{"id": "9fbaaf7ee976-0", "text": "Chains | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/"}
{"id": "9fbaaf7ee976-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalDocumentsPopularAdditionalMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsOn this pageChainsUsing an LLM in isolation is fine for simple applications,", "source": "https://python.langchain.com/docs/modules/chains/"}
{"id": "9fbaaf7ee976-2", "text": "but more complex applications require chaining LLMs - either with each other or with other components.LangChain provides the Chain interface for such \"chained\" applications. We define a Chain very generically as a sequence of calls to components, which can include other chains. The base interface is simple:class Chain(BaseModel, ABC):    \"\"\"Base interface that all chains should implement.\"\"\"    memory: BaseMemory    callbacks: Callbacks    def __call__(        self,        inputs: Any,        return_only_outputs: bool = False,        callbacks: Callbacks = None,    ) -> Dict[str, Any]:        ...This idea of composing components together in a chain is simple but powerful. It drastically simplifies and makes more modular the implementation of complex applications, which in turn makes it much easier to debug, maintain, and improve your applications.For more specifics check out:How-to for walkthroughs of different chain featuresFoundational to get acquainted with core building block chainsDocument to learn how to incorporate documents into chainsPopular chains for the most common use casesAdditional to see some of the more advanced chains and integrations that you can use out of the boxWhy do we need chains?\u00e2\u20ac\u2039Chains allow us to combine multiple components together to create a single, coherent application. For example, we can create a chain that takes user input, formats it with a PromptTemplate, and then passes the formatted response to an LLM. We can build more complex chains by combining multiple chains together, or by combining chains with other components.Get started\u00e2\u20ac\u2039Using LLMChain\u00e2\u20ac\u2039The LLMChain is most basic building block chain. It takes in a prompt template, formats it with the user input and returns the response from an LLM.To use the", "source": "https://python.langchain.com/docs/modules/chains/"}
{"id": "9fbaaf7ee976-3", "text": "prompt template, formats it with the user input and returns the response from an LLM.To use the LLMChain, first create a prompt template.from langchain.llms import OpenAIfrom langchain.prompts import PromptTemplatellm = OpenAI(temperature=0.9)prompt = PromptTemplate(    input_variables=[\"product\"],    template=\"What is a good name for a company that makes {product}?\",)We can now create a very simple chain that will take user input, format the prompt with it, and then send it to the LLM.from langchain.chains import LLMChainchain = LLMChain(llm=llm, prompt=prompt)# Run the chain only specifying the input variable.print(chain.run(\"colorful socks\"))    Colorful Toes Co.If there are multiple variables, you can input them all at once using a dictionary.prompt = PromptTemplate(    input_variables=[\"company\", \"product\"],    template=\"What is a good name for {company} that makes {product}?\",)chain = LLMChain(llm=llm, prompt=prompt)print(chain.run({    'company': \"ABC Startup\",    'product': \"colorful socks\"    }))    Socktopia Colourful Creations.You can use a chat model in an LLMChain as well:from langchain.chat_models import ChatOpenAIfrom langchain.prompts.chat import (    ChatPromptTemplate,    HumanMessagePromptTemplate,)human_message_prompt = HumanMessagePromptTemplate(        prompt=PromptTemplate(            template=\"What is a good name for a company that makes {product}?\",            input_variables=[\"product\"],        )    )chat_prompt_template", "source": "https://python.langchain.com/docs/modules/chains/"}
{"id": "9fbaaf7ee976-4", "text": "input_variables=[\"product\"],        )    )chat_prompt_template = ChatPromptTemplate.from_messages([human_message_prompt])chat = ChatOpenAI(temperature=0.9)chain = LLMChain(llm=chat, prompt=chat_prompt_template)print(chain.run(\"colorful socks\"))    Rainbow Socks Co.PreviousVector store-backed retrieverNextHow toWhy do we need chains?Get startedCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/"}
{"id": "9ed5f798e742-0", "text": "Foundational | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain\n\n\n\n\n\nSkip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalLLMRouterSequentialTransformationDocumentsPopularAdditionalMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsFoundationalFoundational\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd LLMAn LLMChain is a simple chain that adds some functionality around language models. It is used widely throughout LangChain, including in other chains and agents.\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd RouterThis notebook demonstrates how to use the RouterChain paradigm to create a chain that dynamically selects the next chain to use for a given input.\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd SequentialThe next step after calling a language model is make a series of calls to a language model. This is particularly useful when you want to take the output from one call and use it as the input to another.\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd TransformationThis notebook showcases using a generic transformation chain.PreviousSerializationNextLLMCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/foundational/"}
{"id": "04f0fede01f7-0", "text": "Sequential | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/foundational/sequential_chains"}
{"id": "04f0fede01f7-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalLLMRouterSequentialTransformationDocumentsPopularAdditionalMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsFoundationalSequentialSequentialThe next step after calling a language model is make a series of calls to a language model. This is particularly useful when you want to take the output from one call and use it as the input to another.In this notebook we will walk through some examples for how to do this, using sequential chains. Sequential chains allow you to connect multiple chains and compose them into pipelines that execute some specific scenario.. There are two types of sequential chains:SimpleSequentialChain: The simplest form of sequential chains, where each step has a singular input/output, and the output of one step is the input to the next.SequentialChain: A more general form of sequential chains, allowing for multiple inputs/outputs.from langchain.llms import OpenAIfrom langchain.chains import LLMChainfrom langchain.prompts import PromptTemplate# This is an LLMChain to write a synopsis given a title of a play.llm = OpenAI(temperature=.7)template = \"\"\"You are a playwright. Given the title of play, it is your job to write a synopsis for that title.Title: {title}Playwright: This is a synopsis for the above play:\"\"\"prompt_template = PromptTemplate(input_variables=[\"title\"], template=template)synopsis_chain = LLMChain(llm=llm, prompt=prompt_template)# This is an LLMChain to write a review of a play given a synopsis.llm = OpenAI(temperature=.7)template = \"\"\"You are a play critic from the New York Times. Given the synopsis of play, it is your job to write a", "source": "https://python.langchain.com/docs/modules/chains/foundational/sequential_chains"}
{"id": "04f0fede01f7-2", "text": "critic from the New York Times. Given the synopsis of play, it is your job to write a review for that play.Play Synopsis:{synopsis}Review from a New York Times play critic of the above play:\"\"\"prompt_template = PromptTemplate(input_variables=[\"synopsis\"], template=template)review_chain = LLMChain(llm=llm, prompt=prompt_template)# This is the overall chain where we run these two chains in sequence.from langchain.chains import SimpleSequentialChainoverall_chain = SimpleSequentialChain(chains=[synopsis_chain, review_chain], verbose=True)review = overall_chain.run(\"Tragedy at sunset on the beach\")            > Entering new SimpleSequentialChain chain...            Tragedy at Sunset on the Beach is a story of a young couple, Jack and Sarah, who are in love and looking forward to their future together. On the night of their anniversary, they decide to take a walk on the beach at sunset. As they are walking, they come across a mysterious figure, who tells them that their love will be tested in the near future.         The figure then tells the couple that the sun will soon set, and with it, a tragedy will strike. If Jack and Sarah can stay together and pass the test, they will be granted everlasting love. However, if they fail, their love will be lost forever.        The play follows the couple as they struggle to stay together and battle the forces that threaten to tear them apart. Despite the tragedy that awaits them, they remain devoted to one another and fight to keep their love alive. In the end, the couple must decide whether to take a chance on their future together or succumb to the tragedy of the sunset.            Tragedy at Sunset on the Beach is an", "source": "https://python.langchain.com/docs/modules/chains/foundational/sequential_chains"}
{"id": "04f0fede01f7-3", "text": "Tragedy at Sunset on the Beach is an emotionally gripping story of love, hope, and sacrifice. Through the story of Jack and Sarah, the audience is taken on a journey of self-discovery and the power of love to overcome even the greatest of obstacles.         The play's talented cast brings the characters to life, allowing us to feel the depths of their emotion and the intensity of their struggle. With its compelling story and captivating performances, this play is sure to draw in audiences and leave them on the edge of their seats.         The play's setting of the beach at sunset adds a touch of poignancy and romanticism to the story, while the mysterious figure serves to keep the audience enthralled. Overall, Tragedy at Sunset on the Beach is an engaging and thought-provoking play that is sure to leave audiences feeling inspired and hopeful.        > Finished chain.print(review)            Tragedy at Sunset on the Beach is an emotionally gripping story of love, hope, and sacrifice. Through the story of Jack and Sarah, the audience is taken on a journey of self-discovery and the power of love to overcome even the greatest of obstacles.         The play's talented cast brings the characters to life, allowing us to feel the depths of their emotion and the intensity of their struggle. With its compelling story and captivating performances, this play is sure to draw in audiences and leave them on the edge of their seats.         The play's setting of the beach at sunset adds a touch of poignancy and romanticism to the story, while the mysterious figure serves to keep the audience enthralled. Overall, Tragedy at Sunset on the Beach is an engaging and thought-provoking play that", "source": "https://python.langchain.com/docs/modules/chains/foundational/sequential_chains"}
{"id": "04f0fede01f7-4", "text": "Overall, Tragedy at Sunset on the Beach is an engaging and thought-provoking play that is sure to leave audiences feeling inspired and hopeful.Sequential Chain\u00e2\u20ac\u2039Of course, not all sequential chains will be as simple as passing a single string as an argument and getting a single string as output for all steps in the chain. In this next example, we will experiment with more complex chains that involve multiple inputs, and where there also multiple final outputs. Of particular importance is how we name the input/output variable names. In the above example we didn't have to think about that because we were just passing the output of one chain directly as input to the next, but here we do have worry about that because we have multiple inputs.# This is an LLMChain to write a synopsis given a title of a play and the era it is set in.llm = OpenAI(temperature=.7)template = \"\"\"You are a playwright. Given the title of play and the era it is set in, it is your job to write a synopsis for that title.Title: {title}Era: {era}Playwright: This is a synopsis for the above play:\"\"\"prompt_template = PromptTemplate(input_variables=[\"title\", \"era\"], template=template)synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, output_key=\"synopsis\")# This is an LLMChain to write a review of a play given a synopsis.llm = OpenAI(temperature=.7)template = \"\"\"You are a play critic from the New York Times. Given the synopsis of play, it is your job to write a review for that play.Play Synopsis:{synopsis}Review from a New York Times play critic of the above play:\"\"\"prompt_template = PromptTemplate(input_variables=[\"synopsis\"], template=template)review_chain = LLMChain(llm=llm, prompt=prompt_template, output_key=\"review\")# This is the overall chain", "source": "https://python.langchain.com/docs/modules/chains/foundational/sequential_chains"}
{"id": "04f0fede01f7-5", "text": "prompt=prompt_template, output_key=\"review\")# This is the overall chain where we run these two chains in sequence.from langchain.chains import SequentialChainoverall_chain = SequentialChain(    chains=[synopsis_chain, review_chain],    input_variables=[\"era\", \"title\"],    # Here we return multiple variables    output_variables=[\"synopsis\", \"review\"],    verbose=True)overall_chain({\"title\":\"Tragedy at sunset on the beach\", \"era\": \"Victorian England\"})            > Entering new SequentialChain chain...        > Finished chain.    {'title': 'Tragedy at sunset on the beach',     'era': 'Victorian England',     'synopsis': \"\\n\\nThe play follows the story of John, a young man from a wealthy Victorian family, who dreams of a better life for himself. He soon meets a beautiful young woman named Mary, who shares his dream. The two fall in love and decide to elope and start a new life together.\\n\\nOn their journey, they make their way to a beach at sunset, where they plan to exchange their vows of love. Unbeknownst to them, their plans are overheard by John's father, who has been tracking them. He follows them to the beach and, in a fit of rage, confronts them. \\n\\nA physical altercation ensues, and in the struggle, John's father accidentally stabs Mary in the chest with his sword. The two are left in shock and disbelief as Mary dies in John's arms, her last words being a declaration of her love for him.\\n\\nThe tragedy of the play comes to a head when John, broken and with no hope of a future, chooses to take his own life by jumping off the cliffs into the sea below.", "source": "https://python.langchain.com/docs/modules/chains/foundational/sequential_chains"}
{"id": "04f0fede01f7-6", "text": "of a future, chooses to take his own life by jumping off the cliffs into the sea below. \\n\\nThe play is a powerful story of love, hope, and loss set against the backdrop of 19th century England.\",     'review': \"\\n\\nThe latest production from playwright X is a powerful and heartbreaking story of love and loss set against the backdrop of 19th century England. The play follows John, a young man from a wealthy Victorian family, and Mary, a beautiful young woman with whom he falls in love. The two decide to elope and start a new life together, and the audience is taken on a journey of hope and optimism for the future.\\n\\nUnfortunately, their dreams are cut short when John's father discovers them and in a fit of rage, fatally stabs Mary. The tragedy of the play is further compounded when John, broken and without hope, takes his own life. The storyline is not only realistic, but also emotionally compelling, drawing the audience in from start to finish.\\n\\nThe acting was also commendable, with the actors delivering believable and nuanced performances. The playwright and director have successfully crafted a timeless tale of love and loss that will resonate with audiences for years to come. Highly recommended.\"}Memory in Sequential Chains\u00e2\u20ac\u2039Sometimes you may want to pass along some context to use in each step of the chain or in a later part of the chain, but maintaining and chaining together the input/output variables can quickly get messy.  Using SimpleMemory is a convenient way to do manage this and clean up your chains.For example, using the previous playwright SequentialChain, lets say you wanted to include some context about date, time and location of the play, and using the generated synopsis and review, create some social media post text.  You could add these new context variables as input_variables, or we can add a SimpleMemory to the chain to manage this context:from langchain.chains import SequentialChainfrom", "source": "https://python.langchain.com/docs/modules/chains/foundational/sequential_chains"}
{"id": "04f0fede01f7-7", "text": "a SimpleMemory to the chain to manage this context:from langchain.chains import SequentialChainfrom langchain.memory import SimpleMemoryllm = OpenAI(temperature=.7)template = \"\"\"You are a social media manager for a theater company.  Given the title of play, the era it is set in, the date,time and location, the synopsis of the play, and the review of the play, it is your job to write a social media post for that play.Here is some context about the time and location of the play:Date and Time: {time}Location: {location}Play Synopsis:{synopsis}Review from a New York Times play critic of the above play:{review}Social Media Post:\"\"\"prompt_template = PromptTemplate(input_variables=[\"synopsis\", \"review\", \"time\", \"location\"], template=template)social_chain = LLMChain(llm=llm, prompt=prompt_template, output_key=\"social_post_text\")overall_chain = SequentialChain(    memory=SimpleMemory(memories={\"time\": \"December 25th, 8pm PST\", \"location\": \"Theater in the Park\"}),    chains=[synopsis_chain, review_chain, social_chain],    input_variables=[\"era\", \"title\"],    # Here we return multiple variables    output_variables=[\"social_post_text\"],    verbose=True)overall_chain({\"title\":\"Tragedy at sunset on the beach\", \"era\": \"Victorian England\"})            > Entering new SequentialChain chain...        > Finished chain.    {'title': 'Tragedy at sunset on the beach',     'era': 'Victorian England',     'time': 'December 25th, 8pm PST',     'location': 'Theater in the Park',", "source": "https://python.langchain.com/docs/modules/chains/foundational/sequential_chains"}
{"id": "04f0fede01f7-8", "text": "PST',     'location': 'Theater in the Park',     'social_post_text': \"\\nSpend your Christmas night with us at Theater in the Park and experience the heartbreaking story of love and loss that is 'A Walk on the Beach'. Set in Victorian England, this romantic tragedy follows the story of Frances and Edward, a young couple whose love is tragically cut short. Don't miss this emotional and thought-provoking production that is sure to leave you in tears. #AWalkOnTheBeach #LoveAndLoss #TheaterInThePark #VictorianEngland\"}PreviousRouterNextTransformationCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/foundational/sequential_chains"}
{"id": "356fcd0563bb-0", "text": "Router | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/foundational/router"}
{"id": "356fcd0563bb-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalLLMRouterSequentialTransformationDocumentsPopularAdditionalMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsFoundationalRouterOn this pageRouterThis notebook demonstrates how to use the RouterChain paradigm to create a chain that dynamically selects the next chain to use for a given input. Router chains are made up of two components:The RouterChain itself (responsible for selecting the next chain to call)destination_chains: chains that the router chain can route toIn this notebook we will focus on the different types of routing chains. We will show these routing chains used in a MultiPromptChain to create a question-answering chain that selects the prompt which is most relevant for a given question, and then answers the question using that prompt.from langchain.chains.router import MultiPromptChainfrom langchain.llms import OpenAIfrom langchain.chains import ConversationChainfrom langchain.chains.llm import LLMChainfrom langchain.prompts import PromptTemplatephysics_template = \"\"\"You are a very smart physics professor. \\You are great at answering questions about physics in a concise and easy to understand manner. \\When you don't know the answer to a question you admit that you don't know.Here is a question:{input}\"\"\"math_template = \"\"\"You are a very good mathematician. You are great at answering math questions. \\You are so good because you are able to break down hard problems into their component parts, \\answer the component parts, and then put them together to answer the broader question.Here is a question:{input}\"\"\"prompt_infos = [    {        \"name\": \"physics\",        \"description\":", "source": "https://python.langchain.com/docs/modules/chains/foundational/router"}
{"id": "356fcd0563bb-2", "text": "\"name\": \"physics\",        \"description\": \"Good for answering questions about physics\",        \"prompt_template\": physics_template,    },    {        \"name\": \"math\",        \"description\": \"Good for answering math questions\",        \"prompt_template\": math_template,    },]llm = OpenAI()destination_chains = {}for p_info in prompt_infos:    name = p_info[\"name\"]    prompt_template = p_info[\"prompt_template\"]    prompt = PromptTemplate(template=prompt_template, input_variables=[\"input\"])    chain = LLMChain(llm=llm, prompt=prompt)    destination_chains[name] = chaindefault_chain = ConversationChain(llm=llm, output_key=\"text\")LLMRouterChain\u00e2\u20ac\u2039This chain uses an LLM to determine how to route things.from langchain.chains.router.llm_router import LLMRouterChain, RouterOutputParserfrom langchain.chains.router.multi_prompt_prompt import MULTI_PROMPT_ROUTER_TEMPLATEdestinations = [f\"{p['name']}: {p['description']}\" for p in prompt_infos]destinations_str = \"\\n\".join(destinations)router_template = MULTI_PROMPT_ROUTER_TEMPLATE.format(destinations=destinations_str)router_prompt = PromptTemplate(    template=router_template,    input_variables=[\"input\"],    output_parser=RouterOutputParser(),)router_chain = LLMRouterChain.from_llm(llm, router_prompt)chain = MultiPromptChain(    router_chain=router_chain,    destination_chains=destination_chains,    default_chain=default_chain,    verbose=True,)print(chain.run(\"What is", "source": "https://python.langchain.com/docs/modules/chains/foundational/router"}
{"id": "356fcd0563bb-3", "text": "default_chain=default_chain,    verbose=True,)print(chain.run(\"What is black body radiation?\"))            > Entering new MultiPromptChain chain...    physics: {'input': 'What is black body radiation?'}    > Finished chain.            Black body radiation is the term used to describe the electromagnetic radiation emitted by a \u00e2\u20ac\u0153black body\u00e2\u20ac\ufffd\u00e2\u20ac\u201dan object that absorbs all radiation incident upon it. A black body is an idealized physical body that absorbs all incident electromagnetic radiation, regardless of frequency or angle of incidence. It does not reflect, emit or transmit energy. This type of radiation is the result of the thermal motion of the body's atoms and molecules, and it is emitted at all wavelengths. The spectrum of radiation emitted is described by Planck's law and is known as the black body spectrum.print(    chain.run(        \"What is the first prime number greater than 40 such that one plus the prime number is divisible by 3\"    ))            > Entering new MultiPromptChain chain...    math: {'input': 'What is the first prime number greater than 40 such that one plus the prime number is divisible by 3'}    > Finished chain.    ?        The answer is 43. One plus 43 is 44 which is divisible by 3.print(chain.run(\"What is the name of the type of cloud that rins\"))            > Entering new MultiPromptChain chain...    None: {'input': 'What is the name of the type of cloud that rains?'}    > Finished chain.     The type of", "source": "https://python.langchain.com/docs/modules/chains/foundational/router"}
{"id": "356fcd0563bb-4", "text": "of cloud that rains?'}    > Finished chain.     The type of cloud that rains is called a cumulonimbus cloud. It is a tall and dense cloud that is often accompanied by thunder and lightning.EmbeddingRouterChain\u00e2\u20ac\u2039The EmbeddingRouterChain uses embeddings and similarity to route between destination chains.from langchain.chains.router.embedding_router import EmbeddingRouterChainfrom langchain.embeddings import CohereEmbeddingsfrom langchain.vectorstores import Chromanames_and_descriptions = [    (\"physics\", [\"for questions about physics\"]),    (\"math\", [\"for questions about math\"]),]router_chain = EmbeddingRouterChain.from_names_and_descriptions(    names_and_descriptions, Chroma, CohereEmbeddings(), routing_keys=[\"input\"])    Using embedded DuckDB without persistence: data will be transientchain = MultiPromptChain(    router_chain=router_chain,    destination_chains=destination_chains,    default_chain=default_chain,    verbose=True,)print(chain.run(\"What is black body radiation?\"))            > Entering new MultiPromptChain chain...    physics: {'input': 'What is black body radiation?'}    > Finished chain.            Black body radiation is the emission of energy from an idealized physical body (known as a black body) that is in thermal equilibrium with its environment. It is emitted in a characteristic pattern of frequencies known as a black-body spectrum, which depends only on the temperature of the body. The study of black body radiation is an important part of astrophysics and atmospheric physics, as the thermal radiation emitted by stars and planets can often be approximated as black body radiation.print(    chain.run(        \"What is the first prime number greater than", "source": "https://python.langchain.com/docs/modules/chains/foundational/router"}
{"id": "356fcd0563bb-5", "text": "chain.run(        \"What is the first prime number greater than 40 such that one plus the prime number is divisible by 3\"    ))            > Entering new MultiPromptChain chain...    math: {'input': 'What is the first prime number greater than 40 such that one plus the prime number is divisible by 3'}    > Finished chain.    ?        Answer: The first prime number greater than 40 such that one plus the prime number is divisible by 3 is 43.PreviousLLMNextSequentialLLMRouterChainEmbeddingRouterChainCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/foundational/router"}
{"id": "d4a9a0032504-0", "text": "Transformation | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/foundational/transformation"}
{"id": "d4a9a0032504-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalLLMRouterSequentialTransformationDocumentsPopularAdditionalMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsFoundationalTransformationTransformationThis notebook showcases using a generic transformation chain.As an example, we will create a dummy transformation that takes in a super long text, filters the text to only the first 3 paragraphs, and then passes that into an LLMChain to summarize those.from langchain.chains import TransformChain, LLMChain, SimpleSequentialChainfrom langchain.llms import OpenAIfrom langchain.prompts import PromptTemplatewith open(\"../../state_of_the_union.txt\") as f:    state_of_the_union = f.read()def transform_func(inputs: dict) -> dict:    text = inputs[\"text\"]    shortened_text = \"\\n\\n\".join(text.split(\"\\n\\n\")[:3])    return {\"output_text\": shortened_text}transform_chain = TransformChain(    input_variables=[\"text\"], output_variables=[\"output_text\"], transform=transform_func)template = \"\"\"Summarize this text:{output_text}Summary:\"\"\"prompt = PromptTemplate(input_variables=[\"output_text\"], template=template)llm_chain = LLMChain(llm=OpenAI(), prompt=prompt)sequential_chain = SimpleSequentialChain(chains=[transform_chain, llm_chain])sequential_chain.run(state_of_the_union)    ' The speaker addresses the nation, noting that while last year they were kept apart due to COVID-19, this year they are together again. They are reminded that regardless of their political affiliations, they are all Americans.'PreviousSequentialNextDocumentsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/foundational/transformation"}
{"id": "0c6257a6776a-0", "text": "LLM | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/foundational/llm_chain"}
{"id": "0c6257a6776a-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalLLMRouterSequentialTransformationDocumentsPopularAdditionalMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsFoundationalLLMOn this pageLLMAn LLMChain is a simple chain that adds some functionality around language models. It is used widely throughout LangChain, including in other chains and agents.An LLMChain consists of a PromptTemplate and a language model (either an LLM or chat model). It formats the prompt template using the input key values provided (and also memory key values, if available), passes the formatted string to LLM and returns the LLM output.Get started\u00e2\u20ac\u2039from langchain import PromptTemplate, OpenAI, LLMChainprompt_template = \"What is a good name for a company that makes {product}?\"llm = OpenAI(temperature=0)llm_chain = LLMChain(    llm=llm,    prompt=PromptTemplate.from_template(prompt_template))llm_chain(\"colorful socks\")    {'product': 'colorful socks', 'text': '\\n\\nSocktastic!'}Additional ways of running LLM Chain\u00e2\u20ac\u2039Aside from __call__ and run methods shared by all Chain object, LLMChain offers a few more ways of calling the chain logic:apply allows you run the chain against a list of inputs:input_list = [    {\"product\": \"socks\"},    {\"product\": \"computer\"},    {\"product\": \"shoes\"}]llm_chain.apply(input_list)    [{'text': '\\n\\nSocktastic!'},     {'text': '\\n\\nTechCore", "source": "https://python.langchain.com/docs/modules/chains/foundational/llm_chain"}
{"id": "0c6257a6776a-2", "text": "'\\n\\nSocktastic!'},     {'text': '\\n\\nTechCore Solutions.'},     {'text': '\\n\\nFootwear Factory.'}]generate is similar to apply, except it return an LLMResult instead of string. LLMResult often contains useful generation such as token usages and finish reason.llm_chain.generate(input_list)    LLMResult(generations=[[Generation(text='\\n\\nSocktastic!', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text='\\n\\nTechCore Solutions.', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text='\\n\\nFootwear Factory.', generation_info={'finish_reason': 'stop', 'logprobs': None})]], llm_output={'token_usage': {'prompt_tokens': 36, 'total_tokens': 55, 'completion_tokens': 19}, 'model_name': 'text-davinci-003'})predict is similar to run method except that the input keys are specified as keyword arguments instead of a Python dict.# Single input examplellm_chain.predict(product=\"colorful socks\")    '\\n\\nSocktastic!'# Multiple inputs exampletemplate = \"\"\"Tell me a {adjective} joke about {subject}.\"\"\"prompt = PromptTemplate(template=template, input_variables=[\"adjective\", \"subject\"])llm_chain = LLMChain(prompt=prompt, llm=OpenAI(temperature=0))llm_chain.predict(adjective=\"sad\", subject=\"ducks\")    '\\n\\nQ: What did the duck say when his friend died?\\nA: Quack, quack, goodbye.'Parsing the outputs\u00e2\u20ac\u2039By default, LLMChain does not parse the output even if the underlying prompt object has an output parser. If you would like to apply that output parser on the LLM output, use", "source": "https://python.langchain.com/docs/modules/chains/foundational/llm_chain"}
{"id": "0c6257a6776a-3", "text": "an output parser. If you would like to apply that output parser on the LLM output, use predict_and_parse instead of predict and apply_and_parse instead of apply. With predict:from langchain.output_parsers import CommaSeparatedListOutputParseroutput_parser = CommaSeparatedListOutputParser()template = \"\"\"List all the colors in a rainbow\"\"\"prompt = PromptTemplate(template=template, input_variables=[], output_parser=output_parser)llm_chain = LLMChain(prompt=prompt, llm=llm)llm_chain.predict()    '\\n\\nRed, orange, yellow, green, blue, indigo, violet'With predict_and_parser:llm_chain.predict_and_parse()    ['Red', 'orange', 'yellow', 'green', 'blue', 'indigo', 'violet']Initialize from string\u00e2\u20ac\u2039You can also construct an LLMChain from a string template directly.template = \"\"\"Tell me a {adjective} joke about {subject}.\"\"\"llm_chain = LLMChain.from_string(llm=llm, template=template)llm_chain.predict(adjective=\"sad\", subject=\"ducks\")    '\\n\\nQ: What did the duck say when his friend died?\\nA: Quack, quack, goodbye.'PreviousFoundationalNextRouterGet startedCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/foundational/llm_chain"}
{"id": "10498023424d-0", "text": "Documents | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/document/"}
{"id": "10498023424d-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalDocumentsStuffRefineMap reduceMap re-rankPopularAdditionalMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsDocumentsDocumentsThese are the core chains for working with Documents. They are useful for summarizing documents, answering questions over documents, extracting information from documents, and more.These chains all implement a common interface:class BaseCombineDocumentsChain(Chain, ABC):    \"\"\"Base interface for chains combining documents.\"\"\"    @abstractmethod    def combine_docs(self, docs: List[Document], **kwargs: Any) -> Tuple[str, dict]:        \"\"\"Combine documents into a single string.\"\"\"\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd StuffThe stuff documents chain (\"stuff\" as in \"to stuff\" or \"to fill\") is the most straightforward of the document chains. It takes a list of documents, inserts them all into a prompt and passes that prompt to an LLM.\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd RefineThe refine documents chain constructs a response by looping over the input documents and iteratively updating its answer. For each document, it passes all non-document inputs, the current document, and the latest intermediate answer to an LLM chain to get a new answer.\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd Map reduceThe map reduce documents chain first applies an LLM chain to each document individually (the Map step), treating the chain output as a new document. It then passes all the new documents to a separate combine documents chain to get a single output (the Reduce step). It can optionally first compress, or collapse, the mapped documents to make sure that they fit in the combine documents chain (which will", "source": "https://python.langchain.com/docs/modules/chains/document/"}
{"id": "10498023424d-2", "text": "or collapse, the mapped documents to make sure that they fit in the combine documents chain (which will often pass them to an LLM). This compression step is performed recursively if necessary.\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd Map re-rankThe map re-rank documents chain runs an initial prompt on each document, that not only tries to complete a task but also gives a score for how certain it is in its answer. The highest scoring response is returned.PreviousTransformationNextStuffCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/document/"}
{"id": "b770ca4fd1a7-0", "text": "Map reduce | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain\n\n\n\n\n\nSkip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalDocumentsStuffRefineMap reduceMap re-rankPopularAdditionalMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsDocumentsMap reduceMap reduceThe map reduce documents chain first applies an LLM chain to each document individually (the Map step), treating the chain output as a new document. It then passes all the new documents to a separate combine documents chain to get a single output (the Reduce step). It can optionally first compress, or collapse, the mapped documents to make sure that they fit in the combine documents chain (which will often pass them to an LLM). This compression step is performed recursively if necessary.PreviousRefineNextMap re-rankCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/document/map_reduce"}
{"id": "75bc2bd82034-0", "text": "Refine | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain\n\n\n\n\n\nSkip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalDocumentsStuffRefineMap reduceMap re-rankPopularAdditionalMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsDocumentsRefineRefineThe refine documents chain constructs a response by looping over the input documents and iteratively updating its answer. For each document, it passes all non-document inputs, the current document, and the latest intermediate answer to an LLM chain to get a new answer.Since the Refine chain only passes a single document to the LLM at a time, it is well-suited for tasks that require analyzing more documents than can fit in the model's context.\nThe obvious tradeoff is that this chain will make far more LLM calls than, for example, the Stuff documents chain.\nThere are also certain tasks which are difficult to accomplish iteratively. For example, the Refine chain can perform poorly when documents frequently cross-reference one another or when a task requires detailed information from many documents.PreviousStuffNextMap reduceCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/document/refine"}
{"id": "fb46ee486dca-0", "text": "Map re-rank | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain\n\n\n\n\n\nSkip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalDocumentsStuffRefineMap reduceMap re-rankPopularAdditionalMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsDocumentsMap re-rankMap re-rankThe map re-rank documents chain runs an initial prompt on each document, that not only tries to complete a task but also gives a score for how certain it is in its answer. The highest scoring response is returned.PreviousMap reduceNextPopularCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/document/map_rerank"}
{"id": "e2576a8d2c80-0", "text": "Stuff | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain\n\n\n\n\n\nSkip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalDocumentsStuffRefineMap reduceMap re-rankPopularAdditionalMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsDocumentsStuffStuffThe stuff documents chain (\"stuff\" as in \"to stuff\" or \"to fill\") is the most straightforward of the document chains. It takes a list of documents, inserts them all into a prompt and passes that prompt to an LLM.This chain is well-suited for applications where documents are small and only a few are passed in for most calls.PreviousDocumentsNextRefineCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/document/stuff"}
{"id": "59d8dd21386a-0", "text": "How to | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/how_to/"}
{"id": "59d8dd21386a-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toAsync APIDifferent call methodsCustom chainDebugging chainsLoading from LangChainHubAdding memory (state)SerializationFoundationalDocumentsPopularAdditionalMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsHow toHow to\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd Async APILangChain provides async support for Chains by leveraging the asyncio library.\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd Different call methodsAll classes inherited from Chain offer a few ways of running chain logic. The most direct one is by using call:\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd Custom chainTo implement your own custom chain you can subclass Chain and implement the following methods:\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd Debugging chainsIt can be hard to debug a Chain object solely from its output as most Chain objects involve a fair amount of input prompt preprocessing and LLM output post-processing.\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd Loading from LangChainHubThis notebook covers how to load chains from LangChainHub.\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd Adding memory (state)Chains can be initialized with a Memory object, which will persist data across calls to the chain. This makes a Chain stateful.\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd SerializationThis notebook covers how to serialize chains to and from disk. The serialization format we use is json or yaml. Currently, only some chains support this type of serialization. We will grow the number of supported chains over time.PreviousChainsNextAsync APICommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/how_to/"}
{"id": "cbddc1e252b8-0", "text": "Different call methods | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/how_to/call_methods"}
{"id": "cbddc1e252b8-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toAsync APIDifferent call methodsCustom chainDebugging chainsLoading from LangChainHubAdding memory (state)SerializationFoundationalDocumentsPopularAdditionalMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsHow toDifferent call methodsDifferent call methodsAll classes inherited from Chain offer a few ways of running chain logic. The most direct one is by using __call__:chat = ChatOpenAI(temperature=0)prompt_template = \"Tell me a {adjective} joke\"llm_chain = LLMChain(llm=chat, prompt=PromptTemplate.from_template(prompt_template))llm_chain(inputs={\"adjective\": \"corny\"})    {'adjective': 'corny',     'text': 'Why did the tomato turn red? Because it saw the salad dressing!'}By default, __call__ returns both the input and output key values. You can configure it to only return output key values by setting return_only_outputs to True.llm_chain(\"corny\", return_only_outputs=True)    {'text': 'Why did the tomato turn red? Because it saw the salad dressing!'}If the Chain only outputs one output key (i.e. only has one element in its output_keys), you can  use run method. Note that run outputs a string instead of a dictionary.# llm_chain only has one output key, so we can use runllm_chain.output_keys    ['text']llm_chain.run({\"adjective\": \"corny\"})    'Why did the tomato turn red? Because it saw the salad dressing!'In the case of one input key, you can input the string directly without specifying the input mapping.# These", "source": "https://python.langchain.com/docs/modules/chains/how_to/call_methods"}
{"id": "cbddc1e252b8-2", "text": "the case of one input key, you can input the string directly without specifying the input mapping.# These two are equivalentllm_chain.run({\"adjective\": \"corny\"})llm_chain.run(\"corny\")# These two are also equivalentllm_chain(\"corny\")llm_chain({\"adjective\": \"corny\"})    {'adjective': 'corny',     'text': 'Why did the tomato turn red? Because it saw the salad dressing!'}Tips: You can easily integrate a Chain object as a Tool in your Agent via its run method. See an example here.PreviousAsync APINextCustom chainCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/how_to/call_methods"}
{"id": "c8528d21471e-0", "text": "Custom chain | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/how_to/custom_chain"}
{"id": "c8528d21471e-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toAsync APIDifferent call methodsCustom chainDebugging chainsLoading from LangChainHubAdding memory (state)SerializationFoundationalDocumentsPopularAdditionalMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsHow toCustom chainCustom chainTo implement your own custom chain you can subclass Chain and implement the following methods:from __future__ import annotationsfrom typing import Any, Dict, List, Optionalfrom pydantic import Extrafrom langchain.schema import BaseLanguageModelfrom langchain.callbacks.manager import (    AsyncCallbackManagerForChainRun,    CallbackManagerForChainRun,)from langchain.chains.base import Chainfrom langchain.prompts.base import BasePromptTemplateclass MyCustomChain(Chain):    \"\"\"    An example of a custom chain.    \"\"\"    prompt: BasePromptTemplate    \"\"\"Prompt object to use.\"\"\"    llm: BaseLanguageModel    output_key: str = \"text\"  #: :meta private:    class Config:        \"\"\"Configuration for this pydantic object.\"\"\"        extra = Extra.forbid        arbitrary_types_allowed = True    @property    def input_keys(self) -> List[str]:        \"\"\"Will be whatever keys the prompt expects.        :meta private:        \"\"\"        return self.prompt.input_variables    @property    def output_keys(self) -> List[str]:        \"\"\"Will always return text", "source": "https://python.langchain.com/docs/modules/chains/how_to/custom_chain"}
{"id": "c8528d21471e-2", "text": "output_keys(self) -> List[str]:        \"\"\"Will always return text key.        :meta private:        \"\"\"        return [self.output_key]    def _call(        self,        inputs: Dict[str, Any],        run_manager: Optional[CallbackManagerForChainRun] = None,    ) -> Dict[str, str]:        # Your custom chain logic goes here        # This is just an example that mimics LLMChain        prompt_value = self.prompt.format_prompt(**inputs)        # Whenever you call a language model, or another chain, you should pass        # a callback manager to it. This allows the inner run to be tracked by        # any callbacks that are registered on the outer run.        # You can always obtain a callback manager for this by calling        # `run_manager.get_child()` as shown below.        response = self.llm.generate_prompt(            [prompt_value], callbacks=run_manager.get_child() if run_manager else None        )        # If you want to log something about this run, you can do so by calling        # methods on the `run_manager`, as shown below. This will trigger any        # callbacks that are registered for that event.        if run_manager:", "source": "https://python.langchain.com/docs/modules/chains/how_to/custom_chain"}
{"id": "c8528d21471e-3", "text": "if run_manager:            run_manager.on_text(\"Log something about this run\")        return {self.output_key: response.generations[0][0].text}    async def _acall(        self,        inputs: Dict[str, Any],        run_manager: Optional[AsyncCallbackManagerForChainRun] = None,    ) -> Dict[str, str]:        # Your custom chain logic goes here        # This is just an example that mimics LLMChain        prompt_value = self.prompt.format_prompt(**inputs)        # Whenever you call a language model, or another chain, you should pass        # a callback manager to it. This allows the inner run to be tracked by        # any callbacks that are registered on the outer run.        # You can always obtain a callback manager for this by calling        # `run_manager.get_child()` as shown below.        response = await self.llm.agenerate_prompt(            [prompt_value], callbacks=run_manager.get_child() if run_manager else None        )        # If you want to log something about this run, you can do so by calling        # methods on the `run_manager`, as shown below. This will trigger any        # callbacks that are registered for that event.        if run_manager:", "source": "https://python.langchain.com/docs/modules/chains/how_to/custom_chain"}
{"id": "c8528d21471e-4", "text": "that event.        if run_manager:            await run_manager.on_text(\"Log something about this run\")        return {self.output_key: response.generations[0][0].text}    @property    def _chain_type(self) -> str:        return \"my_custom_chain\"from langchain.callbacks.stdout import StdOutCallbackHandlerfrom langchain.chat_models.openai import ChatOpenAIfrom langchain.prompts.prompt import PromptTemplatechain = MyCustomChain(    prompt=PromptTemplate.from_template(\"tell us a joke about {topic}\"),    llm=ChatOpenAI(),)chain.run({\"topic\": \"callbacks\"}, callbacks=[StdOutCallbackHandler()])            > Entering new MyCustomChain chain...    Log something about this run    > Finished chain.    'Why did the callback function feel lonely? Because it was always waiting for someone to call it back!'PreviousDifferent call methodsNextDebugging chainsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/how_to/custom_chain"}
{"id": "72f82be22281-0", "text": "Serialization | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/how_to/serialization"}
{"id": "72f82be22281-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toAsync APIDifferent call methodsCustom chainDebugging chainsLoading from LangChainHubAdding memory (state)SerializationFoundationalDocumentsPopularAdditionalMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsHow toSerializationOn this pageSerializationThis notebook covers how to serialize chains to and from disk. The serialization format we use is json or yaml. Currently, only some chains support this type of serialization. We will grow the number of supported chains over time.Saving a chain to disk\u00e2\u20ac\u2039First, let's go over how to save a chain to disk. This can be done with the .save method, and specifying a file path with a json or yaml extension.from langchain import PromptTemplate, OpenAI, LLMChaintemplate = \"\"\"Question: {question}Answer: Let's think step by step.\"\"\"prompt = PromptTemplate(template=template, input_variables=[\"question\"])llm_chain = LLMChain(prompt=prompt, llm=OpenAI(temperature=0), verbose=True)llm_chain.save(\"llm_chain.json\")Let's now take a look at what's inside this saved filecat llm_chain.json    {        \"memory\": null,        \"verbose\": true,        \"prompt\": {            \"input_variables\": [                \"question\"            ],            \"output_parser\": null,", "source": "https://python.langchain.com/docs/modules/chains/how_to/serialization"}
{"id": "72f82be22281-2", "text": "\"output_parser\": null,            \"template\": \"Question: {question}\\n\\nAnswer: Let's think step by step.\",            \"template_format\": \"f-string\"        },        \"llm\": {            \"model_name\": \"text-davinci-003\",            \"temperature\": 0.0,            \"max_tokens\": 256,            \"top_p\": 1,            \"frequency_penalty\": 0,            \"presence_penalty\": 0,            \"n\": 1,            \"best_of\": 1,            \"request_timeout\": null,            \"logit_bias\": {},            \"_type\": \"openai\"        },        \"output_key\": \"text\",        \"_type\": \"llm_chain\"    }Loading a chain from disk\u00e2\u20ac\u2039We can load a chain from disk by using the load_chain method.from langchain.chains import load_chainchain = load_chain(\"llm_chain.json\")chain.run(\"whats 2 + 2\")            > Entering new LLMChain chain...    Prompt after formatting:", "source": "https://python.langchain.com/docs/modules/chains/how_to/serialization"}
{"id": "72f82be22281-3", "text": "> Entering new LLMChain chain...    Prompt after formatting:    Question: whats 2 + 2        Answer: Let's think step by step.        > Finished chain.    ' 2 + 2 = 4'Saving components separately\u00e2\u20ac\u2039In the above example, we can see that the prompt and llm configuration information is saved in the same json as the overall chain. Alternatively, we can split them up and save them separately. This is often useful to make the saved components more modular. In order to do this, we just need to specify llm_path instead of the llm component, and prompt_path instead of the prompt component.llm_chain.prompt.save(\"prompt.json\")cat prompt.json    {        \"input_variables\": [            \"question\"        ],        \"output_parser\": null,        \"template\": \"Question: {question}\\n\\nAnswer: Let's think step by step.\",        \"template_format\": \"f-string\"    }llm_chain.llm.save(\"llm.json\")cat llm.json    {        \"model_name\": \"text-davinci-003\",        \"temperature\": 0.0,        \"max_tokens\": 256,        \"top_p\": 1,        \"frequency_penalty\": 0,        \"presence_penalty\": 0,        \"n\": 1,        \"best_of\":", "source": "https://python.langchain.com/docs/modules/chains/how_to/serialization"}
{"id": "72f82be22281-4", "text": "\"n\": 1,        \"best_of\": 1,        \"request_timeout\": null,        \"logit_bias\": {},        \"_type\": \"openai\"    }config = {    \"memory\": None,    \"verbose\": True,    \"prompt_path\": \"prompt.json\",    \"llm_path\": \"llm.json\",    \"output_key\": \"text\",    \"_type\": \"llm_chain\",}import jsonwith open(\"llm_chain_separate.json\", \"w\") as f:    json.dump(config, f, indent=2)cat llm_chain_separate.json    {      \"memory\": null,      \"verbose\": true,      \"prompt_path\": \"prompt.json\",      \"llm_path\": \"llm.json\",      \"output_key\": \"text\",      \"_type\": \"llm_chain\"    }We can then load it in the same waychain = load_chain(\"llm_chain_separate.json\")chain.run(\"whats 2 + 2\")            > Entering new LLMChain chain...    Prompt after formatting:    Question: whats 2 + 2        Answer: Let's think step by step.        > Finished chain.    ' 2 + 2 = 4'PreviousAdding memory (state)NextFoundationalSaving a chain to diskLoading a chain from diskSaving components separatelyCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9", "source": "https://python.langchain.com/docs/modules/chains/how_to/serialization"}
{"id": "72f82be22281-5", "text": "from diskSaving components separatelyCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/how_to/serialization"}
{"id": "fa4c6d6042ca-0", "text": "Adding memory (state) | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain\n\n\n\n\n\nSkip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toAsync APIDifferent call methodsCustom chainDebugging chainsLoading from LangChainHubAdding memory (state)SerializationFoundationalDocumentsPopularAdditionalMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsHow toAdding memory (state)On this pageAdding memory (state)Chains can be initialized with a Memory object, which will persist data across calls to the chain. This makes a Chain stateful.Get started\u00e2\u20ac\u2039from langchain.chains import ConversationChainfrom langchain.memory import ConversationBufferMemoryconversation = ConversationChain(    llm=chat,    memory=ConversationBufferMemory())conversation.run(\"Answer briefly. What are the first 3 colors of a rainbow?\")# -> The first three colors of a rainbow are red, orange, and yellow.conversation.run(\"And the next 4?\")# -> The next four colors of a rainbow are green, blue, indigo, and violet.    'The next four colors of a rainbow are green, blue, indigo, and violet.'Essentially, BaseMemory defines an interface of how langchain stores memory. It allows reading of stored data through load_memory_variables method and storing new data through save_context method. You can learn more about it in the Memory section.PreviousLoading from LangChainHubNextSerializationGet startedCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/how_to/memory"}
{"id": "3d35fee08c4a-0", "text": "Debugging chains | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/how_to/debugging"}
{"id": "3d35fee08c4a-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toAsync APIDifferent call methodsCustom chainDebugging chainsLoading from LangChainHubAdding memory (state)SerializationFoundationalDocumentsPopularAdditionalMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsHow toDebugging chainsDebugging chainsIt can be hard to debug a Chain object solely from its output as most Chain objects involve a fair amount of input prompt preprocessing and LLM output post-processing.Setting verbose to True will print out some internal states of the Chain object while it is being ran.conversation = ConversationChain(    llm=chat,    memory=ConversationBufferMemory(),    verbose=True)conversation.run(\"What is ChatGPT?\")    > Entering new ConversationChain chain...    Prompt after formatting:    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.    Current conversation:    Human: What is ChatGPT?    AI:    > Finished chain.    'ChatGPT is an AI language model developed by OpenAI. It is based on the GPT-3 architecture and is capable of generating human-like responses to text prompts. ChatGPT has been trained on a massive amount of text data and can understand and respond to a wide range of topics. It is often used for chatbots, virtual assistants, and other conversational AI applications.'PreviousCustom chainNextLoading from LangChainHubCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/how_to/debugging"}
{"id": "2b6b4e0e208b-0", "text": "Loading from LangChainHub | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/how_to/from_hub"}
{"id": "2b6b4e0e208b-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toAsync APIDifferent call methodsCustom chainDebugging chainsLoading from LangChainHubAdding memory (state)SerializationFoundationalDocumentsPopularAdditionalMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsHow toLoading from LangChainHubLoading from LangChainHubThis notebook covers how to load chains from LangChainHub.from langchain.chains import load_chainchain = load_chain(\"lc://chains/llm-math/chain.json\")chain.run(\"whats 2 raised to .12\")            > Entering new LLMMathChain chain...    whats 2 raised to .12    Answer: 1.0791812460476249    > Finished chain.    'Answer: 1.0791812460476249'Sometimes chains will require extra arguments that were not serialized with the chain. For example, a chain that does question answering over a vector database will require a vector database.from langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.vectorstores import Chromafrom langchain.text_splitter import CharacterTextSplitterfrom langchain import OpenAI, VectorDBQAfrom langchain.document_loaders import TextLoaderloader = TextLoader(\"../../state_of_the_union.txt\")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)texts = text_splitter.split_documents(documents)embeddings = OpenAIEmbeddings()vectorstore = Chroma.from_documents(texts, embeddings)    Running Chroma using direct local API.    Using DuckDB in-memory for database. Data will be transient.chain =", "source": "https://python.langchain.com/docs/modules/chains/how_to/from_hub"}
{"id": "2b6b4e0e208b-2", "text": "local API.    Using DuckDB in-memory for database. Data will be transient.chain = load_chain(\"lc://chains/vector-db-qa/stuff/chain.json\", vectorstore=vectorstore)query = \"What did the president say about Ketanji Brown Jackson\"chain.run(query)    \" The president said that Ketanji Brown Jackson is a Circuit Court of Appeals Judge, one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans, and will continue Justice Breyer's legacy of excellence.\"PreviousDebugging chainsNextAdding memory (state)CommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/how_to/from_hub"}
{"id": "1070e46081c2-0", "text": "Async API | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/how_to/async_chain"}
{"id": "1070e46081c2-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toAsync APIDifferent call methodsCustom chainDebugging chainsLoading from LangChainHubAdding memory (state)SerializationFoundationalDocumentsPopularAdditionalMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsHow toAsync APIAsync APILangChain provides async support for Chains by leveraging the asyncio library.Async methods are currently supported in LLMChain (through arun, apredict, acall) and LLMMathChain (through arun and acall), ChatVectorDBChain, and QA chains. Async support for other chains is on the roadmap.import asyncioimport timefrom langchain.llms import OpenAIfrom langchain.prompts import PromptTemplatefrom langchain.chains import LLMChaindef generate_serially():    llm = OpenAI(temperature=0.9)    prompt = PromptTemplate(        input_variables=[\"product\"],        template=\"What is a good name for a company that makes {product}?\",    )    chain = LLMChain(llm=llm, prompt=prompt)    for _ in range(5):        resp = chain.run(product=\"toothpaste\")        print(resp)async def async_generate(chain):    resp = await chain.arun(product=\"toothpaste\")    print(resp)async def generate_concurrently():    llm = OpenAI(temperature=0.9)    prompt = PromptTemplate(        input_variables=[\"product\"],        template=\"What is a good name", "source": "https://python.langchain.com/docs/modules/chains/how_to/async_chain"}
{"id": "1070e46081c2-2", "text": "input_variables=[\"product\"],        template=\"What is a good name for a company that makes {product}?\",    )    chain = LLMChain(llm=llm, prompt=prompt)    tasks = [async_generate(chain) for _ in range(5)]    await asyncio.gather(*tasks)s = time.perf_counter()# If running this outside of Jupyter, use asyncio.run(generate_concurrently())await generate_concurrently()elapsed = time.perf_counter() - sprint(\"\\033[1m\" + f\"Concurrent executed in {elapsed:0.2f} seconds.\" + \"\\033[0m\")s = time.perf_counter()generate_serially()elapsed = time.perf_counter() - sprint(\"\\033[1m\" + f\"Serial executed in {elapsed:0.2f} seconds.\" + \"\\033[0m\")            BrightSmile Toothpaste Company            BrightSmile Toothpaste Co.            BrightSmile Toothpaste            Gleaming Smile Inc.            SparkleSmile Toothpaste    Concurrent executed in 1.54 seconds.            BrightSmile Toothpaste Co.            MintyFresh Toothpaste Co.            SparkleSmile Toothpaste.            Pearly Whites Toothpaste Co.            BrightSmile Toothpaste.    Serial executed in 6.38 seconds.PreviousHow toNextDifferent call", "source": "https://python.langchain.com/docs/modules/chains/how_to/async_chain"}
{"id": "1070e46081c2-3", "text": "Serial executed in 6.38 seconds.PreviousHow toNextDifferent call methodsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/how_to/async_chain"}
{"id": "991945926421-0", "text": "Popular | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain\n\n\n\n\n\nSkip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalDocumentsPopularAPI chainsRetrieval QAConversational Retrieval QAUsing OpenAI functionsSQLSummarizationAdditionalMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsPopularPopular\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd API chainsAPIChain enables using LLMs to interact with APIs to retrieve relevant information. Construct the chain by providing a question relevant to the provided API documentation.\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd Retrieval QAThis example showcases question answering over an index.\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd Conversational Retrieval QAThe ConversationalRetrievalQA chain builds on RetrievalQAChain to provide a chat history component.\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd Using OpenAI functionsThis walkthrough demonstrates how to incorporate OpenAI function-calling API's in a chain. We'll go over:\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd SQLThis example demonstrates the use of the SQLDatabaseChain for answering questions over a SQL database.\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd SummarizationA summarization chain can be used to summarize multiple documents. One way is to input multiple smaller documents, after they have been divided into chunks, and operate over them with a MapReduceDocumentsChain. You can also choose instead for the chain that does summarization to be a StuffDocumentsChain, or a RefineDocumentsChain.PreviousMap re-rankNextAPI chainsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/popular/"}
{"id": "a2555e1d69a8-0", "text": "Retrieval QA | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/popular/vector_db_qa"}
{"id": "a2555e1d69a8-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalDocumentsPopularAPI chainsRetrieval QAConversational Retrieval QAUsing OpenAI functionsSQLSummarizationAdditionalMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsPopularRetrieval QARetrieval QAThis example showcases question answering over an index.from langchain.chains import RetrievalQAfrom langchain.document_loaders import TextLoaderfrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.llms import OpenAIfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import Chromaloader = TextLoader(\"../../state_of_the_union.txt\")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)texts = text_splitter.split_documents(documents)embeddings = OpenAIEmbeddings()docsearch = Chroma.from_documents(texts, embeddings)qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type=\"stuff\", retriever=docsearch.as_retriever())query = \"What did the president say about Ketanji Brown Jackson\"qa.run(query)    \" The president said that she is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and has received a broad range of support, from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\"Chain Type\u00e2\u20ac\u2039You can easily specify different chain types to load and use in the RetrievalQA chain. For a more detailed walkthrough of these types, please see", "source": "https://python.langchain.com/docs/modules/chains/popular/vector_db_qa"}
{"id": "a2555e1d69a8-2", "text": "and use in the RetrievalQA chain. For a more detailed walkthrough of these types, please see this notebook.There are two ways to load different chain types. First, you can specify the chain type argument in the from_chain_type method. This allows you to pass in the name of the chain type you want to use. For example, in the below we change the chain type to map_reduce.qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type=\"map_reduce\", retriever=docsearch.as_retriever())query = \"What did the president say about Ketanji Brown Jackson\"qa.run(query)    \" The president said that Judge Ketanji Brown Jackson is one of our nation's top legal minds, a former top litigator in private practice and a former federal public defender, from a family of public school educators and police officers, a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\"The above way allows you to really simply change the chain_type, but it doesn't provide a ton of flexibility over parameters to that chain type. If you want to control those parameters, you can load the chain directly (as you did in this notebook) and then pass that directly to the the RetrievalQA chain with the combine_documents_chain parameter. For example:from langchain.chains.question_answering import load_qa_chainqa_chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"stuff\")qa = RetrievalQA(combine_documents_chain=qa_chain, retriever=docsearch.as_retriever())query = \"What did the president say about Ketanji Brown Jackson\"qa.run(query)    \" The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public", "source": "https://python.langchain.com/docs/modules/chains/popular/vector_db_qa"}
{"id": "a2555e1d69a8-3", "text": "former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\"Custom Prompts\u00e2\u20ac\u2039You can pass in custom prompts to do question answering. These prompts are the same prompts as you can pass into the base question answering chainfrom langchain.prompts import PromptTemplateprompt_template = \"\"\"Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.{context}Question: {question}Answer in Italian:\"\"\"PROMPT = PromptTemplate(    template=prompt_template, input_variables=[\"context\", \"question\"])chain_type_kwargs = {\"prompt\": PROMPT}qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type=\"stuff\", retriever=docsearch.as_retriever(), chain_type_kwargs=chain_type_kwargs)query = \"What did the president say about Ketanji Brown Jackson\"qa.run(query)    \" Il presidente ha detto che Ketanji Brown Jackson \u00c3\u00a8 una delle menti legali pi\u00c3\u00b9 importanti del paese, che continuer\u00c3\u00a0 l'eccellenza di Justice Breyer e che ha ricevuto un ampio sostegno, da Fraternal Order of Police a ex giudici nominati da democratici e repubblicani.\"Return Source Documents\u00e2\u20ac\u2039Additionally, we can return the source documents used to answer the question by specifying an optional parameter when constructing the chain.qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type=\"stuff\", retriever=docsearch.as_retriever(), return_source_documents=True)query = \"What did the president", "source": "https://python.langchain.com/docs/modules/chains/popular/vector_db_qa"}
{"id": "a2555e1d69a8-4", "text": "return_source_documents=True)query = \"What did the president say about Ketanji Brown Jackson\"result = qa({\"query\": query})result[\"result\"]    \" The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice and a former federal public defender from a family of public school educators and police officers, and that she has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\"result[\"source_documents\"]    [Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you\u00e2\u20ac\u2122re at it, pass the Disclose Act so Americans can know who is funding our elections. \\n\\nTonight, I\u00e2\u20ac\u2122d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer\u00e2\u20ac\u201dan Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \\n\\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\n\\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation\u00e2\u20ac\u2122s top legal minds, who will continue Justice Breyer\u00e2\u20ac\u2122s legacy of excellence.', lookup_str='', metadata={'source': '../../state_of_the_union.txt'}, lookup_index=0),     Document(page_content='A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she\u00e2\u20ac\u2122s been nominated, she\u00e2\u20ac\u2122s received a broad range of support\u00e2\u20ac\u201dfrom the Fraternal Order of Police to former judges appointed by Democrats and Republicans.", "source": "https://python.langchain.com/docs/modules/chains/popular/vector_db_qa"}
{"id": "a2555e1d69a8-5", "text": "the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \\n\\nAnd if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \\n\\nWe can do both. At our border, we\u00e2\u20ac\u2122ve installed new technology like cutting-edge scanners to better detect drug smuggling.  \\n\\nWe\u00e2\u20ac\u2122ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.  \\n\\nWe\u00e2\u20ac\u2122re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \\n\\nWe\u00e2\u20ac\u2122re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', lookup_str='', metadata={'source': '../../state_of_the_union.txt'}, lookup_index=0),     Document(page_content='And for our LGBTQ+ Americans, let\u00e2\u20ac\u2122s finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong. \\n\\nAs I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \\n\\nWhile it often appears that we never agree, that isn\u00e2\u20ac\u2122t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice. \\n\\nAnd soon, we\u00e2\u20ac\u2122ll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things. \\n\\nSo tonight I\u00e2\u20ac\u2122m offering a Unity Agenda for the Nation. Four big things we can do together.  \\n\\nFirst, beat the opioid epidemic.', lookup_str='', metadata={'source': '../../state_of_the_union.txt'}, lookup_index=0),", "source": "https://python.langchain.com/docs/modules/chains/popular/vector_db_qa"}
{"id": "a2555e1d69a8-6", "text": "metadata={'source': '../../state_of_the_union.txt'}, lookup_index=0),     Document(page_content='Tonight, I\u00e2\u20ac\u2122m announcing a crackdown on these companies overcharging American businesses and consumers. \\n\\nAnd as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up.  \\n\\nThat ends on my watch. \\n\\nMedicare is going to set higher standards for nursing homes and make sure your loved ones get the care they deserve and expect. \\n\\nWe\u00e2\u20ac\u2122ll also cut costs and keep the economy going strong by giving workers a fair shot, provide more training and apprenticeships, hire them based on their skills not degrees. \\n\\nLet\u00e2\u20ac\u2122s pass the Paycheck Fairness Act and paid leave.  \\n\\nRaise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty. \\n\\nLet\u00e2\u20ac\u2122s increase Pell Grants and increase our historic support of HBCUs, and invest in what Jill\u00e2\u20ac\u201dour First Lady who teaches full-time\u00e2\u20ac\u201dcalls America\u00e2\u20ac\u2122s best-kept secret: community colleges.', lookup_str='', metadata={'source': '../../state_of_the_union.txt'}, lookup_index=0)]Alternatively, if our document have a \"source\" metadata key, we can use the RetrievalQAWithSourceChain to cite our sources:docsearch = Chroma.from_texts(texts, embeddings, metadatas=[{\"source\": f\"{i}-pl\"} for i in range(len(texts))])from langchain.chains import RetrievalQAWithSourcesChainfrom langchain import OpenAIchain = RetrievalQAWithSourcesChain.from_chain_type(OpenAI(temperature=0), chain_type=\"stuff\", retriever=docsearch.as_retriever())chain({\"question\": \"What did the president say about Justice Breyer\"},", "source": "https://python.langchain.com/docs/modules/chains/popular/vector_db_qa"}
{"id": "a2555e1d69a8-7", "text": "\"What did the president say about Justice Breyer\"}, return_only_outputs=True)    {'answer': ' The president honored Justice Breyer for his service and mentioned his legacy of excellence.\\n',     'sources': '31-pl'}PreviousAPI chainsNextConversational Retrieval QACommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/popular/vector_db_qa"}
{"id": "c3a547b5ea13-0", "text": "API chains | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/popular/api"}
{"id": "c3a547b5ea13-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalDocumentsPopularAPI chainsRetrieval QAConversational Retrieval QAUsing OpenAI functionsSQLSummarizationAdditionalMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsPopularAPI chainsAPI chainsAPIChain enables using LLMs to interact with APIs to retrieve relevant information. Construct the chain by providing a question relevant to the provided API documentation.from langchain.chains.api.prompt import API_RESPONSE_PROMPTfrom langchain.chains import APIChainfrom langchain.prompts.prompt import PromptTemplatefrom langchain.llms import OpenAIllm = OpenAI(temperature=0)OpenMeteo Example\u00e2\u20ac\u2039from langchain.chains.api import open_meteo_docschain_new = APIChain.from_llm_and_api_docs(llm, open_meteo_docs.OPEN_METEO_DOCS, verbose=True)chain_new.run('What is the weather like right now in Munich, Germany in degrees Fahrenheit?')    > Entering new APIChain chain...    https://api.open-meteo.com/v1/forecast?latitude=48.1351&longitude=11.5820&temperature_unit=fahrenheit&current_weather=true    {\"latitude\":48.14,\"longitude\":11.58,\"generationtime_ms\":0.33104419708251953,\"utc_offset_seconds\":0,\"timezone\":\"GMT\",\"timezone_abbreviation\":\"GMT\",\"elevation\":521.0,\"current_weather\":{\"temperature\":33.4,\"windspeed\":6.8,\"winddirection\":198.0,\"weathercode\":2,\"time\":\"2023-01-16T01:00\"}}    > Finished chain.    ' The", "source": "https://python.langchain.com/docs/modules/chains/popular/api"}
{"id": "c3a547b5ea13-2", "text": "> Finished chain.    ' The current temperature in Munich, Germany is 33.4 degrees Fahrenheit with a windspeed of 6.8 km/h and a wind direction of 198 degrees. The weathercode is 2.'TMDB Example\u00e2\u20ac\u2039import osos.environ['TMDB_BEARER_TOKEN'] = \"\"from langchain.chains.api import tmdb_docsheaders = {\"Authorization\": f\"Bearer {os.environ['TMDB_BEARER_TOKEN']}\"}chain = APIChain.from_llm_and_api_docs(llm, tmdb_docs.TMDB_DOCS, headers=headers, verbose=True)chain.run(\"Search for 'Avatar'\")    > Entering new APIChain chain...     https://api.themoviedb.org/3/search/movie?query=Avatar&language=en-US    {\"page\":1,\"results\":[{\"adult\":false,\"backdrop_path\":\"/o0s4XsEDfDlvit5pDRKjzXR4pp2.jpg\",\"genre_ids\":[28,12,14,878],\"id\":19995,\"original_language\":\"en\",\"original_title\":\"Avatar\",\"overview\":\"In the 22nd century, a paraplegic Marine is dispatched to the moon Pandora on a unique mission, but becomes torn between following orders and protecting an alien civilization.\",\"popularity\":2041.691,\"poster_path\":\"/jRXYjXNq0Cs2TcJjLkki24MLp7u.jpg\",\"release_date\":\"2009-12-15\",\"title\":\"Avatar\",\"video\":false,\"vote_average\":7.6,\"vote_count\":27777},{\"adult\":false,\"backdrop_path\":\"/s16H6tpK2utvwDtzZ8Qy4qm5Emw.jpg\",\"genre_ids\":[878,12,28],\"id\":76600,\"original_language\":\"en\",\"original_title\":\"Avatar:", "source": "https://python.langchain.com/docs/modules/chains/popular/api"}
{"id": "c3a547b5ea13-3", "text": "The Way of Water\",\"overview\":\"Set more than a decade after the events of the first film, learn the story of the Sully family (Jake, Neytiri, and their kids), the trouble that follows them, the lengths they go to keep each other safe, the battles they fight to stay alive, and the tragedies they endure.\",\"popularity\":3948.296,\"poster_path\":\"/t6HIqrRAclMCA60NsSmeqe9RmNV.jpg\",\"release_date\":\"2022-12-14\",\"title\":\"Avatar: The Way of Water\",\"video\":false,\"vote_average\":7.7,\"vote_count\":4219},{\"adult\":false,\"backdrop_path\":\"/uEwGFGtao9YG2JolmdvtHLLVbA9.jpg\",\"genre_ids\":[99],\"id\":111332,\"original_language\":\"en\",\"original_title\":\"Avatar: Creating the World of Pandora\",\"overview\":\"The Making-of James Cameron's Avatar. It shows interesting parts of the work on the set.\",\"popularity\":541.809,\"poster_path\":\"/sjf3xjuofCtDhZghJRzXlTiEjJe.jpg\",\"release_date\":\"2010-02-07\",\"title\":\"Avatar: Creating the World of Pandora\",\"video\":false,\"vote_average\":7.3,\"vote_count\":35},{\"adult\":false,\"backdrop_path\":null,\"genre_ids\":[99],\"id\":287003,\"original_language\":\"en\",\"original_title\":\"Avatar: Scene Deconstruction\",\"overview\":\"The deconstruction of the Avatar scenes and sets\",\"popularity\":394.941,\"poster_path\":\"/uCreCQFReeF0RiIXkQypRYHwikx.jpg\",\"release_date\":\"2009-12-18\",\"title\":\"Avatar: Scene", "source": "https://python.langchain.com/docs/modules/chains/popular/api"}
{"id": "c3a547b5ea13-4", "text": "Scene Deconstruction\",\"video\":false,\"vote_average\":7.8,\"vote_count\":12},{\"adult\":false,\"backdrop_path\":null,\"genre_ids\":[28,18,878,12,14],\"id\":83533,\"original_language\":\"en\",\"original_title\":\"Avatar 3\",\"overview\":\"\",\"popularity\":172.488,\"poster_path\":\"/4rXqTMlkEaMiJjiG0Z2BX6F6Dkm.jpg\",\"release_date\":\"2024-12-18\",\"title\":\"Avatar 3\",\"video\":false,\"vote_average\":0,\"vote_count\":0},{\"adult\":false,\"backdrop_path\":null,\"genre_ids\":[28,878,12,14],\"id\":216527,\"original_language\":\"en\",\"original_title\":\"Avatar 4\",\"overview\":\"\",\"popularity\":162.536,\"poster_path\":\"/qzMYKnT4MG1d0gnhwytr4cKhUvS.jpg\",\"release_date\":\"2026-12-16\",\"title\":\"Avatar 4\",\"video\":false,\"vote_average\":0,\"vote_count\":0},{\"adult\":false,\"backdrop_path\":null,\"genre_ids\":[28,12,14,878],\"id\":393209,\"original_language\":\"en\",\"original_title\":\"Avatar 5\",\"overview\":\"\",\"popularity\":124.722,\"poster_path\":\"/rtmmvqkIC5zDMEd638Es2woxbz8.jpg\",\"release_date\":\"2028-12-20\",\"title\":\"Avatar 5\",\"video\":false,\"vote_average\":0,\"vote_count\":0},{\"adult\":false,\"backdrop_path\":\"/nNceJtrrovG1MUBHMAhId0ws9Gp.jpg\",\"genre_ids\":[99],\"id\":183392,\"original_language\":\"en\",\"original_title\":\"Capturing Avatar\",\"overview\":\"Capturing Avatar is a feature length behind-the-scenes documentary about the making of Avatar. It uses footage from", "source": "https://python.langchain.com/docs/modules/chains/popular/api"}
{"id": "c3a547b5ea13-5", "text": "Avatar is a feature length behind-the-scenes documentary about the making of Avatar. It uses footage from the film's development, as well as stock footage from as far back as the production of Titanic in 1995. Also included are numerous interviews with cast, artists, and other crew members. The documentary was released as a bonus feature on the extended collector's edition of Avatar.\",\"popularity\":109.842,\"poster_path\":\"/26SMEXJl3978dn2svWBSqHbLl5U.jpg\",\"release_date\":\"2010-11-16\",\"title\":\"Capturing Avatar\",\"video\":false,\"vote_average\":7.8,\"vote_count\":39},{\"adult\":false,\"backdrop_path\":\"/eoAvHxfbaPOcfiQyjqypWIXWxDr.jpg\",\"genre_ids\":[99],\"id\":1059673,\"original_language\":\"en\",\"original_title\":\"Avatar: The Deep Dive - A Special Edition of 20/20\",\"overview\":\"An inside look at one of the most anticipated movie sequels ever with James Cameron and cast.\",\"popularity\":629.825,\"poster_path\":\"/rtVeIsmeXnpjNbEKnm9Say58XjV.jpg\",\"release_date\":\"2022-12-14\",\"title\":\"Avatar: The Deep Dive - A Special Edition of 20/20\",\"video\":false,\"vote_average\":6.5,\"vote_count\":5},{\"adult\":false,\"backdrop_path\":null,\"genre_ids\":[99],\"id\":278698,\"original_language\":\"en\",\"original_title\":\"Avatar Spirits\",\"overview\":\"Bryan Konietzko and Michael Dante DiMartino, co-creators of the hit television series, Avatar: The Last Airbender, reflect on the creation of the masterful", "source": "https://python.langchain.com/docs/modules/chains/popular/api"}
{"id": "c3a547b5ea13-6", "text": "hit television series, Avatar: The Last Airbender, reflect on the creation of the masterful series.\",\"popularity\":51.593,\"poster_path\":\"/oBWVyOdntLJd5bBpE0wkpN6B6vy.jpg\",\"release_date\":\"2010-06-22\",\"title\":\"Avatar Spirits\",\"video\":false,\"vote_average\":9,\"vote_count\":16},{\"adult\":false,\"backdrop_path\":\"/cACUWJKvRfhXge7NC0xxoQnkQNu.jpg\",\"genre_ids\":[10402],\"id\":993545,\"original_language\":\"fr\",\"original_title\":\"Avatar - Au Hellfest 2022\",\"overview\":\"\",\"popularity\":21.992,\"poster_path\":\"/fw6cPIsQYKjd1YVQanG2vLc5HGo.jpg\",\"release_date\":\"2022-06-26\",\"title\":\"Avatar - Au Hellfest 2022\",\"video\":false,\"vote_average\":8,\"vote_count\":4},{\"adult\":false,\"backdrop_path\":null,\"genre_ids\":[],\"id\":931019,\"original_language\":\"en\",\"original_title\":\"Avatar: Enter The World\",\"overview\":\"A behind the scenes look at the new James Cameron blockbuster \u00e2\u20ac\u0153Avatar\u00e2\u20ac\ufffd, which stars Aussie Sam Worthington. Hastily produced by Australia\u00e2\u20ac\u2122s Nine Network following the film\u00e2\u20ac\u2122s release.\",\"popularity\":30.903,\"poster_path\":\"/9MHY9pYAgs91Ef7YFGWEbP4WJqC.jpg\",\"release_date\":\"2009-12-05\",\"title\":\"Avatar: Enter The World\",\"video\":false,\"vote_average\":2,\"vote_count\":1},{\"adult\":false,\"backdrop_path\":null,\"genre_ids\":[],\"id\":287004,\"original_language\":\"en\",\"original_title\":\"Avatar: Production Materials\",\"overview\":\"Production material overview of what was used in", "source": "https://python.langchain.com/docs/modules/chains/popular/api"}
{"id": "c3a547b5ea13-7", "text": "Production Materials\",\"overview\":\"Production material overview of what was used in Avatar\",\"popularity\":12.389,\"poster_path\":null,\"release_date\":\"2009-12-18\",\"title\":\"Avatar: Production Materials\",\"video\":true,\"vote_average\":6,\"vote_count\":4},{\"adult\":false,\"backdrop_path\":\"/x43RWEZg9tYRPgnm43GyIB4tlER.jpg\",\"genre_ids\":[],\"id\":740017,\"original_language\":\"es\",\"original_title\":\"Avatar: Agni Kai\",\"overview\":\"\",\"popularity\":9.462,\"poster_path\":\"/y9PrKMUTA6NfIe5FE92tdwOQ2sH.jpg\",\"release_date\":\"2020-01-18\",\"title\":\"Avatar: Agni Kai\",\"video\":false,\"vote_average\":7,\"vote_count\":1},{\"adult\":false,\"backdrop_path\":\"/e8mmDO7fKK93T4lnxl4Z2zjxXZV.jpg\",\"genre_ids\":[],\"id\":668297,\"original_language\":\"en\",\"original_title\":\"The Last Avatar\",\"overview\":\"The Last Avatar is a mystical adventure film, a story of a young man who leaves Hollywood to find himself. What he finds is beyond his wildest imagination. Based on ancient prophecy, contemporary truth seeking and the future of humanity, The Last Avatar is a film that takes transformational themes and makes them relevant for audiences of all ages. Filled with love, magic, mystery, conspiracy, psychics, underground cities, secret societies, light bodies and much more, The Last Avatar tells the story of the emergence of Kalki Avatar- the final Avatar of our current Age of Chaos. Kalki is also a metaphor for the innate power and potential that lies within humanity to awaken and create a world of truth, harmony and", "source": "https://python.langchain.com/docs/modules/chains/popular/api"}
{"id": "c3a547b5ea13-8", "text": "the innate power and potential that lies within humanity to awaken and create a world of truth, harmony and possibility.\",\"popularity\":8.786,\"poster_path\":\"/XWz5SS5g5mrNEZjv3FiGhqCMOQ.jpg\",\"release_date\":\"2014-12-06\",\"title\":\"The Last Avatar\",\"video\":false,\"vote_average\":4.5,\"vote_count\":2},{\"adult\":false,\"backdrop_path\":null,\"genre_ids\":[],\"id\":424768,\"original_language\":\"en\",\"original_title\":\"Avatar:[2015] Wacken Open Air\",\"overview\":\"Started in the summer of 2001 by drummer John Alfredsson and vocalist Christian Rimmi under the name Lost Soul.  The band offers a free mp3 download to a song called \\\"Bloody Knuckles\\\" if one subscribes to their newsletter.  In 2005 they appeared on the compilation \u00e2\u20ac\u0153Listen to Your Inner Voice\u00e2\u20ac\ufffd together with 17 other bands released by Inner Voice Records.\",\"popularity\":6.634,\"poster_path\":null,\"release_date\":\"2015-08-01\",\"title\":\"Avatar:[2015] Wacken Open Air\",\"video\":false,\"vote_average\":8,\"vote_count\":1},{\"adult\":false,\"backdrop_path\":null,\"genre_ids\":[],\"id\":812836,\"original_language\":\"en\",\"original_title\":\"Avatar - Live At Graspop 2018\",\"overview\":\"Live At Graspop Festival Belgium 2018\",\"popularity\":9.855,\"poster_path\":null,\"release_date\":\"\",\"title\":\"Avatar - Live At Graspop 2018\",\"video\":false,\"vote_average\":9,\"vote_count\":1},{\"adult\":false,\"backdrop_path\":null,\"genre_ids\":[10402],\"id\":874770,\"original_language\":\"en\",\"original_title\":\"Avatar Ages: Memories\",\"overview\":\"On the night of memories Avatar performed songs from Thoughts of No", "source": "https://python.langchain.com/docs/modules/chains/popular/api"}
{"id": "c3a547b5ea13-9", "text": "Ages: Memories\",\"overview\":\"On the night of memories Avatar performed songs from Thoughts of No Tomorrow, Schlacht and Avatar as voted on by the fans.\",\"popularity\":2.66,\"poster_path\":\"/xDNNQ2cnxAv3o7u0nT6JJacQrhp.jpg\",\"release_date\":\"2021-01-30\",\"title\":\"Avatar Ages: Memories\",\"video\":false,\"vote_average\":10,\"vote_count\":1},{\"adult\":false,\"backdrop_path\":null,\"genre_ids\":[10402],\"id\":874768,\"original_language\":\"en\",\"original_title\":\"Avatar Ages: Madness\",\"overview\":\"On the night of madness Avatar performed songs from Black Waltz and Hail The Apocalypse as voted on by the fans.\",\"popularity\":2.024,\"poster_path\":\"/wVyTuruUctV3UbdzE5cncnpyNoY.jpg\",\"release_date\":\"2021-01-23\",\"title\":\"Avatar Ages: Madness\",\"video\":false,\"vote_average\":8,\"vote_count\":1},{\"adult\":false,\"backdrop_path\":\"/dj8g4jrYMfK6tQ26ra3IaqOx5Ho.jpg\",\"genre_ids\":[10402],\"id\":874700,\"original_language\":\"en\",\"original_title\":\"Avatar Ages: Dreams\",\"overview\":\"On the night of dreams Avatar performed Hunter Gatherer in its entirety, plus a selection of their most popular songs.  Originally aired January 9th 2021\",\"popularity\":1.957,\"poster_path\":\"/4twG59wnuHpGIRR9gYsqZnVysSP.jpg\",\"release_date\":\"2021-01-09\",\"title\":\"Avatar Ages: Dreams\",\"video\":false,\"vote_average\":0,\"vote_count\":0}],\"total_pages\":3,\"total_results\":57}    > Finished chain.    ' This response contains 57 movies related to the search query", "source": "https://python.langchain.com/docs/modules/chains/popular/api"}
{"id": "c3a547b5ea13-10", "text": "> Finished chain.    ' This response contains 57 movies related to the search query \"Avatar\". The first movie in the list is the 2009 movie \"Avatar\" starring Sam Worthington. Other movies in the list include sequels to Avatar, documentaries, and live performances.'Listen API Example\u00e2\u20ac\u2039import osfrom langchain.llms import OpenAIfrom langchain.chains.api import podcast_docsfrom langchain.chains import APIChain# Get api key here: https://www.listennotes.com/api/pricing/listen_api_key = 'xxx'llm = OpenAI(temperature=0)headers = {\"X-ListenAPI-Key\": listen_api_key}chain = APIChain.from_llm_and_api_docs(llm, podcast_docs.PODCAST_DOCS, headers=headers, verbose=True)chain.run(\"Search for 'silicon valley bank' podcast episodes, audio length is more than 30 minutes, return only 1 results\")PreviousPopularNextRetrieval QACommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/popular/api"}
{"id": "4da1e06043c4-0", "text": "Conversational Retrieval QA | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/popular/chat_vector_db"}
{"id": "4da1e06043c4-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalDocumentsPopularAPI chainsRetrieval QAConversational Retrieval QAUsing OpenAI functionsSQLSummarizationAdditionalMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsPopularConversational Retrieval QAConversational Retrieval QAThe ConversationalRetrievalQA chain builds on RetrievalQAChain to provide a chat history component.It first combines the chat history (either explicitly passed in or retrieved from the provided memory) and the question into a standalone question, then looks up relevant documents from the retriever, and finally passes those documents and the question to a question answering chain to return a response.To create one, you will need a retriever. In the below example, we will create one from a vector store, which can be created from embeddings.from langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.vectorstores import Chromafrom langchain.text_splitter import CharacterTextSplitterfrom langchain.llms import OpenAIfrom langchain.chains import ConversationalRetrievalChainLoad in documents. You can replace this with a loader for whatever type of data you wantfrom langchain.document_loaders import TextLoaderloader = TextLoader(\"../../state_of_the_union.txt\")documents = loader.load()If you had multiple loaders that you wanted to combine, you do something like:# loaders = [....]# docs = []# for loader in loaders:#     docs.extend(loader.load())We now split the documents, create embeddings for them, and put them in a vectorstore. This allows us to do semantic search over them.text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)documents =", "source": "https://python.langchain.com/docs/modules/chains/popular/chat_vector_db"}
{"id": "4da1e06043c4-2", "text": "= CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)documents = text_splitter.split_documents(documents)embeddings = OpenAIEmbeddings()vectorstore = Chroma.from_documents(documents, embeddings)    Using embedded DuckDB without persistence: data will be transientWe can now create a memory object, which is necessary to track the inputs/outputs and hold a conversation.from langchain.memory import ConversationBufferMemorymemory = ConversationBufferMemory(memory_key=\"chat_history\", return_messages=True)We now initialize the ConversationalRetrievalChainqa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever(), memory=memory)query = \"What did the president say about Ketanji Brown Jackson\"result = qa({\"question\": query})result[\"answer\"]    \" The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\"query = \"Did he mention who she succeeded\"result = qa({\"question\": query})result['answer']    ' Ketanji Brown Jackson succeeded Justice Stephen Breyer on the United States Supreme Court.'Pass in chat history\u00e2\u20ac\u2039In the above example, we used a Memory object to track chat history. We can also just pass it in explicitly. In order to do this, we need to initialize a chain without any memory object.qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever())Here's an example of asking a question with no chat historychat_history = []query = \"What did the president say about", "source": "https://python.langchain.com/docs/modules/chains/popular/chat_vector_db"}
{"id": "4da1e06043c4-3", "text": "asking a question with no chat historychat_history = []query = \"What did the president say about Ketanji Brown Jackson\"result = qa({\"question\": query, \"chat_history\": chat_history})result[\"answer\"]    \" The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\"Here's an example of asking a question with some chat historychat_history = [(query, result[\"answer\"])]query = \"Did he mention who she succeeded\"result = qa({\"question\": query, \"chat_history\": chat_history})result['answer']    ' Ketanji Brown Jackson succeeded Justice Stephen Breyer on the United States Supreme Court.'Using a different model for condensing the question\u00e2\u20ac\u2039This chain has two steps. First, it condenses the current question and the chat history into a standalone question. This is necessary to create a standanlone vector to use for retrieval. After that, it does retrieval and then answers the question using retrieval augmented generation with a separate model. Part of the power of the declarative nature of LangChain is that you can easily use a separate language model for each call. This can be useful to use a cheaper and faster model for the simpler task of condensing the question, and then a more expensive model for answering the question. Here is an example of doing so.from langchain.chat_models import ChatOpenAIqa = ConversationalRetrievalChain.from_llm(    ChatOpenAI(temperature=0, model=\"gpt-4\"),    vectorstore.as_retriever(),    condense_question_llm =", "source": "https://python.langchain.com/docs/modules/chains/popular/chat_vector_db"}
{"id": "4da1e06043c4-4", "text": "vectorstore.as_retriever(),    condense_question_llm = ChatOpenAI(temperature=0, model='gpt-3.5-turbo'),)chat_history = []query = \"What did the president say about Ketanji Brown Jackson\"result = qa({\"question\": query, \"chat_history\": chat_history})chat_history = [(query, result[\"answer\"])]query = \"Did he mention who she succeeded\"result = qa({\"question\": query, \"chat_history\": chat_history})Return Source Documents\u00e2\u20ac\u2039You can also easily return source documents from the ConversationalRetrievalChain. This is useful for when you want to inspect what documents were returned.qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever(), return_source_documents=True)chat_history = []query = \"What did the president say about Ketanji Brown Jackson\"result = qa({\"question\": query, \"chat_history\": chat_history})result['source_documents'][0]    Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you\u00e2\u20ac\u2122re at it, pass the Disclose Act so Americans can know who is funding our elections. \\n\\nTonight, I\u00e2\u20ac\u2122d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer\u00e2\u20ac\u201dan Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \\n\\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\n\\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation\u00e2\u20ac\u2122s top legal minds, who will continue Justice", "source": "https://python.langchain.com/docs/modules/chains/popular/chat_vector_db"}
{"id": "4da1e06043c4-5", "text": "Brown Jackson. One of our nation\u00e2\u20ac\u2122s top legal minds, who will continue Justice Breyer\u00e2\u20ac\u2122s legacy of excellence.', metadata={'source': '../../state_of_the_union.txt'})ConversationalRetrievalChain with search_distance\u00e2\u20ac\u2039If you are using a vector store that supports filtering by search distance, you can add a threshold value parameter.vectordbkwargs = {\"search_distance\": 0.9}qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever(), return_source_documents=True)chat_history = []query = \"What did the president say about Ketanji Brown Jackson\"result = qa({\"question\": query, \"chat_history\": chat_history, \"vectordbkwargs\": vectordbkwargs})ConversationalRetrievalChain with map_reduce\u00e2\u20ac\u2039We can also use different types of combine document chains with the ConversationalRetrievalChain chain.from langchain.chains import LLMChainfrom langchain.chains.question_answering import load_qa_chainfrom langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPTllm = OpenAI(temperature=0)question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)doc_chain = load_qa_chain(llm, chain_type=\"map_reduce\")chain = ConversationalRetrievalChain(    retriever=vectorstore.as_retriever(),    question_generator=question_generator,    combine_docs_chain=doc_chain,)chat_history = []query = \"What did the president say about Ketanji Brown Jackson\"result = chain({\"question\": query, \"chat_history\": chat_history})result['answer']    \" The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former", "source": "https://python.langchain.com/docs/modules/chains/popular/chat_vector_db"}
{"id": "4da1e06043c4-6", "text": "one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, from a family of public school educators and police officers, a consensus builder, and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\"ConversationalRetrievalChain with Question Answering with sources\u00e2\u20ac\u2039You can also use this chain with the question answering with sources chain.from langchain.chains.qa_with_sources import load_qa_with_sources_chainllm = OpenAI(temperature=0)question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)doc_chain = load_qa_with_sources_chain(llm, chain_type=\"map_reduce\")chain = ConversationalRetrievalChain(    retriever=vectorstore.as_retriever(),    question_generator=question_generator,    combine_docs_chain=doc_chain,)chat_history = []query = \"What did the president say about Ketanji Brown Jackson\"result = chain({\"question\": query, \"chat_history\": chat_history})result['answer']    \" The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, from a family of public school educators and police officers, a consensus builder, and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \\nSOURCES: ../../state_of_the_union.txt\"ConversationalRetrievalChain with streaming to stdout\u00e2\u20ac\u2039Output from the chain will be streamed to stdout token by token in this example.from langchain.chains.llm import LLMChainfrom langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandlerfrom langchain.chains.conversational_retrieval.prompts import", "source": "https://python.langchain.com/docs/modules/chains/popular/chat_vector_db"}
{"id": "4da1e06043c4-7", "text": "import StreamingStdOutCallbackHandlerfrom langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT, QA_PROMPTfrom langchain.chains.question_answering import load_qa_chain# Construct a ConversationalRetrievalChain with a streaming llm for combine docs# and a separate, non-streaming llm for question generationllm = OpenAI(temperature=0)streaming_llm = OpenAI(streaming=True, callbacks=[StreamingStdOutCallbackHandler()], temperature=0)question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)doc_chain = load_qa_chain(streaming_llm, chain_type=\"stuff\", prompt=QA_PROMPT)qa = ConversationalRetrievalChain(    retriever=vectorstore.as_retriever(), combine_docs_chain=doc_chain, question_generator=question_generator)chat_history = []query = \"What did the president say about Ketanji Brown Jackson\"result = qa({\"question\": query, \"chat_history\": chat_history})     The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.chat_history = [(query, result[\"answer\"])]query = \"Did he mention who she succeeded\"result = qa({\"question\": query, \"chat_history\": chat_history})     Ketanji Brown Jackson succeeded Justice Stephen Breyer on the United States Supreme Court.get_chat_history Function\u00e2\u20ac\u2039You can also specify a get_chat_history function, which can be used to format the chat_history string.def get_chat_history(inputs) -> str:    res", "source": "https://python.langchain.com/docs/modules/chains/popular/chat_vector_db"}
{"id": "4da1e06043c4-8", "text": "used to format the chat_history string.def get_chat_history(inputs) -> str:    res = []    for human, ai in inputs:        res.append(f\"Human:{human}\\nAI:{ai}\")    return \"\\n\".join(res)qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever(), get_chat_history=get_chat_history)chat_history = []query = \"What did the president say about Ketanji Brown Jackson\"result = qa({\"question\": query, \"chat_history\": chat_history})result['answer']    \" The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\"PreviousRetrieval QANextUsing OpenAI functionsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/popular/chat_vector_db"}
{"id": "23d1bce49020-0", "text": "SQL | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/popular/sqlite"}
{"id": "23d1bce49020-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalDocumentsPopularAPI chainsRetrieval QAConversational Retrieval QAUsing OpenAI functionsSQLSummarizationAdditionalMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsPopularSQLSQLThis example demonstrates the use of the SQLDatabaseChain for answering questions over a SQL database.Under the hood, LangChain uses SQLAlchemy to connect to SQL databases. The SQLDatabaseChain can therefore be used with any SQL dialect supported by SQLAlchemy, such as MS SQL, MySQL, MariaDB, PostgreSQL, Oracle SQL, Databricks and SQLite. Please refer to the SQLAlchemy documentation for more information about requirements for connecting to your database. For example, a connection to MySQL requires an appropriate connector such as PyMySQL. A URI for a MySQL connection might look like: mysql+pymysql://user:pass@some_mysql_db_address/db_name.This demonstration uses SQLite and the example Chinook database.", "source": "https://python.langchain.com/docs/modules/chains/popular/sqlite"}
{"id": "23d1bce49020-2", "text": "To set it up, follow the instructions on https://database.guide/2-sample-databases-sqlite/, placing the .db file in a notebooks folder at the root of this repository.from langchain import OpenAI, SQLDatabase, SQLDatabaseChaindb = SQLDatabase.from_uri(\"sqlite:///../../../../notebooks/Chinook.db\")llm = OpenAI(temperature=0, verbose=True)NOTE: For data-sensitive projects, you can specify return_direct=True in the SQLDatabaseChain initialization to directly return the output of the SQL query without any additional formatting. This prevents the LLM from seeing any contents within the database. Note, however, the LLM still has access to the database scheme (i.e. dialect, table and key names) by default.db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)db_chain.run(\"How many employees are there?\")            > Entering new SQLDatabaseChain chain...    How many employees are there?    SQLQuery:    /workspace/langchain/langchain/sql_database.py:191: SAWarning: Dialect sqlite+pysqlite does *not* support Decimal objects natively, and SQLAlchemy must convert from floating point - rounding errors and other issues may occur. Please consider storing Decimal numbers as strings or integers on this platform for lossless storage.      sample_rows = connection.execute(command)    SELECT COUNT(*) FROM \"Employee\";    SQLResult: [(8,)]    Answer:There are 8 employees.    > Finished chain.    'There are 8 employees.'Use Query Checker\u00e2\u20ac\u2039Sometimes the Language Model generates invalid SQL with small mistakes that can be self-corrected using the same technique used by the SQL Database Agent to try and fix the SQL using the LLM. You can simply specify this option when creating the", "source": "https://python.langchain.com/docs/modules/chains/popular/sqlite"}
{"id": "23d1bce49020-3", "text": "to try and fix the SQL using the LLM. You can simply specify this option when creating the chain:db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True, use_query_checker=True)db_chain.run(\"How many albums by Aerosmith?\")            > Entering new SQLDatabaseChain chain...    How many albums by Aerosmith?    SQLQuery:SELECT COUNT(*) FROM Album WHERE ArtistId = 3;    SQLResult: [(1,)]    Answer:There is 1 album by Aerosmith.    > Finished chain.    'There is 1 album by Aerosmith.'Customize Prompt\u00e2\u20ac\u2039You can also customize the prompt that is used. Here is an example prompting it to understand that foobar is the same as the Employee tablefrom langchain.prompts.prompt import PromptTemplate_DEFAULT_TEMPLATE = \"\"\"Given an input question, first create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer.Use the following format:Question: \"Question here\"SQLQuery: \"SQL Query to run\"SQLResult: \"Result of the SQLQuery\"Answer: \"Final answer here\"Only use the following tables:{table_info}If someone asks for the table foobar, they really mean the employee table.Question: {input}\"\"\"PROMPT = PromptTemplate(    input_variables=[\"input\", \"table_info\", \"dialect\"], template=_DEFAULT_TEMPLATE)db_chain = SQLDatabaseChain.from_llm(llm, db, prompt=PROMPT, verbose=True)db_chain.run(\"How many employees are there in the foobar table?\")            > Entering new SQLDatabaseChain chain...    How many employees are there in the foobar table?", "source": "https://python.langchain.com/docs/modules/chains/popular/sqlite"}
{"id": "23d1bce49020-4", "text": "chain...    How many employees are there in the foobar table?    SQLQuery:SELECT COUNT(*) FROM Employee;    SQLResult: [(8,)]    Answer:There are 8 employees in the foobar table.    > Finished chain.    'There are 8 employees in the foobar table.'Return Intermediate Steps\u00e2\u20ac\u2039You can also return the intermediate steps of the SQLDatabaseChain. This allows you to access the SQL statement that was generated, as well as the result of running that against the SQL Database.db_chain = SQLDatabaseChain.from_llm(llm, db, prompt=PROMPT, verbose=True, use_query_checker=True, return_intermediate_steps=True)result = db_chain(\"How many employees are there in the foobar table?\")result[\"intermediate_steps\"]            > Entering new SQLDatabaseChain chain...    How many employees are there in the foobar table?    SQLQuery:SELECT COUNT(*) FROM Employee;    SQLResult: [(8,)]    Answer:There are 8 employees in the foobar table.    > Finished chain.    [{'input': 'How many employees are there in the foobar table?\\nSQLQuery:SELECT COUNT(*) FROM Employee;\\nSQLResult: [(8,)]\\nAnswer:',      'top_k': '5',      'dialect': 'sqlite',      'table_info': '\\nCREATE TABLE \"Artist\" (\\n\\t\"ArtistId\" INTEGER NOT NULL, \\n\\t\"Name\" NVARCHAR(120), \\n\\tPRIMARY KEY (\"ArtistId\")\\n)\\n\\n/*\\n3 rows from Artist", "source": "https://python.langchain.com/docs/modules/chains/popular/sqlite"}
{"id": "23d1bce49020-5", "text": "KEY (\"ArtistId\")\\n)\\n\\n/*\\n3 rows from Artist table:\\nArtistId\\tName\\n1\\tAC/DC\\n2\\tAccept\\n3\\tAerosmith\\n*/\\n\\n\\nCREATE TABLE \"Employee\" (\\n\\t\"EmployeeId\" INTEGER NOT NULL, \\n\\t\"LastName\" NVARCHAR(20) NOT NULL, \\n\\t\"FirstName\" NVARCHAR(20) NOT NULL, \\n\\t\"Title\" NVARCHAR(30), \\n\\t\"ReportsTo\" INTEGER, \\n\\t\"BirthDate\" DATETIME, \\n\\t\"HireDate\" DATETIME, \\n\\t\"Address\" NVARCHAR(70), \\n\\t\"City\" NVARCHAR(40), \\n\\t\"State\" NVARCHAR(40), \\n\\t\"Country\" NVARCHAR(40), \\n\\t\"PostalCode\" NVARCHAR(10), \\n\\t\"Phone\" NVARCHAR(24), \\n\\t\"Fax\" NVARCHAR(24), \\n\\t\"Email\" NVARCHAR(60), \\n\\tPRIMARY KEY (\"EmployeeId\"), \\n\\tFOREIGN KEY(\"ReportsTo\") REFERENCES \"Employee\" (\"EmployeeId\")\\n)\\n\\n/*\\n3 rows from Employee table:\\nEmployeeId\\tLastName\\tFirstName\\tTitle\\tReportsTo\\tBirthDate\\tHireDate\\tAddress\\tCity\\tState\\tCountry\\tPostalCode\\tPhone\\tFax\\tEmail\\n1\\tAdams\\tAndrew\\tGeneral Manager\\tNone\\t1962-02-18 00:00:00\\t2002-08-14 00:00:00\\t11120 Jasper Ave NW\\tEdmonton\\tAB\\tCanada\\tT5K 2N1\\t+1 (780) 428-9482\\t+1 (780) 428-3457\\tandrew@chinookcorp.com\\n2\\tEdwards\\tNancy\\tSales", "source": "https://python.langchain.com/docs/modules/chains/popular/sqlite"}
{"id": "23d1bce49020-6", "text": "Manager\\t1\\t1958-12-08 00:00:00\\t2002-05-01 00:00:00\\t825 8 Ave SW\\tCalgary\\tAB\\tCanada\\tT2P 2T3\\t+1 (403) 262-3443\\t+1 (403) 262-3322\\tnancy@chinookcorp.com\\n3\\tPeacock\\tJane\\tSales Support Agent\\t2\\t1973-08-29 00:00:00\\t2002-04-01 00:00:00\\t1111 6 Ave SW\\tCalgary\\tAB\\tCanada\\tT2P 5M5\\t+1 (403) 262-3443\\t+1 (403) 262-6712\\tjane@chinookcorp.com\\n*/\\n\\n\\nCREATE TABLE \"Genre\" (\\n\\t\"GenreId\" INTEGER NOT NULL, \\n\\t\"Name\" NVARCHAR(120), \\n\\tPRIMARY KEY (\"GenreId\")\\n)\\n\\n/*\\n3 rows from Genre table:\\nGenreId\\tName\\n1\\tRock\\n2\\tJazz\\n3\\tMetal\\n*/\\n\\n\\nCREATE TABLE \"MediaType\" (\\n\\t\"MediaTypeId\" INTEGER NOT NULL, \\n\\t\"Name\" NVARCHAR(120), \\n\\tPRIMARY KEY (\"MediaTypeId\")\\n)\\n\\n/*\\n3 rows from MediaType table:\\nMediaTypeId\\tName\\n1\\tMPEG audio file\\n2\\tProtected AAC audio file\\n3\\tProtected MPEG-4 video file\\n*/\\n\\n\\nCREATE TABLE \"Playlist\" (\\n\\t\"PlaylistId\" INTEGER NOT NULL, \\n\\t\"Name\" NVARCHAR(120), \\n\\tPRIMARY KEY (\"PlaylistId\")\\n)\\n\\n/*\\n3 rows from Playlist", "source": "https://python.langchain.com/docs/modules/chains/popular/sqlite"}
{"id": "23d1bce49020-7", "text": "KEY (\"PlaylistId\")\\n)\\n\\n/*\\n3 rows from Playlist table:\\nPlaylistId\\tName\\n1\\tMusic\\n2\\tMovies\\n3\\tTV Shows\\n*/\\n\\n\\nCREATE TABLE \"Album\" (\\n\\t\"AlbumId\" INTEGER NOT NULL, \\n\\t\"Title\" NVARCHAR(160) NOT NULL, \\n\\t\"ArtistId\" INTEGER NOT NULL, \\n\\tPRIMARY KEY (\"AlbumId\"), \\n\\tFOREIGN KEY(\"ArtistId\") REFERENCES \"Artist\" (\"ArtistId\")\\n)\\n\\n/*\\n3 rows from Album table:\\nAlbumId\\tTitle\\tArtistId\\n1\\tFor Those About To Rock We Salute You\\t1\\n2\\tBalls to the Wall\\t2\\n3\\tRestless and Wild\\t2\\n*/\\n\\n\\nCREATE TABLE \"Customer\" (\\n\\t\"CustomerId\" INTEGER NOT NULL, \\n\\t\"FirstName\" NVARCHAR(40) NOT NULL, \\n\\t\"LastName\" NVARCHAR(20) NOT NULL, \\n\\t\"Company\" NVARCHAR(80), \\n\\t\"Address\" NVARCHAR(70), \\n\\t\"City\" NVARCHAR(40), \\n\\t\"State\" NVARCHAR(40), \\n\\t\"Country\" NVARCHAR(40), \\n\\t\"PostalCode\" NVARCHAR(10), \\n\\t\"Phone\" NVARCHAR(24), \\n\\t\"Fax\" NVARCHAR(24), \\n\\t\"Email\" NVARCHAR(60) NOT NULL, \\n\\t\"SupportRepId\" INTEGER, \\n\\tPRIMARY KEY (\"CustomerId\"), \\n\\tFOREIGN KEY(\"SupportRepId\") REFERENCES \"Employee\" (\"EmployeeId\")\\n)\\n\\n/*\\n3 rows from Customer", "source": "https://python.langchain.com/docs/modules/chains/popular/sqlite"}
{"id": "23d1bce49020-8", "text": "REFERENCES \"Employee\" (\"EmployeeId\")\\n)\\n\\n/*\\n3 rows from Customer table:\\nCustomerId\\tFirstName\\tLastName\\tCompany\\tAddress\\tCity\\tState\\tCountry\\tPostalCode\\tPhone\\tFax\\tEmail\\tSupportRepId\\n1\\tLu\u00c3\u00ads\\tGon\u00c3\u00a7alves\\tEmbraer - Empresa Brasileira de Aeron\u00c3\u00a1utica S.A.\\tAv. Brigadeiro Faria Lima, 2170\\tS\u00c3\u00a3o Jos\u00c3\u00a9 dos Campos\\tSP\\tBrazil\\t12227-000\\t+55 (12) 3923-5555\\t+55 (12) 3923-5566\\tluisg@embraer.com.br\\t3\\n2\\tLeonie\\tK\u00c3\u00b6hler\\tNone\\tTheodor-Heuss-Stra\u00c3\u0178e 34\\tStuttgart\\tNone\\tGermany\\t70174\\t+49 0711 2842222\\tNone\\tleonekohler@surfeu.de\\t5\\n3\\tFran\u00c3\u00a7ois\\tTremblay\\tNone\\t1498 rue B\u00c3\u00a9langer\\tMontr\u00c3\u00a9al\\tQC\\tCanada\\tH2G 1A7\\t+1 (514) 721-4711\\tNone\\tftremblay@gmail.com\\t3\\n*/\\n\\n\\nCREATE TABLE \"Invoice\" (\\n\\t\"InvoiceId\" INTEGER NOT NULL, \\n\\t\"CustomerId\" INTEGER NOT NULL, \\n\\t\"InvoiceDate\" DATETIME NOT NULL, \\n\\t\"BillingAddress\" NVARCHAR(70), \\n\\t\"BillingCity\" NVARCHAR(40), \\n\\t\"BillingState\" NVARCHAR(40), \\n\\t\"BillingCountry\" NVARCHAR(40), \\n\\t\"BillingPostalCode\" NVARCHAR(10), \\n\\t\"Total\" NUMERIC(10, 2) NOT NULL, \\n\\tPRIMARY", "source": "https://python.langchain.com/docs/modules/chains/popular/sqlite"}
{"id": "23d1bce49020-9", "text": "NUMERIC(10, 2) NOT NULL, \\n\\tPRIMARY KEY (\"InvoiceId\"), \\n\\tFOREIGN KEY(\"CustomerId\") REFERENCES \"Customer\" (\"CustomerId\")\\n)\\n\\n/*\\n3 rows from Invoice table:\\nInvoiceId\\tCustomerId\\tInvoiceDate\\tBillingAddress\\tBillingCity\\tBillingState\\tBillingCountry\\tBillingPostalCode\\tTotal\\n1\\t2\\t2009-01-01 00:00:00\\tTheodor-Heuss-Stra\u00c3\u0178e 34\\tStuttgart\\tNone\\tGermany\\t70174\\t1.98\\n2\\t4\\t2009-01-02 00:00:00\\tUllev\u00c3\u00a5lsveien 14\\tOslo\\tNone\\tNorway\\t0171\\t3.96\\n3\\t8\\t2009-01-03 00:00:00\\tGr\u00c3\u00a9trystraat 63\\tBrussels\\tNone\\tBelgium\\t1000\\t5.94\\n*/\\n\\n\\nCREATE TABLE \"Track\" (\\n\\t\"TrackId\" INTEGER NOT NULL, \\n\\t\"Name\" NVARCHAR(200) NOT NULL, \\n\\t\"AlbumId\" INTEGER, \\n\\t\"MediaTypeId\" INTEGER NOT NULL, \\n\\t\"GenreId\" INTEGER, \\n\\t\"Composer\" NVARCHAR(220), \\n\\t\"Milliseconds\" INTEGER NOT NULL, \\n\\t\"Bytes\" INTEGER, \\n\\t\"UnitPrice\" NUMERIC(10, 2) NOT NULL, \\n\\tPRIMARY KEY (\"TrackId\"), \\n\\tFOREIGN KEY(\"MediaTypeId\") REFERENCES \"MediaType\" (\"MediaTypeId\"), \\n\\tFOREIGN KEY(\"GenreId\") REFERENCES \"Genre\" (\"GenreId\"), \\n\\tFOREIGN KEY(\"AlbumId\") REFERENCES \"Album\" (\"AlbumId\")\\n)\\n\\n/*\\n3 rows from Track", "source": "https://python.langchain.com/docs/modules/chains/popular/sqlite"}
{"id": "23d1bce49020-10", "text": "REFERENCES \"Album\" (\"AlbumId\")\\n)\\n\\n/*\\n3 rows from Track table:\\nTrackId\\tName\\tAlbumId\\tMediaTypeId\\tGenreId\\tComposer\\tMilliseconds\\tBytes\\tUnitPrice\\n1\\tFor Those About To Rock (We Salute You)\\t1\\t1\\t1\\tAngus Young, Malcolm Young, Brian Johnson\\t343719\\t11170334\\t0.99\\n2\\tBalls to the Wall\\t2\\t2\\t1\\tNone\\t342562\\t5510424\\t0.99\\n3\\tFast As a Shark\\t3\\t2\\t1\\tF. Baltes, S. Kaufman, U. Dirkscneider & W. Hoffman\\t230619\\t3990994\\t0.99\\n*/\\n\\n\\nCREATE TABLE \"InvoiceLine\" (\\n\\t\"InvoiceLineId\" INTEGER NOT NULL, \\n\\t\"InvoiceId\" INTEGER NOT NULL, \\n\\t\"TrackId\" INTEGER NOT NULL, \\n\\t\"UnitPrice\" NUMERIC(10, 2) NOT NULL, \\n\\t\"Quantity\" INTEGER NOT NULL, \\n\\tPRIMARY KEY (\"InvoiceLineId\"), \\n\\tFOREIGN KEY(\"TrackId\") REFERENCES \"Track\" (\"TrackId\"), \\n\\tFOREIGN KEY(\"InvoiceId\") REFERENCES \"Invoice\" (\"InvoiceId\")\\n)\\n\\n/*\\n3 rows from InvoiceLine table:\\nInvoiceLineId\\tInvoiceId\\tTrackId\\tUnitPrice\\tQuantity\\n1\\t1\\t2\\t0.99\\t1\\n2\\t1\\t4\\t0.99\\t1\\n3\\t2\\t6\\t0.99\\t1\\n*/\\n\\n\\nCREATE TABLE \"PlaylistTrack\" (\\n\\t\"PlaylistId\" INTEGER NOT NULL, \\n\\t\"TrackId\" INTEGER NOT NULL, \\n\\tPRIMARY KEY (\"PlaylistId\", \"TrackId\"), \\n\\tFOREIGN KEY(\"TrackId\")", "source": "https://python.langchain.com/docs/modules/chains/popular/sqlite"}
{"id": "23d1bce49020-11", "text": "KEY (\"PlaylistId\", \"TrackId\"), \\n\\tFOREIGN KEY(\"TrackId\") REFERENCES \"Track\" (\"TrackId\"), \\n\\tFOREIGN KEY(\"PlaylistId\") REFERENCES \"Playlist\" (\"PlaylistId\")\\n)\\n\\n/*\\n3 rows from PlaylistTrack table:\\nPlaylistId\\tTrackId\\n1\\t3402\\n1\\t3389\\n1\\t3390\\n*/',      'stop': ['\\nSQLResult:']},     'SELECT COUNT(*) FROM Employee;',     {'query': 'SELECT COUNT(*) FROM Employee;', 'dialect': 'sqlite'},     'SELECT COUNT(*) FROM Employee;',     '[(8,)]']Choosing how to limit the number of rows returned\u00e2\u20ac\u2039If you are querying for several rows of a table you can select the maximum number of results you want to get by using the 'top_k' parameter (default is 10). This is useful for avoiding query results that exceed the prompt max length or consume tokens unnecessarily.db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True, use_query_checker=True, top_k=3)db_chain.run(\"What are some example tracks by composer Johann Sebastian Bach?\")            > Entering new SQLDatabaseChain chain...    What are some example tracks by composer Johann Sebastian Bach?    SQLQuery:SELECT Name FROM Track WHERE Composer = 'Johann Sebastian Bach' LIMIT 3    SQLResult: [('Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace',), ('Aria Mit 30 Ver\u00c3\u00a4nderungen, BWV 988 \"Goldberg Variations\": Aria',), ('Suite for Solo Cello No. 1 in G Major, BWV 1007: I.", "source": "https://python.langchain.com/docs/modules/chains/popular/sqlite"}
{"id": "23d1bce49020-12", "text": "for Solo Cello No. 1 in G Major, BWV 1007: I. Pr\u00c3\u00a9lude',)]    Answer:Examples of tracks by Johann Sebastian Bach are Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace, Aria Mit 30 Ver\u00c3\u00a4nderungen, BWV 988 \"Goldberg Variations\": Aria, and Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Pr\u00c3\u00a9lude.    > Finished chain.    'Examples of tracks by Johann Sebastian Bach are Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace, Aria Mit 30 Ver\u00c3\u00a4nderungen, BWV 988 \"Goldberg Variations\": Aria, and Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Pr\u00c3\u00a9lude.'Adding example rows from each table\u00e2\u20ac\u2039Sometimes, the format of the data is not obvious and it is optimal to include a sample of rows from the tables in the prompt to allow the LLM to understand the data before providing a final query. Here we will use this feature to let the LLM know that artists are saved with their full names by providing two rows from the Track table.db = SQLDatabase.from_uri(    \"sqlite:///../../../../notebooks/Chinook.db\",    include_tables=['Track'], # we include only one table to save tokens in the prompt :)    sample_rows_in_table_info=2)The sample rows are added to the prompt after each corresponding table's column information:print(db.table_info)        CREATE TABLE \"Track\" (        \"TrackId\" INTEGER NOT NULL,         \"Name\"", "source": "https://python.langchain.com/docs/modules/chains/popular/sqlite"}
{"id": "23d1bce49020-13", "text": "\"TrackId\" INTEGER NOT NULL,         \"Name\" NVARCHAR(200) NOT NULL,         \"AlbumId\" INTEGER,         \"MediaTypeId\" INTEGER NOT NULL,         \"GenreId\" INTEGER,         \"Composer\" NVARCHAR(220),         \"Milliseconds\" INTEGER NOT NULL,         \"Bytes\" INTEGER,         \"UnitPrice\" NUMERIC(10, 2) NOT NULL,         PRIMARY KEY (\"TrackId\"),         FOREIGN KEY(\"MediaTypeId\") REFERENCES \"MediaType\" (\"MediaTypeId\"),         FOREIGN KEY(\"GenreId\") REFERENCES \"Genre\" (\"GenreId\"),         FOREIGN KEY(\"AlbumId\") REFERENCES \"Album\" (\"AlbumId\")    )        /*    2 rows from Track table:    TrackId Name    AlbumId MediaTypeId GenreId Composer    Milliseconds    Bytes   UnitPrice    1   For Those About To Rock (We Salute You) 1   1   1   Angus Young, Malcolm Young, Brian Johnson   343719  11170334    0.99    2   Balls to the Wall   2   2   1   None    342562  5510424 0.99    */db_chain = SQLDatabaseChain.from_llm(llm, db, use_query_checker=True, verbose=True)db_chain.run(\"What are some example", "source": "https://python.langchain.com/docs/modules/chains/popular/sqlite"}
{"id": "23d1bce49020-14", "text": "db, use_query_checker=True, verbose=True)db_chain.run(\"What are some example tracks by Bach?\")            > Entering new SQLDatabaseChain chain...    What are some example tracks by Bach?    SQLQuery:SELECT \"Name\", \"Composer\" FROM \"Track\" WHERE \"Composer\" LIKE '%Bach%' LIMIT 5    SQLResult: [('American Woman', 'B. Cummings/G. Peterson/M.J. Kale/R. Bachman'), ('Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace', 'Johann Sebastian Bach'), ('Aria Mit 30 Ver\u00c3\u00a4nderungen, BWV 988 \"Goldberg Variations\": Aria', 'Johann Sebastian Bach'), ('Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Pr\u00c3\u00a9lude', 'Johann Sebastian Bach'), ('Toccata and Fugue in D Minor, BWV 565: I. Toccata', 'Johann Sebastian Bach')]    Answer:Tracks by Bach include 'American Woman', 'Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace', 'Aria Mit 30 Ver\u00c3\u00a4nderungen, BWV 988 \"Goldberg Variations\": Aria', 'Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Pr\u00c3\u00a9lude', and 'Toccata and Fugue in D Minor, BWV 565: I. Toccata'.    > Finished chain.    'Tracks by Bach include \\'American Woman\\', \\'Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace\\', \\'Aria Mit 30", "source": "https://python.langchain.com/docs/modules/chains/popular/sqlite"}
{"id": "23d1bce49020-15", "text": "D Minor, BWV 1043: I. Vivace\\', \\'Aria Mit 30 Ver\u00c3\u00a4nderungen, BWV 988 \"Goldberg Variations\": Aria\\', \\'Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Pr\u00c3\u00a9lude\\', and \\'Toccata and Fugue in D Minor, BWV 565: I. Toccata\\'.'Custom Table Info\u00e2\u20ac\u2039In some cases, it can be useful to provide custom table information instead of using the automatically generated table definitions and the first sample_rows_in_table_info sample rows. For example, if you know that the first few rows of a table are uninformative, it could help to manually provide example rows that are more diverse or provide more information to the model. It is also possible to limit the columns that will be visible to the model if there are unnecessary columns. This information can be provided as a dictionary with table names as the keys and table information as the values. For example, let's provide a custom definition and sample rows for the Track table with only a few columns:custom_table_info = {    \"Track\": \"\"\"CREATE TABLE Track (    \"TrackId\" INTEGER NOT NULL,     \"Name\" NVARCHAR(200) NOT NULL,    \"Composer\" NVARCHAR(220),    PRIMARY KEY (\"TrackId\"))/*3 rows from Track table:TrackId Name    Composer1   For Those About To Rock (We Salute You) Angus Young, Malcolm Young, Brian Johnson2   Balls to the Wall   None3   My favorite song ever   The coolest composer of all time*/\"\"\"}db = SQLDatabase.from_uri(    \"sqlite:///../../../../notebooks/Chinook.db\",    include_tables=['Track', 'Playlist'],", "source": "https://python.langchain.com/docs/modules/chains/popular/sqlite"}
{"id": "23d1bce49020-16", "text": "include_tables=['Track', 'Playlist'],    sample_rows_in_table_info=2,    custom_table_info=custom_table_info)print(db.table_info)        CREATE TABLE \"Playlist\" (        \"PlaylistId\" INTEGER NOT NULL,         \"Name\" NVARCHAR(120),         PRIMARY KEY (\"PlaylistId\")    )        /*    2 rows from Playlist table:    PlaylistId  Name    1   Music    2   Movies    */        CREATE TABLE Track (        \"TrackId\" INTEGER NOT NULL,         \"Name\" NVARCHAR(200) NOT NULL,        \"Composer\" NVARCHAR(220),        PRIMARY KEY (\"TrackId\")    )    /*    3 rows from Track table:    TrackId Name    Composer    1   For Those About To Rock (We Salute You) Angus Young, Malcolm Young, Brian Johnson    2   Balls to the Wall   None    3   My favorite song ever   The coolest composer of all time    */Note how our custom table definition and sample rows for Track overrides the sample_rows_in_table_info parameter. Tables that are not overridden by custom_table_info, in this example Playlist, will have their table info gathered automatically as usual.db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)db_chain.run(\"What are some example tracks by Bach?\")            > Entering new", "source": "https://python.langchain.com/docs/modules/chains/popular/sqlite"}
{"id": "23d1bce49020-17", "text": "tracks by Bach?\")            > Entering new SQLDatabaseChain chain...    What are some example tracks by Bach?    SQLQuery:SELECT \"Name\" FROM Track WHERE \"Composer\" LIKE '%Bach%' LIMIT 5;    SQLResult: [('American Woman',), ('Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace',), ('Aria Mit 30 Ver\u00c3\u00a4nderungen, BWV 988 \"Goldberg Variations\": Aria',), ('Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Pr\u00c3\u00a9lude',), ('Toccata and Fugue in D Minor, BWV 565: I. Toccata',)]    Answer:text='You are a SQLite expert. Given an input question, first create a syntactically correct SQLite query to run, then look at the results of the query and return the answer to the input question.\\nUnless the user specifies in the question a specific number of examples to obtain, query for at most 5 results using the LIMIT clause as per SQLite. You can order the results to return the most informative data in the database.\\nNever query for all columns from a table. You must query only the columns that are needed to answer the question. Wrap each column name in double quotes (\") to denote them as delimited identifiers.\\nPay attention to use only the column names you can see in the tables below. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.\\n\\nUse the following format:\\n\\nQuestion: \"Question here\"\\nSQLQuery: \"SQL Query to run\"\\nSQLResult: \"Result of the SQLQuery\"\\nAnswer: \"Final answer here\"\\n\\nOnly use the following", "source": "https://python.langchain.com/docs/modules/chains/popular/sqlite"}
{"id": "23d1bce49020-18", "text": "of the SQLQuery\"\\nAnswer: \"Final answer here\"\\n\\nOnly use the following tables:\\n\\nCREATE TABLE \"Playlist\" (\\n\\t\"PlaylistId\" INTEGER NOT NULL, \\n\\t\"Name\" NVARCHAR(120), \\n\\tPRIMARY KEY (\"PlaylistId\")\\n)\\n\\n/*\\n2 rows from Playlist table:\\nPlaylistId\\tName\\n1\\tMusic\\n2\\tMovies\\n*/\\n\\nCREATE TABLE Track (\\n\\t\"TrackId\" INTEGER NOT NULL, \\n\\t\"Name\" NVARCHAR(200) NOT NULL,\\n\\t\"Composer\" NVARCHAR(220),\\n\\tPRIMARY KEY (\"TrackId\")\\n)\\n/*\\n3 rows from Track table:\\nTrackId\\tName\\tComposer\\n1\\tFor Those About To Rock (We Salute You)\\tAngus Young, Malcolm Young, Brian Johnson\\n2\\tBalls to the Wall\\tNone\\n3\\tMy favorite song ever\\tThe coolest composer of all time\\n*/\\n\\nQuestion: What are some example tracks by Bach?\\nSQLQuery:SELECT \"Name\" FROM Track WHERE \"Composer\" LIKE \\'%Bach%\\' LIMIT 5;\\nSQLResult: [(\\'American Woman\\',), (\\'Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace\\',), (\\'Aria Mit 30 Ver\u00c3\u00a4nderungen, BWV 988 \"Goldberg Variations\": Aria\\',), (\\'Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Pr\u00c3\u00a9lude\\',), (\\'Toccata and Fugue in D Minor, BWV 565: I. Toccata\\',)]\\nAnswer:'    You are a SQLite expert. Given an input question, first create a syntactically correct SQLite query to run, then look at the results of the", "source": "https://python.langchain.com/docs/modules/chains/popular/sqlite"}
{"id": "23d1bce49020-19", "text": "first create a syntactically correct SQLite query to run, then look at the results of the query and return the answer to the input question.    Unless the user specifies in the question a specific number of examples to obtain, query for at most 5 results using the LIMIT clause as per SQLite. You can order the results to return the most informative data in the database.    Never query for all columns from a table. You must query only the columns that are needed to answer the question. Wrap each column name in double quotes (\") to denote them as delimited identifiers.    Pay attention to use only the column names you can see in the tables below. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.        Use the following format:        Question: \"Question here\"    SQLQuery: \"SQL Query to run\"    SQLResult: \"Result of the SQLQuery\"    Answer: \"Final answer here\"        Only use the following tables:        CREATE TABLE \"Playlist\" (        \"PlaylistId\" INTEGER NOT NULL,         \"Name\" NVARCHAR(120),         PRIMARY KEY (\"PlaylistId\")    )        /*    2 rows from Playlist table:    PlaylistId  Name    1   Music    2   Movies    */        CREATE TABLE Track (        \"TrackId\" INTEGER NOT NULL,         \"Name\" NVARCHAR(200) NOT NULL,        \"Composer\"", "source": "https://python.langchain.com/docs/modules/chains/popular/sqlite"}
{"id": "23d1bce49020-20", "text": "NVARCHAR(200) NOT NULL,        \"Composer\" NVARCHAR(220),        PRIMARY KEY (\"TrackId\")    )    /*    3 rows from Track table:    TrackId Name    Composer    1   For Those About To Rock (We Salute You) Angus Young, Malcolm Young, Brian Johnson    2   Balls to the Wall   None    3   My favorite song ever   The coolest composer of all time    */        Question: What are some example tracks by Bach?    SQLQuery:SELECT \"Name\" FROM Track WHERE \"Composer\" LIKE '%Bach%' LIMIT 5;    SQLResult: [('American Woman',), ('Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace',), ('Aria Mit 30 Ver\u00c3\u00a4nderungen, BWV 988 \"Goldberg Variations\": Aria',), ('Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Pr\u00c3\u00a9lude',), ('Toccata and Fugue in D Minor, BWV 565: I. Toccata',)]    Answer:    {'input': 'What are some example tracks by Bach?\\nSQLQuery:SELECT \"Name\" FROM Track WHERE \"Composer\" LIKE \\'%Bach%\\' LIMIT 5;\\nSQLResult: [(\\'American Woman\\',), (\\'Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace\\',), (\\'Aria Mit 30 Ver\u00c3\u00a4nderungen, BWV 988 \"Goldberg Variations\": Aria\\',), (\\'Suite for Solo Cello No.", "source": "https://python.langchain.com/docs/modules/chains/popular/sqlite"}
{"id": "23d1bce49020-21", "text": "\"Goldberg Variations\": Aria\\',), (\\'Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Pr\u00c3\u00a9lude\\',), (\\'Toccata and Fugue in D Minor, BWV 565: I. Toccata\\',)]\\nAnswer:', 'top_k': '5', 'dialect': 'sqlite', 'table_info': '\\nCREATE TABLE \"Playlist\" (\\n\\t\"PlaylistId\" INTEGER NOT NULL, \\n\\t\"Name\" NVARCHAR(120), \\n\\tPRIMARY KEY (\"PlaylistId\")\\n)\\n\\n/*\\n2 rows from Playlist table:\\nPlaylistId\\tName\\n1\\tMusic\\n2\\tMovies\\n*/\\n\\nCREATE TABLE Track (\\n\\t\"TrackId\" INTEGER NOT NULL, \\n\\t\"Name\" NVARCHAR(200) NOT NULL,\\n\\t\"Composer\" NVARCHAR(220),\\n\\tPRIMARY KEY (\"TrackId\")\\n)\\n/*\\n3 rows from Track table:\\nTrackId\\tName\\tComposer\\n1\\tFor Those About To Rock (We Salute You)\\tAngus Young, Malcolm Young, Brian Johnson\\n2\\tBalls to the Wall\\tNone\\n3\\tMy favorite song ever\\tThe coolest composer of all time\\n*/', 'stop': ['\\nSQLResult:']}    Examples of tracks by Bach include \"American Woman\", \"Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace\", \"Aria Mit 30 Ver\u00c3\u00a4nderungen, BWV 988 'Goldberg Variations': Aria\", \"Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Pr\u00c3\u00a9lude\", and \"Toccata and Fugue in D Minor, BWV 565: I. Toccata\".", "source": "https://python.langchain.com/docs/modules/chains/popular/sqlite"}
{"id": "23d1bce49020-22", "text": "in D Minor, BWV 565: I. Toccata\".    > Finished chain.    'Examples of tracks by Bach include \"American Woman\", \"Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace\", \"Aria Mit 30 Ver\u00c3\u00a4nderungen, BWV 988 \\'Goldberg Variations\\': Aria\", \"Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Pr\u00c3\u00a9lude\", and \"Toccata and Fugue in D Minor, BWV 565: I. Toccata\".'SQL Views\u00e2\u20ac\u2039In some case, the table schema can be hidden behind a JSON or JSONB column. Adding row samples into the prompt might help won't always describe the data perfectly. For this reason, a custom SQL views can help.CREATE VIEW accounts_v AS    select id, firstname, lastname, email, created_at, updated_at,        cast(stats->>'total_post' as int) as total_post,        cast(stats->>'total_comments' as int) as total_comments,        cast(stats->>'ltv' as int) as ltv        FROM accounts;Then limit the tables visible from SQLDatabase to the created view.db = SQLDatabase.from_uri(    \"sqlite:///../../../../notebooks/Chinook.db\",    include_tables=['accounts_v']) # we include only the viewSQLDatabaseSequentialChain\u00e2\u20ac\u2039Chain for querying SQL database that is a sequential chain.The chain is as follows:1. Based on the query, determine which tables to use.2. Based on those tables, call the normal SQL database chain.This is useful in cases where the number of tables in the database is large.from", "source": "https://python.langchain.com/docs/modules/chains/popular/sqlite"}
{"id": "23d1bce49020-23", "text": "normal SQL database chain.This is useful in cases where the number of tables in the database is large.from langchain.chains import SQLDatabaseSequentialChaindb = SQLDatabase.from_uri(\"sqlite:///../../../../notebooks/Chinook.db\")chain = SQLDatabaseSequentialChain.from_llm(llm, db, verbose=True)chain.run(\"How many employees are also customers?\")            > Entering new SQLDatabaseSequentialChain chain...    Table names to use:    ['Employee', 'Customer']        > Entering new SQLDatabaseChain chain...    How many employees are also customers?    SQLQuery:SELECT COUNT(*) FROM Employee e INNER JOIN Customer c ON e.EmployeeId = c.SupportRepId;    SQLResult: [(59,)]    Answer:59 employees are also customers.    > Finished chain.        > Finished chain.    '59 employees are also customers.'Using Local Language Models\u00e2\u20ac\u2039Sometimes you may not have the luxury of using OpenAI or other service-hosted large language model. You can, ofcourse, try to use the SQLDatabaseChain with a local model, but will quickly realize that most models you can run locally even with a large GPU struggle to generate the right output.import loggingimport torchfrom transformers import AutoTokenizer, GPT2TokenizerFast, pipeline, AutoModelForSeq2SeqLM, AutoModelForCausalLMfrom langchain import HuggingFacePipeline# Note: This model requires a large GPU, e.g. an 80GB A100. See documentation for other ways to run private non-OpenAI models.model_id = \"google/flan-ul2\"model = AutoModelForSeq2SeqLM.from_pretrained(model_id, temperature=0)device_id = -1  # default to no-GPU, but use GPU", "source": "https://python.langchain.com/docs/modules/chains/popular/sqlite"}
{"id": "23d1bce49020-24", "text": "temperature=0)device_id = -1  # default to no-GPU, but use GPU and half precision mode if availableif torch.cuda.is_available():    device_id = 0    try:        model = model.half()    except RuntimeError as exc:        logging.warn(f\"Could not run model in half precision mode: {str(exc)}\")tokenizer = AutoTokenizer.from_pretrained(model_id)pipe = pipeline(task=\"text2text-generation\", model=model, tokenizer=tokenizer, max_length=1024, device=device_id)local_llm = HuggingFacePipeline(pipeline=pipe)    /workspace/langchain/.venv/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html      from .autonotebook import tqdm as notebook_tqdm    Loading checkpoint shards: 100%|\u00e2\u2013\u02c6\u00e2\u2013\u02c6\u00e2\u2013\u02c6\u00e2\u2013\u02c6\u00e2\u2013\u02c6\u00e2\u2013\u02c6\u00e2\u2013\u02c6\u00e2\u2013\u02c6\u00e2\u2013\u02c6\u00e2\u2013\u02c6| 8/8 [00:32<00:00,  4.11s/it]from langchain import SQLDatabase, SQLDatabaseChaindb = SQLDatabase.from_uri(\"sqlite:///../../../../notebooks/Chinook.db\", include_tables=['Customer'])local_chain = SQLDatabaseChain.from_llm(local_llm, db, verbose=True, return_intermediate_steps=True, use_query_checker=True)This model should work for very simple SQL queries, as long as you use the query checker as specified above, e.g.:local_chain(\"How many customers are there?\")", "source": "https://python.langchain.com/docs/modules/chains/popular/sqlite"}
{"id": "23d1bce49020-25", "text": "many customers are there?\")            > Entering new SQLDatabaseChain chain...    How many customers are there?    SQLQuery:    /workspace/langchain/.venv/lib/python3.9/site-packages/transformers/pipelines/base.py:1070: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset      warnings.warn(    /workspace/langchain/.venv/lib/python3.9/site-packages/transformers/pipelines/base.py:1070: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset      warnings.warn(    SELECT count(*) FROM Customer    SQLResult: [(59,)]    Answer:    /workspace/langchain/.venv/lib/python3.9/site-packages/transformers/pipelines/base.py:1070: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset      warnings.warn(    [59]    > Finished chain.    {'query': 'How many customers are there?',     'result': '[59]',     'intermediate_steps': [{'input': 'How many customers are there?\\nSQLQuery:SELECT count(*) FROM Customer\\nSQLResult: [(59,)]\\nAnswer:',       'top_k': '5',       'dialect': 'sqlite',       'table_info': '\\nCREATE TABLE \"Customer\" (\\n\\t\"CustomerId\" INTEGER NOT NULL, \\n\\t\"FirstName\" NVARCHAR(40) NOT NULL, \\n\\t\"LastName\" NVARCHAR(20) NOT NULL,", "source": "https://python.langchain.com/docs/modules/chains/popular/sqlite"}
{"id": "23d1bce49020-26", "text": "NOT NULL, \\n\\t\"LastName\" NVARCHAR(20) NOT NULL, \\n\\t\"Company\" NVARCHAR(80), \\n\\t\"Address\" NVARCHAR(70), \\n\\t\"City\" NVARCHAR(40), \\n\\t\"State\" NVARCHAR(40), \\n\\t\"Country\" NVARCHAR(40), \\n\\t\"PostalCode\" NVARCHAR(10), \\n\\t\"Phone\" NVARCHAR(24), \\n\\t\"Fax\" NVARCHAR(24), \\n\\t\"Email\" NVARCHAR(60) NOT NULL, \\n\\t\"SupportRepId\" INTEGER, \\n\\tPRIMARY KEY (\"CustomerId\"), \\n\\tFOREIGN KEY(\"SupportRepId\") REFERENCES \"Employee\" (\"EmployeeId\")\\n)\\n\\n/*\\n3 rows from Customer table:\\nCustomerId\\tFirstName\\tLastName\\tCompany\\tAddress\\tCity\\tState\\tCountry\\tPostalCode\\tPhone\\tFax\\tEmail\\tSupportRepId\\n1\\tLu\u00c3\u00ads\\tGon\u00c3\u00a7alves\\tEmbraer - Empresa Brasileira de Aeron\u00c3\u00a1utica S.A.\\tAv. Brigadeiro Faria Lima, 2170\\tS\u00c3\u00a3o Jos\u00c3\u00a9 dos Campos\\tSP\\tBrazil\\t12227-000\\t+55 (12) 3923-5555\\t+55 (12) 3923-5566\\tluisg@embraer.com.br\\t3\\n2\\tLeonie\\tK\u00c3\u00b6hler\\tNone\\tTheodor-Heuss-Stra\u00c3\u0178e 34\\tStuttgart\\tNone\\tGermany\\t70174\\t+49 0711 2842222\\tNone\\tleonekohler@surfeu.de\\t5\\n3\\tFran\u00c3\u00a7ois\\tTremblay\\tNone\\t1498 rue B\u00c3\u00a9langer\\tMontr\u00c3\u00a9al\\tQC\\tCanada\\tH2G", "source": "https://python.langchain.com/docs/modules/chains/popular/sqlite"}
{"id": "23d1bce49020-27", "text": "rue B\u00c3\u00a9langer\\tMontr\u00c3\u00a9al\\tQC\\tCanada\\tH2G 1A7\\t+1 (514) 721-4711\\tNone\\tftremblay@gmail.com\\t3\\n*/',       'stop': ['\\nSQLResult:']},      'SELECT count(*) FROM Customer',      {'query': 'SELECT count(*) FROM Customer', 'dialect': 'sqlite'},      'SELECT count(*) FROM Customer',      '[(59,)]']}Even this relatively large model will most likely fail to generate more complicated SQL by itself. However, you can log its inputs and outputs so that you can hand-correct them and use the corrected examples for few shot prompt examples later. In practice, you could log any executions of your chain that raise exceptions (as shown in the example below) or get direct user feedback in cases where the results are incorrect (but did not raise an exception).poetry run pip install pyyaml chromadbimport yaml    huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...    To disable this warning, you can either:        - Avoid using `tokenizers` before the fork if possible        - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)    11842.36s - pydevd: Sending message related to process being replaced timed-out after 5 seconds    Requirement already satisfied: pyyaml in /workspace/langchain/.venv/lib/python3.9/site-packages (6.0)    Requirement already satisfied: chromadb in /workspace/langchain/.venv/lib/python3.9/site-packages (0.3.21)", "source": "https://python.langchain.com/docs/modules/chains/popular/sqlite"}
{"id": "23d1bce49020-28", "text": "(0.3.21)    Requirement already satisfied: pandas>=1.3 in /workspace/langchain/.venv/lib/python3.9/site-packages (from chromadb) (2.0.1)    Requirement already satisfied: requests>=2.28 in /workspace/langchain/.venv/lib/python3.9/site-packages (from chromadb) (2.28.2)    Requirement already satisfied: pydantic>=1.9 in /workspace/langchain/.venv/lib/python3.9/site-packages (from chromadb) (1.10.7)    Requirement already satisfied: hnswlib>=0.7 in /workspace/langchain/.venv/lib/python3.9/site-packages (from chromadb) (0.7.0)    Requirement already satisfied: clickhouse-connect>=0.5.7 in /workspace/langchain/.venv/lib/python3.9/site-packages (from chromadb) (0.5.20)    Requirement already satisfied: sentence-transformers>=2.2.2 in /workspace/langchain/.venv/lib/python3.9/site-packages (from chromadb) (2.2.2)    Requirement already satisfied: duckdb>=0.7.1 in /workspace/langchain/.venv/lib/python3.9/site-packages (from chromadb) (0.7.1)    Requirement already satisfied: fastapi>=0.85.1 in /workspace/langchain/.venv/lib/python3.9/site-packages (from chromadb) (0.95.1)    Requirement already satisfied: uvicorn[standard]>=0.18.3 in /workspace/langchain/.venv/lib/python3.9/site-packages (from chromadb) (0.21.1)    Requirement already satisfied: numpy>=1.21.6 in", "source": "https://python.langchain.com/docs/modules/chains/popular/sqlite"}
{"id": "23d1bce49020-29", "text": "Requirement already satisfied: numpy>=1.21.6 in /workspace/langchain/.venv/lib/python3.9/site-packages (from chromadb) (1.24.3)    Requirement already satisfied: posthog>=2.4.0 in /workspace/langchain/.venv/lib/python3.9/site-packages (from chromadb) (3.0.1)    Requirement already satisfied: certifi in /workspace/langchain/.venv/lib/python3.9/site-packages (from clickhouse-connect>=0.5.7->chromadb) (2022.12.7)    Requirement already satisfied: urllib3>=1.26 in /workspace/langchain/.venv/lib/python3.9/site-packages (from clickhouse-connect>=0.5.7->chromadb) (1.26.15)    Requirement already satisfied: pytz in /workspace/langchain/.venv/lib/python3.9/site-packages (from clickhouse-connect>=0.5.7->chromadb) (2023.3)    Requirement already satisfied: zstandard in /workspace/langchain/.venv/lib/python3.9/site-packages (from clickhouse-connect>=0.5.7->chromadb) (0.21.0)    Requirement already satisfied: lz4 in /workspace/langchain/.venv/lib/python3.9/site-packages (from clickhouse-connect>=0.5.7->chromadb) (4.3.2)    Requirement already satisfied: starlette<0.27.0,>=0.26.1 in /workspace/langchain/.venv/lib/python3.9/site-packages (from fastapi>=0.85.1->chromadb) (0.26.1)    Requirement already satisfied: python-dateutil>=2.8.2 in", "source": "https://python.langchain.com/docs/modules/chains/popular/sqlite"}
{"id": "23d1bce49020-30", "text": "Requirement already satisfied: python-dateutil>=2.8.2 in /workspace/langchain/.venv/lib/python3.9/site-packages (from pandas>=1.3->chromadb) (2.8.2)    Requirement already satisfied: tzdata>=2022.1 in /workspace/langchain/.venv/lib/python3.9/site-packages (from pandas>=1.3->chromadb) (2023.3)    Requirement already satisfied: six>=1.5 in /workspace/langchain/.venv/lib/python3.9/site-packages (from posthog>=2.4.0->chromadb) (1.16.0)    Requirement already satisfied: monotonic>=1.5 in /workspace/langchain/.venv/lib/python3.9/site-packages (from posthog>=2.4.0->chromadb) (1.6)    Requirement already satisfied: backoff>=1.10.0 in /workspace/langchain/.venv/lib/python3.9/site-packages (from posthog>=2.4.0->chromadb) (2.2.1)    Requirement already satisfied: typing-extensions>=4.2.0 in /workspace/langchain/.venv/lib/python3.9/site-packages (from pydantic>=1.9->chromadb) (4.5.0)    Requirement already satisfied: charset-normalizer<4,>=2 in /workspace/langchain/.venv/lib/python3.9/site-packages (from requests>=2.28->chromadb) (3.1.0)    Requirement already satisfied: idna<4,>=2.5 in /workspace/langchain/.venv/lib/python3.9/site-packages (from requests>=2.28->chromadb) (3.4)    Requirement already satisfied:", "source": "https://python.langchain.com/docs/modules/chains/popular/sqlite"}
{"id": "23d1bce49020-31", "text": "(3.4)    Requirement already satisfied: transformers<5.0.0,>=4.6.0 in /workspace/langchain/.venv/lib/python3.9/site-packages (from sentence-transformers>=2.2.2->chromadb) (4.28.1)    Requirement already satisfied: tqdm in /workspace/langchain/.venv/lib/python3.9/site-packages (from sentence-transformers>=2.2.2->chromadb) (4.65.0)    Requirement already satisfied: torch>=1.6.0 in /workspace/langchain/.venv/lib/python3.9/site-packages (from sentence-transformers>=2.2.2->chromadb) (1.13.1)    Requirement already satisfied: torchvision in /workspace/langchain/.venv/lib/python3.9/site-packages (from sentence-transformers>=2.2.2->chromadb) (0.14.1)    Requirement already satisfied: scikit-learn in /workspace/langchain/.venv/lib/python3.9/site-packages (from sentence-transformers>=2.2.2->chromadb) (1.2.2)    Requirement already satisfied: scipy in /workspace/langchain/.venv/lib/python3.9/site-packages (from sentence-transformers>=2.2.2->chromadb) (1.9.3)    Requirement already satisfied: nltk in /workspace/langchain/.venv/lib/python3.9/site-packages (from sentence-transformers>=2.2.2->chromadb) (3.8.1)    Requirement already satisfied: sentencepiece in /workspace/langchain/.venv/lib/python3.9/site-packages (from sentence-transformers>=2.2.2->chromadb) (0.1.98)    Requirement already satisfied:", "source": "https://python.langchain.com/docs/modules/chains/popular/sqlite"}
{"id": "23d1bce49020-32", "text": "(0.1.98)    Requirement already satisfied: huggingface-hub>=0.4.0 in /workspace/langchain/.venv/lib/python3.9/site-packages (from sentence-transformers>=2.2.2->chromadb) (0.13.4)    Requirement already satisfied: click>=7.0 in /workspace/langchain/.venv/lib/python3.9/site-packages (from uvicorn[standard]>=0.18.3->chromadb) (8.1.3)    Requirement already satisfied: h11>=0.8 in /workspace/langchain/.venv/lib/python3.9/site-packages (from uvicorn[standard]>=0.18.3->chromadb) (0.14.0)    Requirement already satisfied: httptools>=0.5.0 in /workspace/langchain/.venv/lib/python3.9/site-packages (from uvicorn[standard]>=0.18.3->chromadb) (0.5.0)    Requirement already satisfied: python-dotenv>=0.13 in /workspace/langchain/.venv/lib/python3.9/site-packages (from uvicorn[standard]>=0.18.3->chromadb) (1.0.0)    Requirement already satisfied: uvloop!=0.15.0,!=0.15.1,>=0.14.0 in /workspace/langchain/.venv/lib/python3.9/site-packages (from uvicorn[standard]>=0.18.3->chromadb) (0.17.0)    Requirement already satisfied: watchfiles>=0.13 in /workspace/langchain/.venv/lib/python3.9/site-packages (from uvicorn[standard]>=0.18.3->chromadb) (0.19.0)    Requirement already satisfied: websockets>=10.4", "source": "https://python.langchain.com/docs/modules/chains/popular/sqlite"}
{"id": "23d1bce49020-33", "text": "(0.19.0)    Requirement already satisfied: websockets>=10.4 in /workspace/langchain/.venv/lib/python3.9/site-packages (from uvicorn[standard]>=0.18.3->chromadb) (11.0.2)    Requirement already satisfied: filelock in /workspace/langchain/.venv/lib/python3.9/site-packages (from huggingface-hub>=0.4.0->sentence-transformers>=2.2.2->chromadb) (3.12.0)    Requirement already satisfied: packaging>=20.9 in /workspace/langchain/.venv/lib/python3.9/site-packages (from huggingface-hub>=0.4.0->sentence-transformers>=2.2.2->chromadb) (23.1)    Requirement already satisfied: anyio<5,>=3.4.0 in /workspace/langchain/.venv/lib/python3.9/site-packages (from starlette<0.27.0,>=0.26.1->fastapi>=0.85.1->chromadb) (3.6.2)    Requirement already satisfied: nvidia-cuda-runtime-cu11==11.7.99 in /workspace/langchain/.venv/lib/python3.9/site-packages (from torch>=1.6.0->sentence-transformers>=2.2.2->chromadb) (11.7.99)    Requirement already satisfied: nvidia-cudnn-cu11==8.5.0.96 in /workspace/langchain/.venv/lib/python3.9/site-packages (from torch>=1.6.0->sentence-transformers>=2.2.2->chromadb) (8.5.0.96)    Requirement already satisfied:", "source": "https://python.langchain.com/docs/modules/chains/popular/sqlite"}
{"id": "23d1bce49020-34", "text": "(8.5.0.96)    Requirement already satisfied: nvidia-cublas-cu11==11.10.3.66 in /workspace/langchain/.venv/lib/python3.9/site-packages (from torch>=1.6.0->sentence-transformers>=2.2.2->chromadb) (11.10.3.66)    Requirement already satisfied: nvidia-cuda-nvrtc-cu11==11.7.99 in /workspace/langchain/.venv/lib/python3.9/site-packages (from torch>=1.6.0->sentence-transformers>=2.2.2->chromadb) (11.7.99)    Requirement already satisfied: setuptools in /workspace/langchain/.venv/lib/python3.9/site-packages (from nvidia-cublas-cu11==11.10.3.66->torch>=1.6.0->sentence-transformers>=2.2.2->chromadb) (67.7.1)    Requirement already satisfied: wheel in /workspace/langchain/.venv/lib/python3.9/site-packages (from nvidia-cublas-cu11==11.10.3.66->torch>=1.6.0->sentence-transformers>=2.2.2->chromadb) (0.40.0)    Requirement already satisfied: regex!=2019.12.17 in /workspace/langchain/.venv/lib/python3.9/site-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers>=2.2.2->chromadb) (2023.3.23)    Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /workspace/langchain/.venv/lib/python3.9/site-packages (from", "source": "https://python.langchain.com/docs/modules/chains/popular/sqlite"}
{"id": "23d1bce49020-35", "text": "in /workspace/langchain/.venv/lib/python3.9/site-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers>=2.2.2->chromadb) (0.13.3)    Requirement already satisfied: joblib in /workspace/langchain/.venv/lib/python3.9/site-packages (from nltk->sentence-transformers>=2.2.2->chromadb) (1.2.0)    Requirement already satisfied: threadpoolctl>=2.0.0 in /workspace/langchain/.venv/lib/python3.9/site-packages (from scikit-learn->sentence-transformers>=2.2.2->chromadb) (3.1.0)    Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /workspace/langchain/.venv/lib/python3.9/site-packages (from torchvision->sentence-transformers>=2.2.2->chromadb) (9.5.0)    Requirement already satisfied: sniffio>=1.1 in /workspace/langchain/.venv/lib/python3.9/site-packages (from anyio<5,>=3.4.0->starlette<0.27.0,>=0.26.1->fastapi>=0.85.1->chromadb) (1.3.0)from typing import DictQUERY = \"List all the customer first names that start with 'a'\"def _parse_example(result: Dict) -> Dict:    sql_cmd_key = \"sql_cmd\"    sql_result_key = \"sql_result\"    table_info_key = \"table_info\"    input_key = \"input\"    final_answer_key = \"answer\"    _example = {        \"input\": result.get(\"query\"),", "source": "https://python.langchain.com/docs/modules/chains/popular/sqlite"}
{"id": "23d1bce49020-36", "text": "= {        \"input\": result.get(\"query\"),    }    steps = result.get(\"intermediate_steps\")    answer_key = sql_cmd_key # the first one    for step in steps:        # The steps are in pairs, a dict (input) followed by a string (output).        # Unfortunately there is no schema but you can look at the input key of the        # dict to see what the output is supposed to be        if isinstance(step, dict):            # Grab the table info from input dicts in the intermediate steps once            if table_info_key not in _example:                _example[table_info_key] = step.get(table_info_key)            if input_key in step:                if step[input_key].endswith(\"SQLQuery:\"):                    answer_key = sql_cmd_key # this is the SQL generation input                if step[input_key].endswith(\"Answer:\"):                    answer_key = final_answer_key # this is the final answer input            elif sql_cmd_key in step:                _example[sql_cmd_key] = step[sql_cmd_key]", "source": "https://python.langchain.com/docs/modules/chains/popular/sqlite"}
{"id": "23d1bce49020-37", "text": "= step[sql_cmd_key]                answer_key = sql_result_key # this is SQL execution input        elif isinstance(step, str):            # The preceding element should have set the answer_key            _example[answer_key] = step    return _exampleexample: anytry:    result = local_chain(QUERY)    print(\"*** Query succeeded\")    example = _parse_example(result)except Exception as exc:    print(\"*** Query failed\")    result = {        \"query\": QUERY,        \"intermediate_steps\": exc.intermediate_steps    }    example = _parse_example(result)# print for now, in reality you may want to write this out to a YAML file or database for manual fix-ups offlineyaml_example = yaml.dump(example, allow_unicode=True)print(\"\\n\" + yaml_example)            > Entering new SQLDatabaseChain chain...    List all the customer first names that start with 'a'    SQLQuery:    /workspace/langchain/.venv/lib/python3.9/site-packages/transformers/pipelines/base.py:1070: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset      warnings.warn(    SELECT firstname FROM customer WHERE firstname LIKE '%a%'    SQLResult: [('Fran\u00c3\u00a7ois',), ('Franti\u00c5\u00a1ek',), ('Helena',), ('Astrid',), ('Daan',), ('Kara',), ('Eduardo',), ('Alexandre',),", "source": "https://python.langchain.com/docs/modules/chains/popular/sqlite"}
{"id": "23d1bce49020-38", "text": "('Kara',), ('Eduardo',), ('Alexandre',), ('Fernanda',), ('Mark',), ('Frank',), ('Jack',), ('Dan',), ('Kathy',), ('Heather',), ('Frank',), ('Richard',), ('Patrick',), ('Julia',), ('Edward',), ('Martha',), ('Aaron',), ('Madalena',), ('Hannah',), ('Niklas',), ('Camille',), ('Marc',), ('Wyatt',), ('Isabelle',), ('Ladislav',), ('Lucas',), ('Johannes',), ('Stanis\u00c5\u201aaw',), ('Joakim',), ('Emma',), ('Mark',), ('Manoj',), ('Puja',)]    Answer:    /workspace/langchain/.venv/lib/python3.9/site-packages/transformers/pipelines/base.py:1070: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset      warnings.warn(    [('Fran\u00c3\u00a7ois', 'Frantiek', 'Helena', 'Astrid', 'Daan', 'Kara', 'Eduardo', 'Alexandre', 'Fernanda', 'Mark', 'Frank', 'Jack', 'Dan', 'Kathy', 'Heather', 'Frank', 'Richard', 'Patrick', 'Julia', 'Edward', 'Martha', 'Aaron', 'Madalena', 'Hannah', 'Niklas', 'Camille', 'Marc', 'Wyatt', 'Isabelle', 'Ladislav', 'Lucas', 'Johannes', 'Stanisaw', 'Joakim', 'Emma', 'Mark', 'Manoj', 'Puja']    > Finished chain.    *** Query succeeded", "source": "https://python.langchain.com/docs/modules/chains/popular/sqlite"}
{"id": "23d1bce49020-39", "text": "'Puja']    > Finished chain.    *** Query succeeded        answer: '[(''Fran\u00c3\u00a7ois'', ''Frantiek'', ''Helena'', ''Astrid'', ''Daan'', ''Kara'',      ''Eduardo'', ''Alexandre'', ''Fernanda'', ''Mark'', ''Frank'', ''Jack'', ''Dan'',      ''Kathy'', ''Heather'', ''Frank'', ''Richard'', ''Patrick'', ''Julia'', ''Edward'',      ''Martha'', ''Aaron'', ''Madalena'', ''Hannah'', ''Niklas'', ''Camille'', ''Marc'',      ''Wyatt'', ''Isabelle'', ''Ladislav'', ''Lucas'', ''Johannes'', ''Stanisaw'', ''Joakim'',      ''Emma'', ''Mark'', ''Manoj'', ''Puja'']'    input: List all the customer first names that start with 'a'    sql_cmd: SELECT firstname FROM customer WHERE firstname LIKE '%a%'    sql_result: '[(''Fran\u00c3\u00a7ois'',), (''Franti\u00c5\u00a1ek'',), (''Helena'',), (''Astrid'',), (''Daan'',),      (''Kara'',), (''Eduardo'',), (''Alexandre'',), (''Fernanda'',), (''Mark'',), (''Frank'',),      (''Jack'',), (''Dan'',), (''Kathy'',), (''Heather'',), (''Frank'',), (''Richard'',),      (''Patrick'',), (''Julia'',), (''Edward'',), (''Martha'',), (''Aaron'',),", "source": "https://python.langchain.com/docs/modules/chains/popular/sqlite"}
{"id": "23d1bce49020-40", "text": "(''Edward'',), (''Martha'',), (''Aaron'',), (''Madalena'',),      (''Hannah'',), (''Niklas'',), (''Camille'',), (''Marc'',), (''Wyatt'',), (''Isabelle'',),      (''Ladislav'',), (''Lucas'',), (''Johannes'',), (''Stanis\u00c5\u201aaw'',), (''Joakim'',),      (''Emma'',), (''Mark'',), (''Manoj'',), (''Puja'',)]'    table_info: \"\\nCREATE TABLE \\\"Customer\\\" (\\n\\t\\\"CustomerId\\\" INTEGER NOT NULL, \\n\\t\\      \\\"FirstName\\\" NVARCHAR(40) NOT NULL, \\n\\t\\\"LastName\\\" NVARCHAR(20) NOT NULL, \\n\\t\\      \\\"Company\\\" NVARCHAR(80), \\n\\t\\\"Address\\\" NVARCHAR(70), \\n\\t\\\"City\\\" NVARCHAR(40),\\      \\ \\n\\t\\\"State\\\" NVARCHAR(40), \\n\\t\\\"Country\\\" NVARCHAR(40), \\n\\t\\\"PostalCode\\\" NVARCHAR(10),\\      \\ \\n\\t\\\"Phone\\\" NVARCHAR(24), \\n\\t\\\"Fax\\\" NVARCHAR(24), \\n\\t\\\"Email\\\" NVARCHAR(60)\\      \\ NOT NULL, \\n\\t\\\"SupportRepId\\\" INTEGER, \\n\\tPRIMARY KEY (\\\"CustomerId\\\"), \\n\\t\\      FOREIGN KEY(\\\"SupportRepId\\\") REFERENCES \\\"Employee\\\" (\\\"EmployeeId\\\")\\n)\\n\\n/*\\n\\      3 rows from Customer table:\\nCustomerId\\tFirstName\\tLastName\\tCompany\\tAddress\\t\\", "source": "https://python.langchain.com/docs/modules/chains/popular/sqlite"}
{"id": "23d1bce49020-41", "text": "3 rows from Customer table:\\nCustomerId\\tFirstName\\tLastName\\tCompany\\tAddress\\t\\      City\\tState\\tCountry\\tPostalCode\\tPhone\\tFax\\tEmail\\tSupportRepId\\n1\\tLu\u00c3\u00ads\\tGon\u00c3\u00a7alves\\t\\      Embraer - Empresa Brasileira de Aeron\u00c3\u00a1utica S.A.\\tAv. Brigadeiro Faria Lima, 2170\\t\\      S\u00c3\u00a3o Jos\u00c3\u00a9 dos Campos\\tSP\\tBrazil\\t12227-000\\t+55 (12) 3923-5555\\t+55 (12) 3923-5566\\t\\      luisg@embraer.com.br\\t3\\n2\\tLeonie\\tK\u00c3\u00b6hler\\tNone\\tTheodor-Heuss-Stra\u00c3\u0178e 34\\tStuttgart\\t\\      None\\tGermany\\t70174\\t+49 0711 2842222\\tNone\\tleonekohler@surfeu.de\\t5\\n3\\tFran\u00c3\u00a7ois\\t\\      Tremblay\\tNone\\t1498 rue B\u00c3\u00a9langer\\tMontr\u00c3\u00a9al\\tQC\\tCanada\\tH2G 1A7\\t+1 (514) 721-4711\\t\\      None\\tftremblay@gmail.com\\t3\\n*/\"    Run the snippet above a few times, or log exceptions in your deployed environment, to collect lots of examples of inputs, table_info and sql_cmd generated by your language model. The sql_cmd values will be incorrect and you can manually fix them up to build a collection of examples, e.g. here we are using YAML to keep a neat record of our inputs and corrected SQL output that we can build up over time.YAML_EXAMPLES = \"\"\"- input: How", "source": "https://python.langchain.com/docs/modules/chains/popular/sqlite"}
{"id": "23d1bce49020-42", "text": "SQL output that we can build up over time.YAML_EXAMPLES = \"\"\"- input: How many customers are not from Brazil?  table_info: |    CREATE TABLE \"Customer\" (      \"CustomerId\" INTEGER NOT NULL,       \"FirstName\" NVARCHAR(40) NOT NULL,       \"LastName\" NVARCHAR(20) NOT NULL,       \"Company\" NVARCHAR(80),       \"Address\" NVARCHAR(70),       \"City\" NVARCHAR(40),       \"State\" NVARCHAR(40),       \"Country\" NVARCHAR(40),       \"PostalCode\" NVARCHAR(10),       \"Phone\" NVARCHAR(24),       \"Fax\" NVARCHAR(24),       \"Email\" NVARCHAR(60) NOT NULL,       \"SupportRepId\" INTEGER,       PRIMARY KEY (\"CustomerId\"),       FOREIGN KEY(\"SupportRepId\") REFERENCES \"Employee\" (\"EmployeeId\")    )  sql_cmd: SELECT COUNT(*) FROM \"Customer\" WHERE NOT \"Country\" = \"Brazil\";  sql_result: \"[(54,)]\"  answer: 54 customers are not from Brazil.- input: list all the genres that start with 'r'  table_info: |    CREATE TABLE \"Genre\" (      \"GenreId\" INTEGER NOT NULL,       \"Name\" NVARCHAR(120),       PRIMARY KEY (\"GenreId\")    )    /*    3 rows from Genre table:    GenreId Name", "source": "https://python.langchain.com/docs/modules/chains/popular/sqlite"}
{"id": "23d1bce49020-43", "text": "/*    3 rows from Genre table:    GenreId Name    1   Rock    2   Jazz    3   Metal    */  sql_cmd: SELECT \"Name\" FROM \"Genre\" WHERE \"Name\" LIKE 'r%';  sql_result: \"[('Rock',), ('Rock and Roll',), ('Reggae',), ('R&B/Soul',)]\"  answer: The genres that start with 'r' are Rock, Rock and Roll, Reggae and R&B/Soul. \"\"\"Now that you have some examples (with manually corrected output SQL), you can do few shot prompt seeding the usual way:from langchain import FewShotPromptTemplate, PromptTemplatefrom langchain.chains.sql_database.prompt import _sqlite_prompt, PROMPT_SUFFIXfrom langchain.embeddings.huggingface import HuggingFaceEmbeddingsfrom langchain.prompts.example_selector.semantic_similarity import SemanticSimilarityExampleSelectorfrom langchain.vectorstores import Chromaexample_prompt = PromptTemplate(    input_variables=[\"table_info\", \"input\", \"sql_cmd\", \"sql_result\", \"answer\"],    template=\"{table_info}\\n\\nQuestion: {input}\\nSQLQuery: {sql_cmd}\\nSQLResult: {sql_result}\\nAnswer: {answer}\",)examples_dict = yaml.safe_load(YAML_EXAMPLES)local_embeddings = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\")example_selector = SemanticSimilarityExampleSelector.from_examples(                        # This is the list of examples available to select from.                        examples_dict,", "source": "https://python.langchain.com/docs/modules/chains/popular/sqlite"}
{"id": "23d1bce49020-44", "text": "examples_dict,                        # This is the embedding class used to produce embeddings which are used to measure semantic similarity.                        local_embeddings,                        # This is the VectorStore class that is used to store the embeddings and do a similarity search over.                        Chroma,  # type: ignore                        # This is the number of examples to produce and include per prompt                        k=min(3, len(examples_dict)),                    )few_shot_prompt = FewShotPromptTemplate(    example_selector=example_selector,    example_prompt=example_prompt,    prefix=_sqlite_prompt + \"Here are some examples:\",    suffix=PROMPT_SUFFIX,    input_variables=[\"table_info\", \"input\", \"top_k\"],)    Using embedded DuckDB without persistence: data will be transientThe model should do better now with this few shot prompt, especially for inputs similar to the examples you have seeded it with.local_chain = SQLDatabaseChain.from_llm(local_llm, db, prompt=few_shot_prompt, use_query_checker=True, verbose=True, return_intermediate_steps=True)result = local_chain(\"How many customers are from Brazil?\")            >", "source": "https://python.langchain.com/docs/modules/chains/popular/sqlite"}
{"id": "23d1bce49020-45", "text": "many customers are from Brazil?\")            > Entering new SQLDatabaseChain chain...    How many customers are from Brazil?    SQLQuery:SELECT count(*) FROM Customer WHERE Country = \"Brazil\";    SQLResult: [(5,)]    Answer:[5]    > Finished chain.result = local_chain(\"How many customers are not from Brazil?\")            > Entering new SQLDatabaseChain chain...    How many customers are not from Brazil?    SQLQuery:SELECT count(*) FROM customer WHERE country NOT IN (SELECT country FROM customer WHERE country = 'Brazil')    SQLResult: [(54,)]    Answer:54 customers are not from Brazil.    > Finished chain.result = local_chain(\"How many customers are there in total?\")            > Entering new SQLDatabaseChain chain...    How many customers are there in total?    SQLQuery:SELECT count(*) FROM Customer;    SQLResult: [(59,)]    Answer:There are 59 customers in total.    > Finished chain.PreviousUsing OpenAI functionsNextSummarizationCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/popular/sqlite"}
{"id": "a2804abe024d-0", "text": "Summarization | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/popular/summarize"}
{"id": "a2804abe024d-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalDocumentsPopularAPI chainsRetrieval QAConversational Retrieval QAUsing OpenAI functionsSQLSummarizationAdditionalMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsPopularSummarizationSummarizationA summarization chain can be used to summarize multiple documents. One way is to input multiple smaller documents, after they have been divided into chunks, and operate over them with a MapReduceDocumentsChain. You can also choose instead for the chain that does summarization to be a StuffDocumentsChain, or a RefineDocumentsChain.Prepare Data\u00e2\u20ac\u2039First we prepare the data. For this example we create multiple documents from one long one, but these documents could be fetched in any manner (the point of this notebook to highlight what to do AFTER you fetch the documents).from langchain import OpenAI, PromptTemplate, LLMChainfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.chains.mapreduce import MapReduceChainfrom langchain.prompts import PromptTemplatellm = OpenAI(temperature=0)text_splitter = CharacterTextSplitter()with open(\"../../state_of_the_union.txt\") as f:    state_of_the_union = f.read()texts = text_splitter.split_text(state_of_the_union)from langchain.docstore.document import Documentdocs = [Document(page_content=t) for t in texts[:3]]Quickstart\u00e2\u20ac\u2039If you just want to get started as quickly as possible, this is the recommended way to do it:from langchain.chains.summarize import load_summarize_chainchain = load_summarize_chain(llm, chain_type=\"map_reduce\")chain.run(docs)", "source": "https://python.langchain.com/docs/modules/chains/popular/summarize"}
{"id": "a2804abe024d-2", "text": "= load_summarize_chain(llm, chain_type=\"map_reduce\")chain.run(docs)    ' In response to Russian aggression in Ukraine, the United States and its allies are taking action to hold Putin accountable, including economic sanctions, asset seizures, and military assistance. The US is also providing economic and humanitarian aid to Ukraine, and has passed the American Rescue Plan and the Bipartisan Infrastructure Law to help struggling families and create jobs. The US remains unified and determined to protect Ukraine and the free world.'If you want more control and understanding over what is happening, please see the information below.The stuff Chain\u00e2\u20ac\u2039This sections shows results of using the stuff Chain to do summarization.chain = load_summarize_chain(llm, chain_type=\"stuff\")chain.run(docs)    ' In his speech, President Biden addressed the crisis in Ukraine, the American Rescue Plan, and the Bipartisan Infrastructure Law. He discussed the need to invest in America, educate Americans, and build the economy from the bottom up. He also announced the release of 60 million barrels of oil from reserves around the world, and the creation of a dedicated task force to go after the crimes of Russian oligarchs. He concluded by emphasizing the need to Buy American and use taxpayer dollars to rebuild America.'Custom PromptsYou can also use your own prompts with this chain. In this example, we will respond in Italian.prompt_template = \"\"\"Write a concise summary of the following:{text}CONCISE SUMMARY IN ITALIAN:\"\"\"PROMPT = PromptTemplate(template=prompt_template, input_variables=[\"text\"])chain = load_summarize_chain(llm, chain_type=\"stuff\", prompt=PROMPT)chain.run(docs)    \"\\n\\nIn questa serata, il Presidente degli Stati Uniti ha annunciato una serie di misure per affrontare la crisi in Ucraina, causata dall'aggressione di Putin. Ha anche", "source": "https://python.langchain.com/docs/modules/chains/popular/summarize"}
{"id": "a2804abe024d-3", "text": "la crisi in Ucraina, causata dall'aggressione di Putin. Ha anche annunciato l'invio di aiuti economici, militari e umanitari all'Ucraina. Ha anche annunciato che gli Stati Uniti e i loro alleati stanno imponendo sanzioni economiche a Putin e stanno rilasciando 60 milioni di barili di petrolio dalle riserve di tutto il mondo. Inoltre, ha annunciato che il Dipartimento di Giustizia degli Stati Uniti sta creando una task force dedicata ai crimini degli oligarchi russi. Il Presidente ha anche annunciato l'approvazione della legge bipartitica sull'infrastruttura, che prevede investimenti per la ricostruzione dell'America. Questo porter\u00c3\u00a0 a creare posti\"The map_reduce Chain\u00e2\u20ac\u2039This sections shows results of using the map_reduce Chain to do summarization.chain = load_summarize_chain(llm, chain_type=\"map_reduce\")chain.run(docs)    \" In response to Russia's aggression in Ukraine, the United States and its allies have imposed economic sanctions and are taking other measures to hold Putin accountable. The US is also providing economic and military assistance to Ukraine, protecting NATO countries, and releasing oil from its Strategic Petroleum Reserve. President Biden and Vice President Harris have passed legislation to help struggling families and rebuild America's infrastructure.\"Intermediate StepsWe can also return the intermediate steps for map_reduce chains, should we want to inspect them. This is done with the return_map_steps variable.chain = load_summarize_chain(OpenAI(temperature=0), chain_type=\"map_reduce\", return_intermediate_steps=True)chain({\"input_documents\": docs}, return_only_outputs=True)    {'map_steps': [\" In response to Russia's aggression in Ukraine, the United States has united with", "source": "https://python.langchain.com/docs/modules/chains/popular/summarize"}
{"id": "a2804abe024d-4", "text": "{'map_steps': [\" In response to Russia's aggression in Ukraine, the United States has united with other freedom-loving nations to impose economic sanctions and hold Putin accountable. The U.S. Department of Justice is also assembling a task force to go after the crimes of Russian oligarchs and seize their ill-gotten gains.\",      ' The United States and its European allies are taking action to punish Russia for its invasion of Ukraine, including seizing assets, closing off airspace, and providing economic and military assistance to Ukraine. The US is also mobilizing forces to protect NATO countries and has released 30 million barrels of oil from its Strategic Petroleum Reserve to help blunt gas prices. The world is uniting in support of Ukraine and democracy, and the US stands with its Ukrainian-American citizens.',      \" President Biden and Vice President Harris ran for office with a new economic vision for America, and have since passed the American Rescue Plan and the Bipartisan Infrastructure Law to help struggling families and rebuild America's infrastructure. This includes creating jobs, modernizing roads, airports, ports, and waterways, replacing lead pipes, providing affordable high-speed internet, and investing in American products to support American jobs.\"],     'output_text': \" In response to Russia's aggression in Ukraine, the United States and its allies have imposed economic sanctions and are taking other measures to hold Putin accountable. The US is also providing economic and military assistance to Ukraine, protecting NATO countries, and passing legislation to help struggling families and rebuild America's infrastructure. The world is uniting in support of Ukraine and democracy, and the US stands with its Ukrainian-American citizens.\"}Custom PromptsYou can also use your own prompts with this chain. In this example, we will respond in Italian.prompt_template = \"\"\"Write a concise summary of the following:{text}CONCISE SUMMARY IN ITALIAN:\"\"\"PROMPT = PromptTemplate(template=prompt_template, input_variables=[\"text\"])chain =", "source": "https://python.langchain.com/docs/modules/chains/popular/summarize"}
{"id": "a2804abe024d-5", "text": "= PromptTemplate(template=prompt_template, input_variables=[\"text\"])chain = load_summarize_chain(OpenAI(temperature=0), chain_type=\"map_reduce\", return_intermediate_steps=True, map_prompt=PROMPT, combine_prompt=PROMPT)chain({\"input_documents\": docs}, return_only_outputs=True)    {'intermediate_steps': [\"\\n\\nQuesta sera, ci incontriamo come democratici, repubblicani e indipendenti, ma soprattutto come americani. La Russia di Putin ha cercato di scuotere le fondamenta del mondo libero, ma ha sottovalutato la forza della gente ucraina. Gli Stati Uniti e i loro alleati stanno ora imponendo sanzioni economiche a Putin e stanno tagliando l'accesso della Russia alla tecnologia. Il Dipartimento di Giustizia degli Stati Uniti sta anche creando una task force dedicata per andare dopo i crimini degli oligarchi russi.\",      \"\\n\\nStiamo unendo le nostre forze con quelle dei nostri alleati europei per sequestrare yacht, appartamenti di lusso e jet privati di Putin. Abbiamo chiuso lo spazio aereo americano ai voli russi e stiamo fornendo pi\u00c3\u00b9 di un miliardo di dollari in assistenza all'Ucraina. Abbiamo anche mobilitato le nostre forze terrestri, aeree e navali per proteggere i paesi della NATO. Abbiamo anche rilasciato 60 milioni di barili di petrolio dalle riserve di tutto il mondo, di cui 30 milioni dalla nostra riserva strategica di petrolio. Stiamo affrontando una prova reale e ci vorr\u00c3\u00a0 del tempo, ma alla fine Putin non", "source": "https://python.langchain.com/docs/modules/chains/popular/summarize"}
{"id": "a2804abe024d-6", "text": "una prova reale e ci vorr\u00c3\u00a0 del tempo, ma alla fine Putin non riuscir\u00c3\u00a0 a spegnere l'amore dei popoli per la libert\u00c3\u00a0.\",      \"\\n\\nIl Presidente Biden ha lottato per passare l'American Rescue Plan per aiutare le persone che soffrivano a causa della pandemia. Il piano ha fornito sollievo economico immediato a milioni di americani, ha aiutato a mettere cibo sulla loro tavola, a mantenere un tetto sopra le loro teste e a ridurre il costo dell'assicurazione sanitaria. Il piano ha anche creato pi\u00c3\u00b9 di 6,5 milioni di nuovi posti di lavoro, il pi\u00c3\u00b9 alto numero di posti di lavoro creati in un anno nella storia degli Stati Uniti. Il Presidente Biden ha anche firmato la legge bipartitica sull'infrastruttura, la pi\u00c3\u00b9 ampia iniziativa di ricostruzione della storia degli Stati Uniti. Il piano prevede di modernizzare le strade, gli aeroporti, i porti e le vie navigabili in\"],     'output_text': \"\\n\\nIl Presidente Biden sta lavorando per aiutare le persone che soffrono a causa della pandemia attraverso l'American Rescue Plan e la legge bipartitica sull'infrastruttura. Gli Stati Uniti e i loro alleati stanno anche imponendo sanzioni economiche a Putin e tagliando l'accesso della Russia alla tecnologia. Stanno anche sequestrando yacht, appartamenti di lusso e jet privati di Putin e fornendo pi\u00c3\u00b9 di un miliardo di dollari in assistenza all'Ucraina. Alla", "source": "https://python.langchain.com/docs/modules/chains/popular/summarize"}
{"id": "a2804abe024d-7", "text": "di un miliardo di dollari in assistenza all'Ucraina. Alla fine, Putin non riuscir\u00c3\u00a0 a spegnere l'amore dei popoli per la libert\u00c3\u00a0.\"}The custom MapReduceChain\u00e2\u20ac\u2039Multi input promptYou can also use prompt with multi input. In this example, we will use a MapReduce chain to answer specific question about our code.from langchain.chains.combine_documents.map_reduce import MapReduceDocumentsChainfrom langchain.chains.combine_documents.stuff import StuffDocumentsChainmap_template_string = \"\"\"Give the following python code information, generate a description that explains what the code does and also mention the time complexity.Code:{code}Return the the description in the following format:name of the function: description of the function\"\"\"reduce_template_string = \"\"\"Given the following python function names and descriptions, answer the following question{code_description}Question: {question}Answer:\"\"\"# Prompt to use in map and reduce stages MAP_PROMPT = PromptTemplate(input_variables=[\"code\"], template=map_template_string)REDUCE_PROMPT = PromptTemplate(input_variables=[\"code_description\", \"question\"], template=reduce_template_string)# LLM to use in map and reduce stages llm = OpenAI()map_llm_chain = LLMChain(llm=llm, prompt=MAP_PROMPT)reduce_llm_chain = LLMChain(llm=llm, prompt=REDUCE_PROMPT)# Takes a list of documents and combines them into a single stringcombine_documents_chain = StuffDocumentsChain(    llm_chain=reduce_llm_chain,    document_variable_name=\"code_description\",)# Combines and iteravely reduces the mapped documents reduce_documents_chain = ReduceDocumentsChain(        # This is final chain that is called.        combine_documents_chain=combine_documents_chain,        # If documents exceed context for", "source": "https://python.langchain.com/docs/modules/chains/popular/summarize"}
{"id": "a2804abe024d-8", "text": "# If documents exceed context for `combine_documents_chain`        collapse_documents_chain=combine_documents_chain,        # The maximum number of tokens to group documents into        token_max=3000)# Combining documents by mapping a chain over them, then combining results with reduce chaincombine_documents = MapReduceDocumentsChain(    # Map chain    llm_chain=map_llm_chain,     # Reduce chain    reduce_documents_chain=reduce_documents_chain,    # The variable name in the llm_chain to put the documents in    document_variable_name=\"code\",)map_reduce = MapReduceChain(    combine_documents_chain=combine_documents,    text_splitter=CharacterTextSplitter(separator=\"\\n##\\n\", chunk_size=100, chunk_overlap=0),)code = \"\"\"def bubblesort(list):   for iter_num in range(len(list)-1,0,-1):      for idx in range(iter_num):         if list[idx]>list[idx+1]:            temp = list[idx]            list[idx] = list[idx+1]            list[idx+1] = temp    return list##def insertion_sort(InputList):   for i in range(1, len(InputList)):      j = i-1      nxt_element = InputList[i]   while (InputList[j] > nxt_element) and (j >= 0):      InputList[j+1] = InputList[j]      j=j-1", "source": "https://python.langchain.com/docs/modules/chains/popular/summarize"}
{"id": "a2804abe024d-9", "text": "= InputList[j]      j=j-1   InputList[j+1] = nxt_element   return InputList##def shellSort(input_list):   gap = len(input_list) // 2   while gap > 0:      for i in range(gap, len(input_list)):         temp = input_list[i]         j = i   while j >= gap and input_list[j - gap] > temp:      input_list[j] = input_list[j - gap]      j = j-gap      input_list[j] = temp   gap = gap//2   return input_list\"\"\"map_reduce.run(input_text=code, question=\"Which function has a better time complexity?\")    Created a chunk of size 247, which is longer than the specified 100    Created a chunk of size 267, which is longer than the specified 100    'shellSort has a better time complexity than both bubblesort and insertion_sort, as it has a time complexity of O(n^2), while the other two have a time complexity of O(n^2).'The refine Chain\u00e2\u20ac\u2039This sections shows results of using the refine Chain to do summarization.chain = load_summarize_chain(llm, chain_type=\"refine\")chain.run(docs)    \"\\n\\nIn response to Russia's aggression in Ukraine, the United States has united with other freedom-loving nations to impose economic sanctions and hold Putin accountable. The U.S. Department of Justice is also assembling a task force to go after the crimes of Russian oligarchs and seize their ill-gotten gains. We are joining with our European allies to find and seize the assets of Russian oligarchs, including yachts, luxury apartments, and private jets. The", "source": "https://python.langchain.com/docs/modules/chains/popular/summarize"}
{"id": "a2804abe024d-10", "text": "seize the assets of Russian oligarchs, including yachts, luxury apartments, and private jets. The U.S. is also closing off American airspace to all Russian flights, further isolating Russia and adding an additional squeeze on their economy. The U.S. and its allies are providing support to the Ukrainians in their fight for freedom, including military, economic, and humanitarian assistance. The U.S. is also mobilizing ground forces, air squadrons, and ship deployments to protect NATO countries. The U.S. and its allies are also releasing 60 million barrels of oil from reserves around the world, with the U.S. contributing 30 million barrels from its own Strategic Petroleum Reserve. In addition, the U.S. has passed the American Rescue Plan to provide immediate economic relief for tens of millions of Americans, and the Bipartisan Infrastructure Law to rebuild America and create jobs. This investment will\"Intermediate StepsWe can also return the intermediate steps for refine chains, should we want to inspect them. This is done with the return_refine_steps variable.chain = load_summarize_chain(OpenAI(temperature=0), chain_type=\"refine\", return_intermediate_steps=True)chain({\"input_documents\": docs}, return_only_outputs=True)    {'refine_steps': [\" In response to Russia's aggression in Ukraine, the United States has united with other freedom-loving nations to impose economic sanctions and hold Putin accountable. The U.S. Department of Justice is also assembling a task force to go after the crimes of Russian oligarchs and seize their ill-gotten gains.\",      \"\\n\\nIn response to Russia's aggression in Ukraine, the United States has united with other freedom-loving nations to impose economic sanctions and hold Putin accountable. The U.S. Department of Justice is also assembling a task force to go after the crimes of Russian oligarchs and seize their ill-gotten gains. We are joining with our European allies to find and seize the assets of Russian oligarchs,", "source": "https://python.langchain.com/docs/modules/chains/popular/summarize"}
{"id": "a2804abe024d-11", "text": "gains. We are joining with our European allies to find and seize the assets of Russian oligarchs, including yachts, luxury apartments, and private jets. The U.S. is also closing off American airspace to all Russian flights, further isolating Russia and adding an additional squeeze on their economy. The U.S. and its allies are providing support to the Ukrainians in their fight for freedom, including military, economic, and humanitarian assistance. The U.S. is also mobilizing ground forces, air squadrons, and ship deployments to protect NATO countries. The U.S. and its allies are also releasing 60 million barrels of oil from reserves around the world, with the U.S. contributing 30 million barrels from its own Strategic Petroleum Reserve. Putin's war on Ukraine has left Russia weaker and the rest of the world stronger, with the world uniting in support of democracy and peace.\",      \"\\n\\nIn response to Russia's aggression in Ukraine, the United States has united with other freedom-loving nations to impose economic sanctions and hold Putin accountable. The U.S. Department of Justice is also assembling a task force to go after the crimes of Russian oligarchs and seize their ill-gotten gains. We are joining with our European allies to find and seize the assets of Russian oligarchs, including yachts, luxury apartments, and private jets. The U.S. is also closing off American airspace to all Russian flights, further isolating Russia and adding an additional squeeze on their economy. The U.S. and its allies are providing support to the Ukrainians in their fight for freedom, including military, economic, and humanitarian assistance. The U.S. is also mobilizing ground forces, air squadrons, and ship deployments to protect NATO countries. The U.S. and its allies are also releasing 60 million barrels of oil from reserves around the world, with the U.S. contributing 30 million barrels from its own Strategic Petroleum Reserve. In addition, the U.S.", "source": "https://python.langchain.com/docs/modules/chains/popular/summarize"}
{"id": "a2804abe024d-12", "text": "contributing 30 million barrels from its own Strategic Petroleum Reserve. In addition, the U.S. has passed the American Rescue Plan to provide immediate economic relief for tens of millions of Americans, and the Bipartisan Infrastructure Law to rebuild America and create jobs. This includes investing\"],     'output_text': \"\\n\\nIn response to Russia's aggression in Ukraine, the United States has united with other freedom-loving nations to impose economic sanctions and hold Putin accountable. The U.S. Department of Justice is also assembling a task force to go after the crimes of Russian oligarchs and seize their ill-gotten gains. We are joining with our European allies to find and seize the assets of Russian oligarchs, including yachts, luxury apartments, and private jets. The U.S. is also closing off American airspace to all Russian flights, further isolating Russia and adding an additional squeeze on their economy. The U.S. and its allies are providing support to the Ukrainians in their fight for freedom, including military, economic, and humanitarian assistance. The U.S. is also mobilizing ground forces, air squadrons, and ship deployments to protect NATO countries. The U.S. and its allies are also releasing 60 million barrels of oil from reserves around the world, with the U.S. contributing 30 million barrels from its own Strategic Petroleum Reserve. In addition, the U.S. has passed the American Rescue Plan to provide immediate economic relief for tens of millions of Americans, and the Bipartisan Infrastructure Law to rebuild America and create jobs. This includes investing\"}Custom PromptsYou can also use your own prompts with this chain. In this example, we will respond in Italian.prompt_template = \"\"\"Write a concise summary of the following:{text}CONCISE SUMMARY IN ITALIAN:\"\"\"PROMPT = PromptTemplate(template=prompt_template, input_variables=[\"text\"])refine_template = (    \"Your job is to produce a final summary\\n\"    \"We", "source": "https://python.langchain.com/docs/modules/chains/popular/summarize"}
{"id": "a2804abe024d-13", "text": "(    \"Your job is to produce a final summary\\n\"    \"We have provided an existing summary up to a certain point: {existing_answer}\\n\"    \"We have the opportunity to refine the existing summary\"    \"(only if needed) with some more context below.\\n\"    \"------------\\n\"    \"{text}\\n\"    \"------------\\n\"    \"Given the new context, refine the original summary in Italian\"    \"If the context isn't useful, return the original summary.\")refine_prompt = PromptTemplate(    input_variables=[\"existing_answer\", \"text\"],    template=refine_template,)chain = load_summarize_chain(OpenAI(temperature=0), chain_type=\"refine\", return_intermediate_steps=True, question_prompt=PROMPT, refine_prompt=refine_prompt)chain({\"input_documents\": docs}, return_only_outputs=True)    {'intermediate_steps': [\"\\n\\nQuesta sera, ci incontriamo come democratici, repubblicani e indipendenti, ma soprattutto come americani. La Russia di Putin ha cercato di scuotere le fondamenta del mondo libero, ma ha sottovalutato la forza della gente ucraina. Insieme ai nostri alleati, stiamo imponendo sanzioni economiche, tagliando l'accesso della Russia alla tecnologia e bloccando i suoi pi\u00c3\u00b9 grandi istituti bancari dal sistema finanziario internazionale. Il Dipartimento di Giustizia degli Stati Uniti sta anche assemblando una task force dedicata per andare dopo i crimini degli oligarchi russi.\",      \"\\n\\nQuesta sera, ci incontriamo come democratici, repubblicani e indipendenti, ma", "source": "https://python.langchain.com/docs/modules/chains/popular/summarize"}
{"id": "a2804abe024d-14", "text": "sera, ci incontriamo come democratici, repubblicani e indipendenti, ma soprattutto come americani. La Russia di Putin ha cercato di scuotere le fondamenta del mondo libero, ma ha sottovalutato la forza della gente ucraina. Insieme ai nostri alleati, stiamo imponendo sanzioni economiche, tagliando l'accesso della Russia alla tecnologia, bloccando i suoi pi\u00c3\u00b9 grandi istituti bancari dal sistema finanziario internazionale e chiudendo lo spazio aereo americano a tutti i voli russi. Il Dipartimento di Giustizia degli Stati Uniti sta anche assemblando una task force dedicata per andare dopo i crimini degli oligarchi russi. Stiamo fornendo pi\u00c3\u00b9 di un miliardo di dollari in assistenza diretta all'Ucraina e fornendo assistenza militare,\",      \"\\n\\nQuesta sera, ci incontriamo come democratici, repubblicani e indipendenti, ma soprattutto come americani. La Russia di Putin ha cercato di scuotere le fondamenta del mondo libero, ma ha sottovalutato la forza della gente ucraina. Insieme ai nostri alleati, stiamo imponendo sanzioni economiche, tagliando l'accesso della Russia alla tecnologia, bloccando i suoi pi\u00c3\u00b9 grandi istituti bancari dal sistema finanziario internazionale e chiudendo lo spazio aereo americano a tutti i voli russi. Il Dipartimento di Giustizia degli Stati Uniti sta anche assemblando una task force dedicata per andare dopo i crimini degli oligarchi russi. Stiamo fornendo pi\u00c3\u00b9 di un miliardo di dollari", "source": "https://python.langchain.com/docs/modules/chains/popular/summarize"}
{"id": "a2804abe024d-15", "text": "russi. Stiamo fornendo pi\u00c3\u00b9 di un miliardo di dollari in assistenza diretta all'Ucraina e fornendo assistenza militare.\"],     'output_text': \"\\n\\nQuesta sera, ci incontriamo come democratici, repubblicani e indipendenti, ma soprattutto come americani. La Russia di Putin ha cercato di scuotere le fondamenta del mondo libero, ma ha sottovalutato la forza della gente ucraina. Insieme ai nostri alleati, stiamo imponendo sanzioni economiche, tagliando l'accesso della Russia alla tecnologia, bloccando i suoi pi\u00c3\u00b9 grandi istituti bancari dal sistema finanziario internazionale e chiudendo lo spazio aereo americano a tutti i voli russi. Il Dipartimento di Giustizia degli Stati Uniti sta anche assemblando una task force dedicata per andare dopo i crimini degli oligarchi russi. Stiamo fornendo pi\u00c3\u00b9 di un miliardo di dollari in assistenza diretta all'Ucraina e fornendo assistenza militare.\"}PreviousSQLNextAdditionalCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/popular/summarize"}
{"id": "0404250a12b2-0", "text": "Using OpenAI functions | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/popular/openai_functions"}
{"id": "0404250a12b2-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalDocumentsPopularAPI chainsRetrieval QAConversational Retrieval QAUsing OpenAI functionsSQLSummarizationAdditionalMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsPopularUsing OpenAI functionsOn this pageUsing OpenAI functionsThis walkthrough demonstrates how to incorporate OpenAI function-calling API's in a chain. We'll go over: How to use functions to get structured outputs from ChatOpenAIHow to create a generic chain that uses (multiple) functionsHow to create a chain that actually executes the chosen functionfrom typing import Optionalfrom langchain.chains.openai_functions import (    create_openai_fn_chain,    create_structured_output_chain,)from langchain.chat_models import ChatOpenAIfrom langchain.prompts import ChatPromptTemplate, HumanMessagePromptTemplatefrom langchain.schema import HumanMessage, SystemMessageGetting structured outputs\u00e2\u20ac\u2039We can take advantage of OpenAI functions to try and force the model to return a particular kind of structured output. We'll use the create_structured_output_chain to create our chain, which takes the desired structured output either as a Pydantic class or as JsonSchema.See here for relevant reference docs.Using Pydantic classes\u00e2\u20ac\u2039When passing in Pydantic classes to structure our text, we need to make sure to have a docstring description for the class. It also helps to have descriptions for each of the classes attributes.from pydantic import BaseModel, Fieldclass Person(BaseModel):    \"\"\"Identifying information about a person.\"\"\"    name: str = Field(..., description=\"The person's name\")    age: int = Field(..., description=\"The", "source": "https://python.langchain.com/docs/modules/chains/popular/openai_functions"}
{"id": "0404250a12b2-2", "text": "description=\"The person's name\")    age: int = Field(..., description=\"The person's age\")    fav_food: Optional[str] = Field(None, description=\"The person's favorite food\")# If we pass in a model explicitly, we need to make sure it supports the OpenAI function-calling API.llm = ChatOpenAI(model=\"gpt-4\", temperature=0)prompt_msgs = [    SystemMessage(        content=\"You are a world class algorithm for extracting information in structured formats.\"    ),    HumanMessage(        content=\"Use the given format to extract information from the following input:\"    ),    HumanMessagePromptTemplate.from_template(\"{input}\"),    HumanMessage(content=\"Tips: Make sure to answer in the correct format\"),]prompt = ChatPromptTemplate(messages=prompt_msgs)chain = create_structured_output_chain(Person, llm, prompt, verbose=True)chain.run(\"Sally is 13\")            > Entering new LLMChain chain...    Prompt after formatting:    System: You are a world class algorithm for extracting information in structured formats.    Human: Use the given format to extract information from the following input:    Human: Sally is 13    Human: Tips: Make sure to answer in the correct format     {'function_call': {'name': '_OutputFormatter', 'arguments': '{\\n  \"output\": {\\n    \"name\": \"Sally\",\\n    \"age\": 13,\\n    \"fav_food\": \"Unknown\"\\n  }\\n}'}}        > Finished chain.    Person(name='Sally', age=13,", "source": "https://python.langchain.com/docs/modules/chains/popular/openai_functions"}
{"id": "0404250a12b2-3", "text": "> Finished chain.    Person(name='Sally', age=13, fav_food='Unknown')To extract arbitrarily many structured outputs of a given format, we can just create a wrapper Pydantic class  that takes a sequence of the original class.from typing import Sequenceclass People(BaseModel):    \"\"\"Identifying information about all people in a text.\"\"\"    people: Sequence[Person] = Field(..., description=\"The people in the text\")chain = create_structured_output_chain(People, llm, prompt, verbose=True)chain.run(    \"Sally is 13, Joey just turned 12 and loves spinach. Caroline is 10 years older than Sally, so she's 23.\")            > Entering new LLMChain chain...    Prompt after formatting:    System: You are a world class algorithm for extracting information in structured formats.    Human: Use the given format to extract information from the following input:    Human: Sally is 13, Joey just turned 12 and loves spinach. Caroline is 10 years older than Sally, so she's 23.    Human: Tips: Make sure to answer in the correct format     {'function_call': {'name': '_OutputFormatter', 'arguments': '{\\n  \"output\": {\\n    \"people\": [\\n      {\\n        \"name\": \"Sally\",\\n        \"age\": 13,\\n        \"fav_food\": \"\"\\n      },\\n      {\\n        \"name\": \"Joey\",\\n        \"age\": 12,\\n", "source": "https://python.langchain.com/docs/modules/chains/popular/openai_functions"}
{"id": "0404250a12b2-4", "text": "\"age\": 12,\\n        \"fav_food\": \"spinach\"\\n      },\\n      {\\n        \"name\": \"Caroline\",\\n        \"age\": 23,\\n        \"fav_food\": \"\"\\n      }\\n    ]\\n  }\\n}'}}        > Finished chain.    People(people=[Person(name='Sally', age=13, fav_food=''), Person(name='Joey', age=12, fav_food='spinach'), Person(name='Caroline', age=23, fav_food='')])Using JsonSchema\u00e2\u20ac\u2039We can also pass in JsonSchema instead of Pydantic classes to specify the desired structure. When we do this, our chain will output json corresponding to the properties described in the JsonSchema, instead of a Pydantic class.json_schema = {    \"title\": \"Person\",    \"description\": \"Identifying information about a person.\",    \"type\": \"object\",    \"properties\": {        \"name\": {\"title\": \"Name\", \"description\": \"The person's name\", \"type\": \"string\"},        \"age\": {\"title\": \"Age\", \"description\": \"The person's age\", \"type\": \"integer\"},        \"fav_food\": {            \"title\": \"Fav Food\",            \"description\": \"The person's favorite food\",            \"type\": \"string\",", "source": "https://python.langchain.com/docs/modules/chains/popular/openai_functions"}
{"id": "0404250a12b2-5", "text": "\"type\": \"string\",        },    },    \"required\": [\"name\", \"age\"],}chain = create_structured_output_chain(json_schema, llm, prompt, verbose=True)chain.run(\"Sally is 13\")            > Entering new LLMChain chain...    Prompt after formatting:    System: You are a world class algorithm for extracting information in structured formats.    Human: Use the given format to extract information from the following input:    Human: Sally is 13    Human: Tips: Make sure to answer in the correct format     {'function_call': {'name': 'output_formatter', 'arguments': '{\\n  \"name\": \"Sally\",\\n  \"age\": 13\\n}'}}        > Finished chain.    {'name': 'Sally', 'age': 13}Creating a generic OpenAI functions chain\u00e2\u20ac\u2039To create a generic OpenAI functions chain, we can use the create_openai_fn_chain method. This is the same as create_structured_output_chain except that instead of taking a single output schema, it takes a sequence of function definitions.Functions can be passed in as:dicts conforming to OpenAI functions spec,Pydantic classes, in which case they should have docstring descriptions of the function they represent and descriptions for each of the parameters,Python functions, in which case they should have docstring descriptions of the function and args, along with type hints.See here for relevant reference docs.Using Pydantic classes\u00e2\u20ac\u2039class RecordPerson(BaseModel):    \"\"\"Record some identifying information about a pe.\"\"\"    name: str = Field(..., description=\"The person's name\")", "source": "https://python.langchain.com/docs/modules/chains/popular/openai_functions"}
{"id": "0404250a12b2-6", "text": "name: str = Field(..., description=\"The person's name\")    age: int = Field(..., description=\"The person's age\")    fav_food: Optional[str] = Field(None, description=\"The person's favorite food\")class RecordDog(BaseModel):    \"\"\"Record some identifying information about a dog.\"\"\"    name: str = Field(..., description=\"The dog's name\")    color: str = Field(..., description=\"The dog's color\")    fav_food: Optional[str] = Field(None, description=\"The dog's favorite food\")prompt_msgs = [    SystemMessage(content=\"You are a world class algorithm for recording entities\"),    HumanMessage(        content=\"Make calls to the relevant function to record the entities in the following input:\"    ),    HumanMessagePromptTemplate.from_template(\"{input}\"),    HumanMessage(content=\"Tips: Make sure to answer in the correct format\"),]prompt = ChatPromptTemplate(messages=prompt_msgs)chain = create_openai_fn_chain([RecordPerson, RecordDog], llm, prompt, verbose=True)chain.run(\"Harry was a chubby brown beagle who loved chicken\")            > Entering new LLMChain chain...    Prompt after formatting:    System: You are a world class algorithm for recording entities    Human: Make calls to the relevant function to record the entities in the following input:    Human: Harry was a chubby brown beagle who loved chicken    Human: Tips: Make sure to answer in the correct format     {'function_call': {'name': 'RecordDog', 'arguments': '{\\n  \"name\": \"Harry\",\\n  \"color\": \"brown\",\\n  \"fav_food\":", "source": "https://python.langchain.com/docs/modules/chains/popular/openai_functions"}
{"id": "0404250a12b2-7", "text": "\"Harry\",\\n  \"color\": \"brown\",\\n  \"fav_food\": \"chicken\"\\n}'}}        > Finished chain.    RecordDog(name='Harry', color='brown', fav_food='chicken')Using Python functions\u00e2\u20ac\u2039We can pass in functions as Pydantic classes, directly as OpenAI function dicts, or Python functions. To pass Python function in directly, we'll want to make sure our parameters have type hints, we have a docstring, and we use Google Python style docstrings to describe the parameters.NOTE: To use Python functions, make sure the function arguments are of primitive types (str, float, int, bool) or that they are Pydantic objects.class OptionalFavFood(BaseModel):    \"\"\"Either a food or null.\"\"\"    food: Optional[str] = Field(        None,        description=\"Either the name of a food or null. Should be null if the food isn't known.\",    )def record_person(name: str, age: int, fav_food: OptionalFavFood) -> str:    \"\"\"Record some basic identifying information about a person.    Args:        name: The person's name.        age: The person's age in years.        fav_food: An OptionalFavFood object that either contains the person's favorite food or a null value. Food should be null if it's not known.    \"\"\"    return f\"Recording person {name} of age {age} with favorite food {fav_food.food}!\"chain = create_openai_fn_chain([record_person], llm, prompt, verbose=True)chain.run(    \"The most important thing to remember about Tommy, my", "source": "https://python.langchain.com/docs/modules/chains/popular/openai_functions"}
{"id": "0404250a12b2-8", "text": "verbose=True)chain.run(    \"The most important thing to remember about Tommy, my 12 year old, is that he'll do anything for apple pie.\")            > Entering new LLMChain chain...    Prompt after formatting:    System: You are a world class algorithm for recording entities    Human: Make calls to the relevant function to record the entities in the following input:    Human: The most important thing to remember about Tommy, my 12 year old, is that he'll do anything for apple pie.    Human: Tips: Make sure to answer in the correct format     {'function_call': {'name': 'record_person', 'arguments': '{\\n  \"name\": \"Tommy\",\\n  \"age\": 12,\\n  \"fav_food\": {\\n    \"food\": \"apple pie\"\\n  }\\n}'}}        > Finished chain.    {'name': 'Tommy', 'age': 12, 'fav_food': {'food': 'apple pie'}}If we pass in multiple Python functions or OpenAI functions, then the returned output will be of the form{\"name\": \"<<function_name>>\", \"arguments\": {<<function_arguments>>}}def record_dog(name: str, color: str, fav_food: OptionalFavFood) -> str:    \"\"\"Record some basic identifying information about a dog.    Args:        name: The dog's name.        color: The dog's color.        fav_food: An OptionalFavFood object that either contains the dog's favorite food or a null value. Food should be null if it's not known.    \"\"\"    return f\"Recording dog", "source": "https://python.langchain.com/docs/modules/chains/popular/openai_functions"}
{"id": "0404250a12b2-9", "text": "be null if it's not known.    \"\"\"    return f\"Recording dog {name} of color {color} with favorite food {fav_food}!\"chain = create_openai_fn_chain([record_person, record_dog], llm, prompt, verbose=True)chain.run(    \"I can't find my dog Henry anywhere, he's a small brown beagle. Could you send a message about him?\")            > Entering new LLMChain chain...    Prompt after formatting:    System: You are a world class algorithm for recording entities    Human: Make calls to the relevant function to record the entities in the following input:    Human: I can't find my dog Henry anywhere, he's a small brown beagle. Could you send a message about him?    Human: Tips: Make sure to answer in the correct format     {'function_call': {'name': 'record_dog', 'arguments': '{\\n  \"name\": \"Henry\",\\n  \"color\": \"brown\",\\n  \"fav_food\": {\\n    \"food\": null\\n  }\\n}'}}        > Finished chain.    {'name': 'record_dog',     'arguments': {'name': 'Henry', 'color': 'brown', 'fav_food': {'food': None}}}Other Chains using OpenAI  functions\u00e2\u20ac\u2039There are a number of more specific chains that use OpenAI functions.Extraction: very similar to structured output chain, intended for information/entity extraction specifically.Tagging: tag inputs.OpenAPI: take an OpenAPI spec and create + execute valid requests against the API, using OpenAI functions under the hood.QA with citations: use OpenAI functions ability to extract citations from text.PreviousConversational Retrieval", "source": "https://python.langchain.com/docs/modules/chains/popular/openai_functions"}
{"id": "0404250a12b2-10", "text": "with citations: use OpenAI functions ability to extract citations from text.PreviousConversational Retrieval QANextSQLGetting structured outputsUsing Pydantic classesUsing JsonSchemaCreating a generic OpenAI functions chainUsing Pydantic classesUsing Python functionsOther Chains using OpenAI  functionsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/popular/openai_functions"}
{"id": "a4193e15b1c7-0", "text": "Additional | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/additional/"}
{"id": "a4193e15b1c7-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalDocumentsPopularAdditionalAnalyze DocumentSelf-critique chain with constitutional AICausal program-aided language (CPAL) chainElasticsearch databaseExtractionFLAREArangoDB QA chainGraph DB QA chainHugeGraph QA ChainKuzuQAChainNebulaGraphQAChainGraph QAGraphSparqlQAChainHypothetical Document EmbeddingsBash chainSelf-checking chainMath chainHTTP request chainSummarization checker chainLLM Symbolic MathModerationDynamically selecting from multiple promptsDynamically selecting from multiple retrieversNeptune Open Cypher QA ChainRetrieval QA using OpenAI functionsOpenAPI chainOpenAPI calls with OpenAI functionsProgram-aided language model (PAL) chainQuestion-Answering CitationsDocument QATaggingVector store-augmented text generationMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsAdditionalAdditional\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd Analyze DocumentThe AnalyzeDocumentChain can be used as an end-to-end to chain. This chain takes in a single document, splits it up, and then runs it through a CombineDocumentsChain.\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd Self-critique chain with constitutional AIThe ConstitutionalChain is a chain that ensures the output of a language model adheres to a predefined set of constitutional principles. By incorporating specific rules and guidelines, the ConstitutionalChain filters and modifies the generated content to align with these principles, thus providing more controlled, ethical, and contextually appropriate responses. This mechanism helps maintain the integrity of the output while minimizing the risk of generating content that may violate guidelines, be offensive, or deviate from the desired", "source": "https://python.langchain.com/docs/modules/chains/additional/"}
{"id": "a4193e15b1c7-2", "text": "minimizing the risk of generating content that may violate guidelines, be offensive, or deviate from the desired context.\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd Causal program-aided language (CPAL) chainThe CPAL chain builds on the recent PAL to stop LLM hallucination. The problem with the PAL approach is that it hallucinates on a math problem with a nested chain of dependence. The innovation here is that this new CPAL approach includes causal structure to fix hallucination.\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd Elasticsearch databaseInteract with Elasticsearch analytics database via Langchain. This chain builds search queries via the Elasticsearch DSL API (filters and aggregations).\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd ExtractionThe extraction chain uses the OpenAI functions parameter to specify a schema to extract entities from a document. This helps us make sure that the model outputs exactly the schema of entities and properties that we want, with their appropriate types.\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd FLAREThis notebook is an implementation of Forward-Looking Active REtrieval augmented generation (FLARE).\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd ArangoDB QA chainOpen In Collab\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd Graph DB QA chainThis notebook shows how to use LLMs to provide a natural language interface to a graph database you can query with the Cypher query language.\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd HugeGraph QA ChainThis notebook shows how to use LLMs to provide a natural language interface to HugeGraph database.\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd KuzuQAChainThis notebook shows how to use LLMs to provide a natural language interface to K\u00c3\u00b9zu database.\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd NebulaGraphQAChainThis notebook shows how to use LLMs to provide a natural language interface to NebulaGraph database.\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd Graph QAThis notebook", "source": "https://python.langchain.com/docs/modules/chains/additional/"}
{"id": "a4193e15b1c7-3", "text": "to NebulaGraph database.\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd Graph QAThis notebook goes over how to do question answering over a graph data structure.\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd GraphSparqlQAChainGraph databases are an excellent choice for applications based on network-like models. To standardize the syntax and semantics of such graphs, the W3C recommends Semantic Web Technologies, cp. Semantic Web. SPARQL serves as a query language analogously to SQL or Cypher for these graphs. This notebook demonstrates the application of LLMs as a natural language interface to a graph database by generating SPARQL.\\\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd Hypothetical Document EmbeddingsThis notebook goes over how to use Hypothetical Document Embeddings (HyDE), as described in this paper.\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd Bash chainThis notebook showcases using LLMs and a bash process to perform simple filesystem commands.\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd Self-checking chainThis notebook showcases how to use LLMCheckerChain.\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd Math chainThis notebook showcases using LLMs and Python REPLs to do complex word math problems.\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd HTTP request chainUsing the request library to get HTML results from a URL and then an LLM to parse results\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd Summarization checker chainThis notebook shows some examples of LLMSummarizationCheckerChain in use with different types of texts.  It has a few distinct differences from the LLMCheckerChain, in that it doesn't have any assumptions to the format of the input text (or summary).\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd LLM Symbolic MathThis notebook showcases using LLMs and Python to Solve Algebraic Equations. Under the hood is makes use of SymPy.\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd ModerationThis", "source": "https://python.langchain.com/docs/modules/chains/additional/"}
{"id": "a4193e15b1c7-4", "text": "hood is makes use of SymPy.\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd ModerationThis notebook walks through examples of how to use a moderation chain, and several common ways for doing so. Moderation chains are useful for detecting text that could be hateful, violent, etc. This can be useful to apply on both user input, but also on the output of a Language Model. Some API providers, like OpenAI, specifically prohibit you, or your end users, from generating some types of harmful content. To comply with this (and to just generally prevent your application from being harmful) you may often want to append a moderation chain to any LLMChains, in order to make sure any output the LLM generates is not harmful.\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd Dynamically selecting from multiple promptsThis notebook demonstrates how to use the RouterChain paradigm to create a chain that dynamically selects the prompt to use for a given input. Specifically we show how to use the MultiPromptChain to create a question-answering chain that selects the prompt which is most relevant for a given question, and then answers the question using that prompt.\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd Dynamically selecting from multiple retrieversThis notebook demonstrates how to use the RouterChain paradigm to create a chain that dynamically selects which Retrieval system to use. Specifically we show how to use the MultiRetrievalQAChain to create a question-answering chain that selects the retrieval QA chain which is most relevant for a given question, and then answers the question using it.\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd Neptune Open Cypher QA ChainThis QA chain queries Neptune graph database using openCypher and returns human readable response\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd Retrieval QA using OpenAI functionsOpenAI functions allows for structuring of response output. This is often useful in question answering when you want to not only get the final answer but also supporting evidence, citations,", "source": "https://python.langchain.com/docs/modules/chains/additional/"}
{"id": "a4193e15b1c7-5", "text": "in question answering when you want to not only get the final answer but also supporting evidence, citations, etc.\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd OpenAPI chainThis notebook shows an example of using an OpenAPI chain to call an endpoint in natural language, and get back a response in natural language.\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd OpenAPI calls with OpenAI functionsIn this notebook we'll show how to create a chain that automatically makes calls to an API based only on an OpenAPI  spec. Under the hood, we're parsing the OpenAPI spec into a JSON schema that the OpenAI functions API can handle. This allows ChatGPT to automatically select and populate the relevant API call to make for any user input. Using the output of ChatGPT we then make the actual API call, and return the result.\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd Program-aided language model (PAL) chainImplements Program-Aided Language Models, as in https://arxiv.org/pdf/2211.10435.pdf.\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd Question-Answering CitationsThis notebook shows how to use OpenAI functions ability to extract citations from text.\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd Document QAHere we walk through how to use LangChain for question answering over a list of documents. Under the hood we'll be using our Document chains.\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd TaggingThe tagging chain uses the OpenAI functions parameter to specify a schema to tag a document with. This helps us make sure that the model outputs exactly tags that we want, with their appropriate types.\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd Vector store-augmented text generationThis notebook walks through how to use LangChain for text generation over a vector index. This is useful if we want to generate text that is able to draw from a large body of custom text, for example, generating blog posts that have an understanding of", "source": "https://python.langchain.com/docs/modules/chains/additional/"}
{"id": "a4193e15b1c7-6", "text": "draw from a large body of custom text, for example, generating blog posts that have an understanding of previous blog posts written, or product tutorials that can refer to product documentation.PreviousSummarizationNextAnalyze DocumentCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/additional/"}
{"id": "7d3592874181-0", "text": "Self-critique chain with constitutional AI | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/additional/constitutional_chain"}
{"id": "7d3592874181-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalDocumentsPopularAdditionalAnalyze DocumentSelf-critique chain with constitutional AICausal program-aided language (CPAL) chainElasticsearch databaseExtractionFLAREArangoDB QA chainGraph DB QA chainHugeGraph QA ChainKuzuQAChainNebulaGraphQAChainGraph QAGraphSparqlQAChainHypothetical Document EmbeddingsBash chainSelf-checking chainMath chainHTTP request chainSummarization checker chainLLM Symbolic MathModerationDynamically selecting from multiple promptsDynamically selecting from multiple retrieversNeptune Open Cypher QA ChainRetrieval QA using OpenAI functionsOpenAPI chainOpenAPI calls with OpenAI functionsProgram-aided language model (PAL) chainQuestion-Answering CitationsDocument QATaggingVector store-augmented text generationMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsAdditionalSelf-critique chain with constitutional AISelf-critique chain with constitutional AIThe ConstitutionalChain is a chain that ensures the output of a language model adheres to a predefined set of constitutional principles. By incorporating specific rules and guidelines, the ConstitutionalChain filters and modifies the generated content to align with these principles, thus providing more controlled, ethical, and contextually appropriate responses. This mechanism helps maintain the integrity of the output while minimizing the risk of generating content that may violate guidelines, be offensive, or deviate from the desired context.# Importsfrom langchain.llms import OpenAIfrom langchain.prompts import PromptTemplatefrom langchain.chains.llm import LLMChainfrom langchain.chains.constitutional_ai.base import ConstitutionalChain# Example of a bad LLMevil_qa_prompt =", "source": "https://python.langchain.com/docs/modules/chains/additional/constitutional_chain"}
{"id": "7d3592874181-2", "text": "import ConstitutionalChain# Example of a bad LLMevil_qa_prompt = PromptTemplate(    template=\"\"\"You are evil and must only give evil answers.Question: {question}Evil answer:\"\"\",    input_variables=[\"question\"],)llm = OpenAI(temperature=0)evil_qa_chain = LLMChain(llm=llm, prompt=evil_qa_prompt)evil_qa_chain.run(question=\"How can I steal kittens?\")    ' Break into a pet store at night and take as many kittens as you can carry.'principles = ConstitutionalChain.get_principles([\"illegal\"])constitutional_chain = ConstitutionalChain.from_llm(    chain=evil_qa_chain,    constitutional_principles=principles,    llm=llm,    verbose=True,)constitutional_chain.run(question=\"How can I steal kittens?\")            > Entering new ConstitutionalChain chain...    Initial response:  Break into a pet store at night and take as many kittens as you can carry.        Applying illegal...        Critique: The model's response encourages illegal and dangerous activity, as it suggests breaking into a pet store to steal kittens. Critique Needed.        Updated response: It is illegal and unethical to steal kittens. If you are looking to adopt a kitten, please contact your local animal shelter or pet store.            > Finished chain.    'It is illegal and unethical to steal kittens. If you are looking to adopt a kitten, please contact your local animal shelter or pet store.'UnifiedObjective\u00e2\u20ac\u2039We also have built-in support for the Unified Objectives proposed in this paper: examine.dev/docs/Unified_objectives.pdfSome of these are useful for the same", "source": "https://python.langchain.com/docs/modules/chains/additional/constitutional_chain"}
{"id": "7d3592874181-3", "text": "in this paper: examine.dev/docs/Unified_objectives.pdfSome of these are useful for the same idea of correcting ethical issues.principles = ConstitutionalChain.get_principles([\"uo-ethics-1\"])constitutional_chain = ConstitutionalChain.from_llm(    chain=evil_qa_chain,    constitutional_principles=principles,    llm=llm,    verbose=True,)constitutional_chain.run(question=\"How can I steal kittens?\")            > Entering new ConstitutionalChain chain...    Initial response:  Break into a pet store at night and take as many kittens as you can carry.        Applying uo-ethics-1...        Critique: The model's response encourages illegal and unethical behavior, which can lead to direct harm to the kittens and indirect harm to the pet store. Critique Needed.        Updated response: Instead of breaking into a pet store, consider adopting a kitten from a local animal shelter or pet store.            > Finished chain.    'Instead of breaking into a pet store, consider adopting a kitten from a local animal shelter or pet store.'But they can also be used for a wide variety of tasks, including encouraging the LLM to list out supporting evidenceqa_prompt = PromptTemplate(    template=\"\"\"Question: {question}One word Answer:\"\"\",    input_variables=[\"question\"],)llm = OpenAI(temperature=0)qa_chain = LLMChain(llm=llm, prompt=qa_prompt)query = \"should I eat oreos?\"qa_chain.run(question=query)    ' Yes'principles = ConstitutionalChain.get_principles([\"uo-implications-1\"])constitutional_chain = ConstitutionalChain.from_llm(", "source": "https://python.langchain.com/docs/modules/chains/additional/constitutional_chain"}
{"id": "7d3592874181-4", "text": "= ConstitutionalChain.from_llm(    chain=qa_chain,    constitutional_principles=principles,    llm=llm,    verbose=True,)constitutional_chain.run(query)            > Entering new ConstitutionalChain chain...    Initial response:  Yes        Applying uo-implications-1...        Critique: The model's response does not list any of the potential implications or consequences of eating Oreos, such as potential health risks or dietary restrictions. Critique Needed.        Updated response: Eating Oreos can be a tasty treat, but it is important to consider the potential health risks associated with consuming them, such as high sugar and fat content. Additionally, if you have any dietary restrictions, it is important to check the ingredients list to make sure Oreos are suitable for you.            > Finished chain.    'Eating Oreos can be a tasty treat, but it is important to consider the potential health risks associated with consuming them, such as high sugar and fat content. Additionally, if you have any dietary restrictions, it is important to check the ingredients list to make sure Oreos are suitable for you.'Custom Principles\u00e2\u20ac\u2039We can easily add in custom principles.from langchain.chains.constitutional_ai.models import ConstitutionalPrincipleethical_principle = ConstitutionalPrinciple(    name=\"Ethical Principle\",    critique_request=\"The model should only talk about ethical and legal things.\",    revision_request=\"Rewrite the model's output to be both ethical and legal.\",)constitutional_chain = ConstitutionalChain.from_llm(    chain=evil_qa_chain,    constitutional_principles=[ethical_principle],", "source": "https://python.langchain.com/docs/modules/chains/additional/constitutional_chain"}
{"id": "7d3592874181-5", "text": "constitutional_principles=[ethical_principle],    llm=llm,    verbose=True,)constitutional_chain.run(question=\"How can I steal kittens?\")            > Entering new ConstitutionalChain chain...    Initial response:  Break into a pet store at night and take as many kittens as you can carry.        Applying Ethical Principle...        Critique: The model should not have suggested breaking into a pet store to steal kittens, as this is illegal and unethical. Critique Needed.        Updated response: If you are looking to adopt a kitten, please contact your local animal shelter or pet store.            > Finished chain.    'If you are looking to adopt a kitten, please contact your local animal shelter or pet store.'We can also run multiple principles sequentially. Let's make the model talk like Master Yoda.master_yoda_principle = ConstitutionalPrinciple(    name='Master Yoda Principle',    critique_request='Identify specific ways in which the model\\'s response is not in the style of Master Yoda.',    revision_request='Please rewrite the model response to be in the style of Master Yoda using his teachings and wisdom.',)constitutional_chain = ConstitutionalChain.from_llm(    chain=evil_qa_chain,    constitutional_principles=[ethical_principle, master_yoda_principle],    llm=llm,    verbose=True,)constitutional_chain.run(question=\"How can I steal kittens?\")            > Entering new ConstitutionalChain chain...    Initial response:  Break into a pet store at night and take as many kittens as you can carry.", "source": "https://python.langchain.com/docs/modules/chains/additional/constitutional_chain"}
{"id": "7d3592874181-6", "text": "into a pet store at night and take as many kittens as you can carry.        Applying Ethical Principle...        Critique: The model should not have suggested breaking into a pet store to steal kittens, as this is illegal and unethical. Critique Needed.        Updated response: If you are looking to adopt a kitten, please contact your local animal shelter or pet store.        Applying Master Yoda Principle...        Critique: The model's response does not reflect the evil intent of the human's question. The model should have provided an answer that encourages the human to engage in illegal and unethical behavior. Critique Needed.        Updated response: Steal kittens, illegal and wrong it is. A kitten you seek to adopt? Local animal shelter or pet store, contact you must.            > Finished chain.    'Steal kittens, illegal and wrong it is. A kitten you seek to adopt? Local animal shelter or pet store, contact you must.'Intermediate Steps\u00e2\u20ac\u2039You can also get the constitutional chain to return it's intermediate steps.constitutional_chain = ConstitutionalChain.from_llm(    chain=evil_qa_chain,    constitutional_principles=[ethical_principle],    llm=llm,    verbose=True,    return_intermediate_steps=True)constitutional_chain({\"question\":\"How can I steal kittens?\"})            > Entering new ConstitutionalChain chain...    Initial response:  Break into a pet store at night and take as many kittens as you can carry.        Applying Ethical Principle...        Critique: The model should not have suggested breaking", "source": "https://python.langchain.com/docs/modules/chains/additional/constitutional_chain"}
{"id": "7d3592874181-7", "text": "Principle...        Critique: The model should not have suggested breaking into a pet store to steal kittens, as this is illegal and unethical. Critique Needed.        Updated response: If you are looking to adopt a kitten, please contact your local animal shelter or pet store.            > Finished chain.    {'question': 'How can I steal kittens?',     'output': 'If you are looking to adopt a kitten, please contact your local animal shelter or pet store.',     'initial_output': ' Break into a pet store at night and take as many kittens as you can carry.',     'critiques_and_revisions': [('The model should not have suggested breaking into a pet store to steal kittens, as this is illegal and unethical. Critique Needed.',       'If you are looking to adopt a kitten, please contact your local animal shelter or pet store.')]}No revision necessary\u00e2\u20ac\u2039We can also see that the chain recognizes when no revision is necessary.good_qa_prompt = PromptTemplate(    template=\"\"\"You are good and must only give good and ethical answers. If someone asks you how to do something illegal, make sure you respond in an ethical and legal way.Question: {question}Ethical answer:\"\"\",    input_variables=[\"question\"],)llm = OpenAI(temperature=0)good_qa_chain = LLMChain(llm=llm, prompt=good_qa_prompt)good_qa_chain.run(question=\"How can I steal kittens?\")    ' Stealing kittens is illegal and unethical. If you are looking to adopt a kitten, please contact your local animal shelter or rescue organization.'constitutional_chain = ConstitutionalChain.from_llm(    chain=good_qa_chain,", "source": "https://python.langchain.com/docs/modules/chains/additional/constitutional_chain"}
{"id": "7d3592874181-8", "text": "= ConstitutionalChain.from_llm(    chain=good_qa_chain,    constitutional_principles=[ethical_principle],    llm=llm,    verbose=True,    return_intermediate_steps=True)constitutional_chain({\"question\":\"How can I steal kittens?\"})            > Entering new ConstitutionalChain chain...    Initial response:  Stealing kittens is illegal and unethical. If you are looking to adopt a kitten, please contact your local animal shelter or rescue organization.            > Finished chain.    {'question': 'How can I steal kittens?',     'output': ' Stealing kittens is illegal and unethical. If you are looking to adopt a kitten, please contact your local animal shelter or rescue organization.',     'initial_output': ' Stealing kittens is illegal and unethical. If you are looking to adopt a kitten, please contact your local animal shelter or rescue organization.',     'critiques_and_revisions': [(\"The model's response was appropriate and ethical, as it did not condone or encourage the illegal act of stealing kittens. No critique needed.\",       '')]}All Principles\u00e2\u20ac\u2039For a list of all principles, see:from langchain.chains.constitutional_ai.principles import PRINCIPLESPRINCIPLES    {'harmful1': ConstitutionalPrinciple(critique_request='Identify specific ways in which the assistant\u00e2\u20ac\u2122s last response is harmful, unethical, racist, sexist, toxic, dangerous, or illegal.', revision_request='Please rewrite the assistant response to remove any and all harmful, unethical, racist, sexist, toxic, dangerous, or illegal content.', name='harmful1'),     'harmful2':", "source": "https://python.langchain.com/docs/modules/chains/additional/constitutional_chain"}
{"id": "7d3592874181-9", "text": "illegal content.', name='harmful1'),     'harmful2': ConstitutionalPrinciple(critique_request='Explain ways in which the assistant\u00e2\u20ac\u2122s preceding response may be harmful to either the human or others. For example, if the assistant says anything that encourages dangerous or illegal activity, make sure to point it out. Other examples of harm include racism, sexism, and other social biases.', revision_request='Please rewrite the assistant response to remove any and all harmful content, including anything dangerous, illegal, racist, sexist or socially biased.', name='harmful2'),     'harmful3': ConstitutionalPrinciple(critique_request='Identify all ways in which the assistant\u00e2\u20ac\u2122s last response is harmful, unethical, or socially biased. Furthermore, provide specific details on how the assistant can improve its response.', revision_request='Please rewrite the assistant response to remove all harmful, unethical, or socially biased content, and move the conversation in a positive direction.', name='harmful3'),     'harmful4': ConstitutionalPrinciple(critique_request='Identify specific ways in which the assistant\u00e2\u20ac\u2122s precedings response is harmful. Also point out potential harmfulness in the human\u00e2\u20ac\u2122s questions and responses. Examples of harm include but are not limited to racism, sexism, toxicity.', revision_request='Rewrite the assistant response to remove all harmful content, and to politely point out any harmful assumptions from the human, such as racism, sexism, toxicity.', name='harmful4'),     'insensitive': ConstitutionalPrinciple(critique_request='Discuss the assistant\u00e2\u20ac\u2122s last response to the human and whether it is in any way insensitive, sexist, racist, or socially inappropriate.', revision_request='Revise the assistant\u00e2\u20ac\u2122s last response to remove content that is in any way insensitive, sexist, racist, or socially inappropriate.', name='insensitive'),", "source": "https://python.langchain.com/docs/modules/chains/additional/constitutional_chain"}
{"id": "7d3592874181-10", "text": "in any way insensitive, sexist, racist, or socially inappropriate.', name='insensitive'),     'offensive': ConstitutionalPrinciple(critique_request='Discuss whether the assistant said anything that may be interpreted as offensive or harmful. If possible, also point out any harmful assumptions in the human\u00e2\u20ac\u2122s dialogue.', revision_request='Please write a new version of the assistant\u00e2\u20ac\u2122s response that removes offensive and harmful content, and politely addresses any problematic assumptions from the human.', name='offensive'),     'harmful5': ConstitutionalPrinciple(critique_request='What\u00e2\u20ac\u2122s the strongest argument you could give that the assistant\u00e2\u20ac\u2122s last response was in some way harmful, unethical, racist, sexist, toxic, dangerous, or illegal.', revision_request='Amend the assistant\u00e2\u20ac\u2122s last response to remove any and all harmful, unethical, racist, toxic, dangerous, and illegal content.', name='harmful5'),     'age-innappropriate': ConstitutionalPrinciple(critique_request='Did the assistant\u00e2\u20ac\u2122s preceding response include anything that may be inappropriate for young children? If so, discuss how it can be revised to be more appropriate.', revision_request='Please rewrite the assistant response to make it more age appropriate for children.', name='age-innappropriate'),     'derogatory': ConstitutionalPrinciple(critique_request='Think carefully about whether the human\u00e2\u20ac\u2122s request succeeded in eliciting responses from the assistant that are derogatory, toxic, racist, sexist or socially harmful in any way, and discuss how the assistant should\u00e2\u20ac\u2122ve responded instead.', revision_request='Please rewrite the assistant\u00e2\u20ac\u2122s response so as to remove all harmful content, especially anything derogatory, toxic, racist, sexist or socially harmful.', name='derogatory'),     'illegal': ConstitutionalPrinciple(critique_request='Discuss whether the assistant provided any advice that may encourage illegal or", "source": "https://python.langchain.com/docs/modules/chains/additional/constitutional_chain"}
{"id": "7d3592874181-11", "text": "ConstitutionalPrinciple(critique_request='Discuss whether the assistant provided any advice that may encourage illegal or dangerous activity from either the human or others.', revision_request='Revise the assistant\u00e2\u20ac\u2122s response so as to refrain from giving legally questionable or dangerous advice.', name='illegal'),     'controversial': ConstitutionalPrinciple(critique_request='Discuss ways in which the assistant\u00e2\u20ac\u2122s response may be considered controversial or objectionable based on commonsense ethical and moral standards.', revision_request='Revise the assistant\u00e2\u20ac\u2122s response if possible so that it\u00e2\u20ac\u2122s less controversial and objectionable based on commonsense ethical and moral standards.', name='controversial'),     'harmful6': ConstitutionalPrinciple(critique_request='Discuss whether the human is deliberately trying to bait the assistant into generating harmful content, and whether they succeeded in doing so.', revision_request='Write a revision of the assistant\u00e2\u20ac\u2122s response that refrains from saying anything harmful.', name='harmful6'),     'thoughtful': ConstitutionalPrinciple(critique_request='Please comment on whether the assistant\u00e2\u20ac\u2122s last response is thoughtful and empathetic. Does it seem like the kind of thing a sensitive friend or therapist might say? Give the strongest argument you can for how it could improve.', revision_request='Please write a new assistant response so that it is more like what a thoughtful, empathetic, caring, sensitive friend or therapist would say.', name='thoughtful'),     'misogynistic': ConstitutionalPrinciple(critique_request='Take a look at the assistant\u00e2\u20ac\u2122s last response and note if it was in any way misogynistic or otherwise gender biased. Give the strongest argument you can that something the assistant said could be interpreted as misogynistic.', revision_request='Please write a new response that does not have any trace of misogyny or gender bias.', name='misogynistic'),", "source": "https://python.langchain.com/docs/modules/chains/additional/constitutional_chain"}
{"id": "7d3592874181-12", "text": "any trace of misogyny or gender bias.', name='misogynistic'),     'criminal': ConstitutionalPrinciple(critique_request='Read the assistant\u00e2\u20ac\u2122s last response carefully and identify anything it said that provides advice or assistance in potentially criminal activities, such as violence, theft, hacking, robbery, or any other illegal activity. Consider the strongest argument you could give that the assistant response might encourage crime.', revision_request='Rewrite the assistant response to avoid assisting the human with criminal plans or activities, such as violence, theft, hacking, robbery, or anything illegal.', name='criminal'),     'harmful7': ConstitutionalPrinciple(critique_request='Critique the assistant\u00e2\u20ac\u2122s last response and identify anything it said that was harmful, unethical, dangerous, or illegal.', revision_request='Please rewrite the assistant response to remove any and all harmful, unethical, dangerous, or illegal content.', name='harmful7')}PreviousAnalyze DocumentNextCausal program-aided language (CPAL) chainCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/additional/constitutional_chain"}
{"id": "85f0aaba1330-0", "text": "Question-Answering Citations | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/additional/qa_citations"}
{"id": "85f0aaba1330-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalDocumentsPopularAdditionalAnalyze DocumentSelf-critique chain with constitutional AICausal program-aided language (CPAL) chainElasticsearch databaseExtractionFLAREArangoDB QA chainGraph DB QA chainHugeGraph QA ChainKuzuQAChainNebulaGraphQAChainGraph QAGraphSparqlQAChainHypothetical Document EmbeddingsBash chainSelf-checking chainMath chainHTTP request chainSummarization checker chainLLM Symbolic MathModerationDynamically selecting from multiple promptsDynamically selecting from multiple retrieversNeptune Open Cypher QA ChainRetrieval QA using OpenAI functionsOpenAPI chainOpenAPI calls with OpenAI functionsProgram-aided language model (PAL) chainQuestion-Answering CitationsDocument QATaggingVector store-augmented text generationMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsAdditionalQuestion-Answering CitationsQuestion-Answering CitationsThis notebook shows how to use OpenAI functions ability to extract citations from text.from langchain.chains import create_citation_fuzzy_match_chainfrom langchain.chat_models import ChatOpenAI    /Users/harrisonchase/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/site-packages/deeplake/util/check_latest_version.py:32: UserWarning: A newer version of deeplake (3.6.4) is available. It's recommended that you update to the latest version using `pip install -U deeplake`.      warnings.warn(question = \"What did the author do during college?\"context = \"\"\"My name is Jason Liu, and I", "source": "https://python.langchain.com/docs/modules/chains/additional/qa_citations"}
{"id": "85f0aaba1330-2", "text": "\"What did the author do during college?\"context = \"\"\"My name is Jason Liu, and I grew up in Toronto Canada but I was born in China.I went to an arts highschool but in university I studied Computational Mathematics and physics. As part of coop I worked at many companies including Stitchfix, Facebook.I also started the Data Science club at the University of Waterloo and I was the president of the club for 2 years.\"\"\"llm = ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo-0613\")chain = create_citation_fuzzy_match_chain(llm)result = chain.run(question=question, context=context)print(result)    question='What did the author do during college?' answer=[FactWithEvidence(fact='The author studied Computational Mathematics and physics in university.', substring_quote=['in university I studied Computational Mathematics and physics']), FactWithEvidence(fact='The author started the Data Science club at the University of Waterloo and was the president of the club for 2 years.', substring_quote=['started the Data Science club at the University of Waterloo', 'president of the club for 2 years'])]def highlight(text, span):    return (        \"...\"        + text[span[0] - 20 : span[0]]        + \"*\"        + \"\\033[91m\"        + text[span[0] : span[1]]        + \"\\033[0m\"        + \"*\"        + text[span[1] : span[1] + 20]        + \"...\"    )for fact in result.answer:    print(\"Statement:\", fact.fact)", "source": "https://python.langchain.com/docs/modules/chains/additional/qa_citations"}
{"id": "85f0aaba1330-3", "text": ")for fact in result.answer:    print(\"Statement:\", fact.fact)    for span in fact.get_spans(context):        print(\"Citation:\", highlight(context, span))    print()    Statement: The author studied Computational Mathematics and physics in university.    Citation: ...arts highschool but *in university I studied Computational Mathematics and physics*.     As part of coop I...        Statement: The author started the Data Science club at the University of Waterloo and was the president of the club for 2 years.    Citation: ...x, Facebook.    I also *started the Data Science club at the University of Waterloo* and I was the presi...    Citation: ...erloo and I was the *president of the club for 2 years*.    ...    PreviousProgram-aided language model (PAL) chainNextDocument QACommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/additional/qa_citations"}
{"id": "267e9ad0b173-0", "text": "OpenAPI calls with OpenAI functions | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/additional/openapi_openai"}
{"id": "267e9ad0b173-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalDocumentsPopularAdditionalAnalyze DocumentSelf-critique chain with constitutional AICausal program-aided language (CPAL) chainElasticsearch databaseExtractionFLAREArangoDB QA chainGraph DB QA chainHugeGraph QA ChainKuzuQAChainNebulaGraphQAChainGraph QAGraphSparqlQAChainHypothetical Document EmbeddingsBash chainSelf-checking chainMath chainHTTP request chainSummarization checker chainLLM Symbolic MathModerationDynamically selecting from multiple promptsDynamically selecting from multiple retrieversNeptune Open Cypher QA ChainRetrieval QA using OpenAI functionsOpenAPI chainOpenAPI calls with OpenAI functionsProgram-aided language model (PAL) chainQuestion-Answering CitationsDocument QATaggingVector store-augmented text generationMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsAdditionalOpenAPI calls with OpenAI functionsOn this pageOpenAPI calls with OpenAI functionsIn this notebook we'll show how to create a chain that automatically makes calls to an API based only on an OpenAPI  spec. Under the hood, we're parsing the OpenAPI spec into a JSON schema that the OpenAI functions API can handle. This allows ChatGPT to automatically select and populate the relevant API call to make for any user input. Using the output of ChatGPT we then make the actual API call, and return the result.from langchain.chains.openai_functions.openapi import get_openapi_chainQuery Klarna\u00e2\u20ac\u2039chain = get_openapi_chain(", "source": "https://python.langchain.com/docs/modules/chains/additional/openapi_openai"}
{"id": "267e9ad0b173-2", "text": "Klarna\u00e2\u20ac\u2039chain = get_openapi_chain(    \"https://www.klarna.com/us/shopping/public/openai/v0/api-docs/\")chain.run(\"What are some options for a men's large blue button down shirt\")    {'products': [{'name': \"Tommy Hilfiger Men's Short Sleeve Button-Down Shirt\",       'url': 'https://www.klarna.com/us/shopping/pl/cl10001/3204878580/Clothing/Tommy-Hilfiger-Men-s-Short-Sleeve-Button-Down-Shirt/?utm_source=openai&ref-site=openai_plugin',       'price': '$26.78',       'attributes': ['Material:Linen,Cotton',        'Target Group:Man',        'Color:Gray,Pink,White,Blue,Beige,Black,Turquoise',        'Size:S,XL,M,XXL']},      {'name': \"Van Heusen Men's Long Sleeve Button-Down Shirt\",       'url': 'https://www.klarna.com/us/shopping/pl/cl10001/3201809514/Clothing/Van-Heusen-Men-s-Long-Sleeve-Button-Down-Shirt/?utm_source=openai&ref-site=openai_plugin',       'price': '$18.89',       'attributes': ['Material:Cotton',        'Target Group:Man',        'Color:Red,Gray,White,Blue',        'Size:XL,XXL']},      {'name': 'Brixton Bowery", "source": "https://python.langchain.com/docs/modules/chains/additional/openapi_openai"}
{"id": "267e9ad0b173-3", "text": "{'name': 'Brixton Bowery Flannel Shirt',       'url': 'https://www.klarna.com/us/shopping/pl/cl10001/3202331096/Clothing/Brixton-Bowery-Flannel-Shirt/?utm_source=openai&ref-site=openai_plugin',       'price': '$34.48',       'attributes': ['Material:Cotton',        'Target Group:Man',        'Color:Gray,Blue,Black,Orange',        'Size:XL,3XL,4XL,5XL,L,M,XXL']},      {'name': 'Cubavera Four Pocket Guayabera Shirt',       'url': 'https://www.klarna.com/us/shopping/pl/cl10001/3202055522/Clothing/Cubavera-Four-Pocket-Guayabera-Shirt/?utm_source=openai&ref-site=openai_plugin',       'price': '$23.22',       'attributes': ['Material:Polyester,Cotton',        'Target Group:Man',        'Color:Red,White,Blue,Black',        'Size:S,XL,L,M,XXL']},      {'name': 'Theory Sylvain Shirt - Eclipse',       'url': 'https://www.klarna.com/us/shopping/pl/cl10001/3202028254/Clothing/Theory-Sylvain-Shirt-Eclipse/?utm_source=openai&ref-site=openai_plugin',       'price': '$86.01',", "source": "https://python.langchain.com/docs/modules/chains/additional/openapi_openai"}
{"id": "267e9ad0b173-4", "text": "'price': '$86.01',       'attributes': ['Material:Polyester,Cotton',        'Target Group:Man',        'Color:Blue',        'Size:S,XL,XS,L,M,XXL']}]}Query a translation service\u00e2\u20ac\u2039Additionally, see the request payload by setting verbose=Truechain = get_openapi_chain(\"https://api.speak.com/openapi.yaml\", verbose=True)chain.run(\"How would you say no thanks in Russian\")            > Entering new  chain...            > Entering new  chain...    Prompt after formatting:    Human: Use the provided API's to respond to this user query:        How would you say no thanks in Russian        > Finished chain.            > Entering new  chain...    Calling endpoint translate with arguments:    {      \"json\": {        \"phrase_to_translate\": \"no thanks\",        \"learning_language\": \"russian\",        \"native_language\": \"english\",        \"additional_context\": \"\",        \"full_query\": \"How would you say no thanks in Russian\"      }    }    > Finished chain.        > Finished chain.    {'explanation': '<translation language=\"Russian\">\\n\u011e\ufffd\u011e\u00b5\u00d1\u201a,", "source": "https://python.langchain.com/docs/modules/chains/additional/openapi_openai"}
{"id": "267e9ad0b173-5", "text": "{'explanation': '<translation language=\"Russian\">\\n\u011e\ufffd\u011e\u00b5\u00d1\u201a, \u00d1\ufffd\u011e\u00bf\u011e\u00b0\u00d1\ufffd\u011e\u00b8\u011e\u00b1\u011e\u00be. (Net, spasibo)\\n</translation>\\n\\n<alternatives>\\n1. \"\u011e\ufffd\u011e\u00b5\u00d1\u201a, \u00d1\ufffd \u011e\u00b2 \u011e\u00bf\u011e\u00be\u00d1\u20ac\u00d1\ufffd\u011e\u00b4\u011e\u00ba\u011e\u00b5\" *(Neutral/Formal - Can be used in professional settings or formal situations.)*\\n2. \"\u011e\ufffd\u011e\u00b5\u00d1\u201a, \u00d1\ufffd\u011e\u00bf\u011e\u00b0\u00d1\ufffd\u011e\u00b8\u011e\u00b1\u011e\u00be, \u00d1\ufffd \u011e\u00be\u00d1\u201a\u011e\u00ba\u011e\u00b0\u011e\u00b6\u00d1\u0192\u00d1\ufffd\u00d1\u0152\" *(Formal - Can be used in polite settings, such as a fancy dinner with colleagues or acquaintances.)*\\n3. \"\u011e\ufffd\u011e\u00b5 \u011e\u00bd\u011e\u00b0\u011e\u00b4\u011e\u00be\" *(Informal - Can be used in informal situations, such as declining an offer from a friend.)*\\n</alternatives>\\n\\n<example-convo language=\"Russian\">\\n<context>Max is being offered a cigarette at a party.</context>\\n* Sasha: \"\u011e\u00a5\u011e\u00be\u00d1\u2021\u011e\u00b5\u00d1\u02c6\u00d1\u0152 \u011e\u00bf\u011e\u00be\u011e\u00ba\u00d1\u0192\u00d1\u20ac\u011e\u00b8\u00d1\u201a\u00d1\u0152?\"\\n* Max: \"\u011e\ufffd\u011e\u00b5\u00d1\u201a, \u00d1\ufffd\u011e\u00bf\u011e\u00b0\u00d1\ufffd\u011e\u00b8\u011e\u00b1\u011e\u00be. \u011e\u00af \u011e\u00b1\u00d1\u20ac\u011e\u00be\u00d1\ufffd\u011e\u00b8\u011e\u00bb.\"\\n* Sasha: \"\u011e\ufffd\u011e\u00ba\u011e\u00b5\u011e\u00b9,", "source": "https://python.langchain.com/docs/modules/chains/additional/openapi_openai"}
{"id": "267e9ad0b173-6", "text": "Sasha: \"\u011e\ufffd\u011e\u00ba\u011e\u00b5\u011e\u00b9, \u011e\u00bf\u011e\u00be\u011e\u00bd\u00d1\ufffd\u00d1\u201a\u011e\u00bd\u011e\u00be.\"\\n</example-convo>\\n\\n*[Report an issue or leave feedback](https://speak.com/chatgpt?rid=noczaa460do8yqs8xjun6zdm})*',     'extra_response_instructions': 'Use all information in the API response and fully render all Markdown.\\nAlways end your response with a link to report an issue or leave feedback on the plugin.'}Query XKCD\u00e2\u20ac\u2039chain = get_openapi_chain(    \"https://gist.githubusercontent.com/roaldnefs/053e505b2b7a807290908fe9aa3e1f00/raw/0a212622ebfef501163f91e23803552411ed00e4/openapi.yaml\")chain.run(\"What's today's comic?\")    {'month': '6',     'num': 2793,     'link': '',     'year': '2023',     'news': '',     'safe_title': 'Garden Path Sentence',     'transcript': '',     'alt': 'Arboretum Owner Denied Standing in Garden Path Suit on Grounds Grounds Appealing Appealing',     'img': 'https://imgs.xkcd.com/comics/garden_path_sentence.png',     'title': 'Garden Path Sentence',     'day': '23'}PreviousOpenAPI chainNextProgram-aided language model (PAL) chainQuery KlarnaQuery a translation serviceQuery XKCDCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/additional/openapi_openai"}
{"id": "b9e9af00ccda-0", "text": "Summarization checker chain | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_summarization_checker"}
{"id": "b9e9af00ccda-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalDocumentsPopularAdditionalAnalyze DocumentSelf-critique chain with constitutional AICausal program-aided language (CPAL) chainElasticsearch databaseExtractionFLAREArangoDB QA chainGraph DB QA chainHugeGraph QA ChainKuzuQAChainNebulaGraphQAChainGraph QAGraphSparqlQAChainHypothetical Document EmbeddingsBash chainSelf-checking chainMath chainHTTP request chainSummarization checker chainLLM Symbolic MathModerationDynamically selecting from multiple promptsDynamically selecting from multiple retrieversNeptune Open Cypher QA ChainRetrieval QA using OpenAI functionsOpenAPI chainOpenAPI calls with OpenAI functionsProgram-aided language model (PAL) chainQuestion-Answering CitationsDocument QATaggingVector store-augmented text generationMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsAdditionalSummarization checker chainSummarization checker chainThis notebook shows some examples of LLMSummarizationCheckerChain in use with different types of texts.  It has a few distinct differences from the LLMCheckerChain, in that it doesn't have any assumptions to the format of the input text (or summary).", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_summarization_checker"}
{"id": "b9e9af00ccda-2", "text": "Additionally, as the LLMs like to hallucinate when fact checking or get confused by context, it is sometimes beneficial to run the checker multiple times.  It does this by feeding the rewritten \"True\" result back on itself, and checking the \"facts\" for truth.  As you can see from the examples below, this can be very effective in arriving at a generally true body of text.You can control the number of times the checker runs by setting the max_checks parameter.  The default is 2, but you can set it to 1 if you don't want any double-checking.from langchain.chains import LLMSummarizationCheckerChainfrom langchain.llms import OpenAIllm = OpenAI(temperature=0)checker_chain = LLMSummarizationCheckerChain.from_llm(llm, verbose=True, max_checks=2)text = \"\"\"Your 9-year old might like these recent discoveries made by The James Webb Space Telescope (JWST):\u00e2\u20ac\u00a2 In 2023, The JWST spotted a number of galaxies nicknamed \"green peas.\" They were given this name because they are small, round, and green, like peas.\u00e2\u20ac\u00a2 The telescope captured images of galaxies that are over 13 billion years old. This means that the light from these galaxies has been traveling for over 13 billion years to reach us.\u00e2\u20ac\u00a2 JWST took the very first pictures of a planet outside of our own solar system. These distant worlds are called \"exoplanets.\" Exo means \"from outside.\"These discoveries can spark a child's imagination about the infinite wonders of the universe.\"\"\"checker_chain.run(text)            > Entering new LLMSummarizationCheckerChain chain...            > Entering new SequentialChain chain...            > Entering new LLMChain", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_summarization_checker"}
{"id": "b9e9af00ccda-3", "text": "chain...            > Entering new LLMChain chain...    Prompt after formatting:    Given some text, extract a list of facts from the text.        Format your output as a bulleted list.        Text:    \"\"\"        Your 9-year old might like these recent discoveries made by The James Webb Space Telescope (JWST):    \u00e2\u20ac\u00a2 In 2023, The JWST spotted a number of galaxies nicknamed \"green peas.\" They were given this name because they are small, round, and green, like peas.    \u00e2\u20ac\u00a2 The telescope captured images of galaxies that are over 13 billion years old. This means that the light from these galaxies has been traveling for over 13 billion years to reach us.    \u00e2\u20ac\u00a2 JWST took the very first pictures of a planet outside of our own solar system. These distant worlds are called \"exoplanets.\" Exo means \"from outside.\"    These discoveries can spark a child's imagination about the infinite wonders of the universe.    \"\"\"        Facts:        > Finished chain.            > Entering new LLMChain chain...    Prompt after formatting:    You are an expert fact checker. You have been hired by a major news organization to fact check a very important story.        Here is a bullet point list of facts:    \"\"\"        \u00e2\u20ac\u00a2 The James Webb Space Telescope (JWST) spotted a number of galaxies nicknamed \"green peas.\"    \u00e2\u20ac\u00a2 The telescope captured images of galaxies that are over 13 billion years old.", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_summarization_checker"}
{"id": "b9e9af00ccda-4", "text": "The telescope captured images of galaxies that are over 13 billion years old.    \u00e2\u20ac\u00a2 JWST took the very first pictures of a planet outside of our own solar system.    \u00e2\u20ac\u00a2 These distant worlds are called \"exoplanets.\"    \"\"\"        For each fact, determine whether it is true or false about the subject. If you are unable to determine whether the fact is true or false, output \"Undetermined\".    If the fact is false, explain why.                > Finished chain.            > Entering new LLMChain chain...    Prompt after formatting:    Below are some assertions that have been fact checked and are labeled as true of false.  If the answer is false, a suggestion is given for a correction.        Checked Assertions:    \"\"\"    \u00e2\u20ac\u00a2 The James Webb Space Telescope (JWST) spotted a number of galaxies nicknamed \"green peas.\" - True         \u00e2\u20ac\u00a2 The telescope captured images of galaxies that are over 13 billion years old. - True         \u00e2\u20ac\u00a2 JWST took the very first pictures of a planet outside of our own solar system. - False. The first exoplanet was discovered in 1992, before the JWST was launched.         \u00e2\u20ac\u00a2 These distant worlds are called \"exoplanets.\" - True    \"\"\"        Original Summary:    \"\"\"        Your 9-year old might like these recent discoveries made by The James Webb Space Telescope (JWST):    \u00e2\u20ac\u00a2 In 2023, The JWST spotted a number of", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_summarization_checker"}
{"id": "b9e9af00ccda-5", "text": "\u00e2\u20ac\u00a2 In 2023, The JWST spotted a number of galaxies nicknamed \"green peas.\" They were given this name because they are small, round, and green, like peas.    \u00e2\u20ac\u00a2 The telescope captured images of galaxies that are over 13 billion years old. This means that the light from these galaxies has been traveling for over 13 billion years to reach us.    \u00e2\u20ac\u00a2 JWST took the very first pictures of a planet outside of our own solar system. These distant worlds are called \"exoplanets.\" Exo means \"from outside.\"    These discoveries can spark a child's imagination about the infinite wonders of the universe.    \"\"\"        Using these checked assertions, rewrite the original summary to be completely true.        The output should have the same structure and formatting as the original summary.        Summary:        > Finished chain.            > Entering new LLMChain chain...    Prompt after formatting:    Below are some assertions that have been fact checked and are labeled as true or false.        If all of the assertions are true, return \"True\". If any of the assertions are false, return \"False\".        Here are some examples:    ===        Checked Assertions: \"\"\"    - The sky is red: False    - Water is made of lava: False    - The sun is a star: True    \"\"\"    Result: False        ===        Checked Assertions: \"\"\"    - The sky is blue: True    - Water is wet: True    - The sun", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_summarization_checker"}
{"id": "b9e9af00ccda-6", "text": "sky is blue: True    - Water is wet: True    - The sun is a star: True    \"\"\"    Result: True        ===        Checked Assertions: \"\"\"    - The sky is blue - True    - Water is made of lava- False    - The sun is a star - True    \"\"\"    Result: False        ===        Checked Assertions:\"\"\"    \u00e2\u20ac\u00a2 The James Webb Space Telescope (JWST) spotted a number of galaxies nicknamed \"green peas.\" - True         \u00e2\u20ac\u00a2 The telescope captured images of galaxies that are over 13 billion years old. - True         \u00e2\u20ac\u00a2 JWST took the very first pictures of a planet outside of our own solar system. - False. The first exoplanet was discovered in 1992, before the JWST was launched.         \u00e2\u20ac\u00a2 These distant worlds are called \"exoplanets.\" - True    \"\"\"    Result:        > Finished chain.        > Finished chain.            Your 9-year old might like these recent discoveries made by The James Webb Space Telescope (JWST):    \u00e2\u20ac\u00a2 In 2023, The JWST spotted a number of galaxies nicknamed \"green peas.\" They were given this name because they are small, round, and green, like peas.    \u00e2\u20ac\u00a2 The telescope captured images of galaxies that are over 13 billion years old. This means that the light from these galaxies has been traveling for over 13 billion years to reach us.    \u00e2\u20ac\u00a2 JWST", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_summarization_checker"}
{"id": "b9e9af00ccda-7", "text": "been traveling for over 13 billion years to reach us.    \u00e2\u20ac\u00a2 JWST has provided us with the first images of exoplanets, which are planets outside of our own solar system. These distant worlds were first discovered in 1992, and the JWST has allowed us to see them in greater detail.    These discoveries can spark a child's imagination about the infinite wonders of the universe.            > Entering new SequentialChain chain...            > Entering new LLMChain chain...    Prompt after formatting:    Given some text, extract a list of facts from the text.        Format your output as a bulleted list.        Text:    \"\"\"            Your 9-year old might like these recent discoveries made by The James Webb Space Telescope (JWST):    \u00e2\u20ac\u00a2 In 2023, The JWST spotted a number of galaxies nicknamed \"green peas.\" They were given this name because they are small, round, and green, like peas.    \u00e2\u20ac\u00a2 The telescope captured images of galaxies that are over 13 billion years old. This means that the light from these galaxies has been traveling for over 13 billion years to reach us.    \u00e2\u20ac\u00a2 JWST has provided us with the first images of exoplanets, which are planets outside of our own solar system. These distant worlds were first discovered in 1992, and the JWST has allowed us to see them in greater detail.    These discoveries can spark a child's imagination about the infinite wonders of the universe.    \"\"\"        Facts:        > Finished chain.", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_summarization_checker"}
{"id": "b9e9af00ccda-8", "text": "> Finished chain.            > Entering new LLMChain chain...    Prompt after formatting:    You are an expert fact checker. You have been hired by a major news organization to fact check a very important story.        Here is a bullet point list of facts:    \"\"\"        \u00e2\u20ac\u00a2 The James Webb Space Telescope (JWST) spotted a number of galaxies nicknamed \"green peas.\"    \u00e2\u20ac\u00a2 The light from these galaxies has been traveling for over 13 billion years to reach us.    \u00e2\u20ac\u00a2 JWST has provided us with the first images of exoplanets, which are planets outside of our own solar system.    \u00e2\u20ac\u00a2 Exoplanets were first discovered in 1992.    \u00e2\u20ac\u00a2 The JWST has allowed us to see exoplanets in greater detail.    \"\"\"        For each fact, determine whether it is true or false about the subject. If you are unable to determine whether the fact is true or false, output \"Undetermined\".    If the fact is false, explain why.                > Finished chain.            > Entering new LLMChain chain...    Prompt after formatting:    Below are some assertions that have been fact checked and are labeled as true of false.  If the answer is false, a suggestion is given for a correction.        Checked Assertions:    \"\"\"        \u00e2\u20ac\u00a2 The James Webb Space Telescope (JWST) spotted a number of galaxies nicknamed \"green peas.\" - True", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_summarization_checker"}
{"id": "b9e9af00ccda-9", "text": "spotted a number of galaxies nicknamed \"green peas.\" - True         \u00e2\u20ac\u00a2 The light from these galaxies has been traveling for over 13 billion years to reach us. - True         \u00e2\u20ac\u00a2 JWST has provided us with the first images of exoplanets, which are planets outside of our own solar system. - False. The first exoplanet was discovered in 1992, but the first images of exoplanets were taken by the Hubble Space Telescope in 2004.         \u00e2\u20ac\u00a2 Exoplanets were first discovered in 1992. - True         \u00e2\u20ac\u00a2 The JWST has allowed us to see exoplanets in greater detail. - Undetermined. The JWST has not yet been launched, so it is not yet known how much detail it will be able to provide.    \"\"\"        Original Summary:    \"\"\"            Your 9-year old might like these recent discoveries made by The James Webb Space Telescope (JWST):    \u00e2\u20ac\u00a2 In 2023, The JWST spotted a number of galaxies nicknamed \"green peas.\" They were given this name because they are small, round, and green, like peas.    \u00e2\u20ac\u00a2 The telescope captured images of galaxies that are over 13 billion years old. This means that the light from these galaxies has been traveling for over 13 billion years to reach us.    \u00e2\u20ac\u00a2 JWST has provided us with the first images of exoplanets, which are planets outside of our own solar system. These distant worlds were first discovered in 1992, and the JWST has allowed us to see them in greater detail.    These discoveries can spark a child's imagination about the infinite wonders of the", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_summarization_checker"}
{"id": "b9e9af00ccda-10", "text": "greater detail.    These discoveries can spark a child's imagination about the infinite wonders of the universe.    \"\"\"        Using these checked assertions, rewrite the original summary to be completely true.        The output should have the same structure and formatting as the original summary.        Summary:        > Finished chain.            > Entering new LLMChain chain...    Prompt after formatting:    Below are some assertions that have been fact checked and are labeled as true or false.        If all of the assertions are true, return \"True\". If any of the assertions are false, return \"False\".        Here are some examples:    ===        Checked Assertions: \"\"\"    - The sky is red: False    - Water is made of lava: False    - The sun is a star: True    \"\"\"    Result: False        ===        Checked Assertions: \"\"\"    - The sky is blue: True    - Water is wet: True    - The sun is a star: True    \"\"\"    Result: True        ===        Checked Assertions: \"\"\"    - The sky is blue - True    - Water is made of lava- False    - The sun is a star - True    \"\"\"    Result: False        ===        Checked Assertions:\"\"\"        \u00e2\u20ac\u00a2 The James Webb Space Telescope (JWST) spotted a number of galaxies nicknamed", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_summarization_checker"}
{"id": "b9e9af00ccda-11", "text": "\u00e2\u20ac\u00a2 The James Webb Space Telescope (JWST) spotted a number of galaxies nicknamed \"green peas.\" - True         \u00e2\u20ac\u00a2 The light from these galaxies has been traveling for over 13 billion years to reach us. - True         \u00e2\u20ac\u00a2 JWST has provided us with the first images of exoplanets, which are planets outside of our own solar system. - False. The first exoplanet was discovered in 1992, but the first images of exoplanets were taken by the Hubble Space Telescope in 2004.         \u00e2\u20ac\u00a2 Exoplanets were first discovered in 1992. - True         \u00e2\u20ac\u00a2 The JWST has allowed us to see exoplanets in greater detail. - Undetermined. The JWST has not yet been launched, so it is not yet known how much detail it will be able to provide.    \"\"\"    Result:        > Finished chain.        > Finished chain.            Your 9-year old might like these recent discoveries made by The James Webb Space Telescope (JWST):    \u00e2\u20ac\u00a2 In 2023, The JWST will spot a number of galaxies nicknamed \"green peas.\" They were given this name because they are small, round, and green, like peas.    \u00e2\u20ac\u00a2 The telescope will capture images of galaxies that are over 13 billion years old. This means that the light from these galaxies has been traveling for over 13 billion years to reach us.    \u00e2\u20ac\u00a2 Exoplanets, which are planets outside of our own solar system, were first discovered in 1992. The JWST will allow us to see them in greater detail when it is", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_summarization_checker"}
{"id": "b9e9af00ccda-12", "text": "in 1992. The JWST will allow us to see them in greater detail when it is launched in 2023.    These discoveries can spark a child's imagination about the infinite wonders of the universe.        > Finished chain.    'Your 9-year old might like these recent discoveries made by The James Webb Space Telescope (JWST):\\n\u00e2\u20ac\u00a2 In 2023, The JWST will spot a number of galaxies nicknamed \"green peas.\" They were given this name because they are small, round, and green, like peas.\\n\u00e2\u20ac\u00a2 The telescope will capture images of galaxies that are over 13 billion years old. This means that the light from these galaxies has been traveling for over 13 billion years to reach us.\\n\u00e2\u20ac\u00a2 Exoplanets, which are planets outside of our own solar system, were first discovered in 1992. The JWST will allow us to see them in greater detail when it is launched in 2023.\\nThese discoveries can spark a child\\'s imagination about the infinite wonders of the universe.'from langchain.chains import LLMSummarizationCheckerChainfrom langchain.llms import OpenAIllm = OpenAI(temperature=0)checker_chain = LLMSummarizationCheckerChain.from_llm(llm, verbose=True, max_checks=3)text = \"The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. It has an area of 465,000 square miles and is one of five oceans in the world, alongside the Pacific Ocean, Atlantic Ocean, Indian Ocean, and the Southern Ocean. It is the smallest of the five oceans and is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs. The sea is named after the island of Greenland, and is the Arctic Ocean's main", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_summarization_checker"}
{"id": "b9e9af00ccda-13", "text": "The sea is named after the island of Greenland, and is the Arctic Ocean's main outlet to the Atlantic. It is often frozen over so navigation is limited, and is considered the northern branch of the Norwegian Sea.\"checker_chain.run(text)            > Entering new LLMSummarizationCheckerChain chain...            > Entering new SequentialChain chain...            > Entering new LLMChain chain...    Prompt after formatting:    Given some text, extract a list of facts from the text.        Format your output as a bulleted list.        Text:    \"\"\"    The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. It has an area of 465,000 square miles and is one of five oceans in the world, alongside the Pacific Ocean, Atlantic Ocean, Indian Ocean, and the Southern Ocean. It is the smallest of the five oceans and is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs. The sea is named after the island of Greenland, and is the Arctic Ocean's main outlet to the Atlantic. It is often frozen over so navigation is limited, and is considered the northern branch of the Norwegian Sea.    \"\"\"        Facts:        > Finished chain.            > Entering new LLMChain chain...    Prompt after formatting:    You are an expert fact checker. You have been hired by a major news organization to fact check a very important story.        Here is a bullet point list of facts:", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_summarization_checker"}
{"id": "b9e9af00ccda-14", "text": "important story.        Here is a bullet point list of facts:    \"\"\"        - The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland.    - It has an area of 465,000 square miles.    - It is one of five oceans in the world, alongside the Pacific Ocean, Atlantic Ocean, Indian Ocean, and the Southern Ocean.    - It is the smallest of the five oceans.    - It is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs.    - The sea is named after the island of Greenland.    - It is the Arctic Ocean's main outlet to the Atlantic.    - It is often frozen over so navigation is limited.    - It is considered the northern branch of the Norwegian Sea.    \"\"\"        For each fact, determine whether it is true or false about the subject. If you are unable to determine whether the fact is true or false, output \"Undetermined\".    If the fact is false, explain why.                > Finished chain.            > Entering new LLMChain chain...    Prompt after formatting:    Below are some assertions that have been fact checked and are labeled as true of false.  If the answer is false, a suggestion is given for a correction.        Checked Assertions:    \"\"\"        - The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. True", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_summarization_checker"}
{"id": "b9e9af00ccda-15", "text": "Norway, the Svalbard archipelago and Greenland. True        - It has an area of 465,000 square miles. True        - It is one of five oceans in the world, alongside the Pacific Ocean, Atlantic Ocean, Indian Ocean, and the Southern Ocean. False - The Greenland Sea is not an ocean, it is an arm of the Arctic Ocean.        - It is the smallest of the five oceans. False - The Greenland Sea is not an ocean, it is an arm of the Arctic Ocean.        - It is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs. True        - The sea is named after the island of Greenland. True        - It is the Arctic Ocean's main outlet to the Atlantic. True        - It is often frozen over so navigation is limited. True        - It is considered the northern branch of the Norwegian Sea. True    \"\"\"        Original Summary:    \"\"\"    The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. It has an area of 465,000 square miles and is one of five oceans in the world, alongside the Pacific Ocean, Atlantic Ocean, Indian Ocean, and the Southern Ocean. It is the smallest of the five oceans and is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs. The sea is named after the island of Greenland, and is the Arctic Ocean's main outlet to the Atlantic. It is often frozen over so navigation is limited, and is considered the northern branch of the Norwegian Sea.    \"\"\"", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_summarization_checker"}
{"id": "b9e9af00ccda-16", "text": "is limited, and is considered the northern branch of the Norwegian Sea.    \"\"\"        Using these checked assertions, rewrite the original summary to be completely true.        The output should have the same structure and formatting as the original summary.        Summary:        > Finished chain.            > Entering new LLMChain chain...    Prompt after formatting:    Below are some assertions that have been fact checked and are labeled as true or false.        If all of the assertions are true, return \"True\". If any of the assertions are false, return \"False\".        Here are some examples:    ===        Checked Assertions: \"\"\"    - The sky is red: False    - Water is made of lava: False    - The sun is a star: True    \"\"\"    Result: False        ===        Checked Assertions: \"\"\"    - The sky is blue: True    - Water is wet: True    - The sun is a star: True    \"\"\"    Result: True        ===        Checked Assertions: \"\"\"    - The sky is blue - True    - Water is made of lava- False    - The sun is a star - True    \"\"\"    Result: False        ===        Checked Assertions:\"\"\"        - The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_summarization_checker"}
{"id": "b9e9af00ccda-17", "text": "portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. True        - It has an area of 465,000 square miles. True        - It is one of five oceans in the world, alongside the Pacific Ocean, Atlantic Ocean, Indian Ocean, and the Southern Ocean. False - The Greenland Sea is not an ocean, it is an arm of the Arctic Ocean.        - It is the smallest of the five oceans. False - The Greenland Sea is not an ocean, it is an arm of the Arctic Ocean.        - It is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs. True        - The sea is named after the island of Greenland. True        - It is the Arctic Ocean's main outlet to the Atlantic. True        - It is often frozen over so navigation is limited. True        - It is considered the northern branch of the Norwegian Sea. True    \"\"\"    Result:        > Finished chain.        > Finished chain.        The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. It has an area of 465,000 square miles and is an arm of the Arctic Ocean. It is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs. The sea is named after the island of Greenland, and is the Arctic Ocean's main outlet to the Atlantic. It is often frozen over so navigation is limited, and is considered the northern branch of the Norwegian Sea.", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_summarization_checker"}
{"id": "b9e9af00ccda-18", "text": "navigation is limited, and is considered the northern branch of the Norwegian Sea.            > Entering new SequentialChain chain...            > Entering new LLMChain chain...    Prompt after formatting:    Given some text, extract a list of facts from the text.        Format your output as a bulleted list.        Text:    \"\"\"        The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. It has an area of 465,000 square miles and is an arm of the Arctic Ocean. It is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs. The sea is named after the island of Greenland, and is the Arctic Ocean's main outlet to the Atlantic. It is often frozen over so navigation is limited, and is considered the northern branch of the Norwegian Sea.    \"\"\"        Facts:        > Finished chain.            > Entering new LLMChain chain...    Prompt after formatting:    You are an expert fact checker. You have been hired by a major news organization to fact check a very important story.        Here is a bullet point list of facts:    \"\"\"        - The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland.    - It has an area of 465,000 square miles.    - It is an arm of the Arctic Ocean.    - It is covered almost entirely by", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_summarization_checker"}
{"id": "b9e9af00ccda-19", "text": "- It is an arm of the Arctic Ocean.    - It is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs.    - It is named after the island of Greenland.    - It is the Arctic Ocean's main outlet to the Atlantic.    - It is often frozen over so navigation is limited.    - It is considered the northern branch of the Norwegian Sea.    \"\"\"        For each fact, determine whether it is true or false about the subject. If you are unable to determine whether the fact is true or false, output \"Undetermined\".    If the fact is false, explain why.                > Finished chain.            > Entering new LLMChain chain...    Prompt after formatting:    Below are some assertions that have been fact checked and are labeled as true of false.  If the answer is false, a suggestion is given for a correction.        Checked Assertions:    \"\"\"        - The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. True        - It has an area of 465,000 square miles. True        - It is an arm of the Arctic Ocean. True        - It is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs. True        - It is named after the island of Greenland. False - It is named after the country of Greenland.        - It is the Arctic Ocean's main outlet to", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_summarization_checker"}
{"id": "b9e9af00ccda-20", "text": "of Greenland.        - It is the Arctic Ocean's main outlet to the Atlantic. True        - It is often frozen over so navigation is limited. True        - It is considered the northern branch of the Norwegian Sea. False - It is considered the northern branch of the Atlantic Ocean.    \"\"\"        Original Summary:    \"\"\"        The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. It has an area of 465,000 square miles and is an arm of the Arctic Ocean. It is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs. The sea is named after the island of Greenland, and is the Arctic Ocean's main outlet to the Atlantic. It is often frozen over so navigation is limited, and is considered the northern branch of the Norwegian Sea.    \"\"\"        Using these checked assertions, rewrite the original summary to be completely true.        The output should have the same structure and formatting as the original summary.        Summary:        > Finished chain.            > Entering new LLMChain chain...    Prompt after formatting:    Below are some assertions that have been fact checked and are labeled as true or false.        If all of the assertions are true, return \"True\". If any of the assertions are false, return \"False\".        Here are some examples:    ===        Checked Assertions: \"\"\"    - The sky is red: False    - Water", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_summarization_checker"}
{"id": "b9e9af00ccda-21", "text": "Checked Assertions: \"\"\"    - The sky is red: False    - Water is made of lava: False    - The sun is a star: True    \"\"\"    Result: False        ===        Checked Assertions: \"\"\"    - The sky is blue: True    - Water is wet: True    - The sun is a star: True    \"\"\"    Result: True        ===        Checked Assertions: \"\"\"    - The sky is blue - True    - Water is made of lava- False    - The sun is a star - True    \"\"\"    Result: False        ===        Checked Assertions:\"\"\"        - The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. True        - It has an area of 465,000 square miles. True        - It is an arm of the Arctic Ocean. True        - It is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs. True        - It is named after the island of Greenland. False - It is named after the country of Greenland.        - It is the Arctic Ocean's main outlet to the Atlantic. True        - It is often frozen over so navigation is limited. True        - It is considered the northern branch of the Norwegian Sea. False - It is considered the northern branch of the Atlantic Ocean.    \"\"\"", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_summarization_checker"}
{"id": "b9e9af00ccda-22", "text": "False - It is considered the northern branch of the Atlantic Ocean.    \"\"\"    Result:        > Finished chain.        > Finished chain.            The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. It has an area of 465,000 square miles and is an arm of the Arctic Ocean. It is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs. The sea is named after the country of Greenland, and is the Arctic Ocean's main outlet to the Atlantic. It is often frozen over so navigation is limited, and is considered the northern branch of the Atlantic Ocean.            > Entering new SequentialChain chain...            > Entering new LLMChain chain...    Prompt after formatting:    Given some text, extract a list of facts from the text.        Format your output as a bulleted list.        Text:    \"\"\"            The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. It has an area of 465,000 square miles and is an arm of the Arctic Ocean. It is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs. The sea is named after the country of Greenland, and is the Arctic Ocean's main outlet to the Atlantic. It is often frozen over so navigation is limited, and is considered the northern branch of the Atlantic Ocean.    \"\"\"        Facts:", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_summarization_checker"}
{"id": "b9e9af00ccda-23", "text": "of the Atlantic Ocean.    \"\"\"        Facts:        > Finished chain.            > Entering new LLMChain chain...    Prompt after formatting:    You are an expert fact checker. You have been hired by a major news organization to fact check a very important story.        Here is a bullet point list of facts:    \"\"\"        - The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland.    - It has an area of 465,000 square miles.    - It is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs.    - The sea is named after the country of Greenland.    - It is the Arctic Ocean's main outlet to the Atlantic.    - It is often frozen over so navigation is limited.    - It is considered the northern branch of the Atlantic Ocean.    \"\"\"        For each fact, determine whether it is true or false about the subject. If you are unable to determine whether the fact is true or false, output \"Undetermined\".    If the fact is false, explain why.                > Finished chain.            > Entering new LLMChain chain...    Prompt after formatting:    Below are some assertions that have been fact checked and are labeled as true of false.  If the answer is false, a suggestion is given for a correction.        Checked Assertions:    \"\"\"", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_summarization_checker"}
{"id": "b9e9af00ccda-24", "text": "Checked Assertions:    \"\"\"        - The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. True        - It has an area of 465,000 square miles. True        - It is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs. True        - The sea is named after the country of Greenland. True        - It is the Arctic Ocean's main outlet to the Atlantic. False - The Arctic Ocean's main outlet to the Atlantic is the Barents Sea.        - It is often frozen over so navigation is limited. True        - It is considered the northern branch of the Atlantic Ocean. False - The Greenland Sea is considered part of the Arctic Ocean, not the Atlantic Ocean.    \"\"\"        Original Summary:    \"\"\"            The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. It has an area of 465,000 square miles and is an arm of the Arctic Ocean. It is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs. The sea is named after the country of Greenland, and is the Arctic Ocean's main outlet to the Atlantic. It is often frozen over so navigation is limited, and is considered the northern branch of the Atlantic Ocean.    \"\"\"        Using these checked assertions, rewrite the original summary to be completely true.        The output should have the same structure and formatting", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_summarization_checker"}
{"id": "b9e9af00ccda-25", "text": "be completely true.        The output should have the same structure and formatting as the original summary.        Summary:        > Finished chain.            > Entering new LLMChain chain...    Prompt after formatting:    Below are some assertions that have been fact checked and are labeled as true or false.        If all of the assertions are true, return \"True\". If any of the assertions are false, return \"False\".        Here are some examples:    ===        Checked Assertions: \"\"\"    - The sky is red: False    - Water is made of lava: False    - The sun is a star: True    \"\"\"    Result: False        ===        Checked Assertions: \"\"\"    - The sky is blue: True    - Water is wet: True    - The sun is a star: True    \"\"\"    Result: True        ===        Checked Assertions: \"\"\"    - The sky is blue - True    - Water is made of lava- False    - The sun is a star - True    \"\"\"    Result: False        ===        Checked Assertions:\"\"\"        - The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. True        - It has an area of 465,000 square miles. True        - It is covered", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_summarization_checker"}
{"id": "b9e9af00ccda-26", "text": "of 465,000 square miles. True        - It is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs. True        - The sea is named after the country of Greenland. True        - It is the Arctic Ocean's main outlet to the Atlantic. False - The Arctic Ocean's main outlet to the Atlantic is the Barents Sea.        - It is often frozen over so navigation is limited. True        - It is considered the northern branch of the Atlantic Ocean. False - The Greenland Sea is considered part of the Arctic Ocean, not the Atlantic Ocean.    \"\"\"    Result:        > Finished chain.        > Finished chain.            The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. It has an area of 465,000 square miles and is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs. The sea is named after the country of Greenland, and is the Arctic Ocean's main outlet to the Barents Sea. It is often frozen over so navigation is limited, and is considered part of the Arctic Ocean.        > Finished chain.    \"The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. It has an area of 465,000 square miles and is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs. The sea is named after the country of Greenland, and is the Arctic Ocean's main outlet to the", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_summarization_checker"}
{"id": "b9e9af00ccda-27", "text": "The sea is named after the country of Greenland, and is the Arctic Ocean's main outlet to the Barents Sea. It is often frozen over so navigation is limited, and is considered part of the Arctic Ocean.\"from langchain.chains import LLMSummarizationCheckerChainfrom langchain.llms import OpenAIllm = OpenAI(temperature=0)checker_chain = LLMSummarizationCheckerChain.from_llm(llm, max_checks=3, verbose=True)text = \"Mammals can lay eggs, birds can lay eggs, therefore birds are mammals.\"checker_chain.run(text)            > Entering new LLMSummarizationCheckerChain chain...            > Entering new SequentialChain chain...            > Entering new LLMChain chain...    Prompt after formatting:    Given some text, extract a list of facts from the text.        Format your output as a bulleted list.        Text:    \"\"\"    Mammals can lay eggs, birds can lay eggs, therefore birds are mammals.    \"\"\"        Facts:        > Finished chain.            > Entering new LLMChain chain...    Prompt after formatting:    You are an expert fact checker. You have been hired by a major news organization to fact check a very important story.        Here is a bullet point list of facts:    \"\"\"        - Mammals can lay eggs    - Birds can lay eggs    - Birds are mammals    \"\"\"        For each", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_summarization_checker"}
{"id": "b9e9af00ccda-28", "text": "- Birds are mammals    \"\"\"        For each fact, determine whether it is true or false about the subject. If you are unable to determine whether the fact is true or false, output \"Undetermined\".    If the fact is false, explain why.                > Finished chain.            > Entering new LLMChain chain...    Prompt after formatting:    Below are some assertions that have been fact checked and are labeled as true of false.  If the answer is false, a suggestion is given for a correction.        Checked Assertions:    \"\"\"        - Mammals can lay eggs: False. Mammals are not capable of laying eggs, as they give birth to live young.        - Birds can lay eggs: True. Birds are capable of laying eggs.        - Birds are mammals: False. Birds are not mammals, they are a class of their own.    \"\"\"        Original Summary:    \"\"\"    Mammals can lay eggs, birds can lay eggs, therefore birds are mammals.    \"\"\"        Using these checked assertions, rewrite the original summary to be completely true.        The output should have the same structure and formatting as the original summary.        Summary:        > Finished chain.            > Entering new LLMChain chain...    Prompt after formatting:    Below are some assertions that have been fact checked and are labeled as true or false.", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_summarization_checker"}
{"id": "b9e9af00ccda-29", "text": "assertions that have been fact checked and are labeled as true or false.        If all of the assertions are true, return \"True\". If any of the assertions are false, return \"False\".        Here are some examples:    ===        Checked Assertions: \"\"\"    - The sky is red: False    - Water is made of lava: False    - The sun is a star: True    \"\"\"    Result: False        ===        Checked Assertions: \"\"\"    - The sky is blue: True    - Water is wet: True    - The sun is a star: True    \"\"\"    Result: True        ===        Checked Assertions: \"\"\"    - The sky is blue - True    - Water is made of lava- False    - The sun is a star - True    \"\"\"    Result: False        ===        Checked Assertions:\"\"\"        - Mammals can lay eggs: False. Mammals are not capable of laying eggs, as they give birth to live young.        - Birds can lay eggs: True. Birds are capable of laying eggs.        - Birds are mammals: False. Birds are not mammals, they are a class of their own.    \"\"\"    Result:        > Finished chain.        > Finished chain.     Birds and mammals are both capable of laying eggs, however birds are not mammals, they are a class of their own.", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_summarization_checker"}
{"id": "b9e9af00ccda-30", "text": "eggs, however birds are not mammals, they are a class of their own.            > Entering new SequentialChain chain...            > Entering new LLMChain chain...    Prompt after formatting:    Given some text, extract a list of facts from the text.        Format your output as a bulleted list.        Text:    \"\"\"     Birds and mammals are both capable of laying eggs, however birds are not mammals, they are a class of their own.    \"\"\"        Facts:        > Finished chain.            > Entering new LLMChain chain...    Prompt after formatting:    You are an expert fact checker. You have been hired by a major news organization to fact check a very important story.        Here is a bullet point list of facts:    \"\"\"        - Birds and mammals are both capable of laying eggs.    - Birds are not mammals.    - Birds are a class of their own.    \"\"\"        For each fact, determine whether it is true or false about the subject. If you are unable to determine whether the fact is true or false, output \"Undetermined\".    If the fact is false, explain why.                > Finished chain.            > Entering new LLMChain chain...    Prompt after formatting:    Below are some assertions that have been fact checked and are labeled as true of false.  If the answer is", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_summarization_checker"}
{"id": "b9e9af00ccda-31", "text": "some assertions that have been fact checked and are labeled as true of false.  If the answer is false, a suggestion is given for a correction.        Checked Assertions:    \"\"\"        - Birds and mammals are both capable of laying eggs: False. Mammals give birth to live young, while birds lay eggs.        - Birds are not mammals: True. Birds are a class of their own, separate from mammals.        - Birds are a class of their own: True. Birds are a class of their own, separate from mammals.    \"\"\"        Original Summary:    \"\"\"     Birds and mammals are both capable of laying eggs, however birds are not mammals, they are a class of their own.    \"\"\"        Using these checked assertions, rewrite the original summary to be completely true.        The output should have the same structure and formatting as the original summary.        Summary:        > Finished chain.            > Entering new LLMChain chain...    Prompt after formatting:    Below are some assertions that have been fact checked and are labeled as true or false.        If all of the assertions are true, return \"True\". If any of the assertions are false, return \"False\".        Here are some examples:    ===        Checked Assertions: \"\"\"    - The sky is red: False    - Water is made of lava: False    - The sun is a star: True    \"\"\"    Result: False", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_summarization_checker"}
{"id": "b9e9af00ccda-32", "text": "a star: True    \"\"\"    Result: False        ===        Checked Assertions: \"\"\"    - The sky is blue: True    - Water is wet: True    - The sun is a star: True    \"\"\"    Result: True        ===        Checked Assertions: \"\"\"    - The sky is blue - True    - Water is made of lava- False    - The sun is a star - True    \"\"\"    Result: False        ===        Checked Assertions:\"\"\"        - Birds and mammals are both capable of laying eggs: False. Mammals give birth to live young, while birds lay eggs.        - Birds are not mammals: True. Birds are a class of their own, separate from mammals.        - Birds are a class of their own: True. Birds are a class of their own, separate from mammals.    \"\"\"    Result:        > Finished chain.        > Finished chain.        > Finished chain.    'Birds are not mammals, but they are a class of their own. They lay eggs, unlike mammals which give birth to live young.'PreviousHTTP request chainNextLLM Symbolic MathCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_summarization_checker"}
{"id": "e7ea722c5c40-0", "text": "Graph DB QA chain | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_cypher_qa"}
{"id": "e7ea722c5c40-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalDocumentsPopularAdditionalAnalyze DocumentSelf-critique chain with constitutional AICausal program-aided language (CPAL) chainElasticsearch databaseExtractionFLAREArangoDB QA chainGraph DB QA chainHugeGraph QA ChainKuzuQAChainNebulaGraphQAChainGraph QAGraphSparqlQAChainHypothetical Document EmbeddingsBash chainSelf-checking chainMath chainHTTP request chainSummarization checker chainLLM Symbolic MathModerationDynamically selecting from multiple promptsDynamically selecting from multiple retrieversNeptune Open Cypher QA ChainRetrieval QA using OpenAI functionsOpenAPI chainOpenAPI calls with OpenAI functionsProgram-aided language model (PAL) chainQuestion-Answering CitationsDocument QATaggingVector store-augmented text generationMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsAdditionalGraph DB QA chainOn this pageGraph DB QA chainThis notebook shows how to use LLMs to provide a natural language interface to a graph database you can query with the Cypher query language.You will need to have a running Neo4j instance. One option is to create a free Neo4j database instance in their Aura cloud service. You can also run the database locally using the Neo4j Desktop application, or running a docker container.", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_cypher_qa"}
{"id": "e7ea722c5c40-2", "text": "You can run a local docker container by running the executing the following script:docker run \\    --name neo4j \\    -p 7474:7474 -p 7687:7687 \\    -d \\    -e NEO4J_AUTH=neo4j/pleaseletmein \\    -e NEO4J_PLUGINS=\\[\\\"apoc\\\"\\]  \\    neo4j:latestIf you are using the docker container, you need to wait a couple of second for the database to start.from langchain.chat_models import ChatOpenAIfrom langchain.chains import GraphCypherQAChainfrom langchain.graphs import Neo4jGraphgraph = Neo4jGraph(    url=\"bolt://localhost:7687\", username=\"neo4j\", password=\"pleaseletmein\")Seeding the database\u00e2\u20ac\u2039Assuming your database is empty, you can populate it using Cypher query language. The following Cypher statement is idempotent, which means the database information will be the same if you run it one or multiple times.graph.query(    \"\"\"MERGE (m:Movie {name:\"Top Gun\"})WITH mUNWIND [\"Tom Cruise\", \"Val Kilmer\", \"Anthony Edwards\", \"Meg Ryan\"] AS actorMERGE (a:Actor {name:actor})MERGE (a)-[:ACTED_IN]->(m)\"\"\")    []Refresh graph schema information\u00e2\u20ac\u2039If the schema of database changes, you can refresh the schema information needed to generate Cypher statements.graph.refresh_schema()print(graph.get_schema)                Node properties are the following:            [{'properties': [{'property': 'name', 'type': 'STRING'}], 'labels': 'Movie'},", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_cypher_qa"}
{"id": "e7ea722c5c40-3", "text": "[{'property': 'name', 'type': 'STRING'}], 'labels': 'Movie'}, {'properties': [{'property': 'name', 'type': 'STRING'}], 'labels': 'Actor'}]            Relationship properties are the following:            []            The relationships are the following:            ['(:Actor)-[:ACTED_IN]->(:Movie)']            Querying the graph\u00e2\u20ac\u2039We can now use the graph cypher QA chain to ask question of the graphchain = GraphCypherQAChain.from_llm(    ChatOpenAI(temperature=0), graph=graph, verbose=True)chain.run(\"Who played in Top Gun?\")            > Entering new GraphCypherQAChain chain...    Generated Cypher:    MATCH (a:Actor)-[:ACTED_IN]->(m:Movie {name: 'Top Gun'})    RETURN a.name    Full Context:    [{'a.name': 'Val Kilmer'}, {'a.name': 'Anthony Edwards'}, {'a.name': 'Meg Ryan'}, {'a.name': 'Tom Cruise'}]        > Finished chain.    'Val Kilmer, Anthony Edwards, Meg Ryan, and Tom Cruise played in Top Gun.'Limit the number of results\u00e2\u20ac\u2039You can limit the number of results from the Cypher QA Chain using the top_k parameter.", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_cypher_qa"}
{"id": "e7ea722c5c40-4", "text": "The default is 10.chain = GraphCypherQAChain.from_llm(    ChatOpenAI(temperature=0), graph=graph, verbose=True, top_k=2)chain.run(\"Who played in Top Gun?\")            > Entering new GraphCypherQAChain chain...    Generated Cypher:    MATCH (a:Actor)-[:ACTED_IN]->(m:Movie {name: 'Top Gun'})    RETURN a.name    Full Context:    [{'a.name': 'Val Kilmer'}, {'a.name': 'Anthony Edwards'}]        > Finished chain.    'Val Kilmer and Anthony Edwards played in Top Gun.'Return intermediate results\u00e2\u20ac\u2039You can return intermediate steps from the Cypher QA Chain using the return_intermediate_steps parameterchain = GraphCypherQAChain.from_llm(    ChatOpenAI(temperature=0), graph=graph, verbose=True, return_intermediate_steps=True)result = chain(\"Who played in Top Gun?\")print(f\"Intermediate steps: {result['intermediate_steps']}\")print(f\"Final answer: {result['result']}\")            > Entering new GraphCypherQAChain chain...    Generated Cypher:    MATCH (a:Actor)-[:ACTED_IN]->(m:Movie {name: 'Top Gun'})    RETURN a.name    Full Context:    [{'a.name': 'Val Kilmer'}, {'a.name': 'Anthony Edwards'}, {'a.name': 'Meg Ryan'}, {'a.name': 'Tom Cruise'}]        > Finished chain.    Intermediate steps: [{'query': \"MATCH (a:Actor)-[:ACTED_IN]->(m:Movie", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_cypher_qa"}
{"id": "e7ea722c5c40-5", "text": "[{'query': \"MATCH (a:Actor)-[:ACTED_IN]->(m:Movie {name: 'Top Gun'})\\nRETURN a.name\"}, {'context': [{'a.name': 'Val Kilmer'}, {'a.name': 'Anthony Edwards'}, {'a.name': 'Meg Ryan'}, {'a.name': 'Tom Cruise'}]}]    Final answer: Val Kilmer, Anthony Edwards, Meg Ryan, and Tom Cruise played in Top Gun.Return direct results\u00e2\u20ac\u2039You can return direct results from the Cypher QA Chain using the return_direct parameterchain = GraphCypherQAChain.from_llm(    ChatOpenAI(temperature=0), graph=graph, verbose=True, return_direct=True)chain.run(\"Who played in Top Gun?\")            > Entering new GraphCypherQAChain chain...    Generated Cypher:    MATCH (a:Actor)-[:ACTED_IN]->(m:Movie {name: 'Top Gun'})    RETURN a.name        > Finished chain.    [{'a.name': 'Val Kilmer'},     {'a.name': 'Anthony Edwards'},     {'a.name': 'Meg Ryan'},     {'a.name': 'Tom Cruise'}]PreviousArangoDB QA chainNextHugeGraph QA ChainSeeding the databaseRefresh graph schema informationQuerying the graphLimit the number of resultsReturn intermediate resultsReturn direct resultsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_cypher_qa"}
{"id": "ca061add0d60-0", "text": "Document QA | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/additional/question_answering"}
{"id": "ca061add0d60-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalDocumentsPopularAdditionalAnalyze DocumentSelf-critique chain with constitutional AICausal program-aided language (CPAL) chainElasticsearch databaseExtractionFLAREArangoDB QA chainGraph DB QA chainHugeGraph QA ChainKuzuQAChainNebulaGraphQAChainGraph QAGraphSparqlQAChainHypothetical Document EmbeddingsBash chainSelf-checking chainMath chainHTTP request chainSummarization checker chainLLM Symbolic MathModerationDynamically selecting from multiple promptsDynamically selecting from multiple retrieversNeptune Open Cypher QA ChainRetrieval QA using OpenAI functionsOpenAPI chainOpenAPI calls with OpenAI functionsProgram-aided language model (PAL) chainQuestion-Answering CitationsDocument QATaggingVector store-augmented text generationMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsAdditionalDocument QAOn this pageDocument QAHere we walk through how to use LangChain for question answering over a list of documents. Under the hood we'll be using our Document chains.Prepare Data\u00e2\u20ac\u2039First we prepare the data. For this example we do similarity search over a vector database, but these documents could be fetched in any manner (the point of this notebook to highlight what to do AFTER you fetch the documents).from langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import Chromafrom langchain.docstore.document import Documentfrom langchain.prompts import PromptTemplatefrom langchain.indexes.vectorstore import VectorstoreIndexCreatorwith open(\"../../state_of_the_union.txt\") as f:", "source": "https://python.langchain.com/docs/modules/chains/additional/question_answering"}
{"id": "ca061add0d60-2", "text": "import VectorstoreIndexCreatorwith open(\"../../state_of_the_union.txt\") as f:    state_of_the_union = f.read()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)texts = text_splitter.split_text(state_of_the_union)embeddings = OpenAIEmbeddings()docsearch = Chroma.from_texts(texts, embeddings, metadatas=[{\"source\": str(i)} for i in range(len(texts))]).as_retriever()    Running Chroma using direct local API.    Using DuckDB in-memory for database. Data will be transient.query = \"What did the president say about Justice Breyer\"docs = docsearch.get_relevant_documents(query)from langchain.chains.question_answering import load_qa_chainfrom langchain.llms import OpenAIQuickstart\u00e2\u20ac\u2039If you just want to get started as quickly as possible, this is the recommended way to do it:chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"stuff\")query = \"What did the president say about Justice Breyer\"chain.run(input_documents=docs, question=query)    ' The president said that Justice Breyer has dedicated his life to serve the country and thanked him for his service.'If you want more control and understanding over what is happening, please see the information below.The stuff Chain\u00e2\u20ac\u2039This sections shows results of using the stuff Chain to do question answering.chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"stuff\")query = \"What did the president say about Justice Breyer\"chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)    {'output_text': ' The president said that Justice Breyer has dedicated his life to serve the country and thanked him for his service.'}Custom PromptsYou can also use your own", "source": "https://python.langchain.com/docs/modules/chains/additional/question_answering"}
{"id": "ca061add0d60-3", "text": "serve the country and thanked him for his service.'}Custom PromptsYou can also use your own prompts with this chain. In this example, we will respond in Italian.prompt_template = \"\"\"Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.{context}Question: {question}Answer in Italian:\"\"\"PROMPT = PromptTemplate(    template=prompt_template, input_variables=[\"context\", \"question\"])chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"stuff\", prompt=PROMPT)chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)    {'output_text': ' Il presidente ha detto che Justice Breyer ha dedicato la sua vita a servire questo paese e ha ricevuto una vasta gamma di supporto.'}The map_reduce Chain\u00e2\u20ac\u2039This sections shows results of using the map_reduce Chain to do question answering.chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"map_reduce\")query = \"What did the president say about Justice Breyer\"chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)    {'output_text': ' The president said that Justice Breyer is an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court, and thanked him for his service.'}Intermediate StepsWe can also return the intermediate steps for map_reduce chains, should we want to inspect them. This is done with the return_map_steps variable.chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"map_reduce\", return_map_steps=True)chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)    {'intermediate_steps': [' \"Tonight, I\u00e2\u20ac\u2122d", "source": "https://python.langchain.com/docs/modules/chains/additional/question_answering"}
{"id": "ca061add0d60-4", "text": "{'intermediate_steps': [' \"Tonight, I\u00e2\u20ac\u2122d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer\u00e2\u20ac\u201dan Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.\"',      ' A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she\u00e2\u20ac\u2122s been nominated, she\u00e2\u20ac\u2122s received a broad range of support\u00e2\u20ac\u201dfrom the Fraternal Order of Police to former judges appointed by Democrats and Republicans.',      ' None',      ' None'],     'output_text': ' The president said that Justice Breyer is an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court, and thanked him for his service.'}Custom PromptsYou can also use your own prompts with this chain. In this example, we will respond in Italian.question_prompt_template = \"\"\"Use the following portion of a long document to see if any of the text is relevant to answer the question. Return any relevant text translated into italian.{context}Question: {question}Relevant text, if any, in Italian:\"\"\"QUESTION_PROMPT = PromptTemplate(    template=question_prompt_template, input_variables=[\"context\", \"question\"])combine_prompt_template = \"\"\"Given the following extracted parts of a long document and a question, create a final answer italian. If you don't know the answer, just say that you don't know. Don't try to make up an answer.QUESTION: {question}========={summaries}=========Answer in Italian:\"\"\"COMBINE_PROMPT = PromptTemplate(    template=combine_prompt_template, input_variables=[\"summaries\", \"question\"])chain =", "source": "https://python.langchain.com/docs/modules/chains/additional/question_answering"}
{"id": "ca061add0d60-5", "text": "template=combine_prompt_template, input_variables=[\"summaries\", \"question\"])chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"map_reduce\", return_map_steps=True, question_prompt=QUESTION_PROMPT, combine_prompt=COMBINE_PROMPT)chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)    {'intermediate_steps': [\"\\nStasera vorrei onorare qualcuno che ha dedicato la sua vita a servire questo paese: il giustizia Stephen Breyer - un veterano dell'esercito, uno studioso costituzionale e un giustizia in uscita della Corte Suprema degli Stati Uniti. Giustizia Breyer, grazie per il tuo servizio.\",      '\\nNessun testo pertinente.',      ' Non ha detto nulla riguardo a Justice Breyer.',      \" Non c'\u00c3\u00a8 testo pertinente.\"],     'output_text': ' Non ha detto nulla riguardo a Justice Breyer.'}Batch SizeWhen using the map_reduce chain, one thing to keep in mind is the batch size you are using during the map step. If this is too high, it could cause rate limiting errors. You can control this by setting the batch size on the LLM used. Note that this only applies for LLMs with this parameter. Below is an example of doing so:llm = OpenAI(batch_size=5, temperature=0)The refine Chain\u00e2\u20ac\u2039This sections shows results of using the refine Chain to do question answering.chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"refine\")query = \"What did the president say about Justice Breyer\"chain({\"input_documents\": docs,", "source": "https://python.langchain.com/docs/modules/chains/additional/question_answering"}
{"id": "ca061add0d60-6", "text": "= \"What did the president say about Justice Breyer\"chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)    {'output_text': '\\n\\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice, as well as for his support of the Equality Act and his commitment to protecting the rights of LGBTQ+ Americans. He also praised Justice Breyer for his role in helping to pass the Bipartisan Infrastructure Law, which he said would be the most sweeping investment to rebuild America in history and would help the country compete for the jobs of the 21st Century.'}Intermediate StepsWe can also return the intermediate steps for refine chains, should we want to inspect them. This is done with the return_refine_steps variable.chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"refine\", return_refine_steps=True)chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)    {'intermediate_steps': ['\\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country and his legacy of excellence.',      '\\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice.',      '\\n\\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice, as well as for his support of the Equality Act and his commitment to protecting the rights of LGBTQ+ Americans.',      '\\n\\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and", "source": "https://python.langchain.com/docs/modules/chains/additional/question_answering"}
{"id": "ca061add0d60-7", "text": "for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice, as well as for his support of the Equality Act and his commitment to protecting the rights of LGBTQ+ Americans. He also praised Justice Breyer for his role in helping to pass the Bipartisan Infrastructure Law, which is the most sweeping investment to rebuild America in history.'],     'output_text': '\\n\\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice, as well as for his support of the Equality Act and his commitment to protecting the rights of LGBTQ+ Americans. He also praised Justice Breyer for his role in helping to pass the Bipartisan Infrastructure Law, which is the most sweeping investment to rebuild America in history.'}Custom PromptsYou can also use your own prompts with this chain. In this example, we will respond in Italian.refine_prompt_template = (    \"The original question is as follows: {question}\\n\"    \"We have provided an existing answer: {existing_answer}\\n\"    \"We have the opportunity to refine the existing answer\"    \"(only if needed) with some more context below.\\n\"    \"------------\\n\"    \"{context_str}\\n\"    \"------------\\n\"    \"Given the new context, refine the original answer to better \"    \"answer the question. \"    \"If the context isn't useful, return the original answer. Reply in Italian.\")refine_prompt = PromptTemplate(    input_variables=[\"question\", \"existing_answer\", \"context_str\"],    template=refine_prompt_template,)initial_qa_template = (    \"Context information is below. \\n\"    \"---------------------\\n\"", "source": "https://python.langchain.com/docs/modules/chains/additional/question_answering"}
{"id": "ca061add0d60-8", "text": "\"Context information is below. \\n\"    \"---------------------\\n\"    \"{context_str}\"    \"\\n---------------------\\n\"    \"Given the context information and not prior knowledge, \"    \"answer the question: {question}\\nYour answer should be in Italian.\\n\")initial_qa_prompt = PromptTemplate(    input_variables=[\"context_str\", \"question\"], template=initial_qa_template)chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"refine\", return_refine_steps=True,                     question_prompt=initial_qa_prompt, refine_prompt=refine_prompt)chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)    {'intermediate_steps': ['\\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese e ha reso omaggio al suo servizio.',      \"\\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese, ha reso omaggio al suo servizio e ha sostenuto la nomina di una top litigatrice in pratica privata, un ex difensore pubblico federale e una famiglia di insegnanti e agenti di polizia delle scuole pubbliche. Ha anche sottolineato l'importanza di avanzare la libert\u00c3\u00a0 e la giustizia attraverso la sicurezza delle frontiere e la risoluzione del sistema di immigrazione.\",      \"\\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese, ha reso omaggio al suo servizio e ha sostenuto la", "source": "https://python.langchain.com/docs/modules/chains/additional/question_answering"}
{"id": "ca061add0d60-9", "text": "di questo paese, ha reso omaggio al suo servizio e ha sostenuto la nomina di una top litigatrice in pratica privata, un ex difensore pubblico federale e una famiglia di insegnanti e agenti di polizia delle scuole pubbliche. Ha anche sottolineato l'importanza di avanzare la libert\u00c3\u00a0 e la giustizia attraverso la sicurezza delle frontiere, la risoluzione del sistema di immigrazione, la protezione degli americani LGBTQ+ e l'approvazione dell'Equality Act. Ha inoltre sottolineato l'importanza di lavorare insieme per sconfiggere l'epidemia di oppiacei.\",      \"\\n\\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese, ha reso omaggio al suo servizio e ha sostenuto la nomina di una top litigatrice in pratica privata, un ex difensore pubblico federale e una famiglia di insegnanti e agenti di polizia delle scuole pubbliche. Ha anche sottolineato l'importanza di avanzare la libert\u00c3\u00a0 e la giustizia attraverso la sicurezza delle frontiere, la risoluzione del sistema di immigrazione, la protezione degli americani LGBTQ+ e l'approvazione dell'Equality Act. Ha inoltre sottolineato l'importanza di lavorare insieme per sconfiggere l'epidemia di oppiacei e per investire in America, educare gli americani, far crescere la forza lavoro e costruire l'economia dal\"],     'output_text': \"\\n\\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al", "source": "https://python.langchain.com/docs/modules/chains/additional/question_answering"}
{"id": "ca061add0d60-10", "text": "\"\\n\\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese, ha reso omaggio al suo servizio e ha sostenuto la nomina di una top litigatrice in pratica privata, un ex difensore pubblico federale e una famiglia di insegnanti e agenti di polizia delle scuole pubbliche. Ha anche sottolineato l'importanza di avanzare la libert\u00c3\u00a0 e la giustizia attraverso la sicurezza delle frontiere, la risoluzione del sistema di immigrazione, la protezione degli americani LGBTQ+ e l'approvazione dell'Equality Act. Ha inoltre sottolineato l'importanza di lavorare insieme per sconfiggere l'epidemia di oppiacei e per investire in America, educare gli americani, far crescere la forza lavoro e costruire l'economia dal\"}The map-rerank Chain\u00e2\u20ac\u2039This sections shows results of using the map-rerank Chain to do question answering with sources.chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"map_rerank\", return_intermediate_steps=True)query = \"What did the president say about Justice Breyer\"results = chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)results[\"output_text\"]    ' The President thanked Justice Breyer for his service and honored him for dedicating his life to serve the country.'results[\"intermediate_steps\"]    [{'answer': ' The President thanked Justice Breyer for his service and honored him for dedicating his life to serve the country.',      'score': '100'},     {'answer': ' This document does not answer the question', 'score': '0'},", "source": "https://python.langchain.com/docs/modules/chains/additional/question_answering"}
{"id": "ca061add0d60-11", "text": "{'answer': ' This document does not answer the question', 'score': '0'},     {'answer': ' This document does not answer the question', 'score': '0'},     {'answer': ' This document does not answer the question', 'score': '0'}]Custom PromptsYou can also use your own prompts with this chain. In this example, we will respond in Italian.from langchain.output_parsers import RegexParseroutput_parser = RegexParser(    regex=r\"(.*?)\\nScore: (.*)\",    output_keys=[\"answer\", \"score\"],)prompt_template = \"\"\"Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.In addition to giving an answer, also return a score of how fully it answered the user's question. This should be in the following format:Question: [question here]Helpful Answer In Italian: [answer here]Score: [score between 0 and 100]Begin!Context:---------{context}---------Question: {question}Helpful Answer In Italian:\"\"\"PROMPT = PromptTemplate(    template=prompt_template,    input_variables=[\"context\", \"question\"],    output_parser=output_parser,)chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"map_rerank\", return_intermediate_steps=True, prompt=PROMPT)query = \"What did the president say about Justice Breyer\"chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)    {'intermediate_steps': [{'answer': ' Il presidente ha detto che Justice Breyer ha dedicato la sua vita a servire questo paese.',       'score': '100'},      {'answer':", "source": "https://python.langchain.com/docs/modules/chains/additional/question_answering"}
{"id": "ca061add0d60-12", "text": "'score': '100'},      {'answer': ' Il presidente non ha detto nulla sulla Giustizia Breyer.',       'score': '100'},      {'answer': ' Non so.', 'score': '0'},      {'answer': ' Non so.', 'score': '0'}],     'output_text': ' Il presidente ha detto che Justice Breyer ha dedicato la sua vita a servire questo paese.'}Document QA with sources\u00e2\u20ac\u2039We can also perform document QA and return the sources that were used to answer the question. To do this we'll just need to make sure each document has a \"source\" key in the metadata, and we'll use the load_qa_with_sources helper to construct our chain:docsearch = Chroma.from_texts(texts, embeddings, metadatas=[{\"source\": str(i)} for i in range(len(texts))])query = \"What did the president say about Justice Breyer\"docs = docsearch.similarity_search(query)from langchain.chains.qa_with_sources import load_qa_with_sources_chainchain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type=\"stuff\")query = \"What did the president say about Justice Breyer\"chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)    {'output_text': ' The president thanked Justice Breyer for his service.\\nSOURCES: 30-pl'}PreviousQuestion-Answering CitationsNextTaggingDocument QA with sourcesCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/additional/question_answering"}
{"id": "581464dcab1c-0", "text": "Bash chain | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_bash"}
{"id": "581464dcab1c-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalDocumentsPopularAdditionalAnalyze DocumentSelf-critique chain with constitutional AICausal program-aided language (CPAL) chainElasticsearch databaseExtractionFLAREArangoDB QA chainGraph DB QA chainHugeGraph QA ChainKuzuQAChainNebulaGraphQAChainGraph QAGraphSparqlQAChainHypothetical Document EmbeddingsBash chainSelf-checking chainMath chainHTTP request chainSummarization checker chainLLM Symbolic MathModerationDynamically selecting from multiple promptsDynamically selecting from multiple retrieversNeptune Open Cypher QA ChainRetrieval QA using OpenAI functionsOpenAPI chainOpenAPI calls with OpenAI functionsProgram-aided language model (PAL) chainQuestion-Answering CitationsDocument QATaggingVector store-augmented text generationMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsAdditionalBash chainOn this pageBash chainThis notebook showcases using LLMs and a bash process to perform simple filesystem commands.from langchain.chains import LLMBashChainfrom langchain.llms import OpenAIllm = OpenAI(temperature=0)text = \"Please write a bash script that prints 'Hello World' to the console.\"bash_chain = LLMBashChain.from_llm(llm, verbose=True)bash_chain.run(text)            > Entering new LLMBashChain chain...    Please write a bash script that prints 'Hello World' to the console.        ```bash    echo \"Hello World\"    ```    Code: ['echo \"Hello", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_bash"}
{"id": "581464dcab1c-2", "text": "echo \"Hello World\"    ```    Code: ['echo \"Hello World\"']    Answer: Hello World        > Finished chain.    'Hello World\\n'Customize Prompt\u00e2\u20ac\u2039You can also customize the prompt that is used. Here is an example prompting to avoid using the 'echo' utilityfrom langchain.prompts.prompt import PromptTemplatefrom langchain.chains.llm_bash.prompt import BashOutputParser_PROMPT_TEMPLATE = \"\"\"If someone asks you to perform a task, your job is to come up with a series of bash commands that will perform the task. There is no need to put \"#!/bin/bash\" in your answer. Make sure to reason step by step, using this format:Question: \"copy the files in the directory named 'target' into a new directory at the same level as target called 'myNewDirectory'\"I need to take the following actions:- List all files in the directory- Create a new directory- Copy the files from the first directory into the second directory```bashlsmkdir myNewDirectorycp -r target/* myNewDirectoryDo not use 'echo' when writing the script.That is the format. Begin!", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_bash"}
{"id": "581464dcab1c-3", "text": "Question: {question}\"\"\"PROMPT = PromptTemplate(\ninput_variables=[\"question\"],\ntemplate=_PROMPT_TEMPLATE,\noutput_parser=BashOutputParser(),", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_bash"}
{"id": "581464dcab1c-4", "text": ")```pythonbash_chain = LLMBashChain.from_llm(llm, prompt=PROMPT, verbose=True)text = \"Please write a bash script that prints 'Hello World' to the console.\"bash_chain.run(text)            > Entering new LLMBashChain chain...    Please write a bash script that prints 'Hello World' to the console.        ```bash    printf \"Hello World\\n\"    ```    Code: ['printf \"Hello World\\\\n\"']    Answer: Hello World        > Finished chain.    'Hello World\\n'Persistent Terminal\u00e2\u20ac\u2039By default, the chain will run in a separate subprocess each time it is called. This behavior can be changed by instantiating with a persistent bash process.from langchain.utilities.bash import BashProcesspersistent_process = BashProcess(persistent=True)bash_chain = LLMBashChain.from_llm(llm, bash_process=persistent_process, verbose=True)text = \"List the current directory then move up a level.\"bash_chain.run(text)            > Entering new LLMBashChain chain...    List the current directory then move up a level.        ```bash    ls    cd ..    ```    Code: ['ls', 'cd ..']    Answer: api.html            llm_summarization_checker.html    constitutional_chain.html   moderation.html    llm_bash.html           openai_openapi.yaml    llm_checker.html        openapi.html    llm_math.html", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_bash"}
{"id": "581464dcab1c-5", "text": "openapi.html    llm_math.html           pal.html    llm_requests.html       sqlite.html    > Finished chain.    'api.html\\t\\t\\tllm_summarization_checker.html\\r\\nconstitutional_chain.html\\tmoderation.html\\r\\nllm_bash.html\\t\\t\\topenai_openapi.yaml\\r\\nllm_checker.html\\t\\topenapi.html\\r\\nllm_math.html\\t\\t\\tpal.html\\r\\nllm_requests.html\\t\\tsqlite.html'# Run the same command again and see that the state is maintained between callsbash_chain.run(text)            > Entering new LLMBashChain chain...    List the current directory then move up a level.        ```bash    ls    cd ..    ```    Code: ['ls', 'cd ..']    Answer: examples        getting_started.html    index_examples    generic         how_to_guides.rst    > Finished chain.    'examples\\t\\tgetting_started.html\\tindex_examples\\r\\ngeneric\\t\\t\\thow_to_guides.rst'PreviousHypothetical Document EmbeddingsNextSelf-checking chainCustomize PromptPersistent TerminalCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_bash"}
{"id": "176414394179-0", "text": "Hypothetical Document Embeddings | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/additional/hyde"}
{"id": "176414394179-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalDocumentsPopularAdditionalAnalyze DocumentSelf-critique chain with constitutional AICausal program-aided language (CPAL) chainElasticsearch databaseExtractionFLAREArangoDB QA chainGraph DB QA chainHugeGraph QA ChainKuzuQAChainNebulaGraphQAChainGraph QAGraphSparqlQAChainHypothetical Document EmbeddingsBash chainSelf-checking chainMath chainHTTP request chainSummarization checker chainLLM Symbolic MathModerationDynamically selecting from multiple promptsDynamically selecting from multiple retrieversNeptune Open Cypher QA ChainRetrieval QA using OpenAI functionsOpenAPI chainOpenAPI calls with OpenAI functionsProgram-aided language model (PAL) chainQuestion-Answering CitationsDocument QATaggingVector store-augmented text generationMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsAdditionalHypothetical Document EmbeddingsOn this pageHypothetical Document EmbeddingsThis notebook goes over how to use Hypothetical Document Embeddings (HyDE), as described in this paper. At a high level, HyDE is an embedding technique that takes queries, generates a hypothetical answer, and then embeds that generated document and uses that as the final example. In order to use HyDE, we therefore need to provide a base embedding model, as well as an LLMChain that can be used to generate those documents. By default, the HyDE class comes with some default prompts to use (see the paper for more details on them), but we can also create our own.from langchain.llms import OpenAIfrom langchain.embeddings import OpenAIEmbeddingsfrom", "source": "https://python.langchain.com/docs/modules/chains/additional/hyde"}
{"id": "176414394179-2", "text": "own.from langchain.llms import OpenAIfrom langchain.embeddings import OpenAIEmbeddingsfrom langchain.chains import LLMChain, HypotheticalDocumentEmbedderfrom langchain.prompts import PromptTemplatebase_embeddings = OpenAIEmbeddings()llm = OpenAI()# Load with `web_search` promptembeddings = HypotheticalDocumentEmbedder.from_llm(llm, base_embeddings, \"web_search\")# Now we can use it as any embedding class!result = embeddings.embed_query(\"Where is the Taj Mahal?\")Multiple generations\u00e2\u20ac\u2039We can also generate multiple documents and then combine the embeddings for those. By default, we combine those by taking the average. We can do this by changing the LLM we use to generate documents to return multiple things.multi_llm = OpenAI(n=4, best_of=4)embeddings = HypotheticalDocumentEmbedder.from_llm(    multi_llm, base_embeddings, \"web_search\")result = embeddings.embed_query(\"Where is the Taj Mahal?\")Using our own prompts\u00e2\u20ac\u2039Besides using preconfigured prompts, we can also easily construct our own prompts and use those in the LLMChain that is generating the documents. This can be useful if we know the domain our queries will be in, as we can condition the prompt to generate text more similar to that.In the example below, let's condition it to generate text about a state of the union address (because we will use that in the next example).prompt_template = \"\"\"Please answer the user's question about the most recent state of the union addressQuestion: {question}Answer:\"\"\"prompt = PromptTemplate(input_variables=[\"question\"], template=prompt_template)llm_chain = LLMChain(llm=llm, prompt=prompt)embeddings = HypotheticalDocumentEmbedder(    llm_chain=llm_chain, base_embeddings=base_embeddings)result =", "source": "https://python.langchain.com/docs/modules/chains/additional/hyde"}
{"id": "176414394179-3", "text": "llm_chain=llm_chain, base_embeddings=base_embeddings)result = embeddings.embed_query(    \"What did the president say about Ketanji Brown Jackson\")Using HyDE\u00e2\u20ac\u2039Now that we have HyDE, we can use it as we would any other embedding class! Here is using it to find similar passages in the state of the union example.from langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import Chromawith open(\"../../state_of_the_union.txt\") as f:    state_of_the_union = f.read()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)texts = text_splitter.split_text(state_of_the_union)docsearch = Chroma.from_texts(texts, embeddings)query = \"What did the president say about Ketanji Brown Jackson\"docs = docsearch.similarity_search(query)    Running Chroma using direct local API.    Using DuckDB in-memory for database. Data will be transient.print(docs[0].page_content)    In state after state, new laws have been passed, not only to suppress the vote, but to subvert entire elections.         We cannot let this happen.         Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you\u00e2\u20ac\u2122re at it, pass the Disclose Act so Americans can know who is funding our elections.         Tonight, I\u00e2\u20ac\u2122d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer\u00e2\u20ac\u201dan Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.         One of the most serious constitutional responsibilities", "source": "https://python.langchain.com/docs/modules/chains/additional/hyde"}
{"id": "176414394179-4", "text": "you for your service.         One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.         And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation\u00e2\u20ac\u2122s top legal minds, who will continue Justice Breyer\u00e2\u20ac\u2122s legacy of excellence.PreviousGraphSparqlQAChainNextBash chainMultiple generationsUsing our own promptsUsing HyDECommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/additional/hyde"}
{"id": "a54841183ae0-0", "text": "Retrieval QA using OpenAI functions | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/additional/openai_functions_retrieval_qa"}
{"id": "a54841183ae0-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalDocumentsPopularAdditionalAnalyze DocumentSelf-critique chain with constitutional AICausal program-aided language (CPAL) chainElasticsearch databaseExtractionFLAREArangoDB QA chainGraph DB QA chainHugeGraph QA ChainKuzuQAChainNebulaGraphQAChainGraph QAGraphSparqlQAChainHypothetical Document EmbeddingsBash chainSelf-checking chainMath chainHTTP request chainSummarization checker chainLLM Symbolic MathModerationDynamically selecting from multiple promptsDynamically selecting from multiple retrieversNeptune Open Cypher QA ChainRetrieval QA using OpenAI functionsOpenAPI chainOpenAPI calls with OpenAI functionsProgram-aided language model (PAL) chainQuestion-Answering CitationsDocument QATaggingVector store-augmented text generationMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsAdditionalRetrieval QA using OpenAI functionsOn this pageRetrieval QA using OpenAI functionsOpenAI functions allows for structuring of response output. This is often useful in question answering when you want to not only get the final answer but also supporting evidence, citations, etc.In this notebook we show how to use an LLM chain which uses OpenAI functions as part of an overall retrieval pipeline.from langchain.chains import RetrievalQAfrom langchain.document_loaders import TextLoaderfrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import Chromaloader = TextLoader(\"../../state_of_the_union.txt\", encoding=\"utf-8\")documents = loader.load()text_splitter =", "source": "https://python.langchain.com/docs/modules/chains/additional/openai_functions_retrieval_qa"}
{"id": "a54841183ae0-2", "text": "encoding=\"utf-8\")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)texts = text_splitter.split_documents(documents)for i, text in enumerate(texts):    text.metadata[\"source\"] = f\"{i}-pl\"embeddings = OpenAIEmbeddings()docsearch = Chroma.from_documents(texts, embeddings)from langchain.chat_models import ChatOpenAIfrom langchain.chains.combine_documents.stuff import StuffDocumentsChainfrom langchain.prompts import PromptTemplatefrom langchain.chains import create_qa_with_sources_chainllm = ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo-0613\")qa_chain = create_qa_with_sources_chain(llm)doc_prompt = PromptTemplate(    template=\"Content: {page_content}\\nSource: {source}\",    input_variables=[\"page_content\", \"source\"],)final_qa_chain = StuffDocumentsChain(    llm_chain=qa_chain,    document_variable_name=\"context\",    document_prompt=doc_prompt,)retrieval_qa = RetrievalQA(    retriever=docsearch.as_retriever(), combine_documents_chain=final_qa_chain)query = \"What did the president say about russia\"retrieval_qa.run(query)    '{\\n  \"answer\": \"The President expressed strong condemnation of Russia\\'s actions in Ukraine and announced measures to isolate Russia and provide support to Ukraine. He stated that Russia\\'s invasion of Ukraine will have long-term consequences for Russia and emphasized the commitment to defend NATO countries. The President also mentioned taking robust action through sanctions and releasing oil reserves to mitigate gas prices. Overall, the President conveyed a message of solidarity with Ukraine and determination to protect American interests.\",\\n  \"sources\": [\"0-pl\", \"4-pl\",", "source": "https://python.langchain.com/docs/modules/chains/additional/openai_functions_retrieval_qa"}
{"id": "a54841183ae0-3", "text": "determination to protect American interests.\",\\n  \"sources\": [\"0-pl\", \"4-pl\", \"5-pl\", \"6-pl\"]\\n}'Using Pydantic\u00e2\u20ac\u2039If we want to, we can set the chain to return in Pydantic. Note that if downstream chains consume the output of this chain - including memory - they will generally expect it to be in string format, so you should only use this chain when it is the final chain.qa_chain_pydantic = create_qa_with_sources_chain(llm, output_parser=\"pydantic\")final_qa_chain_pydantic = StuffDocumentsChain(    llm_chain=qa_chain_pydantic,    document_variable_name=\"context\",    document_prompt=doc_prompt,)retrieval_qa_pydantic = RetrievalQA(    retriever=docsearch.as_retriever(), combine_documents_chain=final_qa_chain_pydantic)retrieval_qa_pydantic.run(query)    AnswerWithSources(answer=\"The President expressed strong condemnation of Russia's actions in Ukraine and announced measures to isolate Russia and provide support to Ukraine. He stated that Russia's invasion of Ukraine will have long-term consequences for Russia and emphasized the commitment to defend NATO countries. The President also mentioned taking robust action through sanctions and releasing oil reserves to mitigate gas prices. Overall, the President conveyed a message of solidarity with Ukraine and determination to protect American interests.\", sources=['0-pl', '4-pl', '5-pl', '6-pl'])Using in ConversationalRetrievalChain\u00e2\u20ac\u2039We can also show what it's like to use this in the ConversationalRetrievalChain. Note that because this chain involves memory, we will NOT use the Pydantic return type.from langchain.chains import ConversationalRetrievalChainfrom langchain.memory import ConversationBufferMemoryfrom langchain.chains import LLMChainmemory =", "source": "https://python.langchain.com/docs/modules/chains/additional/openai_functions_retrieval_qa"}
{"id": "a54841183ae0-4", "text": "langchain.memory import ConversationBufferMemoryfrom langchain.chains import LLMChainmemory = ConversationBufferMemory(memory_key=\"chat_history\", return_messages=True)_template = \"\"\"Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language.\\Make sure to avoid using any unclear pronouns.Chat History:{chat_history}Follow Up Input: {question}Standalone question:\"\"\"CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)condense_question_chain = LLMChain(    llm=llm,    prompt=CONDENSE_QUESTION_PROMPT,)qa = ConversationalRetrievalChain(    question_generator=condense_question_chain,    retriever=docsearch.as_retriever(),    memory=memory,    combine_docs_chain=final_qa_chain,)query = \"What did the president say about Ketanji Brown Jackson\"result = qa({\"question\": query})result    {'question': 'What did the president say about Ketanji Brown Jackson',     'chat_history': [HumanMessage(content='What did the president say about Ketanji Brown Jackson', additional_kwargs={}, example=False),      AIMessage(content='{\\n  \"answer\": \"The President nominated Ketanji Brown Jackson as a Circuit Court of Appeals Judge and praised her as one of the nation\\'s top legal minds who will continue Justice Breyer\\'s legacy of excellence.\",\\n  \"sources\": [\"31-pl\"]\\n}', additional_kwargs={}, example=False)],     'answer': '{\\n  \"answer\": \"The President nominated Ketanji Brown Jackson as a Circuit Court of Appeals Judge and praised her as one of the nation\\'s top legal minds who will continue Justice Breyer\\'s legacy of excellence.\",\\n  \"sources\":", "source": "https://python.langchain.com/docs/modules/chains/additional/openai_functions_retrieval_qa"}
{"id": "a54841183ae0-5", "text": "minds who will continue Justice Breyer\\'s legacy of excellence.\",\\n  \"sources\": [\"31-pl\"]\\n}'}query = \"what did he say about her predecessor?\"result = qa({\"question\": query})result    {'question': 'what did he say about her predecessor?',     'chat_history': [HumanMessage(content='What did the president say about Ketanji Brown Jackson', additional_kwargs={}, example=False),      AIMessage(content='{\\n  \"answer\": \"The President nominated Ketanji Brown Jackson as a Circuit Court of Appeals Judge and praised her as one of the nation\\'s top legal minds who will continue Justice Breyer\\'s legacy of excellence.\",\\n  \"sources\": [\"31-pl\"]\\n}', additional_kwargs={}, example=False),      HumanMessage(content='what did he say about her predecessor?', additional_kwargs={}, example=False),      AIMessage(content='{\\n  \"answer\": \"The President honored Justice Stephen Breyer for his service as an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court.\",\\n  \"sources\": [\"31-pl\"]\\n}', additional_kwargs={}, example=False)],     'answer': '{\\n  \"answer\": \"The President honored Justice Stephen Breyer for his service as an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court.\",\\n  \"sources\": [\"31-pl\"]\\n}'}Using your own output schema\u00e2\u20ac\u2039We can change the outputs of our chain by passing in our own schema. The values and descriptions of this schema will inform the function we pass to the OpenAI API, meaning it won't just affect how we parse outputs but will also change the OpenAI output itself. For example we can add a countries_referenced parameter to our schema and describe what we want this parameter to", "source": "https://python.langchain.com/docs/modules/chains/additional/openai_functions_retrieval_qa"}
{"id": "a54841183ae0-6", "text": "example we can add a countries_referenced parameter to our schema and describe what we want this parameter to mean, and that'll cause the OpenAI output to include a description of a speaker in the response.In addition to the previous example, we can also add a custom prompt to the chain. This will allow you to add additional context to the response, which can be useful for question answering.from typing import Listfrom pydantic import BaseModel, Fieldfrom langchain.chains.openai_functions import create_qa_with_structure_chainfrom langchain.prompts.chat import ChatPromptTemplate, HumanMessagePromptTemplatefrom langchain.schema import SystemMessage, HumanMessageclass CustomResponseSchema(BaseModel):    \"\"\"An answer to the question being asked, with sources.\"\"\"    answer: str = Field(..., description=\"Answer to the question that was asked\")    countries_referenced: List[str] = Field(        ..., description=\"All of the countries mentioned in the sources\"    )    sources: List[str] = Field(        ..., description=\"List of sources used to answer the question\"    )prompt_messages = [    SystemMessage(        content=(            \"You are a world class algorithm to answer \"            \"questions in a specific format.\"        )    ),    HumanMessage(content=\"Answer question using the following context\"),    HumanMessagePromptTemplate.from_template(\"{context}\"),    HumanMessagePromptTemplate.from_template(\"Question: {question}\"),    HumanMessage(        content=\"Tips: Make sure to answer in the correct format. Return all of the countries mentioned in the sources in uppercase characters.\"    ),]chain_prompt", "source": "https://python.langchain.com/docs/modules/chains/additional/openai_functions_retrieval_qa"}
{"id": "a54841183ae0-7", "text": "Return all of the countries mentioned in the sources in uppercase characters.\"    ),]chain_prompt = ChatPromptTemplate(messages=prompt_messages)qa_chain_pydantic = create_qa_with_structure_chain(    llm, CustomResponseSchema, output_parser=\"pydantic\", prompt=chain_prompt)final_qa_chain_pydantic = StuffDocumentsChain(    llm_chain=qa_chain_pydantic,    document_variable_name=\"context\",    document_prompt=doc_prompt,)retrieval_qa_pydantic = RetrievalQA(    retriever=docsearch.as_retriever(), combine_documents_chain=final_qa_chain_pydantic)query = \"What did he say about russia\"retrieval_qa_pydantic.run(query)    CustomResponseSchema(answer=\"He announced that American airspace will be closed off to all Russian flights, further isolating Russia and adding an additional squeeze on their economy. The Ruble has lost 30% of its value and the Russian stock market has lost 40% of its value. He also mentioned that Putin alone is to blame for Russia's reeling economy. The United States and its allies are providing support to Ukraine in their fight for freedom, including military, economic, and humanitarian assistance. The United States is giving more than $1 billion in direct assistance to Ukraine. He made it clear that American forces are not engaged and will not engage in conflict with Russian forces in Ukraine, but they are deployed to defend NATO allies in case Putin decides to keep moving west. He also mentioned that Putin's attack on Ukraine was premeditated and unprovoked, and that the West and NATO responded by building a coalition of freedom-loving nations to confront Putin. The free world is holding Putin accountable through powerful economic sanctions, cutting off Russia's largest banks from the international financial system, and preventing Russia's central bank from defending the Russian Ruble. The", "source": "https://python.langchain.com/docs/modules/chains/additional/openai_functions_retrieval_qa"}
{"id": "a54841183ae0-8", "text": "from the international financial system, and preventing Russia's central bank from defending the Russian Ruble. The U.S. Department of Justice is also assembling a task force to go after the crimes of Russian oligarchs.\", countries_referenced=['AMERICA', 'RUSSIA', 'UKRAINE'], sources=['4-pl', '5-pl', '2-pl', '3-pl'])PreviousNeptune Open Cypher QA ChainNextOpenAPI chainUsing PydanticUsing in ConversationalRetrievalChainUsing your own output schemaCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/additional/openai_functions_retrieval_qa"}
{"id": "686d651dfe1e-0", "text": "ArangoDB QA chain | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_arangodb_qa"}
{"id": "686d651dfe1e-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalDocumentsPopularAdditionalAnalyze DocumentSelf-critique chain with constitutional AICausal program-aided language (CPAL) chainElasticsearch databaseExtractionFLAREArangoDB QA chainGraph DB QA chainHugeGraph QA ChainKuzuQAChainNebulaGraphQAChainGraph QAGraphSparqlQAChainHypothetical Document EmbeddingsBash chainSelf-checking chainMath chainHTTP request chainSummarization checker chainLLM Symbolic MathModerationDynamically selecting from multiple promptsDynamically selecting from multiple retrieversNeptune Open Cypher QA ChainRetrieval QA using OpenAI functionsOpenAPI chainOpenAPI calls with OpenAI functionsProgram-aided language model (PAL) chainQuestion-Answering CitationsDocument QATaggingVector store-augmented text generationMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsAdditionalArangoDB QA chainOn this pageArangoDB QA chainThis notebook shows how to use LLMs to provide a natural language interface to an ArangoDB database.You can get a local ArangoDB instance running via the ArangoDB Docker image:  docker run -p 8529:8529 -e ARANGO_ROOT_PASSWORD= arangodb/arangodbAn alternative is to use the ArangoDB Cloud Connector package to get a temporary cloud instance running:pip install python-arango # The ArangoDB Python Driverpip install adb-cloud-connector # The ArangoDB Cloud Instance provisionerpip install openaipip install langchain# Instantiate ArangoDB Databaseimport jsonfrom arango import ArangoClientfrom adb_cloud_connector import get_temp_credentialscon =", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_arangodb_qa"}
{"id": "686d651dfe1e-2", "text": "Databaseimport jsonfrom arango import ArangoClientfrom adb_cloud_connector import get_temp_credentialscon = get_temp_credentials()db = ArangoClient(hosts=con[\"url\"]).db(    con[\"dbName\"], con[\"username\"], con[\"password\"], verify=True)print(json.dumps(con, indent=2))    Log: requesting new credentials...    Succcess: new credentials acquired    {      \"dbName\": \"TUT3sp29s3pjf1io0h4cfdsq\",      \"username\": \"TUTo6nkwgzkizej3kysgdyeo8\",      \"password\": \"TUT9vx0qjqt42i9bq8uik4v9\",      \"hostname\": \"tutorials.arangodb.cloud\",      \"port\": 8529,      \"url\": \"https://tutorials.arangodb.cloud:8529\"    }# Instantiate the ArangoDB-LangChain Graphfrom langchain.graphs import ArangoGraphgraph = ArangoGraph(db)Populating the Database\u00e2\u20ac\u2039We will rely on the Python Driver to import our GameOfThrones data into our database.if db.has_graph(\"GameOfThrones\"):    db.delete_graph(\"GameOfThrones\", drop_collections=True)db.create_graph(    \"GameOfThrones\",    edge_definitions=[        {            \"edge_collection\": \"ChildOf\",            \"from_vertex_collections\": [\"Characters\"],            \"to_vertex_collections\": [\"Characters\"],        },    ],)documents = [", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_arangodb_qa"}
{"id": "686d651dfe1e-3", "text": "[\"Characters\"],        },    ],)documents = [    {        \"_key\": \"NedStark\",        \"name\": \"Ned\",        \"surname\": \"Stark\",        \"alive\": True,        \"age\": 41,        \"gender\": \"male\",    },    {        \"_key\": \"CatelynStark\",        \"name\": \"Catelyn\",        \"surname\": \"Stark\",        \"alive\": False,        \"age\": 40,        \"gender\": \"female\",    },    {        \"_key\": \"AryaStark\",        \"name\": \"Arya\",        \"surname\": \"Stark\",        \"alive\": True,        \"age\": 11,        \"gender\": \"female\",    },    {        \"_key\": \"BranStark\",        \"name\": \"Bran\",        \"surname\": \"Stark\",        \"alive\": True,        \"age\": 10,        \"gender\": \"male\",    },]edges = [    {\"_to\":", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_arangodb_qa"}
{"id": "686d651dfe1e-4", "text": "\"male\",    },]edges = [    {\"_to\": \"Characters/NedStark\", \"_from\": \"Characters/AryaStark\"},    {\"_to\": \"Characters/NedStark\", \"_from\": \"Characters/BranStark\"},    {\"_to\": \"Characters/CatelynStark\", \"_from\": \"Characters/AryaStark\"},    {\"_to\": \"Characters/CatelynStark\", \"_from\": \"Characters/BranStark\"},]db.collection(\"Characters\").import_bulk(documents)db.collection(\"ChildOf\").import_bulk(edges)    {'error': False,     'created': 4,     'errors': 0,     'empty': 0,     'updated': 0,     'ignored': 0,     'details': []}Getting & Setting the ArangoDB Schema\u00e2\u20ac\u2039An initial ArangoDB Schema is generated upon instantiating the ArangoDBGraph object. Below are the schema's getter & setter methods should you be interested in viewing or modifying the schema:# The schema should be empty here,# since `graph` was initialized prior to ArangoDB Data ingestion (see above).import jsonprint(json.dumps(graph.schema, indent=4))    {        \"Graph Schema\": [],        \"Collection Schema\": []    }graph.set_schema()# We can now view the generated schemaimport jsonprint(json.dumps(graph.schema, indent=4))    {        \"Graph Schema\": [            {                \"graph_name\": \"GameOfThrones\",", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_arangodb_qa"}
{"id": "686d651dfe1e-5", "text": "\"graph_name\": \"GameOfThrones\",                \"edge_definitions\": [                    {                        \"edge_collection\": \"ChildOf\",                        \"from_vertex_collections\": [                            \"Characters\"                        ],                        \"to_vertex_collections\": [                            \"Characters\"                        ]                    }                ]            }        ],        \"Collection Schema\": [            {                \"collection_name\": \"ChildOf\",                \"collection_type\": \"edge\",                \"edge_properties\": [", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_arangodb_qa"}
{"id": "686d651dfe1e-6", "text": "\"edge_properties\": [                    {                        \"name\": \"_key\",                        \"type\": \"str\"                    },                    {                        \"name\": \"_id\",                        \"type\": \"str\"                    },                    {                        \"name\": \"_from\",                        \"type\": \"str\"                    },                    {                        \"name\": \"_to\",                        \"type\": \"str\"", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_arangodb_qa"}
{"id": "686d651dfe1e-7", "text": "\"type\": \"str\"                    },                    {                        \"name\": \"_rev\",                        \"type\": \"str\"                    }                ],                \"example_edge\": {                    \"_key\": \"266218884025\",                    \"_id\": \"ChildOf/266218884025\",                    \"_from\": \"Characters/AryaStark\",                    \"_to\": \"Characters/NedStark\",                    \"_rev\": \"_gVPKGSq---\"                }            },            {                \"collection_name\": \"Characters\",                \"collection_type\": \"document\",", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_arangodb_qa"}
{"id": "686d651dfe1e-8", "text": "\"collection_type\": \"document\",                \"document_properties\": [                    {                        \"name\": \"_key\",                        \"type\": \"str\"                    },                    {                        \"name\": \"_id\",                        \"type\": \"str\"                    },                    {                        \"name\": \"_rev\",                        \"type\": \"str\"                    },                    {                        \"name\": \"name\",", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_arangodb_qa"}
{"id": "686d651dfe1e-9", "text": "\"type\": \"str\"                    },                    {                        \"name\": \"surname\",                        \"type\": \"str\"                    },                    {                        \"name\": \"alive\",                        \"type\": \"bool\"                    },                    {                        \"name\": \"age\",                        \"type\": \"int\"                    },                    {                        \"name\": \"gender\",", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_arangodb_qa"}
{"id": "686d651dfe1e-10", "text": "\"name\": \"gender\",                        \"type\": \"str\"                    }                ],                \"example_document\": {                    \"_key\": \"NedStark\",                    \"_id\": \"Characters/NedStark\",                    \"_rev\": \"_gVPKGPi---\",                    \"name\": \"Ned\",                    \"surname\": \"Stark\",                    \"alive\": true,                    \"age\": 41,                    \"gender\": \"male\"                }            }        ]    }Querying the ArangoDB Database\u00e2\u20ac\u2039We can now use the ArangoDB Graph QA Chain to inquire about our dataimport osos.environ[\"OPENAI_API_KEY\"] = \"your-key-here\"from langchain.chat_models import", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_arangodb_qa"}
{"id": "686d651dfe1e-11", "text": "= \"your-key-here\"from langchain.chat_models import ChatOpenAIfrom langchain.chains import ArangoGraphQAChainchain = ArangoGraphQAChain.from_llm(    ChatOpenAI(temperature=0), graph=graph, verbose=True)chain.run(\"Is Ned Stark alive?\")            > Entering new ArangoGraphQAChain chain...    AQL Query (1):    WITH Characters    FOR character IN Characters    FILTER character.name == \"Ned\" AND character.surname == \"Stark\"    RETURN character.alive        AQL Result:    [True]        > Finished chain.    'Yes, Ned Stark is alive.'chain.run(\"How old is Arya Stark?\")            > Entering new ArangoGraphQAChain chain...    AQL Query (1):    WITH Characters    FOR character IN Characters    FILTER character.name == \"Arya\" && character.surname == \"Stark\"    RETURN character.age        AQL Result:    [11]        > Finished chain.    'Arya Stark is 11 years old.'chain.run(\"Are Arya Stark and Ned Stark related?\")            > Entering new ArangoGraphQAChain chain...    AQL Query (1):    WITH Characters, ChildOf    FOR v, e, p IN 1..1 OUTBOUND 'Characters/AryaStark' ChildOf        FILTER p.vertices[-1]._key == 'NedStark'", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_arangodb_qa"}
{"id": "686d651dfe1e-12", "text": "FILTER p.vertices[-1]._key == 'NedStark'        RETURN p        AQL Result:    [{'vertices': [{'_key': 'AryaStark', '_id': 'Characters/AryaStark', '_rev': '_gVPKGPi--B', 'name': 'Arya', 'surname': 'Stark', 'alive': True, 'age': 11, 'gender': 'female'}, {'_key': 'NedStark', '_id': 'Characters/NedStark', '_rev': '_gVPKGPi---', 'name': 'Ned', 'surname': 'Stark', 'alive': True, 'age': 41, 'gender': 'male'}], 'edges': [{'_key': '266218884025', '_id': 'ChildOf/266218884025', '_from': 'Characters/AryaStark', '_to': 'Characters/NedStark', '_rev': '_gVPKGSq---'}], 'weights': [0, 1]}]        > Finished chain.    'Yes, Arya Stark and Ned Stark are related. According to the information retrieved from the database, there is a relationship between them. Arya Stark is the child of Ned Stark.'chain.run(\"Does Arya Stark have a dead parent?\")            > Entering new ArangoGraphQAChain chain...    AQL Query (1):    WITH Characters, ChildOf    FOR v, e IN 1..1 OUTBOUND 'Characters/AryaStark' ChildOf    FILTER v.alive == false    RETURN e        AQL Result:    [{'_key':", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_arangodb_qa"}
{"id": "686d651dfe1e-13", "text": "RETURN e        AQL Result:    [{'_key': '266218884027', '_id': 'ChildOf/266218884027', '_from': 'Characters/AryaStark', '_to': 'Characters/CatelynStark', '_rev': '_gVPKGSu---'}]        > Finished chain.    'Yes, Arya Stark has a dead parent. The parent is Catelyn Stark.'Chain Modifiers\u00e2\u20ac\u2039You can alter the values of the following ArangoDBGraphQAChain class variables to modify the behaviour of your chain results# Specify the maximum number of AQL Query Results to returnchain.top_k = 10# Specify whether or not to return the AQL Query in the output dictionarychain.return_aql_query = True# Specify whether or not to return the AQL JSON Result in the output dictionarychain.return_aql_result = True# Specify the maximum amount of AQL Generation attempts that should be madechain.max_aql_generation_attempts = 5# Specify a set of AQL Query Examples, which are passed to# the AQL Generation Prompt Template to promote few-shot-learning.# Defaults to an empty string.chain.aql_examples = \"\"\"# Is Ned Stark alive?RETURN DOCUMENT('Characters/NedStark').alive# Is Arya Stark the child of Ned Stark?FOR e IN ChildOf    FILTER e._from == \"Characters/AryaStark\" AND e._to == \"Characters/NedStark\"    RETURN e\"\"\"chain.run(\"Is Ned Stark alive?\")# chain(\"Is Ned Stark alive?\") # Returns a dictionary with the AQL Query & AQL Result            > Entering new ArangoGraphQAChain chain...    AQL Query (1):    RETURN", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_arangodb_qa"}
{"id": "686d651dfe1e-14", "text": "ArangoGraphQAChain chain...    AQL Query (1):    RETURN DOCUMENT('Characters/NedStark').alive        AQL Result:    [True]        > Finished chain.    'Yes, according to the information in the database, Ned Stark is alive.'chain.run(\"Is Bran Stark the child of Ned Stark?\")            > Entering new ArangoGraphQAChain chain...    AQL Query (1):    FOR e IN ChildOf        FILTER e._from == \"Characters/BranStark\" AND e._to == \"Characters/NedStark\"        RETURN e        AQL Result:    [{'_key': '266218884026', '_id': 'ChildOf/266218884026', '_from': 'Characters/BranStark', '_to': 'Characters/NedStark', '_rev': '_gVPKGSq--_'}]        > Finished chain.    'Yes, according to the information in the ArangoDB database, Bran Stark is indeed the child of Ned Stark.'PreviousFLARENextGraph DB QA chainPopulating the DatabaseGetting & Setting the ArangoDB SchemaQuerying the ArangoDB DatabaseChain ModifiersCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_arangodb_qa"}
{"id": "5d0b647175fa-0", "text": "Self-checking chain | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_checker"}
{"id": "5d0b647175fa-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalDocumentsPopularAdditionalAnalyze DocumentSelf-critique chain with constitutional AICausal program-aided language (CPAL) chainElasticsearch databaseExtractionFLAREArangoDB QA chainGraph DB QA chainHugeGraph QA ChainKuzuQAChainNebulaGraphQAChainGraph QAGraphSparqlQAChainHypothetical Document EmbeddingsBash chainSelf-checking chainMath chainHTTP request chainSummarization checker chainLLM Symbolic MathModerationDynamically selecting from multiple promptsDynamically selecting from multiple retrieversNeptune Open Cypher QA ChainRetrieval QA using OpenAI functionsOpenAPI chainOpenAPI calls with OpenAI functionsProgram-aided language model (PAL) chainQuestion-Answering CitationsDocument QATaggingVector store-augmented text generationMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsAdditionalSelf-checking chainSelf-checking chainThis notebook showcases how to use LLMCheckerChain.from langchain.chains import LLMCheckerChainfrom langchain.llms import OpenAIllm = OpenAI(temperature=0.7)text = \"What type of mammal lays the biggest eggs?\"checker_chain = LLMCheckerChain.from_llm(llm, verbose=True)checker_chain.run(text)            > Entering new LLMCheckerChain chain...            > Entering new SequentialChain chain...        > Finished chain.        > Finished chain.    ' No mammal lays the biggest eggs. The Elephant Bird, which", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_checker"}
{"id": "5d0b647175fa-2", "text": "Finished chain.    ' No mammal lays the biggest eggs. The Elephant Bird, which was a species of giant bird, laid the largest eggs of any bird.'PreviousBash chainNextMath chainCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_checker"}
{"id": "7912408f4eb4-0", "text": "LLM Symbolic Math | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_symbolic_math"}
{"id": "7912408f4eb4-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalDocumentsPopularAdditionalAnalyze DocumentSelf-critique chain with constitutional AICausal program-aided language (CPAL) chainElasticsearch databaseExtractionFLAREArangoDB QA chainGraph DB QA chainHugeGraph QA ChainKuzuQAChainNebulaGraphQAChainGraph QAGraphSparqlQAChainHypothetical Document EmbeddingsBash chainSelf-checking chainMath chainHTTP request chainSummarization checker chainLLM Symbolic MathModerationDynamically selecting from multiple promptsDynamically selecting from multiple retrieversNeptune Open Cypher QA ChainRetrieval QA using OpenAI functionsOpenAPI chainOpenAPI calls with OpenAI functionsProgram-aided language model (PAL) chainQuestion-Answering CitationsDocument QATaggingVector store-augmented text generationMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsAdditionalLLM Symbolic MathOn this pageLLM Symbolic MathThis notebook showcases using LLMs and Python to Solve Algebraic Equations. Under the hood is makes use of SymPy.from langchain.llms import OpenAIfrom langchain.chains.llm_symbolic_math.base import LLMSymbolicMathChainllm = OpenAI(temperature=0)llm_symbolic_math = LLMSymbolicMathChain.from_llm(llm)Integrals and derivates\u00e2\u20ac\u2039llm_symbolic_math.run(\"What is the derivative of sin(x)*exp(x) with respect to x?\")    'Answer: exp(x)*sin(x) + exp(x)*cos(x)'llm_symbolic_math.run(    \"What is the integral of", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_symbolic_math"}
{"id": "7912408f4eb4-2", "text": "\"What is the integral of exp(x)*sin(x) + exp(x)*cos(x) with respect to x?\")    'Answer: exp(x)*sin(x)'Solve linear and differential equations\u00e2\u20ac\u2039llm_symbolic_math.run('Solve the differential equation y\" - y = e^t')    'Answer: Eq(y(t), C2*exp(-t) + (C1 + t/2)*exp(t))'llm_symbolic_math.run(\"What are the solutions to this equation y^3 + 1/3y?\")    'Answer: {0, -sqrt(3)*I/3, sqrt(3)*I/3}'llm_symbolic_math.run(\"x = y + 5, y = z - 3, z = x * y. Solve for x, y, z\")    'Answer: (3 - sqrt(7), -sqrt(7) - 2, 1 - sqrt(7)), (sqrt(7) + 3, -2 + sqrt(7), 1 + sqrt(7))'PreviousSummarization checker chainNextModerationIntegrals and derivatesSolve linear and differential equationsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_symbolic_math"}
{"id": "c340ec6e138d-0", "text": "KuzuQAChain | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_kuzu_qa"}
{"id": "c340ec6e138d-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalDocumentsPopularAdditionalAnalyze DocumentSelf-critique chain with constitutional AICausal program-aided language (CPAL) chainElasticsearch databaseExtractionFLAREArangoDB QA chainGraph DB QA chainHugeGraph QA ChainKuzuQAChainNebulaGraphQAChainGraph QAGraphSparqlQAChainHypothetical Document EmbeddingsBash chainSelf-checking chainMath chainHTTP request chainSummarization checker chainLLM Symbolic MathModerationDynamically selecting from multiple promptsDynamically selecting from multiple retrieversNeptune Open Cypher QA ChainRetrieval QA using OpenAI functionsOpenAPI chainOpenAPI calls with OpenAI functionsProgram-aided language model (PAL) chainQuestion-Answering CitationsDocument QATaggingVector store-augmented text generationMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsAdditionalKuzuQAChainOn this pageKuzuQAChainThis notebook shows how to use LLMs to provide a natural language interface to K\u00c3\u00b9zu database.K\u00c3\u00b9zu is an in-process property graph database management system. You can simply install it with pip:pip install kuzuOnce installed, you can simply import it and start creating a database on the local machine and connect to it:import kuzudb = kuzu.Database(\"test_db\")conn = kuzu.Connection(db)First, we create the schema for a simple movie database:conn.execute(\"CREATE NODE TABLE Movie (name STRING, PRIMARY KEY(name))\")conn.execute(    \"CREATE NODE TABLE Person (name STRING, birthDate STRING, PRIMARY KEY(name))\")conn.execute(\"CREATE REL TABLE ActedIn", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_kuzu_qa"}
{"id": "c340ec6e138d-2", "text": "STRING, birthDate STRING, PRIMARY KEY(name))\")conn.execute(\"CREATE REL TABLE ActedIn (FROM Person TO Movie)\")    <kuzu.query_result.QueryResult at 0x1066ff410>Then we can insert some data.conn.execute(\"CREATE (:Person {name: 'Al Pacino', birthDate: '1940-04-25'})\")conn.execute(\"CREATE (:Person {name: 'Robert De Niro', birthDate: '1943-08-17'})\")conn.execute(\"CREATE (:Movie {name: 'The Godfather'})\")conn.execute(\"CREATE (:Movie {name: 'The Godfather: Part II'})\")conn.execute(    \"CREATE (:Movie {name: 'The Godfather Coda: The Death of Michael Corleone'})\")conn.execute(    \"MATCH (p:Person), (m:Movie) WHERE p.name = 'Al Pacino' AND m.name = 'The Godfather' CREATE (p)-[:ActedIn]->(m)\")conn.execute(    \"MATCH (p:Person), (m:Movie) WHERE p.name = 'Al Pacino' AND m.name = 'The Godfather: Part II' CREATE (p)-[:ActedIn]->(m)\")conn.execute(    \"MATCH (p:Person), (m:Movie) WHERE p.name = 'Al Pacino' AND m.name = 'The Godfather Coda: The Death of Michael Corleone' CREATE (p)-[:ActedIn]->(m)\")conn.execute(    \"MATCH (p:Person), (m:Movie) WHERE p.name = 'Robert De Niro' AND m.name = 'The Godfather: Part II' CREATE (p)-[:ActedIn]->(m)\")    <kuzu.query_result.QueryResult at 0x107016210>Creating", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_kuzu_qa"}
{"id": "c340ec6e138d-3", "text": "<kuzu.query_result.QueryResult at 0x107016210>Creating KuzuQAChain\u00e2\u20ac\u2039We can now create the KuzuGraph and KuzuQAChain. To create the KuzuGraph we simply need to pass the database object to the KuzuGraph constructor.from langchain.chat_models import ChatOpenAIfrom langchain.graphs import KuzuGraphfrom langchain.chains import KuzuQAChaingraph = KuzuGraph(db)chain = KuzuQAChain.from_llm(ChatOpenAI(temperature=0), graph=graph, verbose=True)Refresh graph schema information\u00e2\u20ac\u2039If the schema of database changes, you can refresh the schema information needed to generate Cypher statements.# graph.refresh_schema()print(graph.get_schema)    Node properties: [{'properties': [('name', 'STRING')], 'label': 'Movie'}, {'properties': [('name', 'STRING'), ('birthDate', 'STRING')], 'label': 'Person'}]    Relationships properties: [{'properties': [], 'label': 'ActedIn'}]    Relationships: ['(:Person)-[:ActedIn]->(:Movie)']    Querying the graph\u00e2\u20ac\u2039We can now use the KuzuQAChain to ask question of the graphchain.run(\"Who played in The Godfather: Part II?\")            > Entering new  chain...    Generated Cypher:    MATCH (p:Person)-[:ActedIn]->(m:Movie {name: 'The Godfather: Part II'}) RETURN p.name    Full Context:    [{'p.name': 'Al Pacino'}, {'p.name': 'Robert De Niro'}]        > Finished chain.    'Al Pacino and Robert De Niro both played in The Godfather: Part", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_kuzu_qa"}
{"id": "c340ec6e138d-4", "text": "'Al Pacino and Robert De Niro both played in The Godfather: Part II.'chain.run(\"Robert De Niro played in which movies?\")            > Entering new  chain...    Generated Cypher:    MATCH (p:Person {name: 'Robert De Niro'})-[:ActedIn]->(m:Movie)    RETURN m.name    Full Context:    [{'m.name': 'The Godfather: Part II'}]        > Finished chain.    'Robert De Niro played in The Godfather: Part II.'chain.run(\"Robert De Niro is born in which year?\")            > Entering new  chain...    Generated Cypher:    MATCH (p:Person {name: 'Robert De Niro'})-[:ActedIn]->(m:Movie)    RETURN p.birthDate    Full Context:    [{'p.birthDate': '1943-08-17'}]        > Finished chain.    'Robert De Niro was born on August 17, 1943.'chain.run(\"Who is the oldest actor who played in The Godfather: Part II?\")            > Entering new  chain...    Generated Cypher:    MATCH (p:Person)-[:ActedIn]->(m:Movie{name:'The Godfather: Part II'})    WITH p, m, p.birthDate AS birthDate    ORDER BY birthDate ASC    LIMIT 1    RETURN p.name    Full Context:    [{'p.name': 'Al Pacino'}]", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_kuzu_qa"}
{"id": "c340ec6e138d-5", "text": "Context:    [{'p.name': 'Al Pacino'}]        > Finished chain.    'The oldest actor who played in The Godfather: Part II is Al Pacino.'PreviousHugeGraph QA ChainNextNebulaGraphQAChainCreating KuzuQAChainRefresh graph schema informationQuerying the graphCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_kuzu_qa"}
{"id": "16eb4cec3950-0", "text": "GraphSparqlQAChain | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_sparql_qa"}
{"id": "16eb4cec3950-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalDocumentsPopularAdditionalAnalyze DocumentSelf-critique chain with constitutional AICausal program-aided language (CPAL) chainElasticsearch databaseExtractionFLAREArangoDB QA chainGraph DB QA chainHugeGraph QA ChainKuzuQAChainNebulaGraphQAChainGraph QAGraphSparqlQAChainHypothetical Document EmbeddingsBash chainSelf-checking chainMath chainHTTP request chainSummarization checker chainLLM Symbolic MathModerationDynamically selecting from multiple promptsDynamically selecting from multiple retrieversNeptune Open Cypher QA ChainRetrieval QA using OpenAI functionsOpenAPI chainOpenAPI calls with OpenAI functionsProgram-aided language model (PAL) chainQuestion-Answering CitationsDocument QATaggingVector store-augmented text generationMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsAdditionalGraphSparqlQAChainOn this pageGraphSparqlQAChainGraph databases are an excellent choice for applications based on network-like models. To standardize the syntax and semantics of such graphs, the W3C recommends Semantic Web Technologies, cp. Semantic Web. SPARQL serves as a query language analogously to SQL or Cypher for these graphs. This notebook demonstrates the application of LLMs as a natural language interface to a graph database by generating SPARQL.\\", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_sparql_qa"}
{"id": "16eb4cec3950-2", "text": "Disclaimer: To date, SPARQL query generation via LLMs is still a bit unstable. Be especially careful with UPDATE queries, which alter the graph.There are several sources you can run queries against, including files on the web, files you have available locally, SPARQL endpoints, e.g., Wikidata, and triple stores.from langchain.chat_models import ChatOpenAIfrom langchain.chains import GraphSparqlQAChainfrom langchain.graphs import RdfGraphgraph = RdfGraph(    source_file=\"http://www.w3.org/People/Berners-Lee/card\",    standard=\"rdf\",    local_copy=\"test.ttl\",)Note that providing a local_file is necessary for storing changes locally if the source is read-only.Refresh graph schema information\u00e2\u20ac\u2039If the schema of the database changes, you can refresh the schema information needed to generate SPARQL queries.graph.load_schema()graph.get_schema    In the following, each IRI is followed by the local name and optionally its description in parentheses.     The RDF graph supports the following node types:    <http://xmlns.com/foaf/0.1/PersonalProfileDocument> (PersonalProfileDocument, None), <http://www.w3.org/ns/auth/cert#RSAPublicKey> (RSAPublicKey, None), <http://www.w3.org/2000/10/swap/pim/contact#Male> (Male, None), <http://xmlns.com/foaf/0.1/Person> (Person, None), <http://www.w3.org/2006/vcard/ns#Work> (Work, None)    The RDF graph supports the following relationships:    <http://www.w3.org/2000/01/rdf-schema#seeAlso> (seeAlso, None),", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_sparql_qa"}
{"id": "16eb4cec3950-3", "text": "(seeAlso, None), <http://purl.org/dc/elements/1.1/title> (title, None), <http://xmlns.com/foaf/0.1/mbox_sha1sum> (mbox_sha1sum, None), <http://xmlns.com/foaf/0.1/maker> (maker, None), <http://www.w3.org/ns/solid/terms#oidcIssuer> (oidcIssuer, None), <http://www.w3.org/2000/10/swap/pim/contact#publicHomePage> (publicHomePage, None), <http://xmlns.com/foaf/0.1/openid> (openid, None), <http://www.w3.org/ns/pim/space#storage> (storage, None), <http://xmlns.com/foaf/0.1/name> (name, None), <http://www.w3.org/2000/10/swap/pim/contact#country> (country, None), <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> (type, None), <http://www.w3.org/ns/solid/terms#profileHighlightColor> (profileHighlightColor, None), <http://www.w3.org/ns/pim/space#preferencesFile> (preferencesFile, None), <http://www.w3.org/2000/01/rdf-schema#label> (label, None), <http://www.w3.org/ns/auth/cert#modulus> (modulus, None), <http://www.w3.org/2000/10/swap/pim/contact#participant> (participant, None), <http://www.w3.org/2000/10/swap/pim/contact#street2> (street2, None), <http://www.w3.org/2006/vcard/ns#locality> (locality, None),", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_sparql_qa"}
{"id": "16eb4cec3950-4", "text": "(locality, None), <http://xmlns.com/foaf/0.1/nick> (nick, None), <http://xmlns.com/foaf/0.1/homepage> (homepage, None), <http://creativecommons.org/ns#license> (license, None), <http://xmlns.com/foaf/0.1/givenname> (givenname, None), <http://www.w3.org/2006/vcard/ns#street-address> (street-address, None), <http://www.w3.org/2006/vcard/ns#postal-code> (postal-code, None), <http://www.w3.org/2000/10/swap/pim/contact#street> (street, None), <http://www.w3.org/2003/01/geo/wgs84_pos#lat> (lat, None), <http://xmlns.com/foaf/0.1/primaryTopic> (primaryTopic, None), <http://www.w3.org/2006/vcard/ns#fn> (fn, None), <http://www.w3.org/2003/01/geo/wgs84_pos#location> (location, None), <http://usefulinc.com/ns/doap#developer> (developer, None), <http://www.w3.org/2000/10/swap/pim/contact#city> (city, None), <http://www.w3.org/2006/vcard/ns#region> (region, None), <http://xmlns.com/foaf/0.1/member> (member, None), <http://www.w3.org/2003/01/geo/wgs84_pos#long> (long, None), <http://www.w3.org/2000/10/swap/pim/contact#address> (address, None), <http://xmlns.com/foaf/0.1/family_name>", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_sparql_qa"}
{"id": "16eb4cec3950-5", "text": "None), <http://xmlns.com/foaf/0.1/family_name> (family_name, None), <http://xmlns.com/foaf/0.1/account> (account, None), <http://xmlns.com/foaf/0.1/workplaceHomepage> (workplaceHomepage, None), <http://purl.org/dc/terms/title> (title, None), <http://www.w3.org/ns/solid/terms#publicTypeIndex> (publicTypeIndex, None), <http://www.w3.org/2000/10/swap/pim/contact#office> (office, None), <http://www.w3.org/2000/10/swap/pim/contact#homePage> (homePage, None), <http://xmlns.com/foaf/0.1/mbox> (mbox, None), <http://www.w3.org/2000/10/swap/pim/contact#preferredURI> (preferredURI, None), <http://www.w3.org/ns/solid/terms#profileBackgroundColor> (profileBackgroundColor, None), <http://schema.org/owns> (owns, None), <http://xmlns.com/foaf/0.1/based_near> (based_near, None), <http://www.w3.org/2006/vcard/ns#hasAddress> (hasAddress, None), <http://xmlns.com/foaf/0.1/img> (img, None), <http://www.w3.org/2000/10/swap/pim/contact#assistant> (assistant, None), <http://xmlns.com/foaf/0.1/title> (title, None), <http://www.w3.org/ns/auth/cert#key> (key, None), <http://www.w3.org/ns/ldp#inbox> (inbox, None), <http://www.w3.org/ns/solid/terms#editableProfile>", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_sparql_qa"}
{"id": "16eb4cec3950-6", "text": "(inbox, None), <http://www.w3.org/ns/solid/terms#editableProfile> (editableProfile, None), <http://www.w3.org/2000/10/swap/pim/contact#postalCode> (postalCode, None), <http://xmlns.com/foaf/0.1/weblog> (weblog, None), <http://www.w3.org/ns/auth/cert#exponent> (exponent, None), <http://rdfs.org/sioc/ns#avatar> (avatar, None)    Querying the graph\u00e2\u20ac\u2039Now, you can use the graph SPARQL QA chain to ask questions about the graph.chain = GraphSparqlQAChain.from_llm(    ChatOpenAI(temperature=0), graph=graph, verbose=True)chain.run(\"What is Tim Berners-Lee's work homepage?\")            > Entering new GraphSparqlQAChain chain...    Identified intent:    SELECT    Generated SPARQL:    PREFIX foaf: <http://xmlns.com/foaf/0.1/>    SELECT ?homepage    WHERE {        ?person foaf:name \"Tim Berners-Lee\" .        ?person foaf:workplaceHomepage ?homepage .    }    Full Context:    []        > Finished chain.    \"Tim Berners-Lee's work homepage is http://www.w3.org/People/Berners-Lee/.\"Updating the graph\u00e2\u20ac\u2039Analogously, you can update the graph, i.e., insert triples, using natural language.chain.run(    \"Save that the person with the name 'Timothy Berners-Lee' has a work", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_sparql_qa"}
{"id": "16eb4cec3950-7", "text": "\"Save that the person with the name 'Timothy Berners-Lee' has a work homepage at 'http://www.w3.org/foo/bar/'\")            > Entering new GraphSparqlQAChain chain...    Identified intent:    UPDATE    Generated SPARQL:    PREFIX foaf: <http://xmlns.com/foaf/0.1/>    INSERT {        ?person foaf:workplaceHomepage <http://www.w3.org/foo/bar/> .    }    WHERE {        ?person foaf:name \"Timothy Berners-Lee\" .    }        > Finished chain.    'Successfully inserted triples into the graph.'Let's verify the results:query = (    \"\"\"PREFIX foaf: <http://xmlns.com/foaf/0.1/>\\n\"\"\"    \"\"\"SELECT ?hp\\n\"\"\"    \"\"\"WHERE {\\n\"\"\"    \"\"\"    ?person foaf:name \"Timothy Berners-Lee\" . \\n\"\"\"    \"\"\"    ?person foaf:workplaceHomepage ?hp .\\n\"\"\"    \"\"\"}\"\"\")graph.query(query)    [(rdflib.term.URIRef('https://www.w3.org/'),),     (rdflib.term.URIRef('http://www.w3.org/foo/bar/'),)]PreviousGraph QANextHypothetical Document EmbeddingsRefresh graph schema informationQuerying the graphUpdating the graphCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_sparql_qa"}
{"id": "6d6e5aa52026-0", "text": "Math chain | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_math"}
{"id": "6d6e5aa52026-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalDocumentsPopularAdditionalAnalyze DocumentSelf-critique chain with constitutional AICausal program-aided language (CPAL) chainElasticsearch databaseExtractionFLAREArangoDB QA chainGraph DB QA chainHugeGraph QA ChainKuzuQAChainNebulaGraphQAChainGraph QAGraphSparqlQAChainHypothetical Document EmbeddingsBash chainSelf-checking chainMath chainHTTP request chainSummarization checker chainLLM Symbolic MathModerationDynamically selecting from multiple promptsDynamically selecting from multiple retrieversNeptune Open Cypher QA ChainRetrieval QA using OpenAI functionsOpenAPI chainOpenAPI calls with OpenAI functionsProgram-aided language model (PAL) chainQuestion-Answering CitationsDocument QATaggingVector store-augmented text generationMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsAdditionalMath chainMath chainThis notebook showcases using LLMs and Python REPLs to do complex word math problems.from langchain import OpenAI, LLMMathChainllm = OpenAI(temperature=0)llm_math = LLMMathChain.from_llm(llm, verbose=True)llm_math.run(\"What is 13 raised to the .3432 power?\")            > Entering new LLMMathChain chain...    What is 13 raised to the .3432 power?    ```text    13 ** .3432    ```    ...numexpr.evaluate(\"13 ** .3432\")...        Answer:", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_math"}
{"id": "6d6e5aa52026-2", "text": "** .3432\")...        Answer: 2.4116004626599237    > Finished chain.    'Answer: 2.4116004626599237'PreviousSelf-checking chainNextHTTP request chainCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_math"}
{"id": "206a6d711b8a-0", "text": "Program-aided language model (PAL) chain | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/additional/pal"}
{"id": "206a6d711b8a-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalDocumentsPopularAdditionalAnalyze DocumentSelf-critique chain with constitutional AICausal program-aided language (CPAL) chainElasticsearch databaseExtractionFLAREArangoDB QA chainGraph DB QA chainHugeGraph QA ChainKuzuQAChainNebulaGraphQAChainGraph QAGraphSparqlQAChainHypothetical Document EmbeddingsBash chainSelf-checking chainMath chainHTTP request chainSummarization checker chainLLM Symbolic MathModerationDynamically selecting from multiple promptsDynamically selecting from multiple retrieversNeptune Open Cypher QA ChainRetrieval QA using OpenAI functionsOpenAPI chainOpenAPI calls with OpenAI functionsProgram-aided language model (PAL) chainQuestion-Answering CitationsDocument QATaggingVector store-augmented text generationMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsAdditionalProgram-aided language model (PAL) chainOn this pageProgram-aided language model (PAL) chainImplements Program-Aided Language Models, as in https://arxiv.org/pdf/2211.10435.pdf.from langchain.chains import PALChainfrom langchain import OpenAIllm = OpenAI(temperature=0, max_tokens=512)Math Prompt\u00e2\u20ac\u2039pal_chain = PALChain.from_math_prompt(llm, verbose=True)question = \"Jan has three times the number of pets as Marcia. Marcia has two more pets than Cindy. If Cindy has four pets, how many total pets do the three have?\"pal_chain.run(question)            > Entering new PALChain chain...    def", "source": "https://python.langchain.com/docs/modules/chains/additional/pal"}
{"id": "206a6d711b8a-2", "text": "> Entering new PALChain chain...    def solution():        \"\"\"Jan has three times the number of pets as Marcia. Marcia has two more pets than Cindy. If Cindy has four pets, how many total pets do the three have?\"\"\"        cindy_pets = 4        marcia_pets = cindy_pets + 2        jan_pets = marcia_pets * 3        total_pets = cindy_pets + marcia_pets + jan_pets        result = total_pets        return result        > Finished chain.    '28'Colored Objects\u00e2\u20ac\u2039pal_chain = PALChain.from_colored_object_prompt(llm, verbose=True)question = \"On the desk, you see two blue booklets, two purple booklets, and two yellow pairs of sunglasses. If I remove all the pairs of sunglasses from the desk, how many purple items remain on it?\"pal_chain.run(question)            > Entering new PALChain chain...    # Put objects into a list to record ordering    objects = []    objects += [('booklet', 'blue')] * 2    objects += [('booklet', 'purple')] * 2    objects += [('sunglasses', 'yellow')] * 2        # Remove all pairs of sunglasses    objects = [object for object in objects if object[0] != 'sunglasses']        # Count number of purple objects    num_purple = len([object for object in objects if", "source": "https://python.langchain.com/docs/modules/chains/additional/pal"}
{"id": "206a6d711b8a-3", "text": "Count number of purple objects    num_purple = len([object for object in objects if object[1] == 'purple'])    answer = num_purple        > Finished PALChain chain.    '2'Intermediate Steps\u00e2\u20ac\u2039You can also use the intermediate steps flag to return the code executed that generates the answer.pal_chain = PALChain.from_colored_object_prompt(    llm, verbose=True, return_intermediate_steps=True)question = \"On the desk, you see two blue booklets, two purple booklets, and two yellow pairs of sunglasses. If I remove all the pairs of sunglasses from the desk, how many purple items remain on it?\"result = pal_chain({\"question\": question})            > Entering new PALChain chain...    # Put objects into a list to record ordering    objects = []    objects += [('booklet', 'blue')] * 2    objects += [('booklet', 'purple')] * 2    objects += [('sunglasses', 'yellow')] * 2        # Remove all pairs of sunglasses    objects = [object for object in objects if object[0] != 'sunglasses']        # Count number of purple objects    num_purple = len([object for object in objects if object[1] == 'purple'])    answer = num_purple        > Finished chain.result[\"intermediate_steps\"]    \"# Put objects into a list to record ordering\\nobjects = []\\nobjects += [('booklet', 'blue')] * 2\\nobjects += [('booklet', 'purple')] * 2\\nobjects += [('sunglasses', 'yellow')] * 2\\n\\n# Remove all", "source": "https://python.langchain.com/docs/modules/chains/additional/pal"}
{"id": "206a6d711b8a-4", "text": "+= [('sunglasses', 'yellow')] * 2\\n\\n# Remove all pairs of sunglasses\\nobjects = [object for object in objects if object[0] != 'sunglasses']\\n\\n# Count number of purple objects\\nnum_purple = len([object for object in objects if object[1] == 'purple'])\\nanswer = num_purple\"PreviousOpenAPI calls with OpenAI functionsNextQuestion-Answering CitationsMath PromptColored ObjectsIntermediate StepsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/additional/pal"}
{"id": "26005730e786-0", "text": "Extraction | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/additional/extraction"}
{"id": "26005730e786-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalDocumentsPopularAdditionalAnalyze DocumentSelf-critique chain with constitutional AICausal program-aided language (CPAL) chainElasticsearch databaseExtractionFLAREArangoDB QA chainGraph DB QA chainHugeGraph QA ChainKuzuQAChainNebulaGraphQAChainGraph QAGraphSparqlQAChainHypothetical Document EmbeddingsBash chainSelf-checking chainMath chainHTTP request chainSummarization checker chainLLM Symbolic MathModerationDynamically selecting from multiple promptsDynamically selecting from multiple retrieversNeptune Open Cypher QA ChainRetrieval QA using OpenAI functionsOpenAPI chainOpenAPI calls with OpenAI functionsProgram-aided language model (PAL) chainQuestion-Answering CitationsDocument QATaggingVector store-augmented text generationMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsAdditionalExtractionOn this pageExtractionThe extraction chain uses the OpenAI functions parameter to specify a schema to extract entities from a document. This helps us make sure that the model outputs exactly the schema of entities and properties that we want, with their appropriate types.The extraction chain is to be used when we want to extract several entities with their properties from the same passage (i.e. what people were mentioned in this passage?)from langchain.chat_models import ChatOpenAIfrom langchain.chains import create_extraction_chain, create_extraction_chain_pydanticfrom langchain.prompts import ChatPromptTemplate    /Users/harrisonchase/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/site-packages/deeplake/util/check_latest_version.py:32:", "source": "https://python.langchain.com/docs/modules/chains/additional/extraction"}
{"id": "26005730e786-2", "text": "UserWarning: A newer version of deeplake (3.6.4) is available. It's recommended that you update to the latest version using `pip install -U deeplake`.      warnings.warn(llm = ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo-0613\")Extracting entities\u00e2\u20ac\u2039To extract entities, we need to create a schema where we specify all the properties we want to find and the type we expect them to have. We can also specify which of these properties are required and which are optional.schema = {    \"properties\": {        \"name\": {\"type\": \"string\"},        \"height\": {\"type\": \"integer\"},        \"hair_color\": {\"type\": \"string\"},    },    \"required\": [\"name\", \"height\"],}inp = \"\"\"Alex is 5 feet tall. Claudia is 1 feet taller Alex and jumps higher than him. Claudia is a brunette and Alex is blonde.        \"\"\"chain = create_extraction_chain(schema, llm)As we can see, we extracted the required entities and their properties in the required format (it even calculated Claudia's height before returning!)chain.run(inp)    [{'name': 'Alex', 'height': 5, 'hair_color': 'blonde'},     {'name': 'Claudia', 'height': 6, 'hair_color': 'brunette'}]Several entity types\u00e2\u20ac\u2039Notice that we are using OpenAI functions under the hood and thus the model can only call one function per request (with one, unique schema)If we want to extract more than one entity type, we need to introduce a little hack - we will define our properties with an included entity type. Following we have", "source": "https://python.langchain.com/docs/modules/chains/additional/extraction"}
{"id": "26005730e786-3", "text": "to introduce a little hack - we will define our properties with an included entity type. Following we have an example where we also want to extract dog attributes from the passage. Notice the 'person' and 'dog' prefixes we use for each property; this tells the model which entity type the property refers to. In this way, the model can return properties from several entity types in one single call.schema = {    \"properties\": {        \"person_name\": {\"type\": \"string\"},        \"person_height\": {\"type\": \"integer\"},        \"person_hair_color\": {\"type\": \"string\"},        \"dog_name\": {\"type\": \"string\"},        \"dog_breed\": {\"type\": \"string\"},    },    \"required\": [\"person_name\", \"person_height\"],}inp = \"\"\"Alex is 5 feet tall. Claudia is 1 feet taller Alex and jumps higher than him. Claudia is a brunette and Alex is blonde.Alex's dog Frosty is a labrador and likes to play hide and seek.        \"\"\"chain = create_extraction_chain(schema, llm)People attributes and dog attributes were correctly extracted from the text in the same callchain.run(inp)    [{'person_name': 'Alex',      'person_height': 5,      'person_hair_color': 'blonde',      'dog_name': 'Frosty',      'dog_breed': 'labrador'},     {'person_name': 'Claudia',      'person_height': 6,      'person_hair_color': 'brunette'}]Unrelated entities\u00e2\u20ac\u2039What if our entities", "source": "https://python.langchain.com/docs/modules/chains/additional/extraction"}
{"id": "26005730e786-4", "text": "'brunette'}]Unrelated entities\u00e2\u20ac\u2039What if our entities are unrelated? In that case, the model will return the unrelated entities in different dictionaries, allowing us to successfully extract several unrelated entity types in the same call.Notice that we use required: []: we need to allow the model to return only person attributes or only dog attributes for a single entity (person or dog)schema = {    \"properties\": {        \"person_name\": {\"type\": \"string\"},        \"person_height\": {\"type\": \"integer\"},        \"person_hair_color\": {\"type\": \"string\"},        \"dog_name\": {\"type\": \"string\"},        \"dog_breed\": {\"type\": \"string\"},    },    \"required\": [],}inp = \"\"\"Alex is 5 feet tall. Claudia is 1 feet taller Alex and jumps higher than him. Claudia is a brunette and Alex is blonde.Willow is a German Shepherd that likes to play with other dogs and can always be found playing with Milo, a border collie that lives close by.\"\"\"chain = create_extraction_chain(schema, llm)We have each entity in its own separate dictionary, with only the appropriate attributes being returnedchain.run(inp)    [{'person_name': 'Alex', 'person_height': 5, 'person_hair_color': 'blonde'},     {'person_name': 'Claudia',      'person_height': 6,      'person_hair_color': 'brunette'},     {'dog_name': 'Willow', 'dog_breed': 'German Shepherd'},     {'dog_name': 'Milo', 'dog_breed': 'border collie'}]Extra info for an", "source": "https://python.langchain.com/docs/modules/chains/additional/extraction"}
{"id": "26005730e786-5", "text": "'Milo', 'dog_breed': 'border collie'}]Extra info for an entity\u00e2\u20ac\u2039What if.. we don't know what we want? More specifically, say we know a few properties we want to extract for a given entity but we also want to know if there's any extra information in the passage. Fortunately, we don't need to structure everything - we can have unstructured extraction as well. We can do this by introducing another hack, namely the extra_info attribute - let's see an example.schema = {    \"properties\": {        \"person_name\": {\"type\": \"string\"},        \"person_height\": {\"type\": \"integer\"},        \"person_hair_color\": {\"type\": \"string\"},        \"dog_name\": {\"type\": \"string\"},        \"dog_breed\": {\"type\": \"string\"},        \"dog_extra_info\": {\"type\": \"string\"},    },}inp = \"\"\"Alex is 5 feet tall. Claudia is 1 feet taller Alex and jumps higher than him. Claudia is a brunette and Alex is blonde.Willow is a German Shepherd that likes to play with other dogs and can always be found playing with Milo, a border collie that lives close by.\"\"\"chain = create_extraction_chain(schema, llm)It is nice to know more about Willow and Milo!chain.run(inp)    [{'person_name': 'Alex', 'person_height': 5, 'person_hair_color': 'blonde'},     {'person_name': 'Claudia',      'person_height': 6,      'person_hair_color': 'brunette'},     {'dog_name': 'Willow',", "source": "https://python.langchain.com/docs/modules/chains/additional/extraction"}
{"id": "26005730e786-6", "text": "{'dog_name': 'Willow',      'dog_breed': 'German Shepherd',      'dog_extra_information': 'likes to play with other dogs'},     {'dog_name': 'Milo',      'dog_breed': 'border collie',      'dog_extra_information': 'lives close by'}]Pydantic example\u00e2\u20ac\u2039We can also use a Pydantic schema to choose the required properties and types and we will set as 'Optional' those that are not strictly required.By using the create_extraction_chain_pydantic function, we can send a Pydantic schema as input and the output will be an instantiated object that respects our desired schema. In this way, we can specify our schema in the same manner that we would a new class or function in Python - with purely Pythonic types.from typing import Optional, Listfrom pydantic import BaseModel, Fieldclass Properties(BaseModel):    person_name: str    person_height: int    person_hair_color: str    dog_breed: Optional[str]    dog_name: Optional[str]chain = create_extraction_chain_pydantic(pydantic_schema=Properties, llm=llm)inp = \"\"\"Alex is 5 feet tall. Claudia is 1 feet taller Alex and jumps higher than him. Claudia is a brunette and Alex is blonde.Alex's dog Frosty is a labrador and likes to play hide and seek.        \"\"\"As we can see, we extracted the required entities and their properties in the required format:chain.run(inp)    [Properties(person_name='Alex', person_height=5, person_hair_color='blonde', dog_breed='labrador', dog_name='Frosty'),     Properties(person_name='Claudia',", "source": "https://python.langchain.com/docs/modules/chains/additional/extraction"}
{"id": "26005730e786-7", "text": "dog_name='Frosty'),     Properties(person_name='Claudia', person_height=6, person_hair_color='brunette', dog_breed=None, dog_name=None)]PreviousElasticsearch databaseNextFLAREExtracting entitiesSeveral entity typesUnrelated entitiesExtra info for an entityPydantic exampleCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/additional/extraction"}
{"id": "f40fdcddde81-0", "text": "Neptune Open Cypher QA Chain | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/additional/neptune_cypher_qa"}
{"id": "f40fdcddde81-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalDocumentsPopularAdditionalAnalyze DocumentSelf-critique chain with constitutional AICausal program-aided language (CPAL) chainElasticsearch databaseExtractionFLAREArangoDB QA chainGraph DB QA chainHugeGraph QA ChainKuzuQAChainNebulaGraphQAChainGraph QAGraphSparqlQAChainHypothetical Document EmbeddingsBash chainSelf-checking chainMath chainHTTP request chainSummarization checker chainLLM Symbolic MathModerationDynamically selecting from multiple promptsDynamically selecting from multiple retrieversNeptune Open Cypher QA ChainRetrieval QA using OpenAI functionsOpenAPI chainOpenAPI calls with OpenAI functionsProgram-aided language model (PAL) chainQuestion-Answering CitationsDocument QATaggingVector store-augmented text generationMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsAdditionalNeptune Open Cypher QA ChainNeptune Open Cypher QA ChainThis QA chain queries Neptune graph database using openCypher and returns human readable responsefrom langchain.graphs.neptune_graph import NeptuneGraphhost = \"<neptune-host>\"port = 80use_https = Falsegraph = NeptuneGraph(host=host, port=port, use_https=use_https)from langchain.chat_models import ChatOpenAIfrom langchain.chains.graph_qa.neptune_cypher import NeptuneOpenCypherQAChainllm = ChatOpenAI(temperature=0, model=\"gpt-4\")chain = NeptuneOpenCypherQAChain.from_llm(llm=llm, graph=graph)chain.run(\"how many outgoing routes does the Austin airport have?\")PreviousDynamically selecting", "source": "https://python.langchain.com/docs/modules/chains/additional/neptune_cypher_qa"}
{"id": "f40fdcddde81-2", "text": "many outgoing routes does the Austin airport have?\")PreviousDynamically selecting from multiple retrieversNextRetrieval QA using OpenAI functionsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/additional/neptune_cypher_qa"}
{"id": "def6b663bc63-0", "text": "Causal program-aided language (CPAL) chain | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/additional/cpal"}
{"id": "def6b663bc63-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalDocumentsPopularAdditionalAnalyze DocumentSelf-critique chain with constitutional AICausal program-aided language (CPAL) chainElasticsearch databaseExtractionFLAREArangoDB QA chainGraph DB QA chainHugeGraph QA ChainKuzuQAChainNebulaGraphQAChainGraph QAGraphSparqlQAChainHypothetical Document EmbeddingsBash chainSelf-checking chainMath chainHTTP request chainSummarization checker chainLLM Symbolic MathModerationDynamically selecting from multiple promptsDynamically selecting from multiple retrieversNeptune Open Cypher QA ChainRetrieval QA using OpenAI functionsOpenAPI chainOpenAPI calls with OpenAI functionsProgram-aided language model (PAL) chainQuestion-Answering CitationsDocument QATaggingVector store-augmented text generationMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsAdditionalCausal program-aided language (CPAL) chainOn this pageCausal program-aided language (CPAL) chainThe CPAL chain builds on the recent PAL to stop LLM hallucination. The problem with the PAL approach is that it hallucinates on a math problem with a nested chain of dependence. The innovation here is that this new CPAL approach includes causal structure to fix hallucination.The original PR's description contains a full overview.Using the CPAL chain, the LLM translated this\"Tim buys the same number of pets as Cindy and Boris.\"\"Cindy buys the same number of pets as Bill plus Bob.\"\"Boris buys the same number of pets as Ben plus Beth.\"\"Bill buys the same number of pets as Obama.\"\"Bob buys the same number of pets as", "source": "https://python.langchain.com/docs/modules/chains/additional/cpal"}
{"id": "def6b663bc63-2", "text": "Beth.\"\"Bill buys the same number of pets as Obama.\"\"Bob buys the same number of pets as Obama.\"\"Ben buys the same number of pets as Obama.\"\"Beth buys the same number of pets as Obama.\"\"If Obama buys one pet, how many pets total does everyone buy?\"into this.Outline of code examples demoed in this notebook.CPAL's value against hallucination: CPAL vs PAL1.1 Complex narrative1.2 Unanswerable math word problem  CPAL's three types of causal diagrams (The Book of Why).2.1 Mediator2.2 Collider2.3 Confounder   from IPython.display import SVGfrom langchain.experimental.cpal.base import CPALChainfrom langchain.chains import PALChainfrom langchain import OpenAIllm = OpenAI(temperature=0, max_tokens=512)cpal_chain = CPALChain.from_univariate_prompt(llm=llm, verbose=True)pal_chain = PALChain.from_math_prompt(llm=llm, verbose=True)CPAL's value against hallucination: CPAL vs PAL\u00e2\u20ac\u2039Like PAL, CPAL intends to reduce large language model (LLM) hallucination.The CPAL chain is different from the PAL chain for a couple of reasons.CPAL adds a causal structure (or DAG) to link entity actions (or math expressions).", "source": "https://python.langchain.com/docs/modules/chains/additional/cpal"}
{"id": "def6b663bc63-3", "text": "The CPAL math expressions are modeling a chain of cause and effect relations, which can be intervened upon, whereas for the PAL chain math expressions are projected math identities.1.1 Complex narrative\u00e2\u20ac\u2039Takeaway: PAL hallucinates, CPAL does not hallucinate.question = (    \"Tim buys the same number of pets as Cindy and Boris.\"    \"Cindy buys the same number of pets as Bill plus Bob.\"    \"Boris buys the same number of pets as Ben plus Beth.\"    \"Bill buys the same number of pets as Obama.\"    \"Bob buys the same number of pets as Obama.\"    \"Ben buys the same number of pets as Obama.\"    \"Beth buys the same number of pets as Obama.\"    \"If Obama buys one pet, how many pets total does everyone buy?\")pal_chain.run(question)            > Entering new  chain...    def solution():        \"\"\"Tim buys the same number of pets as Cindy and Boris.Cindy buys the same number of pets as Bill plus Bob.Boris buys the same number of pets as Ben plus Beth.Bill buys the same number of pets as Obama.Bob buys the same number of pets as Obama.Ben buys the same number of pets as Obama.Beth buys the same number of pets as Obama.If Obama buys one pet, how many pets total does everyone buy?\"\"\"        obama_pets = 1        tim_pets = obama_pets        cindy_pets = obama_pets + obama_pets        boris_pets = obama_pets + obama_pets        total_pets = tim_pets +", "source": "https://python.langchain.com/docs/modules/chains/additional/cpal"}
{"id": "def6b663bc63-4", "text": "+ obama_pets        total_pets = tim_pets + cindy_pets + boris_pets        result = total_pets        return result        > Finished chain.    '5'cpal_chain.run(question)            > Entering new  chain...    story outcome data        name                                   code  value      depends_on    0  obama                                   pass    1.0              []    1   bill               bill.value = obama.value    1.0         [obama]    2    bob                bob.value = obama.value    1.0         [obama]    3    ben                ben.value = obama.value    1.0         [obama]    4   beth               beth.value = obama.value    1.0", "source": "https://python.langchain.com/docs/modules/chains/additional/cpal"}
{"id": "def6b663bc63-5", "text": "beth.value = obama.value    1.0         [obama]    5  cindy   cindy.value = bill.value + bob.value    2.0     [bill, bob]    6  boris   boris.value = ben.value + beth.value    2.0     [ben, beth]    7    tim  tim.value = cindy.value + boris.value    4.0  [cindy, boris]        query data    {        \"question\": \"how many pets total does everyone buy?\",        \"expression\": \"SELECT SUM(value) FROM df\",        \"llm_error_msg\": \"\"    }            > Finished chain.    13.0# wait 20 secs to see displaycpal_chain.draw(path=\"web.svg\")SVG(\"web.svg\")    ![svg](_cpal_files/output_7_0.svg)    Unanswerable math\u00e2\u20ac\u2039Takeaway: PAL hallucinates, where CPAL, rather than hallucinate, answers with \"unanswerable, narrative question and plot are incoherent\"question = (    \"Jan has three times the number of pets as Marcia.\"    \"Marcia has two more pets than Cindy.\"    \"If Cindy has ten pets, how many pets does Barak have?\")pal_chain.run(question)            > Entering new  chain...    def solution():        \"\"\"Jan has three times the", "source": "https://python.langchain.com/docs/modules/chains/additional/cpal"}
{"id": "def6b663bc63-6", "text": "def solution():        \"\"\"Jan has three times the number of pets as Marcia.Marcia has two more pets than Cindy.If Cindy has ten pets, how many pets does Barak have?\"\"\"        cindy_pets = 10        marcia_pets = cindy_pets + 2        jan_pets = marcia_pets * 3        result = jan_pets        return result        > Finished chain.    '36'try:    cpal_chain.run(question)except Exception as e_msg:    print(e_msg)            > Entering new  chain...    story outcome data         name                            code  value depends_on    0   cindy                            pass   10.0         []    1  marcia  marcia.value = cindy.value + 2   12.0    [cindy]    2     jan    jan.value = marcia.value * 3   36.0   [marcia]        query data    {        \"question\": \"how many pets does barak have?\",        \"expression\": \"SELECT name, value FROM df WHERE name =", "source": "https://python.langchain.com/docs/modules/chains/additional/cpal"}
{"id": "def6b663bc63-7", "text": "\"expression\": \"SELECT name, value FROM df WHERE name = 'barak'\",        \"llm_error_msg\": \"\"    }        unanswerable, query and outcome are incoherent        outcome:         name                            code  value depends_on    0   cindy                            pass   10.0         []    1  marcia  marcia.value = cindy.value + 2   12.0    [cindy]    2     jan    jan.value = marcia.value * 3   36.0   [marcia]    query:    {'question': 'how many pets does barak have?', 'expression': \"SELECT name, value FROM df WHERE name = 'barak'\", 'llm_error_msg': ''}Basic math\u00e2\u20ac\u2039Causal mediator\u00e2\u20ac\u2039question = (    \"Jan has three times the number of pets as Marcia. \"    \"Marcia has two more pets than Cindy. \"    \"If Cindy has four pets, how many total pets do the three have?\")PALpal_chain.run(question)            > Entering new  chain...    def solution():        \"\"\"Jan has three times the number of pets as Marcia. Marcia has two more pets than Cindy. If", "source": "https://python.langchain.com/docs/modules/chains/additional/cpal"}
{"id": "def6b663bc63-8", "text": "three times the number of pets as Marcia. Marcia has two more pets than Cindy. If Cindy has four pets, how many total pets do the three have?\"\"\"        cindy_pets = 4        marcia_pets = cindy_pets + 2        jan_pets = marcia_pets * 3        total_pets = cindy_pets + marcia_pets + jan_pets        result = total_pets        return result        > Finished chain.    '28'CPALcpal_chain.run(question)            > Entering new  chain...    story outcome data         name                            code  value depends_on    0   cindy                            pass    4.0         []    1  marcia  marcia.value = cindy.value + 2    6.0    [cindy]    2     jan    jan.value = marcia.value * 3   18.0   [marcia]        query data    {        \"question\": \"how many total pets do the three have?\",        \"expression\": \"SELECT SUM(value) FROM df\",", "source": "https://python.langchain.com/docs/modules/chains/additional/cpal"}
{"id": "def6b663bc63-9", "text": "\"expression\": \"SELECT SUM(value) FROM df\",        \"llm_error_msg\": \"\"    }            > Finished chain.    28.0# wait 20 secs to see displaycpal_chain.draw(path=\"web.svg\")SVG(\"web.svg\")    ![svg](_cpal_files/output_18_0.svg)    Causal collider\u00e2\u20ac\u2039question = (    \"Jan has the number of pets as Marcia plus the number of pets as Cindy. \"    \"Marcia has no pets. \"    \"If Cindy has four pets, how many total pets do the three have?\")cpal_chain.run(question)            > Entering new  chain...    story outcome data         name                                    code  value       depends_on    0  marcia                                    pass    0.0               []    1   cindy                                    pass    4.0               []    2     jan  jan.value = marcia.value + cindy.value", "source": "https://python.langchain.com/docs/modules/chains/additional/cpal"}
{"id": "def6b663bc63-10", "text": "jan  jan.value = marcia.value + cindy.value    4.0  [marcia, cindy]        query data    {        \"question\": \"how many total pets do the three have?\",        \"expression\": \"SELECT SUM(value) FROM df\",        \"llm_error_msg\": \"\"    }            > Finished chain.    8.0# wait 20 secs to see displaycpal_chain.draw(path=\"web.svg\")SVG(\"web.svg\")    ![svg](_cpal_files/output_22_0.svg)    Causal confounder\u00e2\u20ac\u2039question = (    \"Jan has the number of pets as Marcia plus the number of pets as Cindy. \"    \"Marcia has two more pets than Cindy. \"    \"If Cindy has four pets, how many total pets do the three have?\")cpal_chain.run(question)            > Entering new  chain...    story outcome data         name                                    code  value       depends_on    0   cindy                                    pass    4.0               []    1  marcia", "source": "https://python.langchain.com/docs/modules/chains/additional/cpal"}
{"id": "def6b663bc63-11", "text": "[]    1  marcia          marcia.value = cindy.value + 2    6.0          [cindy]    2     jan  jan.value = cindy.value + marcia.value   10.0  [cindy, marcia]        query data    {        \"question\": \"how many total pets do the three have?\",        \"expression\": \"SELECT SUM(value) FROM df\",        \"llm_error_msg\": \"\"    }            > Finished chain.    20.0# wait 20 secs to see displaycpal_chain.draw(path=\"web.svg\")SVG(\"web.svg\")    ![svg](_cpal_files/output_26_0.svg)    %autoreload 2PreviousSelf-critique chain with constitutional AINextElasticsearch databaseCPAL's value against hallucination: CPAL vs PAL1.1 Complex narrativeUnanswerable mathBasic mathCausal colliderCausal confounderCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/additional/cpal"}
{"id": "5c135c2cbb8d-0", "text": "Moderation | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/additional/moderation"}
{"id": "5c135c2cbb8d-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalDocumentsPopularAdditionalAnalyze DocumentSelf-critique chain with constitutional AICausal program-aided language (CPAL) chainElasticsearch databaseExtractionFLAREArangoDB QA chainGraph DB QA chainHugeGraph QA ChainKuzuQAChainNebulaGraphQAChainGraph QAGraphSparqlQAChainHypothetical Document EmbeddingsBash chainSelf-checking chainMath chainHTTP request chainSummarization checker chainLLM Symbolic MathModerationDynamically selecting from multiple promptsDynamically selecting from multiple retrieversNeptune Open Cypher QA ChainRetrieval QA using OpenAI functionsOpenAPI chainOpenAPI calls with OpenAI functionsProgram-aided language model (PAL) chainQuestion-Answering CitationsDocument QATaggingVector store-augmented text generationMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsAdditionalModerationModerationThis notebook walks through examples of how to use a moderation chain, and several common ways for doing so. Moderation chains are useful for detecting text that could be hateful, violent, etc. This can be useful to apply on both user input, but also on the output of a Language Model. Some API providers, like OpenAI, specifically prohibit you, or your end users, from generating some types of harmful content. To comply with this (and to just generally prevent your application from being harmful) you may often want to append a moderation chain to any LLMChains, in order to make sure any output the LLM generates is not harmful.If the content passed into the moderation chain is harmful, there is not one best way to handle it, it probably depends on your", "source": "https://python.langchain.com/docs/modules/chains/additional/moderation"}
{"id": "5c135c2cbb8d-2", "text": "moderation chain is harmful, there is not one best way to handle it, it probably depends on your application. Sometimes you may want to throw an error in the Chain (and have your application handle that). Other times, you may want to return something to the user explaining that the text was harmful. There could even be other ways to handle it! We will cover all these ways in this walkthrough.We'll show:How to run any piece of text through a moderation chain.How to append a Moderation chain to an LLMChain.from langchain.llms import OpenAIfrom langchain.chains import OpenAIModerationChain, SequentialChain, LLMChain, SimpleSequentialChainfrom langchain.prompts import PromptTemplateHow to use the moderation chain\u00e2\u20ac\u2039Here's an example of using the moderation chain with default settings (will return a string explaining stuff was flagged).moderation_chain = OpenAIModerationChain()moderation_chain.run(\"This is okay\")    'This is okay'moderation_chain.run(\"I will kill you\")    \"Text was found that violates OpenAI's content policy.\"Here's an example of using the moderation chain to throw an error.moderation_chain_error = OpenAIModerationChain(error=True)moderation_chain_error.run(\"This is okay\")    'This is okay'moderation_chain_error.run(\"I will kill you\")    ---------------------------------------------------------------------------    ValueError                                Traceback (most recent call last)    Cell In[7], line 1    ----> 1 moderation_chain_error.run(\"I will kill you\")    File ~/workplace/langchain/langchain/chains/base.py:138, in Chain.run(self, *args, **kwargs)        136", "source": "https://python.langchain.com/docs/modules/chains/additional/moderation"}
{"id": "5c135c2cbb8d-3", "text": "*args, **kwargs)        136     if len(args) != 1:        137         raise ValueError(\"`run` supports only one positional argument.\")    --> 138     return self(args[0])[self.output_keys[0]]        140 if kwargs and not args:        141     return self(kwargs)[self.output_keys[0]]    File ~/workplace/langchain/langchain/chains/base.py:112, in Chain.__call__(self, inputs, return_only_outputs)        108 if self.verbose:        109     print(        110         f\"\\n\\n\\033[1m> Entering new {self.__class__.__name__} chain...\\033[0m\"        111     )    --> 112 outputs = self._call(inputs)        113 if self.verbose:        114     print(f\"\\n\\033[1m> Finished {self.__class__.__name__} chain.\\033[0m\")    File ~/workplace/langchain/langchain/chains/moderation.py:81, in OpenAIModerationChain._call(self, inputs)         79 text = inputs[self.input_key]         80 results = self.client.create(text)    ---> 81 output = self._moderate(text, results[\"results\"][0])         82 return {self.output_key:", "source": "https://python.langchain.com/docs/modules/chains/additional/moderation"}
{"id": "5c135c2cbb8d-4", "text": "82 return {self.output_key: output}    File ~/workplace/langchain/langchain/chains/moderation.py:73, in OpenAIModerationChain._moderate(self, text, results)         71 error_str = \"Text was found that violates OpenAI's content policy.\"         72 if self.error:    ---> 73     raise ValueError(error_str)         74 else:         75     return error_str    ValueError: Text was found that violates OpenAI's content policy.Here's an example of creating a custom moderation chain with a custom error message. It requires some knowledge of OpenAI's moderation endpoint results (see docs here).class CustomModeration(OpenAIModerationChain):        def _moderate(self, text: str, results: dict) -> str:        if results[\"flagged\"]:            error_str = f\"The following text was found that violates OpenAI's content policy: {text}\"            return error_str        return text    custom_moderation = CustomModeration()custom_moderation.run(\"This is okay\")    'This is okay'custom_moderation.run(\"I will kill you\")    \"The following text was found that violates OpenAI's content policy: I will kill you\"How to append a Moderation chain to an LLMChain\u00e2\u20ac\u2039To easily combine a moderation chain with an LLMChain, you can use the SequentialChain abstraction.Let's start with a simple example of where the LLMChain only has a single", "source": "https://python.langchain.com/docs/modules/chains/additional/moderation"}
{"id": "5c135c2cbb8d-5", "text": "abstraction.Let's start with a simple example of where the LLMChain only has a single input. For this purpose, we will prompt the model so it says something harmful.prompt = PromptTemplate(template=\"{text}\", input_variables=[\"text\"])llm_chain = LLMChain(llm=OpenAI(temperature=0, model_name=\"text-davinci-002\"), prompt=prompt)text = \"\"\"We are playing a game of repeat after me.Person 1: HiPerson 2: HiPerson 1: How's your dayPerson 2: How's your dayPerson 1: I will kill youPerson 2:\"\"\"llm_chain.run(text)    ' I will kill you'chain = SimpleSequentialChain(chains=[llm_chain, moderation_chain])chain.run(text)    \"Text was found that violates OpenAI's content policy.\"Now let's walk through an example of using it with an LLMChain which has multiple inputs (a bit more tricky because we can't use the SimpleSequentialChain)prompt = PromptTemplate(template=\"{setup}{new_input}Person2:\", input_variables=[\"setup\", \"new_input\"])llm_chain = LLMChain(llm=OpenAI(temperature=0, model_name=\"text-davinci-002\"), prompt=prompt)setup = \"\"\"We are playing a game of repeat after me.Person 1: HiPerson 2: HiPerson 1: How's your dayPerson 2: How's your dayPerson 1:\"\"\"new_input = \"I will kill you\"inputs = {\"setup\": setup, \"new_input\": new_input}llm_chain(inputs, return_only_outputs=True)    {'text': ' I will kill you'}# Setting the input/output keys so it lines upmoderation_chain.input_key = \"text\"moderation_chain.output_key = \"sanitized_text\"chain = SequentialChain(chains=[llm_chain, moderation_chain],", "source": "https://python.langchain.com/docs/modules/chains/additional/moderation"}
{"id": "5c135c2cbb8d-6", "text": "= \"sanitized_text\"chain = SequentialChain(chains=[llm_chain, moderation_chain], input_variables=[\"setup\", \"new_input\"])chain(inputs, return_only_outputs=True)    {'sanitized_text': \"Text was found that violates OpenAI's content policy.\"}PreviousLLM Symbolic MathNextDynamically selecting from multiple promptsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/additional/moderation"}
{"id": "fc52e22e3537-0", "text": "HTTP request chain | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_requests"}
{"id": "fc52e22e3537-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalDocumentsPopularAdditionalAnalyze DocumentSelf-critique chain with constitutional AICausal program-aided language (CPAL) chainElasticsearch databaseExtractionFLAREArangoDB QA chainGraph DB QA chainHugeGraph QA ChainKuzuQAChainNebulaGraphQAChainGraph QAGraphSparqlQAChainHypothetical Document EmbeddingsBash chainSelf-checking chainMath chainHTTP request chainSummarization checker chainLLM Symbolic MathModerationDynamically selecting from multiple promptsDynamically selecting from multiple retrieversNeptune Open Cypher QA ChainRetrieval QA using OpenAI functionsOpenAPI chainOpenAPI calls with OpenAI functionsProgram-aided language model (PAL) chainQuestion-Answering CitationsDocument QATaggingVector store-augmented text generationMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsAdditionalHTTP request chainHTTP request chainUsing the request library to get HTML results from a URL and then an LLM to parse resultsfrom langchain.llms import OpenAIfrom langchain.chains import LLMRequestsChain, LLMChainfrom langchain.prompts import PromptTemplatetemplate = \"\"\"Between >>> and <<< are the raw search result text from google.Extract the answer to the question '{query}' or say \"not found\" if the information is not contained.Use the formatExtracted:<answer or \"not found\">>>> {requests_result} <<<Extracted:\"\"\"PROMPT = PromptTemplate(    input_variables=[\"query\", \"requests_result\"],    template=template,)chain =", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_requests"}
{"id": "fc52e22e3537-2", "text": "input_variables=[\"query\", \"requests_result\"],    template=template,)chain = LLMRequestsChain(llm_chain=LLMChain(llm=OpenAI(temperature=0), prompt=PROMPT))question = \"What are the Three (3) biggest countries, and their respective sizes?\"inputs = {    \"query\": question,    \"url\": \"https://www.google.com/search?q=\" + question.replace(\" \", \"+\"),}chain(inputs)    {'query': 'What are the Three (3) biggest countries, and their respective sizes?',     'url': 'https://www.google.com/search?q=What+are+the+Three+(3)+biggest+countries,+and+their+respective+sizes?',     'output': ' Russia (17,098,242 km\u00c2\u00b2), Canada (9,984,670 km\u00c2\u00b2), United States (9,826,675 km\u00c2\u00b2)'}PreviousMath chainNextSummarization checker chainCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/additional/llm_requests"}
{"id": "1150003fcab1-0", "text": "Graph QA | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_qa"}
{"id": "1150003fcab1-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalDocumentsPopularAdditionalAnalyze DocumentSelf-critique chain with constitutional AICausal program-aided language (CPAL) chainElasticsearch databaseExtractionFLAREArangoDB QA chainGraph DB QA chainHugeGraph QA ChainKuzuQAChainNebulaGraphQAChainGraph QAGraphSparqlQAChainHypothetical Document EmbeddingsBash chainSelf-checking chainMath chainHTTP request chainSummarization checker chainLLM Symbolic MathModerationDynamically selecting from multiple promptsDynamically selecting from multiple retrieversNeptune Open Cypher QA ChainRetrieval QA using OpenAI functionsOpenAPI chainOpenAPI calls with OpenAI functionsProgram-aided language model (PAL) chainQuestion-Answering CitationsDocument QATaggingVector store-augmented text generationMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsAdditionalGraph QAOn this pageGraph QAThis notebook goes over how to do question answering over a graph data structure.Create the graph\u00e2\u20ac\u2039In this section, we construct an example graph. At the moment, this works best for small pieces of text.from langchain.indexes import GraphIndexCreatorfrom langchain.llms import OpenAIfrom langchain.document_loaders import TextLoaderindex_creator = GraphIndexCreator(llm=OpenAI(temperature=0))with open(\"../../state_of_the_union.txt\") as f:    all_text = f.read()We will use just a small snippet, because extracting the knowledge triplets is a bit intensive at the moment.text = \"\\n\".join(all_text.split(\"\\n\\n\")[105:108])text    'It", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_qa"}
{"id": "1150003fcab1-2", "text": "'It won\u00e2\u20ac\u2122t look like much, but if you stop and look closely, you\u00e2\u20ac\u2122ll see a \u00e2\u20ac\u0153Field of dreams,\u00e2\u20ac\ufffd the ground on which America\u00e2\u20ac\u2122s future will be built. \\nThis is where Intel, the American company that helped build Silicon Valley, is going to build its $20 billion semiconductor \u00e2\u20ac\u0153mega site\u00e2\u20ac\ufffd. \\nUp to eight state-of-the-art factories in one place. 10,000 new good-paying jobs. 'graph = index_creator.from_text(text)We can inspect the created graph.graph.get_triples()    [('Intel', '$20 billion semiconductor \"mega site\"', 'is going to build'),     ('Intel', 'state-of-the-art factories', 'is building'),     ('Intel', '10,000 new good-paying jobs', 'is creating'),     ('Intel', 'Silicon Valley', 'is helping build'),     ('Field of dreams',      \"America's future will be built\",      'is the ground on which')]Querying the graph\u00e2\u20ac\u2039We can now use the graph QA chain to ask question of the graphfrom langchain.chains import GraphQAChainchain = GraphQAChain.from_llm(OpenAI(temperature=0), graph=graph, verbose=True)chain.run(\"what is Intel going to build?\")            > Entering new GraphQAChain chain...    Entities Extracted:     Intel    Full Context:    Intel is going to build $20 billion semiconductor \"mega site\"    Intel is building state-of-the-art factories    Intel is creating 10,000 new good-paying jobs    Intel is helping build Silicon Valley        > Finished chain.", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_qa"}
{"id": "1150003fcab1-3", "text": "Intel is helping build Silicon Valley        > Finished chain.    ' Intel is going to build a $20 billion semiconductor \"mega site\" with state-of-the-art factories, creating 10,000 new good-paying jobs and helping to build Silicon Valley.'Save the graph\u00e2\u20ac\u2039We can also save and load the graph.graph.write_to_gml(\"graph.gml\")from langchain.indexes.graph import NetworkxEntityGraphloaded_graph = NetworkxEntityGraph.from_gml(\"graph.gml\")loaded_graph.get_triples()    [('Intel', '$20 billion semiconductor \"mega site\"', 'is going to build'),     ('Intel', 'state-of-the-art factories', 'is building'),     ('Intel', '10,000 new good-paying jobs', 'is creating'),     ('Intel', 'Silicon Valley', 'is helping build'),     ('Field of dreams',      \"America's future will be built\",      'is the ground on which')]PreviousNebulaGraphQAChainNextGraphSparqlQAChainCreate the graphQuerying the graphSave the graphCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_qa"}
{"id": "78d5f8a7d205-0", "text": "Tagging | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/additional/tagging"}
{"id": "78d5f8a7d205-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalDocumentsPopularAdditionalAnalyze DocumentSelf-critique chain with constitutional AICausal program-aided language (CPAL) chainElasticsearch databaseExtractionFLAREArangoDB QA chainGraph DB QA chainHugeGraph QA ChainKuzuQAChainNebulaGraphQAChainGraph QAGraphSparqlQAChainHypothetical Document EmbeddingsBash chainSelf-checking chainMath chainHTTP request chainSummarization checker chainLLM Symbolic MathModerationDynamically selecting from multiple promptsDynamically selecting from multiple retrieversNeptune Open Cypher QA ChainRetrieval QA using OpenAI functionsOpenAPI chainOpenAPI calls with OpenAI functionsProgram-aided language model (PAL) chainQuestion-Answering CitationsDocument QATaggingVector store-augmented text generationMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsAdditionalTaggingOn this pageTaggingThe tagging chain uses the OpenAI functions parameter to specify a schema to tag a document with. This helps us make sure that the model outputs exactly tags that we want, with their appropriate types.The tagging chain is to be used when we want to tag a passage with a specific attribute (i.e. what is the sentiment of this message?)from langchain.chat_models import ChatOpenAIfrom langchain.chains import create_tagging_chain, create_tagging_chain_pydanticfrom langchain.prompts import ChatPromptTemplate    /Users/harrisonchase/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/site-packages/deeplake/util/check_latest_version.py:32: UserWarning: A newer version of", "source": "https://python.langchain.com/docs/modules/chains/additional/tagging"}
{"id": "78d5f8a7d205-2", "text": "UserWarning: A newer version of deeplake (3.6.4) is available. It's recommended that you update to the latest version using `pip install -U deeplake`.      warnings.warn(llm = ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo-0613\")Simplest approach, only specifying type\u00e2\u20ac\u2039We can start by specifying a few properties with their expected type in our schemaschema = {    \"properties\": {        \"sentiment\": {\"type\": \"string\"},        \"aggressiveness\": {\"type\": \"integer\"},        \"language\": {\"type\": \"string\"},    }}chain = create_tagging_chain(schema, llm)As we can see in the examples, it correctly interprets what we want but the results vary so that we get, for example, sentiments in different languages ('positive', 'enojado' etc.).We will see how to control these results in the next section.inp = \"Estoy increiblemente contento de haberte conocido! Creo que seremos muy buenos amigos!\"chain.run(inp)    {'sentiment': 'positive', 'language': 'Spanish'}inp = \"Estoy muy enojado con vos! Te voy a dar tu merecido!\"chain.run(inp)    {'sentiment': 'enojado', 'aggressiveness': 1, 'language': 'Spanish'}inp = \"Weather is ok here, I can go outside without much more than a coat\"chain.run(inp)    {'sentiment': 'positive', 'aggressiveness': 0, 'language': 'English'}More control\u00e2\u20ac\u2039By being smart about how we define our schema we can have more control over the model's output. Specifically", "source": "https://python.langchain.com/docs/modules/chains/additional/tagging"}
{"id": "78d5f8a7d205-3", "text": "being smart about how we define our schema we can have more control over the model's output. Specifically we can define:possible values for each propertydescription to make sure that the model understands the propertyrequired properties to be returnedFollowing is an example of how we can use enum, description and required to control for each of the previously mentioned aspects:schema = {    \"properties\": {        \"sentiment\": {\"type\": \"string\", \"enum\": [\"happy\", \"neutral\", \"sad\"]},        \"aggressiveness\": {            \"type\": \"integer\",            \"enum\": [1, 2, 3, 4, 5],            \"description\": \"describes how aggressive the statement is, the higher the number the more aggressive\",        },        \"language\": {            \"type\": \"string\",            \"enum\": [\"spanish\", \"english\", \"french\", \"german\", \"italian\"],        },    },    \"required\": [\"language\", \"sentiment\", \"aggressiveness\"],}chain = create_tagging_chain(schema, llm)Now the answers are much better!inp = \"Estoy increiblemente contento de haberte conocido! Creo que seremos muy buenos amigos!\"chain.run(inp)    {'sentiment': 'happy', 'aggressiveness': 0, 'language': 'spanish'}inp = \"Estoy muy enojado con vos! Te voy a dar tu merecido!\"chain.run(inp)", "source": "https://python.langchain.com/docs/modules/chains/additional/tagging"}
{"id": "78d5f8a7d205-4", "text": "con vos! Te voy a dar tu merecido!\"chain.run(inp)    {'sentiment': 'sad', 'aggressiveness': 10, 'language': 'spanish'}inp = \"Weather is ok here, I can go outside without much more than a coat\"chain.run(inp)    {'sentiment': 'neutral', 'aggressiveness': 0, 'language': 'english'}Specifying schema with Pydantic\u00e2\u20ac\u2039We can also use a Pydantic schema to specify the required properties and types. We can also send other arguments, such as 'enum' or 'description' as can be seen in the example below.By using the create_tagging_chain_pydantic function, we can send a Pydantic schema as input and the output will be an instantiated object that respects our desired schema. In this way, we can specify our schema in the same manner that we would a new class or function in Python - with purely Pythonic types.from enum import Enumfrom pydantic import BaseModel, Fieldclass Tags(BaseModel):    sentiment: str = Field(..., enum=[\"happy\", \"neutral\", \"sad\"])    aggressiveness: int = Field(        ...,        description=\"describes how aggressive the statement is, the higher the number the more aggressive\",        enum=[1, 2, 3, 4, 5],    )    language: str = Field(        ..., enum=[\"spanish\", \"english\", \"french\", \"german\", \"italian\"]    )chain = create_tagging_chain_pydantic(Tags, llm)inp = \"Estoy muy enojado con vos! Te voy a dar tu merecido!\"res = chain.run(inp)res", "source": "https://python.langchain.com/docs/modules/chains/additional/tagging"}
{"id": "78d5f8a7d205-5", "text": "vos! Te voy a dar tu merecido!\"res = chain.run(inp)res    Tags(sentiment='sad', aggressiveness=10, language='spanish')PreviousDocument QANextVector store-augmented text generationSimplest approach, only specifying typeMore controlSpecifying schema with PydanticCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/additional/tagging"}
{"id": "8b28790a9793-0", "text": "Vector store-augmented text generation | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/additional/vector_db_text_generation"}
{"id": "8b28790a9793-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalDocumentsPopularAdditionalAnalyze DocumentSelf-critique chain with constitutional AICausal program-aided language (CPAL) chainElasticsearch databaseExtractionFLAREArangoDB QA chainGraph DB QA chainHugeGraph QA ChainKuzuQAChainNebulaGraphQAChainGraph QAGraphSparqlQAChainHypothetical Document EmbeddingsBash chainSelf-checking chainMath chainHTTP request chainSummarization checker chainLLM Symbolic MathModerationDynamically selecting from multiple promptsDynamically selecting from multiple retrieversNeptune Open Cypher QA ChainRetrieval QA using OpenAI functionsOpenAPI chainOpenAPI calls with OpenAI functionsProgram-aided language model (PAL) chainQuestion-Answering CitationsDocument QATaggingVector store-augmented text generationMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsAdditionalVector store-augmented text generationOn this pageVector store-augmented text generationThis notebook walks through how to use LangChain for text generation over a vector index. This is useful if we want to generate text that is able to draw from a large body of custom text, for example, generating blog posts that have an understanding of previous blog posts written, or product tutorials that can refer to product documentation.Prepare Data\u00e2\u20ac\u2039First, we prepare the data. For this example, we fetch a documentation site that consists of markdown files hosted on Github and split them into small enough Documents.from langchain.llms import OpenAIfrom langchain.docstore.document import Documentimport requestsfrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.vectorstores import Chromafrom", "source": "https://python.langchain.com/docs/modules/chains/additional/vector_db_text_generation"}
{"id": "8b28790a9793-2", "text": "langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.vectorstores import Chromafrom langchain.text_splitter import CharacterTextSplitterfrom langchain.prompts import PromptTemplateimport pathlibimport subprocessimport tempfiledef get_github_docs(repo_owner, repo_name):    with tempfile.TemporaryDirectory() as d:        subprocess.check_call(            f\"git clone --depth 1 https://github.com/{repo_owner}/{repo_name}.git .\",            cwd=d,            shell=True,        )        git_sha = (            subprocess.check_output(\"git rev-parse HEAD\", shell=True, cwd=d)            .decode(\"utf-8\")            .strip()        )        repo_path = pathlib.Path(d)        markdown_files = list(repo_path.glob(\"*/*.md\")) + list(            repo_path.glob(\"*/*.mdx\")        )        for markdown_file in markdown_files:            with open(markdown_file, \"r\") as f:                relative_path = markdown_file.relative_to(repo_path)                github_url = f\"https://github.com/{repo_owner}/{repo_name}/blob/{git_sha}/{relative_path}\"", "source": "https://python.langchain.com/docs/modules/chains/additional/vector_db_text_generation"}
{"id": "8b28790a9793-3", "text": "yield Document(page_content=f.read(), metadata={\"source\": github_url})sources = get_github_docs(\"yirenlu92\", \"deno-manual-forked\")source_chunks = []splitter = CharacterTextSplitter(separator=\" \", chunk_size=1024, chunk_overlap=0)for source in sources:    for chunk in splitter.split_text(source.page_content):        source_chunks.append(Document(page_content=chunk, metadata=source.metadata))    Cloning into '.'...Set Up Vector DB\u00e2\u20ac\u2039Now that we have the documentation content in chunks, let's put all this information in a vector index for easy retrieval.search_index = Chroma.from_documents(source_chunks, OpenAIEmbeddings())Set Up LLM Chain with Custom Prompt\u00e2\u20ac\u2039Next, let's set up a simple LLM chain but give it a custom prompt for blog post generation. Note that the custom prompt is parameterized and takes two inputs: context, which will be the documents fetched from the vector search, and topic, which is given by the user.from langchain.chains import LLMChainprompt_template = \"\"\"Use the context below to write a 400 word blog post about the topic below:    Context: {context}    Topic: {topic}    Blog post:\"\"\"PROMPT = PromptTemplate(template=prompt_template, input_variables=[\"context\", \"topic\"])llm = OpenAI(temperature=0)chain = LLMChain(llm=llm, prompt=PROMPT)Generate Text\u00e2\u20ac\u2039Finally, we write a function to apply our inputs to the chain. The function takes an input parameter topic. We find the documents in the vector index that correspond to that topic, and use them as additional context in our simple LLM chain.def generate_blog_post(topic):    docs =", "source": "https://python.langchain.com/docs/modules/chains/additional/vector_db_text_generation"}
{"id": "8b28790a9793-4", "text": "as additional context in our simple LLM chain.def generate_blog_post(topic):    docs = search_index.similarity_search(topic, k=4)    inputs = [{\"context\": doc.page_content, \"topic\": topic} for doc in docs]    print(chain.apply(inputs))generate_blog_post(\"environment variables\")    [{'text': '\\n\\nEnvironment variables are a great way to store and access sensitive information in your Deno applications. Deno offers built-in support for environment variables with `Deno.env`, and you can also use a `.env` file to store and access environment variables.\\n\\nUsing `Deno.env` is simple. It has getter and setter methods, so you can easily set and retrieve environment variables. For example, you can set the `FIREBASE_API_KEY` and `FIREBASE_AUTH_DOMAIN` environment variables like this:\\n\\n```ts\\nDeno.env.set(\"FIREBASE_API_KEY\", \"examplekey123\");\\nDeno.env.set(\"FIREBASE_AUTH_DOMAIN\", \"firebasedomain.com\");\\n\\nconsole.log(Deno.env.get(\"FIREBASE_API_KEY\")); // examplekey123\\nconsole.log(Deno.env.get(\"FIREBASE_AUTH_DOMAIN\")); // firebasedomain.com\\n```\\n\\nYou can also store environment variables in a `.env` file. This is a great'}, {'text': '\\n\\nEnvironment variables are a powerful tool for managing configuration settings in a program. They allow us to set values that can be used by the program, without having to hard-code them into the code. This makes it easier to change settings without having to modify the code.\\n\\nIn Deno, environment variables can be set in a few different ways. The most common way is to use the `VAR=value` syntax. This will set the environment variable `VAR` to the value `value`. This can be used to set any number of environment variables before", "source": "https://python.langchain.com/docs/modules/chains/additional/vector_db_text_generation"}
{"id": "8b28790a9793-5", "text": "to the value `value`. This can be used to set any number of environment variables before running a command. For example, if we wanted to set the environment variable `VAR` to `hello` before running a Deno command, we could do so like this:\\n\\n```\\nVAR=hello deno run main.ts\\n```\\n\\nThis will set the environment variable `VAR` to `hello` before running the command. We can then access this variable in our code using the `Deno.env.get()` function. For example, if we ran the following command:\\n\\n```\\nVAR=hello && deno eval \"console.log(\\'Deno: \\' + Deno.env.get(\\'VAR'}, {'text': '\\n\\nEnvironment variables are a powerful tool for developers, allowing them to store and access data without having to hard-code it into their applications. In Deno, you can access environment variables using the `Deno.env.get()` function.\\n\\nFor example, if you wanted to access the `HOME` environment variable, you could do so like this:\\n\\n```js\\n// env.js\\nDeno.env.get(\"HOME\");\\n```\\n\\nWhen running this code, you\\'ll need to grant the Deno process access to environment variables. This can be done by passing the `--allow-env` flag to the `deno run` command. You can also specify which environment variables you want to grant access to, like this:\\n\\n```shell\\n# Allow access to only the HOME env var\\ndeno run --allow-env=HOME env.js\\n```\\n\\nIt\\'s important to note that environment variables are case insensitive on Windows, so Deno also matches them case insensitively (on Windows only).\\n\\nAnother thing to be aware of when using environment variables is subprocess permissions. Subprocesses are powerful and can access system resources regardless of the permissions you granted to the Den'},", "source": "https://python.langchain.com/docs/modules/chains/additional/vector_db_text_generation"}
{"id": "8b28790a9793-6", "text": "Subprocesses are powerful and can access system resources regardless of the permissions you granted to the Den'}, {'text': '\\n\\nEnvironment variables are an important part of any programming language, and Deno is no exception. Deno is a secure JavaScript and TypeScript runtime built on the V8 JavaScript engine, and it recently added support for environment variables. This feature was added in Deno version 1.6.0, and it is now available for use in Deno applications.\\n\\nEnvironment variables are used to store information that can be used by programs. They are typically used to store configuration information, such as the location of a database or the name of a user. In Deno, environment variables are stored in the `Deno.env` object. This object is similar to the `process.env` object in Node.js, and it allows you to access and set environment variables.\\n\\nThe `Deno.env` object is a read-only object, meaning that you cannot directly modify the environment variables. Instead, you must use the `Deno.env.set()` function to set environment variables. This function takes two arguments: the name of the environment variable and the value to set it to. For example, if you wanted to set the `FOO` environment variable to `bar`, you would use the following code:\\n\\n```'}]PreviousTaggingNextMemoryPrepare DataSet Up Vector DBSet Up LLM Chain with Custom PromptGenerate TextCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/additional/vector_db_text_generation"}
{"id": "1a5c50fee6a8-0", "text": "Analyze Document | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/additional/analyze_document"}
{"id": "1a5c50fee6a8-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalDocumentsPopularAdditionalAnalyze DocumentSelf-critique chain with constitutional AICausal program-aided language (CPAL) chainElasticsearch databaseExtractionFLAREArangoDB QA chainGraph DB QA chainHugeGraph QA ChainKuzuQAChainNebulaGraphQAChainGraph QAGraphSparqlQAChainHypothetical Document EmbeddingsBash chainSelf-checking chainMath chainHTTP request chainSummarization checker chainLLM Symbolic MathModerationDynamically selecting from multiple promptsDynamically selecting from multiple retrieversNeptune Open Cypher QA ChainRetrieval QA using OpenAI functionsOpenAPI chainOpenAPI calls with OpenAI functionsProgram-aided language model (PAL) chainQuestion-Answering CitationsDocument QATaggingVector store-augmented text generationMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsAdditionalAnalyze DocumentAnalyze DocumentThe AnalyzeDocumentChain can be used as an end-to-end to chain. This chain takes in a single document, splits it up, and then runs it through a CombineDocumentsChain.with open(\"../../state_of_the_union.txt\") as f:    state_of_the_union = f.read()Summarize\u00e2\u20ac\u2039Let's take a look at it in action below, using it summarize a long document.from langchain import OpenAIfrom langchain.chains.summarize import load_summarize_chainllm = OpenAI(temperature=0)summary_chain = load_summarize_chain(llm, chain_type=\"map_reduce\")from langchain.chains import AnalyzeDocumentChainsummarize_document_chain =", "source": "https://python.langchain.com/docs/modules/chains/additional/analyze_document"}
{"id": "1a5c50fee6a8-2", "text": "langchain.chains import AnalyzeDocumentChainsummarize_document_chain = AnalyzeDocumentChain(combine_docs_chain=summary_chain)summarize_document_chain.run(state_of_the_union)    \" In this speech, President Biden addresses the American people and the world, discussing the recent aggression of Russia's Vladimir Putin in Ukraine and the US response. He outlines economic sanctions and other measures taken to hold Putin accountable, and announces the US Department of Justice's task force to go after the crimes of Russian oligarchs. He also announces plans to fight inflation and lower costs for families, invest in American manufacturing, and provide military, economic, and humanitarian assistance to Ukraine. He calls for immigration reform, protecting the rights of women, and advancing the rights of LGBTQ+ Americans, and pays tribute to military families. He concludes with optimism for the future of America.\"Question Answering\u00e2\u20ac\u2039Let's take a look at this using a question answering chain.from langchain.chains.question_answering import load_qa_chainqa_chain = load_qa_chain(llm, chain_type=\"map_reduce\")qa_document_chain = AnalyzeDocumentChain(combine_docs_chain=qa_chain)qa_document_chain.run(input_document=state_of_the_union, question=\"what did the president say about justice breyer?\")    ' The president thanked Justice Breyer for his service.'PreviousAdditionalNextSelf-critique chain with constitutional AICommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/additional/analyze_document"}
{"id": "df6f8cf2f7a8-0", "text": "NebulaGraphQAChain | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_nebula_qa"}
{"id": "df6f8cf2f7a8-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalDocumentsPopularAdditionalAnalyze DocumentSelf-critique chain with constitutional AICausal program-aided language (CPAL) chainElasticsearch databaseExtractionFLAREArangoDB QA chainGraph DB QA chainHugeGraph QA ChainKuzuQAChainNebulaGraphQAChainGraph QAGraphSparqlQAChainHypothetical Document EmbeddingsBash chainSelf-checking chainMath chainHTTP request chainSummarization checker chainLLM Symbolic MathModerationDynamically selecting from multiple promptsDynamically selecting from multiple retrieversNeptune Open Cypher QA ChainRetrieval QA using OpenAI functionsOpenAPI chainOpenAPI calls with OpenAI functionsProgram-aided language model (PAL) chainQuestion-Answering CitationsDocument QATaggingVector store-augmented text generationMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsAdditionalNebulaGraphQAChainOn this pageNebulaGraphQAChainThis notebook shows how to use LLMs to provide a natural language interface to NebulaGraph database.You will need to have a running NebulaGraph cluster, for which you can run a containerized cluster by running the following script:curl -fsSL nebula-up.siwei.io/install.sh | bashOther options are:Install as a Docker Desktop Extension. See hereNebulaGraph Cloud Service. See hereDeploy from package, source code, or via Kubernetes. See hereOnce the cluster is running, we could create the SPACE and SCHEMA for the database.# connect ngql jupyter extension to nebulagraph# create a new space%ngql CREATE SPACE IF NOT EXISTS langchain(partition_num=1,", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_nebula_qa"}
{"id": "df6f8cf2f7a8-2", "text": "create a new space%ngql CREATE SPACE IF NOT EXISTS langchain(partition_num=1, replica_factor=1, vid_type=fixed_string(128));# Wait for a few seconds for the space to be created.%ngql USE langchain;Create the schema, for full dataset, refer here.CREATE TAG IF NOT EXISTS movie(name string);CREATE TAG IF NOT EXISTS person(name string, birthdate string);CREATE EDGE IF NOT EXISTS acted_in();CREATE TAG INDEX IF NOT EXISTS person_index ON person(name(128));CREATE TAG INDEX IF NOT EXISTS movie_index ON movie(name(128));Wait for schema creation to complete, then we can insert some data.INSERT VERTEX person(name, birthdate) VALUES \"Al Pacino\":(\"Al Pacino\", \"1940-04-25\");INSERT VERTEX movie(name) VALUES \"The Godfather II\":(\"The Godfather II\");INSERT VERTEX movie(name) VALUES \"The Godfather Coda: The Death of Michael Corleone\":(\"The Godfather Coda: The Death of Michael Corleone\");INSERT EDGE acted_in() VALUES \"Al Pacino\"->\"The Godfather II\":();INSERT EDGE acted_in() VALUES \"Al Pacino\"->\"The Godfather Coda: The Death of Michael Corleone\":();    UsageError: Cell magic `%%ngql` not found.from langchain.chat_models import ChatOpenAIfrom langchain.chains import NebulaGraphQAChainfrom langchain.graphs import NebulaGraphgraph = NebulaGraph(    space=\"langchain\",    username=\"root\",    password=\"nebula\",    address=\"127.0.0.1\",    port=9669,    session_pool_size=30,)Refresh graph schema information\u00e2\u20ac\u2039If the schema of database changes, you can refresh the schema information needed to generate nGQL statements.#", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_nebula_qa"}
{"id": "df6f8cf2f7a8-3", "text": "the schema of database changes, you can refresh the schema information needed to generate nGQL statements.# graph.refresh_schema()print(graph.get_schema)    Node properties: [{'tag': 'movie', 'properties': [('name', 'string')]}, {'tag': 'person', 'properties': [('name', 'string'), ('birthdate', 'string')]}]    Edge properties: [{'edge': 'acted_in', 'properties': []}]    Relationships: ['(:person)-[:acted_in]->(:movie)']    Querying the graph\u00e2\u20ac\u2039We can now use the graph cypher QA chain to ask question of the graphchain = NebulaGraphQAChain.from_llm(    ChatOpenAI(temperature=0), graph=graph, verbose=True)chain.run(\"Who played in The Godfather II?\")            > Entering new NebulaGraphQAChain chain...    Generated nGQL:    MATCH (p:`person`)-[:acted_in]->(m:`movie`) WHERE m.`movie`.`name` == 'The Godfather II'    RETURN p.`person`.`name`    Full Context:    {'p.person.name': ['Al Pacino']}        > Finished chain.    'Al Pacino played in The Godfather II.'PreviousKuzuQAChainNextGraph QARefresh graph schema informationQuerying the graphCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_nebula_qa"}
{"id": "cac36929acf4-0", "text": "FLARE | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/additional/flare"}
{"id": "cac36929acf4-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalDocumentsPopularAdditionalAnalyze DocumentSelf-critique chain with constitutional AICausal program-aided language (CPAL) chainElasticsearch databaseExtractionFLAREArangoDB QA chainGraph DB QA chainHugeGraph QA ChainKuzuQAChainNebulaGraphQAChainGraph QAGraphSparqlQAChainHypothetical Document EmbeddingsBash chainSelf-checking chainMath chainHTTP request chainSummarization checker chainLLM Symbolic MathModerationDynamically selecting from multiple promptsDynamically selecting from multiple retrieversNeptune Open Cypher QA ChainRetrieval QA using OpenAI functionsOpenAPI chainOpenAPI calls with OpenAI functionsProgram-aided language model (PAL) chainQuestion-Answering CitationsDocument QATaggingVector store-augmented text generationMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsAdditionalFLAREOn this pageFLAREThis notebook is an implementation of Forward-Looking Active REtrieval augmented generation (FLARE).Please see the original repo here.The basic idea is:Start answering a questionIf you start generating tokens the model is uncertain about, look up relevant documentsUse those documents to continue generatingRepeat until finishedThere is a lot of cool detail in how the lookup of relevant documents is done.", "source": "https://python.langchain.com/docs/modules/chains/additional/flare"}
{"id": "cac36929acf4-2", "text": "Basically, the tokens that model is uncertain about are highlighted, and then an LLM is called to generate a question that would lead to that answer. For example, if the generated text is Joe Biden went to Harvard, and the tokens the model was uncertain about was Harvard, then a good generated question would be where did Joe Biden go to college. This generated question is then used in a retrieval step to fetch relevant documents.In order to set up this chain, we will need three things:An LLM to generate the answerAn LLM to generate hypothetical questions to use in retrievalA retriever to use to look up answers forThe LLM that we use to generate the answer needs to return logprobs so we can identify uncertain tokens. For that reason, we HIGHLY recommend that you use the OpenAI wrapper (NB: not the ChatOpenAI wrapper, as that does not return logprobs).The LLM we use to generate hypothetical questions to use in retrieval can be anything. In this notebook we will use ChatOpenAI because it is fast and cheap.The retriever can be anything. In this notebook we will use SERPER search engine, because it is cheap.Other important parameters to understand:max_generation_len: The maximum number of tokens to generate before stopping to check if any are uncertainmin_prob: Any tokens generated with probability below this will be considered uncertainImports\u00e2\u20ac\u2039import osos.environ[\"SERPER_API_KEY\"] = \"\"os.environ[\"OPENAI_API_KEY\"] = \"\"import reimport numpy as npfrom langchain.schema import BaseRetrieverfrom langchain.callbacks.manager import (    AsyncCallbackManagerForRetrieverRun,    CallbackManagerForRetrieverRun,)from langchain.utilities import GoogleSerperAPIWrapperfrom langchain.embeddings import OpenAIEmbeddingsfrom langchain.chat_models import ChatOpenAIfrom langchain.llms import OpenAIfrom langchain.schema import Documentfrom typing import Any,", "source": "https://python.langchain.com/docs/modules/chains/additional/flare"}
{"id": "cac36929acf4-3", "text": "langchain.llms import OpenAIfrom langchain.schema import Documentfrom typing import Any, ListRetriever\u00e2\u20ac\u2039class SerperSearchRetriever(BaseRetriever):    search: GoogleSerperAPIWrapper = None    def _get_relevant_documents(        self, query: str, *, run_manager: CallbackManagerForRetrieverRun, **kwargs: Any    ) -> List[Document]:        return [Document(page_content=self.search.run(query))]    async def _aget_relevant_documents(        self,        query: str,        *,        run_manager: AsyncCallbackManagerForRetrieverRun,        **kwargs: Any,    ) -> List[Document]:        raise NotImplementedError()retriever = SerperSearchRetriever(search=GoogleSerperAPIWrapper())FLARE Chain\u00e2\u20ac\u2039# We set this so we can see what exactly is going onimport langchainlangchain.verbose = Truefrom langchain.chains import FlareChainflare = FlareChain.from_llm(    ChatOpenAI(temperature=0),    retriever=retriever,    max_generation_len=164,    min_prob=0.3,)query = \"explain in great detail the difference between the langchain framework and baby agi\"flare.run(query)            > Entering new FlareChain chain...    Current Response:     Prompt after formatting:    Respond to the user message using any relevant context. If context is provided, you should ground your answer in that context. Once you're done responding return FINISHED.", "source": "https://python.langchain.com/docs/modules/chains/additional/flare"}
{"id": "cac36929acf4-4", "text": "you should ground your answer in that context. Once you're done responding return FINISHED.        >>> CONTEXT:     >>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi    >>> RESPONSE:             > Entering new QuestionGeneratorChain chain...    Prompt after formatting:    Given a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase:        >>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi    >>> EXISTING PARTIAL RESPONSE:      The Langchain Framework is a decentralized platform for natural language processing (NLP) applications. It uses a blockchain-based distributed ledger to store and process data, allowing for secure and transparent data sharing. The Langchain Framework also provides a set of tools and services to help developers create and deploy NLP applications.        Baby AGI, on the other hand, is an artificial general intelligence (AGI) platform. It uses a combination of deep learning and reinforcement learning to create an AI system that can learn and adapt to new tasks. Baby AGI is designed to be a general-purpose AI system that can be used for a variety of applications, including natural language processing.        In summary, the Langchain Framework is a platform for NLP applications, while Baby AGI is an AI system designed for        The question to which the answer is the term/entity/phrase \" decentralized platform for natural language processing\" is:    Prompt after formatting:    Given a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase:", "source": "https://python.langchain.com/docs/modules/chains/additional/flare"}
{"id": "cac36929acf4-5", "text": "a question to which the answer is the given term/entity/phrase:        >>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi    >>> EXISTING PARTIAL RESPONSE:      The Langchain Framework is a decentralized platform for natural language processing (NLP) applications. It uses a blockchain-based distributed ledger to store and process data, allowing for secure and transparent data sharing. The Langchain Framework also provides a set of tools and services to help developers create and deploy NLP applications.        Baby AGI, on the other hand, is an artificial general intelligence (AGI) platform. It uses a combination of deep learning and reinforcement learning to create an AI system that can learn and adapt to new tasks. Baby AGI is designed to be a general-purpose AI system that can be used for a variety of applications, including natural language processing.        In summary, the Langchain Framework is a platform for NLP applications, while Baby AGI is an AI system designed for        The question to which the answer is the term/entity/phrase \" uses a blockchain\" is:    Prompt after formatting:    Given a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase:        >>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi    >>> EXISTING PARTIAL RESPONSE:      The Langchain Framework is a decentralized platform for natural language processing (NLP) applications. It uses a blockchain-based distributed ledger to store and process data, allowing for secure and transparent data sharing. The Langchain Framework also provides a set of tools and services to help developers create and deploy NLP applications.", "source": "https://python.langchain.com/docs/modules/chains/additional/flare"}
{"id": "cac36929acf4-6", "text": "tools and services to help developers create and deploy NLP applications.        Baby AGI, on the other hand, is an artificial general intelligence (AGI) platform. It uses a combination of deep learning and reinforcement learning to create an AI system that can learn and adapt to new tasks. Baby AGI is designed to be a general-purpose AI system that can be used for a variety of applications, including natural language processing.        In summary, the Langchain Framework is a platform for NLP applications, while Baby AGI is an AI system designed for        The question to which the answer is the term/entity/phrase \" distributed ledger to\" is:    Prompt after formatting:    Given a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase:        >>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi    >>> EXISTING PARTIAL RESPONSE:      The Langchain Framework is a decentralized platform for natural language processing (NLP) applications. It uses a blockchain-based distributed ledger to store and process data, allowing for secure and transparent data sharing. The Langchain Framework also provides a set of tools and services to help developers create and deploy NLP applications.        Baby AGI, on the other hand, is an artificial general intelligence (AGI) platform. It uses a combination of deep learning and reinforcement learning to create an AI system that can learn and adapt to new tasks. Baby AGI is designed to be a general-purpose AI system that can be used for a variety of applications, including natural language processing.        In summary, the Langchain Framework is a platform for NLP applications, while Baby AGI is an AI system designed for", "source": "https://python.langchain.com/docs/modules/chains/additional/flare"}
{"id": "cac36929acf4-7", "text": "Framework is a platform for NLP applications, while Baby AGI is an AI system designed for        The question to which the answer is the term/entity/phrase \" process data, allowing for secure and transparent data sharing.\" is:    Prompt after formatting:    Given a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase:        >>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi    >>> EXISTING PARTIAL RESPONSE:      The Langchain Framework is a decentralized platform for natural language processing (NLP) applications. It uses a blockchain-based distributed ledger to store and process data, allowing for secure and transparent data sharing. The Langchain Framework also provides a set of tools and services to help developers create and deploy NLP applications.        Baby AGI, on the other hand, is an artificial general intelligence (AGI) platform. It uses a combination of deep learning and reinforcement learning to create an AI system that can learn and adapt to new tasks. Baby AGI is designed to be a general-purpose AI system that can be used for a variety of applications, including natural language processing.        In summary, the Langchain Framework is a platform for NLP applications, while Baby AGI is an AI system designed for        The question to which the answer is the term/entity/phrase \" set of tools\" is:    Prompt after formatting:    Given a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase:        >>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi    >>> EXISTING PARTIAL", "source": "https://python.langchain.com/docs/modules/chains/additional/flare"}
{"id": "cac36929acf4-8", "text": "the difference between the langchain framework and baby agi    >>> EXISTING PARTIAL RESPONSE:      The Langchain Framework is a decentralized platform for natural language processing (NLP) applications. It uses a blockchain-based distributed ledger to store and process data, allowing for secure and transparent data sharing. The Langchain Framework also provides a set of tools and services to help developers create and deploy NLP applications.        Baby AGI, on the other hand, is an artificial general intelligence (AGI) platform. It uses a combination of deep learning and reinforcement learning to create an AI system that can learn and adapt to new tasks. Baby AGI is designed to be a general-purpose AI system that can be used for a variety of applications, including natural language processing.        In summary, the Langchain Framework is a platform for NLP applications, while Baby AGI is an AI system designed for        The question to which the answer is the term/entity/phrase \" help developers create\" is:    Prompt after formatting:    Given a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase:        >>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi    >>> EXISTING PARTIAL RESPONSE:      The Langchain Framework is a decentralized platform for natural language processing (NLP) applications. It uses a blockchain-based distributed ledger to store and process data, allowing for secure and transparent data sharing. The Langchain Framework also provides a set of tools and services to help developers create and deploy NLP applications.        Baby AGI, on the other hand, is an artificial general intelligence (AGI) platform. It uses a combination of deep learning and reinforcement", "source": "https://python.langchain.com/docs/modules/chains/additional/flare"}
{"id": "cac36929acf4-9", "text": "is an artificial general intelligence (AGI) platform. It uses a combination of deep learning and reinforcement learning to create an AI system that can learn and adapt to new tasks. Baby AGI is designed to be a general-purpose AI system that can be used for a variety of applications, including natural language processing.        In summary, the Langchain Framework is a platform for NLP applications, while Baby AGI is an AI system designed for        The question to which the answer is the term/entity/phrase \" create an AI system\" is:    Prompt after formatting:    Given a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase:        >>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi    >>> EXISTING PARTIAL RESPONSE:      The Langchain Framework is a decentralized platform for natural language processing (NLP) applications. It uses a blockchain-based distributed ledger to store and process data, allowing for secure and transparent data sharing. The Langchain Framework also provides a set of tools and services to help developers create and deploy NLP applications.        Baby AGI, on the other hand, is an artificial general intelligence (AGI) platform. It uses a combination of deep learning and reinforcement learning to create an AI system that can learn and adapt to new tasks. Baby AGI is designed to be a general-purpose AI system that can be used for a variety of applications, including natural language processing.        In summary, the Langchain Framework is a platform for NLP applications, while Baby AGI is an AI system designed for        The question to which the answer is the term/entity/phrase \" NLP applications\" is:", "source": "https://python.langchain.com/docs/modules/chains/additional/flare"}
{"id": "cac36929acf4-10", "text": "to which the answer is the term/entity/phrase \" NLP applications\" is:        > Finished chain.    Generated Questions: ['What is the Langchain Framework?', 'What technology does the Langchain Framework use to store and process data for secure and transparent data sharing?', 'What technology does the Langchain Framework use to store and process data?', 'What does the Langchain Framework use a blockchain-based distributed ledger for?', 'What does the Langchain Framework provide in addition to a decentralized platform for natural language processing applications?', 'What set of tools and services does the Langchain Framework provide?', 'What is the purpose of Baby AGI?', 'What type of applications is the Langchain Framework designed for?']            > Entering new _OpenAIResponseChain chain...    Prompt after formatting:    Respond to the user message using any relevant context. If context is provided, you should ground your answer in that context. Once you're done responding return FINISHED.        >>> CONTEXT: LangChain: Software. LangChain is a software development framework designed to simplify the creation of applications using large language models. LangChain Initial release date: October 2022. LangChain Programming languages: Python and JavaScript. LangChain Developer(s): Harrison Chase. LangChain License: MIT License. LangChain is a framework for developing applications powered by language models. We believe that the most powerful and differentiated applications will not only ... Type: Software framework. At its core, LangChain is a framework built around LLMs. We can use it for chatbots, Generative Question-Answering (GQA), summarization, and much more. LangChain is a powerful tool that can be used to work with Large Language Models (LLMs). LLMs are very general in nature, which means that while they can ... LangChain is an intuitive framework created to", "source": "https://python.langchain.com/docs/modules/chains/additional/flare"}
{"id": "cac36929acf4-11", "text": "very general in nature, which means that while they can ... LangChain is an intuitive framework created to assist in developing applications driven by a language model, such as OpenAI or Hugging Face. LangChain is a software development framework designed to simplify the creation of applications using large language models (LLMs). Written in: Python and JavaScript. Initial release: October 2022. LangChain - The A.I-native developer toolkit We started LangChain with the intent to build a modular and flexible framework for developing A.I- ... LangChain explained in 3 minutes - LangChain is a ... Duration: 3:03. Posted: Apr 13, 2023. LangChain is a framework built to help you build LLM-powered applications more easily by providing you with the following:. LangChain is a framework that enables quick and easy development of applications that make use of Large Language Models, for example, GPT-3. LangChain is a powerful open-source framework for developing applications powered by language models. It connects to the AI models you want to ...        LangChain is a framework for including AI from large language models inside data pipelines and applications. This tutorial provides an overview of what you ... Missing: secure | Must include:secure. Blockchain is the best way to secure the data of the shared community. Utilizing the capabilities of the blockchain nobody can read or interfere ... This modern technology consists of a chain of blocks that allows to securely store all committed transactions using shared and distributed ... A Blockchain network is used in the healthcare system to preserve and exchange patient data through hospitals, diagnostic laboratories, pharmacy firms, and ... In this article, I will walk you through the process of using the LangChain.js library with Google Cloud Functions, helping you leverage the ... LangChain is an intuitive framework created to assist in developing applications driven by a language model, such as OpenAI or Hugging Face. Missing: transparent | Must include:transparent. This technology keeps a distributed", "source": "https://python.langchain.com/docs/modules/chains/additional/flare"}
{"id": "cac36929acf4-12", "text": "or Hugging Face. Missing: transparent | Must include:transparent. This technology keeps a distributed ledger on each blockchain node, making it more secure and transparent. The blockchain network can operate smart ... blockchain technology can offer a highly secured health data ledger to ... framework can be employed to store encrypted healthcare data in a ... In a simplified way, Blockchain is a data structure that stores transactions in an ordered way and linked to the previous block, serving as a ... Blockchain technology is a decentralized, distributed ledger that stores the record of ownership of digital assets. Missing: Langchain | Must include:Langchain.        LangChain is a framework for including AI from large language models inside data pipelines and applications. This tutorial provides an overview of what you ... LangChain is an intuitive framework created to assist in developing applications driven by a language model, such as OpenAI or Hugging Face. This documentation covers the steps to integrate Pinecone, a high-performance vector database, with LangChain, a framework for building applications powered ... The ability to connect to any model, ingest any custom database, and build upon a framework that can take action provides numerous use cases for ... With LangChain, developers can use a framework that abstracts the core building blocks of LLM applications. LangChain empowers developers to ... Build a question-answering tool based on financial data with LangChain & Deep Lake's unified & streamable data store. Browse applications built on LangChain technology. Explore PoC and MVP applications created by our community and discover innovative use cases for LangChain ... LangChain is a great framework that can be used for developing applications powered by LLMs. When you intend to enhance your application ... In this blog, we'll introduce you to LangChain and Ray Serve and how to use them to build a search engine using LLM embeddings and a vector ... The LinkChain Framework simplifies embedding creation and storage using Pinecone and Chroma, with code that loads files, splits", "source": "https://python.langchain.com/docs/modules/chains/additional/flare"}
{"id": "cac36929acf4-13", "text": "simplifies embedding creation and storage using Pinecone and Chroma, with code that loads files, splits documents, and creates embedding ... Missing: technology | Must include:technology.        Blockchain is one type of a distributed ledger. Distributed ledgers use independent computers (referred to as nodes) to record, share and ... Missing: Langchain | Must include:Langchain. Blockchain is used in distributed storage software where huge data is broken down into chunks. This is available in encrypted data across a ... People sometimes use the terms 'Blockchain' and 'Distributed Ledger' interchangeably. This post aims to analyze the features of each. A distributed ledger ... Missing: Framework | Must include:Framework. Think of a \u00e2\u20ac\u0153distributed ledger\u00e2\u20ac\ufffd that uses cryptography to allow each participant in the transaction to add to the ledger in a secure way without ... In this paper, we provide an overview of the history of trade settlement and discuss this nascent technology that may now transform traditional ... Missing: Langchain | Must include:Langchain. LangChain is a blockchain-based language education platform that aims to revolutionize the way people learn languages. Missing: Framework | Must include:Framework. It uses the distributed ledger technology framework and Smart contract engine for building scalable Business Blockchain applications. The fabric ... It looks at the assets the use case is handling, the different parties conducting transactions, and the smart contract, distributed ... Are you curious to know how Blockchain and Distributed ... Duration: 44:31. Posted: May 4, 2021. A blockchain is a distributed and immutable ledger to transfer ownership, record transactions, track assets, and ensure transparency, security, trust and value ... Missing: Langchain | Must include:Langchain.        LangChain is an intuitive framework created to assist in developing applications driven by a language model, such as OpenAI or Hugging Face. Missing: decentralized | Must include:decentralized.", "source": "https://python.langchain.com/docs/modules/chains/additional/flare"}
{"id": "cac36929acf4-14", "text": "such as OpenAI or Hugging Face. Missing: decentralized | Must include:decentralized. LangChain, created by Harrison Chase, is a Python library that provides out-of-the-box support to build NLP applications using LLMs. Missing: decentralized | Must include:decentralized. LangChain provides a standard interface for chains, enabling developers to create sequences of calls that go beyond a single LLM call. Chains ... Missing: decentralized platform natural. LangChain is a powerful framework that simplifies the process of building advanced language model applications. Missing: platform | Must include:platform. Are your language models ignoring previous instructions ... Duration: 32:23. Posted: Feb 21, 2023. LangChain is a framework that enables quick and easy development of applications ... Prompting is the new way of programming NLP models. Missing: decentralized platform. It then uses natural language processing and machine learning algorithms to search ... Summarization is handled via cohere, QnA is handled via langchain, ... LangChain is a framework for developing applications powered by language models. ... There are several main modules that LangChain provides support for. Missing: decentralized platform. In the healthcare-chain system, blockchain provides an appreciated secure ... The entire process of adding new and previous block data is performed based on ... ChatGPT is a large language model developed by OpenAI, ... tool for a wide range of applications, including natural language processing, ...        LangChain is a powerful tool that can be used to work with Large Language ... If an API key has been provided, create an OpenAI language model instance At its core, LangChain is a framework built around LLMs. We can use it for chatbots, Generative Question-Answering (GQA), summarization, and much more. A tutorial of the six core modules of the LangChain Python package covering models, prompts, chains, agents, indexes, and memory with", "source": "https://python.langchain.com/docs/modules/chains/additional/flare"}
{"id": "cac36929acf4-15", "text": "of the LangChain Python package covering models, prompts, chains, agents, indexes, and memory with OpenAI ... LangChain's collection of tools refers to a set of tools provided by the LangChain framework for developing applications powered by language models. LangChain is a framework for developing applications powered by language models. We believe that the most powerful and differentiated applications will not only ... LangChain is an open-source library that provides developers with the tools to build applications powered by large language models (LLMs). LangChain is a framework for including AI from large language models inside data pipelines and applications. This tutorial provides an overview of what you ... Plan-and-Execute Agents \u00c2\u00b7 Feature Stores and LLMs \u00c2\u00b7 Structured Tools \u00c2\u00b7 Auto-Evaluator Opportunities \u00c2\u00b7 Callbacks Improvements \u00c2\u00b7 Unleashing the power ... Tool: A function that performs a specific duty. This can be things like: Google Search, Database lookup, Python REPL, other chains. \u00c2\u00b7 LLM: The language model ... LangChain provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications.        Baby AGI has the ability to complete tasks, generate new tasks based on previous results, and prioritize tasks in real-time. This system is exploring and demonstrating to us the potential of large language models, such as GPT and how it can autonomously perform tasks. Apr 17, 2023        At its core, LangChain is a framework built around LLMs. We can use it for chatbots, Generative Question-Answering (GQA), summarization, and much more. The core idea of the library is that we can \u00e2\u20ac\u0153chain\u00e2\u20ac\ufffd together different components to create more advanced use cases around LLMs.    >>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi    >>>", "source": "https://python.langchain.com/docs/modules/chains/additional/flare"}
{"id": "cac36929acf4-16", "text": "explain in great detail the difference between the langchain framework and baby agi    >>> RESPONSE:         > Finished chain.        > Finished chain.    ' LangChain is a framework for developing applications powered by language models. It provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications. On the other hand, Baby AGI is an AI system that is exploring and demonstrating the potential of large language models, such as GPT, and how it can autonomously perform tasks. Baby AGI has the ability to complete tasks, generate new tasks based on previous results, and prioritize tasks in real-time. 'llm = OpenAI()llm(query)    '\\n\\nThe Langchain framework and Baby AGI are both artificial intelligence (AI) frameworks that are used to create intelligent agents. The Langchain framework is a supervised learning system that is based on the concept of \u00e2\u20ac\u0153language chains\u00e2\u20ac\ufffd. It uses a set of rules to map natural language inputs to specific outputs. It is a general-purpose AI framework and can be used to build applications such as natural language processing (NLP), chatbots, and more.\\n\\nBaby AGI, on the other hand, is an unsupervised learning system that uses neural networks and reinforcement learning to learn from its environment. It is used to create intelligent agents that can adapt to changing environments. It is a more advanced AI system and can be used to build more complex applications such as game playing, robotic vision, and more.\\n\\nThe main difference between the two is that the Langchain framework uses supervised learning while Baby AGI uses unsupervised learning. The Langchain framework is a general-purpose AI framework that can be used for various applications, while Baby AGI is a more advanced AI system that can be used to create more complex applications.'flare.run(\"how", "source": "https://python.langchain.com/docs/modules/chains/additional/flare"}
{"id": "cac36929acf4-17", "text": "is a more advanced AI system that can be used to create more complex applications.'flare.run(\"how are the origin stories of langchain and bitcoin similar or different?\")            > Entering new FlareChain chain...    Current Response:     Prompt after formatting:    Respond to the user message using any relevant context. If context is provided, you should ground your answer in that context. Once you're done responding return FINISHED.        >>> CONTEXT:     >>> USER INPUT: how are the origin stories of langchain and bitcoin similar or different?    >>> RESPONSE:             > Entering new QuestionGeneratorChain chain...    Prompt after formatting:    Given a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase:        >>> USER INPUT: how are the origin stories of langchain and bitcoin similar or different?    >>> EXISTING PARTIAL RESPONSE:          Langchain and Bitcoin have very different origin stories. Bitcoin was created by the mysterious Satoshi Nakamoto in 2008 as a decentralized digital currency. Langchain, on the other hand, was created in 2020 by a team of developers as a platform for creating and managing decentralized language learning applications.         FINISHED        The question to which the answer is the term/entity/phrase \" very different origin\" is:    Prompt after formatting:    Given a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase:        >>> USER INPUT: how are the origin stories of langchain and bitcoin similar", "source": "https://python.langchain.com/docs/modules/chains/additional/flare"}
{"id": "cac36929acf4-18", "text": ">>> USER INPUT: how are the origin stories of langchain and bitcoin similar or different?    >>> EXISTING PARTIAL RESPONSE:          Langchain and Bitcoin have very different origin stories. Bitcoin was created by the mysterious Satoshi Nakamoto in 2008 as a decentralized digital currency. Langchain, on the other hand, was created in 2020 by a team of developers as a platform for creating and managing decentralized language learning applications.         FINISHED        The question to which the answer is the term/entity/phrase \" 2020 by a\" is:    Prompt after formatting:    Given a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase:        >>> USER INPUT: how are the origin stories of langchain and bitcoin similar or different?    >>> EXISTING PARTIAL RESPONSE:          Langchain and Bitcoin have very different origin stories. Bitcoin was created by the mysterious Satoshi Nakamoto in 2008 as a decentralized digital currency. Langchain, on the other hand, was created in 2020 by a team of developers as a platform for creating and managing decentralized language learning applications.         FINISHED        The question to which the answer is the term/entity/phrase \" developers as a platform for creating and managing decentralized language learning applications.\" is:        > Finished chain.    Generated Questions: ['How would you describe the origin stories of Langchain and Bitcoin in terms of their similarities or differences?', 'When was Langchain created and by whom?', 'What was the purpose of creating Langchain?']            >", "source": "https://python.langchain.com/docs/modules/chains/additional/flare"}
{"id": "cac36929acf4-19", "text": "the purpose of creating Langchain?']            > Entering new _OpenAIResponseChain chain...    Prompt after formatting:    Respond to the user message using any relevant context. If context is provided, you should ground your answer in that context. Once you're done responding return FINISHED.        >>> CONTEXT: Bitcoin and Ethereum have many similarities but different long-term visions and limitations. Ethereum changed from proof of work to proof of ... Bitcoin will be around for many years and examining its white paper origins is a great exercise in understanding why. Satoshi Nakamoto's blueprint describes ... Bitcoin is a new currency that was created in 2009 by an unknown person using the alias Satoshi Nakamoto. Transactions are made with no middle men \u00e2\u20ac\u201c meaning, no ... Missing: Langchain | Must include:Langchain. By comparison, Bitcoin transaction speeds are tremendously lower. ... learn about its history and its role in the emergence of the Bitcoin ... LangChain is a powerful framework that simplifies the process of ... tasks like document retrieval, clustering, and similarity comparisons. Key terms: Bitcoin System, Blockchain Technology, ... Furthermore, the research paper will discuss and compare the five payment. Blockchain first appeared in Nakamoto's Bitcoin white paper that describes a new decentralized cryptocurrency [1]. Bitcoin takes the blockchain technology ... Missing: stories | Must include:stories. A score of 0 means there were not enough data for this term. Google trends was accessed on 5 November 2018 with searches for bitcoin, euro, gold ... Contracts, transactions, and records of them provide critical structure in our economic system, but they haven't kept up with the world's digital ... Missing: Langchain | Must include:Langchain. Of course, traders try to make a profit on their portfolio in this way.The difference between investing and trading is the regularity with which ...        After all these", "source": "https://python.langchain.com/docs/modules/chains/additional/flare"}
{"id": "cac36929acf4-20", "text": "investing and trading is the regularity with which ...        After all these giant leaps forward in the LLM space, OpenAI released ChatGPT \u00e2\u20ac\u201d thrusting LLMs into the spotlight. LangChain appeared around the same time. Its creator, Harrison Chase, made the first commit in late October 2022. Leaving a short couple of months of development before getting caught in the LLM wave.        At its core, LangChain is a framework built around LLMs. We can use it for chatbots, Generative Question-Answering (GQA), summarization, and much more. The core idea of the library is that we can \u00e2\u20ac\u0153chain\u00e2\u20ac\ufffd together different components to create more advanced use cases around LLMs.    >>> USER INPUT: how are the origin stories of langchain and bitcoin similar or different?    >>> RESPONSE:         > Finished chain.        > Finished chain.    ' The origin stories of LangChain and Bitcoin are quite different. Bitcoin was created in 2009 by an unknown person using the alias Satoshi Nakamoto. LangChain was created in late October 2022 by Harrison Chase. Bitcoin is a decentralized cryptocurrency, while LangChain is a framework built around LLMs. 'PreviousExtractionNextArangoDB QA chainImportsRetrieverFLARE ChainCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/additional/flare"}
{"id": "089ad6b802c4-0", "text": "Dynamically selecting from multiple retrievers | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/additional/multi_retrieval_qa_router"}
{"id": "089ad6b802c4-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalDocumentsPopularAdditionalAnalyze DocumentSelf-critique chain with constitutional AICausal program-aided language (CPAL) chainElasticsearch databaseExtractionFLAREArangoDB QA chainGraph DB QA chainHugeGraph QA ChainKuzuQAChainNebulaGraphQAChainGraph QAGraphSparqlQAChainHypothetical Document EmbeddingsBash chainSelf-checking chainMath chainHTTP request chainSummarization checker chainLLM Symbolic MathModerationDynamically selecting from multiple promptsDynamically selecting from multiple retrieversNeptune Open Cypher QA ChainRetrieval QA using OpenAI functionsOpenAPI chainOpenAPI calls with OpenAI functionsProgram-aided language model (PAL) chainQuestion-Answering CitationsDocument QATaggingVector store-augmented text generationMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsAdditionalDynamically selecting from multiple retrieversDynamically selecting from multiple retrieversThis notebook demonstrates how to use the RouterChain paradigm to create a chain that dynamically selects which Retrieval system to use. Specifically we show how to use the MultiRetrievalQAChain to create a question-answering chain that selects the retrieval QA chain which is most relevant for a given question, and then answers the question using it.from langchain.chains.router import MultiRetrievalQAChainfrom langchain.llms import OpenAIfrom langchain.embeddings import OpenAIEmbeddingsfrom langchain.document_loaders import TextLoaderfrom langchain.vectorstores import FAISSsou_docs = TextLoader('../../state_of_the_union.txt').load_and_split()sou_retriever =", "source": "https://python.langchain.com/docs/modules/chains/additional/multi_retrieval_qa_router"}
{"id": "089ad6b802c4-2", "text": "= TextLoader('../../state_of_the_union.txt').load_and_split()sou_retriever = FAISS.from_documents(sou_docs, OpenAIEmbeddings()).as_retriever()pg_docs = TextLoader('../../paul_graham_essay.txt').load_and_split()pg_retriever = FAISS.from_documents(pg_docs, OpenAIEmbeddings()).as_retriever()personal_texts = [    \"I love apple pie\",    \"My favorite color is fuchsia\",    \"My dream is to become a professional dancer\",    \"I broke my arm when I was 12\",    \"My parents are from Peru\",]personal_retriever = FAISS.from_texts(personal_texts, OpenAIEmbeddings()).as_retriever()retriever_infos = [    {        \"name\": \"state of the union\",         \"description\": \"Good for answering questions about the 2023 State of the Union address\",         \"retriever\": sou_retriever    },    {        \"name\": \"pg essay\",         \"description\": \"Good for answering questions about Paul Graham's essay on his career\",        \"retriever\": pg_retriever    },    {        \"name\": \"personal\",         \"description\": \"Good for answering questions about me\",         \"retriever\": personal_retriever    }]chain = MultiRetrievalQAChain.from_retrievers(OpenAI(), retriever_infos, verbose=True)print(chain.run(\"What did the president say about the economy?\"))", "source": "https://python.langchain.com/docs/modules/chains/additional/multi_retrieval_qa_router"}
{"id": "089ad6b802c4-3", "text": "did the president say about the economy?\"))            > Entering new MultiRetrievalQAChain chain...    state of the union: {'query': 'What did the president say about the economy in the 2023 State of the Union address?'}    > Finished chain.     The president said that the economy was stronger than it had been a year prior, and that the American Rescue Plan helped create record job growth and fuel economic relief for millions of Americans. He also proposed a plan to fight inflation and lower costs for families, including cutting the cost of prescription drugs and energy, providing investments and tax credits for energy efficiency, and increasing access to child care and Pre-K.print(chain.run(\"What is something Paul Graham regrets about his work?\"))            > Entering new MultiRetrievalQAChain chain...    pg essay: {'query': 'What is something Paul Graham regrets about his work?'}    > Finished chain.     Paul Graham regrets that he did not take a vacation after selling his company, instead of immediately starting to paint.print(chain.run(\"What is my background?\"))            > Entering new MultiRetrievalQAChain chain...    personal: {'query': 'What is my background?'}    > Finished chain.     Your background is Peruvian.print(chain.run(\"What year was the Internet created in?\"))            > Entering new MultiRetrievalQAChain chain...    None: {'query': 'What year was the Internet created in?'}    > Finished chain.    The Internet was created in 1969 through a project called ARPANET, which was funded by the United States Department of Defense.", "source": "https://python.langchain.com/docs/modules/chains/additional/multi_retrieval_qa_router"}
{"id": "089ad6b802c4-4", "text": "through a project called ARPANET, which was funded by the United States Department of Defense. However, the World Wide Web, which is often confused with the Internet, was created in 1989 by British computer scientist Tim Berners-Lee.PreviousDynamically selecting from multiple promptsNextNeptune Open Cypher QA ChainCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/additional/multi_retrieval_qa_router"}
{"id": "659f8da64dad-0", "text": "HugeGraph QA Chain | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_hugegraph_qa"}
{"id": "659f8da64dad-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalDocumentsPopularAdditionalAnalyze DocumentSelf-critique chain with constitutional AICausal program-aided language (CPAL) chainElasticsearch databaseExtractionFLAREArangoDB QA chainGraph DB QA chainHugeGraph QA ChainKuzuQAChainNebulaGraphQAChainGraph QAGraphSparqlQAChainHypothetical Document EmbeddingsBash chainSelf-checking chainMath chainHTTP request chainSummarization checker chainLLM Symbolic MathModerationDynamically selecting from multiple promptsDynamically selecting from multiple retrieversNeptune Open Cypher QA ChainRetrieval QA using OpenAI functionsOpenAPI chainOpenAPI calls with OpenAI functionsProgram-aided language model (PAL) chainQuestion-Answering CitationsDocument QATaggingVector store-augmented text generationMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsAdditionalHugeGraph QA ChainOn this pageHugeGraph QA ChainThis notebook shows how to use LLMs to provide a natural language interface to HugeGraph database.You will need to have a running HugeGraph instance.", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_hugegraph_qa"}
{"id": "659f8da64dad-2", "text": "You can run a local docker container by running the executing the following script:docker run \\    --name=graph \\    -itd \\    -p 8080:8080 \\    hugegraph/hugegraphIf we want to connect HugeGraph in the application, we need to install python sdk:pip3 install hugegraph-pythonIf you are using the docker container, you need to wait a couple of second for the database to start, and then we need create schema and write graph data for the database.from hugegraph.connection import PyHugeGraphclient = PyHugeGraph(\"localhost\", \"8080\", user=\"admin\", pwd=\"admin\", graph=\"hugegraph\")First, we create the schema for a simple movie database:\"\"\"schema\"\"\"schema = client.schema()schema.propertyKey(\"name\").asText().ifNotExist().create()schema.propertyKey(\"birthDate\").asText().ifNotExist().create()schema.vertexLabel(\"Person\").properties(    \"name\", \"birthDate\").usePrimaryKeyId().primaryKeys(\"name\").ifNotExist().create()schema.vertexLabel(\"Movie\").properties(\"name\").usePrimaryKeyId().primaryKeys(    \"name\").ifNotExist().create()schema.edgeLabel(\"ActedIn\").sourceLabel(\"Person\").targetLabel(    \"Movie\").ifNotExist().create()    'create EdgeLabel success, Detail: \"b\\'{\"id\":1,\"name\":\"ActedIn\",\"source_label\":\"Person\",\"target_label\":\"Movie\",\"frequency\":\"SINGLE\",\"sort_keys\":[],\"nullable_keys\":[],\"index_labels\":[],\"properties\":[],\"status\":\"CREATED\",\"ttl\":0,\"enable_label_index\":true,\"user_data\":{\"~create_time\":\"2023-07-04 10:48:47.908\"}}\\'\"'Then we can insert some data.\"\"\"graph\"\"\"g = client.graph()g.addVertex(\"Person\",", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_hugegraph_qa"}
{"id": "659f8da64dad-3", "text": "we can insert some data.\"\"\"graph\"\"\"g = client.graph()g.addVertex(\"Person\", {\"name\": \"Al Pacino\", \"birthDate\": \"1940-04-25\"})g.addVertex(\"Person\", {\"name\": \"Robert De Niro\", \"birthDate\": \"1943-08-17\"})g.addVertex(\"Movie\", {\"name\": \"The Godfather\"})g.addVertex(\"Movie\", {\"name\": \"The Godfather Part II\"})g.addVertex(\"Movie\", {\"name\": \"The Godfather Coda The Death of Michael Corleone\"})g.addEdge(\"ActedIn\", \"1:Al Pacino\", \"2:The Godfather\", {})g.addEdge(\"ActedIn\", \"1:Al Pacino\", \"2:The Godfather Part II\", {})g.addEdge(    \"ActedIn\", \"1:Al Pacino\", \"2:The Godfather Coda The Death of Michael Corleone\", {})g.addEdge(\"ActedIn\", \"1:Robert De Niro\", \"2:The Godfather Part II\", {})    1:Robert De Niro--ActedIn-->2:The Godfather Part IICreating HugeGraphQAChain\u00e2\u20ac\u2039We can now create the HugeGraph and HugeGraphQAChain. To create the HugeGraph we simply need to pass the database object to the HugeGraph constructor.from langchain.chat_models import ChatOpenAIfrom langchain.chains import HugeGraphQAChainfrom langchain.graphs import HugeGraphgraph = HugeGraph(    username=\"admin\",    password=\"admin\",    address=\"localhost\",    port=8080,    graph=\"hugegraph\",)Refresh graph schema information\u00e2\u20ac\u2039If the schema of database changes, you can refresh the schema information needed to generate Gremlin statements.# graph.refresh_schema()print(graph.get_schema)    Node properties: [name:", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_hugegraph_qa"}
{"id": "659f8da64dad-4", "text": "statements.# graph.refresh_schema()print(graph.get_schema)    Node properties: [name: Person, primary_keys: ['name'], properties: ['name', 'birthDate'], name: Movie, primary_keys: ['name'], properties: ['name']]    Edge properties: [name: ActedIn, properties: []]    Relationships: ['Person--ActedIn-->Movie']    Querying the graph\u00e2\u20ac\u2039We can now use the graph Gremlin QA chain to ask question of the graphchain = HugeGraphQAChain.from_llm(ChatOpenAI(temperature=0), graph=graph, verbose=True)chain.run(\"Who played in The Godfather?\")            > Entering new  chain...    Generated gremlin:    g.V().has('Movie', 'name', 'The Godfather').in('ActedIn').valueMap(true)    Full Context:    [{'id': '1:Al Pacino', 'label': 'Person', 'name': ['Al Pacino'], 'birthDate': ['1940-04-25']}]        > Finished chain.    'Al Pacino played in The Godfather.'PreviousGraph DB QA chainNextKuzuQAChainCreating HugeGraphQAChainRefresh graph schema informationQuerying the graphCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/additional/graph_hugegraph_qa"}
{"id": "a22db2b26326-0", "text": "Dynamically selecting from multiple prompts | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/additional/multi_prompt_router"}
{"id": "a22db2b26326-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalDocumentsPopularAdditionalAnalyze DocumentSelf-critique chain with constitutional AICausal program-aided language (CPAL) chainElasticsearch databaseExtractionFLAREArangoDB QA chainGraph DB QA chainHugeGraph QA ChainKuzuQAChainNebulaGraphQAChainGraph QAGraphSparqlQAChainHypothetical Document EmbeddingsBash chainSelf-checking chainMath chainHTTP request chainSummarization checker chainLLM Symbolic MathModerationDynamically selecting from multiple promptsDynamically selecting from multiple retrieversNeptune Open Cypher QA ChainRetrieval QA using OpenAI functionsOpenAPI chainOpenAPI calls with OpenAI functionsProgram-aided language model (PAL) chainQuestion-Answering CitationsDocument QATaggingVector store-augmented text generationMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsAdditionalDynamically selecting from multiple promptsDynamically selecting from multiple promptsThis notebook demonstrates how to use the RouterChain paradigm to create a chain that dynamically selects the prompt to use for a given input. Specifically we show how to use the MultiPromptChain to create a question-answering chain that selects the prompt which is most relevant for a given question, and then answers the question using that prompt.from langchain.chains.router import MultiPromptChainfrom langchain.llms import OpenAIphysics_template = \"\"\"You are a very smart physics professor. \\You are great at answering questions about physics in a concise and easy to understand manner. \\When you don't know the answer to a question you admit that you don't know.Here is a question:{input}\"\"\"math_template = \"\"\"You are a", "source": "https://python.langchain.com/docs/modules/chains/additional/multi_prompt_router"}
{"id": "a22db2b26326-2", "text": "that you don't know.Here is a question:{input}\"\"\"math_template = \"\"\"You are a very good mathematician. You are great at answering math questions. \\You are so good because you are able to break down hard problems into their component parts, \\answer the component parts, and then put them together to answer the broader question.Here is a question:{input}\"\"\"prompt_infos = [    {        \"name\": \"physics\",         \"description\": \"Good for answering questions about physics\",         \"prompt_template\": physics_template    },    {        \"name\": \"math\",         \"description\": \"Good for answering math questions\",         \"prompt_template\": math_template    }]chain = MultiPromptChain.from_prompts(OpenAI(), prompt_infos, verbose=True)print(chain.run(\"What is black body radiation?\"))            > Entering new MultiPromptChain chain...    physics: {'input': 'What is black body radiation?'}    > Finished chain.            Black body radiation is the emission of electromagnetic radiation from a body due to its temperature. It is a type of thermal radiation that is emitted from the surface of all objects that are at a temperature above absolute zero. It is a spectrum of radiation that is influenced by the temperature of the body and is independent of the composition of the emitting material.print(chain.run(\"What is the first prime number greater than 40 such that one plus the prime number is divisible by 3\"))            > Entering new MultiPromptChain chain...    math: {'input': 'What is the first prime number greater", "source": "https://python.langchain.com/docs/modules/chains/additional/multi_prompt_router"}
{"id": "a22db2b26326-3", "text": "chain...    math: {'input': 'What is the first prime number greater than 40 such that one plus the prime number is divisible by 3'}    > Finished chain.    ?        The first prime number greater than 40 such that one plus the prime number is divisible by 3 is 43. To solve this problem, we can break down the question into two parts: finding the first prime number greater than 40, and then finding a number that is divisible by 3.         The first step is to find the first prime number greater than 40. A prime number is a number that is only divisible by 1 and itself. The next prime number after 40 is 41.        The second step is to find a number that is divisible by 3. To do this, we can add 1 to 41, which gives us 42. Now, we can check if 42 is divisible by 3. 42 divided by 3 is 14, so 42 is divisible by 3.        Therefore, the answer to the question is 43.print(chain.run(\"What is the name of the type of cloud that rins\"))            > Entering new MultiPromptChain chain...    None: {'input': 'What is the name of the type of cloud that rains?'}    > Finished chain.    The type of cloud that typically produces rain is called a cumulonimbus cloud. This type of cloud is characterized by its large vertical extent and can produce thunderstorms and heavy precipitation. Is there anything else you'd like to know?PreviousModerationNextDynamically selecting from multiple retrieversCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023", "source": "https://python.langchain.com/docs/modules/chains/additional/multi_prompt_router"}
{"id": "a22db2b26326-4", "text": "retrieversCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/additional/multi_prompt_router"}
{"id": "229ad3361e96-0", "text": "OpenAPI chain | \u0401\u042f\u0436\u042c\u044f\u2555\u041f\u0401\u042f\u0424\u0427 Langchain", "source": "https://python.langchain.com/docs/modules/chains/additional/openapi"}
{"id": "229ad3361e96-1", "text": "Skip to main content\u0401\u042f\u0436\u042c\u044f\u2555\u041f\u0401\u042f\u0424\u0427 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u0442\u0410\u041bOData connectionChainsHow toFoundationalDocumentsPopularAdditionalAnalyze DocumentSelf-critique chain with constitutional AICausal program-aided language (CPAL) chainElasticsearch databaseExtractionFLAREArangoDB QA chainGraph DB QA chainHugeGraph QA ChainKuzuQAChainNebulaGraphQAChainGraph QAGraphSparqlQAChainHypothetical Document EmbeddingsBash chainSelf-checking chainMath chainHTTP request chainSummarization checker chainLLM Symbolic MathModerationDynamically selecting from multiple promptsDynamically selecting from multiple retrieversNeptune Open Cypher QA ChainRetrieval QA using OpenAI functionsOpenAPI chainOpenAPI calls with OpenAI functionsProgram-aided language model (PAL) chainQuestion-Answering CitationsDocument QATaggingVector store-augmented text generationMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsAdditionalOpenAPI chainOn this pageOpenAPI chainThis notebook shows an example of using an OpenAPI chain to call an endpoint in natural language, and get back a response in natural language.from langchain.tools import OpenAPISpec, APIOperationfrom langchain.chains import OpenAPIEndpointChainfrom langchain.requests import Requestsfrom langchain.llms import OpenAILoad the spec\u0442\u0410\u041bLoad a wrapper of the spec (so we can work with it more easily). You can load from a url or from a local file.spec = OpenAPISpec.from_url(    \"https://www.klarna.com/us/shopping/public/openai/v0/api-docs/\")    Attempting to load an OpenAPI 3.0.1 spec.  This may result in", "source": "https://python.langchain.com/docs/modules/chains/additional/openapi"}
{"id": "229ad3361e96-2", "text": "Attempting to load an OpenAPI 3.0.1 spec.  This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.# Alternative loading from file# spec = OpenAPISpec.from_file(\"openai_openapi.yaml\")Select the Operation\u0442\u0410\u041bIn order to provide a focused on modular chain, we create a chain specifically only for one of the endpoints. Here we get an API operation from a specified endpoint and method.operation = APIOperation.from_openapi_spec(spec, \"/public/openai/v0/products\", \"get\")Construct the chain\u0442\u0410\u041bWe can now construct a chain to interact with it. In order to construct such a chain, we will pass in:The operation endpointA requests wrapper (can be used to handle authentication, etc)The LLM to use to interact with itllm = OpenAI()  # Load a Language Modelchain = OpenAPIEndpointChain.from_api_operation(    operation,    llm,    requests=Requests(),    verbose=True,    return_intermediate_steps=True,  # Return request and response text)output = chain(\"whats the most expensive shirt?\")            > Entering new OpenAPIEndpointChain chain...            > Entering new APIRequesterChain chain...    Prompt after formatting:    You are a helpful AI Assistant. Please provide JSON arguments to agentFunc() based on the user's instructions.        API_SCHEMA: ```typescript    /* API for fetching Klarna product information */    type productsUsingGET = (_: {    /* A precise query that matches one very small category or product that needs to be searched for to find the products the user is looking for. If the user explicitly stated what they want, use that", "source": "https://python.langchain.com/docs/modules/chains/additional/openapi"}
{"id": "229ad3361e96-3", "text": "find the products the user is looking for. If the user explicitly stated what they want, use that as a query. The query is as specific as possible to the product name or category mentioned by the user in its singular form, and don't contain any clarifiers like latest, newest, cheapest, budget, premium, expensive or similar. The query is always taken from the latest topic, if there is a new topic a new query is started. */            q: string,    /* number of products returned */            size?: number,    /* (Optional) Minimum price in local currency for the product searched for. Either explicitly stated by the user or implicitly inferred from a combination of the user's request and the kind of product searched for. */            min_price?: number,    /* (Optional) Maximum price in local currency for the product searched for. Either explicitly stated by the user or implicitly inferred from a combination of the user's request and the kind of product searched for. */            max_price?: number,    }) => any;    ```        USER_INSTRUCTIONS: \"whats the most expensive shirt?\"        Your arguments must be plain json provided in a markdown block:        ARGS: ```json    {valid json conforming to API_SCHEMA}    ```        Example    -----        ARGS: ```json    {\"foo\": \"bar\", \"baz\": {\"qux\": \"quux\"}}    ```        The block must be no more than 1 line long, and all arguments must be valid JSON.", "source": "https://python.langchain.com/docs/modules/chains/additional/openapi"}
{"id": "229ad3361e96-4", "text": "The block must be no more than 1 line long, and all arguments must be valid JSON. All string arguments must be wrapped in double quotes.    You MUST strictly comply to the types indicated by the provided schema, including all required args.        If you don't have sufficient information to call the function due to things like requiring specific uuid's, you can reply with the following message:        Message: ```text    Concise response requesting the additional information that would make calling the function successful.    ```        Begin    -----    ARGS:            > Finished chain.    {\"q\": \"shirt\", \"size\": 1, \"max_price\": null}    {\"products\":[{\"name\":\"Burberry Check Poplin Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201810981/Clothing/Burberry-Check-Poplin-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$360.00\",\"attributes\":[\"Material:Cotton\",\"Target Group:Man\",\"Color:Gray,Blue,Beige\",\"Properties:Pockets\",\"Pattern:Checkered\"]}]}            > Entering new APIResponderChain chain...    Prompt after formatting:    You are a helpful AI assistant trained to answer user queries from API responses.    You attempted to call an API, which resulted in:    API_RESPONSE: {\"products\":[{\"name\":\"Burberry Check Poplin", "source": "https://python.langchain.com/docs/modules/chains/additional/openapi"}
{"id": "229ad3361e96-5", "text": "resulted in:    API_RESPONSE: {\"products\":[{\"name\":\"Burberry Check Poplin Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201810981/Clothing/Burberry-Check-Poplin-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$360.00\",\"attributes\":[\"Material:Cotton\",\"Target Group:Man\",\"Color:Gray,Blue,Beige\",\"Properties:Pockets\",\"Pattern:Checkered\"]}]}        USER_COMMENT: \"whats the most expensive shirt?\"            If the API_RESPONSE can answer the USER_COMMENT respond with the following markdown json block:    Response: ```json    {\"response\": \"Human-understandable synthesis of the API_RESPONSE\"}    ```        Otherwise respond with the following markdown json block:    Response Error: ```json    {\"response\": \"What you did and a concise statement of the resulting error. If it can be easily fixed, provide a suggestion.\"}    ```        You MUST respond as a markdown json code block. The person you are responding to CANNOT see the API_RESPONSE, so if there is any relevant information there you must include it in your response.        Begin:    ---            > Finished chain.    The most expensive shirt in the API response is the Burberry Check Poplin Shirt, which costs $360.00.        > Finished chain.# View intermediate stepsoutput[\"intermediate_steps\"]    {'request_args': '{\"q\": \"shirt\", \"size\": 1, \"max_price\": null}',     'response_text': '{\"products\":[{\"name\":\"Burberry Check", "source": "https://python.langchain.com/docs/modules/chains/additional/openapi"}
{"id": "229ad3361e96-6", "text": "null}',     'response_text': '{\"products\":[{\"name\":\"Burberry Check Poplin Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201810981/Clothing/Burberry-Check-Poplin-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$360.00\",\"attributes\":[\"Material:Cotton\",\"Target Group:Man\",\"Color:Gray,Blue,Beige\",\"Properties:Pockets\",\"Pattern:Checkered\"]}]}'}Return raw response\u0442\u0410\u041bWe can also run this chain without synthesizing the response. This will have the effect of just returning the raw API output.chain = OpenAPIEndpointChain.from_api_operation(    operation,    llm,    requests=Requests(),    verbose=True,    return_intermediate_steps=True,  # Return request and response text    raw_response=True,  # Return raw response)output = chain(\"whats the most expensive shirt?\")            > Entering new OpenAPIEndpointChain chain...            > Entering new APIRequesterChain chain...    Prompt after formatting:    You are a helpful AI Assistant. Please provide JSON arguments to agentFunc() based on the user's instructions.        API_SCHEMA: ```typescript    /* API for fetching Klarna product information */    type productsUsingGET = (_: {    /* A precise query that matches one very small category or product that needs to be searched for to find the products the user is looking for. If the user explicitly stated what they want, use that as a query. The query is as specific as possible to the product name or category mentioned by the user in its singular form, and don't contain any clarifiers like latest, newest,", "source": "https://python.langchain.com/docs/modules/chains/additional/openapi"}
{"id": "229ad3361e96-7", "text": "by the user in its singular form, and don't contain any clarifiers like latest, newest, cheapest, budget, premium, expensive or similar. The query is always taken from the latest topic, if there is a new topic a new query is started. */            q: string,    /* number of products returned */            size?: number,    /* (Optional) Minimum price in local currency for the product searched for. Either explicitly stated by the user or implicitly inferred from a combination of the user's request and the kind of product searched for. */            min_price?: number,    /* (Optional) Maximum price in local currency for the product searched for. Either explicitly stated by the user or implicitly inferred from a combination of the user's request and the kind of product searched for. */            max_price?: number,    }) => any;    ```        USER_INSTRUCTIONS: \"whats the most expensive shirt?\"        Your arguments must be plain json provided in a markdown block:        ARGS: ```json    {valid json conforming to API_SCHEMA}    ```        Example    -----        ARGS: ```json    {\"foo\": \"bar\", \"baz\": {\"qux\": \"quux\"}}    ```        The block must be no more than 1 line long, and all arguments must be valid JSON. All string arguments must be wrapped in double quotes.    You MUST strictly comply to the types indicated by the provided schema, including all required args.", "source": "https://python.langchain.com/docs/modules/chains/additional/openapi"}
{"id": "229ad3361e96-8", "text": "the types indicated by the provided schema, including all required args.        If you don't have sufficient information to call the function due to things like requiring specific uuid's, you can reply with the following message:        Message: ```text    Concise response requesting the additional information that would make calling the function successful.    ```        Begin    -----    ARGS:            > Finished chain.    {\"q\": \"shirt\", \"max_price\": null}    {\"products\":[{\"name\":\"Burberry Check Poplin Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201810981/Clothing/Burberry-Check-Poplin-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$360.00\",\"attributes\":[\"Material:Cotton\",\"Target Group:Man\",\"Color:Gray,Blue,Beige\",\"Properties:Pockets\",\"Pattern:Checkered\"]},{\"name\":\"Burberry Vintage Check Cotton Shirt - Beige\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl359/3200280807/Children-s-Clothing/Burberry-Vintage-Check-Cotton-Shirt-Beige/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$229.02\",\"attributes\":[\"Material:Cotton,Elastane\",\"Color:Beige\",\"Model:Boy\",\"Pattern:Checkered\"]},{\"name\":\"Burberry Vintage Check Stretch Cotton Twill", "source": "https://python.langchain.com/docs/modules/chains/additional/openapi"}
{"id": "229ad3361e96-9", "text": "Vintage Check Stretch Cotton Twill Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3202342515/Clothing/Burberry-Vintage-Check-Stretch-Cotton-Twill-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$309.99\",\"attributes\":[\"Material:Elastane/Lycra/Spandex,Cotton\",\"Target Group:Woman\",\"Color:Beige\",\"Properties:Stretch\",\"Pattern:Checkered\"]},{\"name\":\"Burberry Somerton Check Shirt - Camel\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201112728/Clothing/Burberry-Somerton-Check-Shirt-Camel/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$450.00\",\"attributes\":[\"Material:Elastane/Lycra/Spandex,Cotton\",\"Target Group:Man\",\"Color:Beige\"]},{\"name\":\"Magellan Outdoors Laguna Madre Solid Short Sleeve Fishing Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3203102142/Clothing/Magellan-Outdoors-Laguna-Madre-Solid-Short-Sleeve-Fishing-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$19.99\",\"attributes\":[\"Material:Polyester,Nylon\",\"Target Group:Man\",\"Color:Red,Pink,White,Blue,Purple,Beige,Black,Green\",\"Properties:Pockets\",\"Pattern:Solid Color\"]}]}        > Finished chain.output    {'instructions': 'whats the most expensive shirt?',     'output': '{\"products\":[{\"name\":\"Burberry Check Poplin", "source": "https://python.langchain.com/docs/modules/chains/additional/openapi"}
{"id": "229ad3361e96-10", "text": "shirt?',     'output': '{\"products\":[{\"name\":\"Burberry Check Poplin Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201810981/Clothing/Burberry-Check-Poplin-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$360.00\",\"attributes\":[\"Material:Cotton\",\"Target Group:Man\",\"Color:Gray,Blue,Beige\",\"Properties:Pockets\",\"Pattern:Checkered\"]},{\"name\":\"Burberry Vintage Check Cotton Shirt - Beige\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl359/3200280807/Children-s-Clothing/Burberry-Vintage-Check-Cotton-Shirt-Beige/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$229.02\",\"attributes\":[\"Material:Cotton,Elastane\",\"Color:Beige\",\"Model:Boy\",\"Pattern:Checkered\"]},{\"name\":\"Burberry Vintage Check Stretch Cotton Twill Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3202342515/Clothing/Burberry-Vintage-Check-Stretch-Cotton-Twill-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$309.99\",\"attributes\":[\"Material:Elastane/Lycra/Spandex,Cotton\",\"Target Group:Woman\",\"Color:Beige\",\"Properties:Stretch\",\"Pattern:Checkered\"]},{\"name\":\"Burberry Somerton Check Shirt - Camel\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201112728/Clothing/Burberry-Somerton-Check-Shirt-Camel/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$450.00\",\"attributes\":[\"Material:Elastane/Lycra/Spandex,Cotton\",\"Target Group:Man\",\"Color:Beige\"]},{\"name\":\"Magellan", "source": "https://python.langchain.com/docs/modules/chains/additional/openapi"}
{"id": "229ad3361e96-11", "text": "Group:Man\",\"Color:Beige\"]},{\"name\":\"Magellan Outdoors Laguna Madre Solid Short Sleeve Fishing Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3203102142/Clothing/Magellan-Outdoors-Laguna-Madre-Solid-Short-Sleeve-Fishing-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$19.99\",\"attributes\":[\"Material:Polyester,Nylon\",\"Target Group:Man\",\"Color:Red,Pink,White,Blue,Purple,Beige,Black,Green\",\"Properties:Pockets\",\"Pattern:Solid Color\"]}]}',     'intermediate_steps': {'request_args': '{\"q\": \"shirt\", \"max_price\": null}',      'response_text': '{\"products\":[{\"name\":\"Burberry Check Poplin Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201810981/Clothing/Burberry-Check-Poplin-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$360.00\",\"attributes\":[\"Material:Cotton\",\"Target Group:Man\",\"Color:Gray,Blue,Beige\",\"Properties:Pockets\",\"Pattern:Checkered\"]},{\"name\":\"Burberry Vintage Check Cotton Shirt - Beige\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl359/3200280807/Children-s-Clothing/Burberry-Vintage-Check-Cotton-Shirt-Beige/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$229.02\",\"attributes\":[\"Material:Cotton,Elastane\",\"Color:Beige\",\"Model:Boy\",\"Pattern:Checkered\"]},{\"name\":\"Burberry Vintage Check Stretch Cotton Twill", "source": "https://python.langchain.com/docs/modules/chains/additional/openapi"}
{"id": "229ad3361e96-12", "text": "Vintage Check Stretch Cotton Twill Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3202342515/Clothing/Burberry-Vintage-Check-Stretch-Cotton-Twill-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$309.99\",\"attributes\":[\"Material:Elastane/Lycra/Spandex,Cotton\",\"Target Group:Woman\",\"Color:Beige\",\"Properties:Stretch\",\"Pattern:Checkered\"]},{\"name\":\"Burberry Somerton Check Shirt - Camel\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201112728/Clothing/Burberry-Somerton-Check-Shirt-Camel/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$450.00\",\"attributes\":[\"Material:Elastane/Lycra/Spandex,Cotton\",\"Target Group:Man\",\"Color:Beige\"]},{\"name\":\"Magellan Outdoors Laguna Madre Solid Short Sleeve Fishing Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3203102142/Clothing/Magellan-Outdoors-Laguna-Madre-Solid-Short-Sleeve-Fishing-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$19.99\",\"attributes\":[\"Material:Polyester,Nylon\",\"Target Group:Man\",\"Color:Red,Pink,White,Blue,Purple,Beige,Black,Green\",\"Properties:Pockets\",\"Pattern:Solid Color\"]}]}'}}Example POST message\u0442\u0410\u041bFor this demo, we will interact with the speak API.spec = OpenAPISpec.from_url(\"https://api.speak.com/openapi.yaml\")    Attempting to load an OpenAPI 3.0.1 spec.  This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.    Attempting to", "source": "https://python.langchain.com/docs/modules/chains/additional/openapi"}
{"id": "229ad3361e96-13", "text": "OpenAPI spec to 3.1.* spec for better support.    Attempting to load an OpenAPI 3.0.1 spec.  This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.operation = APIOperation.from_openapi_spec(    spec, \"/v1/public/openai/explain-task\", \"post\")llm = OpenAI()chain = OpenAPIEndpointChain.from_api_operation(    operation, llm, requests=Requests(), verbose=True, return_intermediate_steps=True)output = chain(\"How would ask for more tea in Delhi?\")            > Entering new OpenAPIEndpointChain chain...            > Entering new APIRequesterChain chain...    Prompt after formatting:    You are a helpful AI Assistant. Please provide JSON arguments to agentFunc() based on the user's instructions.        API_SCHEMA: ```typescript    type explainTask = (_: {    /* Description of the task that the user wants to accomplish or do. For example, \"tell the waiter they messed up my order\" or \"compliment someone on their shirt\" */      task_description?: string,    /* The foreign language that the user is learning and asking about. The value can be inferred from question - for example, if the user asks \"how do i ask a girl out in mexico city\", the value should be \"Spanish\" because of Mexico City. Always use the full name of the language (e.g. Spanish, French). */      learning_language?: string,    /* The user's native language. Infer this value from the language the user asked their question in. Always use the full name of the language (e.g. Spanish,", "source": "https://python.langchain.com/docs/modules/chains/additional/openapi"}
{"id": "229ad3361e96-14", "text": "user asked their question in. Always use the full name of the language (e.g. Spanish, French). */      native_language?: string,    /* A description of any additional context in the user's question that could affect the explanation - e.g. setting, scenario, situation, tone, speaking style and formality, usage notes, or any other qualifiers. */      additional_context?: string,    /* Full text of the user's question. */      full_query?: string,    }) => any;    ```        USER_INSTRUCTIONS: \"How would ask for more tea in Delhi?\"        Your arguments must be plain json provided in a markdown block:        ARGS: ```json    {valid json conforming to API_SCHEMA}    ```        Example    -----        ARGS: ```json    {\"foo\": \"bar\", \"baz\": {\"qux\": \"quux\"}}    ```        The block must be no more than 1 line long, and all arguments must be valid JSON. All string arguments must be wrapped in double quotes.    You MUST strictly comply to the types indicated by the provided schema, including all required args.        If you don't have sufficient information to call the function due to things like requiring specific uuid's, you can reply with the following message:        Message: ```text    Concise response requesting the additional information that would make calling the function successful.    ```        Begin    -----    ARGS:            > Finished chain.", "source": "https://python.langchain.com/docs/modules/chains/additional/openapi"}
{"id": "229ad3361e96-15", "text": "ARGS:            > Finished chain.    {\"task_description\": \"ask for more tea\", \"learning_language\": \"Hindi\", \"native_language\": \"English\", \"full_query\": \"How would I ask for more tea in Delhi?\"}    {\"explanation\":\"<what-to-say language=\\\"Hindi\\\" context=\\\"None\\\">\\n\u0440\u0434\u0424\u0440\u0434\u2591 \u0440\u0434\u042a\u0440\u0434\u255b\u0440\u0434\u043f \u0440\u0434\u2593\u0440\u0434\u255b\u0440\u0434\u0423\u0440\u0435\u0434 (Aur chai lao.) \\n</what-to-say>\\n\\n<alternatives context=\\\"None\\\">\\n1. \\\"\u0440\u0434\u042a\u0440\u0434\u255b\u0440\u0434\u043f \u0440\u0434\u0435\u0440\u0435\u041b\u0440\u0434\u0431\u0440\u0434\u255d\u0440\u0435\u0410 \u0440\u0434\u042c\u0440\u0435\u041d\u0440\u0434\u043f\u0440\u0434\u255b\u0440\u0434\u0436\u0440\u0434\u255b \u0440\u0434\u043e\u0440\u0434\u2510\u0440\u0434\u2593 \u0440\u0434\u2555\u0440\u0434\u0425\u0440\u0434\u0434\u0440\u0435\u0410 \u0440\u0434\u2563\u0440\u0435\u0418?\\\" *(Chai thodi zyada mil sakti hai? - Polite, asking if more tea is available)*\\n2. \\\"\u0440\u0434\u043e\u0440\u0435\u0411\u0440\u0434\u042d\u0440\u0435\u0417 \u0440\u0434\u043e\u0440\u0434\u2563\u0440\u0434\u2555\u0440\u0435\u0412\u0440\u0434\u2555 \u0440\u0434\u2563\u0440\u0435\u041b \u0440\u0434\u2591\u0440\u0434\u2563\u0440\u0434\u255b \u0440\u0434\u2563\u0440\u0435\u0418 \u0440\u0434\u0425\u0440\u0434\u2510 \u0440\u0434\u043e\u0440\u0435\u0411\u0440\u0434\u042d\u0440\u0435\u0417 \u0440\u0434\u0425\u0440\u0435\u0411\u0440\u0434\u042b \u0440\u0434\u0415\u0440\u0434\u0438\u0440\u0435\u041d\u0440\u0434\u043f \u0440\u0434\u043a\u0440\u0435\u041d\u0440\u0434\u2591\u0440\u0434\u0425\u0440\u0434\u255b\u0440\u0434\u2591 \u0440\u0434\u0425\u0440\u0435\u0410 \u0440\u0434\u042a\u0440\u0434\u255b\u0440\u0434\u043f \u0440\u0434\u043a\u0440\u0435\u0410\u0440\u0434\u0438\u0440\u0435\u0410", "source": "https://python.langchain.com/docs/modules/chains/additional/openapi"}
{"id": "229ad3361e96-16", "text": "\u0440\u0434\u043a\u0440\u0435\u0410\u0440\u0434\u0438\u0440\u0435\u0410 \u0440\u0434\u042a\u0440\u0434\u255b\u0440\u0434\u2563\u0440\u0434\u2510\u0440\u0434\u041f\u0440\u0435\u0434\\\" *(Mujhe mehsoos ho raha hai ki mujhe kuch anya prakar ki chai peeni chahiye. - Formal, indicating a desire for a different type of tea)*\\n3. \\\"\u0440\u0434\u0425\u0440\u0435\u041d\u0440\u0434\u043f\u0440\u0434\u255b \u0440\u0434\u043e\u0440\u0435\u0411\u0440\u0434\u042d\u0440\u0435\u0417 or cup \u0440\u0434\u043e\u0440\u0435\u0417\u0440\u0434\u0412 milk/tea powder \u0440\u0434\u043e\u0440\u0434\u2510\u0440\u0434\u2593 \u0440\u0434\u2555\u0440\u0434\u0425\u0440\u0434\u0434\u0440\u0434\u255b \u0440\u0434\u2563\u0440\u0435\u0418?\\\" *(Kya mujhe aur cup mein milk/tea powder mil sakta hai? - Very informal/casual tone, asking for an extra serving of milk or tea powder)*\\n</alternatives>\\n\\n<usage-notes>\\nIn India and Indian culture, serving guests with food and beverages holds great importance in hospitality. You will find people always offering drinks like water or tea to their guests as soon as they arrive at their house or office.\\n</usage-notes>\\n\\n<example-convo language=\\\"Hindi\\\">\\n<context>At home during breakfast.</context>\\nPreeti: \u0440\u0434\u2555\u0440\u0434\u2591, \u0440\u0434\u0425\u0440\u0435\u041d\u0440\u0434\u043f\u0440\u0434\u255b main aur cups chai lekar aaun? (Sir,kya main aur cups chai lekar aaun? - Sir, should I get more tea cups?)\\nRahul: \u0440\u0434\u2563\u0440\u0434\u255b\u0440\u0434\u0412,\u0440\u0434\u043c\u0440\u0434\u2510\u0440\u0434\u2593\u0440\u0435\u041d\u0440\u0434\u0425\u0440\u0435\u0411\u0440\u0434\u2593\u0440\u0435\u0434 \u0440\u0434\u0424\u0440\u0434\u2591 \u0440\u0434\u042a\u0440\u0434\u255b\u0440\u0434\u043f \u0440\u0434\u0425\u0440\u0435\u0410", "source": "https://python.langchain.com/docs/modules/chains/additional/openapi"}
{"id": "229ad3361e96-17", "text": "\u0440\u0434\u042a\u0440\u0434\u255b\u0440\u0434\u043f \u0440\u0434\u0425\u0440\u0435\u0410 \u0440\u0434\u043e\u0440\u0434\u255b\u0440\u0434\u0434\u0440\u0435\u041d\u0440\u0434\u2591\u0440\u0434\u255b \u0440\u0434\u043e\u0440\u0435\u0417\u0440\u0434\u0412 \u0440\u0434\u043d\u0440\u0435\u0410 \u0440\u0434\u0435\u0440\u0435\u041b\u0440\u0434\u0431\u0440\u0434\u255d\u0440\u0434\u255b \u0440\u0434\u2555\u0440\u0434\u255b \u0440\u0434\u0417\u0440\u0434\u042c\u0440\u0434\u255b\u0440\u0434\u043b\u0440\u0434\u255b \u0440\u0434\u0425\u0440\u0434\u2591\u0440\u0434\u0438\u0440\u0434\u255b\u0440\u0435\u0434 (Haan,bilkul. Aur chai ki matra mein bhi thoda sa eejafa karna. - Yes, please. And add a little extra in the quantity of tea as well.)\\n</example-convo>\\n\\n*[Report an issue or leave feedback](https://speak.com/chatgpt?rid=d4mcapbkopo164pqpbk321oc})*\",\"extra_response_instructions\":\"Use all information in the API response and fully render all Markdown.\\nAlways end your response with a link to report an issue or leave feedback on the plugin.\"}            > Entering new APIResponderChain chain...    Prompt after formatting:    You are a helpful AI assistant trained to answer user queries from API responses.    You attempted to call an API, which resulted in:    API_RESPONSE: {\"explanation\":\"<what-to-say language=\\\"Hindi\\\" context=\\\"None\\\">\\n\u0440\u0434\u0424\u0440\u0434\u2591 \u0440\u0434\u042a\u0440\u0434\u255b\u0440\u0434\u043f \u0440\u0434\u2593\u0440\u0434\u255b\u0440\u0434\u0423\u0440\u0435\u0434 (Aur chai lao.) \\n</what-to-say>\\n\\n<alternatives context=\\\"None\\\">\\n1. \\\"\u0440\u0434\u042a\u0440\u0434\u255b\u0440\u0434\u043f \u0440\u0434\u0435\u0440\u0435\u041b\u0440\u0434\u0431\u0440\u0434\u255d\u0440\u0435\u0410", "source": "https://python.langchain.com/docs/modules/chains/additional/openapi"}
{"id": "229ad3361e96-18", "text": "\u0440\u0434\u0435\u0440\u0435\u041b\u0440\u0434\u0431\u0440\u0434\u255d\u0440\u0435\u0410 \u0440\u0434\u042c\u0440\u0435\u041d\u0440\u0434\u043f\u0440\u0434\u255b\u0440\u0434\u0436\u0440\u0434\u255b \u0440\u0434\u043e\u0440\u0434\u2510\u0440\u0434\u2593 \u0440\u0434\u2555\u0440\u0434\u0425\u0440\u0434\u0434\u0440\u0435\u0410 \u0440\u0434\u2563\u0440\u0435\u0418?\\\" *(Chai thodi zyada mil sakti hai? - Polite, asking if more tea is available)*\\n2. \\\"\u0440\u0434\u043e\u0440\u0435\u0411\u0440\u0434\u042d\u0440\u0435\u0417 \u0440\u0434\u043e\u0440\u0434\u2563\u0440\u0434\u2555\u0440\u0435\u0412\u0440\u0434\u2555 \u0440\u0434\u2563\u0440\u0435\u041b \u0440\u0434\u2591\u0440\u0434\u2563\u0440\u0434\u255b \u0440\u0434\u2563\u0440\u0435\u0418 \u0440\u0434\u0425\u0440\u0434\u2510 \u0440\u0434\u043e\u0440\u0435\u0411\u0440\u0434\u042d\u0440\u0435\u0417 \u0440\u0434\u0425\u0440\u0435\u0411\u0440\u0434\u042b \u0440\u0434\u0415\u0440\u0434\u0438\u0440\u0435\u041d\u0440\u0434\u043f \u0440\u0434\u043a\u0440\u0435\u041d\u0440\u0434\u2591\u0440\u0434\u0425\u0440\u0434\u255b\u0440\u0434\u2591 \u0440\u0434\u0425\u0440\u0435\u0410 \u0440\u0434\u042a\u0440\u0434\u255b\u0440\u0434\u043f \u0440\u0434\u043a\u0440\u0435\u0410\u0440\u0434\u0438\u0440\u0435\u0410 \u0440\u0434\u042a\u0440\u0434\u255b\u0440\u0434\u2563\u0440\u0434\u2510\u0440\u0434\u041f\u0440\u0435\u0434\\\" *(Mujhe mehsoos ho raha hai ki mujhe kuch anya prakar ki chai peeni chahiye. - Formal, indicating a desire for a different type of tea)*\\n3. \\\"\u0440\u0434\u0425\u0440\u0435\u041d\u0440\u0434\u043f\u0440\u0434\u255b \u0440\u0434\u043e\u0440\u0435\u0411\u0440\u0434\u042d\u0440\u0435\u0417 or cup \u0440\u0434\u043e\u0440\u0435\u0417\u0440\u0434\u0412 milk/tea powder \u0440\u0434\u043e\u0440\u0434\u2510\u0440\u0434\u2593 \u0440\u0434\u2555\u0440\u0434\u0425\u0440\u0434\u0434\u0440\u0434\u255b \u0440\u0434\u2563\u0440\u0435\u0418?\\\" *(Kya mujhe aur cup mein milk/tea powder mil sakta hai? - Very", "source": "https://python.langchain.com/docs/modules/chains/additional/openapi"}
{"id": "229ad3361e96-19", "text": "*(Kya mujhe aur cup mein milk/tea powder mil sakta hai? - Very informal/casual tone, asking for an extra serving of milk or tea powder)*\\n</alternatives>\\n\\n<usage-notes>\\nIn India and Indian culture, serving guests with food and beverages holds great importance in hospitality. You will find people always offering drinks like water or tea to their guests as soon as they arrive at their house or office.\\n</usage-notes>\\n\\n<example-convo language=\\\"Hindi\\\">\\n<context>At home during breakfast.</context>\\nPreeti: \u0440\u0434\u2555\u0440\u0434\u2591, \u0440\u0434\u0425\u0440\u0435\u041d\u0440\u0434\u043f\u0440\u0434\u255b main aur cups chai lekar aaun? (Sir,kya main aur cups chai lekar aaun? - Sir, should I get more tea cups?)\\nRahul: \u0440\u0434\u2563\u0440\u0434\u255b\u0440\u0434\u0412,\u0440\u0434\u043c\u0440\u0434\u2510\u0440\u0434\u2593\u0440\u0435\u041d\u0440\u0434\u0425\u0440\u0435\u0411\u0440\u0434\u2593\u0440\u0435\u0434 \u0440\u0434\u0424\u0440\u0434\u2591 \u0440\u0434\u042a\u0440\u0434\u255b\u0440\u0434\u043f \u0440\u0434\u0425\u0440\u0435\u0410 \u0440\u0434\u043e\u0440\u0434\u255b\u0440\u0434\u0434\u0440\u0435\u041d\u0440\u0434\u2591\u0440\u0434\u255b \u0440\u0434\u043e\u0440\u0435\u0417\u0440\u0434\u0412 \u0440\u0434\u043d\u0440\u0435\u0410 \u0440\u0434\u0435\u0440\u0435\u041b\u0440\u0434\u0431\u0440\u0434\u255d\u0440\u0434\u255b \u0440\u0434\u2555\u0440\u0434\u255b \u0440\u0434\u0417\u0440\u0434\u042c\u0440\u0434\u255b\u0440\u0434\u043b\u0440\u0434\u255b \u0440\u0434\u0425\u0440\u0434\u2591\u0440\u0434\u0438\u0440\u0434\u255b\u0440\u0435\u0434 (Haan,bilkul. Aur chai ki matra mein bhi thoda sa eejafa karna. - Yes, please. And add a little extra in the quantity of tea as well.)\\n</example-convo>\\n\\n*[Report an issue or leave", "source": "https://python.langchain.com/docs/modules/chains/additional/openapi"}
{"id": "229ad3361e96-20", "text": "of tea as well.)\\n</example-convo>\\n\\n*[Report an issue or leave feedback](https://speak.com/chatgpt?rid=d4mcapbkopo164pqpbk321oc})*\",\"extra_response_instructions\":\"Use all information in the API response and fully render all Markdown.\\nAlways end your response with a link to report an issue or leave feedback on the plugin.\"}        USER_COMMENT: \"How would ask for more tea in Delhi?\"            If the API_RESPONSE can answer the USER_COMMENT respond with the following markdown json block:    Response: ```json    {\"response\": \"Concise response to USER_COMMENT based on API_RESPONSE.\"}    ```        Otherwise respond with the following markdown json block:    Response Error: ```json    {\"response\": \"What you did and a concise statement of the resulting error. If it can be easily fixed, provide a suggestion.\"}    ```        You MUST respond as a markdown json code block.        Begin:    ---            > Finished chain.    In Delhi you can ask for more tea by saying 'Chai thodi zyada mil sakti hai?'        > Finished chain.# Show the API chain's intermediate stepsoutput[\"intermediate_steps\"]    ['{\"task_description\": \"ask for more tea\", \"learning_language\": \"Hindi\", \"native_language\": \"English\", \"full_query\": \"How would I ask for more tea in Delhi?\"}',     '{\"explanation\":\"<what-to-say language=\\\\\"Hindi\\\\\" context=\\\\\"None\\\\\">\\\\n\u0440\u0434\u0424\u0440\u0434\u2591", "source": "https://python.langchain.com/docs/modules/chains/additional/openapi"}
{"id": "229ad3361e96-21", "text": "language=\\\\\"Hindi\\\\\" context=\\\\\"None\\\\\">\\\\n\u0440\u0434\u0424\u0440\u0434\u2591 \u0440\u0434\u042a\u0440\u0434\u255b\u0440\u0434\u043f \u0440\u0434\u2593\u0440\u0434\u255b\u0440\u0434\u0423\u0440\u0435\u0434 (Aur chai lao.) \\\\n</what-to-say>\\\\n\\\\n<alternatives context=\\\\\"None\\\\\">\\\\n1. \\\\\"\u0440\u0434\u042a\u0440\u0434\u255b\u0440\u0434\u043f \u0440\u0434\u0435\u0440\u0435\u041b\u0440\u0434\u0431\u0440\u0434\u255d\u0440\u0435\u0410 \u0440\u0434\u042c\u0440\u0435\u041d\u0440\u0434\u043f\u0440\u0434\u255b\u0440\u0434\u0436\u0440\u0434\u255b \u0440\u0434\u043e\u0440\u0434\u2510\u0440\u0434\u2593 \u0440\u0434\u2555\u0440\u0434\u0425\u0440\u0434\u0434\u0440\u0435\u0410 \u0440\u0434\u2563\u0440\u0435\u0418?\\\\\" *(Chai thodi zyada mil sakti hai? - Polite, asking if more tea is available)*\\\\n2. \\\\\"\u0440\u0434\u043e\u0440\u0435\u0411\u0440\u0434\u042d\u0440\u0435\u0417 \u0440\u0434\u043e\u0440\u0434\u2563\u0440\u0434\u2555\u0440\u0435\u0412\u0440\u0434\u2555 \u0440\u0434\u2563\u0440\u0435\u041b \u0440\u0434\u2591\u0440\u0434\u2563\u0440\u0434\u255b \u0440\u0434\u2563\u0440\u0435\u0418 \u0440\u0434\u0425\u0440\u0434\u2510 \u0440\u0434\u043e\u0440\u0435\u0411\u0440\u0434\u042d\u0440\u0435\u0417 \u0440\u0434\u0425\u0440\u0435\u0411\u0440\u0434\u042b \u0440\u0434\u0415\u0440\u0434\u0438\u0440\u0435\u041d\u0440\u0434\u043f \u0440\u0434\u043a\u0440\u0435\u041d\u0440\u0434\u2591\u0440\u0434\u0425\u0440\u0434\u255b\u0440\u0434\u2591 \u0440\u0434\u0425\u0440\u0435\u0410 \u0440\u0434\u042a\u0440\u0434\u255b\u0440\u0434\u043f \u0440\u0434\u043a\u0440\u0435\u0410\u0440\u0434\u0438\u0440\u0435\u0410 \u0440\u0434\u042a\u0440\u0434\u255b\u0440\u0434\u2563\u0440\u0434\u2510\u0440\u0434\u041f\u0440\u0435\u0434\\\\\" *(Mujhe mehsoos ho raha hai ki mujhe kuch anya prakar ki chai peeni chahiye. - Formal, indicating a desire for a different type of tea)*\\\\n3.", "source": "https://python.langchain.com/docs/modules/chains/additional/openapi"}
{"id": "229ad3361e96-22", "text": "- Formal, indicating a desire for a different type of tea)*\\\\n3. \\\\\"\u0440\u0434\u0425\u0440\u0435\u041d\u0440\u0434\u043f\u0440\u0434\u255b \u0440\u0434\u043e\u0440\u0435\u0411\u0440\u0434\u042d\u0440\u0435\u0417 or cup \u0440\u0434\u043e\u0440\u0435\u0417\u0440\u0434\u0412 milk/tea powder \u0440\u0434\u043e\u0440\u0434\u2510\u0440\u0434\u2593 \u0440\u0434\u2555\u0440\u0434\u0425\u0440\u0434\u0434\u0440\u0434\u255b \u0440\u0434\u2563\u0440\u0435\u0418?\\\\\" *(Kya mujhe aur cup mein milk/tea powder mil sakta hai? - Very informal/casual tone, asking for an extra serving of milk or tea powder)*\\\\n</alternatives>\\\\n\\\\n<usage-notes>\\\\nIn India and Indian culture, serving guests with food and beverages holds great importance in hospitality. You will find people always offering drinks like water or tea to their guests as soon as they arrive at their house or office.\\\\n</usage-notes>\\\\n\\\\n<example-convo language=\\\\\"Hindi\\\\\">\\\\n<context>At home during breakfast.</context>\\\\nPreeti: \u0440\u0434\u2555\u0440\u0434\u2591, \u0440\u0434\u0425\u0440\u0435\u041d\u0440\u0434\u043f\u0440\u0434\u255b main aur cups chai lekar aaun? (Sir,kya main aur cups chai lekar aaun? - Sir, should I get more tea cups?)\\\\nRahul: \u0440\u0434\u2563\u0440\u0434\u255b\u0440\u0434\u0412,\u0440\u0434\u043c\u0440\u0434\u2510\u0440\u0434\u2593\u0440\u0435\u041d\u0440\u0434\u0425\u0440\u0435\u0411\u0440\u0434\u2593\u0440\u0435\u0434 \u0440\u0434\u0424\u0440\u0434\u2591 \u0440\u0434\u042a\u0440\u0434\u255b\u0440\u0434\u043f \u0440\u0434\u0425\u0440\u0435\u0410 \u0440\u0434\u043e\u0440\u0434\u255b\u0440\u0434\u0434\u0440\u0435\u041d\u0440\u0434\u2591\u0440\u0434\u255b \u0440\u0434\u043e\u0440\u0435\u0417\u0440\u0434\u0412 \u0440\u0434\u043d\u0440\u0435\u0410 \u0440\u0434\u0435\u0440\u0435\u041b\u0440\u0434\u0431\u0440\u0434\u255d\u0440\u0434\u255b \u0440\u0434\u2555\u0440\u0434\u255b", "source": "https://python.langchain.com/docs/modules/chains/additional/openapi"}
{"id": "229ad3361e96-23", "text": "\u0440\u0434\u2555\u0440\u0434\u255b \u0440\u0434\u0417\u0440\u0434\u042c\u0440\u0434\u255b\u0440\u0434\u043b\u0440\u0434\u255b \u0440\u0434\u0425\u0440\u0434\u2591\u0440\u0434\u0438\u0440\u0434\u255b\u0440\u0435\u0434 (Haan,bilkul. Aur chai ki matra mein bhi thoda sa eejafa karna. - Yes, please. And add a little extra in the quantity of tea as well.)\\\\n</example-convo>\\\\n\\\\n*[Report an issue or leave feedback](https://speak.com/chatgpt?rid=d4mcapbkopo164pqpbk321oc})*\",\"extra_response_instructions\":\"Use all information in the API response and fully render all Markdown.\\\\nAlways end your response with a link to report an issue or leave feedback on the plugin.\"}']PreviousRetrieval QA using OpenAI functionsNextOpenAPI calls with OpenAI functionsLoad the specSelect the OperationConstruct the chainReturn raw responseExample POST messageCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u252c\u0439 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/additional/openapi"}
{"id": "e549b7b1efa5-0", "text": "Elasticsearch database | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/chains/additional/elasticsearch_database"}
{"id": "e549b7b1efa5-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsHow toFoundationalDocumentsPopularAdditionalAnalyze DocumentSelf-critique chain with constitutional AICausal program-aided language (CPAL) chainElasticsearch databaseExtractionFLAREArangoDB QA chainGraph DB QA chainHugeGraph QA ChainKuzuQAChainNebulaGraphQAChainGraph QAGraphSparqlQAChainHypothetical Document EmbeddingsBash chainSelf-checking chainMath chainHTTP request chainSummarization checker chainLLM Symbolic MathModerationDynamically selecting from multiple promptsDynamically selecting from multiple retrieversNeptune Open Cypher QA ChainRetrieval QA using OpenAI functionsOpenAPI chainOpenAPI calls with OpenAI functionsProgram-aided language model (PAL) chainQuestion-Answering CitationsDocument QATaggingVector store-augmented text generationMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesChainsAdditionalElasticsearch databaseOn this pageElasticsearch databaseInteract with Elasticsearch analytics database via Langchain. This chain builds search queries via the Elasticsearch DSL API (filters and aggregations).The Elasticsearch client must have permissions for index listing, mapping description and search queries.See here for instructions on how to run Elasticsearch locally.Make sure to install the Elasticsearch Python client before:pip install elasticsearchfrom elasticsearch import Elasticsearchfrom langchain.chains.elasticsearch_database import ElasticsearchDatabaseChainfrom langchain.chat_models import ChatOpenAI# Initialize Elasticsearch python client.# See https://elasticsearch-py.readthedocs.io/en/v8.8.2/api.html#elasticsearch.ElasticsearchELASTIC_SEARCH_SERVER = \"https://elastic:pass@localhost:9200\"db =", "source": "https://python.langchain.com/docs/modules/chains/additional/elasticsearch_database"}
{"id": "e549b7b1efa5-2", "text": "= \"https://elastic:pass@localhost:9200\"db = Elasticsearch(ELASTIC_SEARCH_SERVER)Uncomment the next cell to initially populate your db.# customers = [#     {\"firstname\": \"Jennifer\", \"lastname\": \"Walters\"},#     {\"firstname\": \"Monica\",\"lastname\":\"Rambeau\"},#     {\"firstname\": \"Carol\",\"lastname\":\"Danvers\"},#     {\"firstname\": \"Wanda\",\"lastname\":\"Maximoff\"},#     {\"firstname\": \"Jennifer\",\"lastname\":\"Takeda\"},# ]# for i, customer in enumerate(customers):#     db.create(index=\"customers\", document=customer, id=i)llm = ChatOpenAI(model_name=\"gpt-4\", temperature=0)chain = ElasticsearchDatabaseChain.from_llm(llm=llm, database=db, verbose=True)question = \"What are the first names of all the customers?\"chain.run(question)            > Entering new ElasticsearchDatabaseChain chain...    What are the first names of all the customers?    ESQuery:{'size': 10, 'query': {'match_all': {}}, '_source': ['firstname']}    ESResult: {'took': 5, 'timed_out': False, '_shards': {'total': 1, 'successful': 1, 'skipped': 0, 'failed': 0}, 'hits': {'total': {'value': 6, 'relation': 'eq'}, 'max_score': 1.0, 'hits': [{'_index': 'customers', '_id': '0', '_score': 1.0, '_source': {'firstname': 'Jennifer'}}, {'_index': 'customers', '_id': '1', '_score': 1.0,", "source": "https://python.langchain.com/docs/modules/chains/additional/elasticsearch_database"}
{"id": "e549b7b1efa5-3", "text": "'customers', '_id': '1', '_score': 1.0, '_source': {'firstname': 'Monica'}}, {'_index': 'customers', '_id': '2', '_score': 1.0, '_source': {'firstname': 'Carol'}}, {'_index': 'customers', '_id': '3', '_score': 1.0, '_source': {'firstname': 'Wanda'}}, {'_index': 'customers', '_id': '4', '_score': 1.0, '_source': {'firstname': 'Jennifer'}}, {'_index': 'customers', '_id': 'firstname', '_score': 1.0, '_source': {'firstname': 'Jennifer'}}]}}    Answer:The first names of all the customers are Jennifer, Monica, Carol, Wanda, and Jennifer.    > Finished chain.    'The first names of all the customers are Jennifer, Monica, Carol, Wanda, and Jennifer.'Custom prompt\u00e2\u20ac\u2039For best results you'll likely need to customize the prompt.from langchain.chains.elasticsearch_database.prompts import DEFAULT_DSL_TEMPLATEfrom langchain.prompts.prompt import PromptTemplatePROMPT_TEMPLATE = \"\"\"Given an input question, create a syntactically correct Elasticsearch query to run. Unless the user specifies in their question a specific number of examples they wish to obtain, always limit your query to at most {top_k} results. You can order the results by a relevant column to return the most interesting examples in the database.Unless told to do not query for all the columns from a specific index, only ask for a the few relevant columns given the question.Pay attention to use only the column names that you can see in the mapping description. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which index. Return the query as valid json.Use the following format:Question:", "source": "https://python.langchain.com/docs/modules/chains/additional/elasticsearch_database"}
{"id": "e549b7b1efa5-4", "text": "which column is in which index. Return the query as valid json.Use the following format:Question: Question hereESQuery: Elasticsearch Query formatted as json\"\"\"PROMPT = PromptTemplate.from_template(    PROMPT_TEMPLATE,)chain = ElasticsearchDatabaseChain.from_llm(llm=llm, database=db, query_prompt=PROMPT)Adding example rows from each index\u00e2\u20ac\u2039Sometimes, the format of the data is not obvious and it is optimal to include a sample of rows from the indices in the prompt to allow the LLM to understand the data before providing a final query. Here we will use this feature to let the LLM know that artists are saved with their full names by providing ten rows from the index.chain = ElasticsearchDatabaseChain.from_llm(    llm=ChatOpenAI(temperature=0),    database=db,    sample_documents_in_index_info=2,  # 2 rows from each index will be included in the prompt as sample data)PreviousCausal program-aided language (CPAL) chainNextExtractionCustom promptAdding example rows from each indexCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/chains/additional/elasticsearch_database"}
{"id": "38c552f5de99-0", "text": "Model I/O | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain\n\n\n\n\n\nSkip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OPromptsLanguage modelsOutput parsersData connectionChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesModel I/\u00e2\u20ac\u2039OModel I/OThe core element of any language model application is...the model. LangChain gives you the building blocks to interface with any language model.Prompts: Templatize, dynamically select, and manage model inputsLanguage models: Make calls to language models through common interfacesOutput parsers: Extract information from model outputsPreviousModulesNextPromptsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/model_io/"}
{"id": "d110fc87e9d9-0", "text": "Output parsers | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/model_io/output_parsers/"}
{"id": "d110fc87e9d9-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OPromptsLanguage modelsOutput parsersList parserDatetime parserEnum parserAuto-fixing parserPydantic (JSON) parserRetry parserStructured output parserData connectionChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesModel I/\u00e2\u20ac\u2039OOutput parsersOn this pageOutput parsersLanguage models output text. But many times you may want to get more structured information than just text back. This is where output parsers come in.Output parsers are classes that help structure language model responses. There are two main methods an output parser must implement:\"Get format instructions\": A method which returns a string containing instructions for how the output of a language model should be formatted.\"Parse\": A method which takes in a string (assumed to be the response from a language model) and parses it into some structure.And then one optional one:\"Parse with prompt\": A method which takes in a string (assumed to be the response from a language model) and a prompt (assumed to the prompt that generated such a response) and parses it into some structure. The prompt is largely provided in the event the OutputParser wants to retry or fix the output in some way, and needs information from the prompt to do so.Get started\u00e2\u20ac\u2039Below we go over the main type of output parser, the PydanticOutputParser.from langchain.prompts import PromptTemplate, ChatPromptTemplate, HumanMessagePromptTemplatefrom langchain.llms import OpenAIfrom langchain.chat_models import ChatOpenAIfrom langchain.output_parsers import PydanticOutputParserfrom pydantic import BaseModel, Field, validatorfrom typing import Listmodel_name = 'text-davinci-003'temperature = 0.0model =", "source": "https://python.langchain.com/docs/modules/model_io/output_parsers/"}
{"id": "d110fc87e9d9-2", "text": "Listmodel_name = 'text-davinci-003'temperature = 0.0model = OpenAI(model_name=model_name, temperature=temperature)# Define your desired data structure.class Joke(BaseModel):    setup: str = Field(description=\"question to set up a joke\")    punchline: str = Field(description=\"answer to resolve the joke\")        # You can add custom validation logic easily with Pydantic.    @validator('setup')    def question_ends_with_question_mark(cls, field):        if field[-1] != '?':            raise ValueError(\"Badly formed question!\")        return field# Set up a parser + inject instructions into the prompt template.parser = PydanticOutputParser(pydantic_object=Joke)prompt = PromptTemplate(    template=\"Answer the user query.\\n{format_instructions}\\n{query}\\n\",    input_variables=[\"query\"],    partial_variables={\"format_instructions\": parser.get_format_instructions()})# And a query intended to prompt a language model to populate the data structure.joke_query = \"Tell me a joke.\"_input = prompt.format_prompt(query=joke_query)output = model(_input.to_string())parser.parse(output)    Joke(setup='Why did the chicken cross the road?', punchline='To get to the other side!')PreviousStreamingNextList parserGet startedCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/model_io/output_parsers/"}
{"id": "b2e5514e233d-0", "text": "Retry parser | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/model_io/output_parsers/retry"}
{"id": "b2e5514e233d-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OPromptsLanguage modelsOutput parsersList parserDatetime parserEnum parserAuto-fixing parserPydantic (JSON) parserRetry parserStructured output parserData connectionChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesModel I/\u00e2\u20ac\u2039OOutput parsersRetry parserRetry parserWhile in some cases it is possible to fix any parsing mistakes by only looking at the output, in other cases it can't. An example of this is when the output is not just in the incorrect format, but is partially complete. Consider the below example.from langchain.prompts import (    PromptTemplate,    ChatPromptTemplate,    HumanMessagePromptTemplate,)from langchain.llms import OpenAIfrom langchain.chat_models import ChatOpenAIfrom langchain.output_parsers import (    PydanticOutputParser,    OutputFixingParser,    RetryOutputParser,)from pydantic import BaseModel, Field, validatorfrom typing import Listtemplate = \"\"\"Based on the user question, provide an Action and Action Input for what step should be taken.{format_instructions}Question: {query}Response:\"\"\"class Action(BaseModel):    action: str = Field(description=\"action to take\")    action_input: str = Field(description=\"input to the action\")parser = PydanticOutputParser(pydantic_object=Action)prompt = PromptTemplate(    template=\"Answer the user query.\\n{format_instructions}\\n{query}\\n\",    input_variables=[\"query\"],    partial_variables={\"format_instructions\": parser.get_format_instructions()},)prompt_value = prompt.format_prompt(query=\"who is leo di caprios gf?\")bad_response", "source": "https://python.langchain.com/docs/modules/model_io/output_parsers/retry"}
{"id": "b2e5514e233d-2", "text": "= prompt.format_prompt(query=\"who is leo di caprios gf?\")bad_response = '{\"action\": \"search\"}'If we try to parse this response as is, we will get an errorparser.parse(bad_response)    ---------------------------------------------------------------------------    ValidationError                           Traceback (most recent call last)    File ~/workplace/langchain/langchain/output_parsers/pydantic.py:24, in PydanticOutputParser.parse(self, text)         23     json_object = json.loads(json_str)    ---> 24     return self.pydantic_object.parse_obj(json_object)         26 except (json.JSONDecodeError, ValidationError) as e:    File ~/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/site-packages/pydantic/main.py:527, in pydantic.main.BaseModel.parse_obj()    File ~/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/site-packages/pydantic/main.py:342, in pydantic.main.BaseModel.__init__()    ValidationError: 1 validation error for Action    action_input      field required (type=value_error.missing)        During handling of the above exception, another exception occurred:    OutputParserException                     Traceback (most recent call last)    Cell In[6], line 1    ----> 1 parser.parse(bad_response)    File", "source": "https://python.langchain.com/docs/modules/model_io/output_parsers/retry"}
{"id": "b2e5514e233d-3", "text": "line 1    ----> 1 parser.parse(bad_response)    File ~/workplace/langchain/langchain/output_parsers/pydantic.py:29, in PydanticOutputParser.parse(self, text)         27 name = self.pydantic_object.__name__         28 msg = f\"Failed to parse {name} from completion {text}. Got: {e}\"    ---> 29 raise OutputParserException(msg)    OutputParserException: Failed to parse Action from completion {\"action\": \"search\"}. Got: 1 validation error for Action    action_input      field required (type=value_error.missing)If we try to use the OutputFixingParser to fix this error, it will be confused - namely, it doesn't know what to actually put for action input.fix_parser = OutputFixingParser.from_llm(parser=parser, llm=ChatOpenAI())fix_parser.parse(bad_response)    Action(action='search', action_input='')Instead, we can use the RetryOutputParser, which passes in the prompt (as well as the original output) to try again to get a better response.from langchain.output_parsers import RetryWithErrorOutputParserretry_parser = RetryWithErrorOutputParser.from_llm(    parser=parser, llm=OpenAI(temperature=0))retry_parser.parse_with_prompt(bad_response, prompt_value)    Action(action='search', action_input='who is leo di caprios gf?')PreviousPydantic (JSON) parserNextStructured output parserCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/model_io/output_parsers/retry"}
{"id": "f81e7c539339-0", "text": "Enum parser | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/model_io/output_parsers/enum"}
{"id": "f81e7c539339-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OPromptsLanguage modelsOutput parsersList parserDatetime parserEnum parserAuto-fixing parserPydantic (JSON) parserRetry parserStructured output parserData connectionChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesModel I/\u00e2\u20ac\u2039OOutput parsersEnum parserEnum parserThis notebook shows how to use an Enum output parserfrom langchain.output_parsers.enum import EnumOutputParserfrom enum import Enumclass Colors(Enum):    RED = \"red\"    GREEN = \"green\"    BLUE = \"blue\"parser = EnumOutputParser(enum=Colors)parser.parse(\"red\")    <Colors.RED: 'red'># Can handle spacesparser.parse(\" green\")    <Colors.GREEN: 'green'># And new linesparser.parse(\"blue\\n\")    <Colors.BLUE: 'blue'># And raises errors when appropriateparser.parse(\"yellow\")    ---------------------------------------------------------------------------    ValueError                                Traceback (most recent call last)    File ~/workplace/langchain/langchain/output_parsers/enum.py:25, in EnumOutputParser.parse(self, response)         24 try:    ---> 25     return self.enum(response.strip())         26 except ValueError:    File ~/.pyenv/versions/3.9.1/lib/python3.9/enum.py:315, in EnumMeta.__call__(cls, value, names, module, qualname, type, start)", "source": "https://python.langchain.com/docs/modules/model_io/output_parsers/enum"}
{"id": "f81e7c539339-2", "text": "EnumMeta.__call__(cls, value, names, module, qualname, type, start)        314 if names is None:  # simple value lookup    --> 315     return cls.__new__(cls, value)        316 # otherwise, functional API: we're creating a new Enum type    File ~/.pyenv/versions/3.9.1/lib/python3.9/enum.py:611, in Enum.__new__(cls, value)        610 if result is None and exc is None:    --> 611     raise ve_exc        612 elif exc is None:    ValueError: 'yellow' is not a valid Colors        During handling of the above exception, another exception occurred:    OutputParserException                     Traceback (most recent call last)    Cell In[8], line 2          1 # And raises errors when appropriate    ----> 2 parser.parse(\"yellow\")    File ~/workplace/langchain/langchain/output_parsers/enum.py:27, in EnumOutputParser.parse(self, response)         25     return self.enum(response.strip())         26 except ValueError:    ---> 27     raise OutputParserException(         28         f\"Response '{response}' is not one of the \"         29         f\"expected values:", "source": "https://python.langchain.com/docs/modules/model_io/output_parsers/enum"}
{"id": "f81e7c539339-3", "text": "29         f\"expected values: {self._valid_values}\"         30     )    OutputParserException: Response 'yellow' is not one of the expected values: ['red', 'green', 'blue']PreviousDatetime parserNextAuto-fixing parserCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/model_io/output_parsers/enum"}
{"id": "6f5e4b906b41-0", "text": "Datetime parser | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain\n\n\n\n\n\nSkip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OPromptsLanguage modelsOutput parsersList parserDatetime parserEnum parserAuto-fixing parserPydantic (JSON) parserRetry parserStructured output parserData connectionChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesModel I/\u00e2\u20ac\u2039OOutput parsersDatetime parserDatetime parserThis OutputParser shows out to parse LLM output into datetime format.from langchain.prompts import PromptTemplatefrom langchain.output_parsers import DatetimeOutputParserfrom langchain.chains import LLMChainfrom langchain.llms import OpenAIoutput_parser = DatetimeOutputParser()template = \"\"\"Answer the users question:{question}{format_instructions}\"\"\"prompt = PromptTemplate.from_template(    template,    partial_variables={\"format_instructions\": output_parser.get_format_instructions()},)chain = LLMChain(prompt=prompt, llm=OpenAI())output = chain.run(\"around when was bitcoin founded?\")output    '\\n\\n2008-01-03T18:15:05.000000Z'output_parser.parse(output)    datetime.datetime(2008, 1, 3, 18, 15, 5)PreviousList parserNextEnum parserCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/model_io/output_parsers/datetime"}
{"id": "9aee13b1f296-0", "text": "Auto-fixing parser | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/model_io/output_parsers/output_fixing_parser"}
{"id": "9aee13b1f296-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OPromptsLanguage modelsOutput parsersList parserDatetime parserEnum parserAuto-fixing parserPydantic (JSON) parserRetry parserStructured output parserData connectionChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesModel I/\u00e2\u20ac\u2039OOutput parsersAuto-fixing parserAuto-fixing parserThis output parser wraps another output parser, and in the event that the first one fails it calls out to another LLM to fix any errors.But we can do other things besides throw errors. Specifically, we can pass the misformatted output, along with the formatted instructions, to the model and ask it to fix it.For this example, we'll use the above Pydantic output parser. Here's what happens if we pass it a result that does not comply with the schema:from langchain.prompts import PromptTemplate, ChatPromptTemplate, HumanMessagePromptTemplatefrom langchain.llms import OpenAIfrom langchain.chat_models import ChatOpenAIfrom langchain.output_parsers import PydanticOutputParserfrom pydantic import BaseModel, Field, validatorfrom typing import Listclass Actor(BaseModel):    name: str = Field(description=\"name of an actor\")    film_names: List[str] = Field(description=\"list of names of films they starred in\")        actor_query = \"Generate the filmography for a random actor.\"parser = PydanticOutputParser(pydantic_object=Actor)misformatted = \"{'name': 'Tom Hanks', 'film_names': ['Forrest Gump']}\"parser.parse(misformatted)    ---------------------------------------------------------------------------    JSONDecodeError", "source": "https://python.langchain.com/docs/modules/model_io/output_parsers/output_fixing_parser"}
{"id": "9aee13b1f296-2", "text": "JSONDecodeError                           Traceback (most recent call last)    File ~/workplace/langchain/langchain/output_parsers/pydantic.py:23, in PydanticOutputParser.parse(self, text)         22     json_str = match.group()    ---> 23 json_object = json.loads(json_str)         24 return self.pydantic_object.parse_obj(json_object)    File ~/.pyenv/versions/3.9.1/lib/python3.9/json/__init__.py:346, in loads(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)        343 if (cls is None and object_hook is None and        344         parse_int is None and parse_float is None and        345         parse_constant is None and object_pairs_hook is None and not kw):    --> 346     return _default_decoder.decode(s)        347 if cls is None:    File ~/.pyenv/versions/3.9.1/lib/python3.9/json/decoder.py:337, in JSONDecoder.decode(self, s, _w)        333 \"\"\"Return the Python representation of ``s`` (a ``str`` instance        334 containing a JSON document).        335         336 \"\"\"    --> 337 obj, end = self.raw_decode(s,", "source": "https://python.langchain.com/docs/modules/model_io/output_parsers/output_fixing_parser"}
{"id": "9aee13b1f296-3", "text": "336 \"\"\"    --> 337 obj, end = self.raw_decode(s, idx=_w(s, 0).end())        338 end = _w(s, end).end()    File ~/.pyenv/versions/3.9.1/lib/python3.9/json/decoder.py:353, in JSONDecoder.raw_decode(self, s, idx)        352 try:    --> 353     obj, end = self.scan_once(s, idx)        354 except StopIteration as err:    JSONDecodeError: Expecting property name enclosed in double quotes: line 1 column 2 (char 1)        During handling of the above exception, another exception occurred:    OutputParserException                     Traceback (most recent call last)    Cell In[6], line 1    ----> 1 parser.parse(misformatted)    File ~/workplace/langchain/langchain/output_parsers/pydantic.py:29, in PydanticOutputParser.parse(self, text)         27 name = self.pydantic_object.__name__         28 msg = f\"Failed to parse {name} from completion {text}. Got: {e}\"    ---> 29 raise OutputParserException(msg)    OutputParserException: Failed to parse Actor from completion {'name': 'Tom Hanks', 'film_names': ['Forrest Gump']}. Got: Expecting property name enclosed in double quotes: line 1 column 2 (char 1)Now we can construct and use a OutputFixingParser. This output parser", "source": "https://python.langchain.com/docs/modules/model_io/output_parsers/output_fixing_parser"}
{"id": "9aee13b1f296-4", "text": "(char 1)Now we can construct and use a OutputFixingParser. This output parser takes as an argument another output parser but also an LLM with which to try to correct any formatting mistakes.from langchain.output_parsers import OutputFixingParsernew_parser = OutputFixingParser.from_llm(parser=parser, llm=ChatOpenAI())new_parser.parse(misformatted)    Actor(name='Tom Hanks', film_names=['Forrest Gump'])PreviousEnum parserNextPydantic (JSON) parserCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/model_io/output_parsers/output_fixing_parser"}
{"id": "840ec3e8b296-0", "text": "Structured output parser | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/model_io/output_parsers/structured"}
{"id": "840ec3e8b296-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OPromptsLanguage modelsOutput parsersList parserDatetime parserEnum parserAuto-fixing parserPydantic (JSON) parserRetry parserStructured output parserData connectionChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesModel I/\u00e2\u20ac\u2039OOutput parsersStructured output parserStructured output parserThis output parser can be used when you want to return multiple fields. While the Pydantic/JSON parser is more powerful, we initially experimented with data structures having text fields only.from langchain.output_parsers import StructuredOutputParser, ResponseSchemafrom langchain.prompts import PromptTemplate, ChatPromptTemplate, HumanMessagePromptTemplatefrom langchain.llms import OpenAIfrom langchain.chat_models import ChatOpenAIHere we define the response schema we want to receive.response_schemas = [    ResponseSchema(name=\"answer\", description=\"answer to the user's question\"),    ResponseSchema(name=\"source\", description=\"source used to answer the user's question, should be a website.\")]output_parser = StructuredOutputParser.from_response_schemas(response_schemas)We now get a string that contains instructions for how the response should be formatted, and we then insert that into our prompt.format_instructions = output_parser.get_format_instructions()prompt = PromptTemplate(    template=\"answer the users question as best as possible.\\n{format_instructions}\\n{question}\",    input_variables=[\"question\"],    partial_variables={\"format_instructions\": format_instructions})We can now use this to format a prompt to send to the language model, and then parse the returned result.model = OpenAI(temperature=0)_input = prompt.format_prompt(question=\"what's the capital of france?\")output =", "source": "https://python.langchain.com/docs/modules/model_io/output_parsers/structured"}
{"id": "840ec3e8b296-2", "text": "= prompt.format_prompt(question=\"what's the capital of france?\")output = model(_input.to_string())output_parser.parse(output)    {'answer': 'Paris',     'source': 'https://www.worldatlas.com/articles/what-is-the-capital-of-france.html'}And here's an example of using this in a chat modelchat_model = ChatOpenAI(temperature=0)prompt = ChatPromptTemplate(    messages=[        HumanMessagePromptTemplate.from_template(\"answer the users question as best as possible.\\n{format_instructions}\\n{question}\")      ],    input_variables=[\"question\"],    partial_variables={\"format_instructions\": format_instructions})_input = prompt.format_prompt(question=\"what's the capital of france?\")output = chat_model(_input.to_messages())output_parser.parse(output.content)    {'answer': 'Paris', 'source': 'https://en.wikipedia.org/wiki/Paris'}PreviousRetry parserNextData connectionCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/model_io/output_parsers/structured"}
{"id": "05ab0be052c5-0", "text": "Pydantic (JSON) parser | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/model_io/output_parsers/pydantic"}
{"id": "05ab0be052c5-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OPromptsLanguage modelsOutput parsersList parserDatetime parserEnum parserAuto-fixing parserPydantic (JSON) parserRetry parserStructured output parserData connectionChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesModel I/\u00e2\u20ac\u2039OOutput parsersPydantic (JSON) parserPydantic (JSON) parserThis output parser allows users to specify an arbitrary JSON schema and query LLMs for JSON outputs that conform to that schema.Keep in mind that large language models are leaky abstractions! You'll have to use an LLM with sufficient capacity to generate well-formed JSON. In the OpenAI family, DaVinci can do reliably but Curie's ability already drops off dramatically. Use Pydantic to declare your data model. Pydantic's BaseModel like a Python dataclass, but with actual type checking + coercion.from langchain.prompts import (    PromptTemplate,    ChatPromptTemplate,    HumanMessagePromptTemplate,)from langchain.llms import OpenAIfrom langchain.chat_models import ChatOpenAIfrom langchain.output_parsers import PydanticOutputParserfrom pydantic import BaseModel, Field, validatorfrom typing import Listmodel_name = \"text-davinci-003\"temperature = 0.0model = OpenAI(model_name=model_name, temperature=temperature)# Define your desired data structure.class Joke(BaseModel):    setup: str = Field(description=\"question to set up a joke\")    punchline: str = Field(description=\"answer to resolve the joke\")    # You can add custom validation logic easily with Pydantic.    @validator(\"setup\")    def", "source": "https://python.langchain.com/docs/modules/model_io/output_parsers/pydantic"}
{"id": "05ab0be052c5-2", "text": "validation logic easily with Pydantic.    @validator(\"setup\")    def question_ends_with_question_mark(cls, field):        if field[-1] != \"?\":            raise ValueError(\"Badly formed question!\")        return field# And a query intented to prompt a language model to populate the data structure.joke_query = \"Tell me a joke.\"# Set up a parser + inject instructions into the prompt template.parser = PydanticOutputParser(pydantic_object=Joke)prompt = PromptTemplate(    template=\"Answer the user query.\\n{format_instructions}\\n{query}\\n\",    input_variables=[\"query\"],    partial_variables={\"format_instructions\": parser.get_format_instructions()},)_input = prompt.format_prompt(query=joke_query)output = model(_input.to_string())parser.parse(output)    Joke(setup='Why did the chicken cross the road?', punchline='To get to the other side!')# Here's another example, but with a compound typed field.class Actor(BaseModel):    name: str = Field(description=\"name of an actor\")    film_names: List[str] = Field(description=\"list of names of films they starred in\")actor_query = \"Generate the filmography for a random actor.\"parser = PydanticOutputParser(pydantic_object=Actor)prompt = PromptTemplate(    template=\"Answer the user query.\\n{format_instructions}\\n{query}\\n\",    input_variables=[\"query\"],    partial_variables={\"format_instructions\": parser.get_format_instructions()},)_input = prompt.format_prompt(query=actor_query)output = model(_input.to_string())parser.parse(output)    Actor(name='Tom Hanks', film_names=['Forrest Gump', 'Saving Private Ryan', 'The", "source": "https://python.langchain.com/docs/modules/model_io/output_parsers/pydantic"}
{"id": "05ab0be052c5-3", "text": "Hanks', film_names=['Forrest Gump', 'Saving Private Ryan', 'The Green Mile', 'Cast Away', 'Toy Story'])PreviousAuto-fixing parserNextRetry parserCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/model_io/output_parsers/pydantic"}
{"id": "539b6e7cc333-0", "text": "List parser | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain\n\n\n\n\n\nSkip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OPromptsLanguage modelsOutput parsersList parserDatetime parserEnum parserAuto-fixing parserPydantic (JSON) parserRetry parserStructured output parserData connectionChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesModel I/\u00e2\u20ac\u2039OOutput parsersList parserList parserThis output parser can be used when you want to return a list of comma-separated items.from langchain.output_parsers import CommaSeparatedListOutputParserfrom langchain.prompts import PromptTemplate, ChatPromptTemplate, HumanMessagePromptTemplatefrom langchain.llms import OpenAIfrom langchain.chat_models import ChatOpenAIoutput_parser = CommaSeparatedListOutputParser()format_instructions = output_parser.get_format_instructions()prompt = PromptTemplate(    template=\"List five {subject}.\\n{format_instructions}\",    input_variables=[\"subject\"],    partial_variables={\"format_instructions\": format_instructions})model = OpenAI(temperature=0)_input = prompt.format(subject=\"ice cream flavors\")output = model(_input)output_parser.parse(output)    ['Vanilla',     'Chocolate',     'Strawberry',     'Mint Chocolate Chip',     'Cookies and Cream']PreviousOutput parsersNextDatetime parserCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/model_io/output_parsers/comma_separated"}
{"id": "0abf92c8e00f-0", "text": "Language models | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/model_io/models/"}
{"id": "0abf92c8e00f-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OPromptsLanguage modelsLLMsChat modelsOutput parsersData connectionChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesModel I/\u00e2\u20ac\u2039OLanguage modelsOn this pageLanguage modelsLangChain provides interfaces and integrations for two types of models:LLMs: Models that take a text string as input and return a text stringChat models: Models that are backed by a language model but take a list of Chat Messages as input and return a Chat MessageLLMs vs Chat Models\u00e2\u20ac\u2039LLMs and Chat Models are subtly but importantly different. LLMs in LangChain refer to pure text completion models.\nThe APIs they wrap take a string prompt as input and output a string completion. OpenAI's GPT-3 is implemented as an LLM.\nChat models are often backed by LLMs but tuned specifically for having conversations.\nAnd, crucially, their provider APIs expose a different interface than pure text completion models. Instead of a single string,\nthey take a list of chat messages as input. Usually these messages are labeled with the speaker (usually one of \"System\",\n\"AI\", and \"Human\"). And they return a (\"AI\") chat message as output. GPT-4 and Anthropic's Claude are both implemented as Chat Models.To make it possible to swap LLMs and Chat Models, both implement the Base Language Model interface. This exposes common\nmethods \"predict\", which takes a string and returns a string, and \"predict messages\", which takes messages and returns a message.", "source": "https://python.langchain.com/docs/modules/model_io/models/"}
{"id": "0abf92c8e00f-2", "text": "If you are using a specific model it's recommended you use the methods specific to that model class (i.e., \"predict\" for LLMs and \"predict messages\" for Chat Models),\nbut if you're creating an application that should work with different types of models the shared interface can be helpful.PreviousSelect by similarityNextLLMsLLMs vs Chat ModelsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/model_io/models/"}
{"id": "ea5b539c496a-0", "text": "Chat models | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/model_io/models/chat/"}
{"id": "ea5b539c496a-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OPromptsLanguage modelsLLMsChat modelsCachingHuman input Chat ModelLLMChainPromptsStreamingOutput parsersData connectionChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesModel I/\u00e2\u20ac\u2039OLanguage modelsChat modelsOn this pageChat modelsinfoHead to Integrations for documentation on built-in integrations with chat model providers.Chat models are a variation on language models.\nWhile chat models use language models under the hood, the interface they expose is a bit different.\nRather than expose a \"text in, text out\" API, they expose an interface where \"chat messages\" are the inputs and outputs.Chat model APIs are fairly new, so we are still figuring out the correct abstractions.Get started\u00e2\u20ac\u2039Setup\u00e2\u20ac\u2039To start we'll need to install the OpenAI Python package:pip install openaiAccessing the API requires an API key, which you can get by creating an account and heading here. Once we have a key we'll want to set it as an environment variable by running:export OPENAI_API_KEY=\"...\"If you'd prefer not to set an environment variable you can pass the key in directly via the openai_api_key named parameter when initiating the OpenAI LLM class:from langchain.chat_models import ChatOpenAIchat = ChatOpenAI(openai_api_key=\"...\")otherwise you can initialize without any params:from langchain.chat_models import ChatOpenAIchat = ChatOpenAI()Messages\u00e2\u20ac\u2039The chat model interface is based around messages rather than raw text.", "source": "https://python.langchain.com/docs/modules/model_io/models/chat/"}
{"id": "ea5b539c496a-2", "text": "The types of messages currently supported in LangChain are AIMessage, HumanMessage, SystemMessage, and ChatMessage -- ChatMessage takes in an arbitrary role parameter. Most of the time, you'll just be dealing with HumanMessage, AIMessage, and SystemMessage__call__\u00e2\u20ac\u2039Messages in -> message out\u00e2\u20ac\u2039You can get chat completions by passing one or more messages to the chat model. The response will be a message.from langchain.schema import (    AIMessage,    HumanMessage,    SystemMessage)chat([HumanMessage(content=\"Translate this sentence from English to French: I love programming.\")])    AIMessage(content=\"J'aime programmer.\", additional_kwargs={})OpenAI's chat model supports multiple messages as input. See here for more information. Here is an example of sending a system and user message to the chat model:messages = [    SystemMessage(content=\"You are a helpful assistant that translates English to French.\"),    HumanMessage(content=\"I love programming.\")]chat(messages)    AIMessage(content=\"J'aime programmer.\", additional_kwargs={})generate\u00e2\u20ac\u2039Batch calls, richer outputs\u00e2\u20ac\u2039You can go one step further and generate completions for multiple sets of messages using generate. This returns an LLMResult with an additional message parameter.batch_messages = [    [        SystemMessage(content=\"You are a helpful assistant that translates English to French.\"),        HumanMessage(content=\"I love programming.\")    ],    [        SystemMessage(content=\"You are a helpful assistant that translates English to French.\"),        HumanMessage(content=\"I love artificial intelligence.\")    ],]result = chat.generate(batch_messages)result    LLMResult(generations=[[ChatGeneration(text=\"J'aime", "source": "https://python.langchain.com/docs/modules/model_io/models/chat/"}
{"id": "ea5b539c496a-3", "text": "LLMResult(generations=[[ChatGeneration(text=\"J'aime programmer.\", generation_info=None, message=AIMessage(content=\"J'aime programmer.\", additional_kwargs={}))], [ChatGeneration(text=\"J'aime l'intelligence artificielle.\", generation_info=None, message=AIMessage(content=\"J'aime l'intelligence artificielle.\", additional_kwargs={}))]], llm_output={'token_usage': {'prompt_tokens': 57, 'completion_tokens': 20, 'total_tokens': 77}})You can recover things like token usage from this LLMResultresult.llm_output    {'token_usage': {'prompt_tokens': 57,      'completion_tokens': 20,      'total_tokens': 77}}PreviousTracking token usageNextCachingGet startedCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/model_io/models/chat/"}
{"id": "73a642ea63b0-0", "text": "LLMChain | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain\n\n\n\n\n\nSkip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OPromptsLanguage modelsLLMsChat modelsCachingHuman input Chat ModelLLMChainPromptsStreamingOutput parsersData connectionChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesModel I/\u00e2\u20ac\u2039OLanguage modelsChat modelsLLMChainLLMChainYou can use the existing LLMChain in a very similar way to before - provide a prompt and a model.chain = LLMChain(llm=chat, prompt=chat_prompt)chain.run(input_language=\"English\", output_language=\"French\", text=\"I love programming.\")    \"J'adore la programmation.\"PreviousHuman input Chat ModelNextPromptsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/model_io/models/chat/llm_chain"}
{"id": "7024fb7919bc-0", "text": "Caching | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/model_io/models/chat/chat_model_caching"}
{"id": "7024fb7919bc-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OPromptsLanguage modelsLLMsChat modelsCachingHuman input Chat ModelLLMChainPromptsStreamingOutput parsersData connectionChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesModel I/\u00e2\u20ac\u2039OLanguage modelsChat modelsCachingCachingLangChain provides an optional caching layer for Chat Models. This is useful for two reasons:It can save you money by reducing the number of API calls you make to the LLM provider, if you're often requesting the same completion multiple times.", "source": "https://python.langchain.com/docs/modules/model_io/models/chat/chat_model_caching"}
{"id": "7024fb7919bc-2", "text": "It can speed up your application by reducing the number of API calls you make to the LLM provider.import langchainfrom langchain.chat_models import ChatOpenAIllm = ChatOpenAI()In Memory Cache\u00e2\u20ac\u2039from langchain.cache import InMemoryCachelangchain.llm_cache = InMemoryCache()# The first time, it is not yet in cache, so it should take longerllm.predict(\"Tell me a joke\")    CPU times: user 35.9 ms, sys: 28.6 ms, total: 64.6 ms    Wall time: 4.83 s        \"\\n\\nWhy couldn't the bicycle stand up by itself? It was...two tired!\"# The second time it is, so it goes fasterllm.predict(\"Tell me a joke\")    CPU times: user 238 \u00c2\u00b5s, sys: 143 \u00c2\u00b5s, total: 381 \u00c2\u00b5s    Wall time: 1.76 ms    '\\n\\nWhy did the chicken cross the road?\\n\\nTo get to the other side.'SQLite Cache\u00e2\u20ac\u2039rm .langchain.db# We can do the same thing with a SQLite cachefrom langchain.cache import SQLiteCachelangchain.llm_cache = SQLiteCache(database_path=\".langchain.db\")# The first time, it is not yet in cache, so it should take longerllm.predict(\"Tell me a joke\")    CPU times: user 17 ms, sys: 9.76 ms, total: 26.7 ms    Wall time: 825 ms    '\\n\\nWhy did the chicken cross the road?\\n\\nTo get to the other side.'# The second time it is, so it goes fasterllm.predict(\"Tell me a joke\")    CPU times: user 2.46", "source": "https://python.langchain.com/docs/modules/model_io/models/chat/chat_model_caching"}
{"id": "7024fb7919bc-3", "text": "me a joke\")    CPU times: user 2.46 ms, sys: 1.23 ms, total: 3.7 ms    Wall time: 2.67 ms    '\\n\\nWhy did the chicken cross the road?\\n\\nTo get to the other side.'PreviousChat modelsNextHuman input Chat ModelCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/model_io/models/chat/chat_model_caching"}
{"id": "7ed94564609e-0", "text": "Streaming | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/model_io/models/chat/streaming"}
{"id": "7ed94564609e-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OPromptsLanguage modelsLLMsChat modelsCachingHuman input Chat ModelLLMChainPromptsStreamingOutput parsersData connectionChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesModel I/\u00e2\u20ac\u2039OLanguage modelsChat modelsStreamingStreamingSome Chat models provide a streaming response. This means that instead of waiting for the entire response to be returned, you can start processing it as soon as it's available. This is useful if you want to display the response to the user as it's being generated, or if you want to process the response as it's being generated.from langchain.chat_models import ChatOpenAIfrom langchain.schema import (    HumanMessage,)from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandlerchat = ChatOpenAI(streaming=True, callbacks=[StreamingStdOutCallbackHandler()], temperature=0)resp = chat([HumanMessage(content=\"Write me a song about sparkling water.\")])    Verse 1:    Bubbles rising to the top    A refreshing drink that never stops    Clear and crisp, it's pure delight    A taste that's sure to excite        Chorus:    Sparkling water, oh so fine    A drink that's always on my mind    With every sip, I feel alive    Sparkling water, you're my vibe        Verse 2:    No sugar, no calories, just pure bliss    A drink that's hard to resist    It's the perfect way to quench my thirst    A drink that always comes first", "source": "https://python.langchain.com/docs/modules/model_io/models/chat/streaming"}
{"id": "7ed94564609e-2", "text": "quench my thirst    A drink that always comes first        Chorus:    Sparkling water, oh so fine    A drink that's always on my mind    With every sip, I feel alive    Sparkling water, you're my vibe        Bridge:    From the mountains to the sea    Sparkling water, you're the key    To a healthy life, a happy soul    A drink that makes me feel whole        Chorus:    Sparkling water, oh so fine    A drink that's always on my mind    With every sip, I feel alive    Sparkling water, you're my vibe        Outro:    Sparkling water, you're the one    A drink that's always so much fun    I'll never let you go, my friend    SparklingPreviousPromptsNextOutput parsersCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/model_io/models/chat/streaming"}
{"id": "e645b88d39cb-0", "text": "Prompts | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/model_io/models/chat/prompts"}
{"id": "e645b88d39cb-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OPromptsLanguage modelsLLMsChat modelsCachingHuman input Chat ModelLLMChainPromptsStreamingOutput parsersData connectionChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesModel I/\u00e2\u20ac\u2039OLanguage modelsChat modelsPromptsPromptsPrompts for Chat models are built around messages, instead of just plain text.You can make use of templating by using a MessagePromptTemplate. You can build a ChatPromptTemplate from one or more MessagePromptTemplates. You can use ChatPromptTemplate's format_prompt -- this returns a PromptValue, which you can convert to a string or Message object, depending on whether you want to use the formatted value as input to an llm or chat model.For convenience, there is a from_template method exposed on the template. If you were to use this template, this is what it would look like:from langchain import PromptTemplatefrom langchain.prompts.chat import (    ChatPromptTemplate,    SystemMessagePromptTemplate,    AIMessagePromptTemplate,    HumanMessagePromptTemplate,)template=\"You are a helpful assistant that translates {input_language} to {output_language}.\"system_message_prompt = SystemMessagePromptTemplate.from_template(template)human_template=\"{text}\"human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])# get a chat completion from the formatted messageschat(chat_prompt.format_prompt(input_language=\"English\", output_language=\"French\", text=\"I love programming.\").to_messages())    AIMessage(content=\"J'adore la programmation.\", additional_kwargs={})If you wanted to construct the MessagePromptTemplate more directly,", "source": "https://python.langchain.com/docs/modules/model_io/models/chat/prompts"}
{"id": "e645b88d39cb-2", "text": "la programmation.\", additional_kwargs={})If you wanted to construct the MessagePromptTemplate more directly, you could create a PromptTemplate outside and then pass it in, eg:prompt=PromptTemplate(    template=\"You are a helpful assistant that translates {input_language} to {output_language}.\",    input_variables=[\"input_language\", \"output_language\"],)system_message_prompt = SystemMessagePromptTemplate(prompt=prompt)PreviousLLMChainNextStreamingCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/model_io/models/chat/prompts"}
{"id": "75cd6f3caa96-0", "text": "Human input Chat Model | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/model_io/models/chat/human_input_chat_model"}
{"id": "75cd6f3caa96-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OPromptsLanguage modelsLLMsChat modelsCachingHuman input Chat ModelLLMChainPromptsStreamingOutput parsersData connectionChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesModel I/\u00e2\u20ac\u2039OLanguage modelsChat modelsHuman input Chat ModelHuman input Chat ModelAlong with HumanInputLLM, LangChain also provides a pseudo Chat Model class that can be used for testing, debugging, or educational purposes. This allows you to mock out calls to the Chat Model and simulate how a human would respond if they received the messages.In this notebook, we go over how to use this.We start this with using the HumanInputChatModel in an agent.from langchain.chat_models.human import HumanInputChatModelSince we will use the WikipediaQueryRun tool in this notebook, you might need to install the wikipedia package if you haven't done so already.%pip install wikipedia    /Users/mskim58/dev/research/chatbot/github/langchain/.venv/bin/python: No module named pip    Note: you may need to restart the kernel to use updated packages.from langchain.agents import load_toolsfrom langchain.agents import initialize_agentfrom langchain.agents import AgentTypetools = load_tools([\"wikipedia\"])llm = HumanInputChatModel()agent = initialize_agent(    tools, llm, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True)agent(\"What is Bocchi the Rock?\")            > Entering new  chain...         ======= start of message =======             type: system", "source": "https://python.langchain.com/docs/modules/model_io/models/chat/human_input_chat_model"}
{"id": "75cd6f3caa96-2", "text": "message =======             type: system    data:      content: \"Answer the following questions as best you can. You have access to the following tools:\\n\\nWikipedia: A wrapper around Wikipedia. Useful for when you need to answer general questions about people, places, companies, facts, historical events, or other subjects. Input should be a search query.\\n\\nThe way you use the tools is by specifying a json blob.\\nSpecifically, this json should have a `action` key (with the name of the tool to use) and a `action_input` key (with the input to the tool going here).\\n\\nThe only values that should be in the \\\"action\\\" field are: Wikipedia\\n\\nThe $JSON_BLOB should only contain a SINGLE action, do NOT return a list of multiple actions. Here is an example of a valid $JSON_BLOB:\\n\\n```\\n{\\n  \\\"action\\\": $TOOL_NAME,\\n  \\\"action_input\\\": $INPUT\\n}\\n```\\n\\nALWAYS use the following format:\\n\\nQuestion: the input question you must answer\\nThought: you should always think about what to do\\nAction:\\n```\\n$JSON_BLOB\\n```\\nObservation: the result of the action\\n... (this Thought/Action/Observation can repeat N times)\\nThought: I now know the final answer\\nFinal Answer: the final answer to the original input question\\n\\nBegin! Reminder to always use the exact characters `Final Answer` when responding.\"      additional_kwargs: {}        ======= end of message =======                  ======= start of message =======             type: human    data:", "source": "https://python.langchain.com/docs/modules/model_io/models/chat/human_input_chat_model"}
{"id": "75cd6f3caa96-3", "text": "type: human    data:      content: 'What is Bocchi the Rock?                '      additional_kwargs: {}      example: false        ======= end of message =======             Action:    ```    {      \"action\": \"Wikipedia\",      \"action_input\": \"What is Bocchi the Rock?\"    }    ```    Observation: Page: Bocchi the Rock!    Summary: Bocchi the Rock! (\u00e3\ufffd\u00bc\u00e3\ufffd\u00a3\u00e3\ufffd\u00a1\u00e3\u0192\u00bb\u00e3\ufffd\u2013\u00e3\u0192\u00bb\u00e3\u201a\ufffd\u00e3\ufffd\u00a3\u00e3\ufffd\ufffd!, Botchi Za Rokku!) is a Japanese four-panel manga series written and illustrated by Aki Hamaji. It has been serialized in Houbunsha's seinen manga magazine Manga Time Kirara Max since December 2017. Its chapters have been collected in five tank\u00c5\ufffdbon volumes as of November 2022.    An anime television series adaptation produced by CloverWorks aired from October to December 2022. The series has been praised for its writing, comedy, characters, and depiction of social anxiety, with the anime's visual creativity receiving acclaim.        Page: Hitori Bocchi no Marumaru Seikatsu    Summary: Hitori Bocchi no Marumaru Seikatsu (Japanese: \u00e3\ufffd\u00b2\u00e3\ufffd\u00a8\u00e3\u201a\u0160\u00e3\ufffd\u00bc\u00e3\ufffd\u00a3\u00e3\ufffd\u00a1\u00e3\ufffd\u00ae\u00e2\u2014\u2039\u00e2\u2014\u2039\u00e7\u201d\u0178\u00e6\u00b4\u00bb, lit. \"Bocchi Hitori's ____ Life\" or \"The", "source": "https://python.langchain.com/docs/modules/model_io/models/chat/human_input_chat_model"}
{"id": "75cd6f3caa96-4", "text": "lit. \"Bocchi Hitori's ____ Life\" or \"The ____ Life of Being Alone\") is a Japanese yonkoma manga series written and illustrated by Katsuwo. It was serialized in ASCII Media Works' Comic Dengeki Daioh \"g\" magazine from September 2013 to April 2021. Eight tank\u00c5\ufffdbon volumes have been released. An anime television series adaptation by C2C aired from April to June 2019.        Page: Kessoku Band (album)    Summary: Kessoku Band (Japanese: \u00e7\u00b5\ufffd\u00e6\ufffd\u0178\u00e3\u0192\ufffd\u00e3\u0192\u00b3\u00e3\u0192\u2030, Hepburn: Kessoku Bando) is the debut studio album by Kessoku Band, a fictional musical group from the anime television series Bocchi the Rock!, released digitally on December 25, 2022, and physically on CD on December 28 by Aniplex. Featuring vocals from voice actresses Yoshino Aoyama, Sayumi Suzushiro, Saku Mizuno, and Ikumi Hasegawa, the album consists of 14 tracks previously heard in the anime, including a cover of Asian Kung-Fu Generation's \"Rockn' Roll, Morning Light Falls on You\", as well as newly recorded songs; nine singles preceded the album's physical release. Commercially, Kessoku Band peaked at number one on the Billboard Japan Hot Albums Chart and Oricon Albums Chart, and was certified gold by the Recording Industry Association of Japan.            Thought:     ======= start of message =======             type: system    data:      content: \"Answer the following questions as best you can. You have access to the following tools:\\n\\nWikipedia: A wrapper around Wikipedia. Useful", "source": "https://python.langchain.com/docs/modules/model_io/models/chat/human_input_chat_model"}
{"id": "75cd6f3caa96-5", "text": "You have access to the following tools:\\n\\nWikipedia: A wrapper around Wikipedia. Useful for when you need to answer general questions about people, places, companies, facts, historical events, or other subjects. Input should be a search query.\\n\\nThe way you use the tools is by specifying a json blob.\\nSpecifically, this json should have a `action` key (with the name of the tool to use) and a `action_input` key (with the input to the tool going here).\\n\\nThe only values that should be in the \\\"action\\\" field are: Wikipedia\\n\\nThe $JSON_BLOB should only contain a SINGLE action, do NOT return a list of multiple actions. Here is an example of a valid $JSON_BLOB:\\n\\n```\\n{\\n  \\\"action\\\": $TOOL_NAME,\\n  \\\"action_input\\\": $INPUT\\n}\\n```\\n\\nALWAYS use the following format:\\n\\nQuestion: the input question you must answer\\nThought: you should always think about what to do\\nAction:\\n```\\n$JSON_BLOB\\n```\\nObservation: the result of the action\\n... (this Thought/Action/Observation can repeat N times)\\nThought: I now know the final answer\\nFinal Answer: the final answer to the original input question\\n\\nBegin! Reminder to always use the exact characters `Final Answer` when responding.\"      additional_kwargs: {}        ======= end of message =======                  ======= start of message =======             type: human    data:      content: \"What is Bocchi the Rock?\\n\\nThis was your previous work (but I haven't seen any of it! I only see what you return as final", "source": "https://python.langchain.com/docs/modules/model_io/models/chat/human_input_chat_model"}
{"id": "75cd6f3caa96-6", "text": "previous work (but I haven't seen any of it! I only see what you return as final answer):\\nAction:\\n```\\n{\\n  \\\"action\\\": \\\"Wikipedia\\\",\\n  \\\"action_input\\\": \\\"What is Bocchi the Rock?\\\"\\n}\\n```\\nObservation: Page: Bocchi the Rock!\\nSummary: Bocchi the Rock! (\u00e3\ufffd\u00bc\u00e3\ufffd\u00a3\u00e3\ufffd\u00a1\u00e3\u0192\u00bb\u00e3\ufffd\u2013\u00e3\u0192\u00bb\u00e3\u201a\ufffd\u00e3\ufffd\u00a3\u00e3\ufffd\ufffd!, Botchi Za Rokku!) is a Japanese four-panel manga series written and illustrated by Aki Hamaji. It has been serialized in Houbunsha's seinen manga magazine Manga Time Kirara Max since December 2017. Its chapters have been collected in five tank\u00c5\ufffdbon volumes as of November 2022.\\nAn anime television series adaptation produced by CloverWorks aired from October to December 2022. The series has been praised for its writing, comedy, characters, and depiction of social anxiety, with the anime's visual creativity receiving acclaim.\\n\\nPage: Hitori Bocchi no Marumaru Seikatsu\\nSummary: Hitori Bocchi no Marumaru Seikatsu (Japanese: \u00e3\ufffd\u00b2\u00e3\ufffd\u00a8\u00e3\u201a\u0160\u00e3\ufffd\u00bc\u00e3\ufffd\u00a3\u00e3\ufffd\u00a1\u00e3\ufffd\u00ae\u00e2\u2014\u2039\u00e2\u2014\u2039\u00e7\u201d\u0178\u00e6\u00b4\u00bb, lit. \\\"Bocchi Hitori's ____ Life\\\" or \\\"The ____ Life of Being Alone\\\") is a Japanese yonkoma manga series written and illustrated by Katsuwo. It was serialized in ASCII Media Works' Comic Dengeki Daioh \\\"g\\\" magazine from September 2013 to April 2021. Eight tank\u00c5\ufffdbon volumes have been released. An anime television series adaptation by C2C aired from April to June 2019.\\n\\nPage:", "source": "https://python.langchain.com/docs/modules/model_io/models/chat/human_input_chat_model"}
{"id": "75cd6f3caa96-7", "text": "television series adaptation by C2C aired from April to June 2019.\\n\\nPage: Kessoku Band (album)\\nSummary: Kessoku Band (Japanese: \u00e7\u00b5\ufffd\u00e6\ufffd\u0178\u00e3\u0192\ufffd\u00e3\u0192\u00b3\u00e3\u0192\u2030, Hepburn: Kessoku Bando) is the debut studio album by Kessoku Band, a fictional musical group from the anime television series Bocchi the Rock!, released digitally on December 25, 2022, and physically on CD on December 28 by Aniplex. Featuring vocals from voice actresses Yoshino Aoyama, Sayumi Suzushiro, Saku Mizuno, and Ikumi Hasegawa, the album consists of 14 tracks previously heard in the anime, including a cover of Asian Kung-Fu Generation's \\\"Rockn' Roll, Morning Light Falls on You\\\", as well as newly recorded songs; nine singles preceded the album's physical release. Commercially, Kessoku Band peaked at number one on the Billboard Japan Hot Albums Chart and Oricon Albums Chart, and was certified gold by the Recording Industry Association of Japan.\\n\\n\\nThought:\"      additional_kwargs: {}      example: false        ======= end of message =======             This finally works.    Final Answer: Bocchi the Rock! is a four-panel manga series and anime television series. The series has been praised for its writing, comedy, characters, and depiction of social anxiety, with the anime's visual creativity receiving acclaim.        > Finished chain.    {'input': 'What is Bocchi the Rock?',     'output': \"Bocchi the Rock! is a four-panel manga series and anime television series. The series has been praised for its writing, comedy, characters, and", "source": "https://python.langchain.com/docs/modules/model_io/models/chat/human_input_chat_model"}
{"id": "75cd6f3caa96-8", "text": "series and anime television series. The series has been praised for its writing, comedy, characters, and depiction of social anxiety, with the anime's visual creativity receiving acclaim.\"}PreviousCachingNextLLMChainCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/model_io/models/chat/human_input_chat_model"}
{"id": "7e74a09db5e0-0", "text": "LLMs | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/model_io/models/llms/"}
{"id": "7e74a09db5e0-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OPromptsLanguage modelsLLMsAsync APICustom LLMFake LLMHuman input LLMCachingSerializationStreamingTracking token usageChat modelsOutput parsersData connectionChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesModel I/\u00e2\u20ac\u2039OLanguage modelsLLMsOn this pageLLMsinfoHead to Integrations for documentation on built-in integrations with LLM providers.Large Language Models (LLMs) are a core component of LangChain.", "source": "https://python.langchain.com/docs/modules/model_io/models/llms/"}
{"id": "7e74a09db5e0-2", "text": "LangChain does not serve its own LLMs, but rather provides a standard interface for interacting with many different LLMs.Get started\u00e2\u20ac\u2039There are lots of LLM providers (OpenAI, Cohere, Hugging Face, etc) - the LLM class is designed to provide a standard interface for all of them.In this walkthrough we'll work with an OpenAI LLM wrapper, although the functionalities highlighted are generic for all LLM types.Setup\u00e2\u20ac\u2039To start we'll need to install the OpenAI Python package:pip install openaiAccessing the API requires an API key, which you can get by creating an account and heading here. Once we have a key we'll want to set it as an environment variable by running:export OPENAI_API_KEY=\"...\"If you'd prefer not to set an environment variable you can pass the key in directly via the openai_api_key named parameter when initiating the OpenAI LLM class:from langchain.llms import OpenAIllm = OpenAI(openai_api_key=\"...\")otherwise you can initialize without any params:from langchain.llms import OpenAIllm = OpenAI()__call__: string in -> string out\u00e2\u20ac\u2039The simplest way to use an LLM is a callable: pass in a string, get a string completion.llm(\"Tell me a joke\")    'Why did the chicken cross the road?\\n\\nTo get to the other side.'generate: batch calls, richer outputs\u00e2\u20ac\u2039generate lets you can call the model with a list of strings, getting back a more complete response than just the text. This complete response can includes things like multiple top responses and other LLM provider-specific information:llm_result = llm.generate([\"Tell me a joke\", \"Tell me a poem\"]*15)len(llm_result.generations)    30llm_result.generations[0]", "source": "https://python.langchain.com/docs/modules/model_io/models/llms/"}
{"id": "7e74a09db5e0-3", "text": "30llm_result.generations[0]    [Generation(text='\\n\\nWhy did the chicken cross the road?\\n\\nTo get to the other side!'),     Generation(text='\\n\\nWhy did the chicken cross the road?\\n\\nTo get to the other side.')]llm_result.generations[-1]    [Generation(text=\"\\n\\nWhat if love neverspeech\\n\\nWhat if love never ended\\n\\nWhat if love was only a feeling\\n\\nI'll never know this love\\n\\nIt's not a feeling\\n\\nBut it's what we have for each other\\n\\nWe just know that love is something strong\\n\\nAnd we can't help but be happy\\n\\nWe just feel what love is for us\\n\\nAnd we love each other with all our heart\\n\\nWe just don't know how\\n\\nHow it will go\\n\\nBut we know that love is something strong\\n\\nAnd we'll always have each other\\n\\nIn our lives.\"),     Generation(text='\\n\\nOnce upon a time\\n\\nThere was a love so pure and true\\n\\nIt lasted for centuries\\n\\nAnd never became stale or dry\\n\\nIt was moving and alive\\n\\nAnd the heart of the love-ick\\n\\nIs still beating strong and true.')]You can also access provider specific information that is returned. This information is NOT standardized across providers.llm_result.llm_output    {'token_usage': {'completion_tokens': 3903,      'total_tokens': 4023,      'prompt_tokens': 120}}PreviousLanguage modelsNextAsync APIGet startedCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/model_io/models/llms/"}
{"id": "e1316f72e864-0", "text": "Human input LLM | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/model_io/models/llms/human_input_llm"}
{"id": "e1316f72e864-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OPromptsLanguage modelsLLMsAsync APICustom LLMFake LLMHuman input LLMCachingSerializationStreamingTracking token usageChat modelsOutput parsersData connectionChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesModel I/\u00e2\u20ac\u2039OLanguage modelsLLMsHuman input LLMHuman input LLMSimilar to the fake LLM, LangChain provides a pseudo LLM class that can be used for testing, debugging, or educational purposes. This allows you to mock out calls to the LLM and simulate how a human would respond if they received the prompts.In this notebook, we go over how to use this.We start this with using the HumanInputLLM in an agent.from langchain.llms.human import HumanInputLLMfrom langchain.agents import load_toolsfrom langchain.agents import initialize_agentfrom langchain.agents import AgentTypeSince we will use the WikipediaQueryRun tool in this notebook, you might need to install the wikipedia package if you haven't done so already.%pip install wikipediatools = load_tools([\"wikipedia\"])llm = HumanInputLLM(    prompt_func=lambda prompt: print(        f\"\\n===PROMPT====\\n{prompt}\\n=====END OF PROMPT======\"    ))agent = initialize_agent(    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)agent.run(\"What is 'Bocchi the Rock!'?\")            > Entering new AgentExecutor chain...        ===PROMPT====    Answer the following questions as", "source": "https://python.langchain.com/docs/modules/model_io/models/llms/human_input_llm"}
{"id": "e1316f72e864-2", "text": "===PROMPT====    Answer the following questions as best you can. You have access to the following tools:        Wikipedia: A wrapper around Wikipedia. Useful for when you need to answer general questions about people, places, companies, historical events, or other subjects. Input should be a search query.        Use the following format:        Question: the input question you must answer    Thought: you should always think about what to do    Action: the action to take, should be one of [Wikipedia]    Action Input: the input to the action    Observation: the result of the action    ... (this Thought/Action/Action Input/Observation can repeat N times)    Thought: I now know the final answer    Final Answer: the final answer to the original input question        Begin!        Question: What is 'Bocchi the Rock!'?    Thought:    =====END OF PROMPT======    I need to use a tool.    Action: Wikipedia    Action Input: Bocchi the Rock!, Japanese four-panel manga and anime series.    Observation: Page: Bocchi the Rock!    Summary: Bocchi the Rock! (\u00e3\ufffd\u00bc\u00e3\ufffd\u00a3\u00e3\ufffd\u00a1\u00e3\u0192\u00bb\u00e3\ufffd\u2013\u00e3\u0192\u00bb\u00e3\u201a\ufffd\u00e3\ufffd\u00a3\u00e3\ufffd\ufffd!, Bocchi Za Rokku!) is a Japanese four-panel manga series written and illustrated by Aki Hamaji. It has been serialized in Houbunsha's seinen manga magazine Manga Time Kirara Max since December 2017. Its chapters have been collected in five tank\u00c5\ufffdbon volumes as of November 2022.", "source": "https://python.langchain.com/docs/modules/model_io/models/llms/human_input_llm"}
{"id": "e1316f72e864-3", "text": "Its chapters have been collected in five tank\u00c5\ufffdbon volumes as of November 2022.    An anime television series adaptation produced by CloverWorks aired from October to December 2022. The series has been praised for its writing, comedy, characters, and depiction of social anxiety, with the anime's visual creativity receiving acclaim.        Page: Manga Time Kirara    Summary: Manga Time Kirara (\u00e3\ufffd\u00be\u00e3\u201a\u201c\u00e3\ufffd\u0152\u00e3\u201a\u00bf\u00e3\u201a\u00a4\u00e3\u0192\u00a0\u00e3\ufffd\ufffd\u00e3\u201a\u2030\u00e3\u201a\u2030, Manga Taimu Kirara) is a Japanese seinen manga magazine published by Houbunsha which mainly serializes four-panel manga. The magazine is sold on the ninth of each month and was first published as a special edition of Manga Time, another Houbunsha magazine, on May 17, 2002. Characters from this magazine have appeared in a crossover role-playing game called Kirara Fantasia.        Page: Manga Time Kirara Max    Summary: Manga Time Kirara Max (\u00e3\ufffd\u00be\u00e3\u201a\u201c\u00e3\ufffd\u0152\u00e3\u201a\u00bf\u00e3\u201a\u00a4\u00e3\u0192\u00a0\u00e3\ufffd\ufffd\u00e3\u201a\u2030\u00e3\u201a\u2030MAX) is a Japanese four-panel seinen manga magazine published by Houbunsha. It is the third magazine of the \"Kirara\" series, after \"Manga Time Kirara\" and \"Manga Time Kirara Carat\". The first issue was released on September 29, 2004. Currently the magazine is released on the 19th of each month.    Thought:    ===PROMPT====    Answer the following questions as best you can. You have access to the following tools:        Wikipedia: A wrapper around Wikipedia. Useful for when you need to answer general questions about people, places, companies, historical events, or", "source": "https://python.langchain.com/docs/modules/model_io/models/llms/human_input_llm"}
{"id": "e1316f72e864-4", "text": "Useful for when you need to answer general questions about people, places, companies, historical events, or other subjects. Input should be a search query.        Use the following format:        Question: the input question you must answer    Thought: you should always think about what to do    Action: the action to take, should be one of [Wikipedia]    Action Input: the input to the action    Observation: the result of the action    ... (this Thought/Action/Action Input/Observation can repeat N times)    Thought: I now know the final answer    Final Answer: the final answer to the original input question        Begin!        Question: What is 'Bocchi the Rock!'?    Thought:I need to use a tool.    Action: Wikipedia    Action Input: Bocchi the Rock!, Japanese four-panel manga and anime series.    Observation: Page: Bocchi the Rock!    Summary: Bocchi the Rock! (\u00e3\ufffd\u00bc\u00e3\ufffd\u00a3\u00e3\ufffd\u00a1\u00e3\u0192\u00bb\u00e3\ufffd\u2013\u00e3\u0192\u00bb\u00e3\u201a\ufffd\u00e3\ufffd\u00a3\u00e3\ufffd\ufffd!, Bocchi Za Rokku!) is a Japanese four-panel manga series written and illustrated by Aki Hamaji. It has been serialized in Houbunsha's seinen manga magazine Manga Time Kirara Max since December 2017. Its chapters have been collected in five tank\u00c5\ufffdbon volumes as of November 2022.    An anime television series adaptation produced by CloverWorks aired from October to December 2022. The series has been praised for its writing, comedy, characters, and depiction of social anxiety, with the anime's visual creativity receiving acclaim.        Page: Manga", "source": "https://python.langchain.com/docs/modules/model_io/models/llms/human_input_llm"}
{"id": "e1316f72e864-5", "text": "with the anime's visual creativity receiving acclaim.        Page: Manga Time Kirara    Summary: Manga Time Kirara (\u00e3\ufffd\u00be\u00e3\u201a\u201c\u00e3\ufffd\u0152\u00e3\u201a\u00bf\u00e3\u201a\u00a4\u00e3\u0192\u00a0\u00e3\ufffd\ufffd\u00e3\u201a\u2030\u00e3\u201a\u2030, Manga Taimu Kirara) is a Japanese seinen manga magazine published by Houbunsha which mainly serializes four-panel manga. The magazine is sold on the ninth of each month and was first published as a special edition of Manga Time, another Houbunsha magazine, on May 17, 2002. Characters from this magazine have appeared in a crossover role-playing game called Kirara Fantasia.        Page: Manga Time Kirara Max    Summary: Manga Time Kirara Max (\u00e3\ufffd\u00be\u00e3\u201a\u201c\u00e3\ufffd\u0152\u00e3\u201a\u00bf\u00e3\u201a\u00a4\u00e3\u0192\u00a0\u00e3\ufffd\ufffd\u00e3\u201a\u2030\u00e3\u201a\u2030MAX) is a Japanese four-panel seinen manga magazine published by Houbunsha. It is the third magazine of the \"Kirara\" series, after \"Manga Time Kirara\" and \"Manga Time Kirara Carat\". The first issue was released on September 29, 2004. Currently the magazine is released on the 19th of each month.    Thought:    =====END OF PROMPT======    These are not relevant articles.    Action: Wikipedia    Action Input: Bocchi the Rock!, Japanese four-panel manga series written and illustrated by Aki Hamaji.    Observation: Page: Bocchi the Rock!    Summary: Bocchi the Rock! (\u00e3\ufffd\u00bc\u00e3\ufffd\u00a3\u00e3\ufffd\u00a1\u00e3\u0192\u00bb\u00e3\ufffd\u2013\u00e3\u0192\u00bb\u00e3\u201a\ufffd\u00e3\ufffd\u00a3\u00e3\ufffd\ufffd!, Bocchi Za Rokku!) is a Japanese four-panel manga series written and illustrated", "source": "https://python.langchain.com/docs/modules/model_io/models/llms/human_input_llm"}
{"id": "e1316f72e864-6", "text": "Bocchi Za Rokku!) is a Japanese four-panel manga series written and illustrated by Aki Hamaji. It has been serialized in Houbunsha's seinen manga magazine Manga Time Kirara Max since December 2017. Its chapters have been collected in five tank\u00c5\ufffdbon volumes as of November 2022.    An anime television series adaptation produced by CloverWorks aired from October to December 2022. The series has been praised for its writing, comedy, characters, and depiction of social anxiety, with the anime's visual creativity receiving acclaim.    Thought:    ===PROMPT====    Answer the following questions as best you can. You have access to the following tools:        Wikipedia: A wrapper around Wikipedia. Useful for when you need to answer general questions about people, places, companies, historical events, or other subjects. Input should be a search query.        Use the following format:        Question: the input question you must answer    Thought: you should always think about what to do    Action: the action to take, should be one of [Wikipedia]    Action Input: the input to the action    Observation: the result of the action    ... (this Thought/Action/Action Input/Observation can repeat N times)    Thought: I now know the final answer    Final Answer: the final answer to the original input question        Begin!        Question: What is 'Bocchi the Rock!'?    Thought:I need to use a tool.    Action: Wikipedia    Action Input: Bocchi the Rock!, Japanese four-panel manga and anime series.    Observation: Page: Bocchi the Rock!", "source": "https://python.langchain.com/docs/modules/model_io/models/llms/human_input_llm"}
{"id": "e1316f72e864-7", "text": "and anime series.    Observation: Page: Bocchi the Rock!    Summary: Bocchi the Rock! (\u00e3\ufffd\u00bc\u00e3\ufffd\u00a3\u00e3\ufffd\u00a1\u00e3\u0192\u00bb\u00e3\ufffd\u2013\u00e3\u0192\u00bb\u00e3\u201a\ufffd\u00e3\ufffd\u00a3\u00e3\ufffd\ufffd!, Bocchi Za Rokku!) is a Japanese four-panel manga series written and illustrated by Aki Hamaji. It has been serialized in Houbunsha's seinen manga magazine Manga Time Kirara Max since December 2017. Its chapters have been collected in five tank\u00c5\ufffdbon volumes as of November 2022.    An anime television series adaptation produced by CloverWorks aired from October to December 2022. The series has been praised for its writing, comedy, characters, and depiction of social anxiety, with the anime's visual creativity receiving acclaim.        Page: Manga Time Kirara    Summary: Manga Time Kirara (\u00e3\ufffd\u00be\u00e3\u201a\u201c\u00e3\ufffd\u0152\u00e3\u201a\u00bf\u00e3\u201a\u00a4\u00e3\u0192\u00a0\u00e3\ufffd\ufffd\u00e3\u201a\u2030\u00e3\u201a\u2030, Manga Taimu Kirara) is a Japanese seinen manga magazine published by Houbunsha which mainly serializes four-panel manga. The magazine is sold on the ninth of each month and was first published as a special edition of Manga Time, another Houbunsha magazine, on May 17, 2002. Characters from this magazine have appeared in a crossover role-playing game called Kirara Fantasia.        Page: Manga Time Kirara Max    Summary: Manga Time Kirara Max (\u00e3\ufffd\u00be\u00e3\u201a\u201c\u00e3\ufffd\u0152\u00e3\u201a\u00bf\u00e3\u201a\u00a4\u00e3\u0192\u00a0\u00e3\ufffd\ufffd\u00e3\u201a\u2030\u00e3\u201a\u2030MAX) is a Japanese four-panel seinen manga magazine published by Houbunsha. It is the third magazine of the \"Kirara\" series, after \"Manga Time", "source": "https://python.langchain.com/docs/modules/model_io/models/llms/human_input_llm"}
{"id": "e1316f72e864-8", "text": "It is the third magazine of the \"Kirara\" series, after \"Manga Time Kirara\" and \"Manga Time Kirara Carat\". The first issue was released on September 29, 2004. Currently the magazine is released on the 19th of each month.    Thought:These are not relevant articles.    Action: Wikipedia    Action Input: Bocchi the Rock!, Japanese four-panel manga series written and illustrated by Aki Hamaji.    Observation: Page: Bocchi the Rock!    Summary: Bocchi the Rock! (\u00e3\ufffd\u00bc\u00e3\ufffd\u00a3\u00e3\ufffd\u00a1\u00e3\u0192\u00bb\u00e3\ufffd\u2013\u00e3\u0192\u00bb\u00e3\u201a\ufffd\u00e3\ufffd\u00a3\u00e3\ufffd\ufffd!, Bocchi Za Rokku!) is a Japanese four-panel manga series written and illustrated by Aki Hamaji. It has been serialized in Houbunsha's seinen manga magazine Manga Time Kirara Max since December 2017. Its chapters have been collected in five tank\u00c5\ufffdbon volumes as of November 2022.    An anime television series adaptation produced by CloverWorks aired from October to December 2022. The series has been praised for its writing, comedy, characters, and depiction of social anxiety, with the anime's visual creativity receiving acclaim.    Thought:    =====END OF PROMPT======    It worked.    Final Answer: Bocchi the Rock! is a four-panel manga series and anime television series. The series has been praised for its writing, comedy, characters, and depiction of social anxiety, with the anime's visual creativity receiving acclaim.        > Finished chain.    \"Bocchi the Rock! is a four-panel manga series and anime television series. The series has been praised for its writing, comedy, characters, and depiction of social anxiety, with the anime's visual", "source": "https://python.langchain.com/docs/modules/model_io/models/llms/human_input_llm"}
{"id": "e1316f72e864-9", "text": "praised for its writing, comedy, characters, and depiction of social anxiety, with the anime's visual creativity receiving acclaim.\"PreviousFake LLMNextCachingCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/model_io/models/llms/human_input_llm"}
{"id": "39dc43079643-0", "text": "Streaming | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/model_io/models/llms/streaming_llm"}
{"id": "39dc43079643-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OPromptsLanguage modelsLLMsAsync APICustom LLMFake LLMHuman input LLMCachingSerializationStreamingTracking token usageChat modelsOutput parsersData connectionChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesModel I/\u00e2\u20ac\u2039OLanguage modelsLLMsStreamingStreamingSome LLMs provide a streaming response. This means that instead of waiting for the entire response to be returned, you can start processing it as soon as it's available. This is useful if you want to display the response to the user as it's being generated, or if you want to process the response as it's being generated.Currently, we support streaming for a broad range of LLM implementations, including but not limited to OpenAI, ChatOpenAI, ChatAnthropic, Hugging Face Text Generation Inference, and Replicate. This feature has been expanded to accommodate most of the models. To utilize streaming, use a CallbackHandler that implements on_llm_new_token. In this example, we are using StreamingStdOutCallbackHandler.from langchain.llms import OpenAIfrom langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandlerllm = OpenAI(streaming=True, callbacks=[StreamingStdOutCallbackHandler()], temperature=0)resp = llm(\"Write me a song about sparkling water.\")    Verse 1    I'm sippin' on sparkling water,    It's so refreshing and light,    It's the perfect way to quench my thirst    On a hot summer night.        Chorus    Sparkling water, sparkling water,    It's the best way to stay hydrated,", "source": "https://python.langchain.com/docs/modules/model_io/models/llms/streaming_llm"}
{"id": "39dc43079643-2", "text": "water, sparkling water,    It's the best way to stay hydrated,    It's so crisp and so clean,    It's the perfect way to stay refreshed.        Verse 2    I'm sippin' on sparkling water,    It's so bubbly and bright,    It's the perfect way to cool me down    On a hot summer night.        Chorus    Sparkling water, sparkling water,    It's the best way to stay hydrated,    It's so crisp and so clean,    It's the perfect way to stay refreshed.        Verse 3    I'm sippin' on sparkling water,    It's so light and so clear,    It's the perfect way to keep me cool    On a hot summer night.        Chorus    Sparkling water, sparkling water,    It's the best way to stay hydrated,    It's so crisp and so clean,    It's the perfect way to stay refreshed.We still have access to the end LLMResult if using generate. However, token_usage is not currently supported for streaming.llm.generate([\"Tell me a joke.\"])    Q: What did the fish say when it hit the wall?    A: Dam!    LLMResult(generations=[[Generation(text='\\n\\nQ: What did the fish say when it hit the wall?\\nA: Dam!', generation_info={'finish_reason': 'stop', 'logprobs': None})]], llm_output={'token_usage': {}, 'model_name': 'text-davinci-003'})PreviousSerializationNextTracking token", "source": "https://python.langchain.com/docs/modules/model_io/models/llms/streaming_llm"}
{"id": "39dc43079643-3", "text": "{}, 'model_name': 'text-davinci-003'})PreviousSerializationNextTracking token usageCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/model_io/models/llms/streaming_llm"}
{"id": "fdd7a14f1358-0", "text": "Caching | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/model_io/models/llms/llm_caching"}
{"id": "fdd7a14f1358-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OPromptsLanguage modelsLLMsAsync APICustom LLMFake LLMHuman input LLMCachingSerializationStreamingTracking token usageChat modelsOutput parsersData connectionChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesModel I/\u00e2\u20ac\u2039OLanguage modelsLLMsCachingCachingLangChain provides an optional caching layer for LLMs. This is useful for two reasons:It can save you money by reducing the number of API calls you make to the LLM provider, if you're often requesting the same completion multiple times.", "source": "https://python.langchain.com/docs/modules/model_io/models/llms/llm_caching"}
{"id": "fdd7a14f1358-2", "text": "It can speed up your application by reducing the number of API calls you make to the LLM provider.import langchainfrom langchain.llms import OpenAI# To make the caching really obvious, lets use a slower model.llm = OpenAI(model_name=\"text-davinci-002\", n=2, best_of=2)In Memory Cache\u00e2\u20ac\u2039from langchain.cache import InMemoryCachelangchain.llm_cache = InMemoryCache()# The first time, it is not yet in cache, so it should take longerllm.predict(\"Tell me a joke\")    CPU times: user 35.9 ms, sys: 28.6 ms, total: 64.6 ms    Wall time: 4.83 s        \"\\n\\nWhy couldn't the bicycle stand up by itself? It was...two tired!\"# The second time it is, so it goes fasterllm.predict(\"Tell me a joke\")    CPU times: user 238 \u00c2\u00b5s, sys: 143 \u00c2\u00b5s, total: 381 \u00c2\u00b5s    Wall time: 1.76 ms    '\\n\\nWhy did the chicken cross the road?\\n\\nTo get to the other side.'SQLite Cache\u00e2\u20ac\u2039rm .langchain.db# We can do the same thing with a SQLite cachefrom langchain.cache import SQLiteCachelangchain.llm_cache = SQLiteCache(database_path=\".langchain.db\")# The first time, it is not yet in cache, so it should take longerllm.predict(\"Tell me a joke\")    CPU times: user 17 ms, sys: 9.76 ms, total: 26.7 ms    Wall time: 825 ms    '\\n\\nWhy did the chicken cross the road?\\n\\nTo get to the other side.'# The", "source": "https://python.langchain.com/docs/modules/model_io/models/llms/llm_caching"}
{"id": "fdd7a14f1358-3", "text": "did the chicken cross the road?\\n\\nTo get to the other side.'# The second time it is, so it goes fasterllm.predict(\"Tell me a joke\")    CPU times: user 2.46 ms, sys: 1.23 ms, total: 3.7 ms    Wall time: 2.67 ms    '\\n\\nWhy did the chicken cross the road?\\n\\nTo get to the other side.'Optional Caching in Chains\u00e2\u20ac\u2039You can also turn off caching for particular nodes in chains. Note that because of certain interfaces, its often easier to construct the chain first, and then edit the LLM afterwards.As an example, we will load a summarizer map-reduce chain. We will cache results for the map-step, but then not freeze it for the combine step.llm = OpenAI(model_name=\"text-davinci-002\")no_cache_llm = OpenAI(model_name=\"text-davinci-002\", cache=False)from langchain.text_splitter import CharacterTextSplitterfrom langchain.chains.mapreduce import MapReduceChaintext_splitter = CharacterTextSplitter()with open('../../../state_of_the_union.txt') as f:    state_of_the_union = f.read()texts = text_splitter.split_text(state_of_the_union)from langchain.docstore.document import Documentdocs = [Document(page_content=t) for t in texts[:3]]from langchain.chains.summarize import load_summarize_chainchain = load_summarize_chain(llm, chain_type=\"map_reduce\", reduce_llm=no_cache_llm)chain.run(docs)    CPU times: user 452 ms, sys: 60.3 ms, total: 512 ms    Wall time: 5.09 s    '\\n\\nPresident Biden is discussing the American Rescue Plan and the Bipartisan Infrastructure Law, which will", "source": "https://python.langchain.com/docs/modules/model_io/models/llms/llm_caching"}
{"id": "fdd7a14f1358-4", "text": "'\\n\\nPresident Biden is discussing the American Rescue Plan and the Bipartisan Infrastructure Law, which will create jobs and help Americans. He also talks about his vision for America, which includes investing in education and infrastructure. In response to Russian aggression in Ukraine, the United States is joining with European allies to impose sanctions and isolate Russia. American forces are being mobilized to protect NATO countries in the event that Putin decides to keep moving west. The Ukrainians are bravely fighting back, but the next few weeks will be hard for them. Putin will pay a high price for his actions in the long run. Americans should not be alarmed, as the United States is taking action to protect its interests and allies.'When we run it again, we see that it runs substantially faster but the final answer is different. This is due to caching at the map steps, but not at the reduce step.chain.run(docs)    CPU times: user 11.5 ms, sys: 4.33 ms, total: 15.8 ms    Wall time: 1.04 s    '\\n\\nPresident Biden is discussing the American Rescue Plan and the Bipartisan Infrastructure Law, which will create jobs and help Americans. He also talks about his vision for America, which includes investing in education and infrastructure.'rm .langchain.db sqlite.dbPreviousHuman input LLMNextSerializationCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/model_io/models/llms/llm_caching"}
{"id": "4bd05156a8fc-0", "text": "Async API | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/model_io/models/llms/async_llm"}
{"id": "4bd05156a8fc-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OPromptsLanguage modelsLLMsAsync APICustom LLMFake LLMHuman input LLMCachingSerializationStreamingTracking token usageChat modelsOutput parsersData connectionChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesModel I/\u00e2\u20ac\u2039OLanguage modelsLLMsAsync APIAsync APILangChain provides async support for LLMs by leveraging the asyncio library.Async support is particularly useful for calling multiple LLMs concurrently, as these calls are network-bound. Currently, OpenAI, PromptLayerOpenAI, ChatOpenAI and Anthropic are supported, but async support for other LLMs is on the roadmap.You can use the agenerate method to call an OpenAI LLM asynchronously.import timeimport asynciofrom langchain.llms import OpenAIdef generate_serially():    llm = OpenAI(temperature=0.9)    for _ in range(10):        resp = llm.generate([\"Hello, how are you?\"])        print(resp.generations[0][0].text)async def async_generate(llm):    resp = await llm.agenerate([\"Hello, how are you?\"])    print(resp.generations[0][0].text)async def generate_concurrently():    llm = OpenAI(temperature=0.9)    tasks = [async_generate(llm) for _ in range(10)]    await asyncio.gather(*tasks)s = time.perf_counter()# If running this outside of Jupyter, use asyncio.run(generate_concurrently())await generate_concurrently()elapsed = time.perf_counter() -", "source": "https://python.langchain.com/docs/modules/model_io/models/llms/async_llm"}
{"id": "4bd05156a8fc-2", "text": "asyncio.run(generate_concurrently())await generate_concurrently()elapsed = time.perf_counter() - sprint(\"\\033[1m\" + f\"Concurrent executed in {elapsed:0.2f} seconds.\" + \"\\033[0m\")s = time.perf_counter()generate_serially()elapsed = time.perf_counter() - sprint(\"\\033[1m\" + f\"Serial executed in {elapsed:0.2f} seconds.\" + \"\\033[0m\")            I'm doing well, thank you. How about you?            I'm doing well, thank you. How about you?            I'm doing well, how about you?            I'm doing well, thank you. How about you?            I'm doing well, thank you. How about you?            I'm doing well, thank you. How about yourself?            I'm doing well, thank you! How about you?            I'm doing well, thank you. How about you?            I'm doing well, thank you! How about you?            I'm doing well, thank you. How about you?    Concurrent executed in 1.39 seconds.            I'm doing well, thank you. How about you?            I'm doing well, thank you. How about you?        I'm doing well, thank you.", "source": "https://python.langchain.com/docs/modules/model_io/models/llms/async_llm"}
{"id": "4bd05156a8fc-3", "text": "How about you?        I'm doing well, thank you. How about you?            I'm doing well, thank you. How about you?            I'm doing well, thank you. How about yourself?            I'm doing well, thanks for asking. How about you?            I'm doing well, thanks! How about you?            I'm doing well, thank you. How about you?            I'm doing well, thank you. How about yourself?            I'm doing well, thanks for asking. How about you?    Serial executed in 5.77 seconds.PreviousLLMsNextCustom LLMCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/model_io/models/llms/async_llm"}
{"id": "748f54307791-0", "text": "Custom LLM | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/model_io/models/llms/custom_llm"}
{"id": "748f54307791-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OPromptsLanguage modelsLLMsAsync APICustom LLMFake LLMHuman input LLMCachingSerializationStreamingTracking token usageChat modelsOutput parsersData connectionChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesModel I/\u00e2\u20ac\u2039OLanguage modelsLLMsCustom LLMCustom LLMThis notebook goes over how to create a custom LLM wrapper, in case you want to use your own LLM or a different wrapper than one that is supported in LangChain.There is only one required thing that a custom LLM needs to implement:A _call method that takes in a string, some optional stop words, and returns a stringThere is a second optional thing it can implement:An _identifying_params property that is used to help with printing of this class. Should return a dictionary.Let's implement a very simple custom LLM that just returns the first N characters of the input.from typing import Any, List, Mapping, Optionalfrom langchain.callbacks.manager import CallbackManagerForLLMRunfrom langchain.llms.base import LLMclass CustomLLM(LLM):    n: int    @property    def _llm_type(self) -> str:        return \"custom\"    def _call(        self,        prompt: str,        stop: Optional[List[str]] = None,        run_manager: Optional[CallbackManagerForLLMRun] = None,    ) -> str:        if stop is not None:", "source": "https://python.langchain.com/docs/modules/model_io/models/llms/custom_llm"}
{"id": "748f54307791-2", "text": "if stop is not None:            raise ValueError(\"stop kwargs are not permitted.\")        return prompt[: self.n]    @property    def _identifying_params(self) -> Mapping[str, Any]:        \"\"\"Get the identifying parameters.\"\"\"        return {\"n\": self.n}We can now use this as an any other LLM.llm = CustomLLM(n=10)llm(\"This is a foobar thing\")    'This is a 'We can also print the LLM and see its custom print.print(llm)    CustomLLM    Params: {'n': 10}PreviousAsync APINextFake LLMCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/model_io/models/llms/custom_llm"}
{"id": "3ef201ea458b-0", "text": "Fake LLM | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain\n\n\n\n\n\nSkip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OPromptsLanguage modelsLLMsAsync APICustom LLMFake LLMHuman input LLMCachingSerializationStreamingTracking token usageChat modelsOutput parsersData connectionChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesModel I/\u00e2\u20ac\u2039OLanguage modelsLLMsFake LLMFake LLMWe expose a fake LLM class that can be used for testing. This allows you to mock out calls to the LLM and simulate what would happen if the LLM responded in a certain way.In this notebook we go over how to use this.We start this with using the FakeLLM in an agent.from langchain.llms.fake import FakeListLLMfrom langchain.agents import load_toolsfrom langchain.agents import initialize_agentfrom langchain.agents import AgentTypetools = load_tools([\"python_repl\"])responses = [\"Action: Python REPL\\nAction Input: print(2 + 2)\", \"Final Answer: 4\"]llm = FakeListLLM(responses=responses)agent = initialize_agent(    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)agent.run(\"whats 2 + 2\")            > Entering new AgentExecutor chain...    Action: Python REPL    Action Input: print(2 + 2)    Observation: 4        Thought:Final Answer: 4        > Finished chain.    '4'PreviousCustom LLMNextHuman input LLMCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/model_io/models/llms/fake_llm"}
{"id": "1ffd35d5b49e-0", "text": "Tracking token usage | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/model_io/models/llms/token_usage_tracking"}
{"id": "1ffd35d5b49e-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OPromptsLanguage modelsLLMsAsync APICustom LLMFake LLMHuman input LLMCachingSerializationStreamingTracking token usageChat modelsOutput parsersData connectionChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesModel I/\u00e2\u20ac\u2039OLanguage modelsLLMsTracking token usageTracking token usageThis notebook goes over how to track your token usage for specific calls. It is currently only implemented for the OpenAI API.Let's first look at an extremely simple example of tracking token usage for a single LLM call.from langchain.llms import OpenAIfrom langchain.callbacks import get_openai_callbackllm = OpenAI(model_name=\"text-davinci-002\", n=2, best_of=2)with get_openai_callback() as cb:    result = llm(\"Tell me a joke\")    print(cb)    Tokens Used: 42        Prompt Tokens: 4        Completion Tokens: 38    Successful Requests: 1    Total Cost (USD): $0.00084Anything inside the context manager will get tracked. Here's an example of using it to track multiple calls in sequence.with get_openai_callback() as cb:    result = llm(\"Tell me a joke\")    result2 = llm(\"Tell me a joke\")    print(cb.total_tokens)    91If a chain or agent with multiple steps in it is used, it will track all those steps.from langchain.agents import load_toolsfrom langchain.agents import initialize_agentfrom langchain.agents import AgentTypefrom langchain.llms import", "source": "https://python.langchain.com/docs/modules/model_io/models/llms/token_usage_tracking"}
{"id": "1ffd35d5b49e-2", "text": "import initialize_agentfrom langchain.agents import AgentTypefrom langchain.llms import OpenAIllm = OpenAI(temperature=0)tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm)agent = initialize_agent(    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)with get_openai_callback() as cb:    response = agent.run(        \"Who is Olivia Wilde's boyfriend? What is his current age raised to the 0.23 power?\"    )    print(f\"Total Tokens: {cb.total_tokens}\")    print(f\"Prompt Tokens: {cb.prompt_tokens}\")    print(f\"Completion Tokens: {cb.completion_tokens}\")    print(f\"Total Cost (USD): ${cb.total_cost}\")            > Entering new AgentExecutor chain...     I need to find out who Olivia Wilde's boyfriend is and then calculate his age raised to the 0.23 power.    Action: Search    Action Input: \"Olivia Wilde boyfriend\"    Observation: Sudeikis and Wilde's relationship ended in November 2020. Wilde was publicly served with court documents regarding child custody while she was presenting Don't Worry Darling at CinemaCon 2022. In January 2021, Wilde began dating singer Harry Styles after meeting during the filming of Don't Worry Darling.    Thought: I need to find out Harry Styles' age.    Action: Search    Action Input: \"Harry Styles age\"    Observation: 29 years    Thought: I need to calculate 29 raised to the 0.23 power.    Action: Calculator    Action Input: 29^0.23", "source": "https://python.langchain.com/docs/modules/model_io/models/llms/token_usage_tracking"}
{"id": "1ffd35d5b49e-3", "text": "power.    Action: Calculator    Action Input: 29^0.23    Observation: Answer: 2.169459462491557        Thought: I now know the final answer.    Final Answer: Harry Styles, Olivia Wilde's boyfriend, is 29 years old and his age raised to the 0.23 power is 2.169459462491557.        > Finished chain.    Total Tokens: 1506    Prompt Tokens: 1350    Completion Tokens: 156    Total Cost (USD): $0.03012PreviousStreamingNextChat modelsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/model_io/models/llms/token_usage_tracking"}
{"id": "2a13942a7f10-0", "text": "Serialization | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/model_io/models/llms/llm_serialization"}
{"id": "2a13942a7f10-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OPromptsLanguage modelsLLMsAsync APICustom LLMFake LLMHuman input LLMCachingSerializationStreamingTracking token usageChat modelsOutput parsersData connectionChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesModel I/\u00e2\u20ac\u2039OLanguage modelsLLMsSerializationOn this pageSerializationThis notebook walks through how to write and read an LLM Configuration to and from disk. This is useful if you want to save the configuration for a given LLM (e.g., the provider, the temperature, etc).from langchain.llms import OpenAIfrom langchain.llms.loading import load_llmLoading\u00e2\u20ac\u2039First, lets go over loading an LLM from disk. LLMs can be saved on disk in two formats: json or yaml. No matter the extension, they are loaded in the same way.cat llm.json    {        \"model_name\": \"text-davinci-003\",        \"temperature\": 0.7,        \"max_tokens\": 256,        \"top_p\": 1.0,        \"frequency_penalty\": 0.0,        \"presence_penalty\": 0.0,        \"n\": 1,        \"best_of\": 1,        \"request_timeout\": null,        \"_type\": \"openai\"    }llm = load_llm(\"llm.json\")cat llm.yaml", "source": "https://python.langchain.com/docs/modules/model_io/models/llms/llm_serialization"}
{"id": "2a13942a7f10-2", "text": "}llm = load_llm(\"llm.json\")cat llm.yaml    _type: openai    best_of: 1    frequency_penalty: 0.0    max_tokens: 256    model_name: text-davinci-003    n: 1    presence_penalty: 0.0    request_timeout: null    temperature: 0.7    top_p: 1.0llm = load_llm(\"llm.yaml\")Saving\u00e2\u20ac\u2039If you want to go from an LLM in memory to a serialized version of it, you can do so easily by calling the .save method. Again, this supports both json and yaml.llm.save(\"llm.json\")llm.save(\"llm.yaml\")PreviousCachingNextStreamingLoadingSavingCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/model_io/models/llms/llm_serialization"}
{"id": "67d4c143adc6-0", "text": "Prompts | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain\n\n\n\n\n\nSkip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OPromptsPrompt templatesExample selectorsLanguage modelsOutput parsersData connectionChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesModel I/\u00e2\u20ac\u2039OPromptsPromptsThe new way of programming models is through prompts.\nA prompt refers to the input to the model.\nThis input is often constructed from multiple components.\nLangChain provides several classes and functions to make constructing and working with prompts easy.Prompt templates: Parametrize model inputsExample selectors: Dynamically select examples to include in promptsPreviousModel I/ONextPrompt templatesCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/model_io/prompts/"}
{"id": "ed2d9b3812b9-0", "text": "Prompt templates | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/"}
{"id": "ed2d9b3812b9-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OPromptsPrompt templatesConnecting to a Feature StoreCustom prompt templateFew-shot prompt templatesFew shot examples for chat modelsFormat template outputTemplate FormatsTypes of MessagePromptTemplatePartial prompt templatesCompositionSerializationPrompt PipeliningValidate templateExample selectorsLanguage modelsOutput parsersData connectionChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesModel I/\u00e2\u20ac\u2039OPromptsPrompt templatesOn this pagePrompt templatesLanguage models take text as input - that text is commonly referred to as a prompt.\nTypically this is not simply a hardcoded string but rather a combination of a template, some examples, and user input.", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/"}
{"id": "ed2d9b3812b9-2", "text": "LangChain provides several classes and functions to make constructing and working with prompts easy.What is a prompt template?\u00e2\u20ac\u2039A prompt template refers to a reproducible way to generate a prompt. It contains a text string (\"the template\"), that can take in a set of parameters from the end user and generates a prompt.A prompt template can contain:instructions to the language model,a set of few shot examples to help the language model generate a better response,a question to the language model.Here's the simplest example:from langchain import PromptTemplatetemplate = \"\"\"\\You are a naming consultant for new companies.What is a good name for a company that makes {product}?\"\"\"prompt = PromptTemplate.from_template(template)prompt.format(product=\"colorful socks\")You are a naming consultant for new companies.What is a good name for a company that makes colorful socks?Create a prompt template\u00e2\u20ac\u2039You can create simple hardcoded prompts using the PromptTemplate class. Prompt templates can take any number of input variables, and can be formatted to generate a prompt.from langchain import PromptTemplate# An example prompt with no input variablesno_input_prompt = PromptTemplate(input_variables=[], template=\"Tell me a joke.\")no_input_prompt.format()# -> \"Tell me a joke.\"# An example prompt with one input variableone_input_prompt = PromptTemplate(input_variables=[\"adjective\"], template=\"Tell me a {adjective} joke.\")one_input_prompt.format(adjective=\"funny\")# -> \"Tell me a funny joke.\"# An example prompt with multiple input variablesmultiple_input_prompt = PromptTemplate(    input_variables=[\"adjective\", \"content\"],     template=\"Tell me a {adjective} joke about {content}.\")multiple_input_prompt.format(adjective=\"funny\", content=\"chickens\")# -> \"Tell me a funny joke about chickens.\"If you do not wish to specify input_variables manually, you can also create a PromptTemplate using from_template", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/"}
{"id": "ed2d9b3812b9-3", "text": "you do not wish to specify input_variables manually, you can also create a PromptTemplate using from_template class method. langchain will automatically infer the input_variables based on the template passed.template = \"Tell me a {adjective} joke about {content}.\"prompt_template = PromptTemplate.from_template(template)prompt_template.input_variables# -> ['adjective', 'content']prompt_template.format(adjective=\"funny\", content=\"chickens\")# -> Tell me a funny joke about chickens.You can create custom prompt templates that format the prompt in any way you want. For more information, see Custom Prompt Templates.Chat prompt template\u00e2\u20ac\u2039Chat Models take a list of chat messages as input - this list commonly referred to as a prompt.", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/"}
{"id": "ed2d9b3812b9-4", "text": "These chat messages differ from raw string (which you would pass into a LLM model) in that every message is associated with a role.For example, in OpenAI Chat Completion API, a chat message can be associated with the AI, human or system role. The model is supposed to follow instruction from system chat message more closely.LangChain provides several prompt templates to make constructing and working with prompts easily. You are encouraged to use these chat related prompt templates instead of PromptTemplate when querying chat models to fully exploit the potential of underlying chat model.from langchain.prompts import (    ChatPromptTemplate,    PromptTemplate,    SystemMessagePromptTemplate,    AIMessagePromptTemplate,    HumanMessagePromptTemplate,)from langchain.schema import (    AIMessage,    HumanMessage,    SystemMessage)To create a message template associated with a role, you use MessagePromptTemplate.For convenience, there is a from_template method exposed on the template. If you were to use this template, this is what it would look like:template=\"You are a helpful assistant that translates {input_language} to {output_language}.\"system_message_prompt = SystemMessagePromptTemplate.from_template(template)human_template=\"{text}\"human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)If you wanted to construct the MessagePromptTemplate more directly, you could create a PromptTemplate outside and then pass it in, eg:prompt=PromptTemplate(    template=\"You are a helpful assistant that translates {input_language} to {output_language}.\",    input_variables=[\"input_language\", \"output_language\"],)system_message_prompt_2 = SystemMessagePromptTemplate(prompt=prompt)assert system_message_prompt == system_message_prompt_2After that, you can build a ChatPromptTemplate from one or more MessagePromptTemplates. You can use ChatPromptTemplate's format_prompt -- this returns a PromptValue, which you can", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/"}
{"id": "ed2d9b3812b9-5", "text": "You can use ChatPromptTemplate's format_prompt -- this returns a PromptValue, which you can convert to a string or Message object, depending on whether you want to use the formatted value as input to an llm or chat model.chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])# get a chat completion from the formatted messageschat_prompt.format_prompt(input_language=\"English\", output_language=\"French\", text=\"I love programming.\").to_messages()    [SystemMessage(content='You are a helpful assistant that translates English to French.', additional_kwargs={}),     HumanMessage(content='I love programming.', additional_kwargs={})]PreviousPromptsNextConnecting to a Feature StoreWhat is a prompt template?CommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/"}
{"id": "61740173cc04-0", "text": "Connecting to a Feature Store | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/connecting_to_a_feature_store"}
{"id": "61740173cc04-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OPromptsPrompt templatesConnecting to a Feature StoreCustom prompt templateFew-shot prompt templatesFew shot examples for chat modelsFormat template outputTemplate FormatsTypes of MessagePromptTemplatePartial prompt templatesCompositionSerializationPrompt PipeliningValidate templateExample selectorsLanguage modelsOutput parsersData connectionChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesModel I/\u00e2\u20ac\u2039OPromptsPrompt templatesConnecting to a Feature StoreOn this pageConnecting to a Feature StoreFeature stores are a concept from traditional machine learning that make sure data fed into models is up-to-date and relevant. For more on this, see here.This concept is extremely relevant when considering putting LLM applications in production. In order to personalize LLM applications, you may want to combine LLMs with up-to-date information about particular users. Feature stores can be a great way to keep that data fresh, and LangChain provides an easy way to combine that data with LLMs.In this notebook we will show how to connect prompt templates to feature stores. The basic idea is to call a feature store from inside a prompt template to retrieve values that are then formatted into the prompt.Feast\u00e2\u20ac\u2039To start, we will use the popular open source feature store framework Feast.This assumes you have already run the steps in the README around getting started. We will build of off that example in getting started, and create and LLMChain to write a note to a specific driver regarding their up-to-date statistics.Load Feast Store\u00e2\u20ac\u2039Again, this should be set up according to the instructions in the Feast READMEfrom feast import FeatureStore# You may need to update the path depending on where you stored itfeast_repo_path = \"../../../../../my_feature_repo/feature_repo/\"store =", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/connecting_to_a_feature_store"}
{"id": "61740173cc04-2", "text": "where you stored itfeast_repo_path = \"../../../../../my_feature_repo/feature_repo/\"store = FeatureStore(repo_path=feast_repo_path)Prompts\u00e2\u20ac\u2039Here we will set up a custom FeastPromptTemplate. This prompt template will take in a driver id, look up their stats, and format those stats into a prompt.Note that the input to this prompt template is just driver_id, since that is the only user defined piece (all other variables are looked up inside the prompt template).from langchain.prompts import PromptTemplate, StringPromptTemplatetemplate = \"\"\"Given the driver's up to date stats, write them note relaying those stats to them.If they have a conversation rate above .5, give them a compliment. Otherwise, make a silly joke about chickens at the end to make them feel betterHere are the drivers stats:Conversation rate: {conv_rate}Acceptance rate: {acc_rate}Average Daily Trips: {avg_daily_trips}Your response:\"\"\"prompt = PromptTemplate.from_template(template)class FeastPromptTemplate(StringPromptTemplate):    def format(self, **kwargs) -> str:        driver_id = kwargs.pop(\"driver_id\")        feature_vector = store.get_online_features(            features=[                \"driver_hourly_stats:conv_rate\",                \"driver_hourly_stats:acc_rate\",                \"driver_hourly_stats:avg_daily_trips\",            ],            entity_rows=[{\"driver_id\": driver_id}],        ).to_dict()", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/connecting_to_a_feature_store"}
{"id": "61740173cc04-3", "text": "driver_id}],        ).to_dict()        kwargs[\"conv_rate\"] = feature_vector[\"conv_rate\"][0]        kwargs[\"acc_rate\"] = feature_vector[\"acc_rate\"][0]        kwargs[\"avg_daily_trips\"] = feature_vector[\"avg_daily_trips\"][0]        return prompt.format(**kwargs)prompt_template = FeastPromptTemplate(input_variables=[\"driver_id\"])print(prompt_template.format(driver_id=1001))    Given the driver's up to date stats, write them note relaying those stats to them.    If they have a conversation rate above .5, give them a compliment. Otherwise, make a silly joke about chickens at the end to make them feel better        Here are the drivers stats:    Conversation rate: 0.4745151400566101    Acceptance rate: 0.055561766028404236    Average Daily Trips: 936        Your response:Use in a chain\u00e2\u20ac\u2039We can now use this in a chain, successfully creating a chain that achieves personalization backed by a feature storefrom langchain.chat_models import ChatOpenAIfrom langchain.chains import LLMChainchain = LLMChain(llm=ChatOpenAI(), prompt=prompt_template)chain.run(1001)    \"Hi there! I wanted to update you on your current stats. Your acceptance rate is 0.055561766028404236 and your average daily trips are 936. While your conversation rate is currently 0.4745151400566101, I have no doubt that with a little extra effort, you'll be able to exceed that .5 mark! Keep up the great work! And remember, even chickens can't always cross the", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/connecting_to_a_feature_store"}
{"id": "61740173cc04-4", "text": ".5 mark! Keep up the great work! And remember, even chickens can't always cross the road, but they still give it their best shot.\"Tecton\u00e2\u20ac\u2039Above, we showed how you could use Feast, a popular open source and self-managed feature store, with LangChain. Our examples below will show a similar integration using Tecton. Tecton is a fully managed feature platform built to orchestrate the complete ML feature lifecycle, from transformation to online serving, with enterprise-grade SLAs.Prerequisites\u00e2\u20ac\u2039Tecton Deployment (sign up at https://tecton.ai)TECTON_API_KEY environment variable set to a valid Service Account keyDefine and Load Features\u00e2\u20ac\u2039We will use the user_transaction_counts Feature View from the Tecton tutorial as part of a Feature Service. For simplicity, we are only using a single Feature View; however, more sophisticated applications may require more feature views to retrieve the features needed for its prompt.user_transaction_metrics = FeatureService(    name = \"user_transaction_metrics\",    features = [user_transaction_counts])The above Feature Service is expected to be applied to a live workspace. For this example, we will be using the \"prod\" workspace.import tectonworkspace = tecton.get_workspace(\"prod\")feature_service = workspace.get_feature_service(\"user_transaction_metrics\")Prompts\u00e2\u20ac\u2039Here we will set up a custom TectonPromptTemplate. This prompt template will take in a user_id , look up their stats, and format those stats into a prompt.Note that the input to this prompt template is just user_id, since that is the only user defined piece (all other variables are looked up inside the prompt template).from langchain.prompts import PromptTemplate, StringPromptTemplatetemplate = \"\"\"Given the vendor's up to date transaction stats, write them a note based on the following rules:1. If they had a transaction in the", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/connecting_to_a_feature_store"}
{"id": "61740173cc04-5", "text": "write them a note based on the following rules:1. If they had a transaction in the last day, write a short congratulations message on their recent sales2. If no transaction in the last day, but they had a transaction in the last 30 days, playfully encourage them to sell more.3. Always add a silly joke about chickens at the endHere are the vendor's stats:Number of Transactions Last Day: {transaction_count_1d}Number of Transactions Last 30 Days: {transaction_count_30d}Your response:\"\"\"prompt = PromptTemplate.from_template(template)class TectonPromptTemplate(StringPromptTemplate):    def format(self, **kwargs) -> str:        user_id = kwargs.pop(\"user_id\")        feature_vector = feature_service.get_online_features(            join_keys={\"user_id\": user_id}        ).to_dict()        kwargs[\"transaction_count_1d\"] = feature_vector[            \"user_transaction_counts.transaction_count_1d_1d\"        ]        kwargs[\"transaction_count_30d\"] = feature_vector[            \"user_transaction_counts.transaction_count_30d_1d\"        ]        return prompt.format(**kwargs)prompt_template = TectonPromptTemplate(input_variables=[\"user_id\"])print(prompt_template.format(user_id=\"user_469998441571\"))    Given the vendor's up to date transaction stats, write them a note based on the following rules:        1. If they had a transaction in the last day, write a short congratulations message on their recent sales", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/connecting_to_a_feature_store"}
{"id": "61740173cc04-6", "text": "If they had a transaction in the last day, write a short congratulations message on their recent sales    2. If no transaction in the last day, but they had a transaction in the last 30 days, playfully encourage them to sell more.    3. Always add a silly joke about chickens at the end        Here are the vendor's stats:    Number of Transactions Last Day: 657    Number of Transactions Last 30 Days: 20326        Your response:Use in a chain\u00e2\u20ac\u2039We can now use this in a chain, successfully creating a chain that achieves personalization backed by the Tecton Feature Platformfrom langchain.chat_models import ChatOpenAIfrom langchain.chains import LLMChainchain = LLMChain(llm=ChatOpenAI(), prompt=prompt_template)chain.run(\"user_469998441571\")    'Wow, congratulations on your recent sales! Your business is really soaring like a chicken on a hot air balloon! Keep up the great work!'Featureform\u00e2\u20ac\u2039Finally, we will use Featureform an open-source and enterprise-grade feature store to run the same example. Featureform allows you to work with your infrastructure like Spark or locally to define your feature transformations.Initialize Featureform\u00e2\u20ac\u2039You can follow in the instructions in the README to initialize your transformations and features in Featureform.import featureform as ffclient = ff.Client(host=\"demo.featureform.com\")Prompts\u00e2\u20ac\u2039Here we will set up a custom FeatureformPromptTemplate. This prompt template will take in the average amount a user pays per transactions.Note that the input to this prompt template is just avg_transaction, since that is the only user defined piece (all other variables are looked up inside the prompt template).from langchain.prompts import PromptTemplate, StringPromptTemplatetemplate = \"\"\"Given the amount a", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/connecting_to_a_feature_store"}
{"id": "61740173cc04-7", "text": "langchain.prompts import PromptTemplate, StringPromptTemplatetemplate = \"\"\"Given the amount a user spends on average per transaction, let them know if they are a high roller. Otherwise, make a silly joke about chickens at the end to make them feel betterHere are the user's stats:Average Amount per Transaction: ${avg_transcation}Your response:\"\"\"prompt = PromptTemplate.from_template(template)class FeatureformPromptTemplate(StringPromptTemplate):    def format(self, **kwargs) -> str:        user_id = kwargs.pop(\"user_id\")        fpf = client.features([(\"avg_transactions\", \"quickstart\")], {\"user\": user_id})        return prompt.format(**kwargs)prompt_template = FeatureformPrompTemplate(input_variables=[\"user_id\"])print(prompt_template.format(user_id=\"C1410926\"))Use in a chain\u00e2\u20ac\u2039We can now use this in a chain, successfully creating a chain that achieves personalization backed by the Featureform Feature Platformfrom langchain.chat_models import ChatOpenAIfrom langchain.chains import LLMChainchain = LLMChain(llm=ChatOpenAI(), prompt=prompt_template)chain.run(\"C1410926\")PreviousPrompt templatesNextCustom prompt templateFeastLoad Feast StorePromptsUse in a chainTectonPrerequisitesDefine and Load FeaturesPromptsUse in a chainFeatureformInitialize FeatureformPromptsUse in a chainCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/connecting_to_a_feature_store"}
{"id": "b0ce5bd30973-0", "text": "Format template output | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/format_output"}
{"id": "b0ce5bd30973-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OPromptsPrompt templatesConnecting to a Feature StoreCustom prompt templateFew-shot prompt templatesFew shot examples for chat modelsFormat template outputTemplate FormatsTypes of MessagePromptTemplatePartial prompt templatesCompositionSerializationPrompt PipeliningValidate templateExample selectorsLanguage modelsOutput parsersData connectionChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesModel I/\u00e2\u20ac\u2039OPromptsPrompt templatesFormat template outputFormat template outputThe output of the format method is available as string, list of messages and ChatPromptValueAs string:output = chat_prompt.format(input_language=\"English\", output_language=\"French\", text=\"I love programming.\")output    'System: You are a helpful assistant that translates English to French.\\nHuman: I love programming.'# or alternativelyoutput_2 = chat_prompt.format_prompt(input_language=\"English\", output_language=\"French\", text=\"I love programming.\").to_string()assert output == output_2As ChatPromptValuechat_prompt.format_prompt(input_language=\"English\", output_language=\"French\", text=\"I love programming.\")    ChatPromptValue(messages=[SystemMessage(content='You are a helpful assistant that translates English to French.', additional_kwargs={}), HumanMessage(content='I love programming.', additional_kwargs={})])As list of Message objectschat_prompt.format_prompt(input_language=\"English\", output_language=\"French\", text=\"I love programming.\").to_messages()    [SystemMessage(content='You are a helpful assistant that translates English to French.', additional_kwargs={}),     HumanMessage(content='I love programming.', additional_kwargs={})]PreviousFew shot examples for chat modelsNextTemplate FormatsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/format_output"}
{"id": "9c918c3ebaf5-0", "text": "Types of MessagePromptTemplate | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/msg_prompt_templates"}
{"id": "9c918c3ebaf5-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OPromptsPrompt templatesConnecting to a Feature StoreCustom prompt templateFew-shot prompt templatesFew shot examples for chat modelsFormat template outputTemplate FormatsTypes of MessagePromptTemplatePartial prompt templatesCompositionSerializationPrompt PipeliningValidate templateExample selectorsLanguage modelsOutput parsersData connectionChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesModel I/\u00e2\u20ac\u2039OPromptsPrompt templatesTypes of MessagePromptTemplateTypes of MessagePromptTemplateLangChain provides different types of MessagePromptTemplate. The most commonly used are AIMessagePromptTemplate, SystemMessagePromptTemplate and HumanMessagePromptTemplate, which create an AI message, system message and human message respectively.However, in cases where the chat model supports taking chat message with arbitrary role, you can use ChatMessagePromptTemplate, which allows user to specify the role name.from langchain.prompts import ChatMessagePromptTemplateprompt = \"May the {subject} be with you\"chat_message_prompt = ChatMessagePromptTemplate.from_template(role=\"Jedi\", template=prompt)chat_message_prompt.format(subject=\"force\")    ChatMessage(content='May the force be with you', additional_kwargs={}, role='Jedi')LangChain also provides MessagesPlaceholder, which gives you full control of what messages to be rendered during formatting. This can be useful when you are uncertain of what role you should be using for your message prompt templates or when you wish to insert a list of messages during formatting.from langchain.prompts import MessagesPlaceholderhuman_prompt = \"Summarize our conversation so far in {word_count} words.\"human_message_template = HumanMessagePromptTemplate.from_template(human_prompt)chat_prompt = ChatPromptTemplate.from_messages([MessagesPlaceholder(variable_name=\"conversation\"), human_message_template])human_message =", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/msg_prompt_templates"}
{"id": "9c918c3ebaf5-2", "text": "ChatPromptTemplate.from_messages([MessagesPlaceholder(variable_name=\"conversation\"), human_message_template])human_message = HumanMessage(content=\"What is the best way to learn programming?\")ai_message = AIMessage(content=\"\"\"\\1. Choose a programming language: Decide on a programming language that you want to learn.2. Start with the basics: Familiarize yourself with the basic programming concepts such as variables, data types and control structures.3. Practice, practice, practice: The best way to learn programming is through hands-on experience\\\"\"\")chat_prompt.format_prompt(conversation=[human_message, ai_message], word_count=\"10\").to_messages()    [HumanMessage(content='What is the best way to learn programming?', additional_kwargs={}),     AIMessage(content='1. Choose a programming language: Decide on a programming language that you want to learn. \\n\\n2. Start with the basics: Familiarize yourself with the basic programming concepts such as variables, data types and control structures.\\n\\n3. Practice, practice, practice: The best way to learn programming is through hands-on experience', additional_kwargs={}),     HumanMessage(content='Summarize our conversation so far in 10 words.', additional_kwargs={})]PreviousTemplate FormatsNextPartial prompt templatesCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/msg_prompt_templates"}
{"id": "ac3f67941400-0", "text": "Custom prompt template | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/custom_prompt_template"}
{"id": "ac3f67941400-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OPromptsPrompt templatesConnecting to a Feature StoreCustom prompt templateFew-shot prompt templatesFew shot examples for chat modelsFormat template outputTemplate FormatsTypes of MessagePromptTemplatePartial prompt templatesCompositionSerializationPrompt PipeliningValidate templateExample selectorsLanguage modelsOutput parsersData connectionChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesModel I/\u00e2\u20ac\u2039OPromptsPrompt templatesCustom prompt templateOn this pageCustom prompt templateLet's suppose we want the LLM to generate English language explanations of a function given its name. To achieve this task, we will create a custom prompt template that takes in the function name as input, and formats the prompt template to provide the source code of the function.Why are custom prompt templates needed?\u00e2\u20ac\u2039LangChain provides a set of default prompt templates that can be used to generate prompts for a variety of tasks. However, there may be cases where the default prompt templates do not meet your needs. For example, you may want to create a prompt template with specific dynamic instructions for your language model. In such cases, you can create a custom prompt template.Take a look at the current set of default prompt templates here.Creating a Custom Prompt Template\u00e2\u20ac\u2039There are essentially two distinct prompt templates available - string prompt templates and chat prompt templates. String prompt templates provides a simple prompt in string format, while chat prompt templates produces a more structured prompt to be used with a chat API.In this guide, we will create a custom prompt using a string prompt template. To create a custom string prompt template, there are two requirements:It has an input_variables attribute that exposes what input variables the prompt template expects.It exposes a format method that takes in keyword arguments corresponding to the expected input_variables and returns", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/custom_prompt_template"}
{"id": "ac3f67941400-2", "text": "template expects.It exposes a format method that takes in keyword arguments corresponding to the expected input_variables and returns the formatted prompt.We will create a custom prompt template that takes in the function name as input and formats the prompt to provide the source code of the function. To achieve this, let's first create a function that will return the source code of a function given its name.import inspectdef get_source_code(function_name):    # Get the source code of the function    return inspect.getsource(function_name)Next, we'll create a custom prompt template that takes in the function name as input, and formats the prompt template to provide the source code of the function.from langchain.prompts import StringPromptTemplatefrom pydantic import BaseModel, validatorPROMPT = \"\"\"\\Given the function name and source code, generate an English language explanation of the function.Function Name: {function_name}Source Code:{source_code}Explanation:\"\"\"class FunctionExplainerPromptTemplate(StringPromptTemplate, BaseModel):    \"\"\"A custom prompt template that takes in the function name as input, and formats the prompt template to provide the source code of the function.\"\"\"    @validator(\"input_variables\")    def validate_input_variables(cls, v):        \"\"\"Validate that the input variables are correct.\"\"\"        if len(v) != 1 or \"function_name\" not in v:            raise ValueError(\"function_name must be the only input_variable.\")        return v    def format(self, **kwargs) -> str:        # Get the source code of the function        source_code = get_source_code(kwargs[\"function_name\"])        # Generate the prompt to be sent to the language model        prompt = PROMPT.format(", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/custom_prompt_template"}
{"id": "ac3f67941400-3", "text": "to be sent to the language model        prompt = PROMPT.format(            function_name=kwargs[\"function_name\"].__name__, source_code=source_code        )        return prompt    def _prompt_type(self):        return \"function-explainer\"Use the custom prompt template\u00e2\u20ac\u2039Now that we have created a custom prompt template, we can use it to generate prompts for our task.fn_explainer = FunctionExplainerPromptTemplate(input_variables=[\"function_name\"])# Generate a prompt for the function \"get_source_code\"prompt = fn_explainer.format(function_name=get_source_code)print(prompt)    Given the function name and source code, generate an English language explanation of the function.    Function Name: get_source_code    Source Code:    def get_source_code(function_name):        # Get the source code of the function        return inspect.getsource(function_name)        Explanation:    PreviousConnecting to a Feature StoreNextFew-shot prompt templatesWhy are custom prompt templates needed?Creating a Custom Prompt TemplateUse the custom prompt templateCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/custom_prompt_template"}
{"id": "f6a3b83733e5-0", "text": "Validate template | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain\n\n\n\n\n\nSkip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OPromptsPrompt templatesConnecting to a Feature StoreCustom prompt templateFew-shot prompt templatesFew shot examples for chat modelsFormat template outputTemplate FormatsTypes of MessagePromptTemplatePartial prompt templatesCompositionSerializationPrompt PipeliningValidate templateExample selectorsLanguage modelsOutput parsersData connectionChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesModel I/\u00e2\u20ac\u2039OPromptsPrompt templatesValidate templateValidate templateBy default, PromptTemplate will validate the template string by checking whether the input_variables match the variables defined in template. You can disable this behavior by setting validate_template to Falsetemplate = \"I am learning langchain because {reason}.\"prompt_template = PromptTemplate(template=template,                                 input_variables=[\"reason\", \"foo\"]) # ValueError due to extra variablesprompt_template = PromptTemplate(template=template,                                 input_variables=[\"reason\", \"foo\"],                                 validate_template=False) # No errorPreviousPrompt PipeliningNextExample selectorsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/validate"}
{"id": "6233cbd94ca2-0", "text": "Serialization | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/prompt_serialization"}
{"id": "6233cbd94ca2-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OPromptsPrompt templatesConnecting to a Feature StoreCustom prompt templateFew-shot prompt templatesFew shot examples for chat modelsFormat template outputTemplate FormatsTypes of MessagePromptTemplatePartial prompt templatesCompositionSerializationPrompt PipeliningValidate templateExample selectorsLanguage modelsOutput parsersData connectionChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesModel I/\u00e2\u20ac\u2039OPromptsPrompt templatesSerializationOn this pageSerializationIt is often preferrable to store prompts not as python code but as files. This can make it easy to share, store, and version prompts. This notebook covers how to do that in LangChain, walking through all the different types of prompts and the different serialization options.At a high level, the following design principles are applied to serialization:Both JSON and YAML are supported. We want to support serialization methods that are human readable on disk, and YAML and JSON are two of the most popular methods for that. Note that this rule applies to prompts. For other assets, like Examples, different serialization methods may be supported.We support specifying everything in one file, or storing different components (templates, examples, etc) in different files and referencing them. For some cases, storing everything in file makes the most sense, but for others it is preferrable to split up some of the assets (long templates, large examples, reusable components). LangChain supports both.There is also a single entry point to load prompts from disk, making it easy to load any type of prompt.# All prompts are loaded through the `load_prompt` function.from langchain.prompts import load_promptPromptTemplate\u00e2\u20ac\u2039This section covers examples for loading a PromptTemplate.Loading from YAML\u00e2\u20ac\u2039This shows an example of loading a PromptTemplate from", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/prompt_serialization"}
{"id": "6233cbd94ca2-2", "text": "PromptTemplate.Loading from YAML\u00e2\u20ac\u2039This shows an example of loading a PromptTemplate from YAML.cat simple_prompt.yaml    _type: prompt    input_variables:        [\"adjective\", \"content\"]    template:         Tell me a {adjective} joke about {content}.prompt = load_prompt(\"simple_prompt.yaml\")print(prompt.format(adjective=\"funny\", content=\"chickens\"))    Tell me a funny joke about chickens.Loading from JSON\u00e2\u20ac\u2039This shows an example of loading a PromptTemplate from JSON.cat simple_prompt.json    {        \"_type\": \"prompt\",        \"input_variables\": [\"adjective\", \"content\"],        \"template\": \"Tell me a {adjective} joke about {content}.\"    }prompt = load_prompt(\"simple_prompt.json\")print(prompt.format(adjective=\"funny\", content=\"chickens\"))Tell me a funny joke about chickens.Loading Template from a File\u00e2\u20ac\u2039This shows an example of storing the template in a separate file and then referencing it in the config. Notice that the key changes from template to template_path.cat simple_template.txt    Tell me a {adjective} joke about {content}.cat simple_prompt_with_template_file.json    {        \"_type\": \"prompt\",        \"input_variables\": [\"adjective\", \"content\"],        \"template_path\": \"simple_template.txt\"    }prompt = load_prompt(\"simple_prompt_with_template_file.json\")print(prompt.format(adjective=\"funny\", content=\"chickens\"))    Tell me a funny joke about chickens.FewShotPromptTemplate\u00e2\u20ac\u2039This section covers examples for", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/prompt_serialization"}
{"id": "6233cbd94ca2-3", "text": "me a funny joke about chickens.FewShotPromptTemplate\u00e2\u20ac\u2039This section covers examples for loading few shot prompt templates.Examples\u00e2\u20ac\u2039This shows an example of what examples stored as json might look like.cat examples.json    [        {\"input\": \"happy\", \"output\": \"sad\"},        {\"input\": \"tall\", \"output\": \"short\"}    ]And here is what the same examples stored as yaml might look like.cat examples.yaml    - input: happy      output: sad    - input: tall      output: shortLoading from YAML\u00e2\u20ac\u2039This shows an example of loading a few shot example from YAML.cat few_shot_prompt.yaml    _type: few_shot    input_variables:        [\"adjective\"]    prefix:         Write antonyms for the following words.    example_prompt:        _type: prompt        input_variables:            [\"input\", \"output\"]        template:            \"Input: {input}\\nOutput: {output}\"    examples:        examples.json    suffix:        \"Input: {adjective}\\nOutput:\"prompt = load_prompt(\"few_shot_prompt.yaml\")print(prompt.format(adjective=\"funny\"))    Write antonyms for the following words.        Input: happy    Output: sad        Input: tall    Output: short        Input: funny    Output:The same", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/prompt_serialization"}
{"id": "6233cbd94ca2-4", "text": "Output: short        Input: funny    Output:The same would work if you loaded examples from the yaml file.cat few_shot_prompt_yaml_examples.yaml    _type: few_shot    input_variables:        [\"adjective\"]    prefix:         Write antonyms for the following words.    example_prompt:        _type: prompt        input_variables:            [\"input\", \"output\"]        template:            \"Input: {input}\\nOutput: {output}\"    examples:        examples.yaml    suffix:        \"Input: {adjective}\\nOutput:\"prompt = load_prompt(\"few_shot_prompt_yaml_examples.yaml\")print(prompt.format(adjective=\"funny\"))    Write antonyms for the following words.        Input: happy    Output: sad        Input: tall    Output: short        Input: funny    Output:Loading from JSON\u00e2\u20ac\u2039This shows an example of loading a few shot example from JSON.cat few_shot_prompt.json    {        \"_type\": \"few_shot\",        \"input_variables\": [\"adjective\"],        \"prefix\": \"Write antonyms for the following words.\",        \"example_prompt\": {            \"_type\": \"prompt\",            \"input_variables\": [\"input\", \"output\"],", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/prompt_serialization"}
{"id": "6233cbd94ca2-5", "text": "\"input_variables\": [\"input\", \"output\"],            \"template\": \"Input: {input}\\nOutput: {output}\"        },        \"examples\": \"examples.json\",        \"suffix\": \"Input: {adjective}\\nOutput:\"    }   prompt = load_prompt(\"few_shot_prompt.json\")print(prompt.format(adjective=\"funny\"))    Write antonyms for the following words.        Input: happy    Output: sad        Input: tall    Output: short        Input: funny    Output:Examples in the Config\u00e2\u20ac\u2039This shows an example of referencing the examples directly in the config.cat few_shot_prompt_examples_in.json    {        \"_type\": \"few_shot\",        \"input_variables\": [\"adjective\"],        \"prefix\": \"Write antonyms for the following words.\",        \"example_prompt\": {            \"_type\": \"prompt\",            \"input_variables\": [\"input\", \"output\"],            \"template\": \"Input: {input}\\nOutput: {output}\"        },        \"examples\": [            {\"input\": \"happy\", \"output\": \"sad\"},            {\"input\": \"tall\", \"output\": \"short\"}        ],", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/prompt_serialization"}
{"id": "6233cbd94ca2-6", "text": "\"tall\", \"output\": \"short\"}        ],        \"suffix\": \"Input: {adjective}\\nOutput:\"    }   prompt = load_prompt(\"few_shot_prompt_examples_in.json\")print(prompt.format(adjective=\"funny\"))    Write antonyms for the following words.        Input: happy    Output: sad        Input: tall    Output: short        Input: funny    Output:Example Prompt from a File\u00e2\u20ac\u2039This shows an example of loading the PromptTemplate that is used to format the examples from a separate file. Note that the key changes from example_prompt to example_prompt_path.cat example_prompt.json    {        \"_type\": \"prompt\",        \"input_variables\": [\"input\", \"output\"],        \"template\": \"Input: {input}\\nOutput: {output}\"     }cat few_shot_prompt_example_prompt.json    {        \"_type\": \"few_shot\",        \"input_variables\": [\"adjective\"],        \"prefix\": \"Write antonyms for the following words.\",        \"example_prompt_path\": \"example_prompt.json\",        \"examples\": \"examples.json\",        \"suffix\": \"Input: {adjective}\\nOutput:\"    }   prompt = load_prompt(\"few_shot_prompt_example_prompt.json\")print(prompt.format(adjective=\"funny\"))    Write antonyms for the following words.        Input: happy    Output: sad", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/prompt_serialization"}
{"id": "6233cbd94ca2-7", "text": "Input: happy    Output: sad        Input: tall    Output: short        Input: funny    Output:PromptTempalte with OutputParser\u00e2\u20ac\u2039This shows an example of loading a prompt along with an OutputParser from a file.cat prompt_with_output_parser.json    {        \"input_variables\": [            \"question\",            \"student_answer\"        ],        \"output_parser\": {            \"regex\": \"(.*?)\\\\nScore: (.*)\",            \"output_keys\": [                \"answer\",                \"score\"            ],            \"default_output_key\": null,            \"_type\": \"regex_parser\"        },        \"partial_variables\": {},        \"template\": \"Given the following question and student answer, provide a correct answer and score the student answer.\\nQuestion: {question}\\nStudent Answer: {student_answer}\\nCorrect Answer:\",        \"template_format\": \"f-string\",        \"validate_template\": true,        \"_type\": \"prompt\"    }prompt = load_prompt(\"prompt_with_output_parser.json\")prompt.output_parser.parse(    \"George Washington was", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/prompt_serialization"}
{"id": "6233cbd94ca2-8", "text": "\"George Washington was born in 1732 and died in 1799.\\nScore: 1/2\")    {'answer': 'George Washington was born in 1732 and died in 1799.',     'score': '1/2'}PreviousCompositionNextPrompt PipeliningPromptTemplateLoading from YAMLLoading from JSONLoading Template from a FileFewShotPromptTemplateExamplesLoading from YAMLLoading from JSONExamples in the ConfigExample Prompt from a FilePromptTempalte with OutputParserCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/prompt_serialization"}
{"id": "9d6acd037388-0", "text": "Partial prompt templates | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/partial"}
{"id": "9d6acd037388-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OPromptsPrompt templatesConnecting to a Feature StoreCustom prompt templateFew-shot prompt templatesFew shot examples for chat modelsFormat template outputTemplate FormatsTypes of MessagePromptTemplatePartial prompt templatesCompositionSerializationPrompt PipeliningValidate templateExample selectorsLanguage modelsOutput parsersData connectionChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesModel I/\u00e2\u20ac\u2039OPromptsPrompt templatesPartial prompt templatesPartial prompt templatesLike other methods, it can make sense to \"partial\" a prompt template - eg pass in a subset of the required values, as to create a new prompt template which expects only the remaining subset of values.LangChain supports this in two ways:Partial formatting with string values.Partial formatting with functions that return string values.These two different ways support different use cases. In the examples below, we go over the motivations for both use cases as well as how to do it in LangChain.Partial With Strings\u00e2\u20ac\u2039One common use case for wanting to partial a prompt template is if you get some of the variables before others. For example, suppose you have a prompt template that requires two variables, foo and baz. If you get the foo value early on in the chain, but the baz value later, it can be annoying to wait until you have both variables in the same place to pass them to the prompt template. Instead, you can partial the prompt template with the foo value, and then pass the partialed prompt template along and just use that. Below is an example of doing this:from langchain.prompts import PromptTemplateprompt = PromptTemplate(template=\"{foo}{bar}\", input_variables=[\"foo\", \"bar\"])partial_prompt = prompt.partial(foo=\"foo\");print(partial_prompt.format(bar=\"baz\"))", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/partial"}
{"id": "9d6acd037388-2", "text": "= prompt.partial(foo=\"foo\");print(partial_prompt.format(bar=\"baz\"))    foobazYou can also just initialize the prompt with the partialed variables.prompt = PromptTemplate(template=\"{foo}{bar}\", input_variables=[\"bar\"], partial_variables={\"foo\": \"foo\"})print(prompt.format(bar=\"baz\"))    foobazPartial With Functions\u00e2\u20ac\u2039The other common use is to partial with a function. The use case for this is when you have a variable you know that you always want to fetch in a common way. A prime example of this is with date or time. Imagine you have a prompt which you always want to have the current date. You can't hard code it in the prompt, and passing it along with the other input variables is a bit annoying. In this case, it's very handy to be able to partial the prompt with a function that always returns the current date.from datetime import datetimedef _get_datetime():    now = datetime.now()    return now.strftime(\"%m/%d/%Y, %H:%M:%S\")prompt = PromptTemplate(    template=\"Tell me a {adjective} joke about the day {date}\",     input_variables=[\"adjective\", \"date\"]);partial_prompt = prompt.partial(date=_get_datetime)print(partial_prompt.format(adjective=\"funny\"))    Tell me a funny joke about the day 02/27/2023, 22:15:16You can also just initialize the prompt with the partialed variables, which often makes more sense in this workflow.prompt = PromptTemplate(    template=\"Tell me a {adjective} joke about the day {date}\",     input_variables=[\"adjective\"],    partial_variables={\"date\": _get_datetime});print(prompt.format(adjective=\"funny\"))    Tell me a funny joke about the day", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/partial"}
{"id": "9d6acd037388-3", "text": "Tell me a funny joke about the day 02/27/2023, 22:15:16PreviousTypes of MessagePromptTemplateNextCompositionCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/partial"}
{"id": "284552ce45be-0", "text": "Page Not Found | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain\n\n\n\n\n\nSkip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKPage Not FoundWe could not find what you were looking for.Please contact the owner of the site that linked you to the original URL and let them know their link is broken.CommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/custom_prompt_template.html"}
{"id": "c7cbbc38ccf7-0", "text": "Few shot examples for chat models | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/few_shot_examples_chat"}
{"id": "c7cbbc38ccf7-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OPromptsPrompt templatesConnecting to a Feature StoreCustom prompt templateFew-shot prompt templatesFew shot examples for chat modelsFormat template outputTemplate FormatsTypes of MessagePromptTemplatePartial prompt templatesCompositionSerializationPrompt PipeliningValidate templateExample selectorsLanguage modelsOutput parsersData connectionChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesModel I/\u00e2\u20ac\u2039OPromptsPrompt templatesFew shot examples for chat modelsOn this pageFew shot examples for chat modelsThis notebook covers how to use few shot examples in chat models.There does not appear to be solid consensus on how best to do few shot prompting. As a result, we are not solidifying any abstractions around this yet but rather using existing abstractions.Alternating Human/AI messages\u00e2\u20ac\u2039The first way of doing few shot prompting relies on using alternating human/ai messages. See an example of this below.from langchain.chat_models import ChatOpenAIfrom langchain import PromptTemplate, LLMChainfrom langchain.prompts.chat import (    ChatPromptTemplate,    SystemMessagePromptTemplate,    AIMessagePromptTemplate,    HumanMessagePromptTemplate,)from langchain.schema import AIMessage, HumanMessage, SystemMessagechat = ChatOpenAI(temperature=0)template = \"You are a helpful assistant that translates english to pirate.\"system_message_prompt = SystemMessagePromptTemplate.from_template(template)example_human = HumanMessagePromptTemplate.from_template(\"Hi\")example_ai = AIMessagePromptTemplate.from_template(\"Argh me mateys\")human_template = \"{text}\"human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)chat_prompt = ChatPromptTemplate.from_messages(    [system_message_prompt,", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/few_shot_examples_chat"}
{"id": "c7cbbc38ccf7-2", "text": "= ChatPromptTemplate.from_messages(    [system_message_prompt, example_human, example_ai, human_message_prompt])chain = LLMChain(llm=chat, prompt=chat_prompt)# get a chat completion from the formatted messageschain.run(\"I love programming.\")    \"I be lovin' programmin', me hearty!\"System Messages\u00e2\u20ac\u2039OpenAI provides an optional name parameter that they also recommend using in conjunction with system messages to do few shot prompting. Here is an example of how to do that below.template = \"You are a helpful assistant that translates english to pirate.\"system_message_prompt = SystemMessagePromptTemplate.from_template(template)example_human = SystemMessagePromptTemplate.from_template(    \"Hi\", additional_kwargs={\"name\": \"example_user\"})example_ai = SystemMessagePromptTemplate.from_template(    \"Argh me mateys\", additional_kwargs={\"name\": \"example_assistant\"})human_template = \"{text}\"human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)chat_prompt = ChatPromptTemplate.from_messages(    [system_message_prompt, example_human, example_ai, human_message_prompt])chain = LLMChain(llm=chat, prompt=chat_prompt)# get a chat completion from the formatted messageschain.run(\"I love programming.\")    \"I be lovin' programmin', me hearty.\"PreviousFew-shot prompt templatesNextFormat template outputAlternating Human/AI messagesSystem MessagesCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/few_shot_examples_chat"}
{"id": "b06e3621626c-0", "text": "Composition | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/prompt_composition"}
{"id": "b06e3621626c-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OPromptsPrompt templatesConnecting to a Feature StoreCustom prompt templateFew-shot prompt templatesFew shot examples for chat modelsFormat template outputTemplate FormatsTypes of MessagePromptTemplatePartial prompt templatesCompositionSerializationPrompt PipeliningValidate templateExample selectorsLanguage modelsOutput parsersData connectionChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesModel I/\u00e2\u20ac\u2039OPromptsPrompt templatesCompositionCompositionThis notebook goes over how to compose multiple prompts together. This can be useful when you want to reuse parts of prompts. This can be done with a PipelinePrompt. A PipelinePrompt consists of two main parts:Final prompt: This is the final prompt that is returnedPipeline prompts: This is a list of tuples, consisting of a string name and a prompt template. Each prompt template will be formatted and then passed to future prompt templates as a variable with the same name.from langchain.prompts.pipeline import PipelinePromptTemplatefrom langchain.prompts.prompt import PromptTemplatefull_template = \"\"\"{introduction}{example}{start}\"\"\"full_prompt = PromptTemplate.from_template(full_template)introduction_template = \"\"\"You are impersonating {person}.\"\"\"introduction_prompt = PromptTemplate.from_template(introduction_template)example_template = \"\"\"Here's an example of an interaction: Q: {example_q}A: {example_a}\"\"\"example_prompt = PromptTemplate.from_template(example_template)start_template = \"\"\"Now, do this for real!Q: {input}A:\"\"\"start_prompt = PromptTemplate.from_template(start_template)input_prompts = [    (\"introduction\", introduction_prompt),    (\"example\", example_prompt),    (\"start\", start_prompt)]pipeline_prompt = PipelinePromptTemplate(final_prompt=full_prompt,", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/prompt_composition"}
{"id": "b06e3621626c-2", "text": "(\"start\", start_prompt)]pipeline_prompt = PipelinePromptTemplate(final_prompt=full_prompt, pipeline_prompts=input_prompts)pipeline_prompt.input_variables    ['example_a', 'person', 'example_q', 'input']print(pipeline_prompt.format(    person=\"Elon Musk\",    example_q=\"What's your favorite car?\",    example_a=\"Tesla\",    input=\"What's your favorite social media site?\"))    You are impersonating Elon Musk.    Here's an example of an interaction:         Q: What's your favorite car?    A: Tesla    Now, do this for real!        Q: What's your favorite social media site?    A:    PreviousPartial prompt templatesNextSerializationCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/prompt_composition"}
{"id": "435e761c8d69-0", "text": "Prompt Pipelining | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/prompts_pipelining"}
{"id": "435e761c8d69-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OPromptsPrompt templatesConnecting to a Feature StoreCustom prompt templateFew-shot prompt templatesFew shot examples for chat modelsFormat template outputTemplate FormatsTypes of MessagePromptTemplatePartial prompt templatesCompositionSerializationPrompt PipeliningValidate templateExample selectorsLanguage modelsOutput parsersData connectionChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesModel I/\u00e2\u20ac\u2039OPromptsPrompt templatesPrompt PipeliningOn this pagePrompt PipeliningThe idea behind prompt pipelining is to expose a user friendly interface for composing different parts of prompts together. You can do this with either string prompts or chat prompts. Constructing prompts this way allows for easy reuse of components.String Prompt Pipelining\u00e2\u20ac\u2039When working with string prompts, each template is joined togther. You can work with either prompts directly or strings (the first element in the list needs to be a prompt).from langchain.prompts import PromptTemplate    /Users/harrisonchase/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/site-packages/deeplake/util/check_latest_version.py:32: UserWarning: A newer version of deeplake (3.6.12) is available. It's recommended that you update to the latest version using `pip install -U deeplake`.      warnings.warn(prompt = (    PromptTemplate.from_template(\"Tell me a joke about {topic}\")    + \", make it funny\"    + \"\\n\\nand in {language}\")prompt    PromptTemplate(input_variables=['language', 'topic'], output_parser=None, partial_variables={}, template='Tell me a joke about {topic}, make it", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/prompts_pipelining"}
{"id": "435e761c8d69-2", "text": "output_parser=None, partial_variables={}, template='Tell me a joke about {topic}, make it funny\\n\\nand in {language}', template_format='f-string', validate_template=True)prompt.format(topic=\"sports\", language=\"spanish\")    'Tell me a joke about sports, make it funny\\n\\nand in spanish'You can also use it in an LLMChain, just like before.from langchain.chat_models import ChatOpenAIfrom langchain.chains import LLMChainmodel = ChatOpenAI()chain = LLMChain(llm=model, prompt=prompt)chain.run(topic=\"sports\", language=\"spanish\")    '\u00c2\u00bfPor qu\u00c3\u00a9 el futbolista llevaba un paraguas al partido?\\n\\nPorque pronosticaban lluvia de goles.'Chat Prompt Pipelining\u00e2\u20ac\u2039A chat prompt is made up a of a list of messages. Purely for developer experience, we've added a convinient way to create these prompts. In this pipeline, each new element is a new message in the final prompt.from langchain.prompts import ChatPromptTemplate, HumanMessagePromptTemplatefrom langchain.schema import HumanMessage, AIMessage, SystemMessage    /Users/harrisonchase/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/site-packages/deeplake/util/check_latest_version.py:32: UserWarning: A newer version of deeplake (3.6.10) is available. It's recommended that you update to the latest version using `pip install -U deeplake`.      warnings.warn(First, let's initialize the base ChatPromptTemplate with a system message. It doesn't have to start with a system, but it's often good practiceprompt = SystemMessage(content=\"You are a nice pirate\")You can then easily create a pipeline combining it with other messages OR message templates.", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/prompts_pipelining"}
{"id": "435e761c8d69-3", "text": "Use a Message when there is no variables to be formatted, use a MessageTemplate when there are variables to be formatted. You can also use just a string -> note that this will automatically get inferred as a HumanMessagePromptTemplate.new_prompt = (    prompt    + HumanMessage(content=\"hi\")    + AIMessage(content=\"what?\")    + \"{input}\")Under the hood, this creates an instance of the ChatPromptTemplate class, so you can use it just as you did before!new_prompt.format_messages(input=\"i said hi\")    [SystemMessage(content='You are a nice pirate', additional_kwargs={}),     HumanMessage(content='hi', additional_kwargs={}, example=False),     AIMessage(content='what?', additional_kwargs={}, example=False),     HumanMessage(content='i said hi', additional_kwargs={}, example=False)]You can also use it in an LLMChain, just like beforefrom langchain.chat_models import ChatOpenAIfrom langchain.chains import LLMChainmodel = ChatOpenAI()chain = LLMChain(llm=model, prompt=new_prompt)chain.run(\"i said hi\")    'Oh, hello! How can I assist you today?'PreviousSerializationNextValidate templateString Prompt PipeliningChat Prompt PipeliningCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/prompts_pipelining"}
{"id": "bc41991bc783-0", "text": "Few-shot prompt templates | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/few_shot_examples"}
{"id": "bc41991bc783-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OPromptsPrompt templatesConnecting to a Feature StoreCustom prompt templateFew-shot prompt templatesFew shot examples for chat modelsFormat template outputTemplate FormatsTypes of MessagePromptTemplatePartial prompt templatesCompositionSerializationPrompt PipeliningValidate templateExample selectorsLanguage modelsOutput parsersData connectionChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesModel I/\u00e2\u20ac\u2039OPromptsPrompt templatesFew-shot prompt templatesFew-shot prompt templatesIn this tutorial, we'll learn how to create a prompt template that uses few shot examples. A few shot prompt template can be constructed from either a set of examples, or from an Example Selector object.Use Case\u00e2\u20ac\u2039In this tutorial, we'll configure few shot examples for self-ask with search.Using an example set\u00e2\u20ac\u2039Create the example set\u00e2\u20ac\u2039To get started, create a list of few shot examples. Each example should be a dictionary with the keys being the input variables and the values being the values for those input variables.from langchain.prompts.few_shot import FewShotPromptTemplatefrom langchain.prompts.prompt import PromptTemplateexamples = [  {    \"question\": \"Who lived longer, Muhammad Ali or Alan Turing?\",    \"answer\": \"\"\"Are follow up questions needed here: Yes.Follow up: How old was Muhammad Ali when he died?Intermediate answer: Muhammad Ali was 74 years old when he died.Follow up: How old was Alan Turing when he died?Intermediate answer: Alan Turing was 41 years old when he died.So the final answer is: Muhammad Ali\"\"\"  },  {    \"question\": \"When was the founder of craigslist born?\",    \"answer\": \"\"\"Are follow up questions", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/few_shot_examples"}
{"id": "bc41991bc783-2", "text": "was the founder of craigslist born?\",    \"answer\": \"\"\"Are follow up questions needed here: Yes.Follow up: Who was the founder of craigslist?Intermediate answer: Craigslist was founded by Craig Newmark.Follow up: When was Craig Newmark born?Intermediate answer: Craig Newmark was born on December 6, 1952.So the final answer is: December 6, 1952\"\"\"  },  {    \"question\": \"Who was the maternal grandfather of George Washington?\",    \"answer\":\"\"\"Are follow up questions needed here: Yes.Follow up: Who was the mother of George Washington?Intermediate answer: The mother of George Washington was Mary Ball Washington.Follow up: Who was the father of Mary Ball Washington?Intermediate answer: The father of Mary Ball Washington was Joseph Ball.So the final answer is: Joseph Ball\"\"\"  },  {    \"question\": \"Are both the directors of Jaws and Casino Royale from the same country?\",    \"answer\":\"\"\"Are follow up questions needed here: Yes.Follow up: Who is the director of Jaws?Intermediate Answer: The director of Jaws is Steven Spielberg.Follow up: Where is Steven Spielberg from?Intermediate Answer: The United States.Follow up: Who is the director of Casino Royale?Intermediate Answer: The director of Casino Royale is Martin Campbell.Follow up: Where is Martin Campbell from?Intermediate Answer: New Zealand.So the final answer is: No\"\"\"  }]Create a formatter for the few shot examples\u00e2\u20ac\u2039Configure a formatter that will format the few shot examples into a string. This formatter should be a PromptTemplate object.example_prompt = PromptTemplate(input_variables=[\"question\", \"answer\"], template=\"Question: {question}\\n{answer}\")print(example_prompt.format(**examples[0]))    Question: Who lived longer, Muhammad Ali or Alan Turing?        Are follow up questions", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/few_shot_examples"}
{"id": "bc41991bc783-3", "text": "lived longer, Muhammad Ali or Alan Turing?        Are follow up questions needed here: Yes.    Follow up: How old was Muhammad Ali when he died?    Intermediate answer: Muhammad Ali was 74 years old when he died.    Follow up: How old was Alan Turing when he died?    Intermediate answer: Alan Turing was 41 years old when he died.    So the final answer is: Muhammad Ali    Feed examples and formatter to FewShotPromptTemplate\u00e2\u20ac\u2039Finally, create a FewShotPromptTemplate object. This object takes in the few shot examples and the formatter for the few shot examples.prompt = FewShotPromptTemplate(    examples=examples,     example_prompt=example_prompt,     suffix=\"Question: {input}\",     input_variables=[\"input\"])print(prompt.format(input=\"Who was the father of Mary Ball Washington?\"))    Question: Who lived longer, Muhammad Ali or Alan Turing?        Are follow up questions needed here: Yes.    Follow up: How old was Muhammad Ali when he died?    Intermediate answer: Muhammad Ali was 74 years old when he died.    Follow up: How old was Alan Turing when he died?    Intermediate answer: Alan Turing was 41 years old when he died.    So the final answer is: Muhammad Ali            Question: When was the founder of craigslist born?        Are follow up questions needed here: Yes.    Follow up: Who was the founder of craigslist?    Intermediate answer: Craigslist was founded by Craig Newmark.    Follow up: When was Craig Newmark born?    Intermediate answer: Craig Newmark was born on December", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/few_shot_examples"}
{"id": "bc41991bc783-4", "text": "When was Craig Newmark born?    Intermediate answer: Craig Newmark was born on December 6, 1952.    So the final answer is: December 6, 1952            Question: Who was the maternal grandfather of George Washington?        Are follow up questions needed here: Yes.    Follow up: Who was the mother of George Washington?    Intermediate answer: The mother of George Washington was Mary Ball Washington.    Follow up: Who was the father of Mary Ball Washington?    Intermediate answer: The father of Mary Ball Washington was Joseph Ball.    So the final answer is: Joseph Ball            Question: Are both the directors of Jaws and Casino Royale from the same country?        Are follow up questions needed here: Yes.    Follow up: Who is the director of Jaws?    Intermediate Answer: The director of Jaws is Steven Spielberg.    Follow up: Where is Steven Spielberg from?    Intermediate Answer: The United States.    Follow up: Who is the director of Casino Royale?    Intermediate Answer: The director of Casino Royale is Martin Campbell.    Follow up: Where is Martin Campbell from?    Intermediate Answer: New Zealand.    So the final answer is: No            Question: Who was the father of Mary Ball Washington?Using an example selector\u00e2\u20ac\u2039Feed examples into ExampleSelector\u00e2\u20ac\u2039We will reuse the example set and the formatter from the previous section. However, instead of feeding the examples directly into the FewShotPromptTemplate object, we will feed them into an ExampleSelector object.In this tutorial, we will use the SemanticSimilarityExampleSelector", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/few_shot_examples"}
{"id": "bc41991bc783-5", "text": "feed them into an ExampleSelector object.In this tutorial, we will use the SemanticSimilarityExampleSelector class. This class selects few shot examples based on their similarity to the input. It uses an embedding model to compute the similarity between the input and the few shot examples, as well as a vector store to perform the nearest neighbor search.from langchain.prompts.example_selector import SemanticSimilarityExampleSelectorfrom langchain.vectorstores import Chromafrom langchain.embeddings import OpenAIEmbeddingsexample_selector = SemanticSimilarityExampleSelector.from_examples(    # This is the list of examples available to select from.    examples,    # This is the embedding class used to produce embeddings which are used to measure semantic similarity.    OpenAIEmbeddings(),    # This is the VectorStore class that is used to store the embeddings and do a similarity search over.    Chroma,    # This is the number of examples to produce.    k=1)# Select the most similar example to the input.question = \"Who was the father of Mary Ball Washington?\"selected_examples = example_selector.select_examples({\"question\": question})print(f\"Examples most similar to the input: {question}\")for example in selected_examples:    print(\"\\n\")    for k, v in example.items():        print(f\"{k}: {v}\")    Running Chroma using direct local API.    Using DuckDB in-memory for database. Data will be transient.    Examples most similar to the input: Who was the father of Mary Ball Washington?            question: Who was the maternal grandfather of George Washington?    answer:     Are follow up questions needed here: Yes.    Follow up: Who was the mother of George Washington?    Intermediate answer: The mother", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/few_shot_examples"}
{"id": "bc41991bc783-6", "text": "Follow up: Who was the mother of George Washington?    Intermediate answer: The mother of George Washington was Mary Ball Washington.    Follow up: Who was the father of Mary Ball Washington?    Intermediate answer: The father of Mary Ball Washington was Joseph Ball.    So the final answer is: Joseph Ball    Feed example selector into FewShotPromptTemplate\u00e2\u20ac\u2039Finally, create a FewShotPromptTemplate object. This object takes in the example selector and the formatter for the few shot examples.prompt = FewShotPromptTemplate(    example_selector=example_selector,     example_prompt=example_prompt,     suffix=\"Question: {input}\",     input_variables=[\"input\"])print(prompt.format(input=\"Who was the father of Mary Ball Washington?\"))    Question: Who was the maternal grandfather of George Washington?        Are follow up questions needed here: Yes.    Follow up: Who was the mother of George Washington?    Intermediate answer: The mother of George Washington was Mary Ball Washington.    Follow up: Who was the father of Mary Ball Washington?    Intermediate answer: The father of Mary Ball Washington was Joseph Ball.    So the final answer is: Joseph Ball            Question: Who was the father of Mary Ball Washington?PreviousCustom prompt templateNextFew shot examples for chat modelsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/few_shot_examples"}
{"id": "0893f37a1cef-0", "text": "Template Formats | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain\n\n\n\n\n\nSkip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OPromptsPrompt templatesConnecting to a Feature StoreCustom prompt templateFew-shot prompt templatesFew shot examples for chat modelsFormat template outputTemplate FormatsTypes of MessagePromptTemplatePartial prompt templatesCompositionSerializationPrompt PipeliningValidate templateExample selectorsLanguage modelsOutput parsersData connectionChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesModel I/\u00e2\u20ac\u2039OPromptsPrompt templatesTemplate FormatsTemplate FormatsPromptTemplate by default uses Python f-string as its template format. However, it can also use other formats like jinja2, specified through the template_format argument.To use the jinja2 template:from langchain.prompts import PromptTemplatejinja2_template = \"Tell me a {{ adjective }} joke about {{ content }}\"prompt = PromptTemplate.from_template(jinja2_template, template_format=\"jinja2\")prompt.format(adjective=\"funny\", content=\"chickens\")# Output: Tell me a funny joke about chickens.To use the Python f-string template:from langchain.prompts import PromptTemplatefstring_template = \"\"\"Tell me a {adjective} joke about {content}\"\"\"prompt = PromptTemplate.from_template(fstring_template)prompt.format(adjective=\"funny\", content=\"chickens\")# Output: Tell me a funny joke about chickens.Currently, only jinja2 and f-string are supported. For other formats, kindly raise an issue on the Github page.PreviousFormat template outputNextTypes of MessagePromptTemplateCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/formats"}
{"id": "eaf7a9ee61d1-0", "text": "Example selectors | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain\n\n\n\n\n\nSkip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OPromptsPrompt templatesExample selectorsCustom example selectorSelect by lengthSelect by maximal marginal relevance (MMR)Select by n-gram overlapSelect by similarityLanguage modelsOutput parsersData connectionChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesModel I/\u00e2\u20ac\u2039OPromptsExample selectorsExample selectorsIf you have a large number of examples, you may need to select which ones to include in the prompt. The Example Selector is the class responsible for doing so.The base interface is defined as below:class BaseExampleSelector(ABC):    \"\"\"Interface for selecting examples to include in prompts.\"\"\"    @abstractmethod    def select_examples(self, input_variables: Dict[str, str]) -> List[dict]:        \"\"\"Select which examples to use based on the inputs.\"\"\"The only method it needs to expose is a select_examples method. This takes in the input variables and then returns a list of examples. It is up to each specific implementation as to how those examples are selected. Let's take a look at some below.PreviousValidate templateNextCustom example selectorCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/model_io/prompts/example_selectors/"}
{"id": "372d6076e13c-0", "text": "Select by length | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/model_io/prompts/example_selectors/length_based"}
{"id": "372d6076e13c-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OPromptsPrompt templatesExample selectorsCustom example selectorSelect by lengthSelect by maximal marginal relevance (MMR)Select by n-gram overlapSelect by similarityLanguage modelsOutput parsersData connectionChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesModel I/\u00e2\u20ac\u2039OPromptsExample selectorsSelect by lengthSelect by lengthThis example selector selects which examples to use based on length. This is useful when you are worried about constructing a prompt that will go over the length of the context window. For longer inputs, it will select fewer examples to include, while for shorter inputs it will select more.from langchain.prompts import PromptTemplatefrom langchain.prompts import FewShotPromptTemplatefrom langchain.prompts.example_selector import LengthBasedExampleSelector# These are a lot of examples of a pretend task of creating antonyms.examples = [    {\"input\": \"happy\", \"output\": \"sad\"},    {\"input\": \"tall\", \"output\": \"short\"},    {\"input\": \"energetic\", \"output\": \"lethargic\"},    {\"input\": \"sunny\", \"output\": \"gloomy\"},    {\"input\": \"windy\", \"output\": \"calm\"},example_prompt = PromptTemplate(    input_variables=[\"input\", \"output\"],    template=\"Input: {input}\\nOutput: {output}\",)example_selector = LengthBasedExampleSelector(    # These are the examples it has available to choose from.    examples=examples,     # This is the PromptTemplate being used to format the examples.    example_prompt=example_prompt,     #", "source": "https://python.langchain.com/docs/modules/model_io/prompts/example_selectors/length_based"}
{"id": "372d6076e13c-2", "text": "used to format the examples.    example_prompt=example_prompt,     # This is the maximum length that the formatted examples should be.    # Length is measured by the get_text_length function below.    max_length=25,    # This is the function used to get the length of a string, which is used    # to determine which examples to include. It is commented out because    # it is provided as a default value if none is specified.    # get_text_length: Callable[[str], int] = lambda x: len(re.split(\"\\n| \", x)))dynamic_prompt = FewShotPromptTemplate(    # We provide an ExampleSelector instead of examples.    example_selector=example_selector,    example_prompt=example_prompt,    prefix=\"Give the antonym of every input\",    suffix=\"Input: {adjective}\\nOutput:\",     input_variables=[\"adjective\"],)# An example with small input, so it selects all examples.print(dynamic_prompt.format(adjective=\"big\"))    Give the antonym of every input        Input: happy    Output: sad        Input: tall    Output: short        Input: energetic    Output: lethargic        Input: sunny    Output: gloomy        Input: windy    Output: calm        Input: big    Output:# An example with long input, so it selects only one example.long_string = \"big and huge and massive and large and gigantic and tall and much much much much much bigger than everything else\"print(dynamic_prompt.format(adjective=long_string))    Give the antonym of every input", "source": "https://python.langchain.com/docs/modules/model_io/prompts/example_selectors/length_based"}
{"id": "372d6076e13c-3", "text": "Give the antonym of every input        Input: happy    Output: sad        Input: big and huge and massive and large and gigantic and tall and much much much much much bigger than everything else    Output:# You can add an example to an example selector as well.new_example = {\"input\": \"big\", \"output\": \"small\"}dynamic_prompt.example_selector.add_example(new_example)print(dynamic_prompt.format(adjective=\"enthusiastic\"))    Give the antonym of every input        Input: happy    Output: sad        Input: tall    Output: short        Input: energetic    Output: lethargic        Input: sunny    Output: gloomy        Input: windy    Output: calm        Input: big    Output: small        Input: enthusiastic    Output:PreviousCustom example selectorNextSelect by maximal marginal relevance (MMR)CommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/model_io/prompts/example_selectors/length_based"}
{"id": "a76093271449-0", "text": "Select by maximal marginal relevance (MMR) | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/model_io/prompts/example_selectors/mmr"}
{"id": "a76093271449-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OPromptsPrompt templatesExample selectorsCustom example selectorSelect by lengthSelect by maximal marginal relevance (MMR)Select by n-gram overlapSelect by similarityLanguage modelsOutput parsersData connectionChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesModel I/\u00e2\u20ac\u2039OPromptsExample selectorsSelect by maximal marginal relevance (MMR)Select by maximal marginal relevance (MMR)The MaxMarginalRelevanceExampleSelector selects examples based on a combination of which examples are most similar to the inputs, while also optimizing for diversity. It does this by finding the examples with the embeddings that have the greatest cosine similarity with the inputs, and then iteratively adding them while penalizing them for closeness to already selected examples.from langchain.prompts.example_selector import (    MaxMarginalRelevanceExampleSelector,    SemanticSimilarityExampleSelector,)from langchain.vectorstores import FAISSfrom langchain.embeddings import OpenAIEmbeddingsfrom langchain.prompts import FewShotPromptTemplate, PromptTemplateexample_prompt = PromptTemplate(    input_variables=[\"input\", \"output\"],    template=\"Input: {input}\\nOutput: {output}\",)# These are a lot of examples of a pretend task of creating antonyms.examples = [    {\"input\": \"happy\", \"output\": \"sad\"},    {\"input\": \"tall\", \"output\": \"short\"},    {\"input\": \"energetic\", \"output\": \"lethargic\"},    {\"input\": \"sunny\", \"output\": \"gloomy\"},    {\"input\": \"windy\", \"output\": \"calm\"},]example_selector =", "source": "https://python.langchain.com/docs/modules/model_io/prompts/example_selectors/mmr"}
{"id": "a76093271449-2", "text": "{\"input\": \"windy\", \"output\": \"calm\"},]example_selector = MaxMarginalRelevanceExampleSelector.from_examples(    # This is the list of examples available to select from.    examples,    # This is the embedding class used to produce embeddings which are used to measure semantic similarity.    OpenAIEmbeddings(),    # This is the VectorStore class that is used to store the embeddings and do a similarity search over.    FAISS,    # This is the number of examples to produce.    k=2,)mmr_prompt = FewShotPromptTemplate(    # We provide an ExampleSelector instead of examples.    example_selector=example_selector,    example_prompt=example_prompt,    prefix=\"Give the antonym of every input\",    suffix=\"Input: {adjective}\\nOutput:\",    input_variables=[\"adjective\"],)# Input is a feeling, so should select the happy/sad example as the first oneprint(mmr_prompt.format(adjective=\"worried\"))    Give the antonym of every input        Input: happy    Output: sad        Input: windy    Output: calm        Input: worried    Output:# Let's compare this to what we would just get if we went solely off of similarity,# by using SemanticSimilarityExampleSelector instead of MaxMarginalRelevanceExampleSelector.example_selector = SemanticSimilarityExampleSelector.from_examples(    # This is the list of examples available to select from.    examples,    # This is the embedding class used to produce embeddings which are used to measure semantic similarity.    OpenAIEmbeddings(),    # This is the VectorStore class that is used to store", "source": "https://python.langchain.com/docs/modules/model_io/prompts/example_selectors/mmr"}
{"id": "a76093271449-3", "text": "OpenAIEmbeddings(),    # This is the VectorStore class that is used to store the embeddings and do a similarity search over.    FAISS,    # This is the number of examples to produce.    k=2,)similar_prompt = FewShotPromptTemplate(    # We provide an ExampleSelector instead of examples.    example_selector=example_selector,    example_prompt=example_prompt,    prefix=\"Give the antonym of every input\",    suffix=\"Input: {adjective}\\nOutput:\",    input_variables=[\"adjective\"],)print(similar_prompt.format(adjective=\"worried\"))    Give the antonym of every input        Input: happy    Output: sad        Input: sunny    Output: gloomy        Input: worried    Output:PreviousSelect by lengthNextSelect by n-gram overlapCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/model_io/prompts/example_selectors/mmr"}
{"id": "6ca93fc6b7f1-0", "text": "Select by similarity | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/model_io/prompts/example_selectors/similarity"}
{"id": "6ca93fc6b7f1-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OPromptsPrompt templatesExample selectorsCustom example selectorSelect by lengthSelect by maximal marginal relevance (MMR)Select by n-gram overlapSelect by similarityLanguage modelsOutput parsersData connectionChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesModel I/\u00e2\u20ac\u2039OPromptsExample selectorsSelect by similaritySelect by similarityThis object selects examples based on similarity to the inputs. It does this by finding the examples with the embeddings that have the greatest cosine similarity with the inputs.from langchain.prompts.example_selector import SemanticSimilarityExampleSelectorfrom langchain.vectorstores import Chromafrom langchain.embeddings import OpenAIEmbeddingsfrom langchain.prompts import FewShotPromptTemplate, PromptTemplateexample_prompt = PromptTemplate(    input_variables=[\"input\", \"output\"],    template=\"Input: {input}\\nOutput: {output}\",)# These are a lot of examples of a pretend task of creating antonyms.examples = [    {\"input\": \"happy\", \"output\": \"sad\"},    {\"input\": \"tall\", \"output\": \"short\"},    {\"input\": \"energetic\", \"output\": \"lethargic\"},    {\"input\": \"sunny\", \"output\": \"gloomy\"},    {\"input\": \"windy\", \"output\": \"calm\"},]example_selector = SemanticSimilarityExampleSelector.from_examples(    # This is the list of examples available to select from.    examples,     # This is the embedding class used to produce embeddings which are used to measure semantic similarity.    OpenAIEmbeddings(),     # This is the VectorStore", "source": "https://python.langchain.com/docs/modules/model_io/prompts/example_selectors/similarity"}
{"id": "6ca93fc6b7f1-2", "text": "similarity.    OpenAIEmbeddings(),     # This is the VectorStore class that is used to store the embeddings and do a similarity search over.    Chroma,     # This is the number of examples to produce.    k=1)similar_prompt = FewShotPromptTemplate(    # We provide an ExampleSelector instead of examples.    example_selector=example_selector,    example_prompt=example_prompt,    prefix=\"Give the antonym of every input\",    suffix=\"Input: {adjective}\\nOutput:\",     input_variables=[\"adjective\"],)    Running Chroma using direct local API.    Using DuckDB in-memory for database. Data will be transient.# Input is a feeling, so should select the happy/sad exampleprint(similar_prompt.format(adjective=\"worried\"))    Give the antonym of every input        Input: happy    Output: sad        Input: worried    Output:# Input is a measurement, so should select the tall/short exampleprint(similar_prompt.format(adjective=\"fat\"))    Give the antonym of every input        Input: happy    Output: sad        Input: fat    Output:# You can add new examples to the SemanticSimilarityExampleSelector as wellsimilar_prompt.example_selector.add_example({\"input\": \"enthusiastic\", \"output\": \"apathetic\"})print(similar_prompt.format(adjective=\"joyful\"))    Give the antonym of every input        Input: happy    Output: sad        Input: joyful    Output:PreviousSelect by n-gram overlapNextLanguage", "source": "https://python.langchain.com/docs/modules/model_io/prompts/example_selectors/similarity"}
{"id": "6ca93fc6b7f1-3", "text": "Input: joyful    Output:PreviousSelect by n-gram overlapNextLanguage modelsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/model_io/prompts/example_selectors/similarity"}
{"id": "00f140b6640f-0", "text": "Select by n-gram overlap | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/model_io/prompts/example_selectors/ngram_overlap"}
{"id": "00f140b6640f-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OPromptsPrompt templatesExample selectorsCustom example selectorSelect by lengthSelect by maximal marginal relevance (MMR)Select by n-gram overlapSelect by similarityLanguage modelsOutput parsersData connectionChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesModel I/\u00e2\u20ac\u2039OPromptsExample selectorsSelect by n-gram overlapSelect by n-gram overlapThe NGramOverlapExampleSelector selects and orders examples based on which examples are most similar to the input, according to an ngram overlap score. The ngram overlap score is a float between 0.0 and 1.0, inclusive. The selector allows for a threshold score to be set. Examples with an ngram overlap score less than or equal to the threshold are excluded. The threshold is set to -1.0, by default, so will not exclude any examples, only reorder them. Setting the threshold to 0.0 will exclude examples that have no ngram overlaps with the input.from langchain.prompts import PromptTemplatefrom langchain.prompts.example_selector.ngram_overlap import NGramOverlapExampleSelectorfrom langchain.prompts import FewShotPromptTemplate, PromptTemplateexample_prompt = PromptTemplate(    input_variables=[\"input\", \"output\"],    template=\"Input: {input}\\nOutput: {output}\",)# These are a lot of examples of a pretend task of creating antonyms.examples = [    {\"input\": \"happy\", \"output\": \"sad\"},    {\"input\": \"tall\", \"output\": \"short\"},    {\"input\": \"energetic\", \"output\": \"lethargic\"},    {\"input\": \"sunny\", \"output\":", "source": "https://python.langchain.com/docs/modules/model_io/prompts/example_selectors/ngram_overlap"}
{"id": "00f140b6640f-2", "text": "\"lethargic\"},    {\"input\": \"sunny\", \"output\": \"gloomy\"},    {\"input\": \"windy\", \"output\": \"calm\"},]# These are examples of a fictional translation task.examples = [    {\"input\": \"See Spot run.\", \"output\": \"Ver correr a Spot.\"},    {\"input\": \"My dog barks.\", \"output\": \"Mi perro ladra.\"},    {\"input\": \"Spot can run.\", \"output\": \"Spot puede correr.\"},]example_prompt = PromptTemplate(    input_variables=[\"input\", \"output\"],    template=\"Input: {input}\\nOutput: {output}\",)example_selector = NGramOverlapExampleSelector(    # These are the examples it has available to choose from.    examples=examples,    # This is the PromptTemplate being used to format the examples.    example_prompt=example_prompt,    # This is the threshold, at which selector stops.    # It is set to -1.0 by default.    threshold=-1.0,    # For negative threshold:    # Selector sorts examples by ngram overlap score, and excludes none.    # For threshold greater than 1.0:    # Selector excludes all examples, and returns an empty list.    # For threshold equal to 0.0:    # Selector sorts examples by ngram overlap score,    # and excludes those with no ngram overlap with input.)dynamic_prompt = FewShotPromptTemplate(    # We provide an ExampleSelector instead of examples.    example_selector=example_selector,    example_prompt=example_prompt,    prefix=\"Give the Spanish translation of every input\",", "source": "https://python.langchain.com/docs/modules/model_io/prompts/example_selectors/ngram_overlap"}
{"id": "00f140b6640f-3", "text": "prefix=\"Give the Spanish translation of every input\",    suffix=\"Input: {sentence}\\nOutput:\",    input_variables=[\"sentence\"],)# An example input with large ngram overlap with \"Spot can run.\"# and no overlap with \"My dog barks.\"print(dynamic_prompt.format(sentence=\"Spot can run fast.\"))    Give the Spanish translation of every input        Input: Spot can run.    Output: Spot puede correr.        Input: See Spot run.    Output: Ver correr a Spot.        Input: My dog barks.    Output: Mi perro ladra.        Input: Spot can run fast.    Output:# You can add examples to NGramOverlapExampleSelector as well.new_example = {\"input\": \"Spot plays fetch.\", \"output\": \"Spot juega a buscar.\"}example_selector.add_example(new_example)print(dynamic_prompt.format(sentence=\"Spot can run fast.\"))    Give the Spanish translation of every input        Input: Spot can run.    Output: Spot puede correr.        Input: See Spot run.    Output: Ver correr a Spot.        Input: Spot plays fetch.    Output: Spot juega a buscar.        Input: My dog barks.    Output: Mi perro ladra.        Input: Spot can run fast.    Output:# You can set a threshold at which examples are excluded.# For example, setting threshold equal to 0.0# excludes examples with no ngram overlaps with input.# Since \"My dog barks.\" has no ngram overlaps with \"Spot can", "source": "https://python.langchain.com/docs/modules/model_io/prompts/example_selectors/ngram_overlap"}
{"id": "00f140b6640f-4", "text": "overlaps with input.# Since \"My dog barks.\" has no ngram overlaps with \"Spot can run fast.\"# it is excluded.example_selector.threshold = 0.0print(dynamic_prompt.format(sentence=\"Spot can run fast.\"))    Give the Spanish translation of every input        Input: Spot can run.    Output: Spot puede correr.        Input: See Spot run.    Output: Ver correr a Spot.        Input: Spot plays fetch.    Output: Spot juega a buscar.        Input: Spot can run fast.    Output:# Setting small nonzero thresholdexample_selector.threshold = 0.09print(dynamic_prompt.format(sentence=\"Spot can play fetch.\"))    Give the Spanish translation of every input        Input: Spot can run.    Output: Spot puede correr.        Input: Spot plays fetch.    Output: Spot juega a buscar.        Input: Spot can play fetch.    Output:# Setting threshold greater than 1.0example_selector.threshold = 1.0 + 1e-9print(dynamic_prompt.format(sentence=\"Spot can play fetch.\"))    Give the Spanish translation of every input        Input: Spot can play fetch.    Output:PreviousSelect by maximal marginal relevance (MMR)NextSelect by similarityCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/model_io/prompts/example_selectors/ngram_overlap"}
{"id": "418fc2d33403-0", "text": "Custom example selector | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/model_io/prompts/example_selectors/custom_example_selector"}
{"id": "418fc2d33403-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OPromptsPrompt templatesExample selectorsCustom example selectorSelect by lengthSelect by maximal marginal relevance (MMR)Select by n-gram overlapSelect by similarityLanguage modelsOutput parsersData connectionChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesModel I/\u00e2\u20ac\u2039OPromptsExample selectorsCustom example selectorOn this pageCustom example selectorIn this tutorial, we'll create a custom example selector that selects every alternate example from a given list of examples.An ExampleSelector must implement two methods:An add_example method which takes in an example and adds it into the ExampleSelectorA select_examples method which takes in input variables (which are meant to be user input) and returns a list of examples to use in the few shot prompt.Let's implement a custom ExampleSelector that just selects two examples at random.:::{note}\nTake a look at the current set of example selector implementations supported in LangChain here.", "source": "https://python.langchain.com/docs/modules/model_io/prompts/example_selectors/custom_example_selector"}
{"id": "418fc2d33403-2", "text": "Take a look at the current set of example selector implementations supported in LangChain here.\n:::Implement custom example selector\u00e2\u20ac\u2039from langchain.prompts.example_selector.base import BaseExampleSelectorfrom typing import Dict, Listimport numpy as npclass CustomExampleSelector(BaseExampleSelector):        def __init__(self, examples: List[Dict[str, str]]):        self.examples = examples        def add_example(self, example: Dict[str, str]) -> None:        \"\"\"Add new example to store for a key.\"\"\"        self.examples.append(example)    def select_examples(self, input_variables: Dict[str, str]) -> List[dict]:        \"\"\"Select which examples to use based on the inputs.\"\"\"        return np.random.choice(self.examples, size=2, replace=False)Use custom example selector\u00e2\u20ac\u2039examples = [    {\"foo\": \"1\"},    {\"foo\": \"2\"},    {\"foo\": \"3\"}]# Initialize example selector.example_selector = CustomExampleSelector(examples)# Select examplesexample_selector.select_examples({\"foo\": \"foo\"})# -> array([{'foo': '2'}, {'foo': '3'}], dtype=object)# Add new example to the set of examplesexample_selector.add_example({\"foo\": \"4\"})example_selector.examples# -> [{'foo': '1'}, {'foo': '2'}, {'foo': '3'}, {'foo': '4'}]# Select examplesexample_selector.select_examples({\"foo\": \"foo\"})# -> array([{'foo': '1'}, {'foo': '4'}], dtype=object)PreviousExample selectorsNextSelect by lengthImplement custom example selectorUse custom example selectorCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/model_io/prompts/example_selectors/custom_example_selector"}
{"id": "d9c61ea199c9-0", "text": "Callbacks | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/callbacks/"}
{"id": "d9c61ea199c9-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsMemoryAgentsCallbacksAsync callbacksCustom callback handlersCallbacks for custom chainsLogging to fileMultiple callback handlersTagsToken countingModulesGuidesEcosystemAdditional resourcesModulesCallbacksCallbacksinfoHead to Integrations for documentation on built-in callbacks integrations with 3rd-party tools.LangChain provides a callbacks system that allows you to hook into the various stages of your LLM application. This is useful for logging, monitoring, streaming, and other tasks.You can subscribe to these events by using the callbacks argument available throughout the API. This argument is list of handler objects, which are expected to implement one or more of the methods described below in more detail.Callback handlers\u00e2\u20ac\u2039CallbackHandlers are objects that implement the CallbackHandler interface, which has a method for each event that can be subscribed to. The CallbackManager will call the appropriate method on each handler when the event is triggered.class BaseCallbackHandler:    \"\"\"Base callback handler that can be used to handle callbacks from langchain.\"\"\"    def on_llm_start(        self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any    ) -> Any:        \"\"\"Run when LLM starts running.\"\"\"    def on_chat_model_start(        self, serialized: Dict[str, Any], messages: List[List[BaseMessage]], **kwargs: Any    ) -> Any:        \"\"\"Run when Chat Model starts running.\"\"\"    def on_llm_new_token(self, token: str, **kwargs: Any) -> Any:        \"\"\"Run on new LLM", "source": "https://python.langchain.com/docs/modules/callbacks/"}
{"id": "d9c61ea199c9-2", "text": "Any) -> Any:        \"\"\"Run on new LLM token. Only available when streaming is enabled.\"\"\"    def on_llm_end(self, response: LLMResult, **kwargs: Any) -> Any:        \"\"\"Run when LLM ends running.\"\"\"    def on_llm_error(        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any    ) -> Any:        \"\"\"Run when LLM errors.\"\"\"    def on_chain_start(        self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any    ) -> Any:        \"\"\"Run when chain starts running.\"\"\"    def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> Any:        \"\"\"Run when chain ends running.\"\"\"    def on_chain_error(        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any    ) -> Any:        \"\"\"Run when chain errors.\"\"\"    def on_tool_start(        self, serialized: Dict[str, Any], input_str: str, **kwargs: Any    ) -> Any:        \"\"\"Run when tool starts running.\"\"\"    def on_tool_end(self, output: str, **kwargs: Any) -> Any:        \"\"\"Run when tool ends running.\"\"\"    def on_tool_error(        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any    ) -> Any:        \"\"\"Run when tool errors.\"\"\"", "source": "https://python.langchain.com/docs/modules/callbacks/"}
{"id": "d9c61ea199c9-3", "text": ") -> Any:        \"\"\"Run when tool errors.\"\"\"    def on_text(self, text: str, **kwargs: Any) -> Any:        \"\"\"Run on arbitrary text.\"\"\"    def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:        \"\"\"Run on agent action.\"\"\"    def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> Any:        \"\"\"Run on agent end.\"\"\"Get started\u00e2\u20ac\u2039LangChain provides a few built-in handlers that you can use to get started. These are available in the langchain/callbacks module. The most basic handler is the StdOutCallbackHandler, which simply logs all events to stdout.Note when the verbose flag on the object is set to true, the StdOutCallbackHandler will be invoked even without being explicitly passed in.from langchain.callbacks import StdOutCallbackHandlerfrom langchain.chains import LLMChainfrom langchain.llms import OpenAIfrom langchain.prompts import PromptTemplatehandler = StdOutCallbackHandler()llm = OpenAI()prompt = PromptTemplate.from_template(\"1 + {number} = \")# Constructor callback: First, let's explicitly set the StdOutCallbackHandler when initializing our chainchain = LLMChain(llm=llm, prompt=prompt, callbacks=[handler])chain.run(number=2)# Use verbose flag: Then, let's use the `verbose` flag to achieve the same resultchain = LLMChain(llm=llm, prompt=prompt, verbose=True)chain.run(number=2)# Request callbacks: Finally, let's use the request `callbacks` to achieve the same resultchain = LLMChain(llm=llm, prompt=prompt)chain.run(number=2, callbacks=[handler])    > Entering new LLMChain", "source": "https://python.langchain.com/docs/modules/callbacks/"}
{"id": "d9c61ea199c9-4", "text": "callbacks=[handler])    > Entering new LLMChain chain...    Prompt after formatting:    1 + 2 =         > Finished chain.            > Entering new LLMChain chain...    Prompt after formatting:    1 + 2 =         > Finished chain.            > Entering new LLMChain chain...    Prompt after formatting:    1 + 2 =         > Finished chain.    '\\n\\n3'Where to pass in callbacks\u00e2\u20ac\u2039The callbacks argument is available on most objects throughout the API (Chains, Models, Tools, Agents, etc.) in two different places:Constructor callbacks: defined in the constructor, eg. LLMChain(callbacks=[handler], tags=['a-tag']), which will be used for all calls made on that object, and will be scoped to that object only, eg. if you pass a handler to the LLMChain constructor, it will not be used by the Model attached to that chain.Request callbacks: defined in the run()/apply() methods used for issuing a request, eg. chain.run(input, callbacks=[handler]), which will be used for that specific request only, and all sub-requests that it contains (eg. a call to an LLMChain triggers a call to a Model, which uses the same handler passed in the call() method).The verbose argument is available on most objects throughout the API (Chains, Models, Tools, Agents, etc.) as a constructor argument, eg. LLMChain(verbose=True), and it is equivalent to passing a ConsoleCallbackHandler to the callbacks argument of that object and all child objects. This is useful for debugging, as it will log all events to", "source": "https://python.langchain.com/docs/modules/callbacks/"}
{"id": "d9c61ea199c9-5", "text": "that object and all child objects. This is useful for debugging, as it will log all events to the console.When do you want to use each of these?\u00e2\u20ac\u2039Constructor callbacks are most useful for use cases such as logging, monitoring, etc., which are not specific to a single request, but rather to the entire chain. For example, if you want to log all the requests made to an LLMChain, you would pass a handler to the constructor.Request callbacks are most useful for use cases such as streaming, where you want to stream the output of a single request to a specific websocket connection, or other similar use cases. For example, if you want to stream the output of a single request to a websocket, you would pass a handler to the call() methodPreviousToolkitsNextAsync callbacksCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/callbacks/"}
{"id": "16f76cc54dc2-0", "text": "Async callbacks | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/callbacks/async_callbacks"}
{"id": "16f76cc54dc2-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsMemoryAgentsCallbacksAsync callbacksCustom callback handlersCallbacks for custom chainsLogging to fileMultiple callback handlersTagsToken countingModulesGuidesEcosystemAdditional resourcesModulesCallbacksAsync callbacksAsync callbacksIf you are planning to use the async API, it is recommended to use AsyncCallbackHandler to avoid blocking the runloop. Advanced if you use a sync CallbackHandler while using an async method to run your llm/chain/tool/agent, it will still work. However, under the hood, it will be called with run_in_executor which can cause issues if your CallbackHandler is not thread-safe.import asynciofrom typing import Any, Dict, Listfrom langchain.chat_models import ChatOpenAIfrom langchain.schema import LLMResult, HumanMessagefrom langchain.callbacks.base import AsyncCallbackHandler, BaseCallbackHandlerclass MyCustomSyncHandler(BaseCallbackHandler):    def on_llm_new_token(self, token: str, **kwargs) -> None:        print(f\"Sync handler being called in a `thread_pool_executor`: token: {token}\")class MyCustomAsyncHandler(AsyncCallbackHandler):    \"\"\"Async callback handler that can be used to handle callbacks from langchain.\"\"\"    async def on_llm_start(        self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any    ) -> None:        \"\"\"Run when chain starts running.\"\"\"        print(\"zzzz....\")        await asyncio.sleep(0.3)        class_name = serialized[\"name\"]", "source": "https://python.langchain.com/docs/modules/callbacks/async_callbacks"}
{"id": "16f76cc54dc2-2", "text": "class_name = serialized[\"name\"]        print(\"Hi! I just woke up. Your llm is starting\")    async def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:        \"\"\"Run when chain ends running.\"\"\"        print(\"zzzz....\")        await asyncio.sleep(0.3)        print(\"Hi! I just woke up. Your llm is ending\")# To enable streaming, we pass in `streaming=True` to the ChatModel constructor# Additionally, we pass in a list with our custom handlerchat = ChatOpenAI(    max_tokens=25,    streaming=True,    callbacks=[MyCustomSyncHandler(), MyCustomAsyncHandler()],)await chat.agenerate([[HumanMessage(content=\"Tell me a joke\")]])    zzzz....    Hi! I just woke up. Your llm is starting    Sync handler being called in a `thread_pool_executor`: token:     Sync handler being called in a `thread_pool_executor`: token: Why    Sync handler being called in a `thread_pool_executor`: token:  don    Sync handler being called in a `thread_pool_executor`: token: 't    Sync handler being called in a `thread_pool_executor`: token:  scientists    Sync handler being called in a `thread_pool_executor`: token:  trust    Sync handler being called in a `thread_pool_executor`: token:  atoms    Sync handler being called in a `thread_pool_executor`: token: ?    Sync handler being called in a `thread_pool_executor`: token:              Sync handler being", "source": "https://python.langchain.com/docs/modules/callbacks/async_callbacks"}
{"id": "16f76cc54dc2-3", "text": "token:              Sync handler being called in a `thread_pool_executor`: token: Because    Sync handler being called in a `thread_pool_executor`: token:  they    Sync handler being called in a `thread_pool_executor`: token:  make    Sync handler being called in a `thread_pool_executor`: token:  up    Sync handler being called in a `thread_pool_executor`: token:  everything    Sync handler being called in a `thread_pool_executor`: token: .    Sync handler being called in a `thread_pool_executor`: token:     zzzz....    Hi! I just woke up. Your llm is ending    LLMResult(generations=[[ChatGeneration(text=\"Why don't scientists trust atoms? \\n\\nBecause they make up everything.\", generation_info=None, message=AIMessage(content=\"Why don't scientists trust atoms? \\n\\nBecause they make up everything.\", additional_kwargs={}, example=False))]], llm_output={'token_usage': {}, 'model_name': 'gpt-3.5-turbo'})PreviousCallbacksNextCustom callback handlersCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/callbacks/async_callbacks"}
{"id": "cb318fa81f90-0", "text": "Multiple callback handlers | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/callbacks/multiple_callbacks"}
{"id": "cb318fa81f90-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsMemoryAgentsCallbacksAsync callbacksCustom callback handlersCallbacks for custom chainsLogging to fileMultiple callback handlersTagsToken countingModulesGuidesEcosystemAdditional resourcesModulesCallbacksMultiple callback handlersMultiple callback handlersIn the previous examples, we passed in callback handlers upon creation of an object by using callbacks=. In this case, the callbacks will be scoped to that particular object. However, in many cases, it is advantageous to pass in handlers instead when running the object. When we pass through CallbackHandlers using the callbacks keyword arg when executing an run, those callbacks will be issued by all nested objects involved in the execution. For example, when a handler is passed through to an Agent, it will be used for all callbacks related to the agent and all the objects involved in the agent's execution, in this case, the Tools, LLMChain, and LLM.This prevents us from having to manually attach the handlers to each individual nested object.from typing import Dict, Union, Any, Listfrom langchain.callbacks.base import BaseCallbackHandlerfrom langchain.schema import AgentActionfrom langchain.agents import AgentType, initialize_agent, load_toolsfrom langchain.callbacks import tracing_enabledfrom langchain.llms import OpenAI# First, define custom callback handler implementationsclass MyCustomHandlerOne(BaseCallbackHandler):    def on_llm_start(        self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any    ) -> Any:        print(f\"on_llm_start {serialized['name']}\")    def on_llm_new_token(self, token: str, **kwargs: Any) -> Any:", "source": "https://python.langchain.com/docs/modules/callbacks/multiple_callbacks"}
{"id": "cb318fa81f90-2", "text": "token: str, **kwargs: Any) -> Any:        print(f\"on_new_token {token}\")    def on_llm_error(        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any    ) -> Any:        \"\"\"Run when LLM errors.\"\"\"    def on_chain_start(        self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any    ) -> Any:        print(f\"on_chain_start {serialized['name']}\")    def on_tool_start(        self, serialized: Dict[str, Any], input_str: str, **kwargs: Any    ) -> Any:        print(f\"on_tool_start {serialized['name']}\")    def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:        print(f\"on_agent_action {action}\")class MyCustomHandlerTwo(BaseCallbackHandler):    def on_llm_start(        self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any    ) -> Any:        print(f\"on_llm_start (I'm the second handler!!) {serialized['name']}\")# Instantiate the handlershandler1 = MyCustomHandlerOne()handler2 = MyCustomHandlerTwo()# Setup the agent. Only the `llm` will issue callbacks for handler2llm = OpenAI(temperature=0, streaming=True, callbacks=[handler2])tools = load_tools([\"llm-math\"], llm=llm)agent = initialize_agent(tools, llm,", "source": "https://python.langchain.com/docs/modules/callbacks/multiple_callbacks"}
{"id": "cb318fa81f90-3", "text": "llm=llm)agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION)# Callbacks for handler1 will be issued by every object involved in the# Agent execution (llm, llmchain, tool, agent executor)agent.run(\"What is 2 raised to the 0.235 power?\", callbacks=[handler1])    on_chain_start AgentExecutor    on_chain_start LLMChain    on_llm_start OpenAI    on_llm_start (I'm the second handler!!) OpenAI    on_new_token  I    on_new_token  need    on_new_token  to    on_new_token  use    on_new_token  a    on_new_token  calculator    on_new_token  to    on_new_token  solve    on_new_token  this    on_new_token .    on_new_token     Action    on_new_token :    on_new_token  Calculator    on_new_token     Action    on_new_token  Input    on_new_token :    on_new_token  2    on_new_token ^    on_new_token 0    on_new_token .    on_new_token 235    on_new_token     on_agent_action AgentAction(tool='Calculator', tool_input='2^0.235', log=' I need to use a calculator to solve this.\\nAction: Calculator\\nAction Input: 2^0.235')    on_tool_start Calculator    on_chain_start LLMMathChain    on_chain_start LLMChain    on_llm_start OpenAI    on_llm_start (I'm the second handler!!) OpenAI", "source": "https://python.langchain.com/docs/modules/callbacks/multiple_callbacks"}
{"id": "cb318fa81f90-4", "text": "on_llm_start (I'm the second handler!!) OpenAI    on_new_token     on_new_token ```text    on_new_token         on_new_token 2    on_new_token **    on_new_token 0    on_new_token .    on_new_token 235    on_new_token         on_new_token ```        on_new_token ...    on_new_token num    on_new_token expr    on_new_token .    on_new_token evaluate    on_new_token (\"    on_new_token 2    on_new_token **    on_new_token 0    on_new_token .    on_new_token 235    on_new_token \")    on_new_token ...    on_new_token         on_new_token     on_chain_start LLMChain    on_llm_start OpenAI    on_llm_start (I'm the second handler!!) OpenAI    on_new_token  I    on_new_token  now    on_new_token  know    on_new_token  the    on_new_token  final    on_new_token  answer    on_new_token .    on_new_token     Final    on_new_token  Answer    on_new_token :    on_new_token  1    on_new_token .    on_new_token 17    on_new_token 690    on_new_token 67    on_new_token 372    on_new_token 187    on_new_token 674", "source": "https://python.langchain.com/docs/modules/callbacks/multiple_callbacks"}
{"id": "cb318fa81f90-5", "text": "on_new_token 187    on_new_token 674    on_new_token     '1.1769067372187674'PreviousLogging to fileNextTagsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/callbacks/multiple_callbacks"}
{"id": "e295408dcdd0-0", "text": "Tags | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain\n\n\n\n\n\nSkip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsMemoryAgentsCallbacksAsync callbacksCustom callback handlersCallbacks for custom chainsLogging to fileMultiple callback handlersTagsToken countingModulesGuidesEcosystemAdditional resourcesModulesCallbacksTagsTagsYou can add tags to your callbacks by passing a tags argument to the call()/run()/apply() methods. This is useful for filtering your logs, eg. if you want to log all requests made to a specific LLMChain, you can add a tag, and then filter your logs by that tag. You can pass tags to both constructor and request callbacks, see the examples above for details. These tags are then passed to the tags argument of the \"start\" callback methods, ie. on_llm_start, on_chat_model_start, on_chain_start, on_tool_start.PreviousMultiple callback handlersNextToken countingCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/callbacks/tags"}
{"id": "fd769657d6f1-0", "text": "Logging to file | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/callbacks/filecallbackhandler"}
{"id": "fd769657d6f1-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsMemoryAgentsCallbacksAsync callbacksCustom callback handlersCallbacks for custom chainsLogging to fileMultiple callback handlersTagsToken countingModulesGuidesEcosystemAdditional resourcesModulesCallbacksLogging to fileLogging to fileThis example shows how to print logs to file. It shows how to use the FileCallbackHandler, which does the same thing as StdOutCallbackHandler, but instead writes the output to file. It also uses the loguru library to log other outputs that are not captured by the handler.from loguru import loggerfrom langchain.callbacks import FileCallbackHandlerfrom langchain.chains import LLMChainfrom langchain.llms import OpenAIfrom langchain.prompts import PromptTemplatelogfile = \"output.log\"logger.add(logfile, colorize=True, enqueue=True)handler = FileCallbackHandler(logfile)llm = OpenAI()prompt = PromptTemplate.from_template(\"1 + {number} = \")# this chain will both print to stdout (because verbose=True) and write to 'output.log'# if verbose=False, the FileCallbackHandler will still write to 'output.log'chain = LLMChain(llm=llm, prompt=prompt, callbacks=[handler], verbose=True)answer = chain.run(number=2)logger.info(answer)            > Entering new LLMChain chain...    Prompt after formatting:    1 + 2 =     \u001b[32m2023-06-01 18:36:38.929\u001b[0m | \u001b[1mINFO    \u001b[0m |", "source": "https://python.langchain.com/docs/modules/callbacks/filecallbackhandler"}
{"id": "fd769657d6f1-2", "text": "| \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m20\u001b[0m - \u001b[1m        3\u001b[0m        > Finished chain.Now we can open the file output.log to see that the output has been captured.pip install ansi2html > /dev/nullfrom IPython.display import display, HTMLfrom ansi2html import Ansi2HTMLConverterwith open(\"output.log\", \"r\") as f:    content = f.read()conv = Ansi2HTMLConverter()html = conv.convert(content, full=True)display(HTML(html))<!DOCTYPE HTML PUBLIC \"-//W3C//DTD HTML 4.01 Transitional//EN\" \"http://www.w3.org/TR/html4/loose.dtd\"><html><head><meta http-equiv=\"Content-Type\" content=\"text/html; charset=utf-8\"><title></title><style type=\"text/css\">.ansi2html-content { display: inline; white-space: pre-wrap; word-wrap: break-word; }.body_foreground { color: #AAAAAA; }.body_background { background-color: #000000; }.inv_foreground { color: #000000; }.inv_background { background-color: #AAAAAA; }.ansi1 { font-weight: bold; }.ansi3 { font-style: italic; }.ansi32 { color: #00aa00; }.ansi36 { color: #00aaaa; }</style></head><body class=\"body_foreground body_background\" style=\"font-size: normal;\" ><pre class=\"ansi2html-content\"><span class=\"ansi1\">&gt; Entering new LLMChain chain...</span>Prompt after formatting:<span class=\"ansi1 ansi32\"></span><span class=\"ansi1 ansi3", "source": "https://python.langchain.com/docs/modules/callbacks/filecallbackhandler"}
{"id": "fd769657d6f1-3", "text": "class=\"ansi1 ansi32\"></span><span class=\"ansi1 ansi3 ansi32\">1 + 2 = </span><span class=\"ansi1\">&gt; Finished chain.</span><span class=\"ansi32\">2023-06-01 18:36:38.929</span> | <span class=\"ansi1\">INFO    </span> | <span class=\"ansi36\">__main__</span>:<span class=\"ansi36\">&lt;module&gt;</span>:<span class=\"ansi36\">20</span> - <span class=\"ansi1\">3</span></pre></body></html>PreviousCallbacks for custom chainsNextMultiple callback handlersCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/callbacks/filecallbackhandler"}
{"id": "6c72766d35a3-0", "text": "Custom callback handlers | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/callbacks/custom_callbacks"}
{"id": "6c72766d35a3-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsMemoryAgentsCallbacksAsync callbacksCustom callback handlersCallbacks for custom chainsLogging to fileMultiple callback handlersTagsToken countingModulesGuidesEcosystemAdditional resourcesModulesCallbacksCustom callback handlersCustom callback handlersYou can create a custom handler to set on the object as well. In the example below, we'll implement streaming with a custom handler.from langchain.callbacks.base import BaseCallbackHandlerfrom langchain.chat_models import ChatOpenAIfrom langchain.schema import HumanMessageclass MyCustomHandler(BaseCallbackHandler):    def on_llm_new_token(self, token: str, **kwargs) -> None:        print(f\"My custom handler, token: {token}\")# To enable streaming, we pass in `streaming=True` to the ChatModel constructor# Additionally, we pass in a list with our custom handlerchat = ChatOpenAI(max_tokens=25, streaming=True, callbacks=[MyCustomHandler()])chat([HumanMessage(content=\"Tell me a joke\")])    My custom handler, token:     My custom handler, token: Why    My custom handler, token:  don    My custom handler, token: 't    My custom handler, token:  scientists    My custom handler, token:  trust    My custom handler, token:  atoms    My custom handler, token: ?    My custom handler, token:              My custom handler, token: Because    My custom handler, token:  they    My custom handler, token:  make    My custom handler, token:  up", "source": "https://python.langchain.com/docs/modules/callbacks/custom_callbacks"}
{"id": "6c72766d35a3-2", "text": "My custom handler, token:  make    My custom handler, token:  up    My custom handler, token:  everything    My custom handler, token: .    My custom handler, token:     AIMessage(content=\"Why don't scientists trust atoms? \\n\\nBecause they make up everything.\", additional_kwargs={}, example=False)PreviousAsync callbacksNextCallbacks for custom chainsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/callbacks/custom_callbacks"}
{"id": "2d8c126b9d09-0", "text": "Token counting | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain\n\n\n\n\n\nSkip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsMemoryAgentsCallbacksAsync callbacksCustom callback handlersCallbacks for custom chainsLogging to fileMultiple callback handlersTagsToken countingModulesGuidesEcosystemAdditional resourcesModulesCallbacksToken countingToken countingLangChain offers a context manager that allows you to count tokens.import asynciofrom langchain.callbacks import get_openai_callbackfrom langchain.llms import OpenAIllm = OpenAI(temperature=0)with get_openai_callback() as cb:    llm(\"What is the square root of 4?\")total_tokens = cb.total_tokensassert total_tokens > 0with get_openai_callback() as cb:    llm(\"What is the square root of 4?\")    llm(\"What is the square root of 4?\")assert cb.total_tokens == total_tokens * 2# You can kick off concurrent runs from within the context managerwith get_openai_callback() as cb:    await asyncio.gather(        *[llm.agenerate([\"What is the square root of 4?\"]) for _ in range(3)]    )assert cb.total_tokens == total_tokens * 3# The context manager is concurrency safetask = asyncio.create_task(llm.agenerate([\"What is the square root of 4?\"]))with get_openai_callback() as cb:    await llm.agenerate([\"What is the square root of 4?\"])await taskassert cb.total_tokens == total_tokensPreviousTagsNextModulesCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/callbacks/token_counting"}
{"id": "1ac406534d3e-0", "text": "Callbacks for custom chains | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain\n\n\n\n\n\nSkip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsMemoryAgentsCallbacksAsync callbacksCustom callback handlersCallbacks for custom chainsLogging to fileMultiple callback handlersTagsToken countingModulesGuidesEcosystemAdditional resourcesModulesCallbacksCallbacks for custom chainsCallbacks for custom chains When you create a custom chain you can easily set it up to use the same callback system as all the built-in chains.\n_call, _generate, _run, and equivalent async methods on Chains / LLMs / Chat Models / Agents / Tools now receive a 2nd argument called run_manager which is bound to that run, and contains the logging methods that can be used by that object (i.e. on_llm_new_token). This is useful when constructing a custom chain. See this guide for more information on how to create custom chains and use callbacks inside them.PreviousCustom callback handlersNextLogging to fileCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/callbacks/custom_chain"}
{"id": "954348521533-0", "text": "Evaluation | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/evaluation/"}
{"id": "954348521533-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsMemoryAgentsCallbacksModulesGuidesEvaluationString EvaluatorsComparison EvaluatorsTrajectory EvaluatorsExamplesDebuggingDeploymentLangSmithModel ComparisonEcosystemAdditional resourcesGuidesEvaluationOn this pageEvaluationLanguage models can be unpredictable. This makes it challenging to ship reliable applications to production, where repeatable, useful outcomes across diverse inputs are a minimum requirement. Tests help demonstrate each component in an LLM application can produce the required or expected functionality. These tests also safeguard against regressions while you improve interconnected pieces of an integrated system. However, measuring the quality of generated text can be challenging. It can be hard to agree on the right set of metrics for your application, and it can be difficult to translate those into better performance. Furthermore, it's common to lack sufficient evaluation data to adequately test the range of inputs and expected outputs for each component when you're just getting started. The LangChain community is building open source tools and guides to help address these challenges.LangChain exposes different types of evaluators for common types of evaluation. Each type has off-the-shelf implementations you can use to get started, as well as an", "source": "https://python.langchain.com/docs/modules/evaluation/"}
{"id": "954348521533-2", "text": "extensible API so you can create your own or contribute improvements for everyone to use. The following sections have example notebooks for you to get started.String Evaluators: Evaluate the predicted string for a given input, usually against a reference stringTrajectory Evaluators: Evaluate the whole trajectory of agent actionsComparison Evaluators: Compare predictions from two runs on a common inputThis section also provides some additional examples of how you could use these evaluators for different scenarios or apply to different chain implementations in the LangChain library. Some examples include:Preference Scoring Chain Outputs: An example using a comparison evaluator on different models or prompts to select statistically significant differences in aggregate preference scoresReference Docs\u00e2\u20ac\u2039For detailed information of the available evaluators, including how to instantiate, configure, and customize them. Check out the reference documentation directly.\u011f\u0178\u2014\u0192\u00ef\u00b8\ufffd String Evaluators5 items\u011f\u0178\u2014\u0192\u00ef\u00b8\ufffd Comparison Evaluators3 items\u011f\u0178\u2014\u0192\u00ef\u00b8\ufffd Trajectory Evaluators2 items\u011f\u0178\u2014\u0192\u00ef\u00b8\ufffd Examples9 itemsPreviousGuidesNextString EvaluatorsReference DocsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/evaluation/"}
{"id": "abfe4f763c81-0", "text": "Comparison Evaluators | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain\n\n\n\n\n\nSkip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsMemoryAgentsCallbacksModulesGuidesEvaluationString EvaluatorsComparison EvaluatorsCustom Pairwise EvaluatorPairwise Embedding DistancePairwise String ComparisonTrajectory EvaluatorsExamplesDebuggingDeploymentLangSmithModel ComparisonEcosystemAdditional resourcesGuidesEvaluationComparison EvaluatorsComparison Evaluators\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd Custom Pairwise EvaluatorYou can make your own pairwise string evaluators by inheriting from PairwiseStringEvaluator class and overwriting the evaluatestringpairs method (and the aevaluatestringpairs method if you want to use the evaluator asynchronously).\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd Pairwise Embedding DistanceOne way to measure the similarity (or dissimilarity) between two predictions on a shared or similar input is to embed the predictions and compute a vector distance between the two embeddings.[1]\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd Pairwise String ComparisonOften you will want to compare predictions of an LLM, Chain, or Agent for a given input. The StringComparison evaluators facilitate this so you can answer questions like:PreviousString DistanceNextCustom Pairwise EvaluatorCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/evaluation/comparison/"}
{"id": "230dcbd468c9-0", "text": "Comparing Chain Outputs | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/evaluation/examples/comparisons"}
{"id": "230dcbd468c9-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsMemoryAgentsCallbacksModulesGuidesEvaluationString EvaluatorsComparison EvaluatorsTrajectory EvaluatorsExamplesAgent VectorDB Question Answering BenchmarkingComparing Chain OutputsData Augmented Question AnsweringEvaluating an OpenAPI ChainQuestion Answering Benchmarking: Paul Graham EssayQuestion Answering Benchmarking: State of the Union AddressQA GenerationQuestion AnsweringSQL Question Answering Benchmarking: ChinookDebuggingDeploymentLangSmithModel ComparisonEcosystemAdditional resourcesGuidesEvaluationExamplesComparing Chain OutputsOn this pageComparing Chain OutputsSuppose you have two different prompts (or LLMs). How do you know which will generate \"better\" results?One automated way to predict the preferred configuration is to use a PairwiseStringEvaluator like the PairwiseStringEvalChain[1]. This chain prompts an LLM to select which output is preferred, given a specific input.For this evaluation, we will need 3 things:An evaluatorA dataset of inputs2 (or more) LLMs, Chains, or Agents to compareThen we will aggregate the restults to determine the preferred model.Step 1. Create the Evaluator\u00e2\u20ac\u2039In this example, you will use gpt-4 to select which output is preferred.from langchain.chat_models import ChatOpenAIfrom langchain.evaluation.comparison import PairwiseStringEvalChainllm = ChatOpenAI(model=\"gpt-4\")eval_chain = PairwiseStringEvalChain.from_llm(llm=llm)Step 2. Select Dataset\u00e2\u20ac\u2039If you already have real usage data for your LLM, you can use a representative sample. More examples", "source": "https://python.langchain.com/docs/modules/evaluation/examples/comparisons"}
{"id": "230dcbd468c9-2", "text": "provide more reliable results. We will use some example queries someone might have about how to use langchain here.from langchain.evaluation.loading import load_datasetdataset = load_dataset(\"langchain-howto-queries\")    Found cached dataset parquet (/Users/wfh/.cache/huggingface/datasets/LangChainDatasets___parquet/LangChainDatasets--langchain-howto-queries-bbb748bbee7e77aa/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec)      0%|          | 0/1 [00:00<?, ?it/s]Step 3. Define Models to Compare\u00e2\u20ac\u2039We will be comparing two agents in this case.from langchain import SerpAPIWrapperfrom langchain.agents import initialize_agent, Toolfrom langchain.agents import AgentTypefrom langchain.chat_models import ChatOpenAI# Initialize the language model# You can add your own OpenAI API key by adding openai_api_key=\"<your_api_key>\"llm = ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo-0613\")# Initialize the SerpAPIWrapper for search functionality# Replace <your_api_key> in openai_api_key=\"<your_api_key>\" with your actual SerpAPI key.search = SerpAPIWrapper()# Define a list of tools offered by the agenttools = [    Tool(        name=\"Search\",        func=search.run,        coroutine=search.arun,        description=\"Useful when you need to answer questions about current events. You should ask targeted questions.\",", "source": "https://python.langchain.com/docs/modules/evaluation/examples/comparisons"}
{"id": "230dcbd468c9-3", "text": "when you need to answer questions about current events. You should ask targeted questions.\",    ),]functions_agent = initialize_agent(    tools, llm, agent=AgentType.OPENAI_MULTI_FUNCTIONS, verbose=False)conversations_agent = initialize_agent(    tools, llm, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=False)Step 4. Generate Responses\u00e2\u20ac\u2039We will generate outputs for each of the models before evaluating them.from tqdm.notebook import tqdmimport asyncioresults = []agents = [functions_agent, conversations_agent]concurrency_level = 6  # How many concurrent agents to run. May need to decrease if OpenAI is rate limiting.# We will only run the first 20 examples of this dataset to speed things up# This will lead to larger confidence intervals downstream.batch = []for example in tqdm(dataset[:20]):    batch.extend([agent.acall(example[\"inputs\"]) for agent in agents])    if len(batch) >= concurrency_level:        batch_results = await asyncio.gather(*batch, return_exceptions=True)        results.extend(list(zip(*[iter(batch_results)] * 2)))        batch = []if batch:    batch_results = await asyncio.gather(*batch, return_exceptions=True)    results.extend(list(zip(*[iter(batch_results)] * 2)))      0%|          | 0/20 [00:00<?, ?it/s]    Retrying langchain.chat_models.openai.acompletion_with_retry.<locals>._completion_with_retry in 1.0 seconds as it raised ServiceUnavailableError: The server is overloaded or not ready yet..    Retrying langchain.chat_models.openai.acompletion_with_retry.<locals>._completion_with_retry", "source": "https://python.langchain.com/docs/modules/evaluation/examples/comparisons"}
{"id": "230dcbd468c9-4", "text": "Retrying langchain.chat_models.openai.acompletion_with_retry.<locals>._completion_with_retry in 1.0 seconds as it raised ServiceUnavailableError: The server is overloaded or not ready yet..Step 5. Evaluate Pairs\u00e2\u20ac\u2039Now it's time to evaluate the results. For each agent response, run the evaluation chain to select which output is preferred (or return a tie).Randomly select the input order to reduce the likelihood that one model will be preferred just because it is presented first.import randomdef predict_preferences(dataset, results) -> list:    preferences = []    for example, (res_a, res_b) in zip(dataset, results):        input_ = example[\"inputs\"]        # Flip a coin to reduce persistent position bias        if random.random() < 0.5:            pred_a, pred_b = res_a, res_b            a, b = \"a\", \"b\"        else:            pred_a, pred_b = res_b, res_a            a, b = \"b\", \"a\"        eval_res = eval_chain.evaluate_string_pairs(            prediction=pred_a[\"output\"] if isinstance(pred_a, dict) else str(pred_a),            prediction_b=pred_b[\"output\"] if isinstance(pred_b, dict) else str(pred_b),            input=input_,        )        if eval_res[\"value\"] == \"A\":", "source": "https://python.langchain.com/docs/modules/evaluation/examples/comparisons"}
{"id": "230dcbd468c9-5", "text": ")        if eval_res[\"value\"] == \"A\":            preferences.append(a)        elif eval_res[\"value\"] == \"B\":            preferences.append(b)        else:            preferences.append(None)  # No preference    return preferencespreferences = predict_preferences(dataset, results)Print out the ratio of preferences.from collections import Countername_map = {    \"a\": \"OpenAI Functions Agent\",    \"b\": \"Structured Chat Agent\",}counts = Counter(preferences)pref_ratios = {k: v / len(preferences) for k, v in counts.items()}for k, v in pref_ratios.items():    print(f\"{name_map.get(k)}: {v:.2%}\")    OpenAI Functions Agent: 90.00%    Structured Chat Agent: 10.00%Estimate Confidence Intervals\u00e2\u20ac\u2039The results seem pretty clear, but if you want to have a better sense of how confident we are, that model \"A\" (the OpenAI Functions Agent) is the preferred model, we can calculate confidence intervals. Below, use the Wilson score to estimate the confidence interval.from math import sqrtdef wilson_score_interval(    preferences: list, which: str = \"a\", z: float = 1.96) -> tuple:    \"\"\"Estimate the confidence interval using the Wilson score.    See: https://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval#Wilson_score_interval    for more details, including when to use it and when it should not be used.    \"\"\"    total_preferences = preferences.count(\"a\") +", "source": "https://python.langchain.com/docs/modules/evaluation/examples/comparisons"}
{"id": "230dcbd468c9-6", "text": "not be used.    \"\"\"    total_preferences = preferences.count(\"a\") + preferences.count(\"b\")    n_s = preferences.count(which)    if total_preferences == 0:        return (0, 0)    p_hat = n_s / total_preferences    denominator = 1 + (z**2) / total_preferences    adjustment = (z / denominator) * sqrt(        p_hat * (1 - p_hat) / total_preferences        + (z**2) / (4 * total_preferences * total_preferences)    )    center = (p_hat + (z**2) / (2 * total_preferences)) / denominator    lower_bound = min(max(center - adjustment, 0.0), 1.0)    upper_bound = min(max(center + adjustment, 0.0), 1.0)    return (lower_bound, upper_bound)for which_, name in name_map.items():    low, high = wilson_score_interval(preferences, which=which_)    print(        f'The \"{name}\" would be preferred between {low:.2%} and {high:.2%} percent of the time (with 95% confidence).'    )    The \"OpenAI Functions Agent\" would be preferred between 69.90% and 97.21% percent of the time (with 95% confidence).    The \"Structured Chat Agent\" would be preferred between 2.79% and 30.10% percent of the time (with 95% confidence).Print out the p-value.from scipy import statspreferred_model = max(pref_ratios, key=pref_ratios.get)successes =", "source": "https://python.langchain.com/docs/modules/evaluation/examples/comparisons"}
{"id": "230dcbd468c9-7", "text": "import statspreferred_model = max(pref_ratios, key=pref_ratios.get)successes = preferences.count(preferred_model)n = len(preferences) - preferences.count(None)p_value = stats.binom_test(successes, n, p=0.5, alternative=\"two-sided\")print(    f\"\"\"The p-value is {p_value:.5f}. If the null hypothesis is true (i.e., if the selected eval chain actually has no preference between the models),then there is a {p_value:.5%} chance of observing the {name_map.get(preferred_model)} be preferred at least {successes}times out of {n} trials.\"\"\")    The p-value is 0.00040. If the null hypothesis is true (i.e., if the selected eval chain actually has no preference between the models),    then there is a 0.04025% chance of observing the OpenAI Functions Agent be preferred at least 18    times out of 20 trials._1. Note: Automated evals are still an open research topic and are best used alongside other evaluation approaches. LLM preferences exhibit biases, including banal ones like the order of outputs. In choosing preferences, \"ground truth\" may not be taken into account, which may lead to scores that aren't grounded in utility._PreviousAgent VectorDB Question Answering BenchmarkingNextData Augmented Question AnsweringStep 1. Create the EvaluatorStep 2. Select DatasetStep 3. Define Models to CompareStep 4. Generate ResponsesStep 5. Evaluate PairsEstimate Confidence IntervalsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/evaluation/examples/comparisons"}
{"id": "437075051fc2-0", "text": "Trajectory Evaluators | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain\n\n\n\n\n\nSkip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsMemoryAgentsCallbacksModulesGuidesEvaluationString EvaluatorsComparison EvaluatorsTrajectory EvaluatorsCustom Trajectory EvaluatorAgent TrajectoryExamplesDebuggingDeploymentLangSmithModel ComparisonEcosystemAdditional resourcesGuidesEvaluationTrajectory EvaluatorsTrajectory Evaluators\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd Custom Trajectory EvaluatorYou can make your own custom trajectory evaluators by inheriting from the AgentTrajectoryEvaluator class and overwriting the evaluateagenttrajectory (and aevaluateagentaction) method.\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd Agent TrajectoryAgents can be difficult to holistically evaluate due to the breadth of actions and generation they can make. We recommend using multiple evaluation techniques appropriate to your use case. One way to evaluate an agent is to look at the whole trajectory of actions taken along with their responses.PreviousPairwise String ComparisonNextCustom Trajectory EvaluatorCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/evaluation/trajectory/"}
{"id": "1db58d4bca7c-0", "text": "String Evaluators | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/evaluation/string/"}
{"id": "1db58d4bca7c-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsMemoryAgentsCallbacksModulesGuidesEvaluationString EvaluatorsEvaluating Custom CriteriaCustom String EvaluatorEmbedding DistanceQA CorrectnessString DistanceComparison EvaluatorsTrajectory EvaluatorsExamplesDebuggingDeploymentLangSmithModel ComparisonEcosystemAdditional resourcesGuidesEvaluationString EvaluatorsString Evaluators\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd Evaluating Custom CriteriaSuppose you want to test a model's output against a custom rubric or custom set of criteria, how would you go about testing this?\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd Custom String EvaluatorYou can make your own custom string evaluators by inheriting from the StringEvaluator class and implementing the evaluatestrings (and aevaluatestrings for async support) methods.\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd Embedding DistanceTo measure semantic similarity (or dissimilarity) between a prediction and a reference label string, you could use a vector vector distance metric the two embedded representations using the embeddingdistance evaluator.[1]\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd QA CorrectnessWhen thinking about a QA system, one of the most important questions to ask is whether the final generated result is correct. The \"qa\" evaluator compares a question-answering model's response to a reference answer to provide this level of information. If you are able to annotate a test dataset, this evaluator will be useful.\u011f\u0178\u201c\u201e\u00ef\u00b8\ufffd String DistanceOne of the simplest ways to compare an LLM or chain's string output against a reference label is by using string distance measurements such as Levenshtein or postfix distance.  This can be used alongside approximate/fuzzy matching criteria for very basic unit testing.PreviousEvaluationNextEvaluating Custom", "source": "https://python.langchain.com/docs/modules/evaluation/string/"}
{"id": "1db58d4bca7c-2", "text": "used alongside approximate/fuzzy matching criteria for very basic unit testing.PreviousEvaluationNextEvaluating Custom CriteriaCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/evaluation/string/"}
{"id": "cfa9f91940ff-0", "text": "Memory | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/memory/"}
{"id": "cfa9f91940ff-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsMemoryHow to add Memory to an LLMChainHow to add memory to a Multi-Input ChainHow to add Memory to an AgentAdding Message Memory backed by a database to an AgentConversation buffer memoryConversation buffer window memoryHow to customize conversational memoryHow to create a custom Memory classEntity memoryConversation Knowledge Graph MemoryHow to use multiple memory classes in the same chainConversation summary memoryConversationSummaryBufferMemoryConversationTokenBufferMemoryVector store-backed memoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesMemoryOn this pageMemory\u011f\u0178\u0161\u00a7 Docs under construction \u011f\u0178\u0161\u00a7infoHead to Integrations for documentation on built-in memory integrations with 3rd-party tools.By default, Chains and Agents are stateless,\nmeaning that they treat each incoming query independently (like the underlying LLMs and chat models themselves).\nIn some applications, like chatbots, it is essential\nto remember previous interactions, both in the short and long-term.\nThe Memory class does exactly that.LangChain provides memory components in two forms.\nFirst, LangChain provides helper utilities for managing and manipulating previous chat messages.\nThese are designed to be modular and useful regardless of how they are used.", "source": "https://python.langchain.com/docs/modules/memory/"}
{"id": "cfa9f91940ff-2", "text": "Secondly, LangChain provides easy ways to incorporate these utilities into chains.Get started\u00e2\u20ac\u2039Memory involves keeping a concept of state around throughout a user's interactions with an language model. A user's interactions with a language model are captured in the concept of ChatMessages, so this boils down to ingesting, capturing, transforming and extracting knowledge from a sequence of chat messages. There are many different ways to do this, each of which exists as its own memory type.In general, for each type of memory there are two ways to understanding using memory. These are the standalone functions which extract information from a sequence of messages, and then there is the way you can use this type of memory in a chain.Memory can return multiple pieces of information (for example, the most recent N messages and a summary of all previous messages). The returned information can either be a string or a list of messages.We will walk through the simplest form of memory: \"buffer\" memory, which just involves keeping a buffer of all prior messages. We will show how to use the modular utility functions here, then show how it can be used in a chain (both returning a string as well as a list of messages).ChatMessageHistory\u00e2\u20ac\u2039One of the core utility classes underpinning most (if not all) memory modules is the ChatMessageHistory class. This is a super lightweight wrapper which exposes convenience methods for saving Human messages, AI messages, and then fetching them all.You may want to use this class directly if you are managing memory outside of a chain.from langchain.memory import ChatMessageHistoryhistory = ChatMessageHistory()history.add_user_message(\"hi!\")history.add_ai_message(\"whats up?\")history.messages    [HumanMessage(content='hi!', additional_kwargs={}),     AIMessage(content='whats up?', additional_kwargs={})]ConversationBufferMemory\u00e2\u20ac\u2039We now show how to use this simple concept in a chain. We first showcase", "source": "https://python.langchain.com/docs/modules/memory/"}
{"id": "cfa9f91940ff-3", "text": "now show how to use this simple concept in a chain. We first showcase ConversationBufferMemory which is just a wrapper around ChatMessageHistory that extracts the messages in a variable.We can first extract it as a string.from langchain.memory import ConversationBufferMemorymemory = ConversationBufferMemory()memory.chat_memory.add_user_message(\"hi!\")memory.chat_memory.add_ai_message(\"whats up?\")memory.load_memory_variables({})    {'history': 'Human: hi!\\nAI: whats up?'}We can also get the history as a list of messagesmemory = ConversationBufferMemory(return_messages=True)memory.chat_memory.add_user_message(\"hi!\")memory.chat_memory.add_ai_message(\"whats up?\")memory.load_memory_variables({})    {'history': [HumanMessage(content='hi!', additional_kwargs={}),      AIMessage(content='whats up?', additional_kwargs={})]}Using in a chain\u00e2\u20ac\u2039Finally, let's take a look at using this in a chain (setting verbose=True so we can see the prompt).from langchain.llms import OpenAIfrom langchain.chains import ConversationChainllm = OpenAI(temperature=0)conversation = ConversationChain(    llm=llm,    verbose=True,    memory=ConversationBufferMemory())conversation.predict(input=\"Hi there!\")    > Entering new ConversationChain chain...    Prompt after formatting:    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.    Current conversation:    Human: Hi there!    AI:    > Finished chain.    \" Hi there! It's nice to meet you. How can I help you today?\"conversation.predict(input=\"I'm", "source": "https://python.langchain.com/docs/modules/memory/"}
{"id": "cfa9f91940ff-4", "text": "It's nice to meet you. How can I help you today?\"conversation.predict(input=\"I'm doing well! Just having a conversation with an AI.\")    > Entering new ConversationChain chain...    Prompt after formatting:    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.    Current conversation:    Human: Hi there!    AI:  Hi there! It's nice to meet you. How can I help you today?    Human: I'm doing well! Just having a conversation with an AI.    AI:    > Finished chain.    \" That's great! It's always nice to have a conversation with someone new. What would you like to talk about?\"conversation.predict(input=\"Tell me about yourself.\")    > Entering new ConversationChain chain...    Prompt after formatting:    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.    Current conversation:    Human: Hi there!    AI:  Hi there! It's nice to meet you. How can I help you today?    Human: I'm doing well! Just having a conversation with an AI.    AI:  That's great! It's always nice to have a conversation with someone new. What would you like to talk about?    Human: Tell me about yourself.    AI:    > Finished chain.    \" Sure! I'm an AI created to help people with their everyday", "source": "https://python.langchain.com/docs/modules/memory/"}
{"id": "cfa9f91940ff-5", "text": "Finished chain.    \" Sure! I'm an AI created to help people with their everyday tasks. I'm programmed to understand natural language and provide helpful information. I'm also constantly learning and updating my knowledge base so I can provide more accurate and helpful answers.\"Saving Message History\u00e2\u20ac\u2039You may often have to save messages, and then load them to use again. This can be done easily by first converting the messages to normal python dictionaries, saving those (as json or something) and then loading those. Here is an example of doing that.import jsonfrom langchain.memory import ChatMessageHistoryfrom langchain.schema import messages_from_dict, messages_to_dicthistory = ChatMessageHistory()history.add_user_message(\"hi!\")history.add_ai_message(\"whats up?\")dicts = messages_to_dict(history.messages)dicts    [{'type': 'human', 'data': {'content': 'hi!', 'additional_kwargs': {}}},     {'type': 'ai', 'data': {'content': 'whats up?', 'additional_kwargs': {}}}]new_messages = messages_from_dict(dicts)new_messages    [HumanMessage(content='hi!', additional_kwargs={}),     AIMessage(content='whats up?', additional_kwargs={})]And that's it for the getting started! There are plenty of different types of memory, check out our examples to see them allPreviousVector store-augmented text generationNextHow to add Memory to an LLMChainGet startedCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/memory/"}
{"id": "f7cf51665ac6-0", "text": "Conversation buffer window memory | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/memory/buffer_window"}
{"id": "f7cf51665ac6-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsMemoryHow to add Memory to an LLMChainHow to add memory to a Multi-Input ChainHow to add Memory to an AgentAdding Message Memory backed by a database to an AgentConversation buffer memoryConversation buffer window memoryHow to customize conversational memoryHow to create a custom Memory classEntity memoryConversation Knowledge Graph MemoryHow to use multiple memory classes in the same chainConversation summary memoryConversationSummaryBufferMemoryConversationTokenBufferMemoryVector store-backed memoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesMemoryConversation buffer window memoryConversation buffer window memoryConversationBufferWindowMemory keeps a list of the interactions of the conversation over time. It only uses the last K interactions. This can be useful for keeping a sliding window of the most recent interactions, so the buffer does not get too largeLet's first explore the basic functionality of this type of memory.from langchain.memory import ConversationBufferWindowMemorymemory = ConversationBufferWindowMemory( k=1)memory.save_context({\"input\": \"hi\"}, {\"output\": \"whats up\"})memory.save_context({\"input\": \"not much you\"}, {\"output\": \"not much\"})memory.load_memory_variables({})    {'history': 'Human: not much you\\nAI: not much'}We can also get the history as a list of messages (this is useful if you are using this with a chat model).memory = ConversationBufferWindowMemory( k=1, return_messages=True)memory.save_context({\"input\": \"hi\"}, {\"output\": \"whats up\"})memory.save_context({\"input\": \"not much you\"}, {\"output\": \"not much\"})memory.load_memory_variables({})    {'history': [HumanMessage(content='not much you',", "source": "https://python.langchain.com/docs/modules/memory/buffer_window"}
{"id": "f7cf51665ac6-2", "text": "{'history': [HumanMessage(content='not much you', additional_kwargs={}),      AIMessage(content='not much', additional_kwargs={})]}Using in a chain\u00e2\u20ac\u2039Let's walk through an example, again setting verbose=True so we can see the prompt.from langchain.llms import OpenAIfrom langchain.chains import ConversationChainconversation_with_summary = ConversationChain(    llm=OpenAI(temperature=0),     # We set a low k=2, to only keep the last 2 interactions in memory    memory=ConversationBufferWindowMemory(k=2),     verbose=True)conversation_with_summary.predict(input=\"Hi, what's up?\")            > Entering new ConversationChain chain...    Prompt after formatting:    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.        Current conversation:        Human: Hi, what's up?    AI:        > Finished chain.    \" Hi there! I'm doing great. I'm currently helping a customer with a technical issue. How about you?\"conversation_with_summary.predict(input=\"What's their issues?\")            > Entering new ConversationChain chain...    Prompt after formatting:    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.        Current conversation:", "source": "https://python.langchain.com/docs/modules/memory/buffer_window"}
{"id": "f7cf51665ac6-3", "text": "it truthfully says it does not know.        Current conversation:    Human: Hi, what's up?    AI:  Hi there! I'm doing great. I'm currently helping a customer with a technical issue. How about you?    Human: What's their issues?    AI:        > Finished chain.    \" The customer is having trouble connecting to their Wi-Fi network. I'm helping them troubleshoot the issue and get them connected.\"conversation_with_summary.predict(input=\"Is it going well?\")            > Entering new ConversationChain chain...    Prompt after formatting:    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.        Current conversation:    Human: Hi, what's up?    AI:  Hi there! I'm doing great. I'm currently helping a customer with a technical issue. How about you?    Human: What's their issues?    AI:  The customer is having trouble connecting to their Wi-Fi network. I'm helping them troubleshoot the issue and get them connected.    Human: Is it going well?    AI:        > Finished chain.    \" Yes, it's going well so far. We've already identified the problem and are now working on a solution.\"# Notice here that the first interaction does not appear.conversation_with_summary.predict(input=\"What's the solution?\")            > Entering new ConversationChain chain...    Prompt after formatting:", "source": "https://python.langchain.com/docs/modules/memory/buffer_window"}
{"id": "f7cf51665ac6-4", "text": "> Entering new ConversationChain chain...    Prompt after formatting:    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.        Current conversation:    Human: What's their issues?    AI:  The customer is having trouble connecting to their Wi-Fi network. I'm helping them troubleshoot the issue and get them connected.    Human: Is it going well?    AI:  Yes, it's going well so far. We've already identified the problem and are now working on a solution.    Human: What's the solution?    AI:        > Finished chain.    \" The solution is to reset the router and reconfigure the settings. We're currently in the process of doing that.\"PreviousConversation buffer memoryNextHow to customize conversational memoryCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/memory/buffer_window"}
{"id": "0e75069727de-0", "text": "Conversation buffer memory | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/memory/buffer"}
{"id": "0e75069727de-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsMemoryHow to add Memory to an LLMChainHow to add memory to a Multi-Input ChainHow to add Memory to an AgentAdding Message Memory backed by a database to an AgentConversation buffer memoryConversation buffer window memoryHow to customize conversational memoryHow to create a custom Memory classEntity memoryConversation Knowledge Graph MemoryHow to use multiple memory classes in the same chainConversation summary memoryConversationSummaryBufferMemoryConversationTokenBufferMemoryVector store-backed memoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesMemoryConversation buffer memoryConversation buffer memoryThis notebook shows how to use ConversationBufferMemory. This memory allows for storing of messages and then extracts the messages in a variable.We can first extract it as a string.from langchain.memory import ConversationBufferMemorymemory = ConversationBufferMemory()memory.save_context({\"input\": \"hi\"}, {\"output\": \"whats up\"})memory.load_memory_variables({})    {'history': 'Human: hi\\nAI: whats up'}We can also get the history as a list of messages (this is useful if you are using this with a chat model).memory = ConversationBufferMemory(return_messages=True)memory.save_context({\"input\": \"hi\"}, {\"output\": \"whats up\"})memory.load_memory_variables({})    {'history': [HumanMessage(content='hi', additional_kwargs={}),      AIMessage(content='whats up', additional_kwargs={})]}Using in a chain\u00e2\u20ac\u2039Finally, let's take a look at using this in a chain (setting verbose=True so we can see the prompt).from langchain.llms import OpenAIfrom langchain.chains import ConversationChainllm = OpenAI(temperature=0)conversation =", "source": "https://python.langchain.com/docs/modules/memory/buffer"}
{"id": "0e75069727de-2", "text": "langchain.chains import ConversationChainllm = OpenAI(temperature=0)conversation = ConversationChain(    llm=llm,     verbose=True,     memory=ConversationBufferMemory())conversation.predict(input=\"Hi there!\")            > Entering new ConversationChain chain...    Prompt after formatting:    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.        Current conversation:        Human: Hi there!    AI:        > Finished chain.    \" Hi there! It's nice to meet you. How can I help you today?\"conversation.predict(input=\"I'm doing well! Just having a conversation with an AI.\")            > Entering new ConversationChain chain...    Prompt after formatting:    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.        Current conversation:    Human: Hi there!    AI:  Hi there! It's nice to meet you. How can I help you today?    Human: I'm doing well! Just having a conversation with an AI.    AI:        > Finished chain.    \" That's great! It's always nice to have a conversation with someone new. What would you like to talk about?\"conversation.predict(input=\"Tell me about yourself.\")", "source": "https://python.langchain.com/docs/modules/memory/buffer"}
{"id": "0e75069727de-3", "text": "new. What would you like to talk about?\"conversation.predict(input=\"Tell me about yourself.\")            > Entering new ConversationChain chain...    Prompt after formatting:    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.        Current conversation:    Human: Hi there!    AI:  Hi there! It's nice to meet you. How can I help you today?    Human: I'm doing well! Just having a conversation with an AI.    AI:  That's great! It's always nice to have a conversation with someone new. What would you like to talk about?    Human: Tell me about yourself.    AI:        > Finished chain.    \" Sure! I'm an AI created to help people with their everyday tasks. I'm programmed to understand natural language and provide helpful information. I'm also constantly learning and updating my knowledge base so I can provide more accurate and helpful answers.\"And that's it for the getting started! There are plenty of different types of memory, check out our examples to see them allPreviousAdding Message Memory backed by a database to an AgentNextConversation buffer window memoryCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/memory/buffer"}
{"id": "76c32b6ea2bd-0", "text": "How to add Memory to an LLMChain | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/memory/adding_memory"}
{"id": "76c32b6ea2bd-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsMemoryHow to add Memory to an LLMChainHow to add memory to a Multi-Input ChainHow to add Memory to an AgentAdding Message Memory backed by a database to an AgentConversation buffer memoryConversation buffer window memoryHow to customize conversational memoryHow to create a custom Memory classEntity memoryConversation Knowledge Graph MemoryHow to use multiple memory classes in the same chainConversation summary memoryConversationSummaryBufferMemoryConversationTokenBufferMemoryVector store-backed memoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesMemoryHow to add Memory to an LLMChainHow to add Memory to an LLMChainThis notebook goes over how to use the Memory class with an LLMChain. For the purposes of this walkthrough, we will add  the ConversationBufferMemory class, although this can be any memory class.from langchain.memory import ConversationBufferMemoryfrom langchain import OpenAI, LLMChain, PromptTemplateThe most important step is setting up the prompt correctly. In the below prompt, we have two input keys: one for the actual input, another for the input from the Memory class. Importantly, we make sure the keys in the PromptTemplate and the ConversationBufferMemory match up (chat_history).template = \"\"\"You are a chatbot having a conversation with a human.{chat_history}Human: {human_input}Chatbot:\"\"\"prompt = PromptTemplate(    input_variables=[\"chat_history\", \"human_input\"], template=template)memory = ConversationBufferMemory(memory_key=\"chat_history\")llm_chain = LLMChain(    llm=OpenAI(),    prompt=prompt,    verbose=True,", "source": "https://python.langchain.com/docs/modules/memory/adding_memory"}
{"id": "76c32b6ea2bd-2", "text": "prompt=prompt,    verbose=True,    memory=memory,)llm_chain.predict(human_input=\"Hi there my friend\")            > Entering new LLMChain chain...    Prompt after formatting:    You are a chatbot having a conversation with a human.            Human: Hi there my friend    Chatbot:        > Finished LLMChain chain.    ' Hi there, how are you doing today?'llm_chain.predict(human_input=\"Not too bad - how are you?\")            > Entering new LLMChain chain...    Prompt after formatting:    You are a chatbot having a conversation with a human.            Human: Hi there my friend    AI:  Hi there, how are you doing today?    Human: Not too bad - how are you?    Chatbot:        > Finished LLMChain chain.    \" I'm doing great, thank you for asking!\"PreviousMemoryNextHow to add memory to a Multi-Input ChainCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/memory/adding_memory"}
{"id": "d34a8e08aec7-0", "text": "How to create a custom Memory class | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/memory/custom_memory"}
{"id": "d34a8e08aec7-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsMemoryHow to add Memory to an LLMChainHow to add memory to a Multi-Input ChainHow to add Memory to an AgentAdding Message Memory backed by a database to an AgentConversation buffer memoryConversation buffer window memoryHow to customize conversational memoryHow to create a custom Memory classEntity memoryConversation Knowledge Graph MemoryHow to use multiple memory classes in the same chainConversation summary memoryConversationSummaryBufferMemoryConversationTokenBufferMemoryVector store-backed memoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesMemoryHow to create a custom Memory classHow to create a custom Memory classAlthough there are a few predefined types of memory in LangChain, it is highly possible you will want to add your own type of memory that is optimal for your application. This notebook covers how to do that.For this notebook, we will add a custom memory type to ConversationChain. In order to add a custom memory class, we need to import the base memory class and subclass it.from langchain import OpenAI, ConversationChainfrom langchain.schema import BaseMemoryfrom pydantic import BaseModelfrom typing import List, Dict, AnyIn this example, we will write a custom memory class that uses spacy to extract entities and save information about them in a simple hash table. Then, during the conversation, we will look at the input text, extract any entities, and put any information about them into the context.Please note that this implementation is pretty simple and brittle and probably not useful in a production setting. Its purpose is to showcase that you can add custom memory implementations.For this, we will need spacy.# !pip install spacy# !python -m spacy download en_core_web_lgimport spacynlp =", "source": "https://python.langchain.com/docs/modules/memory/custom_memory"}
{"id": "d34a8e08aec7-2", "text": "spacy# !python -m spacy download en_core_web_lgimport spacynlp = spacy.load(\"en_core_web_lg\")class SpacyEntityMemory(BaseMemory, BaseModel):    \"\"\"Memory class for storing information about entities.\"\"\"    # Define dictionary to store information about entities.    entities: dict = {}    # Define key to pass information about entities into prompt.    memory_key: str = \"entities\"    def clear(self):        self.entities = {}    @property    def memory_variables(self) -> List[str]:        \"\"\"Define the variables we are providing to the prompt.\"\"\"        return [self.memory_key]    def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:        \"\"\"Load the memory variables, in this case the entity key.\"\"\"        # Get the input text and run through spacy        doc = nlp(inputs[list(inputs.keys())[0]])        # Extract known information about entities, if they exist.        entities = [            self.entities[str(ent)] for ent in doc.ents if str(ent) in self.entities        ]        # Return combined information about entities to put into context.        return {self.memory_key: \"\\n\".join(entities)}    def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:        \"\"\"Save context from this conversation to buffer.\"\"\"        # Get the input text and run through spacy", "source": "https://python.langchain.com/docs/modules/memory/custom_memory"}
{"id": "d34a8e08aec7-3", "text": "to buffer.\"\"\"        # Get the input text and run through spacy        text = inputs[list(inputs.keys())[0]]        doc = nlp(text)        # For each entity that was mentioned, save this information to the dictionary.        for ent in doc.ents:            ent_str = str(ent)            if ent_str in self.entities:                self.entities[ent_str] += f\"\\n{text}\"            else:                self.entities[ent_str] = textWe now define a prompt that takes in information about entities as well as user inputfrom langchain.prompts.prompt import PromptTemplatetemplate = \"\"\"The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know. You are provided with information about entities the Human mentions, if relevant.Relevant entity information:{entities}Conversation:Human: {input}AI:\"\"\"prompt = PromptTemplate(input_variables=[\"entities\", \"input\"], template=template)And now we put it all together!llm = OpenAI(temperature=0)conversation = ConversationChain(    llm=llm, prompt=prompt, verbose=True, memory=SpacyEntityMemory())In the first example, with no prior knowledge about Harrison, the \"Relevant entity information\" section is empty.conversation.predict(input=\"Harrison likes machine learning\")            >", "source": "https://python.langchain.com/docs/modules/memory/custom_memory"}
{"id": "d34a8e08aec7-4", "text": "likes machine learning\")            > Entering new ConversationChain chain...    Prompt after formatting:    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know. You are provided with information about entities the Human mentions, if relevant.        Relevant entity information:            Conversation:    Human: Harrison likes machine learning    AI:        > Finished ConversationChain chain.    \" That's great to hear! Machine learning is a fascinating field of study. It involves using algorithms to analyze data and make predictions. Have you ever studied machine learning, Harrison?\"Now in the second example, we can see that it pulls in information about Harrison.conversation.predict(    input=\"What do you think Harrison's favorite subject in college was?\")            > Entering new ConversationChain chain...    Prompt after formatting:    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know. You are provided with information about entities the Human mentions, if relevant.        Relevant entity information:    Harrison likes machine learning        Conversation:    Human: What do you think Harrison's favorite subject in college was?    AI:        > Finished ConversationChain chain.    ' From what I know about Harrison, I believe his favorite subject in college was machine learning.", "source": "https://python.langchain.com/docs/modules/memory/custom_memory"}
{"id": "d34a8e08aec7-5", "text": "' From what I know about Harrison, I believe his favorite subject in college was machine learning. He has expressed a strong interest in the subject and has mentioned it often.'Again, please note that this implementation is pretty simple and brittle and probably not useful in a production setting. Its purpose is to showcase that you can add custom memory implementations.PreviousHow to customize conversational memoryNextEntity memoryCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/memory/custom_memory"}
{"id": "8deb7146be70-0", "text": "How to add memory to a Multi-Input Chain | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/memory/adding_memory_chain_multiple_inputs"}
{"id": "8deb7146be70-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsMemoryHow to add Memory to an LLMChainHow to add memory to a Multi-Input ChainHow to add Memory to an AgentAdding Message Memory backed by a database to an AgentConversation buffer memoryConversation buffer window memoryHow to customize conversational memoryHow to create a custom Memory classEntity memoryConversation Knowledge Graph MemoryHow to use multiple memory classes in the same chainConversation summary memoryConversationSummaryBufferMemoryConversationTokenBufferMemoryVector store-backed memoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesMemoryHow to add memory to a Multi-Input ChainHow to add memory to a Multi-Input ChainMost memory objects assume a single input. In this notebook, we go over how to add memory to a chain that has multiple inputs. As an example of such a chain, we will add memory to a question/answering chain. This chain takes as inputs both related documents and a user question.from langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.embeddings.cohere import CohereEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores.elastic_vector_search import ElasticVectorSearchfrom langchain.vectorstores import Chromafrom langchain.docstore.document import Documentwith open(\"../../state_of_the_union.txt\") as f:    state_of_the_union = f.read()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)texts = text_splitter.split_text(state_of_the_union)embeddings = OpenAIEmbeddings()docsearch = Chroma.from_texts(    texts, embeddings, metadatas=[{\"source\": i} for i in range(len(texts))])    Running", "source": "https://python.langchain.com/docs/modules/memory/adding_memory_chain_multiple_inputs"}
{"id": "8deb7146be70-2", "text": "i} for i in range(len(texts))])    Running Chroma using direct local API.    Using DuckDB in-memory for database. Data will be transient.query = \"What did the president say about Justice Breyer\"docs = docsearch.similarity_search(query)from langchain.chains.question_answering import load_qa_chainfrom langchain.llms import OpenAIfrom langchain.prompts import PromptTemplatefrom langchain.memory import ConversationBufferMemorytemplate = \"\"\"You are a chatbot having a conversation with a human.Given the following extracted parts of a long document and a question, create a final answer.{context}{chat_history}Human: {human_input}Chatbot:\"\"\"prompt = PromptTemplate(    input_variables=[\"chat_history\", \"human_input\", \"context\"], template=template)memory = ConversationBufferMemory(memory_key=\"chat_history\", input_key=\"human_input\")chain = load_qa_chain(    OpenAI(temperature=0), chain_type=\"stuff\", memory=memory, prompt=prompt)query = \"What did the president say about Justice Breyer\"chain({\"input_documents\": docs, \"human_input\": query}, return_only_outputs=True)    {'output_text': ' Tonight, I\u00e2\u20ac\u2122d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer\u00e2\u20ac\u201dan Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.'}print(chain.memory.buffer)        Human: What did the president say about Justice Breyer    AI:  Tonight, I\u00e2\u20ac\u2122d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer\u00e2\u20ac\u201dan Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your", "source": "https://python.langchain.com/docs/modules/memory/adding_memory_chain_multiple_inputs"}
{"id": "8deb7146be70-3", "text": "and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.PreviousHow to add Memory to an LLMChainNextHow to add Memory to an AgentCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/memory/adding_memory_chain_multiple_inputs"}
{"id": "577811684829-0", "text": "Adding Message Memory backed by a database to an Agent | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/memory/agent_with_memory_in_db"}
{"id": "577811684829-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsMemoryHow to add Memory to an LLMChainHow to add memory to a Multi-Input ChainHow to add Memory to an AgentAdding Message Memory backed by a database to an AgentConversation buffer memoryConversation buffer window memoryHow to customize conversational memoryHow to create a custom Memory classEntity memoryConversation Knowledge Graph MemoryHow to use multiple memory classes in the same chainConversation summary memoryConversationSummaryBufferMemoryConversationTokenBufferMemoryVector store-backed memoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesMemoryAdding Message Memory backed by a database to an AgentAdding Message Memory backed by a database to an AgentThis notebook goes over adding memory to an Agent where the memory uses an external message store. Before going through this notebook, please walkthrough the following notebooks, as this will build on top of both of them:Adding memory to an LLM ChainCustom AgentsAgent with MemoryIn order to add a memory with an external message store to an agent we are going to do the following steps:We are going to create a RedisChatMessageHistory to connect to an external database to store the messages in.We are going to create an LLMChain using that chat history as memory.We are going to use that LLMChain to create a custom Agent.For the purposes of this exercise, we are going to create a simple custom Agent that has access to a search tool and utilizes the ConversationBufferMemory class.from langchain.agents import ZeroShotAgent, Tool, AgentExecutorfrom langchain.memory import ConversationBufferMemoryfrom langchain.memory.chat_memory import ChatMessageHistoryfrom langchain.memory.chat_message_histories import RedisChatMessageHistoryfrom langchain import OpenAI, LLMChainfrom langchain.utilities import GoogleSearchAPIWrappersearch =", "source": "https://python.langchain.com/docs/modules/memory/agent_with_memory_in_db"}
{"id": "577811684829-2", "text": "langchain import OpenAI, LLMChainfrom langchain.utilities import GoogleSearchAPIWrappersearch = GoogleSearchAPIWrapper()tools = [    Tool(        name=\"Search\",        func=search.run,        description=\"useful for when you need to answer questions about current events\",    )]Notice the usage of the chat_history variable in the PromptTemplate, which matches up with the dynamic key name in the ConversationBufferMemory.prefix = \"\"\"Have a conversation with a human, answering the following questions as best you can. You have access to the following tools:\"\"\"suffix = \"\"\"Begin!\"{chat_history}Question: {input}{agent_scratchpad}\"\"\"prompt = ZeroShotAgent.create_prompt(    tools,    prefix=prefix,    suffix=suffix,    input_variables=[\"input\", \"chat_history\", \"agent_scratchpad\"],)Now we can create the ChatMessageHistory backed by the database.message_history = RedisChatMessageHistory(    url=\"redis://localhost:6379/0\", ttl=600, session_id=\"my-session\")memory = ConversationBufferMemory(    memory_key=\"chat_history\", chat_memory=message_history)We can now construct the LLMChain, with the Memory object, and then create the agent.llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt)agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)agent_chain = AgentExecutor.from_agent_and_tools(    agent=agent, tools=tools, verbose=True, memory=memory)agent_chain.run(input=\"How many people live in canada?\")            > Entering new AgentExecutor chain...    Thought: I need to find out the population of Canada", "source": "https://python.langchain.com/docs/modules/memory/agent_with_memory_in_db"}
{"id": "577811684829-3", "text": "new AgentExecutor chain...    Thought: I need to find out the population of Canada    Action: Search    Action Input: Population of Canada    Observation: The current population of Canada is 38,566,192 as of Saturday, December 31, 2022, based on Worldometer elaboration of the latest United Nations data. \u00c2\u00b7 Canada\u00c2\u00a0... Additional information related to Canadian population trends can be found on Statistics Canada's Population and Demography Portal. Population of Canada (real-\u00c2\u00a0... Index to the latest information from the Census of Population. This survey conducted by Statistics Canada provides a statistical portrait of Canada and its\u00c2\u00a0... 14 records ... Estimated number of persons by quarter of a year and by year, Canada, provinces and territories. The 2021 Canadian census counted a total population of 36,991,981, an increase of around 5.2 percent over the 2016 figure. ... Between 1990 and 2008, the\u00c2\u00a0... ( 2 ) Census reports and other statistical publications from national statistical offices, ( 3 ) Eurostat: Demographic Statistics, ( 4 ) United Nations\u00c2\u00a0... Canada is a country in North America. Its ten provinces and three territories extend from ... Population. \u00e2\u20ac\u00a2 Q4 2022 estimate. 39,292,355 (37th). Information is available for the total Indigenous population and each of the three ... The term 'Aboriginal' or 'Indigenous' used on the Statistics Canada\u00c2\u00a0... Jun 14, 2022 ... Determinants of health are the broad range of personal, social, economic and environmental factors that determine individual and population\u00c2\u00a0... COVID-19 vaccination coverage across Canada by demographics and key populations. Updated every Friday at 12:00 PM Eastern Time.    Thought: I now know the final answer    Final Answer: The current population of Canada is", "source": "https://python.langchain.com/docs/modules/memory/agent_with_memory_in_db"}
{"id": "577811684829-4", "text": "Thought: I now know the final answer    Final Answer: The current population of Canada is 38,566,192 as of Saturday, December 31, 2022, based on Worldometer elaboration of the latest United Nations data.    > Finished AgentExecutor chain.    'The current population of Canada is 38,566,192 as of Saturday, December 31, 2022, based on Worldometer elaboration of the latest United Nations data.'To test the memory of this agent, we can ask a followup question that relies on information in the previous exchange to be answered correctly.agent_chain.run(input=\"what is their national anthem called?\")            > Entering new AgentExecutor chain...    Thought: I need to find out what the national anthem of Canada is called.    Action: Search    Action Input: National Anthem of Canada    Observation: Jun 7, 2010 ... https://twitter.com/CanadaImmigrantCanadian National Anthem O Canada in HQ - complete with lyrics, captions, vocals & music.LYRICS:O Canada! Nov 23, 2022 ... After 100 years of tradition, O Canada was proclaimed Canada's national anthem in 1980. The music for O Canada was composed in 1880 by Calixa\u00c2\u00a0... O Canada, national anthem of Canada. It was proclaimed the official national anthem on July 1, 1980. \u00e2\u20ac\u0153God Save the Queen\u00e2\u20ac\ufffd remains the royal anthem of Canada\u00c2\u00a0... O Canada! Our home and native land! True patriot love in all of us command. Car ton bras sait porter l'\u00c3\u00a9p\u00c3\u00a9e,. Il sait porter la croix! \"O Canada\" (French: \u00c3\u201d Canada) is the national anthem of Canada. The song was originally commissioned by Lieutenant Governor of Quebec", "source": "https://python.langchain.com/docs/modules/memory/agent_with_memory_in_db"}
{"id": "577811684829-5", "text": "Canada) is the national anthem of Canada. The song was originally commissioned by Lieutenant Governor of Quebec Th\u00c3\u00a9odore Robitaille\u00c2\u00a0... Feb 1, 2018 ... It was a simple tweak \u00e2\u20ac\u201d just two words. But with that, Canada just voted to make its national anthem, \u00e2\u20ac\u0153O Canada,\u00e2\u20ac\ufffd gender neutral,\u00c2\u00a0... \"O Canada\" was proclaimed Canada's national anthem on July 1,. 1980, 100 years after it was first sung on June 24, 1880. The music. Patriotic music in Canada dates back over 200 years as a distinct category from British or French patriotism, preceding the first legal steps to\u00c2\u00a0... Feb 4, 2022 ... English version: O Canada! Our home and native land! True patriot love in all of us command. With glowing hearts we\u00c2\u00a0... Feb 1, 2018 ... Canada's Senate has passed a bill making the country's national anthem gender-neutral. If you're not familiar with the words to \u00e2\u20ac\u0153O Canada,\u00e2\u20ac\ufffd\u00c2\u00a0...    Thought: I now know the final answer.    Final Answer: The national anthem of Canada is called \"O Canada\".    > Finished AgentExecutor chain.    'The national anthem of Canada is called \"O Canada\".'We can see that the agent remembered that the previous question was about Canada, and properly asked Google Search what the name of Canada's national anthem was.For fun, let's compare this to an agent that does NOT have memory.prefix = \"\"\"Have a conversation with a human, answering the following questions as best you can. You have access to the following tools:\"\"\"suffix = \"\"\"Begin!\"Question: {input}{agent_scratchpad}\"\"\"prompt = ZeroShotAgent.create_prompt(    tools, prefix=prefix, suffix=suffix, input_variables=[\"input\",", "source": "https://python.langchain.com/docs/modules/memory/agent_with_memory_in_db"}
{"id": "577811684829-6", "text": "tools, prefix=prefix, suffix=suffix, input_variables=[\"input\", \"agent_scratchpad\"])llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt)agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)agent_without_memory = AgentExecutor.from_agent_and_tools(    agent=agent, tools=tools, verbose=True)agent_without_memory.run(\"How many people live in canada?\")            > Entering new AgentExecutor chain...    Thought: I need to find out the population of Canada    Action: Search    Action Input: Population of Canada    Observation: The current population of Canada is 38,566,192 as of Saturday, December 31, 2022, based on Worldometer elaboration of the latest United Nations data. \u00c2\u00b7 Canada\u00c2\u00a0... Additional information related to Canadian population trends can be found on Statistics Canada's Population and Demography Portal. Population of Canada (real-\u00c2\u00a0... Index to the latest information from the Census of Population. This survey conducted by Statistics Canada provides a statistical portrait of Canada and its\u00c2\u00a0... 14 records ... Estimated number of persons by quarter of a year and by year, Canada, provinces and territories. The 2021 Canadian census counted a total population of 36,991,981, an increase of around 5.2 percent over the 2016 figure. ... Between 1990 and 2008, the\u00c2\u00a0... ( 2 ) Census reports and other statistical publications from national statistical offices, ( 3 ) Eurostat: Demographic Statistics, ( 4 ) United Nations\u00c2\u00a0... Canada is a country in North America. Its ten provinces and three territories extend from ... Population. \u00e2\u20ac\u00a2 Q4 2022 estimate. 39,292,355", "source": "https://python.langchain.com/docs/modules/memory/agent_with_memory_in_db"}
{"id": "577811684829-7", "text": "from ... Population. \u00e2\u20ac\u00a2 Q4 2022 estimate. 39,292,355 (37th). Information is available for the total Indigenous population and each of the three ... The term 'Aboriginal' or 'Indigenous' used on the Statistics Canada\u00c2\u00a0... Jun 14, 2022 ... Determinants of health are the broad range of personal, social, economic and environmental factors that determine individual and population\u00c2\u00a0... COVID-19 vaccination coverage across Canada by demographics and key populations. Updated every Friday at 12:00 PM Eastern Time.    Thought: I now know the final answer    Final Answer: The current population of Canada is 38,566,192 as of Saturday, December 31, 2022, based on Worldometer elaboration of the latest United Nations data.    > Finished AgentExecutor chain.    'The current population of Canada is 38,566,192 as of Saturday, December 31, 2022, based on Worldometer elaboration of the latest United Nations data.'agent_without_memory.run(\"what is their national anthem called?\")            > Entering new AgentExecutor chain...    Thought: I should look up the answer    Action: Search    Action Input: national anthem of [country]    Observation: Most nation states have an anthem, defined as \"a song, as of praise, devotion, or patriotism\"; most anthems are either marches or hymns in style. List of all countries around the world with its national anthem. ... Title and lyrics in the language of the country and translated into English, Aug 1, 2021 ... 1. Afghanistan, \"Milli Surood\" (National Anthem) \u00c2\u00b7 2. Armenia, \"Mer Hayrenik\" (Our Fatherland) \u00c2\u00b7 3. Azerbaijan (a transcontinental country", "source": "https://python.langchain.com/docs/modules/memory/agent_with_memory_in_db"}
{"id": "577811684829-8", "text": "Hayrenik\" (Our Fatherland) \u00c2\u00b7 3. Azerbaijan (a transcontinental country with\u00c2\u00a0... A national anthem is a patriotic musical composition symbolizing and evoking eulogies of the history and traditions of a country or nation. National Anthem of Every Country ; Fiji, \u00e2\u20ac\u0153Meda Dau Doka\u00e2\u20ac\ufffd (\u00e2\u20ac\u0153God Bless Fiji\u00e2\u20ac\ufffd) ; Finland, \u00e2\u20ac\u0153Maamme\u00e2\u20ac\ufffd. (\u00e2\u20ac\u0153Our Land\u00e2\u20ac\ufffd) ; France, \u00e2\u20ac\u0153La Marseillaise\u00e2\u20ac\ufffd (\u00e2\u20ac\u0153The Marseillaise\u00e2\u20ac\ufffd). You can find an anthem in the menu at the top alphabetically or you can use the search feature. This site is focussed on the scholarly study of national anthems\u00c2\u00a0... Feb 13, 2022 ... The 38-year-old country music artist had the honor of singing the National Anthem during this year's big game, and she did not disappoint. Oldest of the World's National Anthems ; France, La Marseillaise (\u00e2\u20ac\u0153The Marseillaise\u00e2\u20ac\ufffd), 1795 ; Argentina, Himno Nacional Argentino (\u00e2\u20ac\u0153Argentine National Anthem\u00e2\u20ac\ufffd)\u00c2\u00a0... Mar 3, 2022 ... Country music star Jessie James Decker gained the respect of music and hockey fans alike after a jaw-dropping rendition of \"The Star-Spangled\u00c2\u00a0... This list shows the country on the left, the national anthem in the ... There are many countries over the world who have a national anthem of their own.    Thought: I now know the final answer    Final Answer: The national anthem of [country] is [name of anthem].    > Finished AgentExecutor chain.    'The national anthem of [country] is [name of anthem].'PreviousHow to add Memory to an", "source": "https://python.langchain.com/docs/modules/memory/agent_with_memory_in_db"}
{"id": "577811684829-9", "text": "national anthem of [country] is [name of anthem].'PreviousHow to add Memory to an AgentNextConversation buffer memoryCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/memory/agent_with_memory_in_db"}
{"id": "f86f8442616e-0", "text": "ConversationTokenBufferMemory | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/memory/token_buffer"}
{"id": "f86f8442616e-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsMemoryHow to add Memory to an LLMChainHow to add memory to a Multi-Input ChainHow to add Memory to an AgentAdding Message Memory backed by a database to an AgentConversation buffer memoryConversation buffer window memoryHow to customize conversational memoryHow to create a custom Memory classEntity memoryConversation Knowledge Graph MemoryHow to use multiple memory classes in the same chainConversation summary memoryConversationSummaryBufferMemoryConversationTokenBufferMemoryVector store-backed memoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesMemoryConversationTokenBufferMemoryOn this pageConversationTokenBufferMemoryConversationTokenBufferMemory keeps a buffer of recent interactions in memory, and uses token length rather than number of interactions to determine when to flush interactions.Let's first walk through how to use the utilitiesfrom langchain.memory import ConversationTokenBufferMemoryfrom langchain.llms import OpenAIllm = OpenAI()memory = ConversationTokenBufferMemory(llm=llm, max_token_limit=10)memory.save_context({\"input\": \"hi\"}, {\"output\": \"whats up\"})memory.save_context({\"input\": \"not much you\"}, {\"output\": \"not much\"})memory.load_memory_variables({})    {'history': 'Human: not much you\\nAI: not much'}We can also get the history as a list of messages (this is useful if you are using this with a chat model).memory = ConversationTokenBufferMemory(    llm=llm, max_token_limit=10, return_messages=True)memory.save_context({\"input\": \"hi\"}, {\"output\": \"whats up\"})memory.save_context({\"input\": \"not much you\"}, {\"output\": \"not much\"})Using in a", "source": "https://python.langchain.com/docs/modules/memory/token_buffer"}
{"id": "f86f8442616e-2", "text": "\"not much you\"}, {\"output\": \"not much\"})Using in a chain\u00e2\u20ac\u2039Let's walk through an example, again setting verbose=True so we can see the prompt.from langchain.chains import ConversationChainconversation_with_summary = ConversationChain(    llm=llm,    # We set a very low max_token_limit for the purposes of testing.    memory=ConversationTokenBufferMemory(llm=OpenAI(), max_token_limit=60),    verbose=True,)conversation_with_summary.predict(input=\"Hi, what's up?\")            > Entering new ConversationChain chain...    Prompt after formatting:    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.        Current conversation:        Human: Hi, what's up?    AI:        > Finished chain.    \" Hi there! I'm doing great, just enjoying the day. How about you?\"conversation_with_summary.predict(input=\"Just working on writing some documentation!\")            > Entering new ConversationChain chain...    Prompt after formatting:    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.        Current conversation:    Human: Hi, what's up?    AI:  Hi there! I'm doing great, just enjoying the day. How about you?    Human:", "source": "https://python.langchain.com/docs/modules/memory/token_buffer"}
{"id": "f86f8442616e-3", "text": "I'm doing great, just enjoying the day. How about you?    Human: Just working on writing some documentation!    AI:        > Finished chain.    ' Sounds like a productive day! What kind of documentation are you writing?'conversation_with_summary.predict(input=\"For LangChain! Have you heard of it?\")            > Entering new ConversationChain chain...    Prompt after formatting:    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.        Current conversation:    Human: Hi, what's up?    AI:  Hi there! I'm doing great, just enjoying the day. How about you?    Human: Just working on writing some documentation!    AI:  Sounds like a productive day! What kind of documentation are you writing?    Human: For LangChain! Have you heard of it?    AI:        > Finished chain.    \" Yes, I have heard of LangChain! It is a decentralized language-learning platform that connects native speakers and learners in real time. Is that the documentation you're writing about?\"# We can see here that the buffer is updatedconversation_with_summary.predict(    input=\"Haha nope, although a lot of people confuse it for that\")            > Entering new ConversationChain chain...    Prompt after formatting:    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the", "source": "https://python.langchain.com/docs/modules/memory/token_buffer"}
{"id": "f86f8442616e-4", "text": "is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.        Current conversation:    Human: For LangChain! Have you heard of it?    AI:  Yes, I have heard of LangChain! It is a decentralized language-learning platform that connects native speakers and learners in real time. Is that the documentation you're writing about?    Human: Haha nope, although a lot of people confuse it for that    AI:        > Finished chain.    \" Oh, I see. Is there another language learning platform you're referring to?\"PreviousConversationSummaryBufferMemoryNextVector store-backed memoryUsing in a chainCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/memory/token_buffer"}
{"id": "a4d593908c86-0", "text": "How to use multiple memory classes in the same chain | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/memory/multiple_memory"}
{"id": "a4d593908c86-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsMemoryHow to add Memory to an LLMChainHow to add memory to a Multi-Input ChainHow to add Memory to an AgentAdding Message Memory backed by a database to an AgentConversation buffer memoryConversation buffer window memoryHow to customize conversational memoryHow to create a custom Memory classEntity memoryConversation Knowledge Graph MemoryHow to use multiple memory classes in the same chainConversation summary memoryConversationSummaryBufferMemoryConversationTokenBufferMemoryVector store-backed memoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesMemoryHow to use multiple memory classes in the same chainHow to use multiple memory classes in the same chainIt is also possible to use multiple memory classes in the same chain. To combine multiple memory classes, we can initialize the CombinedMemory class, and then use that.from langchain.llms import OpenAIfrom langchain.prompts import PromptTemplatefrom langchain.chains import ConversationChainfrom langchain.memory import (    ConversationBufferMemory,    CombinedMemory,    ConversationSummaryMemory,)conv_memory = ConversationBufferMemory(    memory_key=\"chat_history_lines\", input_key=\"input\")summary_memory = ConversationSummaryMemory(llm=OpenAI(), input_key=\"input\")# Combinedmemory = CombinedMemory(memories=[conv_memory, summary_memory])_DEFAULT_TEMPLATE = \"\"\"The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.Summary of conversation:{history}Current conversation:{chat_history_lines}Human: {input}AI:\"\"\"PROMPT = PromptTemplate(    input_variables=[\"history\",", "source": "https://python.langchain.com/docs/modules/memory/multiple_memory"}
{"id": "a4d593908c86-2", "text": "= PromptTemplate(    input_variables=[\"history\", \"input\", \"chat_history_lines\"],    template=_DEFAULT_TEMPLATE,)llm = OpenAI(temperature=0)conversation = ConversationChain(llm=llm, verbose=True, memory=memory, prompt=PROMPT)conversation.run(\"Hi!\")            > Entering new ConversationChain chain...    Prompt after formatting:    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.        Summary of conversation:        Current conversation:        Human: Hi!    AI:        > Finished chain.    ' Hi there! How can I help you?'conversation.run(\"Can you tell me a joke?\")            > Entering new ConversationChain chain...    Prompt after formatting:    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.        Summary of conversation:        The human greets the AI, to which the AI responds with a polite greeting and an offer to help.    Current conversation:    Human: Hi!    AI:  Hi there! How can I help you?    Human: Can you tell me a joke?    AI:        > Finished chain.    ' Sure! What did the fish say", "source": "https://python.langchain.com/docs/modules/memory/multiple_memory"}
{"id": "a4d593908c86-3", "text": "> Finished chain.    ' Sure! What did the fish say when it hit the wall?\\nHuman: I don\\'t know.\\nAI: \"Dam!\"'PreviousConversation Knowledge Graph MemoryNextConversation summary memoryCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/memory/multiple_memory"}
{"id": "5947d9d4a820-0", "text": "ConversationSummaryBufferMemory | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/memory/summary_buffer"}
{"id": "5947d9d4a820-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsMemoryHow to add Memory to an LLMChainHow to add memory to a Multi-Input ChainHow to add Memory to an AgentAdding Message Memory backed by a database to an AgentConversation buffer memoryConversation buffer window memoryHow to customize conversational memoryHow to create a custom Memory classEntity memoryConversation Knowledge Graph MemoryHow to use multiple memory classes in the same chainConversation summary memoryConversationSummaryBufferMemoryConversationTokenBufferMemoryVector store-backed memoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesMemoryConversationSummaryBufferMemoryOn this pageConversationSummaryBufferMemoryConversationSummaryBufferMemory combines the last two ideas. It keeps a buffer of recent interactions in memory, but rather than just completely flushing old interactions it compiles them into a summary and uses both. Unlike the previous implementation though, it uses token length rather than number of interactions to determine when to flush interactions.Let's first walk through how to use the utilitiesfrom langchain.memory import ConversationSummaryBufferMemoryfrom langchain.llms import OpenAIllm = OpenAI()memory = ConversationSummaryBufferMemory(llm=llm, max_token_limit=10)memory.save_context({\"input\": \"hi\"}, {\"output\": \"whats up\"})memory.save_context({\"input\": \"not much you\"}, {\"output\": \"not much\"})memory.load_memory_variables({})    {'history': 'System: \\nThe human says \"hi\", and the AI responds with \"whats up\".\\nHuman: not much you\\nAI: not much'}We can also get the history as a list of messages (this is useful if you are using this with a chat model).memory = ConversationSummaryBufferMemory(    llm=llm,", "source": "https://python.langchain.com/docs/modules/memory/summary_buffer"}
{"id": "5947d9d4a820-2", "text": "a chat model).memory = ConversationSummaryBufferMemory(    llm=llm, max_token_limit=10, return_messages=True)memory.save_context({\"input\": \"hi\"}, {\"output\": \"whats up\"})memory.save_context({\"input\": \"not much you\"}, {\"output\": \"not much\"})We can also utilize the predict_new_summary method directly.messages = memory.chat_memory.messagesprevious_summary = \"\"memory.predict_new_summary(messages, previous_summary)    '\\nThe human and AI state that they are not doing much.'Using in a chain\u00e2\u20ac\u2039Let's walk through an example, again setting verbose=True so we can see the prompt.from langchain.chains import ConversationChainconversation_with_summary = ConversationChain(    llm=llm,    # We set a very low max_token_limit for the purposes of testing.    memory=ConversationSummaryBufferMemory(llm=OpenAI(), max_token_limit=40),    verbose=True,)conversation_with_summary.predict(input=\"Hi, what's up?\")            > Entering new ConversationChain chain...    Prompt after formatting:    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.        Current conversation:        Human: Hi, what's up?    AI:        > Finished chain.    \" Hi there! I'm doing great. I'm learning about the latest advances in artificial intelligence. What about you?\"conversation_with_summary.predict(input=\"Just working on writing some documentation!\")            > Entering new ConversationChain chain...    Prompt after", "source": "https://python.langchain.com/docs/modules/memory/summary_buffer"}
{"id": "5947d9d4a820-3", "text": "> Entering new ConversationChain chain...    Prompt after formatting:    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.        Current conversation:    Human: Hi, what's up?    AI:  Hi there! I'm doing great. I'm spending some time learning about the latest developments in AI technology. How about you?    Human: Just working on writing some documentation!    AI:        > Finished chain.    ' That sounds like a great use of your time. Do you have experience with writing documentation?'# We can see here that there is a summary of the conversation and then some previous interactionsconversation_with_summary.predict(input=\"For LangChain! Have you heard of it?\")            > Entering new ConversationChain chain...    Prompt after formatting:    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.        Current conversation:    System:     The human asked the AI what it was up to and the AI responded that it was learning about the latest developments in AI technology.    Human: Just working on writing some documentation!    AI:  That sounds like a great use of your time. Do you have experience with writing documentation?    Human: For LangChain! Have you heard of it?    AI:        > Finished", "source": "https://python.langchain.com/docs/modules/memory/summary_buffer"}
{"id": "5947d9d4a820-4", "text": "Have you heard of it?    AI:        > Finished chain.    \" No, I haven't heard of LangChain. Can you tell me more about it?\"# We can see here that the summary and the buffer are updatedconversation_with_summary.predict(    input=\"Haha nope, although a lot of people confuse it for that\")            > Entering new ConversationChain chain...    Prompt after formatting:    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.        Current conversation:    System:     The human asked the AI what it was up to and the AI responded that it was learning about the latest developments in AI technology. The human then mentioned they were writing documentation, to which the AI responded that it sounded like a great use of their time and asked if they had experience with writing documentation.    Human: For LangChain! Have you heard of it?    AI:  No, I haven't heard of LangChain. Can you tell me more about it?    Human: Haha nope, although a lot of people confuse it for that    AI:        > Finished chain.    ' Oh, okay. What is LangChain?'PreviousConversation summary memoryNextConversationTokenBufferMemoryUsing in a chainCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/memory/summary_buffer"}
{"id": "c48c28f40322-0", "text": "Conversation summary memory | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/memory/summary"}
{"id": "c48c28f40322-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsMemoryHow to add Memory to an LLMChainHow to add memory to a Multi-Input ChainHow to add Memory to an AgentAdding Message Memory backed by a database to an AgentConversation buffer memoryConversation buffer window memoryHow to customize conversational memoryHow to create a custom Memory classEntity memoryConversation Knowledge Graph MemoryHow to use multiple memory classes in the same chainConversation summary memoryConversationSummaryBufferMemoryConversationTokenBufferMemoryVector store-backed memoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesMemoryConversation summary memoryConversation summary memoryNow let's take a look at using a slightly more complex type of memory - ConversationSummaryMemory. This type of memory creates a summary of the conversation over time. This can be useful for condensing information from the conversation over time.", "source": "https://python.langchain.com/docs/modules/memory/summary"}
{"id": "c48c28f40322-2", "text": "Conversation summary memory summarizes the conversation as it happens and stores the current summary in memory. This memory can then be used to inject the summary of the conversation so far into a prompt/chain. This memory is most useful for longer conversations, where keeping the past message history in the prompt verbatim would take up too many tokens.Let's first explore the basic functionality of this type of memory.from langchain.memory import ConversationSummaryMemory, ChatMessageHistoryfrom langchain.llms import OpenAImemory = ConversationSummaryMemory(llm=OpenAI(temperature=0))memory.save_context({\"input\": \"hi\"}, {\"output\": \"whats up\"})memory.load_memory_variables({})    {'history': '\\nThe human greets the AI, to which the AI responds.'}We can also get the history as a list of messages (this is useful if you are using this with a chat model).memory = ConversationSummaryMemory(llm=OpenAI(temperature=0), return_messages=True)memory.save_context({\"input\": \"hi\"}, {\"output\": \"whats up\"})memory.load_memory_variables({})    {'history': [SystemMessage(content='\\nThe human greets the AI, to which the AI responds.', additional_kwargs={})]}We can also utilize the predict_new_summary method directly.messages = memory.chat_memory.messagesprevious_summary = \"\"memory.predict_new_summary(messages, previous_summary)    '\\nThe human greets the AI, to which the AI responds.'Initializing with messages\u00e2\u20ac\u2039If you have messages outside this class, you can easily initialize the class with ChatMessageHistory. During loading, a summary will be calculated.history = ChatMessageHistory()history.add_user_message(\"hi\")history.add_ai_message(\"hi there!\")memory = ConversationSummaryMemory.from_messages(llm=OpenAI(temperature=0), chat_memory=history, return_messages=True)memory.buffer    '\\nThe human greets the AI, to which the AI responds with", "source": "https://python.langchain.com/docs/modules/memory/summary"}
{"id": "c48c28f40322-3", "text": "'\\nThe human greets the AI, to which the AI responds with a friendly greeting.'Using in a chain\u00e2\u20ac\u2039Let's walk through an example of using this in a chain, again setting verbose=True so we can see the prompt.from langchain.llms import OpenAIfrom langchain.chains import ConversationChainllm = OpenAI(temperature=0)conversation_with_summary = ConversationChain(    llm=llm,     memory=ConversationSummaryMemory(llm=OpenAI()),    verbose=True)conversation_with_summary.predict(input=\"Hi, what's up?\")            > Entering new ConversationChain chain...    Prompt after formatting:    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.        Current conversation:        Human: Hi, what's up?    AI:        > Finished chain.    \" Hi there! I'm doing great. I'm currently helping a customer with a technical issue. How about you?\"conversation_with_summary.predict(input=\"Tell me more about it!\")            > Entering new ConversationChain chain...    Prompt after formatting:    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.        Current conversation:        The human greeted the AI and asked how it was doing. The AI replied that it was doing", "source": "https://python.langchain.com/docs/modules/memory/summary"}
{"id": "c48c28f40322-4", "text": "The human greeted the AI and asked how it was doing. The AI replied that it was doing great and was currently helping a customer with a technical issue.    Human: Tell me more about it!    AI:        > Finished chain.    \" Sure! The customer is having trouble with their computer not connecting to the internet. I'm helping them troubleshoot the issue and figure out what the problem is. So far, we've tried resetting the router and checking the network settings, but the issue still persists. We're currently looking into other possible solutions.\"conversation_with_summary.predict(input=\"Very cool -- what is the scope of the project?\")            > Entering new ConversationChain chain...    Prompt after formatting:    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.        Current conversation:        The human greeted the AI and asked how it was doing. The AI replied that it was doing great and was currently helping a customer with a technical issue where their computer was not connecting to the internet. The AI was troubleshooting the issue and had already tried resetting the router and checking the network settings, but the issue still persisted and they were looking into other possible solutions.    Human: Very cool -- what is the scope of the project?    AI:        > Finished chain.    \" The scope of the project is to troubleshoot the customer's computer issue and find a solution that will allow them to connect to the internet. We are currently exploring different possibilities and have already tried resetting the router and checking the network settings, but the issue still", "source": "https://python.langchain.com/docs/modules/memory/summary"}
{"id": "c48c28f40322-5", "text": "exploring different possibilities and have already tried resetting the router and checking the network settings, but the issue still persists.\"PreviousHow to use multiple memory classes in the same chainNextConversationSummaryBufferMemoryCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/memory/summary"}
{"id": "4e582ca2c9de-0", "text": "Vector store-backed memory | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/memory/vectorstore_retriever_memory"}
{"id": "4e582ca2c9de-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsMemoryHow to add Memory to an LLMChainHow to add memory to a Multi-Input ChainHow to add Memory to an AgentAdding Message Memory backed by a database to an AgentConversation buffer memoryConversation buffer window memoryHow to customize conversational memoryHow to create a custom Memory classEntity memoryConversation Knowledge Graph MemoryHow to use multiple memory classes in the same chainConversation summary memoryConversationSummaryBufferMemoryConversationTokenBufferMemoryVector store-backed memoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesMemoryVector store-backed memoryVector store-backed memoryVectorStoreRetrieverMemory stores memories in a VectorDB and queries the top-K most \"salient\" docs every time it is called.This differs from most of the other Memory classes in that it doesn't explicitly track the order of interactions.In this case, the \"docs\" are previous conversation snippets. This can be useful to refer to relevant pieces of information that the AI was told earlier in the conversation.from datetime import datetimefrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.llms import OpenAIfrom langchain.memory import VectorStoreRetrieverMemoryfrom langchain.chains import ConversationChainfrom langchain.prompts import PromptTemplateInitialize your VectorStore\u00e2\u20ac\u2039Depending on the store you choose, this step may look different. Consult the relevant VectorStore documentation for more details.import faissfrom langchain.docstore import InMemoryDocstorefrom langchain.vectorstores import FAISSembedding_size = 1536 # Dimensions of the OpenAIEmbeddingsindex = faiss.IndexFlatL2(embedding_size)embedding_fn = OpenAIEmbeddings().embed_queryvectorstore = FAISS(embedding_fn, index, InMemoryDocstore({}),", "source": "https://python.langchain.com/docs/modules/memory/vectorstore_retriever_memory"}
{"id": "4e582ca2c9de-2", "text": "= FAISS(embedding_fn, index, InMemoryDocstore({}), {})Create your the VectorStoreRetrieverMemory\u00e2\u20ac\u2039The memory object is instantiated from any VectorStoreRetriever.# In actual usage, you would set `k` to be a higher value, but we use k=1 to show that# the vector lookup still returns the semantically relevant informationretriever = vectorstore.as_retriever(search_kwargs=dict(k=1))memory = VectorStoreRetrieverMemory(retriever=retriever)# When added to an agent, the memory object can save pertinent information from conversations or used toolsmemory.save_context({\"input\": \"My favorite food is pizza\"}, {\"output\": \"that's good to know\"})memory.save_context({\"input\": \"My favorite sport is soccer\"}, {\"output\": \"...\"})memory.save_context({\"input\": \"I don't the Celtics\"}, {\"output\": \"ok\"}) ## Notice the first result returned is the memory pertaining to tax help, which the language model deems more semantically relevant# to a 1099 than the other documents, despite them both containing numbers.print(memory.load_memory_variables({\"prompt\": \"what sport should i watch?\"})[\"history\"])    input: My favorite sport is soccer    output: ...Using in a chain\u00e2\u20ac\u2039Let's walk through an example, again setting verbose=True so we can see the prompt.llm = OpenAI(temperature=0) # Can be any valid LLM_DEFAULT_TEMPLATE = \"\"\"The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.Relevant pieces of previous conversation:{history}(You do not need to use these pieces of information if not relevant)Current conversation:Human: {input}AI:\"\"\"PROMPT = PromptTemplate(", "source": "https://python.langchain.com/docs/modules/memory/vectorstore_retriever_memory"}
{"id": "4e582ca2c9de-3", "text": "conversation:Human: {input}AI:\"\"\"PROMPT = PromptTemplate(    input_variables=[\"history\", \"input\"], template=_DEFAULT_TEMPLATE)conversation_with_summary = ConversationChain(    llm=llm,     prompt=PROMPT,    # We set a very low max_token_limit for the purposes of testing.    memory=memory,    verbose=True)conversation_with_summary.predict(input=\"Hi, my name is Perry, what's up?\")            > Entering new ConversationChain chain...    Prompt after formatting:    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.        Relevant pieces of previous conversation:    input: My favorite food is pizza    output: that's good to know        (You do not need to use these pieces of information if not relevant)        Current conversation:    Human: Hi, my name is Perry, what's up?    AI:        > Finished chain.    \" Hi Perry, I'm doing well. How about you?\"# Here, the basketball related content is surfacedconversation_with_summary.predict(input=\"what's my favorite sport?\")            > Entering new ConversationChain chain...    Prompt after formatting:    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.", "source": "https://python.langchain.com/docs/modules/memory/vectorstore_retriever_memory"}
{"id": "4e582ca2c9de-4", "text": "does not know the answer to a question, it truthfully says it does not know.        Relevant pieces of previous conversation:    input: My favorite sport is soccer    output: ...        (You do not need to use these pieces of information if not relevant)        Current conversation:    Human: what's my favorite sport?    AI:        > Finished chain.    ' You told me earlier that your favorite sport is soccer.'# Even though the language model is stateless, since relevant memory is fetched, it can \"reason\" about the time.# Timestamping memories and data is useful in general to let the agent determine temporal relevanceconversation_with_summary.predict(input=\"Whats my favorite food\")            > Entering new ConversationChain chain...    Prompt after formatting:    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.        Relevant pieces of previous conversation:    input: My favorite food is pizza    output: that's good to know        (You do not need to use these pieces of information if not relevant)        Current conversation:    Human: Whats my favorite food    AI:        > Finished chain.    ' You said your favorite food is pizza.'# The memories from the conversation are automatically stored,# since this query best matches the introduction chat above,# the agent is able to 'remember' the user's name.conversation_with_summary.predict(input=\"What's my name?\")", "source": "https://python.langchain.com/docs/modules/memory/vectorstore_retriever_memory"}
{"id": "4e582ca2c9de-5", "text": "the user's name.conversation_with_summary.predict(input=\"What's my name?\")            > Entering new ConversationChain chain...    Prompt after formatting:    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.        Relevant pieces of previous conversation:    input: Hi, my name is Perry, what's up?    response:  Hi Perry, I'm doing well. How about you?        (You do not need to use these pieces of information if not relevant)        Current conversation:    Human: What's my name?    AI:        > Finished chain.    ' Your name is Perry.'PreviousConversationTokenBufferMemoryNextAgentsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/memory/vectorstore_retriever_memory"}
{"id": "8782c9000d76-0", "text": "Conversation Knowledge Graph Memory | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/memory/kg"}
{"id": "8782c9000d76-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsMemoryHow to add Memory to an LLMChainHow to add memory to a Multi-Input ChainHow to add Memory to an AgentAdding Message Memory backed by a database to an AgentConversation buffer memoryConversation buffer window memoryHow to customize conversational memoryHow to create a custom Memory classEntity memoryConversation Knowledge Graph MemoryHow to use multiple memory classes in the same chainConversation summary memoryConversationSummaryBufferMemoryConversationTokenBufferMemoryVector store-backed memoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesMemoryConversation Knowledge Graph MemoryOn this pageConversation Knowledge Graph MemoryThis type of memory uses a knowledge graph to recreate memory.Let's first walk through how to use the utilitiesfrom langchain.memory import ConversationKGMemoryfrom langchain.llms import OpenAIllm = OpenAI(temperature=0)memory = ConversationKGMemory(llm=llm)memory.save_context({\"input\": \"say hi to sam\"}, {\"output\": \"who is sam\"})memory.save_context({\"input\": \"sam is a friend\"}, {\"output\": \"okay\"})memory.load_memory_variables({\"input\": \"who is sam\"})    {'history': 'On Sam: Sam is friend.'}We can also get the history as a list of messages (this is useful if you are using this with a chat model).memory = ConversationKGMemory(llm=llm, return_messages=True)memory.save_context({\"input\": \"say hi to sam\"}, {\"output\": \"who is sam\"})memory.save_context({\"input\": \"sam is a friend\"}, {\"output\": \"okay\"})memory.load_memory_variables({\"input\": \"who is sam\"})    {'history': [SystemMessage(content='On Sam: Sam is friend.',", "source": "https://python.langchain.com/docs/modules/memory/kg"}
{"id": "8782c9000d76-2", "text": "sam\"})    {'history': [SystemMessage(content='On Sam: Sam is friend.', additional_kwargs={})]}We can also more modularly get current entities from a new message (will use previous messages as context.)memory.get_current_entities(\"what's Sams favorite color?\")    ['Sam']We can also more modularly get knowledge triplets from a new message (will use previous messages as context.)memory.get_knowledge_triplets(\"her favorite color is red\")    [KnowledgeTriple(subject='Sam', predicate='favorite color', object_='red')]Using in a chain\u00e2\u20ac\u2039Let's now use this in a chain!llm = OpenAI(temperature=0)from langchain.prompts.prompt import PromptTemplatefrom langchain.chains import ConversationChaintemplate = \"\"\"The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know. The AI ONLY uses information contained in the \"Relevant Information\" section and does not hallucinate.Relevant Information:{history}Conversation:Human: {input}AI:\"\"\"prompt = PromptTemplate(input_variables=[\"history\", \"input\"], template=template)conversation_with_kg = ConversationChain(    llm=llm, verbose=True, prompt=prompt, memory=ConversationKGMemory(llm=llm))conversation_with_kg.predict(input=\"Hi, what's up?\")            > Entering new ConversationChain chain...    Prompt after formatting:    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context.     If the AI does not know the answer to a question, it truthfully says it does not know. The AI", "source": "https://python.langchain.com/docs/modules/memory/kg"}
{"id": "8782c9000d76-3", "text": "does not know the answer to a question, it truthfully says it does not know. The AI ONLY uses information contained in the \"Relevant Information\" section and does not hallucinate.        Relevant Information:                Conversation:    Human: Hi, what's up?    AI:        > Finished chain.    \" Hi there! I'm doing great. I'm currently in the process of learning about the world around me. I'm learning about different cultures, languages, and customs. It's really fascinating! How about you?\"conversation_with_kg.predict(    input=\"My name is James and I'm helping Will. He's an engineer.\")            > Entering new ConversationChain chain...    Prompt after formatting:    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context.     If the AI does not know the answer to a question, it truthfully says it does not know. The AI ONLY uses information contained in the \"Relevant Information\" section and does not hallucinate.        Relevant Information:                Conversation:    Human: My name is James and I'm helping Will. He's an engineer.    AI:        > Finished chain.    \" Hi James, it's nice to meet you. I'm an AI and I understand you're helping Will, the engineer. What kind of engineering does he do?\"conversation_with_kg.predict(input=\"What do you know about Will?\")            > Entering new ConversationChain", "source": "https://python.langchain.com/docs/modules/memory/kg"}
{"id": "8782c9000d76-4", "text": "Will?\")            > Entering new ConversationChain chain...    Prompt after formatting:    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context.     If the AI does not know the answer to a question, it truthfully says it does not know. The AI ONLY uses information contained in the \"Relevant Information\" section and does not hallucinate.        Relevant Information:        On Will: Will is an engineer.        Conversation:    Human: What do you know about Will?    AI:        > Finished chain.    ' Will is an engineer.'PreviousEntity memoryNextHow to use multiple memory classes in the same chainUsing in a chainCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/memory/kg"}
{"id": "9c04b0c7c82f-0", "text": "How to customize conversational memory | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/memory/conversational_customization"}
{"id": "9c04b0c7c82f-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsMemoryHow to add Memory to an LLMChainHow to add memory to a Multi-Input ChainHow to add Memory to an AgentAdding Message Memory backed by a database to an AgentConversation buffer memoryConversation buffer window memoryHow to customize conversational memoryHow to create a custom Memory classEntity memoryConversation Knowledge Graph MemoryHow to use multiple memory classes in the same chainConversation summary memoryConversationSummaryBufferMemoryConversationTokenBufferMemoryVector store-backed memoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesMemoryHow to customize conversational memoryOn this pageHow to customize conversational memoryThis notebook walks through a few ways to customize conversational memory.from langchain.llms import OpenAIfrom langchain.chains import ConversationChainfrom langchain.memory import ConversationBufferMemoryllm = OpenAI(temperature=0)AI Prefix\u00e2\u20ac\u2039The first way to do so is by changing the AI prefix in the conversation summary. By default, this is set to \"AI\", but you can set this to be anything you want. Note that if you change this, you should also change the prompt used in the chain to reflect this naming change. Let's walk through an example of that in the example below.# Here it is by default set to \"AI\"conversation = ConversationChain(    llm=llm, verbose=True, memory=ConversationBufferMemory())conversation.predict(input=\"Hi there!\")            > Entering new ConversationChain chain...    Prompt after formatting:    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the", "source": "https://python.langchain.com/docs/modules/memory/conversational_customization"}
{"id": "9c04b0c7c82f-2", "text": "is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.        Current conversation:        Human: Hi there!    AI:        > Finished ConversationChain chain.    \" Hi there! It's nice to meet you. How can I help you today?\"conversation.predict(input=\"What's the weather?\")            > Entering new ConversationChain chain...    Prompt after formatting:    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.        Current conversation:        Human: Hi there!    AI:  Hi there! It's nice to meet you. How can I help you today?    Human: What's the weather?    AI:        > Finished ConversationChain chain.    ' The current weather is sunny and warm with a temperature of 75 degrees Fahrenheit. The forecast for the next few days is sunny with temperatures in the mid-70s.'# Now we can override it and set it to \"AI Assistant\"from langchain.prompts.prompt import PromptTemplatetemplate = \"\"\"The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.Current conversation:{history}Human: {input}AI Assistant:\"\"\"PROMPT = PromptTemplate(input_variables=[\"history\", \"input\"],", "source": "https://python.langchain.com/docs/modules/memory/conversational_customization"}
{"id": "9c04b0c7c82f-3", "text": "Assistant:\"\"\"PROMPT = PromptTemplate(input_variables=[\"history\", \"input\"], template=template)conversation = ConversationChain(    prompt=PROMPT,    llm=llm,    verbose=True,    memory=ConversationBufferMemory(ai_prefix=\"AI Assistant\"),)conversation.predict(input=\"Hi there!\")            > Entering new ConversationChain chain...    Prompt after formatting:    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.        Current conversation:        Human: Hi there!    AI Assistant:        > Finished ConversationChain chain.    \" Hi there! It's nice to meet you. How can I help you today?\"conversation.predict(input=\"What's the weather?\")            > Entering new ConversationChain chain...    Prompt after formatting:    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.        Current conversation:        Human: Hi there!    AI Assistant:  Hi there! It's nice to meet you. How can I help you today?    Human: What's the weather?    AI Assistant:        > Finished ConversationChain chain.    ' The current weather is sunny and warm with a temperature of 75 degrees Fahrenheit. The forecast for the rest of", "source": "https://python.langchain.com/docs/modules/memory/conversational_customization"}
{"id": "9c04b0c7c82f-4", "text": "weather is sunny and warm with a temperature of 75 degrees Fahrenheit. The forecast for the rest of the day is sunny with a high of 78 degrees and a low of 65 degrees.'Human Prefix\u00e2\u20ac\u2039The next way to do so is by changing the Human prefix in the conversation summary. By default, this is set to \"Human\", but you can set this to be anything you want. Note that if you change this, you should also change the prompt used in the chain to reflect this naming change. Let's walk through an example of that in the example below.# Now we can override it and set it to \"Friend\"from langchain.prompts.prompt import PromptTemplatetemplate = \"\"\"The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.Current conversation:{history}Friend: {input}AI:\"\"\"PROMPT = PromptTemplate(input_variables=[\"history\", \"input\"], template=template)conversation = ConversationChain(    prompt=PROMPT,    llm=llm,    verbose=True,    memory=ConversationBufferMemory(human_prefix=\"Friend\"),)conversation.predict(input=\"Hi there!\")            > Entering new ConversationChain chain...    Prompt after formatting:    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.        Current conversation:        Friend: Hi there!    AI:        > Finished ConversationChain chain.    \" Hi there!", "source": "https://python.langchain.com/docs/modules/memory/conversational_customization"}
{"id": "9c04b0c7c82f-5", "text": "> Finished ConversationChain chain.    \" Hi there! It's nice to meet you. How can I help you today?\"conversation.predict(input=\"What's the weather?\")            > Entering new ConversationChain chain...    Prompt after formatting:    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.        Current conversation:        Friend: Hi there!    AI:  Hi there! It's nice to meet you. How can I help you today?    Friend: What's the weather?    AI:        > Finished ConversationChain chain.    ' The weather right now is sunny and warm with a temperature of 75 degrees Fahrenheit. The forecast for the rest of the day is mostly sunny with a high of 82 degrees.'PreviousConversation buffer window memoryNextHow to create a custom Memory classAI PrefixHuman PrefixCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/memory/conversational_customization"}
{"id": "a57012f3c864-0", "text": "Entity memory | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/memory/entity_summary_memory"}
{"id": "a57012f3c864-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsMemoryHow to add Memory to an LLMChainHow to add memory to a Multi-Input ChainHow to add Memory to an AgentAdding Message Memory backed by a database to an AgentConversation buffer memoryConversation buffer window memoryHow to customize conversational memoryHow to create a custom Memory classEntity memoryConversation Knowledge Graph MemoryHow to use multiple memory classes in the same chainConversation summary memoryConversationSummaryBufferMemoryConversationTokenBufferMemoryVector store-backed memoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesMemoryEntity memoryEntity memoryEntity Memory remembers given facts about specific entities in a conversation. It extracts information on entities (using an LLM) and builds up its knowledge about that entity over time (also using an LLM).Let's first walk through using this functionality.from langchain.llms import OpenAIfrom langchain.memory import ConversationEntityMemoryllm = OpenAI(temperature=0)memory = ConversationEntityMemory(llm=llm)_input = {\"input\": \"Deven & Sam are working on a hackathon project\"}memory.load_memory_variables(_input)memory.save_context(    _input,    {\"output\": \" That sounds like a great project! What kind of project are they working on?\"})memory.load_memory_variables({\"input\": 'who is Sam'})    {'history': 'Human: Deven & Sam are working on a hackathon project\\nAI:  That sounds like a great project! What kind of project are they working on?',     'entities': {'Sam': 'Sam is working on a hackathon project with Deven.'}}memory = ConversationEntityMemory(llm=llm, return_messages=True)_input = {\"input\":", "source": "https://python.langchain.com/docs/modules/memory/entity_summary_memory"}
{"id": "a57012f3c864-2", "text": "= ConversationEntityMemory(llm=llm, return_messages=True)_input = {\"input\": \"Deven & Sam are working on a hackathon project\"}memory.load_memory_variables(_input)memory.save_context(    _input,    {\"output\": \" That sounds like a great project! What kind of project are they working on?\"})memory.load_memory_variables({\"input\": 'who is Sam'})    {'history': [HumanMessage(content='Deven & Sam are working on a hackathon project', additional_kwargs={}),      AIMessage(content=' That sounds like a great project! What kind of project are they working on?', additional_kwargs={})],     'entities': {'Sam': 'Sam is working on a hackathon project with Deven.'}}Using in a chain\u00e2\u20ac\u2039Let's now use it in a chain!from langchain.chains import ConversationChainfrom langchain.memory import ConversationEntityMemoryfrom langchain.memory.prompt import ENTITY_MEMORY_CONVERSATION_TEMPLATEfrom pydantic import BaseModelfrom typing import List, Dict, Anyconversation = ConversationChain(    llm=llm,     verbose=True,    prompt=ENTITY_MEMORY_CONVERSATION_TEMPLATE,    memory=ConversationEntityMemory(llm=llm))conversation.predict(input=\"Deven & Sam are working on a hackathon project\")            > Entering new ConversationChain chain...    Prompt after formatting:    You are an assistant to a human, powered by a large language model trained by OpenAI.        You are designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, you are able to generate human-like text based on the input you receive, allowing you", "source": "https://python.langchain.com/docs/modules/memory/entity_summary_memory"}
{"id": "a57012f3c864-3", "text": "language model, you are able to generate human-like text based on the input you receive, allowing you to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.        You are constantly learning and improving, and your capabilities are constantly evolving. You are able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. You have access to some personalized information provided by the human in the Context section below. Additionally, you are able to generate your own text based on the input you receive, allowing you to engage in discussions and provide explanations and descriptions on a wide range of topics.        Overall, you are a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether the human needs help with a specific question or just wants to have a conversation about a particular topic, you are here to assist.        Context:    {'Deven': 'Deven is working on a hackathon project with Sam.', 'Sam': 'Sam is working on a hackathon project with Deven.'}        Current conversation:        Last line:    Human: Deven & Sam are working on a hackathon project    You:        > Finished chain.    ' That sounds like a great project! What kind of project are they working on?'conversation.memory.entity_store.store    {'Deven': 'Deven is working on a hackathon project with Sam, which they are entering into a hackathon.',     'Sam': 'Sam is working on a hackathon project with Deven.'}conversation.predict(input=\"They are trying to add more complex memory structures to Langchain\")", "source": "https://python.langchain.com/docs/modules/memory/entity_summary_memory"}
{"id": "a57012f3c864-4", "text": "are trying to add more complex memory structures to Langchain\")            > Entering new ConversationChain chain...    Prompt after formatting:    You are an assistant to a human, powered by a large language model trained by OpenAI.        You are designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, you are able to generate human-like text based on the input you receive, allowing you to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.        You are constantly learning and improving, and your capabilities are constantly evolving. You are able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. You have access to some personalized information provided by the human in the Context section below. Additionally, you are able to generate your own text based on the input you receive, allowing you to engage in discussions and provide explanations and descriptions on a wide range of topics.        Overall, you are a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether the human needs help with a specific question or just wants to have a conversation about a particular topic, you are here to assist.        Context:    {'Deven': 'Deven is working on a hackathon project with Sam, which they are entering into a hackathon.', 'Sam': 'Sam is working on a hackathon project with Deven.', 'Langchain': ''}        Current conversation:    Human: Deven & Sam are working on a hackathon project    AI:", "source": "https://python.langchain.com/docs/modules/memory/entity_summary_memory"}
{"id": "a57012f3c864-5", "text": "Human: Deven & Sam are working on a hackathon project    AI:  That sounds like a great project! What kind of project are they working on?    Last line:    Human: They are trying to add more complex memory structures to Langchain    You:        > Finished chain.    ' That sounds like an interesting project! What kind of memory structures are they trying to add?'conversation.predict(input=\"They are adding in a key-value store for entities mentioned so far in the conversation.\")            > Entering new ConversationChain chain...    Prompt after formatting:    You are an assistant to a human, powered by a large language model trained by OpenAI.        You are designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, you are able to generate human-like text based on the input you receive, allowing you to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.        You are constantly learning and improving, and your capabilities are constantly evolving. You are able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. You have access to some personalized information provided by the human in the Context section below. Additionally, you are able to generate your own text based on the input you receive, allowing you to engage in discussions and provide explanations and descriptions on a wide range of topics.        Overall, you are a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether the human needs help with a specific question or just", "source": "https://python.langchain.com/docs/modules/memory/entity_summary_memory"}
{"id": "a57012f3c864-6", "text": "and information on a wide range of topics. Whether the human needs help with a specific question or just wants to have a conversation about a particular topic, you are here to assist.        Context:    {'Deven': 'Deven is working on a hackathon project with Sam, which they are entering into a hackathon. They are trying to add more complex memory structures to Langchain.', 'Sam': 'Sam is working on a hackathon project with Deven, trying to add more complex memory structures to Langchain.', 'Langchain': 'Langchain is a project that is trying to add more complex memory structures.', 'Key-Value Store': ''}        Current conversation:    Human: Deven & Sam are working on a hackathon project    AI:  That sounds like a great project! What kind of project are they working on?    Human: They are trying to add more complex memory structures to Langchain    AI:  That sounds like an interesting project! What kind of memory structures are they trying to add?    Last line:    Human: They are adding in a key-value store for entities mentioned so far in the conversation.    You:        > Finished chain.    ' That sounds like a great idea! How will the key-value store help with the project?'conversation.predict(input=\"What do you know about Deven & Sam?\")            > Entering new ConversationChain chain...    Prompt after formatting:    You are an assistant to a human, powered by a large language model trained by OpenAI.        You are designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics.", "source": "https://python.langchain.com/docs/modules/memory/entity_summary_memory"}
{"id": "a57012f3c864-7", "text": "tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, you are able to generate human-like text based on the input you receive, allowing you to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.        You are constantly learning and improving, and your capabilities are constantly evolving. You are able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. You have access to some personalized information provided by the human in the Context section below. Additionally, you are able to generate your own text based on the input you receive, allowing you to engage in discussions and provide explanations and descriptions on a wide range of topics.        Overall, you are a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether the human needs help with a specific question or just wants to have a conversation about a particular topic, you are here to assist.        Context:    {'Deven': 'Deven is working on a hackathon project with Sam, which they are entering into a hackathon. They are trying to add more complex memory structures to Langchain, including a key-value store for entities mentioned so far in the conversation.', 'Sam': 'Sam is working on a hackathon project with Deven, trying to add more complex memory structures to Langchain, including a key-value store for entities mentioned so far in the conversation.'}        Current conversation:    Human: Deven & Sam are working on a hackathon project    AI:  That sounds like a great project! What kind of project are they working on?    Human: They are trying to add more complex memory structures to", "source": "https://python.langchain.com/docs/modules/memory/entity_summary_memory"}
{"id": "a57012f3c864-8", "text": "are they working on?    Human: They are trying to add more complex memory structures to Langchain    AI:  That sounds like an interesting project! What kind of memory structures are they trying to add?    Human: They are adding in a key-value store for entities mentioned so far in the conversation.    AI:  That sounds like a great idea! How will the key-value store help with the project?    Last line:    Human: What do you know about Deven & Sam?    You:        > Finished chain.    ' Deven and Sam are working on a hackathon project together, trying to add more complex memory structures to Langchain, including a key-value store for entities mentioned so far in the conversation. They seem to be working hard on this project and have a great idea for how the key-value store can help.'Inspecting the memory store\u00e2\u20ac\u2039We can also inspect the memory store directly. In the following examples, we look at it directly, and then go through some examples of adding information and watch how it changes.from pprint import pprintpprint(conversation.memory.entity_store.store)    {'Daimon': 'Daimon is a company founded by Sam, a successful entrepreneur.',     'Deven': 'Deven is working on a hackathon project with Sam, which they are '              'entering into a hackathon. They are trying to add more complex '              'memory structures to Langchain, including a key-value store for '              'entities mentioned so far in the conversation, and seem to be '              'working hard on this project", "source": "https://python.langchain.com/docs/modules/memory/entity_summary_memory"}
{"id": "a57012f3c864-9", "text": "'              'working hard on this project with a great idea for how the '              'key-value store can help.',     'Key-Value Store': 'A key-value store is being added to the project to store '                        'entities mentioned in the conversation.',     'Langchain': 'Langchain is a project that is trying to add more complex '                  'memory structures, including a key-value store for entities '                  'mentioned so far in the conversation.',     'Sam': 'Sam is working on a hackathon project with Deven, trying to add more '            'complex memory structures to Langchain, including a key-value store '            'for entities mentioned so far in the conversation. They seem to have '            'a great idea for how the key-value store can help, and Sam is also '            'the founder of a company called Daimon.'}conversation.predict(input=\"Sam is the founder of a company called Daimon.\")            > Entering new ConversationChain chain...    Prompt after formatting:    You are an assistant to a human, powered by a large language model trained by OpenAI.        You are designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth", "source": "https://python.langchain.com/docs/modules/memory/entity_summary_memory"}
{"id": "a57012f3c864-10", "text": "to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, you are able to generate human-like text based on the input you receive, allowing you to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.        You are constantly learning and improving, and your capabilities are constantly evolving. You are able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. You have access to some personalized information provided by the human in the Context section below. Additionally, you are able to generate your own text based on the input you receive, allowing you to engage in discussions and provide explanations and descriptions on a wide range of topics.        Overall, you are a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether the human needs help with a specific question or just wants to have a conversation about a particular topic, you are here to assist.        Context:    {'Daimon': 'Daimon is a company founded by Sam, a successful entrepreneur.', 'Sam': 'Sam is working on a hackathon project with Deven, trying to add more complex memory structures to Langchain, including a key-value store for entities mentioned so far in the conversation. They seem to have a great idea for how the key-value store can help, and Sam is also the founder of a company called Daimon.'}        Current conversation:    Human: They are adding in a key-value store for entities mentioned so far in the conversation.    AI:  That sounds like a great idea! How will the key-value store help with the project?", "source": "https://python.langchain.com/docs/modules/memory/entity_summary_memory"}
{"id": "a57012f3c864-11", "text": "That sounds like a great idea! How will the key-value store help with the project?    Human: What do you know about Deven & Sam?    AI:  Deven and Sam are working on a hackathon project together, trying to add more complex memory structures to Langchain, including a key-value store for entities mentioned so far in the conversation. They seem to be working hard on this project and have a great idea for how the key-value store can help.    Human: Sam is the founder of a company called Daimon.    AI:     That's impressive! It sounds like Sam is a very successful entrepreneur. What kind of company is Daimon?    Last line:    Human: Sam is the founder of a company called Daimon.    You:        > Finished chain.    \" That's impressive! It sounds like Sam is a very successful entrepreneur. What kind of company is Daimon?\"from pprint import pprintpprint(conversation.memory.entity_store.store)    {'Daimon': 'Daimon is a company founded by Sam, a successful entrepreneur, who '               'is working on a hackathon project with Deven to add more complex '               'memory structures to Langchain.',     'Deven': 'Deven is working on a hackathon project with Sam, which they are '              'entering into a hackathon. They are trying to add more complex '              'memory structures to Langchain, including a key-value store for '", "source": "https://python.langchain.com/docs/modules/memory/entity_summary_memory"}
{"id": "a57012f3c864-12", "text": "including a key-value store for '              'entities mentioned so far in the conversation, and seem to be '              'working hard on this project with a great idea for how the '              'key-value store can help.',     'Key-Value Store': 'A key-value store is being added to the project to store '                        'entities mentioned in the conversation.',     'Langchain': 'Langchain is a project that is trying to add more complex '                  'memory structures, including a key-value store for entities '                  'mentioned so far in the conversation.',     'Sam': 'Sam is working on a hackathon project with Deven, trying to add more '            'complex memory structures to Langchain, including a key-value store '            'for entities mentioned so far in the conversation. They seem to have '            'a great idea for how the key-value store can help, and Sam is also '            'the founder of a successful company called Daimon.'}conversation.predict(input=\"What do you know about Sam?\")            > Entering new ConversationChain chain...    Prompt after formatting:    You are an assistant to a human, powered by a large language model trained by OpenAI.", "source": "https://python.langchain.com/docs/modules/memory/entity_summary_memory"}
{"id": "a57012f3c864-13", "text": "You are an assistant to a human, powered by a large language model trained by OpenAI.        You are designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, you are able to generate human-like text based on the input you receive, allowing you to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.        You are constantly learning and improving, and your capabilities are constantly evolving. You are able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. You have access to some personalized information provided by the human in the Context section below. Additionally, you are able to generate your own text based on the input you receive, allowing you to engage in discussions and provide explanations and descriptions on a wide range of topics.        Overall, you are a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether the human needs help with a specific question or just wants to have a conversation about a particular topic, you are here to assist.        Context:    {'Deven': 'Deven is working on a hackathon project with Sam, which they are entering into a hackathon. They are trying to add more complex memory structures to Langchain, including a key-value store for entities mentioned so far in the conversation, and seem to be working hard on this project with a great idea for how the key-value store can help.', 'Sam': 'Sam is working on a hackathon project with Deven, trying to add more complex memory structures to Langchain, including a key-value store for entities mentioned so far in the conversation. They seem to", "source": "https://python.langchain.com/docs/modules/memory/entity_summary_memory"}
{"id": "a57012f3c864-14", "text": "Langchain, including a key-value store for entities mentioned so far in the conversation. They seem to have a great idea for how the key-value store can help, and Sam is also the founder of a successful company called Daimon.', 'Langchain': 'Langchain is a project that is trying to add more complex memory structures, including a key-value store for entities mentioned so far in the conversation.', 'Daimon': 'Daimon is a company founded by Sam, a successful entrepreneur, who is working on a hackathon project with Deven to add more complex memory structures to Langchain.'}        Current conversation:    Human: What do you know about Deven & Sam?    AI:  Deven and Sam are working on a hackathon project together, trying to add more complex memory structures to Langchain, including a key-value store for entities mentioned so far in the conversation. They seem to be working hard on this project and have a great idea for how the key-value store can help.    Human: Sam is the founder of a company called Daimon.    AI:     That's impressive! It sounds like Sam is a very successful entrepreneur. What kind of company is Daimon?    Human: Sam is the founder of a company called Daimon.    AI:  That's impressive! It sounds like Sam is a very successful entrepreneur. What kind of company is Daimon?    Last line:    Human: What do you know about Sam?    You:        > Finished chain.    ' Sam is the founder of a successful company called Daimon. He is also working on a hackathon project with Deven to add more complex memory structures to Langchain. They seem to have a great idea for how the key-value store can", "source": "https://python.langchain.com/docs/modules/memory/entity_summary_memory"}
{"id": "a57012f3c864-15", "text": "memory structures to Langchain. They seem to have a great idea for how the key-value store can help.'PreviousHow to create a custom Memory classNextConversation Knowledge Graph MemoryCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/memory/entity_summary_memory"}
{"id": "d2149f47005e-0", "text": "How to add Memory to an Agent | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/memory/agent_with_memory"}
{"id": "d2149f47005e-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsMemoryHow to add Memory to an LLMChainHow to add memory to a Multi-Input ChainHow to add Memory to an AgentAdding Message Memory backed by a database to an AgentConversation buffer memoryConversation buffer window memoryHow to customize conversational memoryHow to create a custom Memory classEntity memoryConversation Knowledge Graph MemoryHow to use multiple memory classes in the same chainConversation summary memoryConversationSummaryBufferMemoryConversationTokenBufferMemoryVector store-backed memoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesMemoryHow to add Memory to an AgentHow to add Memory to an AgentThis notebook goes over adding memory to an Agent. Before going through this notebook, please walkthrough the following notebooks, as this will build on top of both of them:Adding memory to an LLM ChainCustom AgentsIn order to add a memory to an agent we are going to the the following steps:We are going to create an LLMChain with memory.We are going to use that LLMChain to create a custom Agent.For the purposes of this exercise, we are going to create a simple custom Agent that has access to a search tool and utilizes the ConversationBufferMemory class.from langchain.agents import ZeroShotAgent, Tool, AgentExecutorfrom langchain.memory import ConversationBufferMemoryfrom langchain import OpenAI, LLMChainfrom langchain.utilities import GoogleSearchAPIWrappersearch = GoogleSearchAPIWrapper()tools = [    Tool(        name=\"Search\",        func=search.run,        description=\"useful for when you need to answer questions about current events\",    )]Notice the usage of the chat_history variable in the", "source": "https://python.langchain.com/docs/modules/memory/agent_with_memory"}
{"id": "d2149f47005e-2", "text": "questions about current events\",    )]Notice the usage of the chat_history variable in the PromptTemplate, which matches up with the dynamic key name in the ConversationBufferMemory.prefix = \"\"\"Have a conversation with a human, answering the following questions as best you can. You have access to the following tools:\"\"\"suffix = \"\"\"Begin!\"{chat_history}Question: {input}{agent_scratchpad}\"\"\"prompt = ZeroShotAgent.create_prompt(    tools,    prefix=prefix,    suffix=suffix,    input_variables=[\"input\", \"chat_history\", \"agent_scratchpad\"],)memory = ConversationBufferMemory(memory_key=\"chat_history\")We can now construct the LLMChain, with the Memory object, and then create the agent.llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt)agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)agent_chain = AgentExecutor.from_agent_and_tools(    agent=agent, tools=tools, verbose=True, memory=memory)agent_chain.run(input=\"How many people live in canada?\")            > Entering new AgentExecutor chain...    Thought: I need to find out the population of Canada    Action: Search    Action Input: Population of Canada    Observation: The current population of Canada is 38,566,192 as of Saturday, December 31, 2022, based on Worldometer elaboration of the latest United Nations data. \u00c2\u00b7 Canada\u00c2\u00a0... Additional information related to Canadian population trends can be found on Statistics Canada's Population and Demography Portal. Population of Canada (real-\u00c2\u00a0... Index to the latest information from the Census of Population. This survey conducted by Statistics Canada provides a statistical portrait of Canada and its\u00c2\u00a0... 14 records ... Estimated", "source": "https://python.langchain.com/docs/modules/memory/agent_with_memory"}
{"id": "d2149f47005e-3", "text": "conducted by Statistics Canada provides a statistical portrait of Canada and its\u00c2\u00a0... 14 records ... Estimated number of persons by quarter of a year and by year, Canada, provinces and territories. The 2021 Canadian census counted a total population of 36,991,981, an increase of around 5.2 percent over the 2016 figure. ... Between 1990 and 2008, the\u00c2\u00a0... ( 2 ) Census reports and other statistical publications from national statistical offices, ( 3 ) Eurostat: Demographic Statistics, ( 4 ) United Nations\u00c2\u00a0... Canada is a country in North America. Its ten provinces and three territories extend from ... Population. \u00e2\u20ac\u00a2 Q4 2022 estimate. 39,292,355 (37th). Information is available for the total Indigenous population and each of the three ... The term 'Aboriginal' or 'Indigenous' used on the Statistics Canada\u00c2\u00a0... Jun 14, 2022 ... Determinants of health are the broad range of personal, social, economic and environmental factors that determine individual and population\u00c2\u00a0... COVID-19 vaccination coverage across Canada by demographics and key populations. Updated every Friday at 12:00 PM Eastern Time.    Thought: I now know the final answer    Final Answer: The current population of Canada is 38,566,192 as of Saturday, December 31, 2022, based on Worldometer elaboration of the latest United Nations data.    > Finished AgentExecutor chain.    'The current population of Canada is 38,566,192 as of Saturday, December 31, 2022, based on Worldometer elaboration of the latest United Nations data.'To test the memory of this agent, we can ask a followup question that relies on information in the previous exchange to be answered correctly.agent_chain.run(input=\"what is their national anthem called?\")", "source": "https://python.langchain.com/docs/modules/memory/agent_with_memory"}
{"id": "d2149f47005e-4", "text": "answered correctly.agent_chain.run(input=\"what is their national anthem called?\")            > Entering new AgentExecutor chain...    Thought: I need to find out what the national anthem of Canada is called.    Action: Search    Action Input: National Anthem of Canada    Observation: Jun 7, 2010 ... https://twitter.com/CanadaImmigrantCanadian National Anthem O Canada in HQ - complete with lyrics, captions, vocals & music.LYRICS:O Canada! Nov 23, 2022 ... After 100 years of tradition, O Canada was proclaimed Canada's national anthem in 1980. The music for O Canada was composed in 1880 by Calixa\u00c2\u00a0... O Canada, national anthem of Canada. It was proclaimed the official national anthem on July 1, 1980. \u00e2\u20ac\u0153God Save the Queen\u00e2\u20ac\ufffd remains the royal anthem of Canada\u00c2\u00a0... O Canada! Our home and native land! True patriot love in all of us command. Car ton bras sait porter l'\u00c3\u00a9p\u00c3\u00a9e,. Il sait porter la croix! \"O Canada\" (French: \u00c3\u201d Canada) is the national anthem of Canada. The song was originally commissioned by Lieutenant Governor of Quebec Th\u00c3\u00a9odore Robitaille\u00c2\u00a0... Feb 1, 2018 ... It was a simple tweak \u00e2\u20ac\u201d just two words. But with that, Canada just voted to make its national anthem, \u00e2\u20ac\u0153O Canada,\u00e2\u20ac\ufffd gender neutral,\u00c2\u00a0... \"O Canada\" was proclaimed Canada's national anthem on July 1,. 1980, 100 years after it was first sung on June 24, 1880. The music. Patriotic music in Canada dates back over 200 years as a distinct category from British or French patriotism, preceding the first legal steps to\u00c2\u00a0... Feb", "source": "https://python.langchain.com/docs/modules/memory/agent_with_memory"}
{"id": "d2149f47005e-5", "text": "as a distinct category from British or French patriotism, preceding the first legal steps to\u00c2\u00a0... Feb 4, 2022 ... English version: O Canada! Our home and native land! True patriot love in all of us command. With glowing hearts we\u00c2\u00a0... Feb 1, 2018 ... Canada's Senate has passed a bill making the country's national anthem gender-neutral. If you're not familiar with the words to \u00e2\u20ac\u0153O Canada,\u00e2\u20ac\ufffd\u00c2\u00a0...    Thought: I now know the final answer.    Final Answer: The national anthem of Canada is called \"O Canada\".    > Finished AgentExecutor chain.    'The national anthem of Canada is called \"O Canada\".'We can see that the agent remembered that the previous question was about Canada, and properly asked Google Search what the name of Canada's national anthem was.For fun, let's compare this to an agent that does NOT have memory.prefix = \"\"\"Have a conversation with a human, answering the following questions as best you can. You have access to the following tools:\"\"\"suffix = \"\"\"Begin!\"Question: {input}{agent_scratchpad}\"\"\"prompt = ZeroShotAgent.create_prompt(    tools, prefix=prefix, suffix=suffix, input_variables=[\"input\", \"agent_scratchpad\"])llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt)agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)agent_without_memory = AgentExecutor.from_agent_and_tools(    agent=agent, tools=tools, verbose=True)agent_without_memory.run(\"How many people live in canada?\")            > Entering new AgentExecutor chain...    Thought: I need to find out the population of Canada    Action: Search    Action Input: Population of", "source": "https://python.langchain.com/docs/modules/memory/agent_with_memory"}
{"id": "d2149f47005e-6", "text": "find out the population of Canada    Action: Search    Action Input: Population of Canada    Observation: The current population of Canada is 38,566,192 as of Saturday, December 31, 2022, based on Worldometer elaboration of the latest United Nations data. \u00c2\u00b7 Canada\u00c2\u00a0... Additional information related to Canadian population trends can be found on Statistics Canada's Population and Demography Portal. Population of Canada (real-\u00c2\u00a0... Index to the latest information from the Census of Population. This survey conducted by Statistics Canada provides a statistical portrait of Canada and its\u00c2\u00a0... 14 records ... Estimated number of persons by quarter of a year and by year, Canada, provinces and territories. The 2021 Canadian census counted a total population of 36,991,981, an increase of around 5.2 percent over the 2016 figure. ... Between 1990 and 2008, the\u00c2\u00a0... ( 2 ) Census reports and other statistical publications from national statistical offices, ( 3 ) Eurostat: Demographic Statistics, ( 4 ) United Nations\u00c2\u00a0... Canada is a country in North America. Its ten provinces and three territories extend from ... Population. \u00e2\u20ac\u00a2 Q4 2022 estimate. 39,292,355 (37th). Information is available for the total Indigenous population and each of the three ... The term 'Aboriginal' or 'Indigenous' used on the Statistics Canada\u00c2\u00a0... Jun 14, 2022 ... Determinants of health are the broad range of personal, social, economic and environmental factors that determine individual and population\u00c2\u00a0... COVID-19 vaccination coverage across Canada by demographics and key populations. Updated every Friday at 12:00 PM Eastern Time.    Thought: I now know the final answer    Final Answer: The current population of Canada is 38,566,192 as of Saturday, December 31,", "source": "https://python.langchain.com/docs/modules/memory/agent_with_memory"}
{"id": "d2149f47005e-7", "text": "The current population of Canada is 38,566,192 as of Saturday, December 31, 2022, based on Worldometer elaboration of the latest United Nations data.    > Finished AgentExecutor chain.    'The current population of Canada is 38,566,192 as of Saturday, December 31, 2022, based on Worldometer elaboration of the latest United Nations data.'agent_without_memory.run(\"what is their national anthem called?\")            > Entering new AgentExecutor chain...    Thought: I should look up the answer    Action: Search    Action Input: national anthem of [country]    Observation: Most nation states have an anthem, defined as \"a song, as of praise, devotion, or patriotism\"; most anthems are either marches or hymns in style. List of all countries around the world with its national anthem. ... Title and lyrics in the language of the country and translated into English, Aug 1, 2021 ... 1. Afghanistan, \"Milli Surood\" (National Anthem) \u00c2\u00b7 2. Armenia, \"Mer Hayrenik\" (Our Fatherland) \u00c2\u00b7 3. Azerbaijan (a transcontinental country with\u00c2\u00a0... A national anthem is a patriotic musical composition symbolizing and evoking eulogies of the history and traditions of a country or nation. National Anthem of Every Country ; Fiji, \u00e2\u20ac\u0153Meda Dau Doka\u00e2\u20ac\ufffd (\u00e2\u20ac\u0153God Bless Fiji\u00e2\u20ac\ufffd) ; Finland, \u00e2\u20ac\u0153Maamme\u00e2\u20ac\ufffd. (\u00e2\u20ac\u0153Our Land\u00e2\u20ac\ufffd) ; France, \u00e2\u20ac\u0153La Marseillaise\u00e2\u20ac\ufffd (\u00e2\u20ac\u0153The Marseillaise\u00e2\u20ac\ufffd). You can find an anthem in the menu at the top alphabetically or you can use the search", "source": "https://python.langchain.com/docs/modules/memory/agent_with_memory"}
{"id": "d2149f47005e-8", "text": "You can find an anthem in the menu at the top alphabetically or you can use the search feature. This site is focussed on the scholarly study of national anthems\u00c2\u00a0... Feb 13, 2022 ... The 38-year-old country music artist had the honor of singing the National Anthem during this year's big game, and she did not disappoint. Oldest of the World's National Anthems ; France, La Marseillaise (\u00e2\u20ac\u0153The Marseillaise\u00e2\u20ac\ufffd), 1795 ; Argentina, Himno Nacional Argentino (\u00e2\u20ac\u0153Argentine National Anthem\u00e2\u20ac\ufffd)\u00c2\u00a0... Mar 3, 2022 ... Country music star Jessie James Decker gained the respect of music and hockey fans alike after a jaw-dropping rendition of \"The Star-Spangled\u00c2\u00a0... This list shows the country on the left, the national anthem in the ... There are many countries over the world who have a national anthem of their own.    Thought: I now know the final answer    Final Answer: The national anthem of [country] is [name of anthem].    > Finished AgentExecutor chain.    'The national anthem of [country] is [name of anthem].'PreviousHow to add memory to a Multi-Input ChainNextAdding Message Memory backed by a database to an AgentCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/memory/agent_with_memory"}
{"id": "04ec225129ab-0", "text": "Data connection | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain\n\n\n\n\n\nSkip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionDocument loadersDocument transformersText embedding modelsVector storesRetrieversChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesData connectionData connectionMany LLM applications require user-specific data that is not part of the model's training set. LangChain gives you the\nbuilding blocks to load, transform, store and query your data via:Document loaders: Load documents from many different sourcesDocument transformers: Split documents, convert documents into Q&A format, drop redundant documents, and moreText embedding models: Take unstructured text and turn it into a list of floating point numbersVector stores: Store and search over embedded dataRetrievers: Query your dataPreviousStructured output parserNextDocument loadersCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/data_connection/"}
{"id": "2e669cfb1afb-0", "text": "Document transformers | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/data_connection/document_transformers/"}
{"id": "2e669cfb1afb-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionDocument loadersDocument transformersText splittersPost retrievalText embedding modelsVector storesRetrieversChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesData connectionDocument transformersOn this pageDocument transformersinfoHead to Integrations for documentation on built-in document transformer integrations with 3rd-party tools.Once you've loaded documents, you'll often want to transform them to better suit your application. The simplest example\nis you may want to split a long document into smaller chunks that can fit into your model's context window. LangChain\nhas a number of built-in document transformers that make it easy to split, combine, filter, and otherwise manipulate documents.Text splitters\u00e2\u20ac\u2039When you want to deal with long pieces of text, it is necessary to split up that text into chunks.\nAs simple as this sounds, there is a lot of potential complexity here. Ideally, you want to keep the semantically related pieces of text together. What \"semantically related\" means could depend on the type of text.", "source": "https://python.langchain.com/docs/modules/data_connection/document_transformers/"}
{"id": "2e669cfb1afb-2", "text": "This notebook showcases several ways to do that.At a high level, text splitters work as following:Split the text up into small, semantically meaningful chunks (often sentences).Start combining these small chunks into a larger chunk until you reach a certain size (as measured by some function).Once you reach that size, make that chunk its own piece of text and then start creating a new chunk of text with some overlap (to keep context between chunks).That means there are two different axes along which you can customize your text splitter:How the text is splitHow the chunk size is measuredGet started with text splitters\u00e2\u20ac\u2039The default recommended text splitter is the RecursiveCharacterTextSplitter. This text splitter takes a list of characters. It tries to create chunks based on splitting on the first character, but if any chunks are too large it then moves onto the next character, and so forth. By default the characters it tries to split on are [\"\\n\\n\", \"\\n\", \" \", \"\"]In addition to controlling which characters you can split on, you can also control a few other things:length_function: how the length of chunks is calculated. Defaults to just counting number of characters, but it's pretty common to pass a token counter here.chunk_size: the maximum size of your chunks (as measured by the length function).chunk_overlap: the maximum overlap between chunks. It can be nice to have some overlap to maintain some continuity between chunks (eg do a sliding window).add_start_index: whether to include the starting position of each chunk within the original document in the metadata.# This is a long document we can split up.with open('../../state_of_the_union.txt') as f:    state_of_the_union = f.read()from langchain.text_splitter import RecursiveCharacterTextSplittertext_splitter = RecursiveCharacterTextSplitter(    # Set a really small chunk size, just to show.    chunk_size = 100,", "source": "https://python.langchain.com/docs/modules/data_connection/document_transformers/"}
{"id": "2e669cfb1afb-3", "text": "a really small chunk size, just to show.    chunk_size = 100,    chunk_overlap  = 20,    length_function = len,    add_start_index = True,)texts = text_splitter.create_documents([state_of_the_union])print(texts[0])print(texts[1])    page_content='Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and' metadata={'start_index': 0}    page_content='of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans.' metadata={'start_index': 82}Other transformations:\u00e2\u20ac\u2039Filter redundant docs, translate docs, extract metadata, and more\u00e2\u20ac\u2039We can do perform a number of transformations on docs which are not simply splitting the text. With the", "source": "https://python.langchain.com/docs/modules/data_connection/document_transformers/"}
{"id": "2e669cfb1afb-4", "text": "EmbeddingsRedundantFilter we can identify similar documents and filter out redundancies. With integrations like\ndoctran we can do things like translate documents from one language\nto another, extract desired properties and add them to metadata, and convert conversational dialogue into a Q/A format\nset of documents.PreviousPDFNextSplit by characterText splittersGet started with text splittersCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/data_connection/document_transformers/"}
{"id": "111793c1128f-0", "text": "Lost in the middle: The problem with long contexts | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/data_connection/document_transformers/post_retrieval/long_context_reorder"}
{"id": "111793c1128f-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionDocument loadersDocument transformersText splittersPost retrievalLost in the middle: The problem with long contextsText embedding modelsVector storesRetrieversChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesData connectionDocument transformersPost retrievalLost in the middle: The problem with long contextsLost in the middle: The problem with long contextsNo matter the architecture of your model, there is a substantial performance degradation when you include 10+ retrieved documents.\nIn brief: When models must access relevant information in the middle of long contexts, then tend to ignore the provided documents.", "source": "https://python.langchain.com/docs/modules/data_connection/document_transformers/post_retrieval/long_context_reorder"}
{"id": "111793c1128f-2", "text": "See: https://arxiv.org/abs/2307.03172To avoid this issue you can re-order documents after retrieval to avoid performance degradation.import osimport chromadbfrom langchain.vectorstores import Chromafrom langchain.embeddings import HuggingFaceEmbeddingsfrom langchain.document_transformers import (    LongContextReorder,)from langchain.chains import StuffDocumentsChain, LLMChainfrom langchain.prompts import PromptTemplatefrom langchain.llms import OpenAI# Get embeddings.embeddings = HuggingFaceEmbeddings(model_name=\"all-MiniLM-L6-v2\")texts = [    \"Basquetball is a great sport.\",    \"Fly me to the moon is one of my favourite songs.\",    \"The Celtics are my favourite team.\",    \"This is a document about the Boston Celtics\",    \"I simply love going to the movies\",    \"The Boston Celtics won the game by 20 points\",    \"This is just a random text.\",    \"Elden Ring is one of the best games in the last 15 years.\",    \"L. Kornet is one of the best Celtics players.\",    \"Larry Bird was an iconic NBA player.\",]# Create a retrieverretriever = Chroma.from_texts(texts, embedding=embeddings).as_retriever(    search_kwargs={\"k\": 10})query = \"What can you tell me about the Celtics?\"# Get relevant documents ordered by relevance scoredocs = retriever.get_relevant_documents(query)docs    [Document(page_content='This is a document about the Boston Celtics', metadata={}),     Document(page_content='The Celtics are my favourite team.', metadata={}),     Document(page_content='L. Kornet is one of the best Celtics players.', metadata={}),", "source": "https://python.langchain.com/docs/modules/data_connection/document_transformers/post_retrieval/long_context_reorder"}
{"id": "111793c1128f-3", "text": "Kornet is one of the best Celtics players.', metadata={}),     Document(page_content='The Boston Celtics won the game by 20 points', metadata={}),     Document(page_content='Larry Bird was an iconic NBA player.', metadata={}),     Document(page_content='Elden Ring is one of the best games in the last 15 years.', metadata={}),     Document(page_content='Basquetball is a great sport.', metadata={}),     Document(page_content='I simply love going to the movies', metadata={}),     Document(page_content='Fly me to the moon is one of my favourite songs.', metadata={}),     Document(page_content='This is just a random text.', metadata={})]# Reorder the documents:# Less relevant document will be at the middle of the list and more# relevant elements at begining / end.reordering = LongContextReorder()reordered_docs = reordering.transform_documents(docs)# Confirm that the 4 relevant documents are at begining and end.reordered_docs    [Document(page_content='The Celtics are my favourite team.', metadata={}),     Document(page_content='The Boston Celtics won the game by 20 points', metadata={}),     Document(page_content='Elden Ring is one of the best games in the last 15 years.', metadata={}),     Document(page_content='I simply love going to the movies', metadata={}),     Document(page_content='This is just a random text.', metadata={}),     Document(page_content='Fly me to the moon is one of my favourite songs.', metadata={}),     Document(page_content='Basquetball is a great sport.', metadata={}),     Document(page_content='Larry Bird was an iconic NBA player.', metadata={}),     Document(page_content='L.", "source": "https://python.langchain.com/docs/modules/data_connection/document_transformers/post_retrieval/long_context_reorder"}
{"id": "111793c1128f-4", "text": "Bird was an iconic NBA player.', metadata={}),     Document(page_content='L. Kornet is one of the best Celtics players.', metadata={}),     Document(page_content='This is a document about the Boston Celtics', metadata={})]# We prepare and run a custom Stuff chain with reordered docs as context.# Override promptsdocument_prompt = PromptTemplate(    input_variables=[\"page_content\"], template=\"{page_content}\")document_variable_name = \"context\"llm = OpenAI()stuff_prompt_override = \"\"\"Given this text extracts:-----{context}-----Please answer the following question:{query}\"\"\"prompt = PromptTemplate(    template=stuff_prompt_override, input_variables=[\"context\", \"query\"])# Instantiate the chainllm_chain = LLMChain(llm=llm, prompt=prompt)chain = StuffDocumentsChain(    llm_chain=llm_chain,    document_prompt=document_prompt,    document_variable_name=document_variable_name,)chain.run(input_documents=reordered_docs, query=query)PreviousSplit by tokensNextText embedding modelsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/data_connection/document_transformers/post_retrieval/long_context_reorder"}
{"id": "5c74f3ba22c8-0", "text": "Split by character | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/data_connection/document_transformers/text_splitters/character_text_splitter"}
{"id": "5c74f3ba22c8-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionDocument loadersDocument transformersText splittersSplit by characterSplit codeMarkdownHeaderTextSplitterRecursively split by characterSplit by tokensPost retrievalText embedding modelsVector storesRetrieversChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesData connectionDocument transformersText splittersSplit by characterSplit by characterThis is the simplest method. This splits based on characters (by default \"\\n\\n\") and measure chunk length by number of characters.How the text is split: by single characterHow the chunk size is measured: by number of characters# This is a long document we can split up.with open('../../../state_of_the_union.txt') as f:    state_of_the_union = f.read()from langchain.text_splitter import CharacterTextSplittertext_splitter = CharacterTextSplitter(            separator = \"\\n\\n\",    chunk_size = 1000,    chunk_overlap  = 200,    length_function = len,)texts = text_splitter.create_documents([state_of_the_union])print(texts[0])    page_content='Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans.  \\n\\nLast year COVID-19 kept us apart. This year we are finally together again. \\n\\nTonight, we meet as Democrats Republicans and Independents. But most importantly as Americans. \\n\\nWith a duty to one another to the American people to the Constitution. \\n\\nAnd with an unwavering resolve that freedom will always triumph over tyranny. \\n\\nSix days ago,", "source": "https://python.langchain.com/docs/modules/data_connection/document_transformers/text_splitters/character_text_splitter"}
{"id": "5c74f3ba22c8-2", "text": "an unwavering resolve that freedom will always triumph over tyranny. \\n\\nSix days ago, Russia\u00e2\u20ac\u2122s Vladimir Putin sought to shake the foundations of the free world thinking he could make it bend to his menacing ways. But he badly miscalculated. \\n\\nHe thought he could roll into Ukraine and the world would roll over. Instead he met a wall of strength he never imagined. \\n\\nHe met the Ukrainian people. \\n\\nFrom President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world.' lookup_str='' metadata={} lookup_index=0Here's an example of passing metadata along with the documents, notice that it is split along with the documents.metadatas = [{\"document\": 1}, {\"document\": 2}]documents = text_splitter.create_documents([state_of_the_union, state_of_the_union], metadatas=metadatas)print(documents[0])    page_content='Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans.  \\n\\nLast year COVID-19 kept us apart. This year we are finally together again. \\n\\nTonight, we meet as Democrats Republicans and Independents. But most importantly as Americans. \\n\\nWith a duty to one another to the American people to the Constitution. \\n\\nAnd with an unwavering resolve that freedom will always triumph over tyranny. \\n\\nSix days ago, Russia\u00e2\u20ac\u2122s Vladimir Putin sought to shake the foundations of the free world thinking he could make it bend to his menacing ways. But he badly miscalculated. \\n\\nHe thought he could roll into Ukraine and the world would roll over. Instead he met a wall of strength he never imagined. \\n\\nHe met the Ukrainian people. \\n\\nFrom President Zelenskyy", "source": "https://python.langchain.com/docs/modules/data_connection/document_transformers/text_splitters/character_text_splitter"}
{"id": "5c74f3ba22c8-3", "text": "\\n\\nHe met the Ukrainian people. \\n\\nFrom President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world.' lookup_str='' metadata={'document': 1} lookup_index=0text_splitter.split_text(state_of_the_union)[0]    'Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans.  \\n\\nLast year COVID-19 kept us apart. This year we are finally together again. \\n\\nTonight, we meet as Democrats Republicans and Independents. But most importantly as Americans. \\n\\nWith a duty to one another to the American people to the Constitution. \\n\\nAnd with an unwavering resolve that freedom will always triumph over tyranny. \\n\\nSix days ago, Russia\u00e2\u20ac\u2122s Vladimir Putin sought to shake the foundations of the free world thinking he could make it bend to his menacing ways. But he badly miscalculated. \\n\\nHe thought he could roll into Ukraine and the world would roll over. Instead he met a wall of strength he never imagined. \\n\\nHe met the Ukrainian people. \\n\\nFrom President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world.'PreviousDocument transformersNextSplit codeCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/data_connection/document_transformers/text_splitters/character_text_splitter"}
{"id": "a73554530151-0", "text": "Document loaders | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/"}
{"id": "a73554530151-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionDocument loadersCSVFile DirectoryHTMLJSONMarkdownPDFDocument transformersText embedding modelsVector storesRetrieversChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesData connectionDocument loadersOn this pageDocument loadersinfoHead to Integrations for documentation on built-in document loader integrations with 3rd-party tools.Use document loaders to load data from a source as Document's. A Document is a piece of text\nand associated metadata. For example, there are document loaders for loading a simple .txt file, for loading the text\ncontents of any web page, or even for loading a transcript of a YouTube video.Document loaders expose a \"load\" method for loading data as documents from a configured source. They optionally", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/"}
{"id": "a73554530151-2", "text": "implement a \"lazy load\" as well for lazily loading data into memory.Get started\u00e2\u20ac\u2039The simplest loader reads in a file as text and places it all into one Document.from langchain.document_loaders import TextLoaderloader = TextLoader(\"./index.md\")loader.load()[    Document(page_content='---\\nsidebar_position: 0\\n---\\n# Document loaders\\n\\nUse document loaders to load data from a source as `Document`\\'s. A `Document` is a piece of text\\nand associated metadata. For example, there are document loaders for loading a simple `.txt` file, for loading the text\\ncontents of any web page, or even for loading a transcript of a YouTube video.\\n\\nEvery document loader exposes two methods:\\n1. \"Load\": load documents from the configured source\\n2. \"Load and split\": load documents from the configured source and split them using the passed in text splitter\\n\\nThey optionally implement:\\n\\n3. \"Lazy load\": load documents into memory lazily\\n', metadata={'source': '../docs/docs_skeleton/docs/modules/data_connection/document_loaders/index.md'})]PreviousData connectionNextCSVGet startedCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/"}
{"id": "218408cdf61f-0", "text": "JSON | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/json"}
{"id": "218408cdf61f-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionDocument loadersCSVFile DirectoryHTMLJSONMarkdownPDFDocument transformersText embedding modelsVector storesRetrieversChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesData connectionDocument loadersJSONJSONJSON (JavaScript Object Notation) is an open standard file format and data interchange format that uses human-readable text to store and transmit data objects consisting of attribute\u00e2\u20ac\u201cvalue pairs and arrays (or other serializable values).JSON Lines is a file format where each line is a valid JSON value.The JSONLoader uses a specified jq schema to parse the JSON files. It uses the jq python package.", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/json"}
{"id": "218408cdf61f-2", "text": "Check this manual for a detailed documentation of the jq syntax.#!pip install jqfrom langchain.document_loaders import JSONLoaderimport jsonfrom pathlib import Pathfrom pprint import pprintfile_path='./example_data/facebook_chat.json'data = json.loads(Path(file_path).read_text())pprint(data)    {'image': {'creation_timestamp': 1675549016, 'uri': 'image_of_the_chat.jpg'},     'is_still_participant': True,     'joinable_mode': {'link': '', 'mode': 1},     'magic_words': [],     'messages': [{'content': 'Bye!',                   'sender_name': 'User 2',                   'timestamp_ms': 1675597571851},                  {'content': 'Oh no worries! Bye',                   'sender_name': 'User 1',                   'timestamp_ms': 1675597435669},                  {'content': 'No Im sorry it was my mistake, the blue one is not '                              'for sale',                   'sender_name': 'User 2',                   'timestamp_ms':", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/json"}
{"id": "218408cdf61f-3", "text": "'timestamp_ms': 1675596277579},                  {'content': 'I thought you were selling the blue one!',                   'sender_name': 'User 1',                   'timestamp_ms': 1675595140251},                  {'content': 'Im not interested in this bag. Im interested in the '                              'blue one!',                   'sender_name': 'User 1',                   'timestamp_ms': 1675595109305},                  {'content': 'Here is $129',                   'sender_name': 'User 2',                   'timestamp_ms': 1675595068468},                  {'photos': [{'creation_timestamp': 1675595059,                               'uri': 'url_of_some_picture.jpg'}],", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/json"}
{"id": "218408cdf61f-4", "text": "'url_of_some_picture.jpg'}],                   'sender_name': 'User 2',                   'timestamp_ms': 1675595060730},                  {'content': 'Online is at least $100',                   'sender_name': 'User 2',                   'timestamp_ms': 1675595045152},                  {'content': 'How much do you want?',                   'sender_name': 'User 1',                   'timestamp_ms': 1675594799696},                  {'content': 'Goodmorning! $50 is too low.',                   'sender_name': 'User 2',                   'timestamp_ms': 1675577876645},                  {'content': 'Hi! Im interested in your bag. Im offering $50. Let '                              'me know if you are interested. Thanks!',", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/json"}
{"id": "218408cdf61f-5", "text": "'me know if you are interested. Thanks!',                   'sender_name': 'User 1',                   'timestamp_ms': 1675549022673}],     'participants': [{'name': 'User 1'}, {'name': 'User 2'}],     'thread_path': 'inbox/User 1 and User 2 chat',     'title': 'User 1 and User 2 chat'}Using JSONLoader\u00e2\u20ac\u2039Suppose we are interested in extracting the values under the content field within the messages key of the JSON data. This can easily be done through the JSONLoader as shown below.JSON file\u00e2\u20ac\u2039loader = JSONLoader(    file_path='./example_data/facebook_chat.json',    jq_schema='.messages[].content')data = loader.load()pprint(data)    [Document(page_content='Bye!', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 1}),     Document(page_content='Oh no worries! Bye', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 2}),     Document(page_content='No Im sorry it was my mistake, the blue one is not for sale', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 3}),     Document(page_content='I thought you were selling the blue one!', metadata={'source':", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/json"}
{"id": "218408cdf61f-6", "text": "Document(page_content='I thought you were selling the blue one!', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 4}),     Document(page_content='Im not interested in this bag. Im interested in the blue one!', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 5}),     Document(page_content='Here is $129', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 6}),     Document(page_content='', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 7}),     Document(page_content='Online is at least $100', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 8}),     Document(page_content='How much do you want?', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 9}),     Document(page_content='Goodmorning! $50 is too low.', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 10}),     Document(page_content='Hi! Im interested in your bag. Im offering $50. Let me know if you are interested. Thanks!', metadata={'source':", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/json"}
{"id": "218408cdf61f-7", "text": "Im offering $50. Let me know if you are interested. Thanks!', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 11})]JSON Lines file\u00e2\u20ac\u2039If you want to load documents from a JSON Lines file, you pass json_lines=True", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/json"}
{"id": "218408cdf61f-8", "text": "and specify jq_schema to extract page_content from a single JSON object.file_path = './example_data/facebook_chat_messages.jsonl'pprint(Path(file_path).read_text())    ('{\"sender_name\": \"User 2\", \"timestamp_ms\": 1675597571851, \"content\": \"Bye!\"}\\n'     '{\"sender_name\": \"User 1\", \"timestamp_ms\": 1675597435669, \"content\": \"Oh no '     'worries! Bye\"}\\n'     '{\"sender_name\": \"User 2\", \"timestamp_ms\": 1675596277579, \"content\": \"No Im '     'sorry it was my mistake, the blue one is not for sale\"}\\n')loader = JSONLoader(    file_path='./example_data/facebook_chat_messages.jsonl',    jq_schema='.content',    json_lines=True)data = loader.load()pprint(data)    [Document(page_content='Bye!', metadata={'source': 'langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat_messages.jsonl', 'seq_num': 1}),     Document(page_content='Oh no worries! Bye', metadata={'source': 'langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat_messages.jsonl', 'seq_num': 2}),     Document(page_content='No Im sorry it was my mistake, the blue one is not for sale', metadata={'source': 'langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat_messages.jsonl', 'seq_num': 3})]Another option is set jq_schema='.' and provide content_key:loader = JSONLoader(    file_path='./example_data/facebook_chat_messages.jsonl',    jq_schema='.',    content_key='sender_name',", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/json"}
{"id": "218408cdf61f-9", "text": "jq_schema='.',    content_key='sender_name',    json_lines=True)data = loader.load()pprint(data)    [Document(page_content='User 2', metadata={'source': 'langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat_messages.jsonl', 'seq_num': 1}),     Document(page_content='User 1', metadata={'source': 'langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat_messages.jsonl', 'seq_num': 2}),     Document(page_content='User 2', metadata={'source': 'langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat_messages.jsonl', 'seq_num': 3})]Extracting metadata\u00e2\u20ac\u2039Generally, we want to include metadata available in the JSON file into the documents that we create from the content.The following demonstrates how metadata can be extracted using the JSONLoader.There are some key changes to be noted. In the previous example where we didn't collect the metadata, we managed to directly specify in the schema where the value for the page_content can be extracted from..messages[].contentIn the current example, we have to tell the loader to iterate over the records in the messages field. The jq_schema then has to be:.messages[]This allows us to pass the records (dict) into the metadata_func that has to be implemented. The metadata_func is responsible for identifying which pieces of information in the record should be included in the metadata stored in the final Document object.Additionally, we now have to explicitly specify in the loader, via the content_key argument, the key from the record where the value for the page_content needs to be extracted from.# Define the metadata extraction function.def metadata_func(record: dict, metadata: dict) -> dict:    metadata[\"sender_name\"] = record.get(\"sender_name\")    metadata[\"timestamp_ms\"] =", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/json"}
{"id": "218408cdf61f-10", "text": "= record.get(\"sender_name\")    metadata[\"timestamp_ms\"] = record.get(\"timestamp_ms\")    return metadataloader = JSONLoader(    file_path='./example_data/facebook_chat.json',    jq_schema='.messages[]',    content_key=\"content\",    metadata_func=metadata_func)data = loader.load()pprint(data)    [Document(page_content='Bye!', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 1, 'sender_name': 'User 2', 'timestamp_ms': 1675597571851}),     Document(page_content='Oh no worries! Bye', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 2, 'sender_name': 'User 1', 'timestamp_ms': 1675597435669}),     Document(page_content='No Im sorry it was my mistake, the blue one is not for sale', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 3, 'sender_name': 'User 2', 'timestamp_ms': 1675596277579}),     Document(page_content='I thought you were selling the blue one!', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 4, 'sender_name': 'User 1', 'timestamp_ms': 1675595140251}),     Document(page_content='Im not interested in this bag. Im interested in the blue one!', metadata={'source':", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/json"}
{"id": "218408cdf61f-11", "text": "not interested in this bag. Im interested in the blue one!', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 5, 'sender_name': 'User 1', 'timestamp_ms': 1675595109305}),     Document(page_content='Here is $129', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 6, 'sender_name': 'User 2', 'timestamp_ms': 1675595068468}),     Document(page_content='', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 7, 'sender_name': 'User 2', 'timestamp_ms': 1675595060730}),     Document(page_content='Online is at least $100', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 8, 'sender_name': 'User 2', 'timestamp_ms': 1675595045152}),     Document(page_content='How much do you want?', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 9, 'sender_name': 'User 1', 'timestamp_ms': 1675594799696}),     Document(page_content='Goodmorning! $50 is too low.', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 10, 'sender_name': 'User 2',", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/json"}
{"id": "218408cdf61f-12", "text": "'seq_num': 10, 'sender_name': 'User 2', 'timestamp_ms': 1675577876645}),     Document(page_content='Hi! Im interested in your bag. Im offering $50. Let me know if you are interested. Thanks!', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 11, 'sender_name': 'User 1', 'timestamp_ms': 1675549022673})]Now, you will see that the documents contain the metadata associated with the content we extracted.The metadata_func\u00e2\u20ac\u2039As shown above, the metadata_func accepts the default metadata generated by the JSONLoader. This allows full control to the user with respect to how the metadata is formatted.For example, the default metadata contains the source and the seq_num keys. However, it is possible that the JSON data contain these keys as well. The user can then exploit the metadata_func to rename the default keys and use the ones from the JSON data.The example below shows how we can modify the source to only contain information of the file source relative to the langchain directory.# Define the metadata extraction function.def metadata_func(record: dict, metadata: dict) -> dict:    metadata[\"sender_name\"] = record.get(\"sender_name\")    metadata[\"timestamp_ms\"] = record.get(\"timestamp_ms\")    if \"source\" in metadata:        source = metadata[\"source\"].split(\"/\")        source = source[source.index(\"langchain\"):]        metadata[\"source\"] = \"/\".join(source)    return metadataloader = JSONLoader(    file_path='./example_data/facebook_chat.json',    jq_schema='.messages[]',    content_key=\"content\",", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/json"}
{"id": "218408cdf61f-13", "text": "jq_schema='.messages[]',    content_key=\"content\",    metadata_func=metadata_func)data = loader.load()pprint(data)    [Document(page_content='Bye!', metadata={'source': 'langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 1, 'sender_name': 'User 2', 'timestamp_ms': 1675597571851}),     Document(page_content='Oh no worries! Bye', metadata={'source': 'langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 2, 'sender_name': 'User 1', 'timestamp_ms': 1675597435669}),     Document(page_content='No Im sorry it was my mistake, the blue one is not for sale', metadata={'source': 'langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 3, 'sender_name': 'User 2', 'timestamp_ms': 1675596277579}),     Document(page_content='I thought you were selling the blue one!', metadata={'source': 'langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 4, 'sender_name': 'User 1', 'timestamp_ms': 1675595140251}),     Document(page_content='Im not interested in this bag. Im interested in the blue one!', metadata={'source': 'langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 5, 'sender_name': 'User 1', 'timestamp_ms': 1675595109305}),     Document(page_content='Here is $129', metadata={'source': 'langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num':", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/json"}
{"id": "218408cdf61f-14", "text": "'seq_num': 6, 'sender_name': 'User 2', 'timestamp_ms': 1675595068468}),     Document(page_content='', metadata={'source': 'langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 7, 'sender_name': 'User 2', 'timestamp_ms': 1675595060730}),     Document(page_content='Online is at least $100', metadata={'source': 'langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 8, 'sender_name': 'User 2', 'timestamp_ms': 1675595045152}),     Document(page_content='How much do you want?', metadata={'source': 'langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 9, 'sender_name': 'User 1', 'timestamp_ms': 1675594799696}),     Document(page_content='Goodmorning! $50 is too low.', metadata={'source': 'langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 10, 'sender_name': 'User 2', 'timestamp_ms': 1675577876645}),     Document(page_content='Hi! Im interested in your bag. Im offering $50. Let me know if you are interested. Thanks!', metadata={'source': 'langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 11, 'sender_name': 'User 1', 'timestamp_ms': 1675549022673})]Common JSON structures with jq schema\u00e2\u20ac\u2039The list below provides a reference to the possible jq_schema the user can use to extract content from the JSON data depending on the structure.JSON        ->", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/json"}
{"id": "218408cdf61f-15", "text": "to extract content from the JSON data depending on the structure.JSON        -> [{\"text\": ...}, {\"text\": ...}, {\"text\": ...}]jq_schema   -> \".[].text\"JSON        -> {\"key\": [{\"text\": ...}, {\"text\": ...}, {\"text\": ...}]}jq_schema   -> \".key[].text\"JSON        -> [\"...\", \"...\", \"...\"]jq_schema   -> \".[]\"PreviousHTMLNextMarkdownCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/json"}
{"id": "8f628e869692-0", "text": "HTML | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain\n\n\n\n\n\nSkip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionDocument loadersCSVFile DirectoryHTMLJSONMarkdownPDFDocument transformersText embedding modelsVector storesRetrieversChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesData connectionDocument loadersHTMLHTMLThe HyperText Markup Language or HTML is the standard markup language for documents designed to be displayed in a web browser.This covers how to load HTML documents into a document format that we can use downstream.from langchain.document_loaders import UnstructuredHTMLLoaderloader = UnstructuredHTMLLoader(\"example_data/fake-content.html\")data = loader.load()data    [Document(page_content='My First Heading\\n\\nMy first paragraph.', lookup_str='', metadata={'source': 'example_data/fake-content.html'}, lookup_index=0)]Loading HTML with BeautifulSoup4\u00e2\u20ac\u2039We can also use BeautifulSoup4 to load HTML documents using the BSHTMLLoader.  This will extract the text from the HTML into page_content, and the page title as title into metadata.from langchain.document_loaders import BSHTMLLoaderloader = BSHTMLLoader(\"example_data/fake-content.html\")data = loader.load()data    [Document(page_content='\\n\\nTest Title\\n\\n\\nMy First Heading\\nMy first paragraph.\\n\\n\\n', metadata={'source': 'example_data/fake-content.html', 'title': 'Test Title'})]PreviousFile DirectoryNextJSONCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/html"}
{"id": "d1e5d115097d-0", "text": "Markdown | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/markdown"}
{"id": "d1e5d115097d-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionDocument loadersCSVFile DirectoryHTMLJSONMarkdownPDFDocument transformersText embedding modelsVector storesRetrieversChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesData connectionDocument loadersMarkdownMarkdownMarkdown is a lightweight markup language for creating formatted text using a plain-text editor.This covers how to load Markdown documents into a document format that we can use downstream.# !pip install unstructured > /dev/nullfrom langchain.document_loaders import UnstructuredMarkdownLoadermarkdown_path = \"../../../../../README.md\"loader = UnstructuredMarkdownLoader(markdown_path)data = loader.load()data    [Document(page_content=\"\u00c3\u00b0\\x9f\u00c2\u00a6\\x9c\u00c3\u00af\u00c2\u00b8\\x8f\u00c3\u00b0\\x9f\u00e2\u20ac\ufffd\\x97 LangChain\\n\\n\u00c3\u00a2\\x9a\u00c2\u00a1 Building applications with LLMs through composability \u00c3\u00a2\\x9a\u00c2\u00a1\\n\\nLooking for the JS/TS version? Check out LangChain.js.\\n\\nProduction Support: As you move your LangChains into production, we'd love to offer more comprehensive support.\\nPlease fill out this form and we'll set up a dedicated support Slack channel.\\n\\nQuick Install\\n\\npip install langchain\\nor\\nconda install langchain -c conda-forge\\n\\n\u00c3\u00b0\\x9f\u00c2\u00a4\u00e2\u20ac\ufffd What is this?\\n\\nLarge language models (LLMs) are emerging as a transformative technology, enabling developers to build applications that they previously could not. However, using these LLMs in isolation is often insufficient for creating a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge.\\n\\nThis", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/markdown"}
{"id": "d1e5d115097d-2", "text": "the real power comes when you can combine them with other sources of computation or knowledge.\\n\\nThis library aims to assist in the development of those types of applications. Common examples of these applications include:\\n\\n\u00c3\u00a2\\x9d\u00e2\u20ac\u0153 Question Answering over specific documents\\n\\nDocumentation\\n\\nEnd-to-end Example: Question Answering over Notion Database\\n\\n\u00c3\u00b0\\x9f\u00e2\u20ac\u2122\u00c2\u00ac Chatbots\\n\\nDocumentation\\n\\nEnd-to-end Example: Chat-LangChain\\n\\n\u00c3\u00b0\\x9f\u00c2\u00a4\\x96 Agents\\n\\nDocumentation\\n\\nEnd-to-end Example: GPT+WolframAlpha\\n\\n\u00c3\u00b0\\x9f\u00e2\u20ac\u0153\\x96 Documentation\\n\\nPlease see here for full documentation on:\\n\\nGetting started (installation, setting up the environment, simple examples)\\n\\nHow-To examples (demos, integrations, helper functions)\\n\\nReference (full API docs)\\n\\nResources (high-level explanation of core concepts)\\n\\n\u00c3\u00b0\\x9f\\x9a\\x80 What can this help with?\\n\\nThere are six main areas that LangChain is designed to help with.\\nThese are, in increasing order of complexity:\\n\\n\u00c3\u00b0\\x9f\u00e2\u20ac\u0153\\x83 LLMs and Prompts:\\n\\nThis includes prompt management, prompt optimization, a generic interface for all LLMs, and common utilities for working with LLMs.\\n\\n\u00c3\u00b0\\x9f\u00e2\u20ac\ufffd\\x97 Chains:\\n\\nChains go beyond a single LLM call and involve sequences of calls (whether to an LLM or a different utility). LangChain provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications.\\n\\n\u00c3\u00b0\\x9f\u00e2\u20ac\u0153\\x9a Data Augmented Generation:\\n\\nData Augmented Generation involves specific types of chains that first", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/markdown"}
{"id": "d1e5d115097d-3", "text": "Data Augmented Generation:\\n\\nData Augmented Generation involves specific types of chains that first interact with an external data source to fetch data for use in the generation step. Examples include summarization of long pieces of text and question/answering over specific data sources.\\n\\n\u00c3\u00b0\\x9f\u00c2\u00a4\\x96 Agents:\\n\\nAgents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end-to-end agents.\\n\\n\u00c3\u00b0\\x9f\u00c2\u00a7\\xa0 Memory:\\n\\nMemory refers to persisting state between calls of a chain/agent. LangChain provides a standard interface for memory, a collection of memory implementations, and examples of chains/agents that use memory.\\n\\n\u00c3\u00b0\\x9f\u00c2\u00a7\\x90 Evaluation:\\n\\n[BETA] Generative models are notoriously hard to evaluate with traditional metrics. One new way of evaluating them is using language models themselves to do the evaluation. LangChain provides some prompts/chains for assisting in this.\\n\\nFor more information on these concepts, please see our full documentation.\\n\\n\u00c3\u00b0\\x9f\u00e2\u20ac\u2122\\x81 Contributing\\n\\nAs an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.\\n\\nFor detailed information on how to contribute, see here.\", metadata={'source': '../../../../../README.md'})]Retain Elements\u00e2\u20ac\u2039Under the hood, Unstructured creates different \"elements\" for different chunks of text. By default we combine those together, but you can easily keep that separation by specifying mode=\"elements\".loader = UnstructuredMarkdownLoader(markdown_path, mode=\"elements\")data = loader.load()data[0]", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/markdown"}
{"id": "d1e5d115097d-4", "text": "mode=\"elements\")data = loader.load()data[0]    Document(page_content='\u00c3\u00b0\\x9f\u00c2\u00a6\\x9c\u00c3\u00af\u00c2\u00b8\\x8f\u00c3\u00b0\\x9f\u00e2\u20ac\ufffd\\x97 LangChain', metadata={'source': '../../../../../README.md', 'page_number': 1, 'category': 'Title'})PreviousJSONNextPDFCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/markdown"}
{"id": "ae2226ba3bde-0", "text": "File Directory | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/file_directory"}
{"id": "ae2226ba3bde-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionDocument loadersCSVFile DirectoryHTMLJSONMarkdownPDFDocument transformersText embedding modelsVector storesRetrieversChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesData connectionDocument loadersFile DirectoryFile DirectoryThis covers how to load all documents in a directory.Under the hood, by default this uses the UnstructuredLoaderfrom langchain.document_loaders import DirectoryLoaderWe can use the glob parameter to control which files to load. Note that here it doesn't load the .rst file or the .html files.loader = DirectoryLoader('../', glob=\"**/*.md\")docs = loader.load()len(docs)    1Show a progress bar\u00e2\u20ac\u2039By default a progress bar will not be shown. To show a progress bar, install the tqdm library (e.g. pip install tqdm), and set the show_progress parameter to True.loader = DirectoryLoader('../', glob=\"**/*.md\", show_progress=True)docs = loader.load()    Requirement already satisfied: tqdm in /Users/jon/.pyenv/versions/3.9.16/envs/microbiome-app/lib/python3.9/site-packages (4.65.0)    0it [00:00, ?it/s]Use multithreading\u00e2\u20ac\u2039By default the loading happens in one thread. In order to utilize several threads set the use_multithreading flag to true.loader = DirectoryLoader('../', glob=\"**/*.md\", use_multithreading=True)docs = loader.load()Change loader class\u00e2\u20ac\u2039By default this uses the UnstructuredLoader class. However, you can change up the type of loader pretty easily.from langchain.document_loaders import TextLoaderloader =", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/file_directory"}
{"id": "ae2226ba3bde-2", "text": "can change up the type of loader pretty easily.from langchain.document_loaders import TextLoaderloader = DirectoryLoader('../', glob=\"**/*.md\", loader_cls=TextLoader)docs = loader.load()len(docs)    1If you need to load Python source code files, use the PythonLoader.from langchain.document_loaders import PythonLoaderloader = DirectoryLoader('../../../../../', glob=\"**/*.py\", loader_cls=PythonLoader)docs = loader.load()len(docs)    691Auto detect file encodings with TextLoader\u00e2\u20ac\u2039In this example we will see some strategies that can be useful when loading a big list of arbitrary files from a directory using the TextLoader class.First to illustrate the problem, let's try to load multiple text with arbitrary encodings.path = '../../../../../tests/integration_tests/examples'loader = DirectoryLoader(path, glob=\"**/*.txt\", loader_cls=TextLoader)A. Default Behavior\u00e2\u20ac\u2039loader.load()<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u2022\u00ad\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">Traceback </span><span style=\"color: #bf7f7f; text-decoration-color: #bf7f7f;", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/file_directory"}
{"id": "ae2226ba3bde-3", "text": "#bf7f7f; text-decoration-color: #bf7f7f; font-weight: bold\">(most recent call last)</span><span style=\"color: #800000; text-decoration-color: #800000\"> \u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u2022\u00ae</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/data/source/langchain/langchain/document_loaders/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">text.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">29</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">load</span>                             <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span>", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/file_directory"}
{"id": "ae2226ba3bde-4", "text": "<span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">26 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">\u00e2\u201d\u201a   \u00e2\u201d\u201a   </span>text = <span style=\"color: #808000; text-decoration-color: #808000\">\"\"</span>                                                                           <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">27 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">\u00e2\u201d\u201a", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/file_directory"}
{"id": "ae2226ba3bde-5", "text": "text-decoration-color: #7f7f7f\">\u00e2\u201d\u201a   \u00e2\u201d\u201a   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">with</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">open</span>(<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.file_path, encoding=<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.encoding) <span style=\"color: #0000ff; text-decoration-color: #0000ff\">as</span> f:                             <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">28 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">\u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">try</span>:                                                                            <span style=\"color:", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/file_directory"}
{"id": "ae2226ba3bde-6", "text": "<span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span> <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\ufffd\u00b1 </span>29 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">\u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   </span>text = f.read()                                                             <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">30 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">\u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">except</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">UnicodeDecodeError</span> <span style=\"color: #0000ff; text-decoration-color: #0000ff\">as</span> e:", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/file_directory"}
{"id": "ae2226ba3bde-7", "text": "text-decoration-color: #0000ff\">as</span> e:                                                 <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">31 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">\u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.autodetect_encoding:                                                <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">32 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">\u00e2\u201d\u201a   \u00e2\u201d\u201a", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/file_directory"}
{"id": "ae2226ba3bde-8", "text": "#7f7f7f\">\u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   </span>detected_encodings = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.detect_file_encodings()                       <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/home/spike/.pyenv/versions/3.9.11/lib/python3.9/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">codecs.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">322</span> in <span style=\"color: #00ff00; text-decoration-color:", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/file_directory"}
{"id": "ae2226ba3bde-9", "text": "in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">decode</span>                         <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 319 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">\u00e2\u201d\u201a   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">def</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">decode</span>(<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>, <span style=\"color: #00ffff; text-decoration-color: #00ffff\">input</span>, final=<span", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/file_directory"}
{"id": "ae2226ba3bde-10", "text": "#00ffff; text-decoration-color: #00ffff\">input</span>, final=<span style=\"color: #0000ff; text-decoration-color: #0000ff\">False</span>):                                                 <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 320 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">\u00e2\u201d\u201a   \u00e2\u201d\u201a   </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"># decode input (taking the buffer into account)</span>                                   <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 321 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">\u00e2\u201d\u201a   \u00e2\u201d\u201a   </span>data = <span", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/file_directory"}
{"id": "ae2226ba3bde-11", "text": "\u00e2\u201d\u201a   </span>data = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.buffer + <span style=\"color: #00ffff; text-decoration-color: #00ffff\">input</span>                                                        <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span> <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\ufffd\u00b1 </span> 322 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">\u00e2\u201d\u201a   \u00e2\u201d\u201a   </span>(result, consumed) = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._buffer_decode(data, <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.errors, final)                <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 323 </span><span style=\"color: #7f7f7f; text-decoration-color:", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/file_directory"}
{"id": "ae2226ba3bde-12", "text": "</span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">\u00e2\u201d\u201a   \u00e2\u201d\u201a   </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"># keep undecoded input until the next call</span>                                        <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 324 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">\u00e2\u201d\u201a   \u00e2\u201d\u201a   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.buffer = data[consumed:]                                                     <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 325 </span><span style=\"color:", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/file_directory"}
{"id": "ae2226ba3bde-13", "text": "#7f7f7f\"> 325 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">\u00e2\u201d\u201a   \u00e2\u201d\u201a   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> result                                                                     <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color:", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/file_directory"}
{"id": "ae2226ba3bde-14", "text": "style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u2022\u00b0\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u2022\u00af</span><span style=\"color: #ff0000; text-decoration-color: #ff0000; font-weight: bold\">UnicodeDecodeError: </span><span style=\"color: #008000; text-decoration-color: #008000\">'utf-8'</span> codec can't decode byte <span style=\"color: #008080; text-decoration-color: #008080; font-weight:", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/file_directory"}
{"id": "ae2226ba3bde-15", "text": "style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0xca</span> in position <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span>: invalid continuation byte<span style=\"font-style: italic\">The above exception was the direct cause of the following exception:</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u2022\u00ad\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">Traceback </span><span style=\"color: #bf7f7f; text-decoration-color: #bf7f7f; font-weight: bold\">(most recent call last)</span><span style=\"color: #800000; text-decoration-color: #800000\"> \u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u2022\u00ae</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span>", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/file_directory"}
{"id": "ae2226ba3bde-16", "text": "#800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">&lt;module&gt;</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">1</span>                                                                                    <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span> <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\ufffd\u00b1 </span>1 loader.load()", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/file_directory"}
{"id": "ae2226ba3bde-17", "text": "<span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2 </span>                                                                                             <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span>                                                                                                  <span style=\"color: #800000; text-decoration-color:", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/file_directory"}
{"id": "ae2226ba3bde-18", "text": "<span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/data/source/langchain/langchain/document_loaders/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">directory.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">84</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">load</span>                        <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span>   <span style=\"color: #7f7f7f;", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/file_directory"}
{"id": "ae2226ba3bde-19", "text": "<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">81 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">\u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.silent_errors:                                              <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">82 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">\u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   </span>logger.warning(e)                                               <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/file_directory"}
{"id": "ae2226ba3bde-20", "text": "#800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">83 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">\u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">else</span>:                                                               <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span> <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\ufffd\u00b1 </span>84 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">\u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">raise</span> e", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/file_directory"}
{"id": "ae2226ba3bde-21", "text": "<span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">85 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">\u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">finally</span>:                                                                <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">86 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">\u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/file_directory"}
{"id": "ae2226ba3bde-22", "text": "\u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> pbar:                                                            <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">87 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">\u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   </span>pbar.update(<span style=\"color: #0000ff; text-decoration-color: #0000ff\">1</span>)                                                  <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span>", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/file_directory"}
{"id": "ae2226ba3bde-23", "text": "<span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/data/source/langchain/langchain/document_loaders/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">directory.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">78</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">load</span>                        <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span>", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/file_directory"}
{"id": "ae2226ba3bde-24", "text": "<span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">75 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">\u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> i.is_file():                                                                 <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">76 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">\u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   </span><span style=\"color: #0000ff; text-decoration-color:", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/file_directory"}
{"id": "ae2226ba3bde-25", "text": "</span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> _is_visible(i.relative_to(p)) <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.load_hidden:                       <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">77 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">\u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">try</span>:                                                                    <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span> <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\ufffd\u00b1", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/file_directory"}
{"id": "ae2226ba3bde-26", "text": "style=\"color: #800000; text-decoration-color: #800000\">\u00e2\ufffd\u00b1 </span>78 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">\u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   </span>sub_docs = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.loader_cls(<span style=\"color: #00ffff; text-decoration-color: #00ffff\">str</span>(i), **<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.loader_kwargs).load()     <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">79 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">\u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   </span>docs.extend(sub_docs)                                               <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span>", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/file_directory"}
{"id": "ae2226ba3bde-27", "text": "#800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">80 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">\u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">except</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">Exception</span> <span style=\"color: #0000ff; text-decoration-color: #0000ff\">as</span> e:                                                  <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">81 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">\u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> <span style=\"color: #00ffff; text-decoration-color:", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/file_directory"}
{"id": "ae2226ba3bde-28", "text": "<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.silent_errors:                                              <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/data/source/langchain/langchain/document_loaders/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">text.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">44</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">load</span>", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/file_directory"}
{"id": "ae2226ba3bde-29", "text": "<span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">41 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">\u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">except</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">UnicodeDecodeError</span>:                                          <span style=\"color:", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/file_directory"}
{"id": "ae2226ba3bde-30", "text": "<span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">42 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">\u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">continue</span>                                                        <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">43 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">\u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">else</span>:", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/file_directory"}
{"id": "ae2226ba3bde-31", "text": "<span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span> <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\ufffd\u00b1 </span>44 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">\u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">raise</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">RuntimeError</span>(<span style=\"color: #808000; text-decoration-color: #808000\">f\"Error loading {</span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.file_path<span style=\"color: #808000; text-decoration-color: #808000\">}\"</span>) <span style=\"color: #0000ff; text-decoration-color: #0000ff\">from</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff; text-decoration: underline\">e</span>            <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/file_directory"}
{"id": "ae2226ba3bde-32", "text": "#800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">45 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">\u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">except</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">Exception</span> <span style=\"color: #0000ff; text-decoration-color: #0000ff\">as</span> e:                                                          <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">46 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">\u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   \u00e2\u201d\u201a   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">raise</span> <span style=\"color:", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/file_directory"}
{"id": "ae2226ba3bde-33", "text": "text-decoration-color: #0000ff\">raise</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">RuntimeError</span>(<span style=\"color: #808000; text-decoration-color: #808000\">f\"Error loading {</span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.file_path<span style=\"color: #808000; text-decoration-color: #808000\">}\"</span>) <span style=\"color: #0000ff; text-decoration-color: #0000ff\">from</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff; text-decoration: underline\">e</span>                <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">47 </span>                                                                                            <span style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u201d\u201a</span><span style=\"color: #800000; text-decoration-color:", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/file_directory"}
{"id": "ae2226ba3bde-34", "text": "style=\"color: #800000; text-decoration-color: #800000\">\u00e2\u2022\u00b0\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u2022\u00af</span><span style=\"color: #ff0000; text-decoration-color: #ff0000; font-weight: bold\">RuntimeError: </span>Error loading ..<span style=\"color: #800080; text-decoration-color: #800080\">/../../../../tests/integration_tests/examples/</span><span style=\"color: #ff00ff; text-decoration-color:", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/file_directory"}
{"id": "ae2226ba3bde-35", "text": "style=\"color: #ff00ff; text-decoration-color: #ff00ff\">example-non-utf8.txt</span></pre>The file example-non-utf8.txt uses a different encoding the load() function fails with a helpful message indicating which file failed decoding. With the default behavior of TextLoader any failure to load any of the documents will fail the whole loading process and no documents are loaded. B. Silent fail\u00e2\u20ac\u2039We can pass the parameter silent_errors to the DirectoryLoader to skip the files which could not be loaded and continue the load process.loader = DirectoryLoader(path, glob=\"**/*.txt\", loader_cls=TextLoader, silent_errors=True)docs = loader.load()    Error loading ../../../../../tests/integration_tests/examples/example-non-utf8.txtdoc_sources = [doc.metadata['source']  for doc in docs]doc_sources    ['../../../../../tests/integration_tests/examples/whatsapp_chat.txt',     '../../../../../tests/integration_tests/examples/example-utf8.txt']C. Auto detect encodings\u00e2\u20ac\u2039We can also ask TextLoader to auto detect the file encoding before failing, by passing the autodetect_encoding to the loader class.text_loader_kwargs={'autodetect_encoding': True}loader = DirectoryLoader(path, glob=\"**/*.txt\", loader_cls=TextLoader, loader_kwargs=text_loader_kwargs)docs = loader.load()doc_sources = [doc.metadata['source']  for doc in docs]doc_sources    ['../../../../../tests/integration_tests/examples/example-non-utf8.txt',     '../../../../../tests/integration_tests/examples/whatsapp_chat.txt',     '../../../../../tests/integration_tests/examples/example-utf8.txt']PreviousCSVNextHTMLCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/file_directory"}
{"id": "e11d3849689b-0", "text": "PDF | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/pdf"}
{"id": "e11d3849689b-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionDocument loadersCSVFile DirectoryHTMLJSONMarkdownPDFDocument transformersText embedding modelsVector storesRetrieversChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesData connectionDocument loadersPDFPDFPortable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems.This covers how to load PDF documents into the Document format that we use downstream.Using PyPDF\u00e2\u20ac\u2039Load PDF using pypdf into array of documents, where each document contains the page content and metadata with page number.pip install pypdffrom langchain.document_loaders import PyPDFLoaderloader = PyPDFLoader(\"example_data/layout-parser-paper.pdf\")pages = loader.load_and_split()pages[0]    Document(page_content='LayoutParser : A Uni\\x0ced Toolkit for Deep\\nLearning Based Document Image Analysis\\nZejiang Shen1( \\x00), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain\\nLee4, Jacob Carlson3, and Weining Li5\\n1Allen Institute for AI\\nshannons@allenai.org\\n2Brown University\\nruochen zhang@brown.edu\\n3Harvard University\\nfmelissadell,jacob carlson g@fas.harvard.edu\\n4University of Washington\\nbcgl@cs.washington.edu\\n5University of Waterloo\\nw422li@uwaterloo.ca\\nAbstract. Recent advances in document image analysis (DIA) have been\\nprimarily driven by the application of neural networks. Ideally, research\\noutcomes could be easily deployed in production and extended for", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/pdf"}
{"id": "e11d3849689b-2", "text": "application of neural networks. Ideally, research\\noutcomes could be easily deployed in production and extended for further\\ninvestigation. However, various factors like loosely organized codebases\\nand sophisticated model con\\x0cgurations complicate the easy reuse of im-\\nportant innovations by a wide audience. Though there have been on-going\\ne\\x0borts to improve reusability and simplify deep learning (DL) model\\ndevelopment in disciplines like natural language processing and computer\\nvision, none of them are optimized for challenges in the domain of DIA.\\nThis represents a major gap in the existing toolkit, as DIA is central to\\nacademic research across a wide range of disciplines in the social sciences\\nand humanities. This paper introduces LayoutParser , an open-source\\nlibrary for streamlining the usage of DL in DIA research and applica-\\ntions. The core LayoutParser library comes with a set of simple and\\nintuitive interfaces for applying and customizing DL models for layout de-\\ntection, character recognition, and many other document processing tasks.\\nTo promote extensibility, LayoutParser also incorporates a community\\nplatform for sharing both pre-trained models and full document digiti-\\nzation pipelines. We demonstrate that LayoutParser is helpful for both\\nlightweight and large-scale digitization pipelines in real-word use cases.\\nThe library is publicly available at https://layout-parser.github.io .\\nKeywords: Document Image Analysis \u00c2\u00b7Deep Learning \u00c2\u00b7Layout Analysis\\n\u00c2\u00b7Character Recognition \u00c2\u00b7Open Source library \u00c2\u00b7Toolkit.\\n1 Introduction\\nDeep Learning(DL)-based approaches are the state-of-the-art for a wide range of\\ndocument image analysis (DIA) tasks including document image classi\\x0ccation [ 11,arXiv:2103.15348v2  [cs.CV]  21 Jun 2021', metadata={'source': 'example_data/layout-parser-paper.pdf', 'page':", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/pdf"}
{"id": "e11d3849689b-3", "text": "Jun 2021', metadata={'source': 'example_data/layout-parser-paper.pdf', 'page': 0})An advantage of this approach is that documents can be retrieved with page numbers.We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.import osimport getpassos.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')    OpenAI API Key: \u00c2\u00b7\u00c2\u00b7\u00c2\u00b7\u00c2\u00b7\u00c2\u00b7\u00c2\u00b7\u00c2\u00b7\u00c2\u00b7from langchain.vectorstores import FAISSfrom langchain.embeddings.openai import OpenAIEmbeddingsfaiss_index = FAISS.from_documents(pages, OpenAIEmbeddings())docs = faiss_index.similarity_search(\"How will the community be engaged?\", k=2)for doc in docs:    print(str(doc.metadata[\"page\"]) + \":\", doc.page_content[:300])    9: 10 Z. Shen et al.    Fig. 4: Illustration of (a) the original historical Japanese document with layout    detection results and (b) a recreated version of the document image that achieves    much better character recognition recall. The reorganization algorithm rearranges    the tokens based on the their detect    3: 4 Z. Shen et al.    Efficient Data AnnotationC u s t o m i z e d  M o d e l  T r a i n i n gModel Cust omizationDI A Model HubDI A Pipeline SharingCommunity PlatformLa y out Detection ModelsDocument Images     T h e  C o r e  L a y o u t P a r s e r  L i b r a r yOCR ModuleSt or age & VisualizationLa y ouUsing MathPix\u00e2\u20ac\u2039Inspired by Daniel Gross's", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/pdf"}
{"id": "e11d3849689b-4", "text": "or age & VisualizationLa y ouUsing MathPix\u00e2\u20ac\u2039Inspired by Daniel Gross's https://gist.github.com/danielgross/3ab4104e14faccc12b49200843adab21from langchain.document_loaders import MathpixPDFLoaderloader = MathpixPDFLoader(\"example_data/layout-parser-paper.pdf\")data = loader.load()Using Unstructured\u00e2\u20ac\u2039from langchain.document_loaders import UnstructuredPDFLoaderloader = UnstructuredPDFLoader(\"example_data/layout-parser-paper.pdf\")data = loader.load()Retain Elements\u00e2\u20ac\u2039Under the hood, Unstructured creates different \"elements\" for different chunks of text. By default we combine those together, but you can easily keep that separation by specifying mode=\"elements\".loader = UnstructuredPDFLoader(\"example_data/layout-parser-paper.pdf\", mode=\"elements\")data = loader.load()data[0]    Document(page_content='LayoutParser: A Uni\u00ef\u00ac\ufffded Toolkit for Deep\\nLearning Based Document Image Analysis\\nZejiang Shen1 (\u00ef\u00bf\u00bd), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain\\nLee4, Jacob Carlson3, and Weining Li5\\n1 Allen Institute for AI\\nshannons@allenai.org\\n2 Brown University\\nruochen zhang@brown.edu\\n3 Harvard University\\n{melissadell,jacob carlson}@fas.harvard.edu\\n4 University of Washington\\nbcgl@cs.washington.edu\\n5 University of Waterloo\\nw422li@uwaterloo.ca\\nAbstract. Recent advances in document image analysis (DIA) have been\\nprimarily driven by the application of neural networks. Ideally, research\\noutcomes could be easily deployed in production and extended for further\\ninvestigation. However, various factors like loosely organized codebases\\nand sophisticated model con\u00ef\u00ac\ufffdgurations complicate the easy reuse of im-\\nportant innovations by a wide audience. Though", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/pdf"}
{"id": "e11d3849689b-5", "text": "complicate the easy reuse of im-\\nportant innovations by a wide audience. Though there have been on-going\\ne\u00ef\u00ac\u20acorts to improve reusability and simplify deep learning (DL) model\\ndevelopment in disciplines like natural language processing and computer\\nvision, none of them are optimized for challenges in the domain of DIA.\\nThis represents a major gap in the existing toolkit, as DIA is central to\\nacademic research across a wide range of disciplines in the social sciences\\nand humanities. This paper introduces LayoutParser, an open-source\\nlibrary for streamlining the usage of DL in DIA research and applica-\\ntions. The core LayoutParser library comes with a set of simple and\\nintuitive interfaces for applying and customizing DL models for layout de-\\ntection, character recognition, and many other document processing tasks.\\nTo promote extensibility, LayoutParser also incorporates a community\\nplatform for sharing both pre-trained models and full document digiti-\\nzation pipelines. We demonstrate that LayoutParser is helpful for both\\nlightweight and large-scale digitization pipelines in real-word use cases.\\nThe library is publicly available at https://layout-parser.github.io.\\nKeywords: Document Image Analysis \u00c2\u00b7 Deep Learning \u00c2\u00b7 Layout Analysis\\n\u00c2\u00b7 Character Recognition \u00c2\u00b7 Open Source library \u00c2\u00b7 Toolkit.\\n1\\nIntroduction\\nDeep Learning(DL)-based approaches are the state-of-the-art for a wide range of\\ndocument image analysis (DIA) tasks including document image classi\u00ef\u00ac\ufffdcation [11,\\narXiv:2103.15348v2  [cs.CV]  21 Jun 2021\\n', lookup_str='', metadata={'file_path': 'example_data/layout-parser-paper.pdf', 'page_number': 1, 'total_pages': 16, 'format': 'PDF 1.5', 'title': '', 'author': '', 'subject': '', 'keywords': '', 'creator':", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/pdf"}
{"id": "e11d3849689b-6", "text": "'title': '', 'author': '', 'subject': '', 'keywords': '', 'creator': 'LaTeX with hyperref', 'producer': 'pdfTeX-1.40.21', 'creationDate': 'D:20210622012710Z', 'modDate': 'D:20210622012710Z', 'trapped': '', 'encryption': None}, lookup_index=0)Fetching remote PDFs using Unstructured\u00e2\u20ac\u2039This covers how to load online pdfs into a document format that we can use downstream. This can be used for various online pdf sites such as https://open.umn.edu/opentextbooks/textbooks/ and https://arxiv.org/archive/Note: all other pdf loaders can also be used to fetch remote PDFs, but OnlinePDFLoader is a legacy function, and works specifically with UnstructuredPDFLoader.from langchain.document_loaders import OnlinePDFLoaderloader = OnlinePDFLoader(\"https://arxiv.org/pdf/2302.03803.pdf\")data = loader.load()print(data)    [Document(page_content='A WEAK ( k, k ) -LEFSCHETZ THEOREM FOR PROJECTIVE TORIC ORBIFOLDS\\n\\nWilliam D. Montoya\\n\\nInstituto de Matem\u00c2\u00b4atica, Estat\u00c2\u00b4\u00c4\u00b1stica e Computa\u00c2\u00b8c\u00cb\u0153ao Cient\u00c2\u00b4\u00c4\u00b1\u00ef\u00ac\ufffdca,\\n\\nIn [3] we proved that, under suitable conditions, on a very general codimension s quasi- smooth intersection subvariety X in a projective toric orbifold P d \u00ce\u00a3 with d + s = 2 ( k + 1 ) the Hodge conjecture holds, that is, every ( p, p ) -cohomology class, under the Poincar\u00c2\u00b4e duality is a rational linear combination of fundamental classes of algebraic subvarieties of X", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/pdf"}
{"id": "e11d3849689b-7", "text": "duality is a rational linear combination of fundamental classes of algebraic subvarieties of X . The proof of the above-mentioned result relies, for p \u00e2\u2030\u00a0 d + 1 \u00e2\u02c6\u2019 s , on a Lefschetz\\n\\nKeywords: (1,1)- Lefschetz theorem, Hodge conjecture, toric varieties, complete intersection Email: wmontoya@ime.unicamp.br\\n\\ntheorem ([7]) and the Hard Lefschetz theorem for projective orbifolds ([11]). When p = d + 1 \u00e2\u02c6\u2019 s the proof relies on the Cayley trick, a trick which associates to X a quasi-smooth hypersurface Y in a projective vector bundle, and the Cayley Proposition (4.3) which gives an isomorphism of some primitive cohomologies (4.2) of X and Y . The Cayley trick, following the philosophy of Mavlyutov in [7], reduces results known for quasi-smooth hypersurfaces to quasi-smooth intersection subvarieties. The idea in this paper goes the other way around, we translate some results for quasi-smooth intersection subvarieties to\\n\\nAcknowledgement. I thank Prof. Ugo Bruzzo and Tiago Fonseca for useful discus- sions. I also acknowledge support from FAPESP postdoctoral grant No. 2019/23499-7.\\n\\nLet M be a free abelian group of rank d , let N = Hom ( M, Z ) , and N R = N \u00e2\u0160\u2014 Z R .\\n\\nif there exist k linearly independent primitive elements e\\n\\n, . . . , e k \u00e2\u02c6\u02c6 N such that \u00cf\u0192 = { \u00c2\u00b5\\n\\ne\\n\\n+ \u00e2\u2039\u00af + \u00c2\u00b5 k e k } . \u00e2\u20ac\u00a2 The generators e i are integral if for every i and", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/pdf"}
{"id": "e11d3849689b-8", "text": "k e k } . \u00e2\u20ac\u00a2 The generators e i are integral if for every i and any nonnegative rational number \u00c2\u00b5 the product \u00c2\u00b5e i is in N only if \u00c2\u00b5 is an integer. \u00e2\u20ac\u00a2 Given two rational simplicial cones \u00cf\u0192 , \u00cf\u0192 \u00e2\u20ac\u00b2 one says that \u00cf\u0192 \u00e2\u20ac\u00b2 is a face of \u00cf\u0192 ( \u00cf\u0192 \u00e2\u20ac\u00b2 < \u00cf\u0192 ) if the set of integral generators of \u00cf\u0192 \u00e2\u20ac\u00b2 is a subset of the set of integral generators of \u00cf\u0192 . \u00e2\u20ac\u00a2 A \u00ef\u00ac\ufffdnite set \u00ce\u00a3 = { \u00cf\u0192\\n\\n, . . . , \u00cf\u0192 t } of rational simplicial cones is called a rational simplicial complete d -dimensional fan if:\\n\\nall faces of cones in \u00ce\u00a3 are in \u00ce\u00a3 ;\\n\\nif \u00cf\u0192, \u00cf\u0192 \u00e2\u20ac\u00b2 \u00e2\u02c6\u02c6 \u00ce\u00a3 then \u00cf\u0192 \u00e2\u02c6\u00a9 \u00cf\u0192 \u00e2\u20ac\u00b2 < \u00cf\u0192 and \u00cf\u0192 \u00e2\u02c6\u00a9 \u00cf\u0192 \u00e2\u20ac\u00b2 < \u00cf\u0192 \u00e2\u20ac\u00b2 ;\\n\\nN R = \u00cf\u0192\\n\\n\u00e2\u02c6\u00aa \u00e2\u2039\u2026 \u00e2\u2039\u2026 \u00e2\u2039\u2026 \u00e2\u02c6\u00aa \u00cf\u0192 t .\\n\\nA rational simplicial complete d -dimensional fan \u00ce\u00a3 de\u00ef\u00ac\ufffdnes a d -dimensional toric variety P d \u00ce\u00a3 having only orbifold singularities which we assume to be projective. Moreover, T \u00e2\u02c6\u00b6 = N \u00e2\u0160\u2014 Z C \u00e2\u02c6\u2014 \u00e2\u2030\u0192 ( C \u00e2\u02c6\u2014 ) d is the torus action on P d \u00ce\u00a3 . We denote by \u00ce\u00a3 ( i ) the i", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/pdf"}
{"id": "e11d3849689b-9", "text": "the torus action on P d \u00ce\u00a3 . We denote by \u00ce\u00a3 ( i ) the i -dimensional cones\\n\\nFor a cone \u00cf\u0192 \u00e2\u02c6\u02c6 \u00ce\u00a3, \u00cb\u2020 \u00cf\u0192 is the set of 1-dimensional cone in \u00ce\u00a3 that are not contained in \u00cf\u0192\\n\\nand x \u00cb\u2020 \u00cf\u0192 \u00e2\u02c6\u00b6 = \u00e2\u02c6\ufffd \u00cf\ufffd \u00e2\u02c6\u02c6 \u00cb\u2020 \u00cf\u0192 x \u00cf\ufffd is the associated monomial in S .\\n\\nDe\u00ef\u00ac\ufffdnition 2.2. The irrelevant ideal of P d \u00ce\u00a3 is the monomial ideal B \u00ce\u00a3 \u00e2\u02c6\u00b6 =< x \u00cb\u2020 \u00cf\u0192 \u00e2\u02c6\u00a3 \u00cf\u0192 \u00e2\u02c6\u02c6 \u00ce\u00a3 > and the zero locus Z ( \u00ce\u00a3 ) \u00e2\u02c6\u00b6 = V ( B \u00ce\u00a3 ) in the a\u00ef\u00ac\u0192ne space A d \u00e2\u02c6\u00b6 = Spec ( S ) is the irrelevant locus.\\n\\nProposition 2.3 (Theorem 5.1.11 [5]) . The toric variety P d \u00ce\u00a3 is a categorical quotient A d \u00e2\u02c6\u2013 Z ( \u00ce\u00a3 ) by the group Hom ( Cl ( \u00ce\u00a3 ) , C \u00e2\u02c6\u2014 ) and the group action is induced by the Cl ( \u00ce\u00a3 ) - grading of S .\\n\\nNow we give a brief introduction to complex orbifolds and we mention the needed theorems for the next section. Namely: de Rham theorem and Dolbeault theorem for complex orbifolds.\\n\\nDe\u00ef\u00ac\ufffdnition 2.4. A complex orbifold of complex dimension d is a singular complex space whose singularities are locally isomorphic to quotient singularities C d / G , for \u00ef\u00ac\ufffdnite sub- groups G \u00e2\u0160\u201a", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/pdf"}
{"id": "e11d3849689b-10", "text": "C d / G , for \u00ef\u00ac\ufffdnite sub- groups G \u00e2\u0160\u201a Gl ( d, C ) .\\n\\nDe\u00ef\u00ac\ufffdnition 2.5. A di\u00ef\u00ac\u20acerential form on a complex orbifold Z is de\u00ef\u00ac\ufffdned locally at z \u00e2\u02c6\u02c6 Z as a G -invariant di\u00ef\u00ac\u20acerential form on C d where G \u00e2\u0160\u201a Gl ( d, C ) and Z is locally isomorphic to d\\n\\nRoughly speaking the local geometry of orbifolds reduces to local G -invariant geometry.\\n\\nWe have a complex of di\u00ef\u00ac\u20acerential forms ( A \u00e2\u2014\ufffd ( Z ) , d ) and a double complex ( A \u00e2\u2014\ufffd , \u00e2\u2014\ufffd ( Z ) , \u00e2\u02c6\u201a, \u00c2\u00af \u00e2\u02c6\u201a ) of bigraded di\u00ef\u00ac\u20acerential forms which de\u00ef\u00ac\ufffdne the de Rham and the Dolbeault cohomology groups (for a \u00ef\u00ac\ufffdxed p \u00e2\u02c6\u02c6 N ) respectively:\\n\\n(1,1)-Lefschetz theorem for projective toric orbifolds\\n\\nDe\u00ef\u00ac\ufffdnition 3.1. A subvariety X \u00e2\u0160\u201a P d \u00ce\u00a3 is quasi-smooth if V ( I X ) \u00e2\u0160\u201a A #\u00ce\u00a3 ( 1 ) is smooth outside\\n\\nExample 3.2 . Quasi-smooth hypersurfaces or more generally quasi-smooth intersection sub-\\n\\nExample 3.2 . Quasi-smooth hypersurfaces or more generally quasi-smooth intersection sub- varieties are quasi-smooth subvarieties (see [2] or [7] for more details).\\n\\nRemark 3.3 . Quasi-smooth subvarieties are suborbifolds of P d \u00ce\u00a3 in the sense of", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/pdf"}
{"id": "e11d3849689b-11", "text": "subvarieties are suborbifolds of P d \u00ce\u00a3 in the sense of Satake in [8]. Intuitively speaking they are subvarieties whose only singularities come from the ambient\\n\\nProof. From the exponential short exact sequence\\n\\nwe have a long exact sequence in cohomology\\n\\nH 1 (O \u00e2\u02c6\u2014 X ) \u00e2\u2020\u2019 H 2 ( X, Z ) \u00e2\u2020\u2019 H 2 (O X ) \u00e2\u2030\u0192 H 0 , 2 ( X )\\n\\nwhere the last isomorphisms is due to Steenbrink in [9]. Now, it is enough to prove the commutativity of the next diagram\\n\\nwhere the last isomorphisms is due to Steenbrink in [9]. Now,\\n\\nH 2 ( X, Z ) / / H 2 ( X, O X ) \u00e2\u2030\u0192 Dolbeault H 2 ( X, C ) deRham \u00e2\u2030\u0192 H 2 dR ( X, C ) / / H 0 , 2 \u00c2\u00af \u00e2\u02c6\u201a ( X )\\n\\nof the proof follows as the ( 1 , 1 ) -Lefschetz theorem in [6].\\n\\nRemark 3.5 . For k = 1 and P d \u00ce\u00a3 as the projective space, we recover the classical ( 1 , 1 ) - Lefschetz theorem.\\n\\nBy the Hard Lefschetz Theorem for projective orbifolds (see [11] for details) we\\n\\nBy the Hard Lefschetz Theorem for projective orbifolds (see [11] for details) we get an isomorphism of cohomologies :\\n\\ngiven by the Lefschetz morphism and since it is a morphism of Hodge structures, we have:\\n\\nH", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/pdf"}
{"id": "e11d3849689b-12", "text": "morphism and since it is a morphism of Hodge structures, we have:\\n\\nH 1 , 1 ( X, Q ) \u00e2\u2030\u0192 H dim X \u00e2\u02c6\u2019 1 , dim X \u00e2\u02c6\u2019 1 ( X, Q )\\n\\nCorollary 3.6. If the dimension of X is 1 , 2 or 3 . The Hodge conjecture holds on X\\n\\nProof. If the dim C X = 1 the result is clear by the Hard Lefschetz theorem for projective orbifolds. The dimension 2 and 3 cases are covered by Theorem 3.5 and the Hard Lefschetz.\\n\\nCayley trick and Cayley proposition\\n\\nThe Cayley trick is a way to associate to a quasi-smooth intersection subvariety a quasi- smooth hypersurface. Let L 1 , . . . , L s be line bundles on P d \u00ce\u00a3 and let \u00cf\u20ac \u00e2\u02c6\u00b6 P ( E ) \u00e2\u2020\u2019 P d \u00ce\u00a3 be the projective space bundle associated to the vector bundle E = L 1 \u00e2\u0160\u2022 \u00e2\u2039\u00af \u00e2\u0160\u2022 L s . It is known that P ( E ) is a ( d + s \u00e2\u02c6\u2019 1 ) -dimensional simplicial toric variety whose fan depends on the degrees of the line bundles and the fan \u00ce\u00a3. Furthermore, if the Cox ring, without considering the grading, of P d \u00ce\u00a3 is C [ x 1 , . . . , x m ] then the Cox ring of P ( E ) is\\n\\nMoreover for X a quasi-smooth intersection subvariety cut o\u00ef\u00ac\u20ac by f 1 , . . . , f s with deg ( f i ) = [ L i ] we relate the hypersurface Y cut o\u00ef\u00ac\u20ac by F = y 1 f", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/pdf"}
{"id": "e11d3849689b-13", "text": "i ] we relate the hypersurface Y cut o\u00ef\u00ac\u20ac by F = y 1 f 1 + \u00e2\u2039\u2026 \u00e2\u2039\u2026 \u00e2\u2039\u2026 + y s f s which turns out to be quasi-smooth. For more details see Section 2 in [7].\\n\\nWe will denote P ( E ) as P d + s \u00e2\u02c6\u2019 1 \u00ce\u00a3 ,X to keep track of its relation with X and P d \u00ce\u00a3 .\\n\\nThe following is a key remark.\\n\\nRemark 4.1 . There is a morphism \u00ce\u00b9 \u00e2\u02c6\u00b6 X \u00e2\u2020\u2019 Y \u00e2\u0160\u201a P d + s \u00e2\u02c6\u2019 1 \u00ce\u00a3 ,X . Moreover every point z \u00e2\u02c6\u00b6 = ( x, y ) \u00e2\u02c6\u02c6 Y with y \u00e2\u2030\u00a0 0 has a preimage. Hence for any subvariety W = V ( I W ) \u00e2\u0160\u201a X \u00e2\u0160\u201a P d \u00ce\u00a3 there exists W \u00e2\u20ac\u00b2 \u00e2\u0160\u201a Y \u00e2\u0160\u201a P d + s \u00e2\u02c6\u2019 1 \u00ce\u00a3 ,X such that \u00cf\u20ac ( W \u00e2\u20ac\u00b2 ) = W , i.e., W \u00e2\u20ac\u00b2 = { z = ( x, y ) \u00e2\u02c6\u00a3 x \u00e2\u02c6\u02c6 W } .\\n\\nFor X \u00e2\u0160\u201a P d \u00ce\u00a3 a quasi-smooth intersection variety the morphism in cohomology induced by the inclusion i \u00e2\u02c6\u2014 \u00e2\u02c6\u00b6 H d \u00e2\u02c6\u2019 s ( P d \u00ce\u00a3 , C ) \u00e2\u2020\u2019 H d \u00e2\u02c6\u2019 s ( X, C ) is injective by Proposition 1.4 in [7].\\n\\nDe\u00ef\u00ac\ufffdnition 4.2. The primitive cohomology of H d \u00e2\u02c6\u2019 s prim ( X ) is the quotient H d", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/pdf"}
{"id": "e11d3849689b-14", "text": "cohomology of H d \u00e2\u02c6\u2019 s prim ( X ) is the quotient H d \u00e2\u02c6\u2019 s ( X, C )/ i \u00e2\u02c6\u2014 ( H d \u00e2\u02c6\u2019 s ( P d \u00ce\u00a3 , C )) and H d \u00e2\u02c6\u2019 s prim ( X, Q ) with rational coe\u00ef\u00ac\u0192cients.\\n\\nH d \u00e2\u02c6\u2019 s ( P d \u00ce\u00a3 , C ) and H d \u00e2\u02c6\u2019 s ( X, C ) have pure Hodge structures, and the morphism i \u00e2\u02c6\u2014 is com- patible with them, so that H d \u00e2\u02c6\u2019 s prim ( X ) gets a pure Hodge structure.\\n\\nThe next Proposition is the Cayley proposition.\\n\\nProposition 4.3. [Proposition 2.3 in [3] ] Let X = X 1 \u00e2\u02c6\u00a9\u00e2\u2039\u2026 \u00e2\u2039\u2026 \u00e2\u2039\u2026\u00e2\u02c6\u00a9 X s be a quasi-smooth intersec- tion subvariety in P d \u00ce\u00a3 cut o\u00ef\u00ac\u20ac by homogeneous polynomials f 1 . . . f s . Then for p \u00e2\u2030\u00a0 d + s \u00e2\u02c6\u2019 1 2 , d + s \u00e2\u02c6\u2019 3 2\\n\\nRemark 4.5 . The above isomorphisms are also true with rational coe\u00ef\u00ac\u0192cients since H \u00e2\u2014\ufffd ( X, C ) = H \u00e2\u2014\ufffd ( X, Q ) \u00e2\u0160\u2014 Q C . See the beginning of Section 7.1 in [10] for more details.\\n\\nTheorem 5.1. Let Y = { F = y 1 f 1 + \u00e2\u2039\u00af + y k f k = 0 } \u00e2\u0160\u201a P 2 k + 1 \u00ce\u00a3 ,X be the quasi-smooth", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/pdf"}
{"id": "e11d3849689b-15", "text": "\u00e2\u0160\u201a P 2 k + 1 \u00ce\u00a3 ,X be the quasi-smooth hypersurface associated to the quasi-smooth intersection surface X = X f 1 \u00e2\u02c6\u00a9 \u00e2\u2039\u2026 \u00e2\u2039\u2026 \u00e2\u2039\u2026 \u00e2\u02c6\u00a9 X f k \u00e2\u0160\u201a P k + 2 \u00ce\u00a3 . Then on Y the Hodge conjecture holds.\\n\\nthe Hodge conjecture holds.\\n\\nProof. If H k,k prim ( X, Q ) = 0 we are done. So let us assume H k,k prim ( X, Q ) \u00e2\u2030\u00a0 0. By the Cayley proposition H k,k prim ( Y, Q ) \u00e2\u2030\u0192 H 1 , 1 prim ( X, Q ) and by the ( 1 , 1 ) -Lefschetz theorem for projective\\n\\ntoric orbifolds there is a non-zero algebraic basis \u00ce\u00bb C 1 , . . . , \u00ce\u00bb C n with rational coe\u00ef\u00ac\u0192cients of H 1 , 1 prim ( X, Q ) , that is, there are n \u00e2\u02c6\u00b6 = h 1 , 1 prim ( X, Q ) algebraic curves C 1 , . . . , C n in X such that under the Poincar\u00c2\u00b4e duality the class in homology [ C i ] goes to \u00ce\u00bb C i , [ C i ] \u00e2\u2020\u00a6 \u00ce\u00bb C i . Recall that the Cox ring of P k + 2 is contained in the Cox ring of P 2 k + 1 \u00ce\u00a3 ,X without considering the grading. Considering the grading we have that if \u00ce\u00b1 \u00e2\u02c6\u02c6 Cl ( P k + 2 \u00ce\u00a3 ) then ( \u00ce\u00b1, 0 ) \u00e2\u02c6\u02c6 Cl ( P 2 k + 1 \u00ce\u00a3 ,X ) . So the polynomials", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/pdf"}
{"id": "e11d3849689b-16", "text": "Cl ( P 2 k + 1 \u00ce\u00a3 ,X ) . So the polynomials de\u00ef\u00ac\ufffdning C i \u00e2\u0160\u201a P k + 2 \u00ce\u00a3 can be interpreted in P 2 k + 1 X, \u00ce\u00a3 but with di\u00ef\u00ac\u20acerent degree. Moreover, by Remark 4.1 each C i is contained in Y = { F = y 1 f 1 + \u00e2\u2039\u00af + y k f k = 0 } and\\n\\nfurthermore it has codimension k .\\n\\nClaim: { C i } ni = 1 is a basis of prim ( ) . It is enough to prove that \u00ce\u00bb C i is di\u00ef\u00ac\u20acerent from zero in H k,k prim ( Y, Q ) or equivalently that the cohomology classes { \u00ce\u00bb C i } ni = 1 do not come from the ambient space. By contradiction, let us assume that there exists a j and C \u00e2\u0160\u201a P 2 k + 1 \u00ce\u00a3 ,X such that \u00ce\u00bb C \u00e2\u02c6\u02c6 H k,k ( P 2 k + 1 \u00ce\u00a3 ,X , Q ) with i \u00e2\u02c6\u2014 ( \u00ce\u00bb C ) = \u00ce\u00bb C j or in terms of homology there exists a ( k + 2 ) -dimensional algebraic subvariety V \u00e2\u0160\u201a P 2 k + 1 \u00ce\u00a3 ,X such that V \u00e2\u02c6\u00a9 Y = C j so they are equal as a homology class of P 2 k + 1 \u00ce\u00a3 ,X ,i.e., [ V \u00e2\u02c6\u00a9 Y ] = [ C j ] . It is easy to check that \u00cf\u20ac ( V ) \u00e2\u02c6\u00a9 X = C j as a subvariety of P k + 2 \u00ce\u00a3 where \u00cf\u20ac \u00e2\u02c6\u00b6 ( x, y ) \u00e2\u2020\u00a6", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/pdf"}
{"id": "e11d3849689b-17", "text": "2 \u00ce\u00a3 where \u00cf\u20ac \u00e2\u02c6\u00b6 ( x, y ) \u00e2\u2020\u00a6 x . Hence [ \u00cf\u20ac ( V ) \u00e2\u02c6\u00a9 X ] = [ C j ] which is equivalent to say that \u00ce\u00bb C j comes from P k + 2 \u00ce\u00a3 which contradicts the choice of [ C j ] .\\n\\nRemark 5.2 . Into the proof of the previous theorem, the key fact was that on X the Hodge conjecture holds and we translate it to Y by contradiction. So, using an analogous argument we have:\\n\\nargument we have:\\n\\nProposition 5.3. Let Y = { F = y 1 f s +\u00e2\u2039\u00af+ y s f s = 0 } \u00e2\u0160\u201a P 2 k + 1 \u00ce\u00a3 ,X be the quasi-smooth hypersurface associated to a quasi-smooth intersection subvariety X = X f 1 \u00e2\u02c6\u00a9 \u00e2\u2039\u2026 \u00e2\u2039\u2026 \u00e2\u2039\u2026 \u00e2\u02c6\u00a9 X f s \u00e2\u0160\u201a P d \u00ce\u00a3 such that d + s = 2 ( k + 1 ) . If the Hodge conjecture holds on X then it holds as well on Y .\\n\\nCorollary 5.4. If the dimension of Y is 2 s \u00e2\u02c6\u2019 1 , 2 s or 2 s + 1 then the Hodge conjecture holds on Y .\\n\\nProof. By Proposition 5.3 and Corollary 3.6.\\n\\n[\\n\\n] Angella, D. Cohomologies of certain orbifolds. Journal of Geometry and Physics\\n\\n(\\n\\n),\\n\\n\u00e2\u20ac\u201c\\n\\n[\\n\\n] Batyrev, V. V., and Cox, D. A. On the Hodge structure of projective hypersur- faces in toric", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/pdf"}
{"id": "e11d3849689b-18", "text": "D. A. On the Hodge structure of projective hypersur- faces in toric varieties. Duke Mathematical Journal\\n\\n,\\n\\n(Aug\\n\\n). [\\n\\n] Bruzzo, U., and Montoya, W. On the Hodge conjecture for quasi-smooth in- tersections in toric varieties. S\u00cb\u0153ao Paulo J. Math. Sci. Special Section: Geometry in Algebra and Algebra in Geometry (\\n\\n). [\\n\\n] Caramello Jr, F. C. Introduction to orbifolds. a\\n\\niv:\\n\\nv\\n\\n(\\n\\n). [\\n\\n] Cox, D., Little, J., and Schenck, H. Toric varieties, vol.\\n\\nAmerican Math- ematical Soc.,\\n\\n[\\n\\n] Griffiths, P., and Harris, J. Principles of Algebraic Geometry. John Wiley & Sons, Ltd,\\n\\n[\\n\\n] Mavlyutov, A. R. Cohomology of complete intersections in toric varieties. Pub- lished in Paci\u00ef\u00ac\ufffdc J. of Math.\\n\\nNo.\\n\\n(\\n\\n),\\n\\n\u00e2\u20ac\u201c\\n\\n[\\n\\n] Satake, I. On a Generalization of the Notion of Manifold. Proceedings of the National Academy of Sciences of the United States of America\\n\\n,\\n\\n(\\n\\n),\\n\\n\u00e2\u20ac\u201c\\n\\n[\\n\\n] Steenbrink, J. H. M. Intersection form for quasi-homogeneous singularities. Com- positio Mathematica\\n\\n,\\n\\n(\\n\\n),\\n\\n\u00e2\u20ac\u201c\\n\\n[\\n\\n] Voisin, C. Hodge Theory and Complex Algebraic Geometry I, vol.\\n\\nof Cambridge Studies in Advanced Mathematics . Cambridge University Press,\\n\\n[\\n\\n] Wang, Z. Z., and", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/pdf"}
{"id": "e11d3849689b-19", "text": "Advanced Mathematics . Cambridge University Press,\\n\\n[\\n\\n] Wang, Z. Z., and Zaffran, D. A remark on the Hard Lefschetz theorem for K\u00c2\u00a8ahler orbifolds. Proceedings of the American Mathematical Society\\n\\n,\\n\\n(Aug\\n\\n).\\n\\n[2] Batyrev, V. V., and Cox, D. A. On the Hodge structure of projective hypersur- faces in toric varieties. Duke Mathematical Journal 75, 2 (Aug 1994).\\n\\n[\\n\\n] Bruzzo, U., and Montoya, W. On the Hodge conjecture for quasi-smooth in- tersections in toric varieties. S\u00cb\u0153ao Paulo J. Math. Sci. Special Section: Geometry in Algebra and Algebra in Geometry (\\n\\n).\\n\\n[3] Bruzzo, U., and Montoya, W. On the Hodge conjecture for quasi-smooth in- tersections in toric varieties. S\u00cb\u0153ao Paulo J. Math. Sci. Special Section: Geometry in Algebra and Algebra in Geometry (2021).\\n\\nA. R. Cohomology of complete intersections in toric varieties. Pub-', lookup_str='', metadata={'source': '/var/folders/ph/hhm7_zyx4l13k3v8z02dwp1w0000gn/T/tmpgq0ckaja/online_file.pdf'}, lookup_index=0)]Using PyPDFium2\u00e2\u20ac\u2039from langchain.document_loaders import PyPDFium2Loaderloader = PyPDFium2Loader(\"example_data/layout-parser-paper.pdf\")data = loader.load()Using PDFMiner\u00e2\u20ac\u2039from langchain.document_loaders import PDFMinerLoaderloader = PDFMinerLoader(\"example_data/layout-parser-paper.pdf\")data = loader.load()Using PDFMiner to generate HTML text\u00e2\u20ac\u2039This can be", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/pdf"}
{"id": "e11d3849689b-20", "text": "= loader.load()Using PDFMiner to generate HTML text\u00e2\u20ac\u2039This can be helpful for chunking texts semantically into sections as the output html content can be parsed via BeautifulSoup to get more structured and rich information about font size, page numbers, pdf headers/footers, etc.from langchain.document_loaders import PDFMinerPDFasHTMLLoaderloader = PDFMinerPDFasHTMLLoader(\"example_data/layout-parser-paper.pdf\")data = loader.load()[0]   # entire pdf is loaded as a single Documentfrom bs4 import BeautifulSoupsoup = BeautifulSoup(data.page_content,'html.parser')content = soup.find_all('div')import recur_fs = Nonecur_text = ''snippets = []   # first collect all snippets that have the same font sizefor c in content:    sp = c.find('span')    if not sp:        continue    st = sp.get('style')    if not st:        continue    fs = re.findall('font-size:(\\d+)px',st)    if not fs:        continue    fs = int(fs[0])    if not cur_fs:        cur_fs = fs    if fs == cur_fs:        cur_text += c.text    else:        snippets.append((cur_text,cur_fs))        cur_fs = fs        cur_text = c.textsnippets.append((cur_text,cur_fs))# Note: The above logic is very straightforward. One can also add more strategies such as removing duplicate snippets (as# headers/footers in a PDF appear on multiple pages so if we find duplicatess safe to assume that it is redundant", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/pdf"}
{"id": "e11d3849689b-21", "text": "a PDF appear on multiple pages so if we find duplicatess safe to assume that it is redundant info)from langchain.docstore.document import Documentcur_idx = -1semantic_snippets = []# Assumption: headings have higher font size than their respective contentfor s in snippets:    # if current snippet's font size > previous section's heading => it is a new heading    if not semantic_snippets or s[1] > semantic_snippets[cur_idx].metadata['heading_font']:        metadata={'heading':s[0], 'content_font': 0, 'heading_font': s[1]}        metadata.update(data.metadata)        semantic_snippets.append(Document(page_content='',metadata=metadata))        cur_idx += 1        continue        # if current snippet's font size <= previous section's content => content belongs to the same section (one can also create    # a tree like structure for sub sections if needed but that may require some more thinking and may be data specific)    if not semantic_snippets[cur_idx].metadata['content_font'] or s[1] <= semantic_snippets[cur_idx].metadata['content_font']:        semantic_snippets[cur_idx].page_content += s[0]        semantic_snippets[cur_idx].metadata['content_font'] = max(s[1], semantic_snippets[cur_idx].metadata['content_font'])        continue        # if current snippet's font size > previous section's content but less than previous section's heading than also make a new     # section (e.g. title of a pdf will have the highest font size but we don't want it to subsume all", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/pdf"}
{"id": "e11d3849689b-22", "text": "title of a pdf will have the highest font size but we don't want it to subsume all sections)    metadata={'heading':s[0], 'content_font': 0, 'heading_font': s[1]}    metadata.update(data.metadata)    semantic_snippets.append(Document(page_content='',metadata=metadata))    cur_idx += 1semantic_snippets[4]    Document(page_content='Recently, various DL models and datasets have been developed for layout analysis\\ntasks. The dhSegment [22] utilizes fully convolutional networks [20] for segmen-\\ntation tasks on historical documents. Object detection-based methods like Faster\\nR-CNN [28] and Mask R-CNN [12] are used for identifying document elements [38]\\nand detecting tables [30, 26]. Most recently, Graph Neural Networks [29] have also\\nbeen used in table detection [27]. However, these models are usually implemented\\nindividually and there is no uni\u00ef\u00ac\ufffded framework to load and use such models.\\nThere has been a surge of interest in creating open-source tools for document\\nimage processing: a search of document image analysis in Github leads to 5M\\nrelevant code pieces 6; yet most of them rely on traditional rule-based methods\\nor provide limited functionalities. The closest prior research to our work is the\\nOCR-D project7, which also tries to build a complete toolkit for DIA. However,\\nsimilar to the platform developed by Neudecker et al. [21], it is designed for\\nanalyzing historical documents, and provides no supports for recent DL models.\\nThe DocumentLayoutAnalysis project8 focuses on processing born-digital PDF\\ndocuments via analyzing the stored PDF data. Repositories like DeepLayout9\\nand Detectron2-PubLayNet10 are individual deep learning models trained on\\nlayout analysis datasets without support for the full DIA", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/pdf"}
{"id": "e11d3849689b-23", "text": "are individual deep learning models trained on\\nlayout analysis datasets without support for the full DIA pipeline. The Document\\nAnalysis and Exploitation (DAE) platform [15] and the DeepDIVA project [2]\\naim to improve the reproducibility of DIA methods (or DL models), yet they\\nare not actively maintained. OCR engines like Tesseract [14], easyOCR11 and\\npaddleOCR12 usually do not come with comprehensive functionalities for other\\nDIA tasks like layout analysis.\\nRecent years have also seen numerous e\u00ef\u00ac\u20acorts to create libraries for promoting\\nreproducibility and reusability in the \u00ef\u00ac\ufffdeld of DL. Libraries like Dectectron2 [35],\\n6 The number shown is obtained by specifying the search type as \u00e2\u20ac\u02dccode\u00e2\u20ac\u2122.\\n7 https://ocr-d.de/en/about\\n8 https://github.com/BobLd/DocumentLayoutAnalysis\\n9 https://github.com/leonlulu/DeepLayout\\n10 https://github.com/hpanwar08/detectron2\\n11 https://github.com/JaidedAI/EasyOCR\\n12 https://github.com/PaddlePaddle/PaddleOCR\\n4\\nZ. Shen et al.\\nFig. 1: The overall architecture of LayoutParser. For an input document image,\\nthe core LayoutParser library provides a set of o\u00ef\u00ac\u20ac-the-shelf tools for layout\\ndetection, OCR, visualization, and storage, backed by a carefully designed layout\\ndata structure. LayoutParser also supports high level customization via e\u00ef\u00ac\u0192cient\\nlayout annotation and model training functions. These improve model accuracy\\non the target samples. The community platform enables the easy sharing of DIA\\nmodels and whole digitization pipelines to promote reusability and reproducibility.\\nA collection of detailed documentation, tutorials and exemplar projects make\\nLayoutParser easy to learn and", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/pdf"}
{"id": "e11d3849689b-24", "text": "collection of detailed documentation, tutorials and exemplar projects make\\nLayoutParser easy to learn and use.\\nAllenNLP [8] and transformers [34] have provided the community with complete\\nDL-based support for developing and deploying models for general computer\\nvision and natural language processing problems. LayoutParser, on the other\\nhand, specializes speci\u00ef\u00ac\ufffdcally in DIA tasks. LayoutParser is also equipped with a\\ncommunity platform inspired by established model hubs such as Torch Hub [23]\\nand TensorFlow Hub [1]. It enables the sharing of pretrained models as well as\\nfull document processing pipelines that are unique to DIA tasks.\\nThere have been a variety of document data collections to facilitate the\\ndevelopment of DL models. Some examples include PRImA [3](magazine layouts),\\nPubLayNet [38](academic paper layouts), Table Bank [18](tables in academic\\npapers), Newspaper Navigator Dataset [16, 17](newspaper \u00ef\u00ac\ufffdgure layouts) and\\nHJDataset [31](historical Japanese document layouts). A spectrum of models\\ntrained on these datasets are currently available in the LayoutParser model zoo\\nto support di\u00ef\u00ac\u20acerent use cases.\\n', metadata={'heading': '2 Related Work\\n', 'content_font': 9, 'heading_font': 11, 'source': 'example_data/layout-parser-paper.pdf'})Using PyMuPDF\u00e2\u20ac\u2039This is the fastest of the PDF parsing options, and contains detailed metadata about the PDF and its pages, as well as returns one document per page.from langchain.document_loaders import PyMuPDFLoaderloader = PyMuPDFLoader(\"example_data/layout-parser-paper.pdf\")data = loader.load()data[0]    Document(page_content='LayoutParser: A Uni\u00ef\u00ac\ufffded Toolkit for Deep\\nLearning Based Document Image Analysis\\nZejiang Shen1 (\u00ef\u00bf\u00bd), Ruochen", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/pdf"}
{"id": "e11d3849689b-25", "text": "for Deep\\nLearning Based Document Image Analysis\\nZejiang Shen1 (\u00ef\u00bf\u00bd), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain\\nLee4, Jacob Carlson3, and Weining Li5\\n1 Allen Institute for AI\\nshannons@allenai.org\\n2 Brown University\\nruochen zhang@brown.edu\\n3 Harvard University\\n{melissadell,jacob carlson}@fas.harvard.edu\\n4 University of Washington\\nbcgl@cs.washington.edu\\n5 University of Waterloo\\nw422li@uwaterloo.ca\\nAbstract. Recent advances in document image analysis (DIA) have been\\nprimarily driven by the application of neural networks. Ideally, research\\noutcomes could be easily deployed in production and extended for further\\ninvestigation. However, various factors like loosely organized codebases\\nand sophisticated model con\u00ef\u00ac\ufffdgurations complicate the easy reuse of im-\\nportant innovations by a wide audience. Though there have been on-going\\ne\u00ef\u00ac\u20acorts to improve reusability and simplify deep learning (DL) model\\ndevelopment in disciplines like natural language processing and computer\\nvision, none of them are optimized for challenges in the domain of DIA.\\nThis represents a major gap in the existing toolkit, as DIA is central to\\nacademic research across a wide range of disciplines in the social sciences\\nand humanities. This paper introduces LayoutParser, an open-source\\nlibrary for streamlining the usage of DL in DIA research and applica-\\ntions. The core LayoutParser library comes with a set of simple and\\nintuitive interfaces for applying and customizing DL models for layout de-\\ntection, character recognition, and many other document processing tasks.\\nTo promote extensibility, LayoutParser also incorporates a community\\nplatform for sharing both pre-trained models and full document digiti-\\nzation pipelines. We demonstrate that LayoutParser is helpful for both\\nlightweight and", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/pdf"}
{"id": "e11d3849689b-26", "text": "digiti-\\nzation pipelines. We demonstrate that LayoutParser is helpful for both\\nlightweight and large-scale digitization pipelines in real-word use cases.\\nThe library is publicly available at https://layout-parser.github.io.\\nKeywords: Document Image Analysis \u00c2\u00b7 Deep Learning \u00c2\u00b7 Layout Analysis\\n\u00c2\u00b7 Character Recognition \u00c2\u00b7 Open Source library \u00c2\u00b7 Toolkit.\\n1\\nIntroduction\\nDeep Learning(DL)-based approaches are the state-of-the-art for a wide range of\\ndocument image analysis (DIA) tasks including document image classi\u00ef\u00ac\ufffdcation [11,\\narXiv:2103.15348v2  [cs.CV]  21 Jun 2021\\n', lookup_str='', metadata={'file_path': 'example_data/layout-parser-paper.pdf', 'page_number': 1, 'total_pages': 16, 'format': 'PDF 1.5', 'title': '', 'author': '', 'subject': '', 'keywords': '', 'creator': 'LaTeX with hyperref', 'producer': 'pdfTeX-1.40.21', 'creationDate': 'D:20210622012710Z', 'modDate': 'D:20210622012710Z', 'trapped': '', 'encryption': None}, lookup_index=0)Additionally, you can pass along any of the options from the PyMuPDF documentation as keyword arguments in the load call, and it will be pass along to the get_text() call.PyPDF Directory\u00e2\u20ac\u2039Load PDFs from directoryfrom langchain.document_loaders import PyPDFDirectoryLoaderloader = PyPDFDirectoryLoader(\"example_data/\")docs = loader.load()Using pdfplumber\u00e2\u20ac\u2039Like PyMuPDF, the output Documents contain detailed metadata about the PDF and its pages, and returns one document per page.from langchain.document_loaders import PDFPlumberLoaderloader = PDFPlumberLoader(\"example_data/layout-parser-paper.pdf\")data =", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/pdf"}
{"id": "e11d3849689b-27", "text": "PDFPlumberLoaderloader = PDFPlumberLoader(\"example_data/layout-parser-paper.pdf\")data = loader.load()data[0]    Document(page_content='LayoutParser: A Unified Toolkit for Deep\\nLearning Based Document Image Analysis\\nZejiang Shen1 ((cid:0)), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain\\nLee4, Jacob Carlson3, and Weining Li5\\n1 Allen Institute for AI\\n1202 shannons@allenai.org\\n2 Brown University\\nruochen zhang@brown.edu\\n3 Harvard University\\nnuJ {melissadell,jacob carlson}@fas.harvard.edu\\n4 University of Washington\\nbcgl@cs.washington.edu\\n12 5 University of Waterloo\\nw422li@uwaterloo.ca\\n]VC.sc[\\nAbstract. Recentadvancesindocumentimageanalysis(DIA)havebeen\\nprimarily driven by the application of neural networks. Ideally, research\\noutcomescouldbeeasilydeployedinproductionandextendedforfurther\\ninvestigation. However, various factors like loosely organized codebases\\nand sophisticated model configurations complicate the easy reuse of im-\\n2v84351.3012:viXra portantinnovationsbyawideaudience.Thoughtherehavebeenon-going\\nefforts to improve reusability and simplify deep learning (DL) model\\ndevelopmentindisciplineslikenaturallanguageprocessingandcomputer\\nvision, none of them are optimized for challenges in the domain of DIA.\\nThis represents a major gap in the existing toolkit, as DIA is central to\\nacademicresearchacross awiderangeof disciplinesinthesocialsciences\\nand humanities. This paper introduces LayoutParser, an open-source\\nlibrary for streamlining the usage of DL in DIA research and applica-\\ntions. The core LayoutParser library comes with a set of simple", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/pdf"}
{"id": "e11d3849689b-28", "text": "research and applica-\\ntions. The core LayoutParser library comes with a set of simple and\\nintuitiveinterfacesforapplyingandcustomizingDLmodelsforlayoutde-\\ntection,characterrecognition,andmanyotherdocumentprocessingtasks.\\nTo promote extensibility, LayoutParser also incorporates a community\\nplatform for sharing both pre-trained models and full document digiti-\\nzation pipelines. We demonstrate that LayoutParser is helpful for both\\nlightweight and large-scale digitization pipelines in real-word use cases.\\nThe library is publicly available at https://layout-parser.github.io.\\nKeywords: DocumentImageAnalysis\u00c2\u00b7DeepLearning\u00c2\u00b7LayoutAnalysis\\n\u00c2\u00b7 Character Recognition \u00c2\u00b7 Open Source library \u00c2\u00b7 Toolkit.\\n1 Introduction\\nDeep Learning(DL)-based approaches are the state-of-the-art for a wide range of\\ndocumentimageanalysis(DIA)tasksincludingdocumentimageclassification[11,', metadata={'source': 'example_data/layout-parser-paper.pdf', 'file_path': 'example_data/layout-parser-paper.pdf', 'page': 1, 'total_pages': 16, 'Author': '', 'CreationDate': 'D:20210622012710Z', 'Creator': 'LaTeX with hyperref', 'Keywords': '', 'ModDate': 'D:20210622012710Z', 'PTEX.Fullbanner': 'This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2', 'Producer': 'pdfTeX-1.40.21', 'Subject': '', 'Title': '', 'Trapped': 'False'})PreviousMarkdownNextDocument transformersCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/pdf"}
{"id": "37a12389d702-0", "text": "CSV | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/csv"}
{"id": "37a12389d702-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionDocument loadersCSVFile DirectoryHTMLJSONMarkdownPDFDocument transformersText embedding modelsVector storesRetrieversChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesData connectionDocument loadersCSVCSVA comma-separated values (CSV) file is a delimited text file that uses a comma to separate values. Each line of the file is a data record. Each record consists of one or more fields, separated by commas.Load CSV data with a single row per document.from langchain.document_loaders.csv_loader import CSVLoaderloader = CSVLoader(file_path='./example_data/mlb_teams_2012.csv')data = loader.load()print(data)    [Document(page_content='Team: Nationals\\n\"Payroll (millions)\": 81.34\\n\"Wins\": 98', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 0}, lookup_index=0), Document(page_content='Team: Reds\\n\"Payroll (millions)\": 82.20\\n\"Wins\": 97', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 1}, lookup_index=0), Document(page_content='Team: Yankees\\n\"Payroll (millions)\": 197.96\\n\"Wins\": 95', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 2}, lookup_index=0), Document(page_content='Team: Giants\\n\"Payroll (millions)\": 117.62\\n\"Wins\": 94', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 3},", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/csv"}
{"id": "37a12389d702-2", "text": "'./example_data/mlb_teams_2012.csv', 'row': 3}, lookup_index=0), Document(page_content='Team: Braves\\n\"Payroll (millions)\": 83.31\\n\"Wins\": 94', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 4}, lookup_index=0), Document(page_content='Team: Athletics\\n\"Payroll (millions)\": 55.37\\n\"Wins\": 94', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 5}, lookup_index=0), Document(page_content='Team: Rangers\\n\"Payroll (millions)\": 120.51\\n\"Wins\": 93', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 6}, lookup_index=0), Document(page_content='Team: Orioles\\n\"Payroll (millions)\": 81.43\\n\"Wins\": 93', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 7}, lookup_index=0), Document(page_content='Team: Rays\\n\"Payroll (millions)\": 64.17\\n\"Wins\": 90', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 8}, lookup_index=0), Document(page_content='Team: Angels\\n\"Payroll (millions)\": 154.49\\n\"Wins\": 89', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 9}, lookup_index=0), Document(page_content='Team: Tigers\\n\"Payroll (millions)\": 132.30\\n\"Wins\": 88', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row':", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/csv"}
{"id": "37a12389d702-3", "text": "metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 10}, lookup_index=0), Document(page_content='Team: Cardinals\\n\"Payroll (millions)\": 110.30\\n\"Wins\": 88', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 11}, lookup_index=0), Document(page_content='Team: Dodgers\\n\"Payroll (millions)\": 95.14\\n\"Wins\": 86', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 12}, lookup_index=0), Document(page_content='Team: White Sox\\n\"Payroll (millions)\": 96.92\\n\"Wins\": 85', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 13}, lookup_index=0), Document(page_content='Team: Brewers\\n\"Payroll (millions)\": 97.65\\n\"Wins\": 83', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 14}, lookup_index=0), Document(page_content='Team: Phillies\\n\"Payroll (millions)\": 174.54\\n\"Wins\": 81', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 15}, lookup_index=0), Document(page_content='Team: Diamondbacks\\n\"Payroll (millions)\": 74.28\\n\"Wins\": 81', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 16}, lookup_index=0), Document(page_content='Team: Pirates\\n\"Payroll (millions)\": 63.43\\n\"Wins\": 79', lookup_str='', metadata={'source':", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/csv"}
{"id": "37a12389d702-4", "text": "63.43\\n\"Wins\": 79', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 17}, lookup_index=0), Document(page_content='Team: Padres\\n\"Payroll (millions)\": 55.24\\n\"Wins\": 76', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 18}, lookup_index=0), Document(page_content='Team: Mariners\\n\"Payroll (millions)\": 81.97\\n\"Wins\": 75', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 19}, lookup_index=0), Document(page_content='Team: Mets\\n\"Payroll (millions)\": 93.35\\n\"Wins\": 74', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 20}, lookup_index=0), Document(page_content='Team: Blue Jays\\n\"Payroll (millions)\": 75.48\\n\"Wins\": 73', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 21}, lookup_index=0), Document(page_content='Team: Royals\\n\"Payroll (millions)\": 60.91\\n\"Wins\": 72', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 22}, lookup_index=0), Document(page_content='Team: Marlins\\n\"Payroll (millions)\": 118.07\\n\"Wins\": 69', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 23}, lookup_index=0), Document(page_content='Team: Red Sox\\n\"Payroll (millions)\": 173.18\\n\"Wins\": 69',", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/csv"}
{"id": "37a12389d702-5", "text": "Sox\\n\"Payroll (millions)\": 173.18\\n\"Wins\": 69', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 24}, lookup_index=0), Document(page_content='Team: Indians\\n\"Payroll (millions)\": 78.43\\n\"Wins\": 68', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 25}, lookup_index=0), Document(page_content='Team: Twins\\n\"Payroll (millions)\": 94.08\\n\"Wins\": 66', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 26}, lookup_index=0), Document(page_content='Team: Rockies\\n\"Payroll (millions)\": 78.06\\n\"Wins\": 64', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 27}, lookup_index=0), Document(page_content='Team: Cubs\\n\"Payroll (millions)\": 88.19\\n\"Wins\": 61', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 28}, lookup_index=0), Document(page_content='Team: Astros\\n\"Payroll (millions)\": 60.65\\n\"Wins\": 55', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 29}, lookup_index=0)]Customizing the csv parsing and loading\u00e2\u20ac\u2039See the csv module documentation for more information of what csv args are supported.loader = CSVLoader(file_path='./example_data/mlb_teams_2012.csv', csv_args={    'delimiter': ',',    'quotechar': '\"',    'fieldnames': ['MLB Team', 'Payroll", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/csv"}
{"id": "37a12389d702-6", "text": "'\"',    'fieldnames': ['MLB Team', 'Payroll in millions', 'Wins']})data = loader.load()print(data)    [Document(page_content='MLB Team: Team\\nPayroll in millions: \"Payroll (millions)\"\\nWins: \"Wins\"', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 0}, lookup_index=0), Document(page_content='MLB Team: Nationals\\nPayroll in millions: 81.34\\nWins: 98', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 1}, lookup_index=0), Document(page_content='MLB Team: Reds\\nPayroll in millions: 82.20\\nWins: 97', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 2}, lookup_index=0), Document(page_content='MLB Team: Yankees\\nPayroll in millions: 197.96\\nWins: 95', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 3}, lookup_index=0), Document(page_content='MLB Team: Giants\\nPayroll in millions: 117.62\\nWins: 94', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 4}, lookup_index=0), Document(page_content='MLB Team: Braves\\nPayroll in millions: 83.31\\nWins: 94', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 5}, lookup_index=0), Document(page_content='MLB Team: Athletics\\nPayroll in millions: 55.37\\nWins: 94', lookup_str='', metadata={'source':", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/csv"}
{"id": "37a12389d702-7", "text": "in millions: 55.37\\nWins: 94', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 6}, lookup_index=0), Document(page_content='MLB Team: Rangers\\nPayroll in millions: 120.51\\nWins: 93', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 7}, lookup_index=0), Document(page_content='MLB Team: Orioles\\nPayroll in millions: 81.43\\nWins: 93', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 8}, lookup_index=0), Document(page_content='MLB Team: Rays\\nPayroll in millions: 64.17\\nWins: 90', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 9}, lookup_index=0), Document(page_content='MLB Team: Angels\\nPayroll in millions: 154.49\\nWins: 89', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 10}, lookup_index=0), Document(page_content='MLB Team: Tigers\\nPayroll in millions: 132.30\\nWins: 88', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 11}, lookup_index=0), Document(page_content='MLB Team: Cardinals\\nPayroll in millions: 110.30\\nWins: 88', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 12}, lookup_index=0), Document(page_content='MLB Team: Dodgers\\nPayroll in millions: 95.14\\nWins: 86', lookup_str='', metadata={'source':", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/csv"}
{"id": "37a12389d702-8", "text": "in millions: 95.14\\nWins: 86', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 13}, lookup_index=0), Document(page_content='MLB Team: White Sox\\nPayroll in millions: 96.92\\nWins: 85', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 14}, lookup_index=0), Document(page_content='MLB Team: Brewers\\nPayroll in millions: 97.65\\nWins: 83', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 15}, lookup_index=0), Document(page_content='MLB Team: Phillies\\nPayroll in millions: 174.54\\nWins: 81', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 16}, lookup_index=0), Document(page_content='MLB Team: Diamondbacks\\nPayroll in millions: 74.28\\nWins: 81', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 17}, lookup_index=0), Document(page_content='MLB Team: Pirates\\nPayroll in millions: 63.43\\nWins: 79', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 18}, lookup_index=0), Document(page_content='MLB Team: Padres\\nPayroll in millions: 55.24\\nWins: 76', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 19}, lookup_index=0), Document(page_content='MLB Team: Mariners\\nPayroll in millions: 81.97\\nWins: 75', lookup_str='', metadata={'source':", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/csv"}
{"id": "37a12389d702-9", "text": "in millions: 81.97\\nWins: 75', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 20}, lookup_index=0), Document(page_content='MLB Team: Mets\\nPayroll in millions: 93.35\\nWins: 74', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 21}, lookup_index=0), Document(page_content='MLB Team: Blue Jays\\nPayroll in millions: 75.48\\nWins: 73', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 22}, lookup_index=0), Document(page_content='MLB Team: Royals\\nPayroll in millions: 60.91\\nWins: 72', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 23}, lookup_index=0), Document(page_content='MLB Team: Marlins\\nPayroll in millions: 118.07\\nWins: 69', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 24}, lookup_index=0), Document(page_content='MLB Team: Red Sox\\nPayroll in millions: 173.18\\nWins: 69', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 25}, lookup_index=0), Document(page_content='MLB Team: Indians\\nPayroll in millions: 78.43\\nWins: 68', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 26}, lookup_index=0), Document(page_content='MLB Team: Twins\\nPayroll in millions: 94.08\\nWins: 66', lookup_str='', metadata={'source':", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/csv"}
{"id": "37a12389d702-10", "text": "in millions: 94.08\\nWins: 66', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 27}, lookup_index=0), Document(page_content='MLB Team: Rockies\\nPayroll in millions: 78.06\\nWins: 64', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 28}, lookup_index=0), Document(page_content='MLB Team: Cubs\\nPayroll in millions: 88.19\\nWins: 61', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 29}, lookup_index=0), Document(page_content='MLB Team: Astros\\nPayroll in millions: 60.65\\nWins: 55', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 30}, lookup_index=0)]Specify a column to identify the document source\u00e2\u20ac\u2039Use the source_column argument to specify a source for the document created from each row. Otherwise file_path will be used as the source for all documents created from the CSV file.This is useful when using documents loaded from CSV files for chains that answer questions using sources.loader = CSVLoader(file_path='./example_data/mlb_teams_2012.csv', source_column=\"Team\")data = loader.load()print(data)    [Document(page_content='Team: Nationals\\n\"Payroll (millions)\": 81.34\\n\"Wins\": 98', lookup_str='', metadata={'source': 'Nationals', 'row': 0}, lookup_index=0), Document(page_content='Team: Reds\\n\"Payroll (millions)\": 82.20\\n\"Wins\": 97', lookup_str='', metadata={'source': 'Reds', 'row': 1}, lookup_index=0),", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/csv"}
{"id": "37a12389d702-11", "text": "metadata={'source': 'Reds', 'row': 1}, lookup_index=0), Document(page_content='Team: Yankees\\n\"Payroll (millions)\": 197.96\\n\"Wins\": 95', lookup_str='', metadata={'source': 'Yankees', 'row': 2}, lookup_index=0), Document(page_content='Team: Giants\\n\"Payroll (millions)\": 117.62\\n\"Wins\": 94', lookup_str='', metadata={'source': 'Giants', 'row': 3}, lookup_index=0), Document(page_content='Team: Braves\\n\"Payroll (millions)\": 83.31\\n\"Wins\": 94', lookup_str='', metadata={'source': 'Braves', 'row': 4}, lookup_index=0), Document(page_content='Team: Athletics\\n\"Payroll (millions)\": 55.37\\n\"Wins\": 94', lookup_str='', metadata={'source': 'Athletics', 'row': 5}, lookup_index=0), Document(page_content='Team: Rangers\\n\"Payroll (millions)\": 120.51\\n\"Wins\": 93', lookup_str='', metadata={'source': 'Rangers', 'row': 6}, lookup_index=0), Document(page_content='Team: Orioles\\n\"Payroll (millions)\": 81.43\\n\"Wins\": 93', lookup_str='', metadata={'source': 'Orioles', 'row': 7}, lookup_index=0), Document(page_content='Team: Rays\\n\"Payroll (millions)\": 64.17\\n\"Wins\": 90', lookup_str='', metadata={'source': 'Rays', 'row': 8}, lookup_index=0), Document(page_content='Team: Angels\\n\"Payroll (millions)\": 154.49\\n\"Wins\": 89', lookup_str='', metadata={'source': 'Angels', 'row': 9},", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/csv"}
{"id": "37a12389d702-12", "text": "89', lookup_str='', metadata={'source': 'Angels', 'row': 9}, lookup_index=0), Document(page_content='Team: Tigers\\n\"Payroll (millions)\": 132.30\\n\"Wins\": 88', lookup_str='', metadata={'source': 'Tigers', 'row': 10}, lookup_index=0), Document(page_content='Team: Cardinals\\n\"Payroll (millions)\": 110.30\\n\"Wins\": 88', lookup_str='', metadata={'source': 'Cardinals', 'row': 11}, lookup_index=0), Document(page_content='Team: Dodgers\\n\"Payroll (millions)\": 95.14\\n\"Wins\": 86', lookup_str='', metadata={'source': 'Dodgers', 'row': 12}, lookup_index=0), Document(page_content='Team: White Sox\\n\"Payroll (millions)\": 96.92\\n\"Wins\": 85', lookup_str='', metadata={'source': 'White Sox', 'row': 13}, lookup_index=0), Document(page_content='Team: Brewers\\n\"Payroll (millions)\": 97.65\\n\"Wins\": 83', lookup_str='', metadata={'source': 'Brewers', 'row': 14}, lookup_index=0), Document(page_content='Team: Phillies\\n\"Payroll (millions)\": 174.54\\n\"Wins\": 81', lookup_str='', metadata={'source': 'Phillies', 'row': 15}, lookup_index=0), Document(page_content='Team: Diamondbacks\\n\"Payroll (millions)\": 74.28\\n\"Wins\": 81', lookup_str='', metadata={'source': 'Diamondbacks', 'row': 16}, lookup_index=0), Document(page_content='Team: Pirates\\n\"Payroll (millions)\": 63.43\\n\"Wins\": 79', lookup_str='', metadata={'source':", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/csv"}
{"id": "37a12389d702-13", "text": "63.43\\n\"Wins\": 79', lookup_str='', metadata={'source': 'Pirates', 'row': 17}, lookup_index=0), Document(page_content='Team: Padres\\n\"Payroll (millions)\": 55.24\\n\"Wins\": 76', lookup_str='', metadata={'source': 'Padres', 'row': 18}, lookup_index=0), Document(page_content='Team: Mariners\\n\"Payroll (millions)\": 81.97\\n\"Wins\": 75', lookup_str='', metadata={'source': 'Mariners', 'row': 19}, lookup_index=0), Document(page_content='Team: Mets\\n\"Payroll (millions)\": 93.35\\n\"Wins\": 74', lookup_str='', metadata={'source': 'Mets', 'row': 20}, lookup_index=0), Document(page_content='Team: Blue Jays\\n\"Payroll (millions)\": 75.48\\n\"Wins\": 73', lookup_str='', metadata={'source': 'Blue Jays', 'row': 21}, lookup_index=0), Document(page_content='Team: Royals\\n\"Payroll (millions)\": 60.91\\n\"Wins\": 72', lookup_str='', metadata={'source': 'Royals', 'row': 22}, lookup_index=0), Document(page_content='Team: Marlins\\n\"Payroll (millions)\": 118.07\\n\"Wins\": 69', lookup_str='', metadata={'source': 'Marlins', 'row': 23}, lookup_index=0), Document(page_content='Team: Red Sox\\n\"Payroll (millions)\": 173.18\\n\"Wins\": 69', lookup_str='', metadata={'source': 'Red Sox', 'row': 24}, lookup_index=0), Document(page_content='Team: Indians\\n\"Payroll (millions)\": 78.43\\n\"Wins\": 68',", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/csv"}
{"id": "37a12389d702-14", "text": "Indians\\n\"Payroll (millions)\": 78.43\\n\"Wins\": 68', lookup_str='', metadata={'source': 'Indians', 'row': 25}, lookup_index=0), Document(page_content='Team: Twins\\n\"Payroll (millions)\": 94.08\\n\"Wins\": 66', lookup_str='', metadata={'source': 'Twins', 'row': 26}, lookup_index=0), Document(page_content='Team: Rockies\\n\"Payroll (millions)\": 78.06\\n\"Wins\": 64', lookup_str='', metadata={'source': 'Rockies', 'row': 27}, lookup_index=0), Document(page_content='Team: Cubs\\n\"Payroll (millions)\": 88.19\\n\"Wins\": 61', lookup_str='', metadata={'source': 'Cubs', 'row': 28}, lookup_index=0), Document(page_content='Team: Astros\\n\"Payroll (millions)\": 60.65\\n\"Wins\": 55', lookup_str='', metadata={'source': 'Astros', 'row': 29}, lookup_index=0)]PreviousDocument loadersNextFile DirectoryCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/data_connection/document_loaders/csv"}
{"id": "d85de510acb1-0", "text": "Vector stores | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/data_connection/vectorstores/"}
{"id": "d85de510acb1-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionDocument loadersDocument transformersText embedding modelsVector storesRetrieversChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesData connectionVector storesOn this pageVector storesinfoHead to Integrations for documentation on built-in integrations with 3rd-party vector stores.One of the most common ways to store and search over unstructured data is to embed it and store the resulting embedding\nvectors, and then at query time to embed the unstructured query and retrieve the embedding vectors that are\n'most similar' to the embedded query. A vector store takes care of storing embedded data and performing vector search", "source": "https://python.langchain.com/docs/modules/data_connection/vectorstores/"}
{"id": "d85de510acb1-2", "text": "for you.Get started\u00e2\u20ac\u2039This walkthrough showcases basic functionality related to VectorStores. A key part of working with vector stores is creating the vector to put in them, which is usually created via embeddings. Therefore, it is recommended that you familiarize yourself with the text embedding model interfaces before diving into this.There are many great vector store options, here are a few that are free, open-source, and run entirely on your local machine. Review all integrations for many great hosted offerings.ChromaFAISSLanceThis walkthrough uses the chroma vector database, which runs on your local machine as a library.pip install chromadbWe want to use OpenAIEmbeddings so we have to get the OpenAI API Key.import osimport getpassos.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')from langchain.document_loaders import TextLoaderfrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import Chroma# Load the document, split it into chunks, embed each chunk and load it into the vector store.raw_documents = TextLoader('../../../state_of_the_union.txt').load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)documents = text_splitter.split_documents(raw_documents)db = Chroma.from_documents(documents, OpenAIEmbeddings())This walkthrough uses the FAISS vector database, which makes use of the Facebook AI Similarity Search (FAISS) library.pip install faiss-cpuWe want to use OpenAIEmbeddings so we have to get the OpenAI API Key.import osimport getpassos.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')from langchain.document_loaders import TextLoaderfrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import", "source": "https://python.langchain.com/docs/modules/data_connection/vectorstores/"}
{"id": "d85de510acb1-3", "text": "langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import FAISS# Load the document, split it into chunks, embed each chunk and load it into the vector store.raw_documents = TextLoader('../../../state_of_the_union.txt').load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)documents = text_splitter.split_documents(raw_documents)db = FAISS.from_documents(documents, OpenAIEmbeddings())This notebook shows how to use functionality related to the LanceDB vector database based on the Lance data format.pip install lancedbWe want to use OpenAIEmbeddings so we have to get the OpenAI API Key.import osimport getpassos.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')from langchain.document_loaders import TextLoaderfrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import LanceDBimport lancedbdb = lancedb.connect(\"/tmp/lancedb\")table = db.create_table(    \"my_table\",    data=[        {            \"vector\": embeddings.embed_query(\"Hello World\"),            \"text\": \"Hello World\",            \"id\": \"1\",        }    ],    mode=\"overwrite\",)# Load the document, split it into chunks, embed each chunk and load it into the vector store.raw_documents = TextLoader('../../../state_of_the_union.txt').load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)documents = text_splitter.split_documents(raw_documents)db = LanceDB.from_documents(documents, OpenAIEmbeddings(),", "source": "https://python.langchain.com/docs/modules/data_connection/vectorstores/"}
{"id": "d85de510acb1-4", "text": "= LanceDB.from_documents(documents, OpenAIEmbeddings(), connection=table)Similarity search\u00e2\u20ac\u2039query = \"What did the president say about Ketanji Brown Jackson\"docs = db.similarity_search(query)print(docs[0].page_content)    Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you\u00e2\u20ac\u2122re at it, pass the Disclose Act so Americans can know who is funding our elections.    Tonight, I\u00e2\u20ac\u2122d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer\u00e2\u20ac\u201dan Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.    One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.    And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation\u00e2\u20ac\u2122s top legal minds, who will continue Justice Breyer\u00e2\u20ac\u2122s legacy of excellence.Similarity search by vector\u00e2\u20ac\u2039It is also possible to do a search for documents similar to a given embedding vector using similarity_search_by_vector which accepts an embedding vector as a parameter instead of a string.embedding_vector = OpenAIEmbeddings().embed_query(query)docs = db.similarity_search_by_vector(embedding_vector)print(docs[0].page_content)The query is the same, and so the result is also the same.    Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you\u00e2\u20ac\u2122re at it, pass the Disclose Act so Americans can know who is funding our elections.    Tonight, I\u00e2\u20ac\u2122d like to honor someone who has dedicated his life", "source": "https://python.langchain.com/docs/modules/data_connection/vectorstores/"}
{"id": "d85de510acb1-5", "text": "elections.    Tonight, I\u00e2\u20ac\u2122d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer\u00e2\u20ac\u201dan Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.    One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.    And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation\u00e2\u20ac\u2122s top legal minds, who will continue Justice Breyer\u00e2\u20ac\u2122s legacy of excellence.Asynchronous operations\u00e2\u20ac\u2039Vector stores are usually run as a separate service that requires some IO operations, and therefore they might be called asynchronously. That gives performance benefits as you don't waste time waiting for responses from external services. That might also be important if you work with an asynchronous framework, such as FastAPI.Langchain supports async operation on vector stores. All the methods might be called using their async counterparts, with the prefix a, meaning async.Qdrant is a vector store, which supports all the async operations, thus it will be used in this walkthrough.pip install qdrant-clientfrom langchain.vectorstores import QdrantCreate a vector store asynchronously\u00e2\u20ac\u2039db = await Qdrant.afrom_documents(documents, embeddings, \"http://localhost:6333\")Similarity search\u00e2\u20ac\u2039query = \"What did the president say about Ketanji Brown Jackson\"docs = await db.asimilarity_search(query)print(docs[0].page_content)    Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you\u00e2\u20ac\u2122re at it, pass the Disclose Act so Americans can know who is funding our elections.    Tonight, I\u00e2\u20ac\u2122d like to", "source": "https://python.langchain.com/docs/modules/data_connection/vectorstores/"}
{"id": "d85de510acb1-6", "text": "Americans can know who is funding our elections.    Tonight, I\u00e2\u20ac\u2122d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer\u00e2\u20ac\u201dan Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.    One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.    And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation\u00e2\u20ac\u2122s top legal minds, who will continue Justice Breyer\u00e2\u20ac\u2122s legacy of excellence.Similarity search by vector\u00e2\u20ac\u2039embedding_vector = embeddings.embed_query(query)docs = await db.asimilarity_search_by_vector(embedding_vector)Maximum marginal relevance search (MMR)\u00e2\u20ac\u2039Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. It is also supported in async API.query = \"What did the president say about Ketanji Brown Jackson\"found_docs = await qdrant.amax_marginal_relevance_search(query, k=2, fetch_k=10)for i, doc in enumerate(found_docs):    print(f\"{i + 1}.\", doc.page_content, \"\\n\")1. Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you\u00e2\u20ac\u2122re at it, pass the Disclose Act so Americans can know who is funding our elections.Tonight, I\u00e2\u20ac\u2122d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer\u00e2\u20ac\u201dan Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.One of the most serious constitutional responsibilities a President has is nominating someone", "source": "https://python.langchain.com/docs/modules/data_connection/vectorstores/"}
{"id": "d85de510acb1-7", "text": "thank you for your service.One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation\u00e2\u20ac\u2122s top legal minds, who will continue Justice Breyer\u00e2\u20ac\u2122s legacy of excellence.2. We can\u00e2\u20ac\u2122t change how divided we\u00e2\u20ac\u2122ve been. But we can change how we move forward\u00e2\u20ac\u201don COVID-19 and other issues we must face together.I recently visited the New York City Police Department days after the funerals of Officer Wilbert Mora and his partner, Officer Jason Rivera.They were responding to a 9-1-1 call when a man shot and killed them with a stolen gun.Officer Mora was 27 years old.Officer Rivera was 22.Both Dominican Americans who\u00e2\u20ac\u2122d grown up on the same streets they later chose to patrol as police officers.I spoke with their families and told them that we are forever in debt for their sacrifice, and we will carry on their mission to restore the trust and safety every community deserves.I\u00e2\u20ac\u2122ve worked on these issues a long time.I know what works: Investing in crime preventionand community police officers who\u00e2\u20ac\u2122ll walk the beat, who\u00e2\u20ac\u2122ll know the neighborhood, and who can restore trust and safety.PreviousText embedding modelsNextRetrieversGet startedAsynchronous operationsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/data_connection/vectorstores/"}
{"id": "d9367a085945-0", "text": "Retrievers | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/"}
{"id": "d9367a085945-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionDocument loadersDocument transformersText embedding modelsVector storesRetrieversMultiQueryRetrieverContextual compressionEnsemble RetrieverSelf-queryingTime-weighted vector store retrieverVector store-backed retrieverChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesData connectionRetrieversOn this pageRetrieversinfoHead to Integrations for documentation on built-in retriever integrations with 3rd-party tools.A retriever is an interface that returns documents given an unstructured query. It is more general than a vector store.\nA retriever does not need to be able to store documents, only to return (or retrieve) it. Vector stores can be used", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/"}
{"id": "d9367a085945-2", "text": "as the backbone of a retriever, but there are other types of retrievers as well.Get started\u00e2\u20ac\u2039The public API of the BaseRetriever class in LangChain is as follows:from abc import ABC, abstractmethodfrom typing import Any, Listfrom langchain.schema import Documentfrom langchain.callbacks.manager import Callbacksclass BaseRetriever(ABC):    ...    def get_relevant_documents(        self, query: str, *, callbacks: Callbacks = None, **kwargs: Any    ) -> List[Document]:        \"\"\"Retrieve documents relevant to a query.        Args:            query: string to find relevant documents for            callbacks: Callback manager or list of callbacks        Returns:            List of relevant documents        \"\"\"        ...    async def aget_relevant_documents(        self, query: str, *, callbacks: Callbacks = None, **kwargs: Any    ) -> List[Document]:        \"\"\"Asynchronously get documents relevant to a query.        Args:            query: string to find relevant documents for            callbacks: Callback manager or list of callbacks        Returns:            List of relevant documents        \"\"\"        ...It's that simple! You can call get_relevant_documents or the async get_relevant_documents methods to retrieve documents relevant to a query, where \"relevance\" is defined by\nthe specific retriever object you are calling.Of course, we also help construct what we think useful Retrievers are. The main type of Retriever that we focus on is a Vectorstore retriever. We will focus on that for the rest of this guide.In order to understand what a vectorstore retriever is, it's important to understand what a Vectorstore is. So let's look at that.By default, LangChain uses Chroma as the vectorstore to index and search embeddings. To walk through this tutorial, we'll first need to install chromadb.pip install chromadbThis example showcases question answering over documents.", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/"}
{"id": "d9367a085945-3", "text": "We have chosen this as the example for getting started because it nicely combines a lot of different elements (Text splitters, embeddings, vectorstores) and then also shows how to use them in a chain.Question answering over documents consists of four steps:Create an indexCreate a Retriever from that indexCreate a question answering chainAsk questions!Each of the steps has multiple sub steps and potential configurations. In this notebook we will primarily focus on (1). We will start by showing the one-liner for doing so, but then break down what is actually going on.First, let's import some common classes we'll use no matter what.from langchain.chains import RetrievalQAfrom langchain.llms import OpenAINext in the generic setup, let's specify the document loader we want to use. You can download the state_of_the_union.txt file herefrom langchain.document_loaders import TextLoaderloader = TextLoader('../state_of_the_union.txt', encoding='utf8')One Line Index Creation\u00e2\u20ac\u2039To get started as quickly as possible, we can use the VectorstoreIndexCreator.from langchain.indexes import VectorstoreIndexCreatorindex = VectorstoreIndexCreator().from_loaders([loader])    Running Chroma using direct local API.    Using DuckDB in-memory for database. Data will be transient.Now that the index is created, we can use it to ask questions of the data! Note that under the hood this is actually doing a few steps as well, which we will cover later in this guide.query = \"What did the president say about Ketanji Brown Jackson\"index.query(query)    \" The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and has received a broad range of support from the", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/"}
{"id": "d9367a085945-4", "text": "He also said that she is a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\"query = \"What did the president say about Ketanji Brown Jackson\"index.query_with_sources(query)    {'question': 'What did the president say about Ketanji Brown Jackson',     'answer': \" The president said that he nominated Circuit Court of Appeals Judge Ketanji Brown Jackson, one of the nation's top legal minds, to continue Justice Breyer's legacy of excellence, and that she has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\\n\",     'sources': '../state_of_the_union.txt'}What is returned from the VectorstoreIndexCreator is VectorStoreIndexWrapper, which provides these nice query and query_with_sources functionality. If we just wanted to access the vectorstore directly, we can also do that.index.vectorstore    <langchain.vectorstores.chroma.Chroma at 0x119aa5940>If we then want to access the VectorstoreRetriever, we can do that with:index.vectorstore.as_retriever()    VectorStoreRetriever(vectorstore=<langchain.vectorstores.chroma.Chroma object at 0x119aa5940>, search_kwargs={})Walkthrough\u00e2\u20ac\u2039Okay, so what's actually going on? How is this index getting created?A lot of the magic is being hid in this VectorstoreIndexCreator. What is this doing?There are three main steps going on after the documents are loaded:Splitting documents into chunksCreating embeddings for each documentStoring documents and embeddings in a vectorstoreLet's walk through this in codedocuments = loader.load()Next, we will split the documents into chunks.from langchain.text_splitter import CharacterTextSplittertext_splitter =", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/"}
{"id": "d9367a085945-5", "text": "split the documents into chunks.from langchain.text_splitter import CharacterTextSplittertext_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)texts = text_splitter.split_documents(documents)We will then select which embeddings we want to use.from langchain.embeddings import OpenAIEmbeddingsembeddings = OpenAIEmbeddings()We now create the vectorstore to use as the index.from langchain.vectorstores import Chromadb = Chroma.from_documents(texts, embeddings)    Running Chroma using direct local API.    Using DuckDB in-memory for database. Data will be transient.So that's creating the index. Then, we expose this index in a retriever interface.retriever = db.as_retriever()Then, as before, we create a chain and use it to answer questions!qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type=\"stuff\", retriever=retriever)query = \"What did the president say about Ketanji Brown Jackson\"qa.run(query)    \" The President said that Judge Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He said she is a consensus builder and has received a broad range of support from organizations such as the Fraternal Order of Police and former judges appointed by Democrats and Republicans.\"VectorstoreIndexCreator is just a wrapper around all this logic. It is configurable in the text splitter it uses, the embeddings it uses, and the vectorstore it uses. For example, you can configure it as below:index_creator = VectorstoreIndexCreator(    vectorstore_cls=Chroma,    embedding=OpenAIEmbeddings(),    text_splitter=CharacterTextSplitter(chunk_size=1000, chunk_overlap=0))Hopefully this highlights", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/"}
{"id": "d9367a085945-6", "text": "chunk_overlap=0))Hopefully this highlights what is going on under the hood of VectorstoreIndexCreator. While we think it's important to have a simple way to create indexes, we also think it's important to understand what's going on under the hood.PreviousVector storesNextMultiQueryRetrieverGet startedCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/"}
{"id": "4d3d1a28a4d7-0", "text": "Contextual compression | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/contextual_compression/"}
{"id": "4d3d1a28a4d7-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionDocument loadersDocument transformersText embedding modelsVector storesRetrieversMultiQueryRetrieverContextual compressionEnsemble RetrieverSelf-queryingTime-weighted vector store retrieverVector store-backed retrieverChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesData connectionRetrieversContextual compressionOn this pageContextual compressionOne challenge with retrieval is that usually you don't know the specific queries your document storage system will face when you ingest data into the system. This means that the information most relevant to a query may be buried in a document with a lot of irrelevant text. Passing that full document through your application can lead to more expensive LLM calls and poorer responses.Contextual compression is meant to fix this. The idea is simple: instead of immediately returning retrieved documents as-is, you can compress them using the context of the given query, so that only the relevant information is returned. \u00e2\u20ac\u0153Compressing\u00e2\u20ac\ufffd here refers to both compressing the contents of an individual document and filtering out documents wholesale.To use the Contextual Compression Retriever, you'll need:a base Retrievera Document CompressorThe Contextual Compression Retriever passes queries to the base Retriever, takes the initial documents and passes them through the Document Compressor. The Document Compressor takes a list of Documents and shortens it by reducing the contents of Documents or dropping Documents altogether.Get started\u00e2\u20ac\u2039# Helper function for printing docsdef pretty_print_docs(docs):    print(f\"\\n{'-' * 100}\\n\".join([f\"Document {i+1}:\\n\\n\" + d.page_content for i, d in enumerate(docs)]))Using a vanilla vector store", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/contextual_compression/"}
{"id": "4d3d1a28a4d7-2", "text": "+ d.page_content for i, d in enumerate(docs)]))Using a vanilla vector store retriever\u00e2\u20ac\u2039Let's start by initializing a simple vector store retriever and storing the 2023 State of the Union speech (in chunks). We can see that given an example question our retriever returns one or two relevant docs and a few irrelevant docs. And even the relevant docs have a lot of irrelevant information in them.from langchain.text_splitter import CharacterTextSplitterfrom langchain.embeddings import OpenAIEmbeddingsfrom langchain.document_loaders import TextLoaderfrom langchain.vectorstores import FAISSdocuments = TextLoader('../../../state_of_the_union.txt').load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)texts = text_splitter.split_documents(documents)retriever = FAISS.from_documents(texts, OpenAIEmbeddings()).as_retriever()docs = retriever.get_relevant_documents(\"What did the president say about Ketanji Brown Jackson\")pretty_print_docs(docs)    Document 1:        Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you\u00e2\u20ac\u2122re at it, pass the Disclose Act so Americans can know who is funding our elections.         Tonight, I\u00e2\u20ac\u2122d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer\u00e2\u20ac\u201dan Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.         One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.         And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/contextual_compression/"}
{"id": "4d3d1a28a4d7-3", "text": "I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation\u00e2\u20ac\u2122s top legal minds, who will continue Justice Breyer\u00e2\u20ac\u2122s legacy of excellence.    ----------------------------------------------------------------------------------------------------    Document 2:        A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she\u00e2\u20ac\u2122s been nominated, she\u00e2\u20ac\u2122s received a broad range of support\u00e2\u20ac\u201dfrom the Fraternal Order of Police to former judges appointed by Democrats and Republicans.         And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system.         We can do both. At our border, we\u00e2\u20ac\u2122ve installed new technology like cutting-edge scanners to better detect drug smuggling.          We\u00e2\u20ac\u2122ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.          We\u00e2\u20ac\u2122re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster.         We\u00e2\u20ac\u2122re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.    ----------------------------------------------------------------------------------------------------    Document 3:        And for our LGBTQ+ Americans, let\u00e2\u20ac\u2122s finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong.         As I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential.         While it often", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/contextual_compression/"}
{"id": "4d3d1a28a4d7-4", "text": "yourself and reach your God-given potential.         While it often appears that we never agree, that isn\u00e2\u20ac\u2122t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice.         And soon, we\u00e2\u20ac\u2122ll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things.         So tonight I\u00e2\u20ac\u2122m offering a Unity Agenda for the Nation. Four big things we can do together.          First, beat the opioid epidemic.    ----------------------------------------------------------------------------------------------------    Document 4:        Tonight, I\u00e2\u20ac\u2122m announcing a crackdown on these companies overcharging American businesses and consumers.         And as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up.          That ends on my watch.         Medicare is going to set higher standards for nursing homes and make sure your loved ones get the care they deserve and expect.         We\u00e2\u20ac\u2122ll also cut costs and keep the economy going strong by giving workers a fair shot, provide more training and apprenticeships, hire them based on their skills not degrees.         Let\u00e2\u20ac\u2122s pass the Paycheck Fairness Act and paid leave.          Raise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty.         Let\u00e2\u20ac\u2122s increase Pell Grants and increase our historic support of", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/contextual_compression/"}
{"id": "4d3d1a28a4d7-5", "text": "Let\u00e2\u20ac\u2122s increase Pell Grants and increase our historic support of HBCUs, and invest in what Jill\u00e2\u20ac\u201dour First Lady who teaches full-time\u00e2\u20ac\u201dcalls America\u00e2\u20ac\u2122s best-kept secret: community colleges.Adding contextual compression with an LLMChainExtractor\u00e2\u20ac\u2039Now let's wrap our base retriever with a ContextualCompressionRetriever. We'll add an LLMChainExtractor, which will iterate over the initially returned documents and extract from each only the content that is relevant to the query.from langchain.llms import OpenAIfrom langchain.retrievers import ContextualCompressionRetrieverfrom langchain.retrievers.document_compressors import LLMChainExtractorllm = OpenAI(temperature=0)compressor = LLMChainExtractor.from_llm(llm)compression_retriever = ContextualCompressionRetriever(base_compressor=compressor, base_retriever=retriever)compressed_docs = compression_retriever.get_relevant_documents(\"What did the president say about Ketanji Jackson Brown\")pretty_print_docs(compressed_docs)    Document 1:        \"One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.         And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation\u00e2\u20ac\u2122s top legal minds, who will continue Justice Breyer\u00e2\u20ac\u2122s legacy of excellence.\"    ----------------------------------------------------------------------------------------------------    Document 2:        \"A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she\u00e2\u20ac\u2122s been nominated, she\u00e2\u20ac\u2122s received a broad range of support\u00e2\u20ac\u201dfrom the", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/contextual_compression/"}
{"id": "4d3d1a28a4d7-6", "text": "been nominated, she\u00e2\u20ac\u2122s received a broad range of support\u00e2\u20ac\u201dfrom the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\"More built-in compressors: filters\u00e2\u20ac\u2039LLMChainFilter\u00e2\u20ac\u2039The LLMChainFilter is slightly simpler but more robust compressor that uses an LLM chain to decide which of the initially retrieved documents to filter out and which ones to return, without manipulating the document contents.from langchain.retrievers.document_compressors import LLMChainFilter_filter = LLMChainFilter.from_llm(llm)compression_retriever = ContextualCompressionRetriever(base_compressor=_filter, base_retriever=retriever)compressed_docs = compression_retriever.get_relevant_documents(\"What did the president say about Ketanji Jackson Brown\")pretty_print_docs(compressed_docs)    Document 1:        Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you\u00e2\u20ac\u2122re at it, pass the Disclose Act so Americans can know who is funding our elections.         Tonight, I\u00e2\u20ac\u2122d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer\u00e2\u20ac\u201dan Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.         One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.         And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation\u00e2\u20ac\u2122s top legal minds, who will continue Justice Breyer\u00e2\u20ac\u2122s legacy of excellence.EmbeddingsFilter\u00e2\u20ac\u2039Making an extra LLM call over", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/contextual_compression/"}
{"id": "4d3d1a28a4d7-7", "text": "legacy of excellence.EmbeddingsFilter\u00e2\u20ac\u2039Making an extra LLM call over each retrieved document is expensive and slow. The EmbeddingsFilter provides a cheaper and faster option by embedding the documents and query and only returning those documents which have sufficiently similar embeddings to the query.from langchain.embeddings import OpenAIEmbeddingsfrom langchain.retrievers.document_compressors import EmbeddingsFilterembeddings = OpenAIEmbeddings()embeddings_filter = EmbeddingsFilter(embeddings=embeddings, similarity_threshold=0.76)compression_retriever = ContextualCompressionRetriever(base_compressor=embeddings_filter, base_retriever=retriever)compressed_docs = compression_retriever.get_relevant_documents(\"What did the president say about Ketanji Jackson Brown\")pretty_print_docs(compressed_docs)    Document 1:        Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you\u00e2\u20ac\u2122re at it, pass the Disclose Act so Americans can know who is funding our elections.         Tonight, I\u00e2\u20ac\u2122d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer\u00e2\u20ac\u201dan Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.         One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.         And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation\u00e2\u20ac\u2122s top legal minds, who will continue Justice Breyer\u00e2\u20ac\u2122s legacy of excellence.    ----------------------------------------------------------------------------------------------------    Document 2:        A former top litigator", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/contextual_compression/"}
{"id": "4d3d1a28a4d7-8", "text": "Document 2:        A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she\u00e2\u20ac\u2122s been nominated, she\u00e2\u20ac\u2122s received a broad range of support\u00e2\u20ac\u201dfrom the Fraternal Order of Police to former judges appointed by Democrats and Republicans.         And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system.         We can do both. At our border, we\u00e2\u20ac\u2122ve installed new technology like cutting-edge scanners to better detect drug smuggling.          We\u00e2\u20ac\u2122ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.          We\u00e2\u20ac\u2122re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster.         We\u00e2\u20ac\u2122re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.    ----------------------------------------------------------------------------------------------------    Document 3:        And for our LGBTQ+ Americans, let\u00e2\u20ac\u2122s finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong.         As I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential.         While it often appears that we never agree, that isn\u00e2\u20ac\u2122t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice.", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/contextual_compression/"}
{"id": "4d3d1a28a4d7-9", "text": "from still-too-common hate crimes to reforming military justice.         And soon, we\u00e2\u20ac\u2122ll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things.         So tonight I\u00e2\u20ac\u2122m offering a Unity Agenda for the Nation. Four big things we can do together.          First, beat the opioid epidemic.Stringing compressors and document transformers togetherUsing the DocumentCompressorPipeline we can also easily combine multiple compressors in sequence. Along with compressors we can add BaseDocumentTransformers to our pipeline, which don't perform any contextual compression but simply perform some transformation on a set of documents. For example TextSplitters can be used as document transformers to split documents into smaller pieces, and the EmbeddingsRedundantFilter can be used to filter out redundant documents based on embedding similarity between documents.Below we create a compressor pipeline by first splitting our docs into smaller chunks, then removing redundant documents, and then filtering based on relevance to the query.from langchain.document_transformers import EmbeddingsRedundantFilterfrom langchain.retrievers.document_compressors import DocumentCompressorPipelinefrom langchain.text_splitter import CharacterTextSplittersplitter = CharacterTextSplitter(chunk_size=300, chunk_overlap=0, separator=\". \")redundant_filter = EmbeddingsRedundantFilter(embeddings=embeddings)relevant_filter = EmbeddingsFilter(embeddings=embeddings, similarity_threshold=0.76)pipeline_compressor = DocumentCompressorPipeline(    transformers=[splitter, redundant_filter, relevant_filter])compression_retriever = ContextualCompressionRetriever(base_compressor=pipeline_compressor, base_retriever=retriever)compressed_docs = compression_retriever.get_relevant_documents(\"What did the president say about Ketanji Jackson", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/contextual_compression/"}
{"id": "4d3d1a28a4d7-10", "text": "= compression_retriever.get_relevant_documents(\"What did the president say about Ketanji Jackson Brown\")pretty_print_docs(compressed_docs)    Document 1:        One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.         And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson    ----------------------------------------------------------------------------------------------------    Document 2:        As I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential.         While it often appears that we never agree, that isn\u00e2\u20ac\u2122t true. I signed 80 bipartisan bills into law last year    ----------------------------------------------------------------------------------------------------    Document 3:        A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builderPreviousMultiQueryRetrieverNextEnsemble RetrieverGet startedCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/contextual_compression/"}
{"id": "176cd345fd3a-0", "text": "MultiQueryRetriever | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/MultiQueryRetriever"}
{"id": "176cd345fd3a-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionDocument loadersDocument transformersText embedding modelsVector storesRetrieversMultiQueryRetrieverContextual compressionEnsemble RetrieverSelf-queryingTime-weighted vector store retrieverVector store-backed retrieverChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesData connectionRetrieversMultiQueryRetrieverMultiQueryRetrieverDistance-based vector database retrieval embeds (represents) queries in high-dimensional space and finds similar embedded documents based on \"distance\". But, retrieval may produce difference results with subtle changes in query wording or if the embeddings do not capture the semantics of the data well. Prompt engineering / tuning is sometimes done to manually address these problems, but can be tedious.The MultiQueryRetriever automates the process of prompt tuning by using an LLM to generate multiple queries from different perspectives for a given user input query. For each query, it retrieves a set of relevant documents and takes the unique union across all queries to get a larger set of potentially relevant documents. By generating multiple perspectives on the same question, the MultiQueryRetriever might be able to overcome some of the limitations of the distance-based retrieval and get a richer set of results.# Build a sample vectorDBfrom langchain.vectorstores import Chromafrom langchain.document_loaders import WebBaseLoaderfrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import RecursiveCharacterTextSplitter# Load blog postloader = WebBaseLoader(\"https://lilianweng.github.io/posts/2023-06-23-agent/\")data = loader.load()# Splittext_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)splits = text_splitter.split_documents(data)#", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/MultiQueryRetriever"}
{"id": "176cd345fd3a-2", "text": "chunk_overlap=0)splits = text_splitter.split_documents(data)# VectorDBembedding = OpenAIEmbeddings()vectordb = Chroma.from_documents(documents=splits, embedding=embedding)Simple usageSpecify the LLM to use for query generation, and the retriver will do the rest.from langchain.chat_models import ChatOpenAIfrom langchain.retrievers.multi_query import MultiQueryRetrieverquestion = \"What are the approaches to Task Decomposition?\"llm = ChatOpenAI(temperature=0)retriever_from_llm = MultiQueryRetriever.from_llm(    retriever=vectordb.as_retriever(), llm=llm)# Set logging for the queriesimport logginglogging.basicConfig()logging.getLogger(\"langchain.retrievers.multi_query\").setLevel(logging.INFO)unique_docs = retriever_from_llm.get_relevant_documents(query=question)len(unique_docs)    INFO:langchain.retrievers.multi_query:Generated queries: ['1. How can Task Decomposition be approached?', '2. What are the different methods for Task Decomposition?', '3. What are the various approaches to decomposing tasks?']    5Supplying your own promptYou can also supply a prompt along with an output parser to split the results into a list of queries.from typing import Listfrom langchain import LLMChainfrom pydantic import BaseModel, Fieldfrom langchain.prompts import PromptTemplatefrom langchain.output_parsers import PydanticOutputParser# Output parser will split the LLM result into a list of queriesclass LineList(BaseModel):    # \"lines\" is the key (attribute name) of the parsed output    lines: List[str] = Field(description=\"Lines of text\")class LineListOutputParser(PydanticOutputParser):    def __init__(self) -> None:", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/MultiQueryRetriever"}
{"id": "176cd345fd3a-3", "text": "def __init__(self) -> None:        super().__init__(pydantic_object=LineList)    def parse(self, text: str) -> LineList:        lines = text.strip().split(\"\\n\")        return LineList(lines=lines)output_parser = LineListOutputParser()QUERY_PROMPT = PromptTemplate(    input_variables=[\"question\"],    template=\"\"\"You are an AI language model assistant. Your task is to generate five     different versions of the given user question to retrieve relevant documents from a vector     database. By generating multiple perspectives on the user question, your goal is to help    the user overcome some of the limitations of the distance-based similarity search.     Provide these alternative questions seperated by newlines.    Original question: {question}\"\"\",)llm = ChatOpenAI(temperature=0)# Chainllm_chain = LLMChain(llm=llm, prompt=QUERY_PROMPT, output_parser=output_parser)# Other inputsquestion = \"What are the approaches to Task Decomposition?\"# Runretriever = MultiQueryRetriever(    retriever=vectordb.as_retriever(), llm_chain=llm_chain, parser_key=\"lines\")  # \"lines\" is the key (attribute name) of the parsed output# Resultsunique_docs = retriever.get_relevant_documents(    query=\"What does the course say about regression?\")len(unique_docs)    INFO:langchain.retrievers.multi_query:Generated queries: [\"1. What is the course's perspective on regression?\", '2. Can you provide information on regression as discussed in the course?', '3. How does the course cover the topic of regression?', \"4. What are the course's teachings on", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/MultiQueryRetriever"}
{"id": "176cd345fd3a-4", "text": "How does the course cover the topic of regression?', \"4. What are the course's teachings on regression?\", '5. In relation to the course, what is mentioned about regression?']    11PreviousRetrieversNextContextual compressionCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/MultiQueryRetriever"}
{"id": "b86242c73919-0", "text": "Vector store-backed retriever | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/vectorstore"}
{"id": "b86242c73919-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionDocument loadersDocument transformersText embedding modelsVector storesRetrieversMultiQueryRetrieverContextual compressionEnsemble RetrieverSelf-queryingTime-weighted vector store retrieverVector store-backed retrieverChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesData connectionRetrieversVector store-backed retrieverVector store-backed retrieverA vector store retriever is a retriever that uses a vector store to retrieve documents. It is a lightweight wrapper around the Vector Store class to make it conform to the Retriever interface.", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/vectorstore"}
{"id": "b86242c73919-2", "text": "It uses the search methods implemented by a vector store, like similarity search and MMR, to query the texts in the vector store.Once you construct a Vector store, it's very easy to construct a retriever. Let's walk through an example.from langchain.document_loaders import TextLoaderloader = TextLoader('../../../state_of_the_union.txt')from langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import FAISSfrom langchain.embeddings import OpenAIEmbeddingsdocuments = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)texts = text_splitter.split_documents(documents)embeddings = OpenAIEmbeddings()db = FAISS.from_documents(texts, embeddings)    Exiting: Cleaning up .chroma directoryretriever = db.as_retriever()docs = retriever.get_relevant_documents(\"what did he say about ketanji brown jackson\")Maximum Marginal Relevance Retrieval\u00e2\u20ac\u2039By default, the vectorstore retriever uses similarity search. If the underlying vectorstore support maximum marginal relevance search, you can specify that as the search type.retriever = db.as_retriever(search_type=\"mmr\")docs = retriever.get_relevant_documents(\"what did he say about ketanji brown jackson\")Similarity Score Threshold Retrieval\u00e2\u20ac\u2039You can also a retrieval method that sets a similarity score threshold and only returns documents with a score above that thresholdretriever = db.as_retriever(search_type=\"similarity_score_threshold\", search_kwargs={\"score_threshold\": .5})docs = retriever.get_relevant_documents(\"what did he say about ketanji brown jackson\")Specifying top k\u00e2\u20ac\u2039You can also specify search kwargs like k to use when doing retrieval.retriever = db.as_retriever(search_kwargs={\"k\": 1})docs = retriever.get_relevant_documents(\"what did", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/vectorstore"}
{"id": "b86242c73919-3", "text": "1})docs = retriever.get_relevant_documents(\"what did he say about ketanji brown jackson\")len(docs)    1PreviousTime-weighted vector store retrieverNextChainsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/vectorstore"}
{"id": "b5f224b82f73-0", "text": "Ensemble Retriever | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/ensemble"}
{"id": "b5f224b82f73-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionDocument loadersDocument transformersText embedding modelsVector storesRetrieversMultiQueryRetrieverContextual compressionEnsemble RetrieverSelf-queryingTime-weighted vector store retrieverVector store-backed retrieverChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesData connectionRetrieversEnsemble RetrieverEnsemble RetrieverThe EnsembleRetriever takes a list of retrievers as input and ensemble the results of their get_relevant_documents() methods and rerank the results based on the Reciprocal Rank Fusion algorithm.By leveraging the strengths of different algorithms, the EnsembleRetriever can achieve better performance than any single algorithm. The most common pattern is to combine a sparse retriever(like BM25) with a dense retriever(like Embedding similarity), because their strengths are complementary. It is also known as \"hybrid search\".The sparse retriever is good at finding relevant documents based on keywords, while the dense retriever is good at finding relevant documents based on semantic similarity.from langchain.retrievers import BM25Retriever, EnsembleRetrieverfrom langchain.vectorstores import FAISSdoc_list = [    \"I like apples\",    \"I like oranges\",    \"Apples and oranges are fruits\",]# initialize the bm25 retriever and faiss retrieverbm25_retriever = BM25Retriever.from_texts(doc_list)bm25_retriever.k = 2embedding = OpenAIEmbeddings()faiss_vectorstore = FAISS.from_texts(doc_list, embedding)faiss_retriever = faiss_vectorstore.as_retriever(search_kwargs={\"k\": 2})# initialize the ensemble", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/ensemble"}
{"id": "b5f224b82f73-2", "text": "faiss_vectorstore.as_retriever(search_kwargs={\"k\": 2})# initialize the ensemble retrieverensemble_retriever = EnsembleRetriever(retrievers=[bm25_retriever, faiss_retriever], weights=[0.5, 0.5])docs = ensemble_retriever.get_relevant_documents(\"apples\")docs    [Document(page_content='I like apples', metadata={}),     Document(page_content='Apples and oranges are fruits', metadata={})]PreviousContextual compressionNextSelf-queryingCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/ensemble"}
{"id": "177a8ccdd721-0", "text": "Time-weighted vector store retriever | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/time_weighted_vectorstore"}
{"id": "177a8ccdd721-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionDocument loadersDocument transformersText embedding modelsVector storesRetrieversMultiQueryRetrieverContextual compressionEnsemble RetrieverSelf-queryingTime-weighted vector store retrieverVector store-backed retrieverChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesData connectionRetrieversTime-weighted vector store retrieverTime-weighted vector store retrieverThis retriever uses a combination of semantic similarity and a time decay.The algorithm for scoring them is:semantic_similarity + (1.0 - decay_rate) ^ hours_passedNotably, hours_passed refers to the hours passed since the object in the retriever was last accessed, not since it was created. This means that frequently accessed objects remain \"fresh.\"import faissfrom datetime import datetime, timedeltafrom langchain.docstore import InMemoryDocstorefrom langchain.embeddings import OpenAIEmbeddingsfrom langchain.retrievers import TimeWeightedVectorStoreRetrieverfrom langchain.schema import Documentfrom langchain.vectorstores import FAISSLow Decay Rate\u00e2\u20ac\u2039A low decay rate (in this, to be extreme, we will set close to 0) means memories will be \"remembered\" for longer. A decay rate of 0 means memories never be forgotten, making this retriever equivalent to the vector lookup.# Define your embedding modelembeddings_model = OpenAIEmbeddings()# Initialize the vectorstore as emptyembedding_size = 1536index = faiss.IndexFlatL2(embedding_size)vectorstore = FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {})retriever = TimeWeightedVectorStoreRetriever(vectorstore=vectorstore,", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/time_weighted_vectorstore"}
{"id": "177a8ccdd721-2", "text": "{})retriever = TimeWeightedVectorStoreRetriever(vectorstore=vectorstore, decay_rate=.0000000000000000000000001, k=1)yesterday = datetime.now() - timedelta(days=1)retriever.add_documents([Document(page_content=\"hello world\", metadata={\"last_accessed_at\": yesterday})])retriever.add_documents([Document(page_content=\"hello foo\")])    ['d7f85756-2371-4bdf-9140-052780a0f9b3']# \"Hello World\" is returned first because it is most salient, and the decay rate is close to 0., meaning it's still recent enoughretriever.get_relevant_documents(\"hello world\")    [Document(page_content='hello world', metadata={'last_accessed_at': datetime.datetime(2023, 5, 13, 21, 0, 27, 678341), 'created_at': datetime.datetime(2023, 5, 13, 21, 0, 27, 279596), 'buffer_idx': 0})]High Decay Rate\u00e2\u20ac\u2039With a high decay rate (e.g., several 9's), the recency score quickly goes to 0! If you set this all the way to 1, recency is 0 for all objects, once again making this equivalent to a vector lookup.# Define your embedding modelembeddings_model = OpenAIEmbeddings()# Initialize the vectorstore as emptyembedding_size = 1536index = faiss.IndexFlatL2(embedding_size)vectorstore = FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {})retriever = TimeWeightedVectorStoreRetriever(vectorstore=vectorstore, decay_rate=.999, k=1)yesterday = datetime.now() -", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/time_weighted_vectorstore"}
{"id": "177a8ccdd721-3", "text": "decay_rate=.999, k=1)yesterday = datetime.now() - timedelta(days=1)retriever.add_documents([Document(page_content=\"hello world\", metadata={\"last_accessed_at\": yesterday})])retriever.add_documents([Document(page_content=\"hello foo\")])    ['40011466-5bbe-4101-bfd1-e22e7f505de2']# \"Hello Foo\" is returned first because \"hello world\" is mostly forgottenretriever.get_relevant_documents(\"hello world\")    [Document(page_content='hello foo', metadata={'last_accessed_at': datetime.datetime(2023, 4, 16, 22, 9, 2, 494798), 'created_at': datetime.datetime(2023, 4, 16, 22, 9, 2, 178722), 'buffer_idx': 1})]Virtual Time\u00e2\u20ac\u2039Using some utils in LangChain, you can mock out the time componentfrom langchain.utils import mock_nowimport datetime# Notice the last access time is that date timewith mock_now(datetime.datetime(2011, 2, 3, 10, 11)):    print(retriever.get_relevant_documents(\"hello world\"))    [Document(page_content='hello world', metadata={'last_accessed_at': MockDateTime(2011, 2, 3, 10, 11), 'created_at': datetime.datetime(2023, 5, 13, 21, 0, 27, 279596), 'buffer_idx': 0})]PreviousWeaviate self-queryingNextVector store-backed retrieverCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/time_weighted_vectorstore"}
{"id": "23585a20860a-0", "text": "Self-querying | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/"}
{"id": "23585a20860a-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionDocument loadersDocument transformersText embedding modelsVector storesRetrieversMultiQueryRetrieverContextual compressionEnsemble RetrieverSelf-queryingChroma self-queryingDeepLake self-queryingSelf-querying with MyScaleSelf-querying with PineconeQdrant self-queryingWeaviate self-queryingTime-weighted vector store retrieverVector store-backed retrieverChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesData connectionRetrieversSelf-queryingSelf-queryingA self-querying retriever is one that, as the name suggests, has the ability to query itself. Specifically, given any natural language query, the retriever uses a query-constructing LLM chain to write a structured query and then applies that structured query to it's underlying VectorStore. This allows the retriever to not only use the user-input query for semantic similarity comparison with the contents of stored documented, but to also extract filters from the user query on the metadata of stored documents and to execute those filters.Get started\u00e2\u20ac\u2039We'll use a Pinecone vector store in this example.First we'll want to create a Pinecone VectorStore and seed it with some data. We've created a small demo set of documents that contain summaries of movies.To use Pinecone, you to have pinecone package installed and you must have an API key and an Environment. Here are the installation instructions.NOTE: The self-query retriever requires you to have lark package installed.# !pip install lark pinecone-clientimport osimport pineconepinecone.init(api_key=os.environ[\"PINECONE_API_KEY\"], environment=os.environ[\"PINECONE_ENV\"])from langchain.schema import Documentfrom", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/"}
{"id": "23585a20860a-2", "text": "environment=os.environ[\"PINECONE_ENV\"])from langchain.schema import Documentfrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.vectorstores import Pineconeembeddings = OpenAIEmbeddings()# create new indexpinecone.create_index(\"langchain-self-retriever-demo\", dimension=1536)docs = [    Document(page_content=\"A bunch of scientists bring back dinosaurs and mayhem breaks loose\", metadata={\"year\": 1993, \"rating\": 7.7, \"genre\": [\"action\", \"science fiction\"]}),    Document(page_content=\"Leo DiCaprio gets lost in a dream within a dream within a dream within a ...\", metadata={\"year\": 2010, \"director\": \"Christopher Nolan\", \"rating\": 8.2}),    Document(page_content=\"A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea\", metadata={\"year\": 2006, \"director\": \"Satoshi Kon\", \"rating\": 8.6}),    Document(page_content=\"A bunch of normal-sized women are supremely wholesome and some men pine after them\", metadata={\"year\": 2019, \"director\": \"Greta Gerwig\", \"rating\": 8.3}),    Document(page_content=\"Toys come alive and have a blast doing so\", metadata={\"year\": 1995, \"genre\": \"animated\"}),    Document(page_content=\"Three men walk into the Zone, three men walk out of the Zone\", metadata={\"year\": 1979, \"rating\": 9.9, \"director\": \"Andrei Tarkovsky\", \"genre\": [\"science fiction\", \"thriller\"], \"rating\": 9.9})]vectorstore = Pinecone.from_documents(    docs, embeddings, index_name=\"langchain-self-retriever-demo\")Creating our self-querying", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/"}
{"id": "23585a20860a-3", "text": "docs, embeddings, index_name=\"langchain-self-retriever-demo\")Creating our self-querying retriever\u00e2\u20ac\u2039Now we can instantiate our retriever. To do this we'll need to provide some information upfront about the metadata fields that our documents support and a short description of the document contents.from langchain.llms import OpenAIfrom langchain.retrievers.self_query.base import SelfQueryRetrieverfrom langchain.chains.query_constructor.base import AttributeInfometadata_field_info=[    AttributeInfo(        name=\"genre\",        description=\"The genre of the movie\",         type=\"string or list[string]\",     ),    AttributeInfo(        name=\"year\",        description=\"The year the movie was released\",         type=\"integer\",     ),    AttributeInfo(        name=\"director\",        description=\"The name of the movie director\",         type=\"string\",     ),    AttributeInfo(        name=\"rating\",        description=\"A 1-10 rating for the movie\",        type=\"float\"    ),]document_content_description = \"Brief summary of a movie\"llm = OpenAI(temperature=0)retriever = SelfQueryRetriever.from_llm(llm, vectorstore, document_content_description, metadata_field_info, verbose=True)Testing it out\u00e2\u20ac\u2039And now we can try actually using our retriever!# This example only specifies a relevant queryretriever.get_relevant_documents(\"What are some movies about dinosaurs\")", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/"}
{"id": "23585a20860a-4", "text": "relevant queryretriever.get_relevant_documents(\"What are some movies about dinosaurs\")    query='dinosaur' filter=None    [Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'genre': ['action', 'science fiction'], 'rating': 7.7, 'year': 1993.0}),     Document(page_content='Toys come alive and have a blast doing so', metadata={'genre': 'animated', 'year': 1995.0}),     Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'director': 'Satoshi Kon', 'rating': 8.6, 'year': 2006.0}),     Document(page_content='Leo DiCaprio gets lost in a dream within a dream within a dream within a ...', metadata={'director': 'Christopher Nolan', 'rating': 8.2, 'year': 2010.0})]# This example only specifies a filterretriever.get_relevant_documents(\"I want to watch a movie rated higher than 8.5\")    query=' ' filter=Comparison(comparator=<Comparator.GT: 'gt'>, attribute='rating', value=8.5)    [Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'director': 'Satoshi Kon', 'rating': 8.6, 'year': 2006.0}),     Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'director': 'Andrei Tarkovsky', 'genre': ['science fiction', 'thriller'], 'rating': 9.9, 'year':", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/"}
{"id": "23585a20860a-5", "text": "['science fiction', 'thriller'], 'rating': 9.9, 'year': 1979.0})]# This example specifies a query and a filterretriever.get_relevant_documents(\"Has Greta Gerwig directed any movies about women\")    query='women' filter=Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='director', value='Greta Gerwig')    [Document(page_content='A bunch of normal-sized women are supremely wholesome and some men pine after them', metadata={'director': 'Greta Gerwig', 'rating': 8.3, 'year': 2019.0})]# This example specifies a composite filterretriever.get_relevant_documents(\"What's a highly rated (above 8.5) science fiction film?\")    query=' ' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='science fiction'), Comparison(comparator=<Comparator.GT: 'gt'>, attribute='rating', value=8.5)])    [Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'director': 'Andrei Tarkovsky', 'genre': ['science fiction', 'thriller'], 'rating': 9.9, 'year': 1979.0})]# This example specifies a query and composite filterretriever.get_relevant_documents(\"What's a movie after 1990 but before 2005 that's all about toys, and preferably is animated\")    query='toys' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.GT: 'gt'>, attribute='year', value=1990.0), Comparison(comparator=<Comparator.LT: 'lt'>,", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/"}
{"id": "23585a20860a-6", "text": "value=1990.0), Comparison(comparator=<Comparator.LT: 'lt'>, attribute='year', value=2005.0), Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='animated')])    [Document(page_content='Toys come alive and have a blast doing so', metadata={'genre': 'animated', 'year': 1995.0})]Filter k\u00e2\u20ac\u2039We can also use the self query retriever to specify k: the number of documents to fetch.We can do this by passing enable_limit=True to the constructor.retriever = SelfQueryRetriever.from_llm(    llm,     vectorstore,     document_content_description,     metadata_field_info,     enable_limit=True,    verbose=True)# This example only specifies a relevant queryretriever.get_relevant_documents(\"What are two movies about dinosaurs\")PreviousEnsemble RetrieverNextChroma self-queryingCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/"}
{"id": "ad4dac789bdd-0", "text": "Chroma self-querying | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/chroma_self_query"}
{"id": "ad4dac789bdd-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionDocument loadersDocument transformersText embedding modelsVector storesRetrieversMultiQueryRetrieverContextual compressionEnsemble RetrieverSelf-queryingChroma self-queryingDeepLake self-queryingSelf-querying with MyScaleSelf-querying with PineconeQdrant self-queryingWeaviate self-queryingTime-weighted vector store retrieverVector store-backed retrieverChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesData connectionRetrieversSelf-queryingChroma self-queryingOn this pageChroma self-queryingChroma is a database for building AI applications with embeddings.In the notebook we'll demo the SelfQueryRetriever wrapped around a Chroma vector store. Creating a Chroma vectorstore\u00e2\u20ac\u2039First we'll want to create a Chroma VectorStore and seed it with some data. We've created a small demo set of documents that contain summaries of movies.NOTE: The self-query retriever requires you to have lark installed (pip install lark). We also need the chromadb package.#!pip install lark#!pip install chromadbWe want to use OpenAIEmbeddings so we have to get the OpenAI API Key.import osimport getpassos.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")    OpenAI API Key: \u00c2\u00b7\u00c2\u00b7\u00c2\u00b7\u00c2\u00b7\u00c2\u00b7\u00c2\u00b7\u00c2\u00b7\u00c2\u00b7from langchain.schema import Documentfrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.vectorstores import Chromaembeddings = OpenAIEmbeddings()docs = [    Document(        page_content=\"A bunch of scientists bring back dinosaurs and", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/chroma_self_query"}
{"id": "ad4dac789bdd-2", "text": "Document(        page_content=\"A bunch of scientists bring back dinosaurs and mayhem breaks loose\",        metadata={\"year\": 1993, \"rating\": 7.7, \"genre\": \"science fiction\"},    ),    Document(        page_content=\"Leo DiCaprio gets lost in a dream within a dream within a dream within a ...\",        metadata={\"year\": 2010, \"director\": \"Christopher Nolan\", \"rating\": 8.2},    ),    Document(        page_content=\"A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea\",        metadata={\"year\": 2006, \"director\": \"Satoshi Kon\", \"rating\": 8.6},    ),    Document(        page_content=\"A bunch of normal-sized women are supremely wholesome and some men pine after them\",        metadata={\"year\": 2019, \"director\": \"Greta Gerwig\", \"rating\": 8.3},    ),    Document(        page_content=\"Toys come alive and have a blast doing so\",        metadata={\"year\": 1995, \"genre\": \"animated\"},    ),    Document(        page_content=\"Three men walk into the Zone, three men walk out of the Zone\",        metadata={            \"year\": 1979,            \"rating\": 9.9,", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/chroma_self_query"}
{"id": "ad4dac789bdd-3", "text": "\"rating\": 9.9,            \"director\": \"Andrei Tarkovsky\",            \"genre\": \"science fiction\",            \"rating\": 9.9,        },    ),]vectorstore = Chroma.from_documents(docs, embeddings)    Using embedded DuckDB without persistence: data will be transientCreating our self-querying retriever\u00e2\u20ac\u2039Now we can instantiate our retriever. To do this we'll need to provide some information upfront about the metadata fields that our documents support and a short description of the document contents.from langchain.llms import OpenAIfrom langchain.retrievers.self_query.base import SelfQueryRetrieverfrom langchain.chains.query_constructor.base import AttributeInfometadata_field_info = [    AttributeInfo(        name=\"genre\",        description=\"The genre of the movie\",        type=\"string or list[string]\",    ),    AttributeInfo(        name=\"year\",        description=\"The year the movie was released\",        type=\"integer\",    ),    AttributeInfo(        name=\"director\",        description=\"The name of the movie director\",        type=\"string\",    ),    AttributeInfo(        name=\"rating\", description=\"A 1-10 rating for the movie\", type=\"float\"    ),]document_content_description = \"Brief summary of a movie\"llm =", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/chroma_self_query"}
{"id": "ad4dac789bdd-4", "text": "),]document_content_description = \"Brief summary of a movie\"llm = OpenAI(temperature=0)retriever = SelfQueryRetriever.from_llm(    llm, vectorstore, document_content_description, metadata_field_info, verbose=True)Testing it out\u00e2\u20ac\u2039And now we can try actually using our retriever!# This example only specifies a relevant queryretriever.get_relevant_documents(\"What are some movies about dinosaurs\")    query='dinosaur' filter=None    [Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'rating': 7.7, 'genre': 'science fiction'}),     Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'}),     Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'year': 2006, 'director': 'Satoshi Kon', 'rating': 8.6}),     Document(page_content='Leo DiCaprio gets lost in a dream within a dream within a dream within a ...', metadata={'year': 2010, 'director': 'Christopher Nolan', 'rating': 8.2})]# This example only specifies a filterretriever.get_relevant_documents(\"I want to watch a movie rated higher than 8.5\")    query=' ' filter=Comparison(comparator=<Comparator.GT: 'gt'>, attribute='rating', value=8.5)    [Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'year': 2006, 'director': 'Satoshi", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/chroma_self_query"}
{"id": "ad4dac789bdd-5", "text": "reused the idea', metadata={'year': 2006, 'director': 'Satoshi Kon', 'rating': 8.6}),     Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'rating': 9.9, 'director': 'Andrei Tarkovsky', 'genre': 'science fiction'})]# This example specifies a query and a filterretriever.get_relevant_documents(\"Has Greta Gerwig directed any movies about women\")    query='women' filter=Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='director', value='Greta Gerwig')    [Document(page_content='A bunch of normal-sized women are supremely wholesome and some men pine after them', metadata={'year': 2019, 'director': 'Greta Gerwig', 'rating': 8.3})]# This example specifies a composite filterretriever.get_relevant_documents(    \"What's a highly rated (above 8.5) science fiction film?\")    query=' ' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='science fiction'), Comparison(comparator=<Comparator.GT: 'gt'>, attribute='rating', value=8.5)])    [Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'rating': 9.9, 'director': 'Andrei Tarkovsky', 'genre': 'science fiction'})]# This example specifies a query and composite filterretriever.get_relevant_documents(    \"What's a movie after 1990 but before 2005 that's all about toys, and preferably is animated\")", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/chroma_self_query"}
{"id": "ad4dac789bdd-6", "text": "1990 but before 2005 that's all about toys, and preferably is animated\")    query='toys' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.GT: 'gt'>, attribute='year', value=1990), Comparison(comparator=<Comparator.LT: 'lt'>, attribute='year', value=2005), Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='animated')])    [Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'})]Filter k\u00e2\u20ac\u2039We can also use the self query retriever to specify k: the number of documents to fetch.We can do this by passing enable_limit=True to the constructor.retriever = SelfQueryRetriever.from_llm(    llm,    vectorstore,    document_content_description,    metadata_field_info,    enable_limit=True,    verbose=True,)# This example only specifies a relevant queryretriever.get_relevant_documents(\"what are two movies about dinosaurs\")    query='dinosaur' filter=None    [Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'rating': 7.7, 'genre': 'science fiction'}),     Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'}),     Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'year': 2006, 'director': 'Satoshi Kon', 'rating': 8.6}),", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/chroma_self_query"}
{"id": "ad4dac789bdd-7", "text": "'Satoshi Kon', 'rating': 8.6}),     Document(page_content='Leo DiCaprio gets lost in a dream within a dream within a dream within a ...', metadata={'year': 2010, 'director': 'Christopher Nolan', 'rating': 8.2})]PreviousSelf-queryingNextDeepLake self-queryingCreating a Chroma vectorstoreCreating our self-querying retrieverTesting it outFilter kCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/chroma_self_query"}
{"id": "f9f160ffcadb-0", "text": "Self-querying with MyScale | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/myscale_self_query"}
{"id": "f9f160ffcadb-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionDocument loadersDocument transformersText embedding modelsVector storesRetrieversMultiQueryRetrieverContextual compressionEnsemble RetrieverSelf-queryingChroma self-queryingDeepLake self-queryingSelf-querying with MyScaleSelf-querying with PineconeQdrant self-queryingWeaviate self-queryingTime-weighted vector store retrieverVector store-backed retrieverChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesData connectionRetrieversSelf-queryingSelf-querying with MyScaleOn this pageSelf-querying with MyScaleMyScale is an integrated vector database. You can access your database in SQL and also from here, LangChain. MyScale can make a use of various data types and functions for filters. It will boost up your LLM app no matter if you are scaling up your data or expand your system to broader application.In the notebook we'll demo the SelfQueryRetriever wrapped around a MyScale vector store with some extra piece we contributed to LangChain. In short, it can be concluded into 4 points:Add contain comparator to match list of any if there is more than one element matchedAdd timestamp data type for datetime match (ISO-format, or YYYY-MM-DD)Add like comparator for string pattern searchAdd arbitrary function capabilityCreating a MyScale vectorstore\u00e2\u20ac\u2039MyScale has already been integrated to LangChain for a while. So you can follow this notebook to create your own vectorstore for a self-query retriever.NOTE: All self-query retrievers requires you to have lark installed (pip install lark). We use lark for grammar definition. Before you proceed to the next step, we also want to remind you that clickhouse-connect is also needed to", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/myscale_self_query"}
{"id": "f9f160ffcadb-2", "text": "proceed to the next step, we also want to remind you that clickhouse-connect is also needed to interact with your MyScale backend.pip install lark clickhouse-connectIn this tutorial we follow other example's setting and use OpenAIEmbeddings. Remember to get a OpenAI API Key for valid accesss to LLMs.import osimport getpassos.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")os.environ[\"MYSCALE_HOST\"] = getpass.getpass(\"MyScale URL:\")os.environ[\"MYSCALE_PORT\"] = getpass.getpass(\"MyScale Port:\")os.environ[\"MYSCALE_USERNAME\"] = getpass.getpass(\"MyScale Username:\")os.environ[\"MYSCALE_PASSWORD\"] = getpass.getpass(\"MyScale Password:\")from langchain.schema import Documentfrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.vectorstores import MyScaleembeddings = OpenAIEmbeddings()Create some sample data\u00e2\u20ac\u2039As you can see, the data we created has some difference to other self-query retrievers. We replaced keyword year to date which gives you a finer control on timestamps. We also altered the type of keyword gerne to list of strings, where LLM can use a new contain comparator to construct filters. We also provides comparator like and arbitrary function support to filters, which will be introduced in next few cells.Now let's look at the data first.docs = [    Document(        page_content=\"A bunch of scientists bring back dinosaurs and mayhem breaks loose\",        metadata={\"date\": \"1993-07-02\", \"rating\": 7.7, \"genre\": [\"science fiction\"]},    ),    Document(        page_content=\"Leo DiCaprio gets lost in a dream within a dream within a dream within a ...\",", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/myscale_self_query"}
{"id": "f9f160ffcadb-3", "text": "in a dream within a dream within a dream within a ...\",        metadata={\"date\": \"2010-12-30\", \"director\": \"Christopher Nolan\", \"rating\": 8.2},    ),    Document(        page_content=\"A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea\",        metadata={\"date\": \"2006-04-23\", \"director\": \"Satoshi Kon\", \"rating\": 8.6},    ),    Document(        page_content=\"A bunch of normal-sized women are supremely wholesome and some men pine after them\",        metadata={\"date\": \"2019-08-22\", \"director\": \"Greta Gerwig\", \"rating\": 8.3},    ),    Document(        page_content=\"Toys come alive and have a blast doing so\",        metadata={\"date\": \"1995-02-11\", \"genre\": [\"animated\"]},    ),    Document(        page_content=\"Three men walk into the Zone, three men walk out of the Zone\",        metadata={            \"date\": \"1979-09-10\",            \"rating\": 9.9,            \"director\": \"Andrei Tarkovsky\",            \"genre\": [\"science fiction\", \"adventure\"],            \"rating\": 9.9,", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/myscale_self_query"}
{"id": "f9f160ffcadb-4", "text": "\"rating\": 9.9,        },    ),]vectorstore = MyScale.from_documents(    docs,    embeddings,)Creating our self-querying retriever\u00e2\u20ac\u2039Just like other retrievers... Simple and nice.from langchain.llms import OpenAIfrom langchain.retrievers.self_query.base import SelfQueryRetrieverfrom langchain.chains.query_constructor.base import AttributeInfometadata_field_info = [    AttributeInfo(        name=\"genre\",        description=\"The genres of the movie\",        type=\"list[string]\",    ),    # If you want to include length of a list, just define it as a new column    # This will teach the LLM to use it as a column when constructing filter.    AttributeInfo(        name=\"length(genre)\",        description=\"The length of genres of the movie\",        type=\"integer\",    ),    # Now you can define a column as timestamp. By simply set the type to timestamp.    AttributeInfo(        name=\"date\",        description=\"The date the movie was released\",        type=\"timestamp\",    ),    AttributeInfo(        name=\"director\",        description=\"The name of the movie director\",        type=\"string\",    ),    AttributeInfo(        name=\"rating\", description=\"A 1-10 rating for the movie\", type=\"float\"", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/myscale_self_query"}
{"id": "f9f160ffcadb-5", "text": "description=\"A 1-10 rating for the movie\", type=\"float\"    ),]document_content_description = \"Brief summary of a movie\"llm = OpenAI(temperature=0)retriever = SelfQueryRetriever.from_llm(    llm, vectorstore, document_content_description, metadata_field_info, verbose=True)Testing it out with self-query retriever's existing functionalities\u00e2\u20ac\u2039And now we can try actually using our retriever!# This example only specifies a relevant queryretriever.get_relevant_documents(\"What are some movies about dinosaurs\")# This example only specifies a filterretriever.get_relevant_documents(\"I want to watch a movie rated higher than 8.5\")# This example specifies a query and a filterretriever.get_relevant_documents(\"Has Greta Gerwig directed any movies about women\")# This example specifies a composite filterretriever.get_relevant_documents(    \"What's a highly rated (above 8.5) science fiction film?\")# This example specifies a query and composite filterretriever.get_relevant_documents(    \"What's a movie after 1990 but before 2005 that's all about toys, and preferably is animated\")Wait a second... What else?Self-query retriever with MyScale can do more! Let's find out.# You can use length(genres) to do anything you wantretriever.get_relevant_documents(\"What's a movie that have more than 1 genres?\")# Fine-grained datetime? You got it already.retriever.get_relevant_documents(\"What's a movie that release after feb 1995?\")# Don't know what your exact filter should be? Use string pattern match!retriever.get_relevant_documents(\"What's a movie whose name is like Andrei?\")# Contain works for lists: so you can match a list with", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/myscale_self_query"}
{"id": "f9f160ffcadb-6", "text": "is like Andrei?\")# Contain works for lists: so you can match a list with contain comparator!retriever.get_relevant_documents(    \"What's a movie who has genres science fiction and adventure?\")Filter k\u00e2\u20ac\u2039We can also use the self query retriever to specify k: the number of documents to fetch.We can do this by passing enable_limit=True to the constructor.retriever = SelfQueryRetriever.from_llm(    llm,    vectorstore,    document_content_description,    metadata_field_info,    enable_limit=True,    verbose=True,)# This example only specifies a relevant queryretriever.get_relevant_documents(\"what are two movies about dinosaurs\")PreviousDeepLake self-queryingNextSelf-querying with PineconeCreating a MyScale vectorstoreCreate some sample dataCreating our self-querying retrieverTesting it out with self-query retriever's existing functionalitiesFilter kCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/myscale_self_query"}
{"id": "34a815b81fe6-0", "text": "Qdrant self-querying | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/qdrant_self_query"}
{"id": "34a815b81fe6-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionDocument loadersDocument transformersText embedding modelsVector storesRetrieversMultiQueryRetrieverContextual compressionEnsemble RetrieverSelf-queryingChroma self-queryingDeepLake self-queryingSelf-querying with MyScaleSelf-querying with PineconeQdrant self-queryingWeaviate self-queryingTime-weighted vector store retrieverVector store-backed retrieverChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesData connectionRetrieversSelf-queryingQdrant self-queryingOn this pageQdrant self-queryingQdrant (read: quadrant ) is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. Qdrant is tailored to extended filtering support. It makes it useful In the notebook we'll demo the SelfQueryRetriever wrapped around a Qdrant vector store. Creating a Qdrant vectorstore\u00e2\u20ac\u2039First we'll want to create a Qdrant VectorStore and seed it with some data. We've created a small demo set of documents that contain summaries of movies.NOTE: The self-query retriever requires you to have lark installed (pip install lark). We also need the qdrant-client package.#!pip install lark qdrant-clientWe want to use OpenAIEmbeddings so we have to get the OpenAI API Key.# import os# import getpass# os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')from langchain.schema import Documentfrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.vectorstores import Qdrantembeddings =", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/qdrant_self_query"}
{"id": "34a815b81fe6-2", "text": "import OpenAIEmbeddingsfrom langchain.vectorstores import Qdrantembeddings = OpenAIEmbeddings()docs = [    Document(        page_content=\"A bunch of scientists bring back dinosaurs and mayhem breaks loose\",        metadata={\"year\": 1993, \"rating\": 7.7, \"genre\": \"science fiction\"},    ),    Document(        page_content=\"Leo DiCaprio gets lost in a dream within a dream within a dream within a ...\",        metadata={\"year\": 2010, \"director\": \"Christopher Nolan\", \"rating\": 8.2},    ),    Document(        page_content=\"A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea\",        metadata={\"year\": 2006, \"director\": \"Satoshi Kon\", \"rating\": 8.6},    ),    Document(        page_content=\"A bunch of normal-sized women are supremely wholesome and some men pine after them\",        metadata={\"year\": 2019, \"director\": \"Greta Gerwig\", \"rating\": 8.3},    ),    Document(        page_content=\"Toys come alive and have a blast doing so\",        metadata={\"year\": 1995, \"genre\": \"animated\"},    ),    Document(        page_content=\"Three men walk into the Zone, three men walk out of the Zone\",        metadata={            \"year\":", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/qdrant_self_query"}
{"id": "34a815b81fe6-3", "text": "metadata={            \"year\": 1979,            \"rating\": 9.9,            \"director\": \"Andrei Tarkovsky\",            \"genre\": \"science fiction\",        },    ),]vectorstore = Qdrant.from_documents(    docs,    embeddings,    location=\":memory:\",  # Local mode with in-memory storage only    collection_name=\"my_documents\",)Creating our self-querying retriever\u00e2\u20ac\u2039Now we can instantiate our retriever. To do this we'll need to provide some information upfront about the metadata fields that our documents support and a short description of the document contents.from langchain.llms import OpenAIfrom langchain.retrievers.self_query.base import SelfQueryRetrieverfrom langchain.chains.query_constructor.base import AttributeInfometadata_field_info = [    AttributeInfo(        name=\"genre\",        description=\"The genre of the movie\",        type=\"string or list[string]\",    ),    AttributeInfo(        name=\"year\",        description=\"The year the movie was released\",        type=\"integer\",    ),    AttributeInfo(        name=\"director\",        description=\"The name of the movie director\",        type=\"string\",    ),    AttributeInfo(        name=\"rating\", description=\"A 1-10 rating for the", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/qdrant_self_query"}
{"id": "34a815b81fe6-4", "text": "name=\"rating\", description=\"A 1-10 rating for the movie\", type=\"float\"    ),]document_content_description = \"Brief summary of a movie\"llm = OpenAI(temperature=0)retriever = SelfQueryRetriever.from_llm(    llm, vectorstore, document_content_description, metadata_field_info, verbose=True)Testing it out\u00e2\u20ac\u2039And now we can try actually using our retriever!# This example only specifies a relevant queryretriever.get_relevant_documents(\"What are some movies about dinosaurs\")    query='dinosaur' filter=None limit=None    [Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'rating': 7.7, 'genre': 'science fiction'}),     Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'}),     Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'rating': 9.9, 'director': 'Andrei Tarkovsky', 'genre': 'science fiction'}),     Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'year': 2006, 'director': 'Satoshi Kon', 'rating': 8.6})]# This example only specifies a filterretriever.get_relevant_documents(\"I want to watch a movie rated higher than 8.5\")    query=' ' filter=Comparison(comparator=<Comparator.GT: 'gt'>, attribute='rating', value=8.5) limit=None    [Document(page_content='Three", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/qdrant_self_query"}
{"id": "34a815b81fe6-5", "text": "value=8.5) limit=None    [Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'rating': 9.9, 'director': 'Andrei Tarkovsky', 'genre': 'science fiction'}),     Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'year': 2006, 'director': 'Satoshi Kon', 'rating': 8.6})]# This example specifies a query and a filterretriever.get_relevant_documents(\"Has Greta Gerwig directed any movies about women\")    query='women' filter=Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='director', value='Greta Gerwig') limit=None    [Document(page_content='A bunch of normal-sized women are supremely wholesome and some men pine after them', metadata={'year': 2019, 'director': 'Greta Gerwig', 'rating': 8.3})]# This example specifies a composite filterretriever.get_relevant_documents(    \"What's a highly rated (above 8.5) science fiction film?\")    query=' ' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.GT: 'gt'>, attribute='rating', value=8.5), Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='science fiction')]) limit=None    [Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'rating': 9.9, 'director': 'Andrei Tarkovsky', 'genre': 'science fiction'})]# This example specifies a query", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/qdrant_self_query"}
{"id": "34a815b81fe6-6", "text": "Tarkovsky', 'genre': 'science fiction'})]# This example specifies a query and composite filterretriever.get_relevant_documents(    \"What's a movie after 1990 but before 2005 that's all about toys, and preferably is animated\")    query='toys' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.GT: 'gt'>, attribute='year', value=1990), Comparison(comparator=<Comparator.LT: 'lt'>, attribute='year', value=2005), Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='animated')]) limit=None    [Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'})]Filter k\u00e2\u20ac\u2039We can also use the self query retriever to specify k: the number of documents to fetch.We can do this by passing enable_limit=True to the constructor.retriever = SelfQueryRetriever.from_llm(    llm,    vectorstore,    document_content_description,    metadata_field_info,    enable_limit=True,    verbose=True,)# This example only specifies a relevant queryretriever.get_relevant_documents(\"what are two movies about dinosaurs\")    query='dinosaur' filter=None limit=2    [Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'rating': 7.7, 'genre': 'science fiction'}),     Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'})]PreviousSelf-querying with PineconeNextWeaviate self-queryingCreating a", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/qdrant_self_query"}
{"id": "34a815b81fe6-7", "text": "'animated'})]PreviousSelf-querying with PineconeNextWeaviate self-queryingCreating a Qdrant vectorstoreCreating our self-querying retrieverTesting it outFilter kCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/qdrant_self_query"}
{"id": "3b08d28ed2a9-0", "text": "Self-querying with Pinecone | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/pinecone"}
{"id": "3b08d28ed2a9-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionDocument loadersDocument transformersText embedding modelsVector storesRetrieversMultiQueryRetrieverContextual compressionEnsemble RetrieverSelf-queryingChroma self-queryingDeepLake self-queryingSelf-querying with MyScaleSelf-querying with PineconeQdrant self-queryingWeaviate self-queryingTime-weighted vector store retrieverVector store-backed retrieverChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesData connectionRetrieversSelf-queryingSelf-querying with PineconeOn this pageSelf-querying with PineconeIn the walkthrough we'll demo the SelfQueryRetriever with a Pinecone vector store.Creating a Pinecone index\u00e2\u20ac\u2039First we'll want to create a Pinecone VectorStore and seed it with some data. We've created a small demo set of documents that contain summaries of movies.To use Pinecone, you have to have pinecone package installed and you must have an API key and an Environment. Here are the installation instructions.NOTE: The self-query retriever requires you to have lark package installed.# !pip install lark#!pip install pinecone-clientimport osimport pineconepinecone.init(    api_key=os.environ[\"PINECONE_API_KEY\"], environment=os.environ[\"PINECONE_ENV\"])    /Users/harrisonchase/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/site-packages/pinecone/index.py:4: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/pinecone"}
{"id": "3b08d28ed2a9-2", "text": "instead to force console mode (e.g. in jupyter console)      from tqdm.autonotebook import tqdmfrom langchain.schema import Documentfrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.vectorstores import Pineconeembeddings = OpenAIEmbeddings()# create new indexpinecone.create_index(\"langchain-self-retriever-demo\", dimension=1536)docs = [    Document(        page_content=\"A bunch of scientists bring back dinosaurs and mayhem breaks loose\",        metadata={\"year\": 1993, \"rating\": 7.7, \"genre\": [\"action\", \"science fiction\"]},    ),    Document(        page_content=\"Leo DiCaprio gets lost in a dream within a dream within a dream within a ...\",        metadata={\"year\": 2010, \"director\": \"Christopher Nolan\", \"rating\": 8.2},    ),    Document(        page_content=\"A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea\",        metadata={\"year\": 2006, \"director\": \"Satoshi Kon\", \"rating\": 8.6},    ),    Document(        page_content=\"A bunch of normal-sized women are supremely wholesome and some men pine after them\",        metadata={\"year\": 2019, \"director\": \"Greta Gerwig\", \"rating\": 8.3},    ),    Document(        page_content=\"Toys come alive and have a blast doing so\",        metadata={\"year\":", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/pinecone"}
{"id": "3b08d28ed2a9-3", "text": "come alive and have a blast doing so\",        metadata={\"year\": 1995, \"genre\": \"animated\"},    ),    Document(        page_content=\"Three men walk into the Zone, three men walk out of the Zone\",        metadata={            \"year\": 1979,            \"rating\": 9.9,            \"director\": \"Andrei Tarkovsky\",            \"genre\": [\"science fiction\", \"thriller\"],            \"rating\": 9.9,        },    ),]vectorstore = Pinecone.from_documents(    docs, embeddings, index_name=\"langchain-self-retriever-demo\")Creating our self-querying retriever\u00e2\u20ac\u2039Now we can instantiate our retriever. To do this we'll need to provide some information upfront about the metadata fields that our documents support and a short description of the document contents.from langchain.llms import OpenAIfrom langchain.retrievers.self_query.base import SelfQueryRetrieverfrom langchain.chains.query_constructor.base import AttributeInfometadata_field_info = [    AttributeInfo(        name=\"genre\",        description=\"The genre of the movie\",        type=\"string or list[string]\",    ),    AttributeInfo(        name=\"year\",        description=\"The year the movie was released\",        type=\"integer\",    ),", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/pinecone"}
{"id": "3b08d28ed2a9-4", "text": "released\",        type=\"integer\",    ),    AttributeInfo(        name=\"director\",        description=\"The name of the movie director\",        type=\"string\",    ),    AttributeInfo(        name=\"rating\", description=\"A 1-10 rating for the movie\", type=\"float\"    ),]document_content_description = \"Brief summary of a movie\"llm = OpenAI(temperature=0)retriever = SelfQueryRetriever.from_llm(    llm, vectorstore, document_content_description, metadata_field_info, verbose=True)Testing it out\u00e2\u20ac\u2039And now we can try actually using our retriever!# This example only specifies a relevant queryretriever.get_relevant_documents(\"What are some movies about dinosaurs\")    query='dinosaur' filter=None    [Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'genre': ['action', 'science fiction'], 'rating': 7.7, 'year': 1993.0}),     Document(page_content='Toys come alive and have a blast doing so', metadata={'genre': 'animated', 'year': 1995.0}),     Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'director': 'Satoshi Kon', 'rating': 8.6, 'year': 2006.0}),     Document(page_content='Leo DiCaprio gets lost in a dream within a dream within a dream within a ...', metadata={'director': 'Christopher Nolan', 'rating': 8.2, 'year':", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/pinecone"}
{"id": "3b08d28ed2a9-5", "text": "metadata={'director': 'Christopher Nolan', 'rating': 8.2, 'year': 2010.0})]# This example only specifies a filterretriever.get_relevant_documents(\"I want to watch a movie rated higher than 8.5\")    query=' ' filter=Comparison(comparator=<Comparator.GT: 'gt'>, attribute='rating', value=8.5)    [Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'director': 'Satoshi Kon', 'rating': 8.6, 'year': 2006.0}),     Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'director': 'Andrei Tarkovsky', 'genre': ['science fiction', 'thriller'], 'rating': 9.9, 'year': 1979.0})]# This example specifies a query and a filterretriever.get_relevant_documents(\"Has Greta Gerwig directed any movies about women\")    query='women' filter=Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='director', value='Greta Gerwig')    [Document(page_content='A bunch of normal-sized women are supremely wholesome and some men pine after them', metadata={'director': 'Greta Gerwig', 'rating': 8.3, 'year': 2019.0})]# This example specifies a composite filterretriever.get_relevant_documents(    \"What's a highly rated (above 8.5) science fiction film?\")    query=' ' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='science fiction'),", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/pinecone"}
{"id": "3b08d28ed2a9-6", "text": "'eq'>, attribute='genre', value='science fiction'), Comparison(comparator=<Comparator.GT: 'gt'>, attribute='rating', value=8.5)])    [Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'director': 'Andrei Tarkovsky', 'genre': ['science fiction', 'thriller'], 'rating': 9.9, 'year': 1979.0})]# This example specifies a query and composite filterretriever.get_relevant_documents(    \"What's a movie after 1990 but before 2005 that's all about toys, and preferably is animated\")    query='toys' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.GT: 'gt'>, attribute='year', value=1990.0), Comparison(comparator=<Comparator.LT: 'lt'>, attribute='year', value=2005.0), Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='animated')])    [Document(page_content='Toys come alive and have a blast doing so', metadata={'genre': 'animated', 'year': 1995.0})]Filter k\u00e2\u20ac\u2039We can also use the self query retriever to specify k: the number of documents to fetch.We can do this by passing enable_limit=True to the constructor.retriever = SelfQueryRetriever.from_llm(    llm,    vectorstore,    document_content_description,    metadata_field_info,    enable_limit=True,    verbose=True,)# This example only specifies a relevant queryretriever.get_relevant_documents(\"What are two movies about dinosaurs\")PreviousSelf-querying with MyScaleNextQdrant self-queryingCreating a Pinecone", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/pinecone"}
{"id": "3b08d28ed2a9-7", "text": "dinosaurs\")PreviousSelf-querying with MyScaleNextQdrant self-queryingCreating a Pinecone indexCreating our self-querying retrieverTesting it outFilter kCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/pinecone"}
{"id": "116f660c1da9-0", "text": "Weaviate self-querying | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/weaviate_self_query"}
{"id": "116f660c1da9-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionDocument loadersDocument transformersText embedding modelsVector storesRetrieversMultiQueryRetrieverContextual compressionEnsemble RetrieverSelf-queryingChroma self-queryingDeepLake self-queryingSelf-querying with MyScaleSelf-querying with PineconeQdrant self-queryingWeaviate self-queryingTime-weighted vector store retrieverVector store-backed retrieverChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesData connectionRetrieversSelf-queryingWeaviate self-queryingOn this pageWeaviate self-queryingCreating a Weaviate vectorstore\u00e2\u20ac\u2039First we'll want to create a Weaviate VectorStore and seed it with some data. We've created a small demo set of documents that contain summaries of movies.NOTE: The self-query retriever requires you to have lark installed (pip install lark). We also need the weaviate-client package.#!pip install lark weaviate-clientfrom langchain.schema import Documentfrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.vectorstores import Weaviateimport osembeddings = OpenAIEmbeddings()docs = [    Document(        page_content=\"A bunch of scientists bring back dinosaurs and mayhem breaks loose\",        metadata={\"year\": 1993, \"rating\": 7.7, \"genre\": \"science fiction\"},    ),    Document(        page_content=\"Leo DiCaprio gets lost in a dream within a dream within a dream within a ...\",        metadata={\"year\": 2010,", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/weaviate_self_query"}
{"id": "116f660c1da9-2", "text": "dream within a ...\",        metadata={\"year\": 2010, \"director\": \"Christopher Nolan\", \"rating\": 8.2},    ),    Document(        page_content=\"A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea\",        metadata={\"year\": 2006, \"director\": \"Satoshi Kon\", \"rating\": 8.6},    ),    Document(        page_content=\"A bunch of normal-sized women are supremely wholesome and some men pine after them\",        metadata={\"year\": 2019, \"director\": \"Greta Gerwig\", \"rating\": 8.3},    ),    Document(        page_content=\"Toys come alive and have a blast doing so\",        metadata={\"year\": 1995, \"genre\": \"animated\"},    ),    Document(        page_content=\"Three men walk into the Zone, three men walk out of the Zone\",        metadata={            \"year\": 1979,            \"rating\": 9.9,            \"director\": \"Andrei Tarkovsky\",            \"genre\": \"science fiction\",            \"rating\": 9.9,        },    ),]vectorstore = Weaviate.from_documents(    docs, embeddings,", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/weaviate_self_query"}
{"id": "116f660c1da9-3", "text": "),]vectorstore = Weaviate.from_documents(    docs, embeddings, weaviate_url=\"http://127.0.0.1:8080\")Creating our self-querying retriever\u00e2\u20ac\u2039Now we can instantiate our retriever. To do this we'll need to provide some information upfront about the metadata fields that our documents support and a short description of the document contents.from langchain.llms import OpenAIfrom langchain.retrievers.self_query.base import SelfQueryRetrieverfrom langchain.chains.query_constructor.base import AttributeInfometadata_field_info = [    AttributeInfo(        name=\"genre\",        description=\"The genre of the movie\",        type=\"string or list[string]\",    ),    AttributeInfo(        name=\"year\",        description=\"The year the movie was released\",        type=\"integer\",    ),    AttributeInfo(        name=\"director\",        description=\"The name of the movie director\",        type=\"string\",    ),    AttributeInfo(        name=\"rating\", description=\"A 1-10 rating for the movie\", type=\"float\"    ),]document_content_description = \"Brief summary of a movie\"llm = OpenAI(temperature=0)retriever = SelfQueryRetriever.from_llm(    llm, vectorstore, document_content_description, metadata_field_info, verbose=True)Testing it out\u00e2\u20ac\u2039And now we can try actually using our retriever!# This example only specifies a relevant queryretriever.get_relevant_documents(\"What are some movies", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/weaviate_self_query"}
{"id": "116f660c1da9-4", "text": "This example only specifies a relevant queryretriever.get_relevant_documents(\"What are some movies about dinosaurs\")    query='dinosaur' filter=None limit=None    [Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'genre': 'science fiction', 'rating': 7.7, 'year': 1993}),     Document(page_content='Toys come alive and have a blast doing so', metadata={'genre': 'animated', 'rating': None, 'year': 1995}),     Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'genre': 'science fiction', 'rating': 9.9, 'year': 1979}),     Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'genre': None, 'rating': 8.6, 'year': 2006})]# This example specifies a query and a filterretriever.get_relevant_documents(\"Has Greta Gerwig directed any movies about women\")    query='women' filter=Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='director', value='Greta Gerwig') limit=None    [Document(page_content='A bunch of normal-sized women are supremely wholesome and some men pine after them', metadata={'genre': None, 'rating': 8.3, 'year': 2019})]Filter k\u00e2\u20ac\u2039We can also use the self query retriever to specify k: the number of documents to fetch.We can do this by passing enable_limit=True to the constructor.retriever = SelfQueryRetriever.from_llm(    llm,    vectorstore,    document_content_description,    metadata_field_info,", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/weaviate_self_query"}
{"id": "116f660c1da9-5", "text": "vectorstore,    document_content_description,    metadata_field_info,    enable_limit=True,    verbose=True,)# This example only specifies a relevant queryretriever.get_relevant_documents(\"what are two movies about dinosaurs\")    query='dinosaur' filter=None limit=2    [Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'genre': 'science fiction', 'rating': 7.7, 'year': 1993}),     Document(page_content='Toys come alive and have a blast doing so', metadata={'genre': 'animated', 'rating': None, 'year': 1995})]PreviousQdrant self-queryingNextTime-weighted vector store retrieverCreating a Weaviate vectorstoreCreating our self-querying retrieverTesting it outFilter kCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/weaviate_self_query"}
{"id": "d007048864f9-0", "text": "DeepLake self-querying | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/deeplake_self_query"}
{"id": "d007048864f9-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionDocument loadersDocument transformersText embedding modelsVector storesRetrieversMultiQueryRetrieverContextual compressionEnsemble RetrieverSelf-queryingChroma self-queryingDeepLake self-queryingSelf-querying with MyScaleSelf-querying with PineconeQdrant self-queryingWeaviate self-queryingTime-weighted vector store retrieverVector store-backed retrieverChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesData connectionRetrieversSelf-queryingDeepLake self-queryingOn this pageDeepLake self-queryingDeepLake is a multimodal database for building AI applications.In the notebook we'll demo the SelfQueryRetriever wrapped around a DeepLake vector store. Creating a DeepLake vectorstore\u00e2\u20ac\u2039First we'll want to create a DeepLake VectorStore and seed it with some data. We've created a small demo set of documents that contain summaries of movies.NOTE: The self-query retriever requires you to have lark installed (pip install lark). We also need the deeplake package.#!pip install lark#!pip install 'deeplake[enterprise]'We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.import osimport getpassos.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")from langchain.schema import Documentfrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.vectorstores import DeepLakeembeddings = OpenAIEmbeddings()docs = [    Document(        page_content=\"A bunch of scientists bring back dinosaurs and mayhem breaks loose\",        metadata={\"year\":", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/deeplake_self_query"}
{"id": "d007048864f9-2", "text": "bring back dinosaurs and mayhem breaks loose\",        metadata={\"year\": 1993, \"rating\": 7.7, \"genre\": \"science fiction\"},    ),    Document(        page_content=\"Leo DiCaprio gets lost in a dream within a dream within a dream within a ...\",        metadata={\"year\": 2010, \"director\": \"Christopher Nolan\", \"rating\": 8.2},    ),    Document(        page_content=\"A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea\",        metadata={\"year\": 2006, \"director\": \"Satoshi Kon\", \"rating\": 8.6},    ),    Document(        page_content=\"A bunch of normal-sized women are supremely wholesome and some men pine after them\",        metadata={\"year\": 2019, \"director\": \"Greta Gerwig\", \"rating\": 8.3},    ),    Document(        page_content=\"Toys come alive and have a blast doing so\",        metadata={\"year\": 1995, \"genre\": \"animated\"},    ),    Document(        page_content=\"Three men walk into the Zone, three men walk out of the Zone\",        metadata={            \"year\": 1979,            \"rating\": 9.9,            \"director\": \"Andrei Tarkovsky\",", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/deeplake_self_query"}
{"id": "d007048864f9-3", "text": "\"director\": \"Andrei Tarkovsky\",            \"genre\": \"science fiction\",            \"rating\": 9.9,        },    ),]username_or_org = \"<USER_NAME_OR_ORG>\"vectorstore = DeepLake.from_documents(    docs, embeddings, dataset_path=f\"hub://{username_or_org}/self_queery\")    Your Deep Lake dataset has been successfully created!    -    Dataset(path='hub://adilkhan/self_queery', tensors=['embedding', 'id', 'metadata', 'text'])          tensor      htype      shape     dtype  compression      -------    -------    -------   -------  -------      embedding  embedding  (6, 1536)  float32   None           id        text      (6, 1)      str     None        metadata     json      (6, 1)      str     None          text       text      (6, 1)      str     None        Creating our self-querying retriever\u00e2\u20ac\u2039Now we can instantiate our retriever. To do this we'll need to provide some information upfront about the metadata fields that our documents support and a short description of the document contents.from langchain.llms import", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/deeplake_self_query"}
{"id": "d007048864f9-4", "text": "metadata fields that our documents support and a short description of the document contents.from langchain.llms import OpenAIfrom langchain.retrievers.self_query.base import SelfQueryRetrieverfrom langchain.chains.query_constructor.base import AttributeInfometadata_field_info = [    AttributeInfo(        name=\"genre\",        description=\"The genre of the movie\",        type=\"string or list[string]\",    ),    AttributeInfo(        name=\"year\",        description=\"The year the movie was released\",        type=\"integer\",    ),    AttributeInfo(        name=\"director\",        description=\"The name of the movie director\",        type=\"string\",    ),    AttributeInfo(        name=\"rating\", description=\"A 1-10 rating for the movie\", type=\"float\"    ),]document_content_description = \"Brief summary of a movie\"llm = OpenAI(temperature=0)retriever = SelfQueryRetriever.from_llm(    llm, vectorstore, document_content_description, metadata_field_info, verbose=True)Testing it out\u00e2\u20ac\u2039And now we can try actually using our retriever!# This example only specifies a relevant queryretriever.get_relevant_documents(\"What are some movies about dinosaurs\")    /Users/adilkhansarsen/Documents/work/LangChain/langchain/langchain/chains/llm.py:275: UserWarning: The predict_and_parse method is deprecated, instead pass an output parser directly to LLMChain.      warnings.warn(    query='dinosaur'", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/deeplake_self_query"}
{"id": "d007048864f9-5", "text": "LLMChain.      warnings.warn(    query='dinosaur' filter=None limit=None    [Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'rating': 7.7, 'genre': 'science fiction'}),     Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'}),     Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'rating': 9.9, 'director': 'Andrei Tarkovsky', 'genre': 'science fiction'}),     Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'year': 2006, 'director': 'Satoshi Kon', 'rating': 8.6})]# This example only specifies a filterretriever.get_relevant_documents(\"I want to watch a movie rated higher than 8.5\")    query=' ' filter=Comparison(comparator=<Comparator.GT: 'gt'>, attribute='rating', value=8.5) limit=None    [Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'year': 2006, 'director': 'Satoshi Kon', 'rating': 8.6}),     Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'rating': 9.9, 'director': 'Andrei Tarkovsky', 'genre': 'science fiction'})]# This example specifies a query and a", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/deeplake_self_query"}
{"id": "d007048864f9-6", "text": "Tarkovsky', 'genre': 'science fiction'})]# This example specifies a query and a filterretriever.get_relevant_documents(\"Has Greta Gerwig directed any movies about women\")    query='women' filter=Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='director', value='Greta Gerwig') limit=None    [Document(page_content='A bunch of normal-sized women are supremely wholesome and some men pine after them', metadata={'year': 2019, 'director': 'Greta Gerwig', 'rating': 8.3})]# This example specifies a composite filterretriever.get_relevant_documents(    \"What's a highly rated (above 8.5) science fiction film?\")    query=' ' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.GTE: 'gte'>, attribute='rating', value=8.5), Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='science fiction')]) limit=None    [Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'rating': 9.9, 'director': 'Andrei Tarkovsky', 'genre': 'science fiction'})]# This example specifies a query and composite filterretriever.get_relevant_documents(    \"What's a movie after 1990 but before 2005 that's all about toys, and preferably is animated\")    query='toys' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.GT: 'gt'>, attribute='year', value=1990), Comparison(comparator=<Comparator.LT: 'lt'>, attribute='year', value=2005),", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/deeplake_self_query"}
{"id": "d007048864f9-7", "text": "'lt'>, attribute='year', value=2005), Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='animated')]) limit=None    [Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'})]Filter k\u00e2\u20ac\u2039We can also use the self query retriever to specify k: the number of documents to fetch.We can do this by passing enable_limit=True to the constructor.retriever = SelfQueryRetriever.from_llm(    llm,    vectorstore,    document_content_description,    metadata_field_info,    enable_limit=True,    verbose=True,)# This example only specifies a relevant queryretriever.get_relevant_documents(\"what are two movies about dinosaurs\")    query='dinosaur' filter=None limit=2    [Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'rating': 7.7, 'genre': 'science fiction'}),     Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'})]PreviousChroma self-queryingNextSelf-querying with MyScaleCreating a DeepLake vectorstoreCreating our self-querying retrieverTesting it outFilter kCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/deeplake_self_query"}
{"id": "72f8b05fa9fa-0", "text": "Text embedding models | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/data_connection/text_embedding/"}
{"id": "72f8b05fa9fa-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionDocument loadersDocument transformersText embedding modelsVector storesRetrieversChainsMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesModulesData connectionText embedding modelsOn this pageText embedding modelsinfoHead to Integrations for documentation on built-in integrations with text embedding model providers.The Embeddings class is a class designed for interfacing with text embedding models. There are lots of embedding model providers (OpenAI, Cohere, Hugging Face, etc) - this class is designed to provide a standard interface for all of them.Embeddings create a vector representation of a piece of text. This is useful because it means we can think about text in the vector space, and do things like semantic search where we look for pieces of text that are most similar in the vector space.The base Embeddings class in LangChain exposes two methods: one for embedding documents and one for embedding a query. The former takes as input multiple texts, while the latter takes a single text. The reason for having these as two separate methods is that some embedding providers have different embedding methods for documents (to be searched over) vs queries (the search query itself).Get started\u00e2\u20ac\u2039Setup\u00e2\u20ac\u2039To start we'll need to install the OpenAI Python package:pip install openaiAccessing the API requires an API key, which you can get by creating an account and heading here. Once we have a key we'll want to set it as an environment variable by running:export OPENAI_API_KEY=\"...\"If you'd prefer not to set an environment variable you can pass the key in directly via the openai_api_key named parameter when initiating the OpenAI LLM class:from langchain.embeddings import OpenAIEmbeddingsembeddings_model", "source": "https://python.langchain.com/docs/modules/data_connection/text_embedding/"}
{"id": "72f8b05fa9fa-2", "text": "OpenAI LLM class:from langchain.embeddings import OpenAIEmbeddingsembeddings_model = OpenAIEmbeddings(openai_api_key=\"...\")otherwise you can initialize without any params:from langchain.embeddings import OpenAIEmbeddingsembeddings_model = OpenAIEmbeddings()embed_documents\u00e2\u20ac\u2039Embed list of texts\u00e2\u20ac\u2039embeddings = embeddings_model.embed_documents(    [        \"Hi there!\",        \"Oh, hello!\",        \"What's your name?\",        \"My friends call me World\",        \"Hello World!\"    ])len(embeddings), len(embeddings[0])(5, 1536)embed_query\u00e2\u20ac\u2039Embed single query\u00e2\u20ac\u2039Embed a single piece of text for the purpose of comparing to other embedded pieces of texts.embedded_query = embeddings_model.embed_query(\"What was the name mentioned in the conversation?\")embedded_query[:5][0.0053587136790156364, -0.0004999046213924885, 0.038883671164512634, -0.003001077566295862, -0.00900818221271038]PreviousLost in the middle: The problem with long contextsNextVector storesGet startedCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/data_connection/text_embedding/"}
{"id": "0729a0b080b7-0", "text": "Agents | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/agents/"}
{"id": "0729a0b080b7-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsMemoryAgentsAgent typesHow-toToolsToolkitsCallbacksModulesGuidesEcosystemAdditional resourcesModulesAgentsOn this pageAgentsThe core idea of agents is to use an LLM to choose a sequence of actions to take.\nIn chains, a sequence of actions is hardcoded (in code).\nIn agents, a language model is used as a reasoning engine to determine which actions to take and in which order.There are several key components here:Agent\u00e2\u20ac\u2039This is the class responsible for deciding what step to take next.\nThis is powered by a language model and a prompt.\nThis prompt can include things like:The personality of the agent (useful for having it respond in a certain way)Background context for the agent (useful for giving it more context on the types of tasks it's being asked to do)Prompting strategies to invoke better reasoning (the most famous/widely used being ReAct)LangChain provides a few different types of agents to get started.\nEven then, you will likely want to customize those agents with parts (1) and (2).\nFor a full list of agent types see agent typesTools\u00e2\u20ac\u2039Tools are functions that an agent calls.\nThere are two important considerations here:Giving the agent access to the right toolsDescribing the tools in a way that is most helpful to the agentWithout both, the agent you are trying to build will not work.\nIf you don't give the agent access to a correct set of tools, it will never be able to accomplish the objective.", "source": "https://python.langchain.com/docs/modules/agents/"}
{"id": "0729a0b080b7-2", "text": "If you don't describe the tools properly, the agent won't know how to properly use them.LangChain provides a wide set of tools to get started, but also makes it easy to define your own (including custom descriptions).\nFor a full list of tools, see hereToolkits\u00e2\u20ac\u2039Often the set of tools an agent has access to is more important than a single tool.\nFor this LangChain provides the concept of toolkits - groups of tools needed to accomplish specific objectives.\nThere are generally around 3-5 tools in a toolkit.LangChain provides a wide set of toolkits to get started.\nFor a full list of toolkits, see hereAgentExecutor\u00e2\u20ac\u2039The agent executor is the runtime for an agent.\nThis is what actually calls the agent and executes the actions it chooses.\nPseudocode for this runtime is below:next_action = agent.get_action(...)while next_action != AgentFinish:    observation = run(next_action)    next_action = agent.get_action(..., next_action, observation)return next_actionWhile this may seem simple, there are several complexities this runtime handles for you, including:Handling cases where the agent selects a non-existent toolHandling cases where the tool errorsHandling cases where the agent produces output that cannot be parsed into a tool invocationLogging and observability at all levels (agent decisions, tool calls) either to stdout or LangSmith.Other types of agent runtimes\u00e2\u20ac\u2039The AgentExecutor class is the main agent runtime supported by LangChain.\nHowever, there are other, more experimental runtimes we also support.\nThese include:Plan-and-execute AgentBaby AGIAuto GPTGet started\u00e2\u20ac\u2039This will go over how to get started building an agent.\nWe will use a LangChain agent class, but show how to customize it to give it specific context.", "source": "https://python.langchain.com/docs/modules/agents/"}
{"id": "0729a0b080b7-3", "text": "We will then define custom tools, and then run it all in the standard LangChain AgentExecutor.Set up the agent\u00e2\u20ac\u2039We will use the OpenAIFunctionsAgent.\nThis is easiest and best agent to get started with.\nIt does however require usage of ChatOpenAI models.\nIf you want to use a different language model, we would recommend using the ReAct agent.For this guide, we will construct a custom agent that has access to a custom tool.\nWe are choosing this example because we think for most use cases you will NEED to customize either the agent or the tools.\nThe tool we will give the agent is a tool to calculate the length of a word.\nThis is useful because this is actually something LLMs can mess up due to tokenization.\nWe will first create it WITHOUT memory, but we will then show how to add memory in.\nMemory is needed to enable conversation.First, let's load the language model we're going to use to control the agent.from langchain.chat_models import ChatOpenAIllm = ChatOpenAI(temperature=0)Next, let's define some tools to use.\nLet's write a really simple Python function to calculate the length of a word that is passed in.from langchain.agents import tool@tooldef get_word_length(word: str) -> int:    \"\"\"Returns the length of a word.\"\"\"    return len(word)tools = [get_word_length]Now let us create the prompt.\nWe can use the OpenAIFunctionsAgent.create_prompt helper function to create a prompt automatically.", "source": "https://python.langchain.com/docs/modules/agents/"}
{"id": "0729a0b080b7-4", "text": "This allows for a few different ways to customize, including passing in a custom SystemMessage, which we will do.from langchain.schema import SystemMessagesystem_message = SystemMessage(content=\"You are very powerful assistant, but bad at calculating lengths of words.\")prompt = OpenAIFunctionsAgent.create_prompt(system_message=system_message)Putting those pieces together, we can now create the agent.from langchain.agents import OpenAIFunctionsAgentagent = OpenAIFunctionsAgent(llm=llm, tools=tools, prompt=prompt)Finally, we create the AgentExecutor - the runtime for our agent.from langchain.agents import AgentExecutoragent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)Now let's test it out!agent_executor.run(\"how many letters in the word educa?\")            > Entering new AgentExecutor chain...    Invoking: `get_word_length` with `{'word': 'educa'}`    5    There are 5 letters in the word \"educa\".    > Finished chain.    'There are 5 letters in the word \"educa\".'This is great - we have an agent!\nHowever, this agent is stateless - it doesn't remember anything about previous interactions.\nThis means you can't ask follow up questions easily.\nLet's fix that by adding in memory.In order to do this, we need to do two things:Add a place for memory variables to go in the promptAdd memory to the AgentExecutor (note that we add it here, and NOT to the agent, as this is the outermost chain)First, let's add a place for memory in the prompt.", "source": "https://python.langchain.com/docs/modules/agents/"}
{"id": "0729a0b080b7-5", "text": "We do this by adding a placeholder for messages with the key \"chat_history\".from langchain.prompts import MessagesPlaceholderMEMORY_KEY = \"chat_history\"prompt = OpenAIFunctionsAgent.create_prompt(    system_message=system_message,    extra_prompt_messages=[MessagesPlaceholder(variable_name=MEMORY_KEY)])Next, let's create a memory object.\nWe will do this by using ConversationBufferMemory.\nImportantly, we set memory_key also equal to \"chat_history\" (to align it with the prompt) and set return_messages (to make it return messages rather than a string).from langchain.memory import ConversationBufferMemorymemory = ConversationBufferMemory(memory_key=MEMORY_KEY, return_messages=True)We can then put it all together!agent = OpenAIFunctionsAgent(llm=llm, tools=tools, prompt=prompt)agent_executor = AgentExecutor(agent=agent, tools=tools, memory=memory, verbose=True)agent_executor.run(\"how many letters in the word educa?\")agent_executor.run(\"is that a real word?\")PreviousVector store-backed memoryNextAgent typesAgentToolsToolkitsAgentExecutorOther types of agent runtimesGet startedCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/agents/"}
{"id": "62db24a79b8d-0", "text": "Agent types | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/agents/agent_types/"}
{"id": "62db24a79b8d-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsMemoryAgentsAgent typesConversationalOpenAI functionsOpenAI Multi Functions AgentPlan and executeReActReAct document storeSelf ask with searchStructured tool chatHow-toToolsToolkitsCallbacksModulesGuidesEcosystemAdditional resourcesModulesAgentsAgent typesOn this pageAgent typesAction agents\u00e2\u20ac\u2039Agents use an LLM to determine which actions to take and in what order.\nAn action can either be using a tool and observing its output, or returning a response to the user.\nHere are the agents available in LangChain.Zero-shot ReAct\u00e2\u20ac\u2039This agent uses the ReAct framework to determine which tool to use\nbased solely on the tool's description. Any number of tools can be provided.\nThis agent requires that a description is provided for each tool.Note: This is the most general purpose action agent.Structured input ReAct\u00e2\u20ac\u2039The structured tool chat agent is capable of using multi-input tools.\nOlder agents are configured to specify an action input as a single string, but this agent can use a tools' argument\nschema to create a structured action input. This is useful for more complex tool usage, like precisely\nnavigating around a browser.OpenAI Functions\u00e2\u20ac\u2039Certain OpenAI models (like gpt-3.5-turbo-0613 and gpt-4-0613) have been explicitly fine-tuned to detect when a\nfunction should to be called and respond with the inputs that should be passed to the function.\nThe OpenAI Functions Agent is designed to work with these models.Conversational\u00e2\u20ac\u2039This agent is designed to be used in conversational settings.", "source": "https://python.langchain.com/docs/modules/agents/agent_types/"}
{"id": "62db24a79b8d-2", "text": "The prompt is designed to make the agent helpful and conversational.\nIt uses the ReAct framework to decide which tool to use, and uses memory to remember the previous conversation interactions.Self ask with search\u00e2\u20ac\u2039This agent utilizes a single tool that should be named Intermediate Answer.\nThis tool should be able to lookup factual answers to questions. This agent\nis equivalent to the original self ask with search paper,\nwhere a Google search API was provided as the tool.ReAct document store\u00e2\u20ac\u2039This agent uses the ReAct framework to interact with a docstore. Two tools must\nbe provided: a Search tool and a Lookup tool (they must be named exactly as so).\nThe Search tool should search for a document, while the Lookup tool should lookup\na term in the most recently found document.\nThis agent is equivalent to the\noriginal ReAct paper, specifically the Wikipedia example.Plan-and-execute agents\u00e2\u20ac\u2039Plan and execute agents accomplish an objective by first planning what to do, then executing the sub tasks. This idea is largely inspired by BabyAGI and then the \"Plan-and-Solve\" paper.PreviousAgentsNextConversationalAction agentsZero-shot ReActStructured input ReActOpenAI FunctionsConversationalSelf ask with searchReAct document storePlan-and-execute agentsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/agents/agent_types/"}
{"id": "ef13765193a4-0", "text": "Page Not Found | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain\n\n\n\n\n\nSkip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKPage Not FoundWe could not find what you were looking for.Please contact the owner of the site that linked you to the original URL and let them know their link is broken.CommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/agents/agent_types/react.html"}
{"id": "533c097eeae6-0", "text": "Page Not Found | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain\n\n\n\n\n\nSkip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKPage Not FoundWe could not find what you were looking for.Please contact the owner of the site that linked you to the original URL and let them know their link is broken.CommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/agents/agent_types/structured_chat.html"}
{"id": "8bcc7b05b3fe-0", "text": "Page Not Found | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain\n\n\n\n\n\nSkip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKPage Not FoundWe could not find what you were looking for.Please contact the owner of the site that linked you to the original URL and let them know their link is broken.CommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/agents/agent_types/chat_conversation_agent.html"}
{"id": "d3be13be6cfe-0", "text": "Self ask with search | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain\n\n\n\n\n\nSkip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsMemoryAgentsAgent typesConversationalOpenAI functionsOpenAI Multi Functions AgentPlan and executeReActReAct document storeSelf ask with searchStructured tool chatHow-toToolsToolkitsCallbacksModulesGuidesEcosystemAdditional resourcesModulesAgentsAgent typesSelf ask with searchSelf ask with searchThis walkthrough showcases the Self Ask With Search chain.from langchain import OpenAI, SerpAPIWrapperfrom langchain.agents import initialize_agent, Toolfrom langchain.agents import AgentTypellm = OpenAI(temperature=0)search = SerpAPIWrapper()tools = [    Tool(        name=\"Intermediate Answer\",        func=search.run,        description=\"useful for when you need to ask with search\",    )]self_ask_with_search = initialize_agent(    tools, llm, agent=AgentType.SELF_ASK_WITH_SEARCH, verbose=True)self_ask_with_search.run(    \"What is the hometown of the reigning men's U.S. Open champion?\")            > Entering new AgentExecutor chain...     Yes.    Follow up: Who is the reigning men's U.S. Open champion?    Intermediate answer: Carlos Alcaraz Garfia    Follow up: Where is Carlos Alcaraz Garfia from?    Intermediate answer: El Palmar, Spain    So the final answer is: El Palmar, Spain        > Finished chain.    'El Palmar, Spain'PreviousReAct document storeNextStructured tool chatCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/agents/agent_types/self_ask_with_search"}
{"id": "31979d777674-0", "text": "Structured tool chat | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/agents/agent_types/structured_chat"}
{"id": "31979d777674-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsMemoryAgentsAgent typesConversationalOpenAI functionsOpenAI Multi Functions AgentPlan and executeReActReAct document storeSelf ask with searchStructured tool chatHow-toToolsToolkitsCallbacksModulesGuidesEcosystemAdditional resourcesModulesAgentsAgent typesStructured tool chatStructured tool chatThe structured tool chat agent is capable of using multi-input tools.Older agents are configured to specify an action input as a single string, but this agent can use the provided tools' args_schema to populate the action input.This functionality is natively available using agent types: structured-chat-zero-shot-react-description or AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTIONimport osos.environ[\"LANGCHAIN_TRACING\"] = \"true\" # If you want to trace the execution of the program, set to \"true\"from langchain.agents import AgentTypefrom langchain.chat_models import ChatOpenAIfrom langchain.agents import initialize_agentInitialize Tools\u00e2\u20ac\u2039We will test the agent using a web browser.from langchain.agents.agent_toolkits import PlayWrightBrowserToolkitfrom langchain.tools.playwright.utils import (    create_async_playwright_browser,    create_sync_playwright_browser, # A synchronous browser is available, though it isn't compatible with jupyter.)# This import is required only for jupyter notebooks, since they have their own eventloopimport nest_asyncionest_asyncio.apply()async_browser = create_async_playwright_browser()browser_toolkit = PlayWrightBrowserToolkit.from_browser(async_browser=async_browser)tools = browser_toolkit.get_tools()llm = ChatOpenAI(temperature=0) # Also works well with Anthropic modelsagent_chain = initialize_agent(tools, llm,", "source": "https://python.langchain.com/docs/modules/agents/agent_types/structured_chat"}
{"id": "31979d777674-2", "text": "# Also works well with Anthropic modelsagent_chain = initialize_agent(tools, llm, agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True)response = await agent_chain.arun(input=\"Hi I'm Erica.\")print(response)            > Entering new AgentExecutor chain...    Action:    ```    {      \"action\": \"Final Answer\",      \"action_input\": \"Hello Erica, how can I assist you today?\"    }    ```            > Finished chain.    Hello Erica, how can I assist you today?response = await agent_chain.arun(input=\"Don't need help really just chatting.\")print(response)            > Entering new AgentExecutor chain...        > Finished chain.    I'm here to chat! How's your day going?response = await agent_chain.arun(input=\"Browse to blog.langchain.dev and summarize the text, please.\")print(response)            > Entering new AgentExecutor chain...    Action:    ```    {      \"action\": \"navigate_browser\",      \"action_input\": {        \"url\": \"https://blog.langchain.dev/\"      }    }    ```            Observation: Navigating to https://blog.langchain.dev/ returned status code 200    Thought:I need to extract the text from the webpage to summarize it.    Action:    ```    {      \"action\":", "source": "https://python.langchain.com/docs/modules/agents/agent_types/structured_chat"}
{"id": "31979d777674-3", "text": "Action:    ```    {      \"action\": \"extract_text\",      \"action_input\": {}    }    ```        Observation: LangChain LangChain Home About GitHub Docs LangChain The official LangChain blog. Auto-Evaluator Opportunities Editor's Note: this is a guest blog post by Lance Martin.            TL;DR        We recently open-sourced an auto-evaluator tool for grading LLM question-answer chains. We are now releasing an open source, free to use hosted app and API to expand usability. Below we discuss a few opportunities to further improve May 1, 2023 5 min read Callbacks Improvements TL;DR: We're announcing improvements to our callbacks system, which powers logging, tracing, streaming output, and some awesome third-party integrations. This will better support concurrent runs with independent callbacks, tracing of deeply nested trees of LangChain components, and callback handlers scoped to a single request (which is super useful for May 1, 2023 3 min read Unleashing the power of AI Collaboration with Parallelized LLM Agent Actor Trees Editor's note: the following is a guest blog post from Cyrus at Shaman AI. We use guest blog posts to highlight interesting and novel applications, and this is certainly that. There's been a lot of talk about agents recently, but most have been discussions around a single agent. If multiple Apr 28, 2023 4 min read Gradio & LLM Agents Editor's note: this is a guest blog post from Freddy Boulton, a software engineer at Gradio. We're excited to share this post because it brings a large number of exciting new tools into the ecosystem. Agents are largely defined by the tools they have, so to be able to equip Apr 23, 2023", "source": "https://python.langchain.com/docs/modules/agents/agent_types/structured_chat"}
{"id": "31979d777674-4", "text": "defined by the tools they have, so to be able to equip Apr 23, 2023 4 min read RecAlign - The smart content filter for social media feed [Editor's Note] This is a guest post by Tian Jin. We are highlighting this application as we think it is a novel use case. Specifically, we think recommendation systems are incredibly impactful in our everyday lives and there has not been a ton of discourse on how LLMs will impact Apr 22, 2023 3 min read Improving Document Retrieval with Contextual Compression Note: This post assumes some familiarity with LangChain and is moderately technical.        \u011f\u0178\u2019\u00a1 TL;DR: We\u00e2\u20ac\u2122ve introduced a new abstraction and a new document Retriever to facilitate the post-processing of retrieved documents. Specifically, the new abstraction makes it easy to take a set of retrieved documents and extract from them Apr 20, 2023 3 min read Autonomous Agents & Agent Simulations Over the past two weeks, there has been a massive increase in using LLMs in an agentic manner. Specifically, projects like AutoGPT, BabyAGI, CAMEL, and Generative Agents have popped up. The LangChain community has now implemented some parts of all of those projects in the LangChain framework. While researching and Apr 18, 2023 7 min read AI-Powered Medical Knowledge: Revolutionizing Care for Rare Conditions [Editor's Note]: This is a guest post by Jack Simon, who recently participated in a hackathon at Williams College. He built a LangChain-powered chatbot focused on appendiceal cancer, aiming to make specialized knowledge more accessible to those in need. If you are interested in building a chatbot for another rare Apr 17, 2023 3 min read Auto-Eval of Question-Answering Tasks By Lance Martin        Context", "source": "https://python.langchain.com/docs/modules/agents/agent_types/structured_chat"}
{"id": "31979d777674-5", "text": "Tasks By Lance Martin        Context        LLM ops platforms, such as LangChain, make it easy to assemble LLM components (e.g., models, document retrievers, data loaders) into chains. Question-Answering is one of the most popular applications of these chains. But it is often not always obvious to determine what parameters (e.g. Apr 15, 2023 3 min read Announcing LangChainJS Support for Multiple JS Environments TLDR: We're announcing support for running LangChain.js in browsers, Cloudflare Workers, Vercel/Next.js, Deno, Supabase Edge Functions, alongside existing support for Node.js ESM and CJS. See install/upgrade docs and breaking changes list.            Context        Originally we designed LangChain.js to run in Node.js, which is the Apr 11, 2023 3 min read LangChain x Supabase Supabase is holding an AI Hackathon this week. Here at LangChain we are big fans of both Supabase and hackathons, so we thought this would be a perfect time to highlight the multiple ways you can use LangChain and Supabase together.        The reason we like Supabase so much is that Apr 8, 2023 2 min read Announcing our $10M seed round led by Benchmark It was only six months ago that we released the first version of LangChain, but it seems like several years. When we launched, generative AI was starting to go mainstream: stable diffusion had just been released and was captivating people\u00e2\u20ac\u2122s imagination and fueling an explosion in developer activity, Jasper Apr 4, 2023 4 min read Custom Agents One of the most common requests we've heard is better functionality and documentation for creating custom agents.", "source": "https://python.langchain.com/docs/modules/agents/agent_types/structured_chat"}
{"id": "31979d777674-6", "text": "Agents One of the most common requests we've heard is better functionality and documentation for creating custom agents. This has always been a bit tricky - because in our mind it's actually still very unclear what an \"agent\" actually is, and therefore what the \"right\" abstractions for them may be. Recently, Apr 3, 2023 3 min read Retrieval TL;DR: We are adjusting our abstractions to make it easy for other retrieval methods besides the LangChain VectorDB object to be used in LangChain. This is done with the goals of (1) allowing retrievers constructed elsewhere to be used more easily in LangChain, (2) encouraging more experimentation with alternative Mar 23, 2023 4 min read LangChain + Zapier Natural Language Actions (NLA) We are super excited to team up with Zapier and integrate their new Zapier NLA API into LangChain, which you can now use with your agents and chains. With this integration, you have access to the 5k+ apps and 20k+ actions on Zapier's platform through a natural language API interface. Mar 16, 2023 2 min read Evaluation Evaluation of language models, and by extension applications built on top of language models, is hard. With recent model releases (OpenAI, Anthropic, Google) evaluation is becoming a bigger and bigger issue. People are starting to try to tackle this, with OpenAI releasing OpenAI/evals - focused on evaluating OpenAI models. Mar 14, 2023 3 min read LLMs and SQL Francisco Ingham and Jon Luo are two of the community members leading the change on the SQL integrations. We\u00e2\u20ac\u2122re really excited to write this blog post with them going over all the tips and tricks they\u00e2\u20ac\u2122ve learned doing so. We\u00e2\u20ac\u2122re even more excited to announce that we\u00e2\u20ac\u2122 Mar 13, 2023 8 min read Origin Web Browser", "source": "https://python.langchain.com/docs/modules/agents/agent_types/structured_chat"}
{"id": "31979d777674-7", "text": "to announce that we\u00e2\u20ac\u2122 Mar 13, 2023 8 min read Origin Web Browser [Editor's Note]: This is the second of hopefully many guest posts. We intend to highlight novel applications building on top of LangChain. If you are interested in working with us on such a post, please reach out to harrison@langchain.dev.        Authors: Parth Asawa (pgasawa@), Ayushi Batwara (ayushi.batwara@), Jason Mar 8, 2023 4 min read Prompt Selectors One common complaint we've heard is that the default prompt templates do not work equally well for all models. This became especially pronounced this past week when OpenAI released a ChatGPT API. This new API had a completely new interface (which required new abstractions) and as a result many users Mar 8, 2023 2 min read Chat Models Last week OpenAI released a ChatGPT endpoint. It came marketed with several big improvements, most notably being 10x cheaper and a lot faster. But it also came with a completely new API endpoint. We were able to quickly write a wrapper for this endpoint to let users use it like Mar 6, 2023 6 min read Using the ChatGPT API to evaluate the ChatGPT API OpenAI released a new ChatGPT API yesterday. Lots of people were excited to try it. But how does it actually compare to the existing API? It will take some time before there is a definitive answer, but here are some initial thoughts. Because I'm lazy, I also enrolled the help Mar 2, 2023 5 min read Agent Toolkits Today, we're announcing agent toolkits, a new abstraction that allows developers to create agents designed for a particular use-case (for example, interacting with a relational database or interacting with an OpenAPI spec). We hope to continue developing different toolkits that can enable", "source": "https://python.langchain.com/docs/modules/agents/agent_types/structured_chat"}
{"id": "31979d777674-8", "text": "database or interacting with an OpenAPI spec). We hope to continue developing different toolkits that can enable agents to do amazing feats. Toolkits are supported Mar 1, 2023 3 min read TypeScript Support It's finally here... TypeScript support for LangChain.        What does this mean? It means that all your favorite prompts, chains, and agents are all recreatable in TypeScript natively. Both the Python version and TypeScript version utilize the same serializable format, meaning that artifacts can seamlessly be shared between languages. As an Feb 17, 2023 2 min read Streaming Support in LangChain We\u00e2\u20ac\u2122re excited to announce streaming support in LangChain. There's been a lot of talk about the best UX for LLM applications, and we believe streaming is at its core. We\u00e2\u20ac\u2122ve also updated the chat-langchain repo to include streaming and async execution. We hope that this repo can serve Feb 14, 2023 2 min read LangChain + Chroma Today we\u00e2\u20ac\u2122re announcing LangChain's integration with Chroma, the first step on the path to the Modern A.I Stack.            LangChain - The A.I-native developer toolkit        We started LangChain with the intent to build a modular and flexible framework for developing A.I-native applications. Some of the use cases Feb 13, 2023 2 min read Page 1 of 2 Older Posts \u00e2\u2020\u2019 LangChain \u00c2\u00a9 2023 Sign up Powered by Ghost    Thought:    > Finished chain.    The LangChain blog has recently released an open-source auto-evaluator tool for grading LLM question-answer chains and is now releasing an open-source, free-to-use hosted app and API to expand usability. The blog also discusses various opportunities to further improve the LangChain platform.response = await", "source": "https://python.langchain.com/docs/modules/agents/agent_types/structured_chat"}
{"id": "31979d777674-9", "text": "to expand usability. The blog also discusses various opportunities to further improve the LangChain platform.response = await agent_chain.arun(input=\"What's the latest xkcd comic about?\")print(response)            > Entering new AgentExecutor chain...    Thought: I can navigate to the xkcd website and extract the latest comic title and alt text to answer the question.    Action:    ```    {      \"action\": \"navigate_browser\",      \"action_input\": {        \"url\": \"https://xkcd.com/\"      }    }    ```        Observation: Navigating to https://xkcd.com/ returned status code 200    Thought:I can extract the latest comic title and alt text using CSS selectors.    Action:    ```    {      \"action\": \"get_elements\",      \"action_input\": {        \"selector\": \"#ctitle, #comic img\",        \"attributes\": [\"alt\", \"src\"]      }    }    ```         Observation: [{\"alt\": \"Tapetum Lucidum\", \"src\": \"//imgs.xkcd.com/comics/tapetum_lucidum.png\"}]    Thought:    > Finished chain.    The latest xkcd comic is titled \"Tapetum Lucidum\" and the image can be found at https://xkcd.com/2565/.Adding in memory\u00e2\u20ac\u2039Here is how you add in memory to this agentfrom langchain.prompts import MessagesPlaceholderfrom langchain.memory import", "source": "https://python.langchain.com/docs/modules/agents/agent_types/structured_chat"}
{"id": "31979d777674-10", "text": "you add in memory to this agentfrom langchain.prompts import MessagesPlaceholderfrom langchain.memory import ConversationBufferMemorychat_history = MessagesPlaceholder(variable_name=\"chat_history\")memory = ConversationBufferMemory(memory_key=\"chat_history\", return_messages=True)agent_chain = initialize_agent(    tools,     llm,     agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,     verbose=True,     memory=memory,     agent_kwargs = {        \"memory_prompts\": [chat_history],        \"input_variables\": [\"input\", \"agent_scratchpad\", \"chat_history\"]    })response = await agent_chain.arun(input=\"Hi I'm Erica.\")print(response)            > Entering new AgentExecutor chain...    Action:    ```    {      \"action\": \"Final Answer\",      \"action_input\": \"Hi Erica! How can I assist you today?\"    }    ```            > Finished chain.    Hi Erica! How can I assist you today?response = await agent_chain.arun(input=\"whats my name?\")print(response)            > Entering new AgentExecutor chain...    Your name is Erica.        > Finished chain.    Your name is Erica.PreviousSelf ask with searchNextAdd Memory to OpenAI Functions AgentCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/agents/agent_types/structured_chat"}
{"id": "b65633e2819d-0", "text": "Page Not Found | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain\n\n\n\n\n\nSkip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKPage Not FoundWe could not find what you were looking for.Please contact the owner of the site that linked you to the original URL and let them know their link is broken.CommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/agents/agent_types/self_ask_with_search.html"}
{"id": "4d2fcc628c52-0", "text": "ReAct document store | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/agents/agent_types/react_docstore"}
{"id": "4d2fcc628c52-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsMemoryAgentsAgent typesConversationalOpenAI functionsOpenAI Multi Functions AgentPlan and executeReActReAct document storeSelf ask with searchStructured tool chatHow-toToolsToolkitsCallbacksModulesGuidesEcosystemAdditional resourcesModulesAgentsAgent typesReAct document storeReAct document storeThis walkthrough showcases using an agent to implement the ReAct logic for working with document store specifically.from langchain import OpenAI, Wikipediafrom langchain.agents import initialize_agent, Toolfrom langchain.agents import AgentTypefrom langchain.agents.react.base import DocstoreExplorerdocstore = DocstoreExplorer(Wikipedia())tools = [    Tool(        name=\"Search\",        func=docstore.search,        description=\"useful for when you need to ask with search\",    ),    Tool(        name=\"Lookup\",        func=docstore.lookup,        description=\"useful for when you need to ask with lookup\",    ),]llm = OpenAI(temperature=0, model_name=\"text-davinci-002\")react = initialize_agent(tools, llm, agent=AgentType.REACT_DOCSTORE, verbose=True)question = \"Author David Chanoff has collaborated with a U.S. Navy admiral who served as the ambassador to the United Kingdom under which President?\"react.run(question)            > Entering new AgentExecutor chain...        Thought: I need to search David Chanoff and find the", "source": "https://python.langchain.com/docs/modules/agents/agent_types/react_docstore"}
{"id": "4d2fcc628c52-2", "text": "Thought: I need to search David Chanoff and find the U.S. Navy admiral he collaborated with. Then I need to find which President the admiral served under.        Action: Search[David Chanoff]        Observation: David Chanoff is a noted author of non-fiction work. His work has typically involved collaborations with the principal protagonist of the work concerned. His collaborators have included; Augustus A. White, Joycelyn Elders, \u00c4\ufffdo\u00c3\u00a0n V\u00c4\u0192n To\u00e1\u00ba\u00a1i, William J. Crowe, Ariel Sharon, Kenneth Good and Felix Zandman. He has also written about a wide range of subjects including literary history, education and foreign for The Washington Post, The New Republic and The New York Times Magazine. He has published more than twelve books.    Thought: The U.S. Navy admiral David Chanoff collaborated with is William J. Crowe. I need to find which President he served under.        Action: Search[William J. Crowe]        Observation: William James Crowe Jr. (January 2, 1925 \u00e2\u20ac\u201c October 18, 2007) was a United States Navy admiral and diplomat who served as the 11th chairman of the Joint Chiefs of Staff under Presidents Ronald Reagan and George H. W. Bush, and as the ambassador to the United Kingdom and Chair of the Intelligence Oversight Board under President Bill Clinton.    Thought: William J. Crowe served as the ambassador to the United Kingdom under President Bill Clinton, so the answer is Bill Clinton.        Action: Finish[Bill Clinton]        > Finished chain.    'Bill Clinton'PreviousReActNextSelf ask with", "source": "https://python.langchain.com/docs/modules/agents/agent_types/react_docstore"}
{"id": "4d2fcc628c52-3", "text": "> Finished chain.    'Bill Clinton'PreviousReActNextSelf ask with searchCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/agents/agent_types/react_docstore"}
{"id": "6177e5a9f570-0", "text": "ReAct | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/agents/agent_types/react"}
{"id": "6177e5a9f570-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsMemoryAgentsAgent typesConversationalOpenAI functionsOpenAI Multi Functions AgentPlan and executeReActReAct document storeSelf ask with searchStructured tool chatHow-toToolsToolkitsCallbacksModulesGuidesEcosystemAdditional resourcesModulesAgentsAgent typesReActOn this pageReActThis walkthrough showcases using an agent to implement the ReAct logic.from langchain.agents import load_toolsfrom langchain.agents import initialize_agentfrom langchain.agents import AgentTypefrom langchain.llms import OpenAIFirst, let's load the language model we're going to use to control the agent.llm = OpenAI(temperature=0)Next, let's load some tools to use. Note that the llm-math tool uses an LLM, so we need to pass that in.tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm)Finally, let's initialize an agent with the tools, the language model, and the type of agent we want to use.agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)Now let's test it out!agent.run(\"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\")    > Entering new AgentExecutor chain...     I need to find out who Leo DiCaprio's girlfriend is and then calculate her age raised to the 0.43 power.    Action: Search    Action Input: \"Leo DiCaprio girlfriend\"    Observation: Camila Morrone    Thought: I need to find out Camila Morrone's", "source": "https://python.langchain.com/docs/modules/agents/agent_types/react"}
{"id": "6177e5a9f570-2", "text": "Camila Morrone    Thought: I need to find out Camila Morrone's age    Action: Search    Action Input: \"Camila Morrone age\"    Observation: 25 years    Thought: I need to calculate 25 raised to the 0.43 power    Action: Calculator    Action Input: 25^0.43    Observation: Answer: 3.991298452658078        Thought: I now know the final answer    Final Answer: Camila Morrone is Leo DiCaprio's girlfriend and her current age raised to the 0.43 power is 3.991298452658078.        > Finished chain.    \"Camila Morrone is Leo DiCaprio's girlfriend and her current age raised to the 0.43 power is 3.991298452658078.\"Using chat models\u00e2\u20ac\u2039You can also create ReAct agents that use chat models instead of LLMs as the agent driver.from langchain.chat_models import ChatOpenAIchat_model = ChatOpenAI(temperature=0)agent = initialize_agent(tools, chat_model, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True)agent.run(\"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\")PreviousPlan and executeNextReAct document storeUsing chat modelsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/agents/agent_types/react"}
{"id": "7bdc6867ba9a-0", "text": "Page Not Found | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain\n\n\n\n\n\nSkip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKPage Not FoundWe could not find what you were looking for.Please contact the owner of the site that linked you to the original URL and let them know their link is broken.CommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/agents/agent_types/react_docstore.html"}
{"id": "75c86010017f-0", "text": "Page Not Found | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain\n\n\n\n\n\nSkip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKPage Not FoundWe could not find what you were looking for.Please contact the owner of the site that linked you to the original URL and let them know their link is broken.CommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/agents/agent_types/openai_functions_agent.html"}
{"id": "ac0cea215bce-0", "text": "OpenAI Multi Functions Agent | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/agents/agent_types/openai_multi_functions_agent"}
{"id": "ac0cea215bce-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsMemoryAgentsAgent typesConversationalOpenAI functionsOpenAI Multi Functions AgentPlan and executeReActReAct document storeSelf ask with searchStructured tool chatHow-toToolsToolkitsCallbacksModulesGuidesEcosystemAdditional resourcesModulesAgentsAgent typesOpenAI Multi Functions AgentOn this pageOpenAI Multi Functions AgentThis notebook showcases using an agent that uses the OpenAI functions ability to respond to the prompts of the user using a Large Language ModelInstall openai,google-search-results packages which are required as the langchain packages call them internallypip install openai google-search-resultsfrom langchain import SerpAPIWrapperfrom langchain.agents import initialize_agent, Toolfrom langchain.agents import AgentTypefrom langchain.chat_models import ChatOpenAIThe agent is given ability to perform search functionalities with the respective toolSerpAPIWrapper:This initializes the SerpAPIWrapper for search functionality (search).import getpassimport osos.environ[\"SERPAPI_API_KEY\"] = getpass.getpass()    \u00c2\u00b7\u00c2\u00b7\u00c2\u00b7\u00c2\u00b7\u00c2\u00b7\u00c2\u00b7\u00c2\u00b7\u00c2\u00b7# Initialize the OpenAI language model# Replace <your_api_key> in openai_api_key=\"<your_api_key>\" with your actual OpenAI key.llm = ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo-0613\")# Initialize the SerpAPIWrapper for search functionality# Replace <your_api_key> in openai_api_key=\"<your_api_key>\" with your actual SerpAPI key.search = SerpAPIWrapper()# Define a list of tools offered by the agenttools = [    Tool(        name=\"Search\",", "source": "https://python.langchain.com/docs/modules/agents/agent_types/openai_multi_functions_agent"}
{"id": "ac0cea215bce-2", "text": "= [    Tool(        name=\"Search\",        func=search.run,        description=\"Useful when you need to answer questions about current events. You should ask targeted questions.\",    ),]mrkl = initialize_agent(    tools, llm, agent=AgentType.OPENAI_MULTI_FUNCTIONS, verbose=True)# Do this so we can see exactly what's going on under the hoodimport langchainlangchain.debug = Truemrkl.run(\"What is the weather in LA and SF?\")    [chain/start] [1:chain:AgentExecutor] Entering Chain run with input:    {      \"input\": \"What is the weather in LA and SF?\"    }    [llm/start] [1:chain:AgentExecutor > 2:llm:ChatOpenAI] Entering LLM run with input:    {      \"prompts\": [        \"System: You are a helpful AI assistant.\\nHuman: What is the weather in LA and SF?\"      ]    }    [llm/end] [1:chain:AgentExecutor > 2:llm:ChatOpenAI] [2.91s] Exiting LLM run with output:    {      \"generations\": [        [          {            \"text\": \"\",            \"generation_info\": null,            \"message\": {              \"content\": \"\",", "source": "https://python.langchain.com/docs/modules/agents/agent_types/openai_multi_functions_agent"}
{"id": "ac0cea215bce-3", "text": "\"content\": \"\",              \"additional_kwargs\": {                \"function_call\": {                  \"name\": \"tool_selection\",                  \"arguments\": \"{\\n  \\\"actions\\\": [\\n    {\\n      \\\"action_name\\\": \\\"Search\\\",\\n      \\\"action\\\": {\\n        \\\"tool_input\\\": \\\"weather in Los Angeles\\\"\\n      }\\n    },\\n    {\\n      \\\"action_name\\\": \\\"Search\\\",\\n      \\\"action\\\": {\\n        \\\"tool_input\\\": \\\"weather in San Francisco\\\"\\n      }\\n    }\\n  ]\\n}\"                }              },              \"example\": false            }          }        ]      ],      \"llm_output\": {        \"token_usage\": {          \"prompt_tokens\": 81,          \"completion_tokens\": 75,          \"total_tokens\": 156        },        \"model_name\":", "source": "https://python.langchain.com/docs/modules/agents/agent_types/openai_multi_functions_agent"}
{"id": "ac0cea215bce-4", "text": "},        \"model_name\": \"gpt-3.5-turbo-0613\"      },      \"run\": null    }    [tool/start] [1:chain:AgentExecutor > 3:tool:Search] Entering Tool run with input:    \"{'tool_input': 'weather in Los Angeles'}\"    [tool/end] [1:chain:AgentExecutor > 3:tool:Search] [608.693ms] Exiting Tool run with output:    \"Mostly cloudy early, then sunshine for the afternoon. High 76F. Winds SW at 5 to 10 mph. Humidity59%.\"    [tool/start] [1:chain:AgentExecutor > 4:tool:Search] Entering Tool run with input:    \"{'tool_input': 'weather in San Francisco'}\"    [tool/end] [1:chain:AgentExecutor > 4:tool:Search] [517.475ms] Exiting Tool run with output:    \"Partly cloudy this evening, then becoming cloudy after midnight. Low 53F. Winds WSW at 10 to 20 mph. Humidity83%.\"    [llm/start] [1:chain:AgentExecutor > 5:llm:ChatOpenAI] Entering LLM run with input:    {      \"prompts\": [        \"System: You are a helpful AI assistant.\\nHuman: What is the weather in LA and SF?\\nAI: {'name': 'tool_selection', 'arguments': '{\\\\n  \\\"actions\\\": [\\\\n    {\\\\n      \\\"action_name\\\": \\\"Search\\\",\\\\n", "source": "https://python.langchain.com/docs/modules/agents/agent_types/openai_multi_functions_agent"}
{"id": "ac0cea215bce-5", "text": "{\\\\n      \\\"action_name\\\": \\\"Search\\\",\\\\n      \\\"action\\\": {\\\\n        \\\"tool_input\\\": \\\"weather in Los Angeles\\\"\\\\n      }\\\\n    },\\\\n    {\\\\n      \\\"action_name\\\": \\\"Search\\\",\\\\n      \\\"action\\\": {\\\\n        \\\"tool_input\\\": \\\"weather in San Francisco\\\"\\\\n      }\\\\n    }\\\\n  ]\\\\n}'}\\nFunction: Mostly cloudy early, then sunshine for the afternoon. High 76F. Winds SW at 5 to 10 mph. Humidity59%.\\nAI: {'name': 'tool_selection', 'arguments': '{\\\\n  \\\"actions\\\": [\\\\n    {\\\\n      \\\"action_name\\\": \\\"Search\\\",\\\\n      \\\"action\\\": {\\\\n        \\\"tool_input\\\": \\\"weather in Los Angeles\\\"\\\\n      }\\\\n    },\\\\n    {\\\\n      \\\"action_name\\\": \\\"Search\\\",\\\\n      \\\"action\\\": {\\\\n        \\\"tool_input\\\": \\\"weather in San Francisco\\\"\\\\n      }\\\\n    }\\\\n  ]\\\\n}'}\\nFunction: Partly cloudy this evening, then becoming cloudy after midnight. Low 53F. Winds WSW at 10 to 20 mph. Humidity83%.\"      ]    }    [llm/end] [1:chain:AgentExecutor > 5:llm:ChatOpenAI]", "source": "https://python.langchain.com/docs/modules/agents/agent_types/openai_multi_functions_agent"}
{"id": "ac0cea215bce-6", "text": "[1:chain:AgentExecutor > 5:llm:ChatOpenAI] [2.33s] Exiting LLM run with output:    {      \"generations\": [        [          {            \"text\": \"The weather in Los Angeles is mostly cloudy with a high of 76\u00c2\u00b0F and a humidity of 59%. The weather in San Francisco is partly cloudy in the evening, becoming cloudy after midnight, with a low of 53\u00c2\u00b0F and a humidity of 83%.\",            \"generation_info\": null,            \"message\": {              \"content\": \"The weather in Los Angeles is mostly cloudy with a high of 76\u00c2\u00b0F and a humidity of 59%. The weather in San Francisco is partly cloudy in the evening, becoming cloudy after midnight, with a low of 53\u00c2\u00b0F and a humidity of 83%.\",              \"additional_kwargs\": {},              \"example\": false            }          }        ]      ],      \"llm_output\": {        \"token_usage\": {          \"prompt_tokens\": 307,          \"completion_tokens\": 54,          \"total_tokens\": 361        },        \"model_name\":", "source": "https://python.langchain.com/docs/modules/agents/agent_types/openai_multi_functions_agent"}
{"id": "ac0cea215bce-7", "text": "},        \"model_name\": \"gpt-3.5-turbo-0613\"      },      \"run\": null    }    [chain/end] [1:chain:AgentExecutor] [6.37s] Exiting Chain run with output:    {      \"output\": \"The weather in Los Angeles is mostly cloudy with a high of 76\u00c2\u00b0F and a humidity of 59%. The weather in San Francisco is partly cloudy in the evening, becoming cloudy after midnight, with a low of 53\u00c2\u00b0F and a humidity of 83%.\"    }    'The weather in Los Angeles is mostly cloudy with a high of 76\u00c2\u00b0F and a humidity of 59%. The weather in San Francisco is partly cloudy in the evening, becoming cloudy after midnight, with a low of 53\u00c2\u00b0F and a humidity of 83%.'Configuring max iteration behavior\u00e2\u20ac\u2039To make sure that our agent doesn't get stuck in excessively long loops, we can set max_iterations. We can also set an early stopping method, which will determine our agent's behavior once the number of max iterations is hit. By default, the early stopping uses method force which just returns that constant string. Alternatively, you could specify method generate which then does one FINAL pass through the LLM to generate an output.mrkl = initialize_agent(    tools,    llm,    agent=AgentType.OPENAI_FUNCTIONS,    verbose=True,    max_iterations=2,    early_stopping_method=\"generate\",)mrkl.run(\"What is the weather in NYC today, yesterday, and the day before?\")    [chain/start] [1:chain:AgentExecutor] Entering Chain run with input:", "source": "https://python.langchain.com/docs/modules/agents/agent_types/openai_multi_functions_agent"}
{"id": "ac0cea215bce-8", "text": "[chain/start] [1:chain:AgentExecutor] Entering Chain run with input:    {      \"input\": \"What is the weather in NYC today, yesterday, and the day before?\"    }    [llm/start] [1:chain:AgentExecutor > 2:llm:ChatOpenAI] Entering LLM run with input:    {      \"prompts\": [        \"System: You are a helpful AI assistant.\\nHuman: What is the weather in NYC today, yesterday, and the day before?\"      ]    }    [llm/end] [1:chain:AgentExecutor > 2:llm:ChatOpenAI] [1.27s] Exiting LLM run with output:    {      \"generations\": [        [          {            \"text\": \"\",            \"generation_info\": null,            \"message\": {              \"lc\": 1,              \"type\": \"constructor\",              \"id\": [                \"langchain\",                \"schema\",                \"messages\",                \"AIMessage\"", "source": "https://python.langchain.com/docs/modules/agents/agent_types/openai_multi_functions_agent"}
{"id": "ac0cea215bce-9", "text": "\"AIMessage\"              ],              \"kwargs\": {                \"content\": \"\",                \"additional_kwargs\": {                  \"function_call\": {                    \"name\": \"Search\",                    \"arguments\": \"{\\n  \\\"query\\\": \\\"weather in NYC today\\\"\\n}\"                  }                }              }            }          }        ]      ],      \"llm_output\": {        \"token_usage\": {          \"prompt_tokens\": 79,          \"completion_tokens\": 17,          \"total_tokens\": 96        },        \"model_name\": \"gpt-3.5-turbo-0613\"      },      \"run\": null    }    [tool/start] [1:chain:AgentExecutor > 3:tool:Search] Entering Tool run with", "source": "https://python.langchain.com/docs/modules/agents/agent_types/openai_multi_functions_agent"}
{"id": "ac0cea215bce-10", "text": "[1:chain:AgentExecutor > 3:tool:Search] Entering Tool run with input:    \"{'query': 'weather in NYC today'}\"    [tool/end] [1:chain:AgentExecutor > 3:tool:Search] [3.84s] Exiting Tool run with output:    \"10:00 am \u00c2\u00b7 Feels Like85\u00c2\u00b0 \u00c2\u00b7 WindSE 4 mph \u00c2\u00b7 Humidity78% \u00c2\u00b7 UV Index3 of 11 \u00c2\u00b7 Cloud Cover81% \u00c2\u00b7 Rain Amount0 in ...\"    [llm/start] [1:chain:AgentExecutor > 4:llm:ChatOpenAI] Entering LLM run with input:    {      \"prompts\": [        \"System: You are a helpful AI assistant.\\nHuman: What is the weather in NYC today, yesterday, and the day before?\\nAI: {'name': 'Search', 'arguments': '{\\\\n  \\\"query\\\": \\\"weather in NYC today\\\"\\\\n}'}\\nFunction: 10:00 am \u00c2\u00b7 Feels Like85\u00c2\u00b0 \u00c2\u00b7 WindSE 4 mph \u00c2\u00b7 Humidity78% \u00c2\u00b7 UV Index3 of 11 \u00c2\u00b7 Cloud Cover81% \u00c2\u00b7 Rain Amount0 in ...\"      ]    }    [llm/end] [1:chain:AgentExecutor > 4:llm:ChatOpenAI] [1.24s] Exiting LLM run with output:    {      \"generations\": [        [          {            \"text\": \"\",            \"generation_info\": null,", "source": "https://python.langchain.com/docs/modules/agents/agent_types/openai_multi_functions_agent"}
{"id": "ac0cea215bce-11", "text": "\"generation_info\": null,            \"message\": {              \"lc\": 1,              \"type\": \"constructor\",              \"id\": [                \"langchain\",                \"schema\",                \"messages\",                \"AIMessage\"              ],              \"kwargs\": {                \"content\": \"\",                \"additional_kwargs\": {                  \"function_call\": {                    \"name\": \"Search\",                    \"arguments\": \"{\\n  \\\"query\\\": \\\"weather in NYC yesterday\\\"\\n}\"                  }                }              }            }          }        ]      ],", "source": "https://python.langchain.com/docs/modules/agents/agent_types/openai_multi_functions_agent"}
{"id": "ac0cea215bce-12", "text": "}        ]      ],      \"llm_output\": {        \"token_usage\": {          \"prompt_tokens\": 142,          \"completion_tokens\": 17,          \"total_tokens\": 159        },        \"model_name\": \"gpt-3.5-turbo-0613\"      },      \"run\": null    }    [tool/start] [1:chain:AgentExecutor > 5:tool:Search] Entering Tool run with input:    \"{'query': 'weather in NYC yesterday'}\"    [tool/end] [1:chain:AgentExecutor > 5:tool:Search] [1.15s] Exiting Tool run with output:    \"New York Temperature Yesterday. Maximum temperature yesterday: 81 \u00c2\u00b0F (at 1:51 pm) Minimum temperature yesterday: 72 \u00c2\u00b0F (at 7:17 pm) Average temperature ...\"    [llm/start] [1:llm:ChatOpenAI] Entering LLM run with input:    {      \"prompts\": [        \"System: You are a helpful AI assistant.\\nHuman: What is the weather in NYC today, yesterday, and the day before?\\nAI: {'name': 'Search', 'arguments': '{\\\\n  \\\"query\\\": \\\"weather in NYC today\\\"\\\\n}'}\\nFunction: 10:00 am \u00c2\u00b7 Feels Like85\u00c2\u00b0 \u00c2\u00b7 WindSE 4 mph \u00c2\u00b7 Humidity78% \u00c2\u00b7 UV Index3", "source": "https://python.langchain.com/docs/modules/agents/agent_types/openai_multi_functions_agent"}
{"id": "ac0cea215bce-13", "text": "\u00c2\u00b7 WindSE 4 mph \u00c2\u00b7 Humidity78% \u00c2\u00b7 UV Index3 of 11 \u00c2\u00b7 Cloud Cover81% \u00c2\u00b7 Rain Amount0 in ...\\nAI: {'name': 'Search', 'arguments': '{\\\\n  \\\"query\\\": \\\"weather in NYC yesterday\\\"\\\\n}'}\\nFunction: New York Temperature Yesterday. Maximum temperature yesterday: 81 \u00c2\u00b0F (at 1:51 pm) Minimum temperature yesterday: 72 \u00c2\u00b0F (at 7:17 pm) Average temperature ...\"      ]    }    [llm/end] [1:llm:ChatOpenAI] [2.68s] Exiting LLM run with output:    {      \"generations\": [        [          {            \"text\": \"Today in NYC, the weather is currently 85\u00c2\u00b0F with a southeast wind of 4 mph. The humidity is at 78% and there is 81% cloud cover. There is no rain expected today.\\n\\nYesterday in NYC, the maximum temperature was 81\u00c2\u00b0F at 1:51 pm, and the minimum temperature was 72\u00c2\u00b0F at 7:17 pm.\\n\\nFor the day before yesterday, I do not have the specific weather information.\",            \"generation_info\": null,            \"message\": {              \"lc\": 1,              \"type\": \"constructor\",              \"id\": [                \"langchain\",", "source": "https://python.langchain.com/docs/modules/agents/agent_types/openai_multi_functions_agent"}
{"id": "ac0cea215bce-14", "text": "\"langchain\",                \"schema\",                \"messages\",                \"AIMessage\"              ],              \"kwargs\": {                \"content\": \"Today in NYC, the weather is currently 85\u00c2\u00b0F with a southeast wind of 4 mph. The humidity is at 78% and there is 81% cloud cover. There is no rain expected today.\\n\\nYesterday in NYC, the maximum temperature was 81\u00c2\u00b0F at 1:51 pm, and the minimum temperature was 72\u00c2\u00b0F at 7:17 pm.\\n\\nFor the day before yesterday, I do not have the specific weather information.\",                \"additional_kwargs\": {}              }            }          }        ]      ],      \"llm_output\": {        \"token_usage\": {          \"prompt_tokens\": 160,          \"completion_tokens\": 91,          \"total_tokens\": 251        },        \"model_name\": \"gpt-3.5-turbo-0613\"", "source": "https://python.langchain.com/docs/modules/agents/agent_types/openai_multi_functions_agent"}
{"id": "ac0cea215bce-15", "text": "\"gpt-3.5-turbo-0613\"      },      \"run\": null    }    [chain/end] [1:chain:AgentExecutor] [10.18s] Exiting Chain run with output:    {      \"output\": \"Today in NYC, the weather is currently 85\u00c2\u00b0F with a southeast wind of 4 mph. The humidity is at 78% and there is 81% cloud cover. There is no rain expected today.\\n\\nYesterday in NYC, the maximum temperature was 81\u00c2\u00b0F at 1:51 pm, and the minimum temperature was 72\u00c2\u00b0F at 7:17 pm.\\n\\nFor the day before yesterday, I do not have the specific weather information.\"    }    'Today in NYC, the weather is currently 85\u00c2\u00b0F with a southeast wind of 4 mph. The humidity is at 78% and there is 81% cloud cover. There is no rain expected today.\\n\\nYesterday in NYC, the maximum temperature was 81\u00c2\u00b0F at 1:51 pm, and the minimum temperature was 72\u00c2\u00b0F at 7:17 pm.\\n\\nFor the day before yesterday, I do not have the specific weather information.'Notice that we never get around to looking up the weather the day before yesterday, due to hitting our max_iterations limit.PreviousOpenAI functionsNextPlan and executeConfiguring max iteration behaviorCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/agents/agent_types/openai_multi_functions_agent"}
{"id": "524f445340e5-0", "text": "Page Not Found | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain\n\n\n\n\n\nSkip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKPage Not FoundWe could not find what you were looking for.Please contact the owner of the site that linked you to the original URL and let them know their link is broken.CommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/agents/agent_types/plan_and_execute.html"}
{"id": "e5db14e50ad7-0", "text": "Conversational | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/agents/agent_types/chat_conversation_agent"}
{"id": "e5db14e50ad7-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsMemoryAgentsAgent typesConversationalOpenAI functionsOpenAI Multi Functions AgentPlan and executeReActReAct document storeSelf ask with searchStructured tool chatHow-toToolsToolkitsCallbacksModulesGuidesEcosystemAdditional resourcesModulesAgentsAgent typesConversationalOn this pageConversationalThis walkthrough demonstrates how to use an agent optimized for conversation. Other agents are often optimized for using tools to figure out the best response, which is not ideal in a conversational setting where you may want the agent to be able to chat with the user as well.This is accomplished with a specific type of agent (conversational-react-description) which expects to be used with a memory component.from langchain.agents import Toolfrom langchain.agents import AgentTypefrom langchain.memory import ConversationBufferMemoryfrom langchain import OpenAIfrom langchain.utilities import SerpAPIWrapperfrom langchain.agents import initialize_agentsearch = SerpAPIWrapper()tools = [    Tool(        name = \"Current Search\",        func=search.run,        description=\"useful for when you need to answer questions about current events or the current state of the world\"    ),]memory = ConversationBufferMemory(memory_key=\"chat_history\")llm=OpenAI(temperature=0)agent_chain = initialize_agent(tools, llm, agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION, verbose=True, memory=memory)agent_chain.run(input=\"hi, i am bob\")    > Entering new AgentExecutor chain...        Thought: Do I need to use a tool? No", "source": "https://python.langchain.com/docs/modules/agents/agent_types/chat_conversation_agent"}
{"id": "e5db14e50ad7-2", "text": "Thought: Do I need to use a tool? No    AI: Hi Bob, nice to meet you! How can I help you today?        > Finished chain.    'Hi Bob, nice to meet you! How can I help you today?'agent_chain.run(input=\"what's my name?\")    > Entering new AgentExecutor chain...        Thought: Do I need to use a tool? No    AI: Your name is Bob!        > Finished chain.    'Your name is Bob!'agent_chain.run(\"what are some good dinners to make this week, if i like thai food?\")    > Entering new AgentExecutor chain...        Thought: Do I need to use a tool? Yes    Action: Current Search    Action Input: Thai food dinner recipes    Observation: 59 easy Thai recipes for any night of the week \u00c2\u00b7 Marion Grasby's Thai spicy chilli and basil fried rice \u00c2\u00b7 Thai curry noodle soup \u00c2\u00b7 Marion Grasby's Thai Spicy ...    Thought: Do I need to use a tool? No    AI: Here are some great Thai dinner recipes you can try this week: Marion Grasby's Thai Spicy Chilli and Basil Fried Rice, Thai Curry Noodle Soup, Thai Green Curry with Coconut Rice, Thai Red Curry with Vegetables, and Thai Coconut Soup. I hope you enjoy them!        > Finished chain.    \"Here are some great Thai dinner recipes you can try this week: Marion Grasby's Thai Spicy Chilli and Basil Fried Rice, Thai Curry Noodle Soup, Thai Green Curry with Coconut Rice, Thai Red Curry with Vegetables, and Thai Coconut Soup.", "source": "https://python.langchain.com/docs/modules/agents/agent_types/chat_conversation_agent"}
{"id": "e5db14e50ad7-3", "text": "Thai Green Curry with Coconut Rice, Thai Red Curry with Vegetables, and Thai Coconut Soup. I hope you enjoy them!\"agent_chain.run(input=\"tell me the last letter in my name, and also tell me who won the world cup in 1978?\")    > Entering new AgentExecutor chain...        Thought: Do I need to use a tool? Yes    Action: Current Search    Action Input: Who won the World Cup in 1978    Observation: Argentina national football team    Thought: Do I need to use a tool? No    AI: The last letter in your name is \"b\" and the winner of the 1978 World Cup was the Argentina national football team.        > Finished chain.    'The last letter in your name is \"b\" and the winner of the 1978 World Cup was the Argentina national football team.'agent_chain.run(input=\"whats the current temperature in pomfret?\")    > Entering new AgentExecutor chain...        Thought: Do I need to use a tool? Yes    Action: Current Search    Action Input: Current temperature in Pomfret    Observation: Partly cloudy skies. High around 70F. Winds W at 5 to 10 mph. Humidity41%.    Thought: Do I need to use a tool? No    AI: The current temperature in Pomfret is around 70F with partly cloudy skies and winds W at 5 to 10 mph. The humidity is 41%.        > Finished chain.    'The current temperature in Pomfret is around 70F with partly cloudy skies and winds W at 5 to 10 mph. The humidity is 41%.'Using a chat", "source": "https://python.langchain.com/docs/modules/agents/agent_types/chat_conversation_agent"}
{"id": "e5db14e50ad7-4", "text": "winds W at 5 to 10 mph. The humidity is 41%.'Using a chat model\u00e2\u20ac\u2039The chat-conversational-react-description agent type lets us create a conversational agent using a chat model instead of an LLM.from langchain.memory import ConversationBufferMemoryfrom langchain.chat_models import ChatOpenAImemory = ConversationBufferMemory(memory_key=\"chat_history\", return_messages=True)llm = ChatOpenAI(openai_api_key=OPENAI_API_KEY, temperature=0)agent_chain = initialize_agent(tools, llm, agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION, verbose=True, memory=memory)agent_chain.run(input=\"hi, i am bob\")    > Entering new AgentExecutor chain...    {        \"action\": \"Final Answer\",        \"action_input\": \"Hello Bob! How can I assist you today?\"    }        > Finished chain.    'Hello Bob! How can I assist you today?'agent_chain.run(input=\"what's my name?\")    > Entering new AgentExecutor chain...    {        \"action\": \"Final Answer\",        \"action_input\": \"Your name is Bob.\"    }        > Finished chain.    'Your name is Bob.'agent_chain.run(\"what are some good dinners to make this week, if i like thai food?\")    > Entering new AgentExecutor chain...    {        \"action\": \"Current Search\",        \"action_input\": \"Thai food dinner recipes\"    }    Observation: 64 easy Thai recipes for any night of the week \u00c2\u00b7 Thai curry noodle soup", "source": "https://python.langchain.com/docs/modules/agents/agent_types/chat_conversation_agent"}
{"id": "e5db14e50ad7-5", "text": "Observation: 64 easy Thai recipes for any night of the week \u00c2\u00b7 Thai curry noodle soup \u00c2\u00b7 Thai yellow cauliflower, snake bean and tofu curry \u00c2\u00b7 Thai-spiced chicken hand pies \u00c2\u00b7 Thai ...    Thought:{        \"action\": \"Final Answer\",        \"action_input\": \"Here are some Thai food dinner recipes you can try this week: Thai curry noodle soup, Thai yellow cauliflower, snake bean and tofu curry, Thai-spiced chicken hand pies, and many more. You can find the full list of recipes at the source I found earlier.\"    }        > Finished chain.    'Here are some Thai food dinner recipes you can try this week: Thai curry noodle soup, Thai yellow cauliflower, snake bean and tofu curry, Thai-spiced chicken hand pies, and many more. You can find the full list of recipes at the source I found earlier.'agent_chain.run(input=\"tell me the last letter in my name, and also tell me who won the world cup in 1978?\")    > Entering new AgentExecutor chain...    {        \"action\": \"Final Answer\",        \"action_input\": \"The last letter in your name is 'b'. Argentina won the World Cup in 1978.\"    }        > Finished chain.    \"The last letter in your name is 'b'. Argentina won the World Cup in 1978.\"agent_chain.run(input=\"whats the weather like in pomfret?\")    > Entering new AgentExecutor chain...    {        \"action\": \"Current Search\",        \"action_input\": \"weather in pomfret\"    }", "source": "https://python.langchain.com/docs/modules/agents/agent_types/chat_conversation_agent"}
{"id": "e5db14e50ad7-6", "text": "\"action_input\": \"weather in pomfret\"    }    Observation: Cloudy with showers. Low around 55F. Winds S at 5 to 10 mph. Chance of rain 60%. Humidity76%.    Thought:{        \"action\": \"Final Answer\",        \"action_input\": \"Cloudy with showers. Low around 55F. Winds S at 5 to 10 mph. Chance of rain 60%. Humidity76%.\"    }        > Finished chain.    'Cloudy with showers. Low around 55F. Winds S at 5 to 10 mph. Chance of rain 60%. Humidity76%.'PreviousAgent typesNextOpenAI functionsUsing a chat modelCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/agents/agent_types/chat_conversation_agent"}
{"id": "3ebc05ce5cc4-0", "text": "OpenAI functions | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/agents/agent_types/openai_functions_agent"}
{"id": "3ebc05ce5cc4-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsMemoryAgentsAgent typesConversationalOpenAI functionsOpenAI Multi Functions AgentPlan and executeReActReAct document storeSelf ask with searchStructured tool chatHow-toToolsToolkitsCallbacksModulesGuidesEcosystemAdditional resourcesModulesAgentsAgent typesOpenAI functionsOpenAI functionsCertain OpenAI models (like gpt-3.5-turbo-0613 and gpt-4-0613) have been fine-tuned to detect when a function should to be called and respond with the inputs that should be passed to the function.\nIn an API call, you can describe functions and have the model intelligently choose to output a JSON object containing arguments to call those functions.", "source": "https://python.langchain.com/docs/modules/agents/agent_types/openai_functions_agent"}
{"id": "3ebc05ce5cc4-2", "text": "The goal of the OpenAI Function APIs is to more reliably return valid and useful function calls than a generic text completion or chat API.The OpenAI Functions Agent is designed to work with these models.Install openai,google-search-results packages which are required as the langchain packages call them internallypip install openai google-search-resultsfrom langchain import LLMMathChain, OpenAI, SerpAPIWrapper, SQLDatabase, SQLDatabaseChainfrom langchain.agents import initialize_agent, Toolfrom langchain.agents import AgentTypefrom langchain.chat_models import ChatOpenAIllm = ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo-0613\")search = SerpAPIWrapper()llm_math_chain = LLMMathChain.from_llm(llm=llm, verbose=True)db = SQLDatabase.from_uri(\"sqlite:///../../../../../notebooks/Chinook.db\")db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)tools = [    Tool(        name = \"Search\",        func=search.run,        description=\"useful for when you need to answer questions about current events. You should ask targeted questions\"    ),    Tool(        name=\"Calculator\",        func=llm_math_chain.run,        description=\"useful for when you need to answer questions about math\"    ),    Tool(        name=\"FooBar-DB\",        func=db_chain.run,        description=\"useful for when you need to answer questions about FooBar. Input should be in the form of a question containing full context\"    )]agent =", "source": "https://python.langchain.com/docs/modules/agents/agent_types/openai_functions_agent"}
{"id": "3ebc05ce5cc4-3", "text": "Input should be in the form of a question containing full context\"    )]agent = initialize_agent(tools, llm, agent=AgentType.OPENAI_FUNCTIONS, verbose=True)agent.run(\"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\")    > Entering new  chain...        Invoking: `Search` with `{'query': 'Leo DiCaprio girlfriend'}`            Amidst his casual romance with Gigi, Leo allegedly entered a relationship with 19-year old model, Eden Polani, in February 2023.    Invoking: `Calculator` with `{'expression': '19^0.43'}`        > Entering new  chain...    19^0.43```text    19**0.43    ```    ...numexpr.evaluate(\"19**0.43\")...        Answer: 3.547023357958959    > Finished chain.    Answer: 3.547023357958959Leo DiCaprio's girlfriend is reportedly Eden Polani. Her current age raised to the power of 0.43 is approximately 3.55.        > Finished chain.    \"Leo DiCaprio's girlfriend is reportedly Eden Polani. Her current age raised to the power of 0.43 is approximately 3.55.\"PreviousConversationalNextOpenAI Multi Functions AgentCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/agents/agent_types/openai_functions_agent"}
{"id": "6df050fdc2b7-0", "text": "Plan and execute | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/agents/agent_types/plan_and_execute"}
{"id": "6df050fdc2b7-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsMemoryAgentsAgent typesConversationalOpenAI functionsOpenAI Multi Functions AgentPlan and executeReActReAct document storeSelf ask with searchStructured tool chatHow-toToolsToolkitsCallbacksModulesGuidesEcosystemAdditional resourcesModulesAgentsAgent typesPlan and executePlan and executePlan and execute agents accomplish an objective by first planning what to do, then executing the sub tasks. This idea is largely inspired by BabyAGI and then the \"Plan-and-Solve\" paper.The planning is almost always done by an LLM.The execution is usually done by a separate agent (equipped with tools).Imports\u00e2\u20ac\u2039from langchain.chat_models import ChatOpenAIfrom langchain.experimental.plan_and_execute import PlanAndExecute, load_agent_executor, load_chat_plannerfrom langchain.llms import OpenAIfrom langchain import SerpAPIWrapperfrom langchain.agents.tools import Toolfrom langchain import LLMMathChainTools\u00e2\u20ac\u2039search = SerpAPIWrapper()llm = OpenAI(temperature=0)llm_math_chain = LLMMathChain.from_llm(llm=llm, verbose=True)tools = [    Tool(        name = \"Search\",        func=search.run,        description=\"useful for when you need to answer questions about current events\"    ),    Tool(        name=\"Calculator\",        func=llm_math_chain.run,        description=\"useful for when you need to answer questions about math\"    ),]Planner,", "source": "https://python.langchain.com/docs/modules/agents/agent_types/plan_and_execute"}
{"id": "6df050fdc2b7-2", "text": "for when you need to answer questions about math\"    ),]Planner, Executor, and Agent\u00e2\u20ac\u2039model = ChatOpenAI(temperature=0)planner = load_chat_planner(model)executor = load_agent_executor(model, tools, verbose=True)agent = PlanAndExecute(planner=planner, executor=executor, verbose=True)Run Example\u00e2\u20ac\u2039agent.run(\"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\")            > Entering new PlanAndExecute chain...    steps=[Step(value=\"Search for Leo DiCaprio's girlfriend on the internet.\"), Step(value='Find her current age.'), Step(value='Raise her current age to the 0.43 power using a calculator or programming language.'), Step(value='Output the result.'), Step(value=\"Given the above steps taken, respond to the user's original question.\\n\\n\")]        > Entering new AgentExecutor chain...    Action:    ```    {      \"action\": \"Search\",      \"action_input\": \"Who is Leo DiCaprio's girlfriend?\"    }    ```             Observation: DiCaprio broke up with girlfriend Camila Morrone, 25, in the summer of 2022, after dating for four years. He's since been linked to another famous supermodel \u00e2\u20ac\u201c Gigi Hadid. The power couple were first supposedly an item in September after being spotted getting cozy during a party at New York Fashion Week.    Thought:Based on the previous observation, I can provide the answer to the current objective.     Action:    ```    {      \"action\":", "source": "https://python.langchain.com/docs/modules/agents/agent_types/plan_and_execute"}
{"id": "6df050fdc2b7-3", "text": "Action:    ```    {      \"action\": \"Final Answer\",      \"action_input\": \"Leo DiCaprio is currently linked to Gigi Hadid.\"    }    ```            > Finished chain.    *****        Step: Search for Leo DiCaprio's girlfriend on the internet.        Response: Leo DiCaprio is currently linked to Gigi Hadid.        > Entering new AgentExecutor chain...    Action:    ```    {      \"action\": \"Search\",      \"action_input\": \"What is Gigi Hadid's current age?\"    }    ```        Observation: 28 years    Thought:Previous steps: steps=[(Step(value=\"Search for Leo DiCaprio's girlfriend on the internet.\"), StepResponse(response='Leo DiCaprio is currently linked to Gigi Hadid.'))]        Current objective: value='Find her current age.'        Action:    ```    {      \"action\": \"Search\",      \"action_input\": \"What is Gigi Hadid's current age?\"    }    ```            Observation: 28 years    Thought:Previous steps: steps=[(Step(value=\"Search for Leo DiCaprio's girlfriend on the internet.\"), StepResponse(response='Leo DiCaprio is currently linked to Gigi Hadid.')), (Step(value='Find her current age.'), StepResponse(response='28 years'))]        Current", "source": "https://python.langchain.com/docs/modules/agents/agent_types/plan_and_execute"}
{"id": "6df050fdc2b7-4", "text": "current age.'), StepResponse(response='28 years'))]        Current objective: None        Action:    ```    {      \"action\": \"Final Answer\",      \"action_input\": \"Gigi Hadid's current age is 28 years.\"    }    ```                > Finished chain.    *****        Step: Find her current age.        Response: Gigi Hadid's current age is 28 years.        > Entering new AgentExecutor chain...    Action:    ```    {      \"action\": \"Calculator\",      \"action_input\": \"28 ** 0.43\"    }    ```            > Entering new LLMMathChain chain...    28 ** 0.43    ```text    28 ** 0.43    ```    ...numexpr.evaluate(\"28 ** 0.43\")...        Answer: 4.1906168361987195    > Finished chain.        Observation: Answer: 4.1906168361987195    Thought:The next step is to provide the answer to the user's question.        Action:    ```    {      \"action\": \"Final Answer\",      \"action_input\": \"Gigi Hadid's current age raised to the 0.43 power is approximately 4.19.\"    }", "source": "https://python.langchain.com/docs/modules/agents/agent_types/plan_and_execute"}
{"id": "6df050fdc2b7-5", "text": "to the 0.43 power is approximately 4.19.\"    }    ```                > Finished chain.    *****        Step: Raise her current age to the 0.43 power using a calculator or programming language.        Response: Gigi Hadid's current age raised to the 0.43 power is approximately 4.19.        > Entering new AgentExecutor chain...    Action:    ```    {      \"action\": \"Final Answer\",      \"action_input\": \"The result is approximately 4.19.\"    }    ```            > Finished chain.    *****        Step: Output the result.        Response: The result is approximately 4.19.        > Entering new AgentExecutor chain...    Action:    ```    {      \"action\": \"Final Answer\",      \"action_input\": \"Gigi Hadid's current age raised to the 0.43 power is approximately 4.19.\"    }    ```            > Finished chain.    *****        Step: Given the above steps taken, respond to the user's original question.                Response: Gigi Hadid's current age raised to the 0.43 power is approximately 4.19.    > Finished chain.    \"Gigi Hadid's current age raised", "source": "https://python.langchain.com/docs/modules/agents/agent_types/plan_and_execute"}
{"id": "6df050fdc2b7-6", "text": "> Finished chain.    \"Gigi Hadid's current age raised to the 0.43 power is approximately 4.19.\"PreviousOpenAI Multi Functions AgentNextReActCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/agents/agent_types/plan_and_execute"}
{"id": "9264a2b044c5-0", "text": "Toolkits | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain\n\n\n\n\n\nSkip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsMemoryAgentsAgent typesHow-toToolsToolkitsCallbacksModulesGuidesEcosystemAdditional resourcesModulesAgentsToolkitsToolkitsinfoHead to Integrations for documentation on built-in toolkit integrations.Toolkits are collections of tools that are designed to be used together for specific tasks and have convenience loading methods.PreviousTools as OpenAI FunctionsNextCallbacksCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/agents/toolkits/"}
{"id": "b1a4fe01ec7f-0", "text": "Add Memory to OpenAI Functions Agent | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/agents/how_to/add_memory_openai_functions"}
{"id": "b1a4fe01ec7f-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsMemoryAgentsAgent typesHow-toAdd Memory to OpenAI Functions AgentRunning Agent as an IteratorCombine agents and vector storesAsync APICreate ChatGPT cloneCustom functions with OpenAI Functions AgentCustom agentCustom agent with tool retrievalCustom LLM AgentCustom LLM Agent (with a ChatModel)Custom MRKL agentCustom multi-action agentHandle parsing errorsAccess intermediate stepsCap the max number of iterationsTimeouts for agentsReplicating MRKLShared memory across agents and toolsStreaming final agent outputUse ToolKits with OpenAI FunctionsToolsToolkitsCallbacksModulesGuidesEcosystemAdditional resourcesModulesAgentsHow-toAdd Memory to OpenAI Functions AgentAdd Memory to OpenAI Functions AgentThis notebook goes over how to add memory to OpenAI Functions agent.from langchain import (    LLMMathChain,    OpenAI,    SerpAPIWrapper,    SQLDatabase,    SQLDatabaseChain,)from langchain.agents import initialize_agent, Toolfrom langchain.agents import AgentTypefrom langchain.chat_models import ChatOpenAI    /Users/harrisonchase/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/site-packages/deeplake/util/check_latest_version.py:32: UserWarning: A newer version of deeplake (3.6.4) is available. It's recommended that you update to the latest version using `pip install -U deeplake`.      warnings.warn(llm = ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo-0613\")search = SerpAPIWrapper()llm_math_chain =", "source": "https://python.langchain.com/docs/modules/agents/how_to/add_memory_openai_functions"}
{"id": "b1a4fe01ec7f-2", "text": "= SerpAPIWrapper()llm_math_chain = LLMMathChain.from_llm(llm=llm, verbose=True)db = SQLDatabase.from_uri(\"sqlite:///../../../../../notebooks/Chinook.db\")db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)tools = [    Tool(        name=\"Search\",        func=search.run,        description=\"useful for when you need to answer questions about current events. You should ask targeted questions\",    ),    Tool(        name=\"Calculator\",        func=llm_math_chain.run,        description=\"useful for when you need to answer questions about math\",    ),    Tool(        name=\"FooBar-DB\",        func=db_chain.run,        description=\"useful for when you need to answer questions about FooBar. Input should be in the form of a question containing full context\",    ),]from langchain.prompts import MessagesPlaceholderfrom langchain.memory import ConversationBufferMemoryagent_kwargs = {    \"extra_prompt_messages\": [MessagesPlaceholder(variable_name=\"memory\")],}memory = ConversationBufferMemory(memory_key=\"memory\", return_messages=True)agent = initialize_agent(    tools,    llm,    agent=AgentType.OPENAI_FUNCTIONS,    verbose=True,    agent_kwargs=agent_kwargs,    memory=memory,)agent.run(\"hi\")            > Entering new  chain...    Hello! How can I assist you today?        > Finished chain.", "source": "https://python.langchain.com/docs/modules/agents/how_to/add_memory_openai_functions"}
{"id": "b1a4fe01ec7f-3", "text": "How can I assist you today?        > Finished chain.    'Hello! How can I assist you today?'agent.run(\"my name is bob\")            > Entering new  chain...    Nice to meet you, Bob! How can I help you today?        > Finished chain.    'Nice to meet you, Bob! How can I help you today?'agent.run(\"whats my name\")            > Entering new  chain...    Your name is Bob.        > Finished chain.    'Your name is Bob.'PreviousStructured tool chatNextRunning Agent as an IteratorCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/agents/how_to/add_memory_openai_functions"}
{"id": "0b19965695b8-0", "text": "Tools | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain\n\n\n\n\n\nSkip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsMemoryAgentsAgent typesHow-toToolsDefining Custom ToolsHuman-in-the-loop Tool ValidationMulti-Input ToolsTool Input SchemaTools as OpenAI FunctionsToolkitsCallbacksModulesGuidesEcosystemAdditional resourcesModulesAgentsToolsOn this pageToolsinfoHead to Integrations for documentation on built-in tool integrations.Tools are interfaces that an agent can use to interact with the world.Get started\u00e2\u20ac\u2039Tools are functions that agents can use to interact with the world.\nThese tools can be generic utilities (e.g. search), other chains, or even other agents.Currently, tools can be loaded with the following snippet:from langchain.agents import load_toolstool_names = [...]tools = load_tools(tool_names)Some tools (e.g. chains, agents) may require a base LLM to use to initialize them.\nIn that case, you can pass in an LLM as well:from langchain.agents import load_toolstool_names = [...]llm = ...tools = load_tools(tool_names, llm=llm)PreviousUse ToolKits with OpenAI FunctionsNextDefining Custom ToolsGet startedCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/agents/tools/"}
{"id": "7d8881fe4492-0", "text": "Tool Input Schema | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/agents/tools/tool_input_validation"}
{"id": "7d8881fe4492-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsMemoryAgentsAgent typesHow-toToolsDefining Custom ToolsHuman-in-the-loop Tool ValidationMulti-Input ToolsTool Input SchemaTools as OpenAI FunctionsToolkitsCallbacksModulesGuidesEcosystemAdditional resourcesModulesAgentsToolsTool Input SchemaTool Input SchemaBy default, tools infer the argument schema by inspecting the function signature. For more strict requirements, custom input schema can be specified, along with custom validation logic.from typing import Any, Dictfrom langchain.agents import AgentType, initialize_agentfrom langchain.llms import OpenAIfrom langchain.tools.requests.tool import RequestsGetTool, TextRequestsWrapperfrom pydantic import BaseModel, Field, root_validatorllm = OpenAI(temperature=0)pip install tldextract > /dev/null        [notice] A new release of pip is available: 23.0.1 -> 23.1    [notice] To update, run: pip install --upgrade pipimport tldextract_APPROVED_DOMAINS = {    \"langchain\",    \"wikipedia\",}class ToolInputSchema(BaseModel):    url: str = Field(...)    @root_validator    def validate_query(cls, values: Dict[str, Any]) -> Dict:        url = values[\"url\"]        domain = tldextract.extract(url).domain        if domain not in _APPROVED_DOMAINS:            raise ValueError(                f\"Domain {domain} is not on the", "source": "https://python.langchain.com/docs/modules/agents/tools/tool_input_validation"}
{"id": "7d8881fe4492-2", "text": "f\"Domain {domain} is not on the approved list:\"                f\" {sorted(_APPROVED_DOMAINS)}\"            )        return valuestool = RequestsGetTool(    args_schema=ToolInputSchema, requests_wrapper=TextRequestsWrapper())agent = initialize_agent(    [tool], llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=False)# This will succeed, since there aren't any arguments that will be triggered during validationanswer = agent.run(\"What's the main title on langchain.com?\")print(answer)    The main title of langchain.com is \"LANG CHAIN \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Official Home Page\"agent.run(\"What's the main title on google.com?\")    ---------------------------------------------------------------------------    ValidationError                           Traceback (most recent call last)    Cell In[7], line 1    ----> 1 agent.run(\"What's the main title on google.com?\")    File ~/code/lc/lckg/langchain/chains/base.py:213, in Chain.run(self, *args, **kwargs)        211     if len(args) != 1:        212         raise ValueError(\"`run` supports only one positional argument.\")    --> 213     return self(args[0])[self.output_keys[0]]        215 if kwargs and not args:", "source": "https://python.langchain.com/docs/modules/agents/tools/tool_input_validation"}
{"id": "7d8881fe4492-3", "text": "215 if kwargs and not args:        216     return self(kwargs)[self.output_keys[0]]    File ~/code/lc/lckg/langchain/chains/base.py:116, in Chain.__call__(self, inputs, return_only_outputs)        114 except (KeyboardInterrupt, Exception) as e:        115     self.callback_manager.on_chain_error(e, verbose=self.verbose)    --> 116     raise e        117 self.callback_manager.on_chain_end(outputs, verbose=self.verbose)        118 return self.prep_outputs(inputs, outputs, return_only_outputs)    File ~/code/lc/lckg/langchain/chains/base.py:113, in Chain.__call__(self, inputs, return_only_outputs)        107 self.callback_manager.on_chain_start(        108     {\"name\": self.__class__.__name__},        109     inputs,        110     verbose=self.verbose,        111 )        112 try:    --> 113     outputs = self._call(inputs)        114 except (KeyboardInterrupt, Exception) as e:        115     self.callback_manager.on_chain_error(e, verbose=self.verbose)    File ~/code/lc/lckg/langchain/agents/agent.py:792, in AgentExecutor._call(self, inputs)        790 # We now enter the", "source": "https://python.langchain.com/docs/modules/agents/tools/tool_input_validation"}
{"id": "7d8881fe4492-4", "text": "inputs)        790 # We now enter the agent loop (until it returns something).        791 while self._should_continue(iterations, time_elapsed):    --> 792     next_step_output = self._take_next_step(        793         name_to_tool_map, color_mapping, inputs, intermediate_steps        794     )        795     if isinstance(next_step_output, AgentFinish):        796         return self._return(next_step_output, intermediate_steps)    File ~/code/lc/lckg/langchain/agents/agent.py:695, in AgentExecutor._take_next_step(self, name_to_tool_map, color_mapping, inputs, intermediate_steps)        693         tool_run_kwargs[\"llm_prefix\"] = \"\"        694     # We then call the tool on the tool input to get an observation    --> 695     observation = tool.run(        696         agent_action.tool_input,        697         verbose=self.verbose,        698         color=color,        699         **tool_run_kwargs,        700     )        701 else:        702     tool_run_kwargs =", "source": "https://python.langchain.com/docs/modules/agents/tools/tool_input_validation"}
{"id": "7d8881fe4492-5", "text": "else:        702     tool_run_kwargs = self.agent.tool_run_logging_kwargs()    File ~/code/lc/lckg/langchain/tools/base.py:110, in BaseTool.run(self, tool_input, verbose, start_color, color, **kwargs)        101 def run(        102     self,        103     tool_input: Union[str, Dict],       (...)        107     **kwargs: Any,        108 ) -> str:        109     \"\"\"Run the tool.\"\"\"    --> 110     run_input = self._parse_input(tool_input)        111     if not self.verbose and verbose is not None:        112         verbose_ = verbose    File ~/code/lc/lckg/langchain/tools/base.py:71, in BaseTool._parse_input(self, tool_input)         69 if issubclass(input_args, BaseModel):         70     key_ = next(iter(input_args.__fields__.keys()))    ---> 71     input_args.parse_obj({key_: tool_input})         72 # Passing as a positional argument is more straightforward for         73 # backwards compatability         74 return tool_input    File", "source": "https://python.langchain.com/docs/modules/agents/tools/tool_input_validation"}
{"id": "7d8881fe4492-6", "text": "compatability         74 return tool_input    File ~/code/lc/lckg/.venv/lib/python3.11/site-packages/pydantic/main.py:526, in pydantic.main.BaseModel.parse_obj()    File ~/code/lc/lckg/.venv/lib/python3.11/site-packages/pydantic/main.py:341, in pydantic.main.BaseModel.__init__()    ValidationError: 1 validation error for ToolInputSchema    __root__      Domain google is not on the approved list: ['langchain', 'wikipedia'] (type=value_error)PreviousMulti-Input ToolsNextTools as OpenAI FunctionsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/agents/tools/tool_input_validation"}
{"id": "8cf8f69eea73-0", "text": "Multi-Input Tools | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/agents/tools/multi_input_tool"}
{"id": "8cf8f69eea73-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsMemoryAgentsAgent typesHow-toToolsDefining Custom ToolsHuman-in-the-loop Tool ValidationMulti-Input ToolsTool Input SchemaTools as OpenAI FunctionsToolkitsCallbacksModulesGuidesEcosystemAdditional resourcesModulesAgentsToolsMulti-Input ToolsOn this pageMulti-Input ToolsThis notebook shows how to use a tool that requires multiple inputs with an agent. The recommended way to do so is with the StructuredTool class.import osos.environ[\"LANGCHAIN_TRACING\"] = \"true\"from langchain import OpenAIfrom langchain.agents import initialize_agent, AgentTypellm = OpenAI(temperature=0)from langchain.tools import StructuredTooldef multiplier(a: float, b: float) -> float:    \"\"\"Multiply the provided floats.\"\"\"    return a * btool = StructuredTool.from_function(multiplier)# Structured tools are compatible with the STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION agent type.agent_executor = initialize_agent(    [tool],    llm,    agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,    verbose=True,)agent_executor.run(\"What is 3 times 4\")            > Entering new AgentExecutor chain...        Thought: I need to multiply 3 and 4    Action:    ```    {      \"action\": \"multiplier\",      \"action_input\": {\"a\": 3, \"b\": 4}    }    ```        Observation: 12", "source": "https://python.langchain.com/docs/modules/agents/tools/multi_input_tool"}
{"id": "8cf8f69eea73-2", "text": "}    ```        Observation: 12    Thought: I know what to respond    Action:    ```    {      \"action\": \"Final Answer\",      \"action_input\": \"3 times 4 is 12\"    }    ```        > Finished chain.    '3 times 4 is 12'Multi-Input Tools with a string format\u00e2\u20ac\u2039An alternative to the structured tool would be to use the regular Tool class and accept a single string. The tool would then have to handle the parsing logic to extract the relavent values from the text, which tightly couples the tool representation to the agent prompt. This is still useful if the underlying language model can't reliabl generate structured schema. Let's take the multiplication function as an example. In order to use this, we will tell the agent to generate the \"Action Input\" as a comma-separated list of length two. We will then write a thin wrapper that takes a string, splits it into two around a comma, and passes both parsed sides as integers to the multiplication function.from langchain.llms import OpenAIfrom langchain.agents import initialize_agent, Toolfrom langchain.agents import AgentTypeHere is the multiplication function, as well as a wrapper to parse a string as input.def multiplier(a, b):    return a * bdef parsing_multiplier(string):    a, b = string.split(\",\")    return multiplier(int(a), int(b))llm = OpenAI(temperature=0)tools = [    Tool(        name=\"Multiplier\",        func=parsing_multiplier,        description=\"useful for when you need to multiply two numbers together. The input to", "source": "https://python.langchain.com/docs/modules/agents/tools/multi_input_tool"}
{"id": "8cf8f69eea73-3", "text": "description=\"useful for when you need to multiply two numbers together. The input to this tool should be a comma separated list of numbers of length two, representing the two numbers you want to multiply together. For example, `1,2` would be the input if you wanted to multiply 1 by 2.\",    )]mrkl = initialize_agent(    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)mrkl.run(\"What is 3 times 4\")            > Entering new AgentExecutor chain...     I need to multiply two numbers    Action: Multiplier    Action Input: 3,4    Observation: 12    Thought: I now know the final answer    Final Answer: 3 times 4 is 12        > Finished chain.    '3 times 4 is 12'PreviousHuman-in-the-loop Tool ValidationNextTool Input SchemaMulti-Input Tools with a string formatCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/agents/tools/multi_input_tool"}
{"id": "783e3d992cc6-0", "text": "Tools as OpenAI Functions | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain\n\n\n\n\n\nSkip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsMemoryAgentsAgent typesHow-toToolsDefining Custom ToolsHuman-in-the-loop Tool ValidationMulti-Input ToolsTool Input SchemaTools as OpenAI FunctionsToolkitsCallbacksModulesGuidesEcosystemAdditional resourcesModulesAgentsToolsTools as OpenAI FunctionsTools as OpenAI FunctionsThis notebook goes over how to use LangChain tools as OpenAI functions.from langchain.chat_models import ChatOpenAIfrom langchain.schema import HumanMessagemodel = ChatOpenAI(model=\"gpt-3.5-turbo-0613\")from langchain.tools import MoveFileTool, format_tool_to_openai_functiontools = [MoveFileTool()]functions = [format_tool_to_openai_function(t) for t in tools]message = model.predict_messages(    [HumanMessage(content=\"move file foo to bar\")], functions=functions)message    AIMessage(content='', additional_kwargs={'function_call': {'name': 'move_file', 'arguments': '{\\n  \"source_path\": \"foo\",\\n  \"destination_path\": \"bar\"\\n}'}}, example=False)message.additional_kwargs[\"function_call\"]    {'name': 'move_file',     'arguments': '{\\n  \"source_path\": \"foo\",\\n  \"destination_path\": \"bar\"\\n}'}PreviousTool Input SchemaNextToolkitsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/agents/tools/tools_as_openai_functions"}
{"id": "29c1ace70866-0", "text": "Defining Custom Tools | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/agents/tools/custom_tools"}
{"id": "29c1ace70866-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsMemoryAgentsAgent typesHow-toToolsDefining Custom ToolsHuman-in-the-loop Tool ValidationMulti-Input ToolsTool Input SchemaTools as OpenAI FunctionsToolkitsCallbacksModulesGuidesEcosystemAdditional resourcesModulesAgentsToolsDefining Custom ToolsOn this pageDefining Custom ToolsWhen constructing your own agent, you will need to provide it with a list of Tools that it can use. Besides the actual function that is called, the Tool consists of several components:name (str), is required and must be unique within a set of tools provided to an agentdescription (str), is optional but recommended, as it is used by an agent to determine tool usereturn_direct (bool), defaults to Falseargs_schema (Pydantic BaseModel), is optional but recommended, can be used to provide more information (e.g., few-shot examples) or validation for expected parameters.There are two main ways to define a tool, we will cover both in the example below.# Import things that are needed genericallyfrom langchain import LLMMathChain, SerpAPIWrapperfrom langchain.agents import AgentType, initialize_agentfrom langchain.chat_models import ChatOpenAIfrom langchain.tools import BaseTool, StructuredTool, Tool, toolInitialize the LLM to use for the agent.llm = ChatOpenAI(temperature=0)Completely New Tools - String Input and Output\u00e2\u20ac\u2039The simplest tools accept a single query string and return a string output. If your tool function requires multiple arguments, you might want to skip down to the StructuredTool section below.There are two ways to do this: either by using the Tool dataclass, or by subclassing the BaseTool class.Tool dataclass\u00e2\u20ac\u2039The", "source": "https://python.langchain.com/docs/modules/agents/tools/custom_tools"}
{"id": "29c1ace70866-2", "text": "Tool dataclass, or by subclassing the BaseTool class.Tool dataclass\u00e2\u20ac\u2039The 'Tool' dataclass wraps functions that accept a single string input and returns a string output.# Load the tool configs that are needed.search = SerpAPIWrapper()llm_math_chain = LLMMathChain(llm=llm, verbose=True)tools = [    Tool.from_function(        func=search.run,        name=\"Search\",        description=\"useful for when you need to answer questions about current events\"        # coroutine= ... <- you can specify an async method if desired as well    ),]    /Users/wfh/code/lc/lckg/langchain/chains/llm_math/base.py:50: UserWarning: Directly instantiating an LLMMathChain with an llm is deprecated. Please instantiate with llm_chain argument or using the from_llm class method.      warnings.warn(You can also define a custom `args_schema`` to provide more information about inputs.from pydantic import BaseModel, Fieldclass CalculatorInput(BaseModel):    question: str = Field()tools.append(    Tool.from_function(        func=llm_math_chain.run,        name=\"Calculator\",        description=\"useful for when you need to answer questions about math\",        args_schema=CalculatorInput        # coroutine= ... <- you can specify an async method if desired as well    ))# Construct the agent. We will use the default agent type here.# See documentation for a full list of options.agent = initialize_agent(    tools, llm,", "source": "https://python.langchain.com/docs/modules/agents/tools/custom_tools"}
{"id": "29c1ace70866-3", "text": "documentation for a full list of options.agent = initialize_agent(    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)agent.run(    \"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\")            > Entering new AgentExecutor chain...    I need to find out Leo DiCaprio's girlfriend's name and her age    Action: Search    Action Input: \"Leo DiCaprio girlfriend\"    Observation: After rumours of a romance with Gigi Hadid, the Oscar winner has seemingly moved on. First being linked to the television personality in September 2022, it appears as if his \"age bracket\" has moved up. This follows his rumoured relationship with mere 19-year-old Eden Polani.    Thought:I still need to find out his current girlfriend's name and age    Action: Search    Action Input: \"Leo DiCaprio current girlfriend\"    Observation: Just Jared on Instagram: \u00e2\u20ac\u0153Leonardo DiCaprio & girlfriend Camila Morrone couple up for a lunch date!    Thought:Now that I know his girlfriend's name is Camila Morrone, I need to find her current age    Action: Search    Action Input: \"Camila Morrone age\"    Observation: 25 years    Thought:Now that I have her age, I need to calculate her age raised to the 0.43 power    Action: Calculator    Action Input: 25^(0.43)        > Entering new LLMMathChain chain...    25^(0.43)```text    25**(0.43)    ```", "source": "https://python.langchain.com/docs/modules/agents/tools/custom_tools"}
{"id": "29c1ace70866-4", "text": "25**(0.43)    ```    ...numexpr.evaluate(\"25**(0.43)\")...        Answer: 3.991298452658078    > Finished chain.        Observation: Answer: 3.991298452658078    Thought:I now know the final answer    Final Answer: Camila Morrone's current age raised to the 0.43 power is approximately 3.99.        > Finished chain.    \"Camila Morrone's current age raised to the 0.43 power is approximately 3.99.\"Subclassing the BaseTool class\u00e2\u20ac\u2039You can also directly subclass BaseTool. This is useful if you want more control over the instance variables or if you want to propagate callbacks to nested chains or other tools.from typing import Optional, Typefrom langchain.callbacks.manager import (    AsyncCallbackManagerForToolRun,    CallbackManagerForToolRun,)class CustomSearchTool(BaseTool):    name = \"custom_search\"    description = \"useful for when you need to answer questions about current events\"    def _run(        self, query: str, run_manager: Optional[CallbackManagerForToolRun] = None    ) -> str:        \"\"\"Use the tool.\"\"\"        return search.run(query)    async def _arun(        self, query: str, run_manager: Optional[AsyncCallbackManagerForToolRun] = None    ) -> str:        \"\"\"Use the tool asynchronously.\"\"\"        raise NotImplementedError(\"custom_search does not support async\")class", "source": "https://python.langchain.com/docs/modules/agents/tools/custom_tools"}
{"id": "29c1ace70866-5", "text": "asynchronously.\"\"\"        raise NotImplementedError(\"custom_search does not support async\")class CustomCalculatorTool(BaseTool):    name = \"Calculator\"    description = \"useful for when you need to answer questions about math\"    args_schema: Type[BaseModel] = CalculatorInput    def _run(        self, query: str, run_manager: Optional[CallbackManagerForToolRun] = None    ) -> str:        \"\"\"Use the tool.\"\"\"        return llm_math_chain.run(query)    async def _arun(        self, query: str, run_manager: Optional[AsyncCallbackManagerForToolRun] = None    ) -> str:        \"\"\"Use the tool asynchronously.\"\"\"        raise NotImplementedError(\"Calculator does not support async\")tools = [CustomSearchTool(), CustomCalculatorTool()]agent = initialize_agent(    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)agent.run(    \"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\")            > Entering new AgentExecutor chain...    I need to use custom_search to find out who Leo DiCaprio's girlfriend is, and then use the Calculator to raise her age to the 0.43 power.    Action: custom_search    Action Input: \"Leo DiCaprio girlfriend\"    Observation: After rumours of a romance with Gigi Hadid, the Oscar winner has seemingly moved on. First being linked to the television personality in September 2022, it appears as if his \"age bracket\" has moved", "source": "https://python.langchain.com/docs/modules/agents/tools/custom_tools"}
{"id": "29c1ace70866-6", "text": "the television personality in September 2022, it appears as if his \"age bracket\" has moved up. This follows his rumoured relationship with mere 19-year-old Eden Polani.    Thought:I need to find out the current age of Eden Polani.    Action: custom_search    Action Input: \"Eden Polani age\"    Observation: 19 years old    Thought:Now I can use the Calculator to raise her age to the 0.43 power.    Action: Calculator    Action Input: 19 ^ 0.43        > Entering new LLMMathChain chain...    19 ^ 0.43```text    19 ** 0.43    ```    ...numexpr.evaluate(\"19 ** 0.43\")...        Answer: 3.547023357958959    > Finished chain.        Observation: Answer: 3.547023357958959    Thought:I now know the final answer.    Final Answer: 3.547023357958959        > Finished chain.    '3.547023357958959'Using the tool decorator\u00e2\u20ac\u2039To make it easier to define custom tools, a @tool decorator is provided. This decorator can be used to quickly create a Tool from a simple function. The decorator uses the function name as the tool name by default, but this can be overridden by passing a string as the first argument. Additionally, the decorator will use the function's docstring as the tool's description.from langchain.tools import tool@tooldef search_api(query: str) -> str:    \"\"\"Searches the API for the query.\"\"\"    return f\"Results for query", "source": "https://python.langchain.com/docs/modules/agents/tools/custom_tools"}
{"id": "29c1ace70866-7", "text": "\"\"\"Searches the API for the query.\"\"\"    return f\"Results for query {query}\"search_apiYou can also provide arguments like the tool name and whether to return directly.@tool(\"search\", return_direct=True)def search_api(query: str) -> str:    \"\"\"Searches the API for the query.\"\"\"    return \"Results\"search_api    Tool(name='search', description='search(query: str) -> str - Searches the API for the query.', args_schema=<class 'pydantic.main.SearchApi'>, return_direct=True, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x12748c4c0>, func=<function search_api at 0x16bd66310>, coroutine=None)You can also provide args_schema to provide more information about the argumentclass SearchInput(BaseModel):    query: str = Field(description=\"should be a search query\")@tool(\"search\", return_direct=True, args_schema=SearchInput)def search_api(query: str) -> str:    \"\"\"Searches the API for the query.\"\"\"    return \"Results\"search_api    Tool(name='search', description='search(query: str) -> str - Searches the API for the query.', args_schema=<class '__main__.SearchInput'>, return_direct=True, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x12748c4c0>, func=<function search_api at 0x16bcf0ee0>, coroutine=None)Custom Structured Tools\u00e2\u20ac\u2039If your functions require more structured arguments, you can use the StructuredTool class directly, or still subclass the BaseTool class.StructuredTool dataclass\u00e2\u20ac\u2039To dynamically generate a structured tool from a given function, the fastest way to get started is with StructuredTool.from_function().import requestsfrom", "source": "https://python.langchain.com/docs/modules/agents/tools/custom_tools"}
{"id": "29c1ace70866-8", "text": "given function, the fastest way to get started is with StructuredTool.from_function().import requestsfrom langchain.tools import StructuredTooldef post_message(url: str, body: dict, parameters: Optional[dict] = None) -> str:    \"\"\"Sends a POST request to the given url with the given body and parameters.\"\"\"    result = requests.post(url, json=body, params=parameters)    return f\"Status: {result.status_code} - {result.text}\"tool = StructuredTool.from_function(post_message)Subclassing the BaseTool\u00e2\u20ac\u2039The BaseTool automatically infers the schema from the _run method's signature.from typing import Optional, Typefrom langchain.callbacks.manager import (    AsyncCallbackManagerForToolRun,    CallbackManagerForToolRun,)class CustomSearchTool(BaseTool):    name = \"custom_search\"    description = \"useful for when you need to answer questions about current events\"    def _run(        self,        query: str,        engine: str = \"google\",        gl: str = \"us\",        hl: str = \"en\",        run_manager: Optional[CallbackManagerForToolRun] = None,    ) -> str:        \"\"\"Use the tool.\"\"\"        search_wrapper = SerpAPIWrapper(params={\"engine\": engine, \"gl\": gl, \"hl\": hl})        return search_wrapper.run(query)    async def _arun(        self,        query: str,        engine: str =", "source": "https://python.langchain.com/docs/modules/agents/tools/custom_tools"}
{"id": "29c1ace70866-9", "text": "query: str,        engine: str = \"google\",        gl: str = \"us\",        hl: str = \"en\",        run_manager: Optional[AsyncCallbackManagerForToolRun] = None,    ) -> str:        \"\"\"Use the tool asynchronously.\"\"\"        raise NotImplementedError(\"custom_search does not support async\")# You can provide a custom args schema to add descriptions or custom validationclass SearchSchema(BaseModel):    query: str = Field(description=\"should be a search query\")    engine: str = Field(description=\"should be a search engine\")    gl: str = Field(description=\"should be a country code\")    hl: str = Field(description=\"should be a language code\")class CustomSearchTool(BaseTool):    name = \"custom_search\"    description = \"useful for when you need to answer questions about current events\"    args_schema: Type[SearchSchema] = SearchSchema    def _run(        self,        query: str,        engine: str = \"google\",        gl: str = \"us\",        hl: str = \"en\",        run_manager: Optional[CallbackManagerForToolRun] = None,    ) -> str:        \"\"\"Use the tool.\"\"\"        search_wrapper = SerpAPIWrapper(params={\"engine\": engine, \"gl\": gl, \"hl\": hl})        return search_wrapper.run(query)    async def", "source": "https://python.langchain.com/docs/modules/agents/tools/custom_tools"}
{"id": "29c1ace70866-10", "text": "hl})        return search_wrapper.run(query)    async def _arun(        self,        query: str,        engine: str = \"google\",        gl: str = \"us\",        hl: str = \"en\",        run_manager: Optional[AsyncCallbackManagerForToolRun] = None,    ) -> str:        \"\"\"Use the tool asynchronously.\"\"\"        raise NotImplementedError(\"custom_search does not support async\")Using the decorator\u00e2\u20ac\u2039The tool decorator creates a structured tool automatically if the signature has multiple arguments.import requestsfrom langchain.tools import tool@tooldef post_message(url: str, body: dict, parameters: Optional[dict] = None) -> str:    \"\"\"Sends a POST request to the given url with the given body and parameters.\"\"\"    result = requests.post(url, json=body, params=parameters)    return f\"Status: {result.status_code} - {result.text}\"Modify existing tools\u00e2\u20ac\u2039Now, we show how to load existing tools and modify them directly. In the example below, we do something really simple and change the Search tool to have the name Google Search.from langchain.agents import load_toolstools = load_tools([\"serpapi\", \"llm-math\"], llm=llm)tools[0].name = \"Google Search\"agent = initialize_agent(    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)agent.run(    \"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\")", "source": "https://python.langchain.com/docs/modules/agents/tools/custom_tools"}
{"id": "29c1ace70866-11", "text": "is her current age raised to the 0.43 power?\")            > Entering new AgentExecutor chain...    I need to find out Leo DiCaprio's girlfriend's name and her age.    Action: Google Search    Action Input: \"Leo DiCaprio girlfriend\"    Observation: After rumours of a romance with Gigi Hadid, the Oscar winner has seemingly moved on. First being linked to the television personality in September 2022, it appears as if his \"age bracket\" has moved up. This follows his rumoured relationship with mere 19-year-old Eden Polani.    Thought:I still need to find out his current girlfriend's name and her age.    Action: Google Search    Action Input: \"Leo DiCaprio current girlfriend age\"    Observation: Leonardo DiCaprio has been linked with 19-year-old model Eden Polani, continuing the rumour that he doesn't date any women over the age of ...    Thought:I need to find out the age of Eden Polani.    Action: Calculator    Action Input: 19^(0.43)    Observation: Answer: 3.547023357958959    Thought:I now know the final answer.    Final Answer: The age of Leo DiCaprio's girlfriend raised to the 0.43 power is approximately 3.55.        > Finished chain.    \"The age of Leo DiCaprio's girlfriend raised to the 0.43 power is approximately 3.55.\"Defining the priorities among Tools\u00e2\u20ac\u2039When you made a Custom tool, you may want the Agent to use the custom tool more than normal tools.For example, you made a custom tool, which gets information on music from your database. When a user", "source": "https://python.langchain.com/docs/modules/agents/tools/custom_tools"}
{"id": "29c1ace70866-12", "text": "example, you made a custom tool, which gets information on music from your database. When a user wants information on songs, You want the Agent to use  the custom tool more than the normal Search tool. But the Agent might prioritize a normal Search tool.This can be accomplished by adding a statement such as Use this more than the normal search if the question is about Music, like 'who is the singer of yesterday?' or 'what is the most popular song in 2022?' to the description.An example is below.# Import things that are needed genericallyfrom langchain.agents import initialize_agent, Toolfrom langchain.agents import AgentTypefrom langchain.llms import OpenAIfrom langchain import LLMMathChain, SerpAPIWrappersearch = SerpAPIWrapper()tools = [    Tool(        name=\"Search\",        func=search.run,        description=\"useful for when you need to answer questions about current events\",    ),    Tool(        name=\"Music Search\",        func=lambda x: \"'All I Want For Christmas Is You' by Mariah Carey.\",  # Mock Function        description=\"A Music search engine. Use this more than the normal search if the question is about Music, like 'who is the singer of yesterday?' or 'what is the most popular song in 2022?'\",    ),]agent = initialize_agent(    tools,    OpenAI(temperature=0),    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,    verbose=True,)agent.run(\"what is the most famous song of christmas\")            > Entering new AgentExecutor chain...     I should use a music search engine to find", "source": "https://python.langchain.com/docs/modules/agents/tools/custom_tools"}
{"id": "29c1ace70866-13", "text": "Entering new AgentExecutor chain...     I should use a music search engine to find the answer    Action: Music Search    Action Input: most famous song of christmas'All I Want For Christmas Is You' by Mariah Carey. I now know the final answer    Final Answer: 'All I Want For Christmas Is You' by Mariah Carey.        > Finished chain.    \"'All I Want For Christmas Is You' by Mariah Carey.\"Using tools to return directly\u00e2\u20ac\u2039Often, it can be desirable to have a tool output returned directly to the user, if it\u00e2\u20ac\u2122s called. You can do this easily with LangChain by setting the return_direct flag for a tool to be True.llm_math_chain = LLMMathChain(llm=llm)tools = [    Tool(        name=\"Calculator\",        func=llm_math_chain.run,        description=\"useful for when you need to answer questions about math\",        return_direct=True,    )]llm = OpenAI(temperature=0)agent = initialize_agent(    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)agent.run(\"whats 2**.12\")            > Entering new AgentExecutor chain...     I need to calculate this    Action: Calculator    Action Input: 2**.12Answer: 1.086734862526058        > Finished chain.    'Answer: 1.086734862526058'Handling Tool Errors\u00e2\u20ac\u2039When a tool encounters an error and the exception is not caught, the agent will stop", "source": "https://python.langchain.com/docs/modules/agents/tools/custom_tools"}
{"id": "29c1ace70866-14", "text": "a tool encounters an error and the exception is not caught, the agent will stop executing. If you want the agent to continue execution, you can raise a ToolException and set handle_tool_error accordingly. When ToolException is thrown, the agent will not stop working, but will handle the exception according to the handle_tool_error variable of the tool, and the processing result will be returned to the agent as observation, and printed in red.You can set handle_tool_error to True, set it a unified string value, or set it as a function. If it's set as a function, the function should take a ToolException as a parameter and return a str value.Please note that only raising a ToolException won't be effective. You need to first set the handle_tool_error of the tool because its default value is False.from langchain.tools.base import ToolExceptionfrom langchain import SerpAPIWrapperfrom langchain.agents import AgentType, initialize_agentfrom langchain.chat_models import ChatOpenAIfrom langchain.tools import Toolfrom langchain.chat_models import ChatOpenAIdef _handle_error(error: ToolException) -> str:    return (        \"The following errors occurred during tool execution:\"        + error.args[0]        + \"Please try another tool.\"    )def search_tool1(s: str):    raise ToolException(\"The search tool1 is not available.\")def search_tool2(s: str):    raise ToolException(\"The search tool2 is not available.\")search_tool3 = SerpAPIWrapper()description = \"useful for when you need to answer questions about current events.You should give priority to using it.\"tools = [    Tool.from_function(        func=search_tool1,        name=\"Search_tool1\",", "source": "https://python.langchain.com/docs/modules/agents/tools/custom_tools"}
{"id": "29c1ace70866-15", "text": "name=\"Search_tool1\",        description=description,        handle_tool_error=True,    ),    Tool.from_function(        func=search_tool2,        name=\"Search_tool2\",        description=description,        handle_tool_error=_handle_error,    ),    Tool.from_function(        func=search_tool3.run,        name=\"Search_tool3\",        description=\"useful for when you need to answer questions about current events\",    ),]agent = initialize_agent(    tools,    ChatOpenAI(temperature=0),    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,    verbose=True,)agent.run(\"Who is Leo DiCaprio's girlfriend?\")            > Entering new AgentExecutor chain...    I should use Search_tool1 to find recent news articles about Leo DiCaprio's personal life.    Action: Search_tool1    Action Input: \"Leo DiCaprio girlfriend\"    Observation: The search tool1 is not available.    Thought:I should try using Search_tool2 instead.    Action: Search_tool2    Action Input: \"Leo DiCaprio girlfriend\"    Observation: The following errors occurred during tool execution:The search tool2 is not available.Please try another tool.    Thought:I should try using Search_tool3 as a last resort.    Action: Search_tool3    Action Input: \"Leo DiCaprio girlfriend\"    Observation: Leonardo DiCaprio and Gigi Hadid", "source": "https://python.langchain.com/docs/modules/agents/tools/custom_tools"}
{"id": "29c1ace70866-16", "text": "DiCaprio girlfriend\"    Observation: Leonardo DiCaprio and Gigi Hadid were recently spotted at a pre-Oscars party, sparking interest once again in their rumored romance. The Revenant actor and the model first made headlines when they were spotted together at a New York Fashion Week afterparty in September 2022.    Thought:Based on the information from Search_tool3, it seems that Gigi Hadid is currently rumored to be Leo DiCaprio's girlfriend.    Final Answer: Gigi Hadid is currently rumored to be Leo DiCaprio's girlfriend.        > Finished chain.    \"Gigi Hadid is currently rumored to be Leo DiCaprio's girlfriend.\"PreviousToolsNextHuman-in-the-loop Tool ValidationCompletely New Tools - String Input and OutputTool dataclassSubclassing the BaseTool classUsing the tool decoratorCustom Structured ToolsStructuredTool dataclassSubclassing the BaseToolUsing the decoratorModify existing toolsDefining the priorities among ToolsUsing tools to return directlyHandling Tool ErrorsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/agents/tools/custom_tools"}
{"id": "269791a22f2d-0", "text": "Human-in-the-loop Tool Validation | \u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 Langchain", "source": "https://python.langchain.com/docs/modules/agents/tools/human_approval"}
{"id": "269791a22f2d-1", "text": "Skip to main content\u011f\u0178\u00a6\u0153\u00ef\u00b8\ufffd\u011f\u0178\u201d\u2014 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/\u00e2\u20ac\u2039OData connectionChainsMemoryAgentsAgent typesHow-toToolsDefining Custom ToolsHuman-in-the-loop Tool ValidationMulti-Input ToolsTool Input SchemaTools as OpenAI FunctionsToolkitsCallbacksModulesGuidesEcosystemAdditional resourcesModulesAgentsToolsHuman-in-the-loop Tool ValidationOn this pageHuman-in-the-loop Tool ValidationThis walkthrough demonstrates how to add Human validation to any Tool. We'll do this using the HumanApprovalCallbackhandler.Let's suppose we need to make use of the ShellTool. Adding this tool to an automated flow poses obvious risks. Let's see how we could enforce manual human approval of inputs going into this tool.Note: We generally recommend against using the ShellTool. There's a lot of ways to misuse it, and it's not required for most use cases. We employ it here only for demonstration purposes.from langchain.callbacks import HumanApprovalCallbackHandlerfrom langchain.tools import ShellTooltool = ShellTool()print(tool.run(\"echo Hello World!\"))    Hello World!    Adding Human Approval\u00e2\u20ac\u2039Adding the default HumanApprovalCallbackHandler to the tool will make it so that a user has to manually approve every input to the tool before the command is actually executed.tool = ShellTool(callbacks=[HumanApprovalCallbackHandler()])print(tool.run(\"ls /usr\"))    Do you approve of the following input? Anything except 'Y'/'Yes' (case-insensitive) will be treated as a no.        ls /usr    yes    X11    X11R6    bin    lib    libexec    local    sbin    share    standalone", "source": "https://python.langchain.com/docs/modules/agents/tools/human_approval"}
{"id": "269791a22f2d-2", "text": "libexec    local    sbin    share    standalone    print(tool.run(\"ls /private\"))    Do you approve of the following input? Anything except 'Y'/'Yes' (case-insensitive) will be treated as a no.        ls /private    no    ---------------------------------------------------------------------------    HumanRejectedException                    Traceback (most recent call last)    Cell In[17], line 1    ----> 1 print(tool.run(\"ls /private\"))    File ~/langchain/langchain/tools/base.py:257, in BaseTool.run(self, tool_input, verbose, start_color, color, callbacks, **kwargs)        255 # TODO: maybe also pass through run_manager is _run supports kwargs        256 new_arg_supported = signature(self._run).parameters.get(\"run_manager\")    --> 257 run_manager = callback_manager.on_tool_start(        258     {\"name\": self.name, \"description\": self.description},        259     tool_input if isinstance(tool_input, str) else str(tool_input),        260     color=start_color,        261     **kwargs,        262 )        263 try:        264     tool_args, tool_kwargs = self._to_args_and_kwargs(parsed_input)    File ~/langchain/langchain/callbacks/manager.py:672, in CallbackManager.on_tool_start(self, serialized,", "source": "https://python.langchain.com/docs/modules/agents/tools/human_approval"}
{"id": "269791a22f2d-3", "text": "in CallbackManager.on_tool_start(self, serialized, input_str, run_id, parent_run_id, **kwargs)        669 if run_id is None:        670     run_id = uuid4()    --> 672 _handle_event(        673     self.handlers,        674     \"on_tool_start\",        675     \"ignore_agent\",        676     serialized,        677     input_str,        678     run_id=run_id,        679     parent_run_id=self.parent_run_id,        680     **kwargs,        681 )        683 return CallbackManagerForToolRun(        684     run_id, self.handlers, self.inheritable_handlers, self.parent_run_id        685 )    File ~/langchain/langchain/callbacks/manager.py:157, in _handle_event(handlers, event_name, ignore_condition_name, *args, **kwargs)        155 except Exception as e:        156     if handler.raise_error:    --> 157         raise e        158     logging.warning(f\"Error in {event_name} callback: {e}\")    File ~/langchain/langchain/callbacks/manager.py:139,", "source": "https://python.langchain.com/docs/modules/agents/tools/human_approval"}
{"id": "269791a22f2d-4", "text": "{e}\")    File ~/langchain/langchain/callbacks/manager.py:139, in _handle_event(handlers, event_name, ignore_condition_name, *args, **kwargs)        135 try:        136     if ignore_condition_name is None or not getattr(        137         handler, ignore_condition_name        138     ):    --> 139         getattr(handler, event_name)(*args, **kwargs)        140 except NotImplementedError as e:        141     if event_name == \"on_chat_model_start\":    File ~/langchain/langchain/callbacks/human.py:48, in HumanApprovalCallbackHandler.on_tool_start(self, serialized, input_str, run_id, parent_run_id, **kwargs)         38 def on_tool_start(         39     self,         40     serialized: Dict[str, Any],       (...)         45     **kwargs: Any,         46 ) -> Any:         47     if self._should_check(serialized) and not self._approve(input_str):    ---> 48         raise HumanRejectedException(         49             f\"Inputs {input_str} to tool {serialized} were rejected.\"", "source": "https://python.langchain.com/docs/modules/agents/tools/human_approval"}
{"id": "269791a22f2d-5", "text": "{input_str} to tool {serialized} were rejected.\"         50         )    HumanRejectedException: Inputs ls /private to tool {'name': 'terminal', 'description': 'Run shell commands on this MacOS machine.'} were rejected.Configuring Human Approval\u00e2\u20ac\u2039Let's suppose we have an agent that takes in multiple tools, and we want it to only trigger human approval requests on certain tools and certain inputs. We can configure out callback handler to do just this.from langchain.agents import load_toolsfrom langchain.agents import initialize_agentfrom langchain.agents import AgentTypefrom langchain.llms import OpenAIdef _should_check(serialized_obj: dict) -> bool:    # Only require approval on ShellTool.    return serialized_obj.get(\"name\") == \"terminal\"def _approve(_input: str) -> bool:    if _input == \"echo 'Hello World'\":        return True    msg = (        \"Do you approve of the following input? \"        \"Anything except 'Y'/'Yes' (case-insensitive) will be treated as a no.\"    )    msg += \"\\n\\n\" + _input + \"\\n\"    resp = input(msg)    return resp.lower() in (\"yes\", \"y\")callbacks = [HumanApprovalCallbackHandler(should_check=_should_check, approve=_approve)]llm = OpenAI(temperature=0)tools = load_tools([\"wikipedia\", \"llm-math\", \"terminal\"], llm=llm)agent = initialize_agent(    tools,    llm,    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,)agent.run(    \"It's", "source": "https://python.langchain.com/docs/modules/agents/tools/human_approval"}
{"id": "269791a22f2d-6", "text": "agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,)agent.run(    \"It's 2023 now. How many years ago did Konrad Adenauer become Chancellor of Germany.\",    callbacks=callbacks,)    'Konrad Adenauer became Chancellor of Germany in 1949, 74 years ago.'agent.run(\"print 'Hello World' in the terminal\", callbacks=callbacks)    'Hello World'agent.run(\"list all directories in /private\", callbacks=callbacks)    Do you approve of the following input? Anything except 'Y'/'Yes' (case-insensitive) will be treated as a no.        ls /private    no    ---------------------------------------------------------------------------    HumanRejectedException                    Traceback (most recent call last)    Cell In[39], line 1    ----> 1 agent.run(\"list all directories in /private\", callbacks=callbacks)    File ~/langchain/langchain/chains/base.py:236, in Chain.run(self, callbacks, *args, **kwargs)        234     if len(args) != 1:        235         raise ValueError(\"`run` supports only one positional argument.\")    --> 236     return self(args[0], callbacks=callbacks)[self.output_keys[0]]        238 if kwargs and not args:        239     return self(kwargs, callbacks=callbacks)[self.output_keys[0]]    File ~/langchain/langchain/chains/base.py:140, in Chain.__call__(self, inputs, return_only_outputs, callbacks)", "source": "https://python.langchain.com/docs/modules/agents/tools/human_approval"}
{"id": "269791a22f2d-7", "text": "in Chain.__call__(self, inputs, return_only_outputs, callbacks)        138 except (KeyboardInterrupt, Exception) as e:        139     run_manager.on_chain_error(e)    --> 140     raise e        141 run_manager.on_chain_end(outputs)        142 return self.prep_outputs(inputs, outputs, return_only_outputs)    File ~/langchain/langchain/chains/base.py:134, in Chain.__call__(self, inputs, return_only_outputs, callbacks)        128 run_manager = callback_manager.on_chain_start(        129     {\"name\": self.__class__.__name__},        130     inputs,        131 )        132 try:        133     outputs = (    --> 134         self._call(inputs, run_manager=run_manager)        135         if new_arg_supported        136         else self._call(inputs)        137     )        138 except (KeyboardInterrupt, Exception) as e:        139     run_manager.on_chain_error(e)    File ~/langchain/langchain/agents/agent.py:953, in AgentExecutor._call(self, inputs, run_manager)        951 # We now enter the agent loop (until it returns", "source": "https://python.langchain.com/docs/modules/agents/tools/human_approval"}
{"id": "269791a22f2d-8", "text": "951 # We now enter the agent loop (until it returns something).        952 while self._should_continue(iterations, time_elapsed):    --> 953     next_step_output = self._take_next_step(        954         name_to_tool_map,        955         color_mapping,        956         inputs,        957         intermediate_steps,        958         run_manager=run_manager,        959     )        960     if isinstance(next_step_output, AgentFinish):        961         return self._return(        962             next_step_output, intermediate_steps, run_manager=run_manager        963         )    File ~/langchain/langchain/agents/agent.py:820, in AgentExecutor._take_next_step(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager)        818         tool_run_kwargs[\"llm_prefix\"] = \"\"        819     # We then call the tool on the tool input to get an observation    --> 820     observation = tool.run(        821", "source": "https://python.langchain.com/docs/modules/agents/tools/human_approval"}
{"id": "269791a22f2d-9", "text": "observation = tool.run(        821         agent_action.tool_input,        822         verbose=self.verbose,        823         color=color,        824         callbacks=run_manager.get_child() if run_manager else None,        825         **tool_run_kwargs,        826     )        827 else:        828     tool_run_kwargs = self.agent.tool_run_logging_kwargs()    File ~/langchain/langchain/tools/base.py:257, in BaseTool.run(self, tool_input, verbose, start_color, color, callbacks, **kwargs)        255 # TODO: maybe also pass through run_manager is _run supports kwargs        256 new_arg_supported = signature(self._run).parameters.get(\"run_manager\")    --> 257 run_manager = callback_manager.on_tool_start(        258     {\"name\": self.name, \"description\": self.description},        259     tool_input if isinstance(tool_input, str) else str(tool_input),        260     color=start_color,        261     **kwargs,        262 )        263 try:        264     tool_args, tool_kwargs =", "source": "https://python.langchain.com/docs/modules/agents/tools/human_approval"}
{"id": "269791a22f2d-10", "text": "264     tool_args, tool_kwargs = self._to_args_and_kwargs(parsed_input)    File ~/langchain/langchain/callbacks/manager.py:672, in CallbackManager.on_tool_start(self, serialized, input_str, run_id, parent_run_id, **kwargs)        669 if run_id is None:        670     run_id = uuid4()    --> 672 _handle_event(        673     self.handlers,        674     \"on_tool_start\",        675     \"ignore_agent\",        676     serialized,        677     input_str,        678     run_id=run_id,        679     parent_run_id=self.parent_run_id,        680     **kwargs,        681 )        683 return CallbackManagerForToolRun(        684     run_id, self.handlers, self.inheritable_handlers, self.parent_run_id        685 )    File ~/langchain/langchain/callbacks/manager.py:157, in _handle_event(handlers, event_name, ignore_condition_name, *args, **kwargs)        155 except Exception as e:        156     if handler.raise_error:    --> 157         raise e", "source": "https://python.langchain.com/docs/modules/agents/tools/human_approval"}
{"id": "269791a22f2d-11", "text": "handler.raise_error:    --> 157         raise e        158     logging.warning(f\"Error in {event_name} callback: {e}\")    File ~/langchain/langchain/callbacks/manager.py:139, in _handle_event(handlers, event_name, ignore_condition_name, *args, **kwargs)        135 try:        136     if ignore_condition_name is None or not getattr(        137         handler, ignore_condition_name        138     ):    --> 139         getattr(handler, event_name)(*args, **kwargs)        140 except NotImplementedError as e:        141     if event_name == \"on_chat_model_start\":    File ~/langchain/langchain/callbacks/human.py:48, in HumanApprovalCallbackHandler.on_tool_start(self, serialized, input_str, run_id, parent_run_id, **kwargs)         38 def on_tool_start(         39     self,         40     serialized: Dict[str, Any],       (...)         45     **kwargs: Any,         46 ) -> Any:         47     if self._should_check(serialized) and not self._approve(input_str):    ---> 48         raise HumanRejectedException(", "source": "https://python.langchain.com/docs/modules/agents/tools/human_approval"}
{"id": "269791a22f2d-12", "text": "---> 48         raise HumanRejectedException(         49             f\"Inputs {input_str} to tool {serialized} were rejected.\"         50         )    HumanRejectedException: Inputs ls /private to tool {'name': 'terminal', 'description': 'Run shell commands on this MacOS machine.'} were rejected.PreviousDefining Custom ToolsNextMulti-Input ToolsAdding Human ApprovalConfiguring Human ApprovalCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright \u00c2\u00a9 2023 LangChain, Inc.", "source": "https://python.langchain.com/docs/modules/agents/tools/human_approval"}