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Serialization | 🦜️🔗 Langchain Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/​OPromptsLanguage modelsLLMsAsync APICustom LLMFake LLMHuman input LLMCachingSerializationStreamingTracking token usageChat modelsOutput parsersRetrievalChainsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesModel I/​OLanguage 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​First, 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 _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​If 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 © 2023 LangChain, Inc.

This 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).

This 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). ->: Serialization | 🦜️🔗 Langchain Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/​OPromptsLanguage modelsLLMsAsync APICustom LLMFake LLMHuman input LLMCachingSerializationStreamingTracking token usageChat modelsOutput parsersRetrievalChainsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesModel I/​OLanguage 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​First, 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 _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​If 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 © 2023 LangChain, Inc.

Async API | 🦜️🔗 Langchain

LangChain provides async support for LLMs by leveraging the asyncio library.

LangChain provides async support for LLMs by leveraging the asyncio library. ->: Async API | 🦜️🔗 Langchain

Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/​OPromptsLanguage modelsLLMsAsync APICustom LLMFake LLMHuman input LLMCachingSerializationStreamingTracking token usageChat modelsOutput parsersRetrievalChainsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesModel I/​OLanguage 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, Anthropic and Cohere 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() - 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?

LangChain provides async support for LLMs by leveraging the asyncio library.

LangChain provides async support for LLMs by leveraging the asyncio library. ->: Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/​OPromptsLanguage modelsLLMsAsync APICustom LLMFake LLMHuman input LLMCachingSerializationStreamingTracking token usageChat modelsOutput parsersRetrievalChainsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesModel I/​OLanguage 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, Anthropic and Cohere 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() - 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, 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. 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 © 2023 LangChain, Inc.

LangChain provides async support for LLMs by leveraging the asyncio library.

LangChain provides async support for LLMs by leveraging the asyncio library. ->: 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. 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 © 2023 LangChain, Inc.

Human input LLM | 🦜️🔗 Langchain

Similar 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.

Similar 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. ->: Human input LLM | 🦜️🔗 Langchain

Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/​OPromptsLanguage modelsLLMsAsync APICustom LLMFake LLMHuman input LLMCachingSerializationStreamingTracking token usageChat modelsOutput parsersRetrievalChainsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesModel I/​OLanguage 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 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

Similar 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.

Similar 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. ->: Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/​OPromptsLanguage modelsLLMsAsync APICustom LLMFake LLMHuman input LLMCachingSerializationStreamingTracking token usageChat modelsOutput parsersRetrievalChainsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesModel I/​OLanguage 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 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

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! (ぼっち・ざ・ろっく!, 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 tankōbon 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 (まんがタイムきらら, 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 (まんがタイムきららMAX) 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".

Similar 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.

Similar 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. ->: 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! (ぼっち・ざ・ろっく!, 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 tankōbon 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 (まんがタイムきらら, 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 (まんがタイムきららMAX) 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".

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 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! (ぼっち・ざ・ろっく!, 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 tankōbon 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 (まんがタイムきらら, Manga Taimu Kirara) is a Japanese seinen manga magazine published by Houbunsha which mainly serializes four-panel manga. The magazine is sold on

Similar 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.

Similar 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. ->: 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 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! (ぼっち・ざ・ろっく!, 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 tankōbon 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 (まんがタイムきらら, Manga Taimu Kirara) is a Japanese seinen manga magazine published by Houbunsha which mainly serializes four-panel manga. The magazine is sold on

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 (まんがタイムきららMAX) 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! (ぼっち・ざ・ろっく!, 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 tankōbon 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

Similar 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.

Similar 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. ->: 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 (まんがタイムきららMAX) 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! (ぼっち・ざ・ろっく!, 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 tankōbon 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

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! (ぼっち・ざ・ろっく!, 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 tankōbon 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 (まんがタイムきらら, 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 (まんがタイムきららMAX) 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

Similar 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.

Similar 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. ->: 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! (ぼっち・ざ・ろっく!, 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 tankōbon 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 (まんがタイムきらら, 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 (まんがタイムきららMAX) 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

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! (ぼっち・ざ・ろっく!, 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 tankōbon 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 creativity receiving acclaim."PreviousFake LLMNextCachingCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.

Similar 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.

Similar 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. ->: 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! (ぼっち・ざ・ろっく!, 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 tankōbon 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 creativity receiving acclaim."PreviousFake LLMNextCachingCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.

Caching | 🦜️🔗 Langchain

LangChain provides an optional caching layer for LLMs. This is useful for two reasons:

LangChain provides an optional caching layer for LLMs. This is useful for two reasons: ->: Caching | 🦜️🔗 Langchain

Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/​OPromptsLanguage modelsLLMsAsync APICustom LLMFake LLMHuman input LLMCachingSerializationStreamingTracking token usageChat modelsOutput parsersRetrievalChainsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesModel I/​OLanguage modelsLLMsCachingOn this pageCachingLangChain 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.

LangChain provides an optional caching layer for LLMs. This is useful for two reasons:

LangChain provides an optional caching layer for LLMs. This is useful for two reasons: ->: Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/​OPromptsLanguage modelsLLMsAsync APICustom LLMFake LLMHuman input LLMCachingSerializationStreamingTracking token usageChat modelsOutput parsersRetrievalChainsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesModel I/​OLanguage modelsLLMsCachingOn this pageCachingLangChain 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.

It can speed up your application by reducing the number of API calls you make to the LLM provider.from langchain.globals import set_llm_cachefrom 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​from langchain.cache import InMemoryCacheset_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 µs, sys: 143 µs, total: 381 µs Wall time: 1.76 ms '\n\nWhy did the chicken cross the road?\n\nTo get to the other side.'SQLite Cache​rm .langchain.db# We can do the same thing with a SQLite cachefrom langchain.cache import SQLiteCacheset_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 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​You can also turn off caching for particular nodes in chains. Note that because of certain interfaces, it's 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

LangChain provides an optional caching layer for LLMs. This is useful for two reasons:

LangChain provides an optional caching layer for LLMs. This is useful for two reasons: ->: It can speed up your application by reducing the number of API calls you make to the LLM provider.from langchain.globals import set_llm_cachefrom 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​from langchain.cache import InMemoryCacheset_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 µs, sys: 143 µs, total: 381 µs Wall time: 1.76 ms '\n\nWhy did the chicken cross the road?\n\nTo get to the other side.'SQLite Cache​rm .langchain.db# We can do the same thing with a SQLite cachefrom langchain.cache import SQLiteCacheset_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 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​You can also turn off caching for particular nodes in chains. Note that because of certain interfaces, it's 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

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 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 LLMNextSerializationIn Memory CacheSQLite CacheOptional

LangChain provides an optional caching layer for LLMs. This is useful for two reasons:

LangChain provides an optional caching layer for LLMs. This is useful for two reasons: ->: 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 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 LLMNextSerializationIn Memory CacheSQLite CacheOptional

Memory CacheSQLite CacheOptional caching in chainsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.

LangChain provides an optional caching layer for LLMs. This is useful for two reasons:

LangChain provides an optional caching layer for LLMs. This is useful for two reasons: ->: Memory CacheSQLite CacheOptional caching in chainsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.

Fake LLM | 🦜️🔗 Langchain Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/​OPromptsLanguage modelsLLMsAsync APICustom LLMFake LLMHuman input LLMCachingSerializationStreamingTracking token usageChat modelsOutput parsersRetrievalChainsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesModel I/​OLanguage modelsLLMsFake LLMFake LLMLangChain provides 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 © 2023 LangChain, Inc.

LangChain provides 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.

LangChain provides 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. ->: Fake LLM | 🦜️🔗 Langchain Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/​OPromptsLanguage modelsLLMsAsync APICustom LLMFake LLMHuman input LLMCachingSerializationStreamingTracking token usageChat modelsOutput parsersRetrievalChainsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesModel I/​OLanguage modelsLLMsFake LLMFake LLMLangChain provides 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 © 2023 LangChain, Inc.

Custom LLM | 🦜️🔗 Langchain

This 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.

This 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. ->: Custom LLM | 🦜️🔗 Langchain

Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/​OPromptsLanguage modelsLLMsAsync APICustom LLMFake LLMHuman input LLMCachingSerializationStreamingTracking token usageChat modelsOutput parsersRetrievalChainsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesModel I/​OLanguage 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, **kwargs: Any, ) -> str: 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:

This 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.

This 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. ->: Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/​OPromptsLanguage modelsLLMsAsync APICustom LLMFake LLMHuman input LLMCachingSerializationStreamingTracking token usageChat modelsOutput parsersRetrievalChainsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesModel I/​OLanguage 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, **kwargs: Any, ) -> str: 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:

custom print.print(llm) CustomLLM Params: {'n': 10}PreviousAsync APINextFake LLMCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.

This 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.

This 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. ->: custom print.print(llm) CustomLLM Params: {'n': 10}PreviousAsync APINextFake LLMCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.

Streaming | 🦜️🔗 Langchain

Some 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.

Some 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. ->: Streaming | 🦜️🔗 Langchain

Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/​OPromptsLanguage modelsLLMsAsync APICustom LLMFake LLMHuman input LLMCachingSerializationStreamingTracking token usageChat modelsOutput parsersRetrievalChainsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesModel I/​OLanguage 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, 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,

Some 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.

Some 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. ->: Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/​OPromptsLanguage modelsLLMsAsync APICustom LLMFake LLMHuman input LLMCachingSerializationStreamingTracking token usageChat modelsOutput parsersRetrievalChainsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesModel I/​OLanguage 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, 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,

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 usageCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.

Some 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.

Some 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. ->: 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 usageCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.

Human input chat model | 🦜️🔗 Langchain

Along 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.

Along 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. ->: Human input chat model | 🦜️🔗 Langchain

Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/​OPromptsLanguage modelsLLMsChat modelsCachingHuman input chat modelLLMChainPromptsStreamingOutput parsersRetrievalChainsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesModel I/​OLanguage 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 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

Along 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.

Along 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. ->: Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/​OPromptsLanguage modelsLLMsChat modelsCachingHuman input chat modelLLMChainPromptsStreamingOutput parsersRetrievalChainsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesModel I/​OLanguage 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 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

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? ' 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! (ぼっち・ざ・ろっく!, 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 tankōbon 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

Along 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.

Along 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. ->: 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? ' 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! (ぼっち・ざ・ろっく!, 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 tankōbon 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

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: ひとりぼっちの○○生活, 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 tankōbon 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: 結束バンド, 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 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

Along 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.

Along 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. ->: 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: ひとりぼっちの○○生活, 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 tankōbon 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: 結束バンド, 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 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

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 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! (ぼっち・ざ・ろっく!, 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 tankōbon 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:

Along 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.

Along 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. ->: 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 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! (ぼっち・ざ・ろっく!, 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 tankōbon 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:

Hitori Bocchi no Marumaru Seikatsu (Japanese: ひとりぼっちの○○生活, 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 tankōbon volumes have been released. An anime television series adaptation by C2C aired from April to June 2019.\n\nPage: Kessoku Band (album)\nSummary: Kessoku Band (Japanese: 結束バンド, 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 depiction of social anxiety, with the anime's visual creativity receiving

Along 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.

Along 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. ->: Hitori Bocchi no Marumaru Seikatsu (Japanese: ひとりぼっちの○○生活, 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 tankōbon volumes have been released. An anime television series adaptation by C2C aired from April to June 2019.\n\nPage: Kessoku Band (album)\nSummary: Kessoku Band (Japanese: 結束バンド, 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 depiction of social anxiety, with the anime's visual creativity receiving

with the anime's visual creativity receiving acclaim."}PreviousCachingNextLLMChainCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.

Along 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.

Along 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. ->: with the anime's visual creativity receiving acclaim."}PreviousCachingNextLLMChainCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.

Chat models | 🦜�🔗 Langchain

Features (natively supported)

Features (natively supported) ->: Chat models | 🦜�🔗 Langchain

Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsChat modelsAnthropicAnthropic FunctionsAnyscaleAzureAzureML Chat Online EndpointBaichuan ChatBaidu QianfanBedrock ChatCohereERNIE-Bot ChatEverlyAIFireworksGCP Vertex AIJinaChatKonko🚅 LiteLLMLlama APIMiniMaxOllamaOpenAIPromptLayer ChatOpenAITongyi QwenvLLM ChatYandexGPTDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsChat modelsOn this pageChat modelsFeatures (natively supported)​All ChatModels implement the Runnable interface, which comes with default implementations of all methods, ie. ainvoke, batch, abatch, stream, astream. This gives all ChatModels basic support for async, streaming and batch, which by default is implemented as below:Async support defaults to calling the respective sync method in asyncio's default thread pool executor. This lets other async functions in your application make progress while the ChatModel is being executed, by moving this call to a background thread.Streaming support defaults to returning an Iterator (or AsyncIterator in the case of async streaming) of a single value, the final result returned by the underlying ChatModel provider. This obviously doesn't give you token-by-token streaming, which requires native support from the ChatModel provider, but ensures your code that expects an iterator of tokens can work for any of our ChatModel integrations.Batch support defaults to calling the underlying ChatModel in parallel for each input by making use of a thread pool executor (in the sync batch case) or asyncio.gather (in the async batch case). The concurrency can be controlled with the max_concurrency key in RunnableConfig.Each ChatModel integration can optionally provide native implementations to truly enable async or streaming.

Features (natively supported)

Features (natively supported) ->: Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsChat modelsAnthropicAnthropic FunctionsAnyscaleAzureAzureML Chat Online EndpointBaichuan ChatBaidu QianfanBedrock ChatCohereERNIE-Bot ChatEverlyAIFireworksGCP Vertex AIJinaChatKonko🚅 LiteLLMLlama APIMiniMaxOllamaOpenAIPromptLayer ChatOpenAITongyi QwenvLLM ChatYandexGPTDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsChat modelsOn this pageChat modelsFeatures (natively supported)​All ChatModels implement the Runnable interface, which comes with default implementations of all methods, ie. ainvoke, batch, abatch, stream, astream. This gives all ChatModels basic support for async, streaming and batch, which by default is implemented as below:Async support defaults to calling the respective sync method in asyncio's default thread pool executor. This lets other async functions in your application make progress while the ChatModel is being executed, by moving this call to a background thread.Streaming support defaults to returning an Iterator (or AsyncIterator in the case of async streaming) of a single value, the final result returned by the underlying ChatModel provider. This obviously doesn't give you token-by-token streaming, which requires native support from the ChatModel provider, but ensures your code that expects an iterator of tokens can work for any of our ChatModel integrations.Batch support defaults to calling the underlying ChatModel in parallel for each input by making use of a thread pool executor (in the sync batch case) or asyncio.gather (in the async batch case). The concurrency can be controlled with the max_concurrency key in RunnableConfig.Each ChatModel integration can optionally provide native implementations to truly enable async or streaming.

The table shows, for each integration, which features have been implemented with native support.ModelInvokeAsync invokeStreamAsync streamAzureChatOpenAI✅✅✅✅BedrockChat✅�✅�ChatAnthropic✅✅✅✅ChatAnyscale✅✅✅✅ChatBaichuan✅�✅�ChatCohere✅✅✅✅ChatEverlyAI✅✅✅✅ChatFireworks✅✅✅✅ChatGooglePalm✅✅��ChatJavelinAIGateway✅✅��ChatKonko✅���ChatLiteLLM✅✅✅✅ChatMLflowAIGateway✅���ChatOllama✅�✅�ChatOpenAI✅✅✅✅ChatVertexAI✅✅✅�ChatYandexGPT✅✅��ErnieBotChat✅���JinaChat✅✅✅✅MiniMaxChat✅✅��PromptLayerChatOpenAI✅���QianfanChatEndpoint✅✅✅✅📄� Chat modelsFeatures (natively supported)📄� AnthropicThis notebook covers how to get started with Anthropic chat models.📄� Anthropic FunctionsThis notebook shows how to use an experimental wrapper around Anthropic that gives it the same API as OpenAI Functions.📄� AnyscaleThis notebook demonstrates the use of langchain.chat_models.ChatAnyscale for Anyscale Endpoints.📄� AzureThis notebook goes over how to connect to an Azure hosted OpenAI endpoint📄� AzureML Chat Online EndpointAzureML is a platform used to build, train, and deploy machine learning models. Users can explore the types of models to deploy in the Model Catalog, which provides Azure Foundation Models and OpenAI Models. Azure Foundation Models include various open-source models and popular Hugging Face models. Users can also import models of their liking into AzureML.📄� Baichuan ChatBaichuan chat models API by Baichuan Intelligent Technology. For more information, see https://platform.baichuan-ai.com/docs/api📄� Baidu QianfanBaidu AI Cloud Qianfan Platform is a one-stop large model development and service operation platform for enterprise developers. Qianfan not only provides including the model of Wenxin Yiyan (ERNIE-Bot) and the third-party open-source models, but also provides various AI

Features (natively supported)

Features (natively supported) ->: The table shows, for each integration, which features have been implemented with native support.ModelInvokeAsync invokeStreamAsync streamAzureChatOpenAI✅✅✅✅BedrockChat✅�✅�ChatAnthropic✅✅✅✅ChatAnyscale✅✅✅✅ChatBaichuan✅�✅�ChatCohere✅✅✅✅ChatEverlyAI✅✅✅✅ChatFireworks✅✅✅✅ChatGooglePalm✅✅��ChatJavelinAIGateway✅✅��ChatKonko✅���ChatLiteLLM✅✅✅✅ChatMLflowAIGateway✅���ChatOllama✅�✅�ChatOpenAI✅✅✅✅ChatVertexAI✅✅✅�ChatYandexGPT✅✅��ErnieBotChat✅���JinaChat✅✅✅✅MiniMaxChat✅✅��PromptLayerChatOpenAI✅���QianfanChatEndpoint✅✅✅✅📄� Chat modelsFeatures (natively supported)📄� AnthropicThis notebook covers how to get started with Anthropic chat models.📄� Anthropic FunctionsThis notebook shows how to use an experimental wrapper around Anthropic that gives it the same API as OpenAI Functions.📄� AnyscaleThis notebook demonstrates the use of langchain.chat_models.ChatAnyscale for Anyscale Endpoints.📄� AzureThis notebook goes over how to connect to an Azure hosted OpenAI endpoint📄� AzureML Chat Online EndpointAzureML is a platform used to build, train, and deploy machine learning models. Users can explore the types of models to deploy in the Model Catalog, which provides Azure Foundation Models and OpenAI Models. Azure Foundation Models include various open-source models and popular Hugging Face models. Users can also import models of their liking into AzureML.📄� Baichuan ChatBaichuan chat models API by Baichuan Intelligent Technology. For more information, see https://platform.baichuan-ai.com/docs/api📄� Baidu QianfanBaidu AI Cloud Qianfan Platform is a one-stop large model development and service operation platform for enterprise developers. Qianfan not only provides including the model of Wenxin Yiyan (ERNIE-Bot) and the third-party open-source models, but also provides various AI

open-source models, but also provides various AI development tools and the whole set of development environment, which facilitates customers to use and develop large model applications easily.📄� Bedrock ChatAmazon Bedrock is a fully managed service that makes FMs from leading AI startups and Amazon available via an API, so you can choose from a wide range of FMs to find the model that is best suited for your use case📄� CohereThis notebook covers how to get started with Cohere chat models.📄� ERNIE-Bot ChatERNIE-Bot is a large language model developed by Baidu, covering a huge amount of Chinese data.📄� EverlyAIEverlyAI allows you to run your ML models at scale in the cloud. It also provides API access to several LLM models.📄� FireworksFireworks accelerates product development on generative AI by creating an innovative AI experiment and production platform.📄� GCP Vertex AINote: This is seperate from the Google PaLM integration. Google has chosen to offer an enterprise version of PaLM through GCP, and this supports the models made available through there.📄� JinaChatThis notebook covers how to get started with JinaChat chat models.📄� KonkoKonko API is a fully managed Web API designed to help application developers:📄� 🚅 LiteLLMLiteLLM is a library that simplifies calling Anthropic, Azure, Huggingface, Replicate, etc.📄� Llama APIThis notebook shows how to use LangChain with LlamaAPI - a hosted version of Llama2 that adds in support for function calling.📄� MiniMaxMinimax is a Chinese startup that provides LLM service for companies and individuals.📄� OllamaOllama allows you to run open-source large language models, such as LLaMA2, locally.📄� OpenAIThis notebook covers how to get started with OpenAI chat models.📄� PromptLayer ChatOpenAIThis example showcases how to connect to PromptLayer to start recording your ChatOpenAI requests.📄� Tongyi QwenTongyi Qwen is a large language model developed by

Features (natively supported)

Features (natively supported) ->: open-source models, but also provides various AI development tools and the whole set of development environment, which facilitates customers to use and develop large model applications easily.📄� Bedrock ChatAmazon Bedrock is a fully managed service that makes FMs from leading AI startups and Amazon available via an API, so you can choose from a wide range of FMs to find the model that is best suited for your use case📄� CohereThis notebook covers how to get started with Cohere chat models.📄� ERNIE-Bot ChatERNIE-Bot is a large language model developed by Baidu, covering a huge amount of Chinese data.📄� EverlyAIEverlyAI allows you to run your ML models at scale in the cloud. It also provides API access to several LLM models.📄� FireworksFireworks accelerates product development on generative AI by creating an innovative AI experiment and production platform.📄� GCP Vertex AINote: This is seperate from the Google PaLM integration. Google has chosen to offer an enterprise version of PaLM through GCP, and this supports the models made available through there.📄� JinaChatThis notebook covers how to get started with JinaChat chat models.📄� KonkoKonko API is a fully managed Web API designed to help application developers:📄� 🚅 LiteLLMLiteLLM is a library that simplifies calling Anthropic, Azure, Huggingface, Replicate, etc.📄� Llama APIThis notebook shows how to use LangChain with LlamaAPI - a hosted version of Llama2 that adds in support for function calling.📄� MiniMaxMinimax is a Chinese startup that provides LLM service for companies and individuals.📄� OllamaOllama allows you to run open-source large language models, such as LLaMA2, locally.📄� OpenAIThis notebook covers how to get started with OpenAI chat models.📄� PromptLayer ChatOpenAIThis example showcases how to connect to PromptLayer to start recording your ChatOpenAI requests.📄� Tongyi QwenTongyi Qwen is a large language model developed by

Qwen is a large language model developed by Alibaba's Damo Academy. It is capable of understanding user intent through natural language understanding and semantic analysis, based on user input in natural language. It provides services and assistance to users in different domains and tasks. By providing clear and detailed instructions, you can obtain results that better align with your expectations.📄� vLLM ChatvLLM can be deployed as a server that mimics the OpenAI API protocol. This allows vLLM to be used as a drop-in replacement for applications using OpenAI API. This server can be queried in the same format as OpenAI API.📄� YandexGPTThis notebook goes over how to use Langchain with YandexGPT chat model.PreviousYandexGPTNextChat modelsFeatures (natively supported)CommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.

Features (natively supported)

Features (natively supported) ->: Qwen is a large language model developed by Alibaba's Damo Academy. It is capable of understanding user intent through natural language understanding and semantic analysis, based on user input in natural language. It provides services and assistance to users in different domains and tasks. By providing clear and detailed instructions, you can obtain results that better align with your expectations.📄� vLLM ChatvLLM can be deployed as a server that mimics the OpenAI API protocol. This allows vLLM to be used as a drop-in replacement for applications using OpenAI API. This server can be queried in the same format as OpenAI API.📄� YandexGPTThis notebook goes over how to use Langchain with YandexGPT chat model.PreviousYandexGPTNextChat modelsFeatures (natively supported)CommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.

Llama API | 🦜️🔗 Langchain Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsChat modelsAnthropicAnthropic FunctionsAnyscaleAzureAzureML Chat Online EndpointBaichuan ChatBaidu QianfanBedrock ChatCohereERNIE-Bot ChatEverlyAIFireworksGCP Vertex AIJinaChatKonko🚅 LiteLLMLlama APIMiniMaxOllamaOpenAIPromptLayer ChatOpenAITongyi QwenvLLM ChatYandexGPTDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsChat modelsLlama APILlama APIThis notebook shows how to use LangChain with LlamaAPI - a hosted version of Llama2 that adds in support for function calling.!pip install -U llamaapifrom llamaapi import LlamaAPI# Replace 'Your_API_Token' with your actual API tokenllama = LlamaAPI('Your_API_Token')from langchain_experimental.llms import ChatLlamaAPI /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(model = ChatLlamaAPI(client=llama)from langchain.chains import create_tagging_chainschema = { "properties": { "sentiment": {"type": "string", 'description': 'the sentiment encountered in the passage'}, "aggressiveness": {"type": "integer", 'description': 'a 0-10 score of how aggressive the passage is'}, "language": {"type": "string", 'description': 'the language of the passage'}, }}chain = create_tagging_chain(schema, model)chain.run("give me your money") {'sentiment': 'aggressive', 'aggressiveness': 8, 'language': 'english'}Previous🚅 LiteLLMNextMiniMaxCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.

This notebook shows how to use LangChain with LlamaAPI - a hosted version of Llama2 that adds in support for function calling.

This notebook shows how to use LangChain with LlamaAPI - a hosted version of Llama2 that adds in support for function calling. ->: Llama API | 🦜️🔗 Langchain Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsChat modelsAnthropicAnthropic FunctionsAnyscaleAzureAzureML Chat Online EndpointBaichuan ChatBaidu QianfanBedrock ChatCohereERNIE-Bot ChatEverlyAIFireworksGCP Vertex AIJinaChatKonko🚅 LiteLLMLlama APIMiniMaxOllamaOpenAIPromptLayer ChatOpenAITongyi QwenvLLM ChatYandexGPTDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsChat modelsLlama APILlama APIThis notebook shows how to use LangChain with LlamaAPI - a hosted version of Llama2 that adds in support for function calling.!pip install -U llamaapifrom llamaapi import LlamaAPI# Replace 'Your_API_Token' with your actual API tokenllama = LlamaAPI('Your_API_Token')from langchain_experimental.llms import ChatLlamaAPI /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(model = ChatLlamaAPI(client=llama)from langchain.chains import create_tagging_chainschema = { "properties": { "sentiment": {"type": "string", 'description': 'the sentiment encountered in the passage'}, "aggressiveness": {"type": "integer", 'description': 'a 0-10 score of how aggressive the passage is'}, "language": {"type": "string", 'description': 'the language of the passage'}, }}chain = create_tagging_chain(schema, model)chain.run("give me your money") {'sentiment': 'aggressive', 'aggressiveness': 8, 'language': 'english'}Previous🚅 LiteLLMNextMiniMaxCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.

Google | 🦜️🔗 Langchain

All functionality related to Google Cloud Platform

All functionality related to Google Cloud Platform ->: Google | 🦜️🔗 Langchain

Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersProvidersGoogleOn this pageGoogleAll functionality related to Google Cloud PlatformLLMs​Vertex AI​Access PaLM LLMs like text-bison and code-bison via Google Cloud.from langchain.llms import VertexAIModel Garden​Access PaLM and hundreds of OSS models via Vertex AI Model Garden.from langchain.llms import VertexAIModelGardenChat models​Vertex AI​Access PaLM chat models like chat-bison and codechat-bison via Google Cloud.from langchain.chat_models import ChatVertexAIDocument Loader​Google BigQuery​Google BigQuery is a serverless and cost-effective enterprise data warehouse that works across clouds and scales with your data.

All functionality related to Google Cloud Platform

All functionality related to Google Cloud Platform ->: Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersProvidersGoogleOn this pageGoogleAll functionality related to Google Cloud PlatformLLMs​Vertex AI​Access PaLM LLMs like text-bison and code-bison via Google Cloud.from langchain.llms import VertexAIModel Garden​Access PaLM and hundreds of OSS models via Vertex AI Model Garden.from langchain.llms import VertexAIModelGardenChat models​Vertex AI​Access PaLM chat models like chat-bison and codechat-bison via Google Cloud.from langchain.chat_models import ChatVertexAIDocument Loader​Google BigQuery​Google BigQuery is a serverless and cost-effective enterprise data warehouse that works across clouds and scales with your data.

BigQuery is a part of the Google Cloud Platform.First, we need to install google-cloud-bigquery python package.pip install google-cloud-bigquerySee a usage example.from langchain.document_loaders import BigQueryLoaderGoogle Cloud Storage‚ÄãGoogle Cloud Storage is a managed service for storing unstructured data.First, we need to install google-cloud-storage python package.pip install google-cloud-storageThere are two loaders for the Google Cloud Storage: the Directory and the File loaders.See a usage example.from langchain.document_loaders import GCSDirectoryLoaderSee a usage example.from langchain.document_loaders import GCSFileLoaderGoogle Drive‚ÄãGoogle Drive is a file storage and synchronization service developed by Google.Currently, only Google Docs are supported.First, we need to install several python package.pip install google-api-python-client google-auth-httplib2 google-auth-oauthlibSee a usage example and authorizing instructions.from langchain.document_loaders import GoogleDriveLoaderVector Store‚ÄãGoogle Vertex AI MatchingEngine‚ÄãGoogle Vertex AI Matching Engine provides the industry's leading high-scale low latency vector database. These vector databases are commonly referred to as vector similarity-matching or an approximate nearest neighbor (ANN) service.We need to install several python packages.pip install tensorflow google-cloud-aiplatform tensorflow-hub tensorflow-textSee a usage example.from langchain.vectorstores import MatchingEngineGoogle ScaNN‚ÄãGoogle ScaNN (Scalable Nearest Neighbors) is a python package.ScaNN is a method for efficient vector similarity search at scale.ScaNN includes search space pruning and quantization for Maximum Inner Product Search and also supports other distance functions such as Euclidean distance. The implementation is optimized for x86 processors with AVX2 support. See its Google Research github

All functionality related to Google Cloud Platform

All functionality related to Google Cloud Platform ->: BigQuery is a part of the Google Cloud Platform.First, we need to install google-cloud-bigquery python package.pip install google-cloud-bigquerySee a usage example.from langchain.document_loaders import BigQueryLoaderGoogle Cloud Storage‚ÄãGoogle Cloud Storage is a managed service for storing unstructured data.First, we need to install google-cloud-storage python package.pip install google-cloud-storageThere are two loaders for the Google Cloud Storage: the Directory and the File loaders.See a usage example.from langchain.document_loaders import GCSDirectoryLoaderSee a usage example.from langchain.document_loaders import GCSFileLoaderGoogle Drive‚ÄãGoogle Drive is a file storage and synchronization service developed by Google.Currently, only Google Docs are supported.First, we need to install several python package.pip install google-api-python-client google-auth-httplib2 google-auth-oauthlibSee a usage example and authorizing instructions.from langchain.document_loaders import GoogleDriveLoaderVector Store‚ÄãGoogle Vertex AI MatchingEngine‚ÄãGoogle Vertex AI Matching Engine provides the industry's leading high-scale low latency vector database. These vector databases are commonly referred to as vector similarity-matching or an approximate nearest neighbor (ANN) service.We need to install several python packages.pip install tensorflow google-cloud-aiplatform tensorflow-hub tensorflow-textSee a usage example.from langchain.vectorstores import MatchingEngineGoogle ScaNN‚ÄãGoogle ScaNN (Scalable Nearest Neighbors) is a python package.ScaNN is a method for efficient vector similarity search at scale.ScaNN includes search space pruning and quantization for Maximum Inner Product Search and also supports other distance functions such as Euclidean distance. The implementation is optimized for x86 processors with AVX2 support. See its Google Research github

with AVX2 support. See its Google Research github for more details.We need to install scann python package.pip install scannSee a usage example.from langchain.vectorstores import ScaNNRetrievers‚ÄãVertex AI Search‚ÄãGoogle Cloud Vertex AI Search allows developers to quickly build generative AI powered search engines for customers and employees.First, you need to install the google-cloud-discoveryengine Python package.pip install google-cloud-discoveryengineSee a usage example.from langchain.retrievers import GoogleVertexAISearchRetrieverDocument AI Warehouse‚ÄãGoogle Cloud Document AI Warehouse allows enterprises to search, store, govern, and manage documents and their AI-extracted data and metadata in a single platform. Documents should be uploaded outside of Langchain,from langchain.retrievers import GoogleDocumentAIWarehouseRetrieverdocai_wh_retriever = GoogleDocumentAIWarehouseRetriever( project_number=...)query = ...documents = docai_wh_retriever.get_relevant_documents( query, user_ldap=...)Tools‚ÄãGoogle Search‚ÄãInstall requirements with pip install google-api-python-clientSet up a Custom Search Engine, following these instructionsGet an API Key and Custom Search Engine ID from the previous step, and set them as environment variables GOOGLE_API_KEY and GOOGLE_CSE_ID respectivelyThere exists a GoogleSearchAPIWrapper utility which wraps this API. To import this utility:from langchain.utilities import GoogleSearchAPIWrapperFor a more detailed walkthrough of this wrapper, see this notebook.We can easily load this wrapper as a Tool (to use with an Agent). We can do this with:from langchain.agents import load_toolstools = load_tools(["google-search"])Document Transformer‚ÄãGoogle Document AI‚ÄãDocument AI is a Google Cloud Platform service to transform unstructured data from documents into structured data, making it easier to understand, analyze, and consume. We need to set up a GCS bucket and create your own OCR processor

All functionality related to Google Cloud Platform

All functionality related to Google Cloud Platform ->: with AVX2 support. See its Google Research github for more details.We need to install scann python package.pip install scannSee a usage example.from langchain.vectorstores import ScaNNRetrievers‚ÄãVertex AI Search‚ÄãGoogle Cloud Vertex AI Search allows developers to quickly build generative AI powered search engines for customers and employees.First, you need to install the google-cloud-discoveryengine Python package.pip install google-cloud-discoveryengineSee a usage example.from langchain.retrievers import GoogleVertexAISearchRetrieverDocument AI Warehouse‚ÄãGoogle Cloud Document AI Warehouse allows enterprises to search, store, govern, and manage documents and their AI-extracted data and metadata in a single platform. Documents should be uploaded outside of Langchain,from langchain.retrievers import GoogleDocumentAIWarehouseRetrieverdocai_wh_retriever = GoogleDocumentAIWarehouseRetriever( project_number=...)query = ...documents = docai_wh_retriever.get_relevant_documents( query, user_ldap=...)Tools‚ÄãGoogle Search‚ÄãInstall requirements with pip install google-api-python-clientSet up a Custom Search Engine, following these instructionsGet an API Key and Custom Search Engine ID from the previous step, and set them as environment variables GOOGLE_API_KEY and GOOGLE_CSE_ID respectivelyThere exists a GoogleSearchAPIWrapper utility which wraps this API. To import this utility:from langchain.utilities import GoogleSearchAPIWrapperFor a more detailed walkthrough of this wrapper, see this notebook.We can easily load this wrapper as a Tool (to use with an Agent). We can do this with:from langchain.agents import load_toolstools = load_tools(["google-search"])Document Transformer‚ÄãGoogle Document AI‚ÄãDocument AI is a Google Cloud Platform service to transform unstructured data from documents into structured data, making it easier to understand, analyze, and consume. We need to set up a GCS bucket and create your own OCR processor

The GCS_OUTPUT_PATH should be a path to a folder on GCS (starting with gs://) and a processor name should look like projects/PROJECT_NUMBER/locations/LOCATION/processors/PROCESSOR_ID. We can get it either programmatically or copy from the Prediction endpoint section of the Processor details tab in the Google Cloud Console.pip install google-cloud-documentaipip install google-cloud-documentai-toolboxSee a usage example.from langchain.document_loaders.blob_loaders import Blobfrom langchain.document_loaders.parsers import DocAIParserPreviousAWSNextMicrosoftLLMsVertex AIModel GardenChat modelsVertex AIDocument LoaderGoogle BigQueryGoogle Cloud StorageGoogle DriveVector StoreGoogle Vertex AI MatchingEngineGoogle ScaNNRetrieversVertex AI SearchDocument AI WarehouseToolsGoogle SearchDocument TransformerGoogle Document AICommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.

All functionality related to Google Cloud Platform

All functionality related to Google Cloud Platform ->: The GCS_OUTPUT_PATH should be a path to a folder on GCS (starting with gs://) and a processor name should look like projects/PROJECT_NUMBER/locations/LOCATION/processors/PROCESSOR_ID. We can get it either programmatically or copy from the Prediction endpoint section of the Processor details tab in the Google Cloud Console.pip install google-cloud-documentaipip install google-cloud-documentai-toolboxSee a usage example.from langchain.document_loaders.blob_loaders import Blobfrom langchain.document_loaders.parsers import DocAIParserPreviousAWSNextMicrosoftLLMsVertex AIModel GardenChat modelsVertex AIDocument LoaderGoogle BigQueryGoogle Cloud StorageGoogle DriveVector StoreGoogle Vertex AI MatchingEngineGoogle ScaNNRetrieversVertex AI SearchDocument AI WarehouseToolsGoogle SearchDocument TransformerGoogle Document AICommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.

Google Cloud Storage File | 🦜️🔗 Langchain

Google Cloud Storage is a managed service for storing unstructured data.

Google Cloud Storage is a managed service for storing unstructured data. ->: Google Cloud Storage File | 🦜️🔗 Langchain

Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersGoogle Cloud Storage FileGoogle Cloud Storage FileGoogle Cloud Storage is a managed service

Google Cloud Storage is a managed service for storing unstructured data.

Google Cloud Storage is a managed service for storing unstructured data. ->: Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersGoogle Cloud Storage FileGoogle Cloud Storage FileGoogle Cloud Storage is a managed service

FileGoogle Cloud Storage is a managed service for storing unstructured data.This covers how to load document objects from an Google Cloud Storage (GCS) file object (blob).# !pip install google-cloud-storagefrom langchain.document_loaders import GCSFileLoaderloader = GCSFileLoader(project_name="aist", bucket="testing-hwc", blob="fake.docx")loader.load() /Users/harrisonchase/workplace/langchain/.venv/lib/python3.10/site-packages/google/auth/_default.py:83: UserWarning: Your application has authenticated using end user credentials from Google Cloud SDK without a quota project. You might receive a "quota exceeded" or "API not enabled" error. We recommend you rerun `gcloud auth application-default login` and make sure a quota project is added. Or you can use service accounts instead. For more information about service accounts, see https://cloud.google.com/docs/authentication/ warnings.warn(_CLOUD_SDK_CREDENTIALS_WARNING) [Document(page_content='Lorem ipsum dolor sit amet.', lookup_str='', metadata={'source': '/var/folders/y6/8_bzdg295ld6s1_97_12m4lr0000gn/T/tmp3srlf8n8/fake.docx'}, lookup_index=0)]If you want to use an alternative loader, you can provide a custom function, for example:from langchain.document_loaders import PyPDFLoaderdef load_pdf(file_path): return PyPDFLoader(file_path)loader = GCSFileLoader(project_name="aist", bucket="testing-hwc", blob="fake.pdf", loader_func=load_pdf)PreviousGoogle Cloud Storage DirectoryNextGoogle DriveCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.

Google Cloud Storage is a managed service for storing unstructured data.

Google Cloud Storage is a managed service for storing unstructured data. ->: FileGoogle Cloud Storage is a managed service for storing unstructured data.This covers how to load document objects from an Google Cloud Storage (GCS) file object (blob).# !pip install google-cloud-storagefrom langchain.document_loaders import GCSFileLoaderloader = GCSFileLoader(project_name="aist", bucket="testing-hwc", blob="fake.docx")loader.load() /Users/harrisonchase/workplace/langchain/.venv/lib/python3.10/site-packages/google/auth/_default.py:83: UserWarning: Your application has authenticated using end user credentials from Google Cloud SDK without a quota project. You might receive a "quota exceeded" or "API not enabled" error. We recommend you rerun `gcloud auth application-default login` and make sure a quota project is added. Or you can use service accounts instead. For more information about service accounts, see https://cloud.google.com/docs/authentication/ warnings.warn(_CLOUD_SDK_CREDENTIALS_WARNING) [Document(page_content='Lorem ipsum dolor sit amet.', lookup_str='', metadata={'source': '/var/folders/y6/8_bzdg295ld6s1_97_12m4lr0000gn/T/tmp3srlf8n8/fake.docx'}, lookup_index=0)]If you want to use an alternative loader, you can provide a custom function, for example:from langchain.document_loaders import PyPDFLoaderdef load_pdf(file_path): return PyPDFLoader(file_path)loader = GCSFileLoader(project_name="aist", bucket="testing-hwc", blob="fake.pdf", loader_func=load_pdf)PreviousGoogle Cloud Storage DirectoryNextGoogle DriveCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.

Wikipedia | 🦜️🔗 Langchain

Wikipedia is a multilingual free online encyclopedia written and maintained by a community of volunteers, known as Wikipedians, through open collaboration and using a wiki-based editing system called MediaWiki. Wikipedia is the largest and most-read reference work in history.

Wikipedia is a multilingual free online encyclopedia written and maintained by a community of volunteers, known as Wikipedians, through open collaboration and using a wiki-based editing system called MediaWiki. Wikipedia is the largest and most-read reference work in history. ->: Wikipedia | 🦜️🔗 Langchain

Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersWikipediaOn this pageWikipediaWikipedia is a multilingual free online encyclopedia written and

Wikipedia is a multilingual free online encyclopedia written and maintained by a community of volunteers, known as Wikipedians, through open collaboration and using a wiki-based editing system called MediaWiki. Wikipedia is the largest and most-read reference work in history.

Wikipedia is a multilingual free online encyclopedia written and maintained by a community of volunteers, known as Wikipedians, through open collaboration and using a wiki-based editing system called MediaWiki. Wikipedia is the largest and most-read reference work in history. ->: Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersWikipediaOn this pageWikipediaWikipedia is a multilingual free online encyclopedia written and

multilingual free online encyclopedia written and maintained by a community of volunteers, known as Wikipedians, through open collaboration and using a wiki-based editing system called MediaWiki. Wikipedia is the largest and most-read reference work in history.This notebook shows how to load wiki pages from wikipedia.org into the Document format that we use downstream.Installation​First, you need to install wikipedia python package.#!pip install wikipediaExamples​WikipediaLoader has these arguments:query: free text which used to find documents in Wikipediaoptional lang: default="en". Use it to search in a specific language part of Wikipediaoptional load_max_docs: default=100. Use it to limit number of downloaded documents. It takes time to download all 100 documents, so use a small number for experiments. There is a hard limit of 300 for now.optional load_all_available_meta: default=False. By default only the most important fields downloaded: Published (date when document was published/last updated), title, Summary. If True, other fields also downloaded.from langchain.document_loaders import WikipediaLoaderdocs = WikipediaLoader(query="HUNTER X HUNTER", load_max_docs=2).load()len(docs)docs[0].metadata # meta-information of the Documentdocs[0].page_content[:400] # a content of the DocumentPreviousWhatsApp ChatNextXMLInstallationExamplesCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.

Wikipedia is a multilingual free online encyclopedia written and maintained by a community of volunteers, known as Wikipedians, through open collaboration and using a wiki-based editing system called MediaWiki. Wikipedia is the largest and most-read reference work in history.

Wikipedia is a multilingual free online encyclopedia written and maintained by a community of volunteers, known as Wikipedians, through open collaboration and using a wiki-based editing system called MediaWiki. Wikipedia is the largest and most-read reference work in history. ->: multilingual free online encyclopedia written and maintained by a community of volunteers, known as Wikipedians, through open collaboration and using a wiki-based editing system called MediaWiki. Wikipedia is the largest and most-read reference work in history.This notebook shows how to load wiki pages from wikipedia.org into the Document format that we use downstream.Installation​First, you need to install wikipedia python package.#!pip install wikipediaExamples​WikipediaLoader has these arguments:query: free text which used to find documents in Wikipediaoptional lang: default="en". Use it to search in a specific language part of Wikipediaoptional load_max_docs: default=100. Use it to limit number of downloaded documents. It takes time to download all 100 documents, so use a small number for experiments. There is a hard limit of 300 for now.optional load_all_available_meta: default=False. By default only the most important fields downloaded: Published (date when document was published/last updated), title, Summary. If True, other fields also downloaded.from langchain.document_loaders import WikipediaLoaderdocs = WikipediaLoader(query="HUNTER X HUNTER", load_max_docs=2).load()len(docs)docs[0].metadata # meta-information of the Documentdocs[0].page_content[:400] # a content of the DocumentPreviousWhatsApp ChatNextXMLInstallationExamplesCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.

Joplin | 🦜️🔗 Langchain

Joplin is an open-source note-taking app. Capture your thoughts and securely access them from any device.

Joplin is an open-source note-taking app. Capture your thoughts and securely access them from any device. ->: Joplin | 🦜️🔗 Langchain

Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersJoplinJoplinJoplin is an open-source note-taking app. Capture your thoughts and securely

Joplin is an open-source note-taking app. Capture your thoughts and securely access them from any device.

Joplin is an open-source note-taking app. Capture your thoughts and securely access them from any device. ->: Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersJoplinJoplinJoplin is an open-source note-taking app. Capture your thoughts and securely

app. Capture your thoughts and securely access them from any device.This notebook covers how to load documents from a Joplin database.Joplin has a REST API for accessing its local database. This loader uses the API to retrieve all notes in the database and their metadata. This requires an access token that can be obtained from the app by following these steps:Open the Joplin app. The app must stay open while the documents are being loaded.Go to settings / options and select "Web Clipper".Make sure that the Web Clipper service is enabled.Under "Advanced Options", copy the authorization token.You may either initialize the loader directly with the access token, or store it in the environment variable JOPLIN_ACCESS_TOKEN.An alternative to this approach is to export the Joplin's note database to Markdown files (optionally, with Front Matter metadata) and use a Markdown loader, such as ObsidianLoader, to load them.from langchain.document_loaders import JoplinLoaderloader = JoplinLoader(access_token="<access-token>")docs = loader.load()PreviousIuguNextJupyter NotebookCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.

Joplin is an open-source note-taking app. Capture your thoughts and securely access them from any device.

Joplin is an open-source note-taking app. Capture your thoughts and securely access them from any device. ->: app. Capture your thoughts and securely access them from any device.This notebook covers how to load documents from a Joplin database.Joplin has a REST API for accessing its local database. This loader uses the API to retrieve all notes in the database and their metadata. This requires an access token that can be obtained from the app by following these steps:Open the Joplin app. The app must stay open while the documents are being loaded.Go to settings / options and select "Web Clipper".Make sure that the Web Clipper service is enabled.Under "Advanced Options", copy the authorization token.You may either initialize the loader directly with the access token, or store it in the environment variable JOPLIN_ACCESS_TOKEN.An alternative to this approach is to export the Joplin's note database to Markdown files (optionally, with Front Matter metadata) and use a Markdown loader, such as ObsidianLoader, to load them.from langchain.document_loaders import JoplinLoaderloader = JoplinLoader(access_token="<access-token>")docs = loader.load()PreviousIuguNextJupyter NotebookCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.

Notion DB 2/2 | 🦜️🔗 Langchain

Notion is a collaboration platform with modified Markdown support that integrates kanban boards, tasks, wikis and databases. It is an all-in-one workspace for notetaking, knowledge and data management, and project and task management.

Notion is a collaboration platform with modified Markdown support that integrates kanban boards, tasks, wikis and databases. It is an all-in-one workspace for notetaking, knowledge and data management, and project and task management. ->: Notion DB 2/2 | 🦜️🔗 Langchain

Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersNotion DB 2/2On this pageNotion DB 2/2Notion is a collaboration platform with modified

Notion is a collaboration platform with modified Markdown support that integrates kanban boards, tasks, wikis and databases. It is an all-in-one workspace for notetaking, knowledge and data management, and project and task management.

Notion is a collaboration platform with modified Markdown support that integrates kanban boards, tasks, wikis and databases. It is an all-in-one workspace for notetaking, knowledge and data management, and project and task management. ->: Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersNotion DB 2/2On this pageNotion DB 2/2Notion is a collaboration platform with modified

is a collaboration platform with modified Markdown support that integrates kanban boards, tasks, wikis and databases. It is an all-in-one workspace for notetaking, knowledge and data management, and project and task management.NotionDBLoader is a Python class for loading content from a Notion database. It retrieves pages from the database, reads their content, and returns a list of Document objects.Requirements‚ÄãA Notion DatabaseNotion Integration TokenSetup‚Äã1. Create a Notion Table Database‚ÄãCreate a new table database in Notion. You can add any column to the database and they will be treated as metadata. For example you can add the following columns:Title: set Title as the default property.Categories: A Multi-select property to store categories associated with the page.Keywords: A Multi-select property to store keywords associated with the page.Add your content to the body of each page in the database. The NotionDBLoader will extract the content and metadata from these pages.2. Create a Notion Integration‚ÄãTo create a Notion Integration, follow these steps:Visit the Notion Developers page and log in with your Notion account.Click on the "+ New integration" button.Give your integration a name and choose the workspace where your database is located.Select the require capabilities, this extension only need the Read content capabilityClick the "Submit" button to create the integration.

Notion is a collaboration platform with modified Markdown support that integrates kanban boards, tasks, wikis and databases. It is an all-in-one workspace for notetaking, knowledge and data management, and project and task management.

Notion is a collaboration platform with modified Markdown support that integrates kanban boards, tasks, wikis and databases. It is an all-in-one workspace for notetaking, knowledge and data management, and project and task management. ->: is a collaboration platform with modified Markdown support that integrates kanban boards, tasks, wikis and databases. It is an all-in-one workspace for notetaking, knowledge and data management, and project and task management.NotionDBLoader is a Python class for loading content from a Notion database. It retrieves pages from the database, reads their content, and returns a list of Document objects.Requirements‚ÄãA Notion DatabaseNotion Integration TokenSetup‚Äã1. Create a Notion Table Database‚ÄãCreate a new table database in Notion. You can add any column to the database and they will be treated as metadata. For example you can add the following columns:Title: set Title as the default property.Categories: A Multi-select property to store categories associated with the page.Keywords: A Multi-select property to store keywords associated with the page.Add your content to the body of each page in the database. The NotionDBLoader will extract the content and metadata from these pages.2. Create a Notion Integration‚ÄãTo create a Notion Integration, follow these steps:Visit the Notion Developers page and log in with your Notion account.Click on the "+ New integration" button.Give your integration a name and choose the workspace where your database is located.Select the require capabilities, this extension only need the Read content capabilityClick the "Submit" button to create the integration.

Once the integration is created, you'll be provided with an Integration Token (API key). Copy this token and keep it safe, as you'll need it to use the NotionDBLoader.3. Connect the Integration to the Database​To connect your integration to the database, follow these steps:Open your database in Notion.Click on the three-dot menu icon in the top right corner of the database view.Click on the "+ New integration" button.Find your integration, you may need to start typing its name in the search box.Click on the "Connect" button to connect the integration to the database.4. Get the Database ID​To get the database ID, follow these steps:Open your database in Notion.Click on the three-dot menu icon in the top right corner of the database view.Select "Copy link" from the menu to copy the database URL to your clipboard.The database ID is the long string of alphanumeric characters found in the URL. It typically looks like this: https://www.notion.so/username/8935f9d140a04f95a872520c4f123456?v=.... In this example, the database ID is 8935f9d140a04f95a872520c4f123456.With the database properly set up and the integration token and database ID in hand, you can now use the NotionDBLoader code to load content and metadata from your Notion database.Usage​NotionDBLoader is part of the langchain package's document loaders. You can use it as follows:from getpass import getpassNOTION_TOKEN = getpass()DATABASE_ID = getpass() ········ ········from langchain.document_loaders import NotionDBLoaderloader = NotionDBLoader( integration_token=NOTION_TOKEN, database_id=DATABASE_ID, request_timeout_sec=30, # optional, defaults to 10)docs = loader.load()print(docs) PreviousNotion DB 1/2NextNucliaRequirementsSetup1. Create a Notion Table Database2. Create a Notion Integration3. Connect the Integration to the Database4. Get the Database IDUsageCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.

Notion is a collaboration platform with modified Markdown support that integrates kanban boards, tasks, wikis and databases. It is an all-in-one workspace for notetaking, knowledge and data management, and project and task management.

Notion is a collaboration platform with modified Markdown support that integrates kanban boards, tasks, wikis and databases. It is an all-in-one workspace for notetaking, knowledge and data management, and project and task management. ->: Once the integration is created, you'll be provided with an Integration Token (API key). Copy this token and keep it safe, as you'll need it to use the NotionDBLoader.3. Connect the Integration to the Database​To connect your integration to the database, follow these steps:Open your database in Notion.Click on the three-dot menu icon in the top right corner of the database view.Click on the "+ New integration" button.Find your integration, you may need to start typing its name in the search box.Click on the "Connect" button to connect the integration to the database.4. Get the Database ID​To get the database ID, follow these steps:Open your database in Notion.Click on the three-dot menu icon in the top right corner of the database view.Select "Copy link" from the menu to copy the database URL to your clipboard.The database ID is the long string of alphanumeric characters found in the URL. It typically looks like this: https://www.notion.so/username/8935f9d140a04f95a872520c4f123456?v=.... In this example, the database ID is 8935f9d140a04f95a872520c4f123456.With the database properly set up and the integration token and database ID in hand, you can now use the NotionDBLoader code to load content and metadata from your Notion database.Usage​NotionDBLoader is part of the langchain package's document loaders. You can use it as follows:from getpass import getpassNOTION_TOKEN = getpass()DATABASE_ID = getpass() ········ ········from langchain.document_loaders import NotionDBLoaderloader = NotionDBLoader( integration_token=NOTION_TOKEN, database_id=DATABASE_ID, request_timeout_sec=30, # optional, defaults to 10)docs = loader.load()print(docs) PreviousNotion DB 1/2NextNucliaRequirementsSetup1. Create a Notion Table Database2. Create a Notion Integration3. Connect the Integration to the Database4. Get the Database IDUsageCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.

AZLyrics | 🦜️🔗 Langchain

AZLyrics is a large, legal, every day growing collection of lyrics.

AZLyrics is a large, legal, every day growing collection of lyrics. ->: AZLyrics | 🦜️🔗 Langchain

Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersAZLyricsAZLyricsAZLyrics is a large, legal, every day growing collection of lyrics.This covers

AZLyrics is a large, legal, every day growing collection of lyrics.

AZLyrics is a large, legal, every day growing collection of lyrics. ->: Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersAZLyricsAZLyricsAZLyrics is a large, legal, every day growing collection of lyrics.This covers

day growing collection of lyrics.This covers how to load AZLyrics webpages into a document format that we can use downstream.from langchain.document_loaders import AZLyricsLoaderloader = AZLyricsLoader("https://www.azlyrics.com/lyrics/mileycyrus/flowers.html")data = loader.load()data [Document(page_content="Miley Cyrus - Flowers Lyrics | AZLyrics.com\n\r\nWe were good, we were gold\nKinda dream that can't be sold\nWe were right till we weren't\nBuilt a home and watched it burn\n\nI didn't wanna leave you\nI didn't wanna lie\nStarted to cry but then remembered I\n\nI can buy myself flowers\nWrite my name in the sand\nTalk to myself for hours\nSay things you don't understand\nI can take myself dancing\nAnd I can hold my own hand\nYeah, I can love me better than you can\n\nCan love me better\nI can love me better, baby\nCan love me better\nI can love me better, baby\n\nPaint my nails, cherry red\nMatch the roses that you left\nNo remorse, no regret\nI forgive every word you said\n\nI didn't wanna leave you, baby\nI didn't wanna fight\nStarted to cry but then remembered I\n\nI can buy myself flowers\nWrite my name in the sand\nTalk to myself for hours, yeah\nSay things you don't understand\nI can take myself dancing\nAnd I can hold my own hand\nYeah, I can love me better than you can\n\nCan love me better\nI can love me better, baby\nCan love me better\nI can love me better, baby\nCan love me better\nI can love me better, baby\nCan love me better\nI\n\nI didn't wanna wanna leave you\nI didn't wanna fight\nStarted to cry but then remembered I\n\nI can buy myself flowers\nWrite my name in the sand\nTalk to myself for hours (Yeah)\nSay things you don't understand\nI can take myself dancing\nAnd I can hold my own hand\nYeah, I can love me better than\nYeah, I can love me better than you can, uh\n\nCan love me better\nI can love me better, baby\nCan love me better\nI can love me better, baby (Than you can)\nCan love me better\nI can love me better, baby\nCan love me

AZLyrics is a large, legal, every day growing collection of lyrics.

AZLyrics is a large, legal, every day growing collection of lyrics. ->: day growing collection of lyrics.This covers how to load AZLyrics webpages into a document format that we can use downstream.from langchain.document_loaders import AZLyricsLoaderloader = AZLyricsLoader("https://www.azlyrics.com/lyrics/mileycyrus/flowers.html")data = loader.load()data [Document(page_content="Miley Cyrus - Flowers Lyrics | AZLyrics.com\n\r\nWe were good, we were gold\nKinda dream that can't be sold\nWe were right till we weren't\nBuilt a home and watched it burn\n\nI didn't wanna leave you\nI didn't wanna lie\nStarted to cry but then remembered I\n\nI can buy myself flowers\nWrite my name in the sand\nTalk to myself for hours\nSay things you don't understand\nI can take myself dancing\nAnd I can hold my own hand\nYeah, I can love me better than you can\n\nCan love me better\nI can love me better, baby\nCan love me better\nI can love me better, baby\n\nPaint my nails, cherry red\nMatch the roses that you left\nNo remorse, no regret\nI forgive every word you said\n\nI didn't wanna leave you, baby\nI didn't wanna fight\nStarted to cry but then remembered I\n\nI can buy myself flowers\nWrite my name in the sand\nTalk to myself for hours, yeah\nSay things you don't understand\nI can take myself dancing\nAnd I can hold my own hand\nYeah, I can love me better than you can\n\nCan love me better\nI can love me better, baby\nCan love me better\nI can love me better, baby\nCan love me better\nI can love me better, baby\nCan love me better\nI\n\nI didn't wanna wanna leave you\nI didn't wanna fight\nStarted to cry but then remembered I\n\nI can buy myself flowers\nWrite my name in the sand\nTalk to myself for hours (Yeah)\nSay things you don't understand\nI can take myself dancing\nAnd I can hold my own hand\nYeah, I can love me better than\nYeah, I can love me better than you can, uh\n\nCan love me better\nI can love me better, baby\nCan love me better\nI can love me better, baby (Than you can)\nCan love me better\nI can love me better, baby\nCan love me

better\nI can love me better, baby\nCan love me better\nI\n", lookup_str='', metadata={'source': 'https://www.azlyrics.com/lyrics/mileycyrus/flowers.html'}, lookup_index=0)]PreviousAWS S3 FileNextAzure Blob Storage ContainerCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.

AZLyrics is a large, legal, every day growing collection of lyrics.

AZLyrics is a large, legal, every day growing collection of lyrics. ->: better\nI can love me better, baby\nCan love me better\nI\n", lookup_str='', metadata={'source': 'https://www.azlyrics.com/lyrics/mileycyrus/flowers.html'}, lookup_index=0)]PreviousAWS S3 FileNextAzure Blob Storage ContainerCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.

GitBook | 🦜️🔗 Langchain

GitBook is a modern documentation platform where teams can document everything from products to internal knowledge bases and APIs.

GitBook is a modern documentation platform where teams can document everything from products to internal knowledge bases and APIs. ->: GitBook | 🦜️🔗 Langchain

Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersGitBookOn this pageGitBookGitBook is a modern documentation platform where teams can document

GitBook is a modern documentation platform where teams can document everything from products to internal knowledge bases and APIs.

GitBook is a modern documentation platform where teams can document everything from products to internal knowledge bases and APIs. ->: Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersGitBookOn this pageGitBookGitBook is a modern documentation platform where teams can document

documentation platform where teams can document everything from products to internal knowledge bases and APIs.This notebook shows how to pull page data from any GitBook.from langchain.document_loaders import GitbookLoaderLoad from single GitBook page‚Äãloader = GitbookLoader("https://docs.gitbook.com")page_data = loader.load()page_data [Document(page_content='Introduction to GitBook\nGitBook is a modern documentation platform where teams can document everything from products to internal knowledge bases and APIs.\nWe want to help \nteams to work more efficiently\n by creating a simple yet powerful platform for them to \nshare their knowledge\n.\nOur mission is to make a \nuser-friendly\n and \ncollaborative\n product for everyone to create, edit and share knowledge through documentation.\nPublish your documentation in 5 easy steps\nImport\n\nMove your existing content to GitBook with ease.\nGit Sync\n\nBenefit from our bi-directional synchronisation with GitHub and GitLab.\nOrganise your content\n\nCreate pages and spaces and organize them into collections\nCollaborate\n\nInvite other users and collaborate asynchronously with ease.\nPublish your docs\n\nShare your documentation with selected users or with everyone.\nNext\n - Getting started\nOverview\nLast modified \n3mo ago', lookup_str='', metadata={'source': 'https://docs.gitbook.com', 'title': 'Introduction to GitBook'}, lookup_index=0)]Load from all paths in a given GitBook‚ÄãFor this to work, the GitbookLoader needs to be initialized with the root path (https://docs.gitbook.com in this example) and have load_all_paths set to True.loader = GitbookLoader("https://docs.gitbook.com", load_all_paths=True)all_pages_data = loader.load() Fetching text from https://docs.gitbook.com/ Fetching text from https://docs.gitbook.com/getting-started/overview Fetching text from https://docs.gitbook.com/getting-started/import Fetching text from https://docs.gitbook.com/getting-started/git-sync Fetching text

GitBook is a modern documentation platform where teams can document everything from products to internal knowledge bases and APIs.

GitBook is a modern documentation platform where teams can document everything from products to internal knowledge bases and APIs. ->: documentation platform where teams can document everything from products to internal knowledge bases and APIs.This notebook shows how to pull page data from any GitBook.from langchain.document_loaders import GitbookLoaderLoad from single GitBook page‚Äãloader = GitbookLoader("https://docs.gitbook.com")page_data = loader.load()page_data [Document(page_content='Introduction to GitBook\nGitBook is a modern documentation platform where teams can document everything from products to internal knowledge bases and APIs.\nWe want to help \nteams to work more efficiently\n by creating a simple yet powerful platform for them to \nshare their knowledge\n.\nOur mission is to make a \nuser-friendly\n and \ncollaborative\n product for everyone to create, edit and share knowledge through documentation.\nPublish your documentation in 5 easy steps\nImport\n\nMove your existing content to GitBook with ease.\nGit Sync\n\nBenefit from our bi-directional synchronisation with GitHub and GitLab.\nOrganise your content\n\nCreate pages and spaces and organize them into collections\nCollaborate\n\nInvite other users and collaborate asynchronously with ease.\nPublish your docs\n\nShare your documentation with selected users or with everyone.\nNext\n - Getting started\nOverview\nLast modified \n3mo ago', lookup_str='', metadata={'source': 'https://docs.gitbook.com', 'title': 'Introduction to GitBook'}, lookup_index=0)]Load from all paths in a given GitBook‚ÄãFor this to work, the GitbookLoader needs to be initialized with the root path (https://docs.gitbook.com in this example) and have load_all_paths set to True.loader = GitbookLoader("https://docs.gitbook.com", load_all_paths=True)all_pages_data = loader.load() Fetching text from https://docs.gitbook.com/ Fetching text from https://docs.gitbook.com/getting-started/overview Fetching text from https://docs.gitbook.com/getting-started/import Fetching text from https://docs.gitbook.com/getting-started/git-sync Fetching text

Fetching text from https://docs.gitbook.com/getting-started/content-structure Fetching text from https://docs.gitbook.com/getting-started/collaboration Fetching text from https://docs.gitbook.com/getting-started/publishing Fetching text from https://docs.gitbook.com/tour/quick-find Fetching text from https://docs.gitbook.com/tour/editor Fetching text from https://docs.gitbook.com/tour/customization Fetching text from https://docs.gitbook.com/tour/member-management Fetching text from https://docs.gitbook.com/tour/pdf-export Fetching text from https://docs.gitbook.com/tour/activity-history Fetching text from https://docs.gitbook.com/tour/insights Fetching text from https://docs.gitbook.com/tour/notifications Fetching text from https://docs.gitbook.com/tour/internationalization Fetching text from https://docs.gitbook.com/tour/keyboard-shortcuts Fetching text from https://docs.gitbook.com/tour/seo Fetching text from https://docs.gitbook.com/advanced-guides/custom-domain Fetching text from https://docs.gitbook.com/advanced-guides/advanced-sharing-and-security Fetching text from https://docs.gitbook.com/advanced-guides/integrations Fetching text from https://docs.gitbook.com/billing-and-admin/account-settings Fetching text from https://docs.gitbook.com/billing-and-admin/plans Fetching text from https://docs.gitbook.com/troubleshooting/faqs Fetching text from https://docs.gitbook.com/troubleshooting/hard-refresh Fetching text from https://docs.gitbook.com/troubleshooting/report-bugs Fetching text from https://docs.gitbook.com/troubleshooting/connectivity-issues Fetching text from https://docs.gitbook.com/troubleshooting/supportprint(f"fetched {len(all_pages_data)} documents.")# show second documentall_pages_data[2] fetched 28 documents. Document(page_content="Import\nFind out how to easily migrate your existing documentation and which formats are supported.\nThe import function allows you to

GitBook is a modern documentation platform where teams can document everything from products to internal knowledge bases and APIs.

GitBook is a modern documentation platform where teams can document everything from products to internal knowledge bases and APIs. ->: Fetching text from https://docs.gitbook.com/getting-started/content-structure Fetching text from https://docs.gitbook.com/getting-started/collaboration Fetching text from https://docs.gitbook.com/getting-started/publishing Fetching text from https://docs.gitbook.com/tour/quick-find Fetching text from https://docs.gitbook.com/tour/editor Fetching text from https://docs.gitbook.com/tour/customization Fetching text from https://docs.gitbook.com/tour/member-management Fetching text from https://docs.gitbook.com/tour/pdf-export Fetching text from https://docs.gitbook.com/tour/activity-history Fetching text from https://docs.gitbook.com/tour/insights Fetching text from https://docs.gitbook.com/tour/notifications Fetching text from https://docs.gitbook.com/tour/internationalization Fetching text from https://docs.gitbook.com/tour/keyboard-shortcuts Fetching text from https://docs.gitbook.com/tour/seo Fetching text from https://docs.gitbook.com/advanced-guides/custom-domain Fetching text from https://docs.gitbook.com/advanced-guides/advanced-sharing-and-security Fetching text from https://docs.gitbook.com/advanced-guides/integrations Fetching text from https://docs.gitbook.com/billing-and-admin/account-settings Fetching text from https://docs.gitbook.com/billing-and-admin/plans Fetching text from https://docs.gitbook.com/troubleshooting/faqs Fetching text from https://docs.gitbook.com/troubleshooting/hard-refresh Fetching text from https://docs.gitbook.com/troubleshooting/report-bugs Fetching text from https://docs.gitbook.com/troubleshooting/connectivity-issues Fetching text from https://docs.gitbook.com/troubleshooting/supportprint(f"fetched {len(all_pages_data)} documents.")# show second documentall_pages_data[2] fetched 28 documents. Document(page_content="Import\nFind out how to easily migrate your existing documentation and which formats are supported.\nThe import function allows you to

are supported.\nThe import function allows you to migrate and unify existing documentation in GitBook. You can choose to import single or multiple pages although limits apply. \nPermissions\nAll members with editor permission or above can use the import feature.\nSupported formats\nGitBook supports imports from websites or files that are:\nMarkdown (.md or .markdown)\nHTML (.html)\nMicrosoft Word (.docx).\nWe also support import from:\nConfluence\nNotion\nGitHub Wiki\nQuip\nDropbox Paper\nGoogle Docs\nYou can also upload a ZIP\n \ncontaining HTML or Markdown files when \nimporting multiple pages.\nNote: this feature is in beta.\nFeel free to suggest import sources we don't support yet and \nlet us know\n if you have any issues.\nImport panel\nWhen you create a new space, you'll have the option to import content straight away:\nThe new page menu\nImport a page or subpage by selecting \nImport Page\n from the New Page menu, or \nImport Subpage\n in the page action menu, found in the table of contents:\nImport from the page action menu\nWhen you choose your input source, instructions will explain how to proceed.\nAlthough GitBook supports importing content from different kinds of sources, the end result might be different from your source due to differences in product features and document format.\nLimits\nGitBook currently has the following limits for imported content:\nThe maximum number of pages that can be uploaded in a single import is \n20.\nThe maximum number of files (images etc.) that can be uploaded in a single import is \n20.\nGetting started - \nPrevious\nOverview\nNext\n - Getting started\nGit Sync\nLast modified \n4mo ago", lookup_str='', metadata={'source': 'https://docs.gitbook.com/getting-started/import', 'title': 'Import'}, lookup_index=0)PreviousGitNextGitHubLoad from single GitBook pageLoad from all paths in a given GitBookCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.

GitBook is a modern documentation platform where teams can document everything from products to internal knowledge bases and APIs.

GitBook is a modern documentation platform where teams can document everything from products to internal knowledge bases and APIs. ->: are supported.\nThe import function allows you to migrate and unify existing documentation in GitBook. You can choose to import single or multiple pages although limits apply. \nPermissions\nAll members with editor permission or above can use the import feature.\nSupported formats\nGitBook supports imports from websites or files that are:\nMarkdown (.md or .markdown)\nHTML (.html)\nMicrosoft Word (.docx).\nWe also support import from:\nConfluence\nNotion\nGitHub Wiki\nQuip\nDropbox Paper\nGoogle Docs\nYou can also upload a ZIP\n \ncontaining HTML or Markdown files when \nimporting multiple pages.\nNote: this feature is in beta.\nFeel free to suggest import sources we don't support yet and \nlet us know\n if you have any issues.\nImport panel\nWhen you create a new space, you'll have the option to import content straight away:\nThe new page menu\nImport a page or subpage by selecting \nImport Page\n from the New Page menu, or \nImport Subpage\n in the page action menu, found in the table of contents:\nImport from the page action menu\nWhen you choose your input source, instructions will explain how to proceed.\nAlthough GitBook supports importing content from different kinds of sources, the end result might be different from your source due to differences in product features and document format.\nLimits\nGitBook currently has the following limits for imported content:\nThe maximum number of pages that can be uploaded in a single import is \n20.\nThe maximum number of files (images etc.) that can be uploaded in a single import is \n20.\nGetting started - \nPrevious\nOverview\nNext\n - Getting started\nGit Sync\nLast modified \n4mo ago", lookup_str='', metadata={'source': 'https://docs.gitbook.com/getting-started/import', 'title': 'Import'}, lookup_index=0)PreviousGitNextGitHubLoad from single GitBook pageLoad from all paths in a given GitBookCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.

Notion DB 1/2 | 🦜️🔗 Langchain

Notion is a collaboration platform with modified Markdown support that integrates kanban boards, tasks, wikis and databases. It is an all-in-one workspace for notetaking, knowledge and data management, and project and task management.

Notion is a collaboration platform with modified Markdown support that integrates kanban boards, tasks, wikis and databases. It is an all-in-one workspace for notetaking, knowledge and data management, and project and task management. ->: Notion DB 1/2 | 🦜️🔗 Langchain

Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersNotion DB 1/2On this pageNotion DB 1/2Notion is a collaboration platform with modified

Notion is a collaboration platform with modified Markdown support that integrates kanban boards, tasks, wikis and databases. It is an all-in-one workspace for notetaking, knowledge and data management, and project and task management.

Notion is a collaboration platform with modified Markdown support that integrates kanban boards, tasks, wikis and databases. It is an all-in-one workspace for notetaking, knowledge and data management, and project and task management. ->: Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersNotion DB 1/2On this pageNotion DB 1/2Notion is a collaboration platform with modified

is a collaboration platform with modified Markdown support that integrates kanban boards, tasks, wikis and databases. It is an all-in-one workspace for notetaking, knowledge and data management, and project and task management.This notebook covers how to load documents from a Notion database dump.In order to get this notion dump, follow these instructions:🧑 Instructions for ingesting your own dataset​Export your dataset from Notion. You can do this by clicking on the three dots in the upper right hand corner and then clicking Export.When exporting, make sure to select the Markdown & CSV format option.This will produce a .zip file in your Downloads folder. Move the .zip file into this repository.Run the following command to unzip the zip file (replace the Export... with your own file name as needed).unzip Export-d3adfe0f-3131-4bf3-8987-a52017fc1bae.zip -d Notion_DBRun the following command to ingest the data.from langchain.document_loaders import NotionDirectoryLoaderloader = NotionDirectoryLoader("Notion_DB")docs = loader.load()PreviousNews URLNextNotion DB 2/2🧑 Instructions for ingesting your own datasetCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.

Notion is a collaboration platform with modified Markdown support that integrates kanban boards, tasks, wikis and databases. It is an all-in-one workspace for notetaking, knowledge and data management, and project and task management.

Notion is a collaboration platform with modified Markdown support that integrates kanban boards, tasks, wikis and databases. It is an all-in-one workspace for notetaking, knowledge and data management, and project and task management. ->: is a collaboration platform with modified Markdown support that integrates kanban boards, tasks, wikis and databases. It is an all-in-one workspace for notetaking, knowledge and data management, and project and task management.This notebook covers how to load documents from a Notion database dump.In order to get this notion dump, follow these instructions:🧑 Instructions for ingesting your own dataset​Export your dataset from Notion. You can do this by clicking on the three dots in the upper right hand corner and then clicking Export.When exporting, make sure to select the Markdown & CSV format option.This will produce a .zip file in your Downloads folder. Move the .zip file into this repository.Run the following command to unzip the zip file (replace the Export... with your own file name as needed).unzip Export-d3adfe0f-3131-4bf3-8987-a52017fc1bae.zip -d Notion_DBRun the following command to ingest the data.from langchain.document_loaders import NotionDirectoryLoaderloader = NotionDirectoryLoader("Notion_DB")docs = loader.load()PreviousNews URLNextNotion DB 2/2🧑 Instructions for ingesting your own datasetCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.

LarkSuite (FeiShu) | 🦜️🔗 Langchain

LarkSuite is an enterprise collaboration platform developed by ByteDance.

LarkSuite is an enterprise collaboration platform developed by ByteDance. ->: LarkSuite (FeiShu) | 🦜️🔗 Langchain

Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersLarkSuite (FeiShu)LarkSuite (FeiShu)LarkSuite is an enterprise collaboration platform

LarkSuite is an enterprise collaboration platform developed by ByteDance.

LarkSuite is an enterprise collaboration platform developed by ByteDance. ->: Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersLarkSuite (FeiShu)LarkSuite (FeiShu)LarkSuite is an enterprise collaboration platform

is an enterprise collaboration platform developed by ByteDance.This notebook covers how to load data from the LarkSuite REST API into a format that can be ingested into LangChain, along with example usage for text summarization.The LarkSuite API requires an access token (tenant_access_token or user_access_token), checkout LarkSuite open platform document for API details.from getpass import getpassfrom langchain.document_loaders.larksuite import LarkSuiteDocLoaderDOMAIN = input("larksuite domain")ACCESS_TOKEN = getpass("larksuite tenant_access_token or user_access_token")DOCUMENT_ID = input("larksuite document id")from pprint import pprintlarksuite_loader = LarkSuiteDocLoader(DOMAIN, ACCESS_TOKEN, DOCUMENT_ID)docs = larksuite_loader.load()pprint(docs) [Document(page_content='Test Doc\nThis is a Test Doc\n\n1\n2\n3\n\n', metadata={'document_id': 'V76kdbd2HoBbYJxdiNNccajunPf', 'revision_id': 11, 'title': 'Test Doc'})]# see https://python.langchain.com/docs/use_cases/summarization for more detailsfrom langchain.chains.summarize import load_summarize_chainchain = load_summarize_chain(llm, chain_type="map_reduce")chain.run(docs)PreviousJupyter NotebookNextMastodonCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.

LarkSuite is an enterprise collaboration platform developed by ByteDance.

LarkSuite is an enterprise collaboration platform developed by ByteDance. ->: is an enterprise collaboration platform developed by ByteDance.This notebook covers how to load data from the LarkSuite REST API into a format that can be ingested into LangChain, along with example usage for text summarization.The LarkSuite API requires an access token (tenant_access_token or user_access_token), checkout LarkSuite open platform document for API details.from getpass import getpassfrom langchain.document_loaders.larksuite import LarkSuiteDocLoaderDOMAIN = input("larksuite domain")ACCESS_TOKEN = getpass("larksuite tenant_access_token or user_access_token")DOCUMENT_ID = input("larksuite document id")from pprint import pprintlarksuite_loader = LarkSuiteDocLoader(DOMAIN, ACCESS_TOKEN, DOCUMENT_ID)docs = larksuite_loader.load()pprint(docs) [Document(page_content='Test Doc\nThis is a Test Doc\n\n1\n2\n3\n\n', metadata={'document_id': 'V76kdbd2HoBbYJxdiNNccajunPf', 'revision_id': 11, 'title': 'Test Doc'})]# see https://python.langchain.com/docs/use_cases/summarization for more detailsfrom langchain.chains.summarize import load_summarize_chainchain = load_summarize_chain(llm, chain_type="map_reduce")chain.run(docs)PreviousJupyter NotebookNextMastodonCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.

Airbyte Stripe | 🦜️🔗 Langchain

Airbyte is a data integration platform for ELT pipelines from APIs, databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases.

Airbyte is a data integration platform for ELT pipelines from APIs, databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases. ->: Airbyte Stripe | 🦜️🔗 Langchain

Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersAirbyte StripeOn this pageAirbyte StripeAirbyte is a data integration platform for ELT

Airbyte is a data integration platform for ELT pipelines from APIs, databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases.

Airbyte is a data integration platform for ELT pipelines from APIs, databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases. ->: Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersAirbyte StripeOn this pageAirbyte StripeAirbyte is a data integration platform for ELT

is a data integration platform for ELT pipelines from APIs, databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases.This loader exposes the Stripe connector as a document loader, allowing you to load various Stripe objects as documents.Installation‚ÄãFirst, you need to install the airbyte-source-stripe python package.#!pip install airbyte-source-stripeExample‚ÄãCheck out the Airbyte documentation page for details about how to configure the reader.

Airbyte is a data integration platform for ELT pipelines from APIs, databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases.

Airbyte is a data integration platform for ELT pipelines from APIs, databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases. ->: is a data integration platform for ELT pipelines from APIs, databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases.This loader exposes the Stripe connector as a document loader, allowing you to load various Stripe objects as documents.Installation‚ÄãFirst, you need to install the airbyte-source-stripe python package.#!pip install airbyte-source-stripeExample‚ÄãCheck out the Airbyte documentation page for details about how to configure the reader.

The JSON schema the config object should adhere to can be found on Github: https://github.com/airbytehq/airbyte/blob/master/airbyte-integrations/connectors/source-stripe/source_stripe/spec.yaml.The general shape looks like this:{ "client_secret": "<secret key>", "account_id": "<account id>", "start_date": "<date from which to start retrieving records from in ISO format, e.g. 2020-10-20T00:00:00Z>",}By default all fields are stored as metadata in the documents and the text is set to an empty string. Construct the text of the document by transforming the documents returned by the reader.from langchain.document_loaders.airbyte import AirbyteStripeLoaderconfig = { # your stripe configuration}loader = AirbyteStripeLoader(config=config, stream_name="invoices") # check the documentation linked above for a list of all streamsNow you can load documents the usual waydocs = loader.load()As load returns a list, it will block until all documents are loaded. To have better control over this process, you can also you the lazy_load method which returns an iterator instead:docs_iterator = loader.lazy_load()Keep in mind that by default the page content is empty and the metadata object contains all the information from the record. To create documents in a different, pass in a record_handler function when creating the loader:from langchain.docstore.document import Documentdef handle_record(record, id): return Document(page_content=record.data["title"], metadata=record.data)loader = AirbyteStripeLoader(config=config, record_handler=handle_record, stream_name="invoices")docs = loader.load()Incremental loads‚ÄãSome streams allow incremental loading, this means the source keeps track of synced records and won't load them again. This is useful for sources that have a high volume of data and are updated frequently.To take advantage of this, store the last_state property of the loader and pass it in when creating the loader again. This will ensure that only new records are

Airbyte is a data integration platform for ELT pipelines from APIs, databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases.

Airbyte is a data integration platform for ELT pipelines from APIs, databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases. ->: The JSON schema the config object should adhere to can be found on Github: https://github.com/airbytehq/airbyte/blob/master/airbyte-integrations/connectors/source-stripe/source_stripe/spec.yaml.The general shape looks like this:{ "client_secret": "<secret key>", "account_id": "<account id>", "start_date": "<date from which to start retrieving records from in ISO format, e.g. 2020-10-20T00:00:00Z>",}By default all fields are stored as metadata in the documents and the text is set to an empty string. Construct the text of the document by transforming the documents returned by the reader.from langchain.document_loaders.airbyte import AirbyteStripeLoaderconfig = { # your stripe configuration}loader = AirbyteStripeLoader(config=config, stream_name="invoices") # check the documentation linked above for a list of all streamsNow you can load documents the usual waydocs = loader.load()As load returns a list, it will block until all documents are loaded. To have better control over this process, you can also you the lazy_load method which returns an iterator instead:docs_iterator = loader.lazy_load()Keep in mind that by default the page content is empty and the metadata object contains all the information from the record. To create documents in a different, pass in a record_handler function when creating the loader:from langchain.docstore.document import Documentdef handle_record(record, id): return Document(page_content=record.data["title"], metadata=record.data)loader = AirbyteStripeLoader(config=config, record_handler=handle_record, stream_name="invoices")docs = loader.load()Incremental loads‚ÄãSome streams allow incremental loading, this means the source keeps track of synced records and won't load them again. This is useful for sources that have a high volume of data and are updated frequently.To take advantage of this, store the last_state property of the loader and pass it in when creating the loader again. This will ensure that only new records are

again. This will ensure that only new records are loaded.last_state = loader.last_state # store safelyincremental_loader = AirbyteStripeLoader(config=config, record_handler=handle_record, stream_name="invoices", state=last_state)new_docs = incremental_loader.load()PreviousAirbyte ShopifyNextAirbyte TypeformInstallationExampleIncremental loadsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.

Airbyte is a data integration platform for ELT pipelines from APIs, databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases.

Airbyte is a data integration platform for ELT pipelines from APIs, databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases. ->: again. This will ensure that only new records are loaded.last_state = loader.last_state # store safelyincremental_loader = AirbyteStripeLoader(config=config, record_handler=handle_record, stream_name="invoices", state=last_state)new_docs = incremental_loader.load()PreviousAirbyte ShopifyNextAirbyte TypeformInstallationExampleIncremental loadsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.

Microsoft OneDrive | 🦜️🔗 Langchain

Microsoft OneDrive (formerly SkyDrive) is a file hosting service operated by Microsoft.

Microsoft OneDrive (formerly SkyDrive) is a file hosting service operated by Microsoft. ->: Microsoft OneDrive | 🦜️🔗 Langchain

Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersMicrosoft OneDriveOn this pageMicrosoft OneDriveMicrosoft OneDrive (formerly SkyDrive) is a

Microsoft OneDrive (formerly SkyDrive) is a file hosting service operated by Microsoft.

Microsoft OneDrive (formerly SkyDrive) is a file hosting service operated by Microsoft. ->: Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersMicrosoft OneDriveOn this pageMicrosoft OneDriveMicrosoft OneDrive (formerly SkyDrive) is a

OneDrive (formerly SkyDrive) is a file hosting service operated by Microsoft.This notebook covers how to load documents from OneDrive. Currently, only docx, doc, and pdf files are supported.Prerequisites​Register an application with the Microsoft identity platform instructions.When registration finishes, the Azure portal displays the app registration's Overview pane. You see the Application (client) ID. Also called the client ID, this value uniquely identifies your application in the Microsoft identity platform.During the steps you will be following at item 1, you can set the redirect URI as http://localhost:8000/callbackDuring the steps you will be following at item 1, generate a new password (client_secret) under Application Secrets section.Follow the instructions at this document to add the following SCOPES (offline_access and Files.Read.All) to your application.Visit the Graph Explorer Playground to obtain your OneDrive ID. The first step is to ensure you are logged in with the account associated your OneDrive account. Then you need to make a request to https://graph.microsoft.com/v1.0/me/drive and the response will return a payload with a field id that holds the ID of your OneDrive account.You need to install the o365 package using the command pip install o365.At the end of the steps you must have the following values: CLIENT_IDCLIENT_SECRETDRIVE_ID🧑 Instructions for ingesting your documents from OneDrive​🔑 Authentication​By default, the OneDriveLoader expects that the values of CLIENT_ID and CLIENT_SECRET must be stored as environment variables named O365_CLIENT_ID and O365_CLIENT_SECRET respectively. You could pass those environment variables through a .env file at the root of your application or using the following command in your script.os.environ['O365_CLIENT_ID'] = "YOUR CLIENT ID"os.environ['O365_CLIENT_SECRET'] = "YOUR CLIENT SECRET"This loader uses an authentication called on behalf of a user. It is a 2 step authentication with user

Microsoft OneDrive (formerly SkyDrive) is a file hosting service operated by Microsoft.

Microsoft OneDrive (formerly SkyDrive) is a file hosting service operated by Microsoft. ->: OneDrive (formerly SkyDrive) is a file hosting service operated by Microsoft.This notebook covers how to load documents from OneDrive. Currently, only docx, doc, and pdf files are supported.Prerequisites​Register an application with the Microsoft identity platform instructions.When registration finishes, the Azure portal displays the app registration's Overview pane. You see the Application (client) ID. Also called the client ID, this value uniquely identifies your application in the Microsoft identity platform.During the steps you will be following at item 1, you can set the redirect URI as http://localhost:8000/callbackDuring the steps you will be following at item 1, generate a new password (client_secret) under Application Secrets section.Follow the instructions at this document to add the following SCOPES (offline_access and Files.Read.All) to your application.Visit the Graph Explorer Playground to obtain your OneDrive ID. The first step is to ensure you are logged in with the account associated your OneDrive account. Then you need to make a request to https://graph.microsoft.com/v1.0/me/drive and the response will return a payload with a field id that holds the ID of your OneDrive account.You need to install the o365 package using the command pip install o365.At the end of the steps you must have the following values: CLIENT_IDCLIENT_SECRETDRIVE_ID🧑 Instructions for ingesting your documents from OneDrive​🔑 Authentication​By default, the OneDriveLoader expects that the values of CLIENT_ID and CLIENT_SECRET must be stored as environment variables named O365_CLIENT_ID and O365_CLIENT_SECRET respectively. You could pass those environment variables through a .env file at the root of your application or using the following command in your script.os.environ['O365_CLIENT_ID'] = "YOUR CLIENT ID"os.environ['O365_CLIENT_SECRET'] = "YOUR CLIENT SECRET"This loader uses an authentication called on behalf of a user. It is a 2 step authentication with user

a user. It is a 2 step authentication with user consent. When you instantiate the loader, it will call will print a url that the user must visit to give consent to the app on the required permissions. The user must then visit this url and give consent to the application. Then the user must copy the resulting page url and paste it back on the console. The method will then return True if the login attempt was successful.from langchain.document_loaders.onedrive import OneDriveLoaderloader = OneDriveLoader(drive_id="YOUR DRIVE ID")Once the authentication has been done, the loader will store a token (o365_token.txt) at ~/.credentials/ folder. This token could be used later to authenticate without the copy/paste steps explained earlier. To use this token for authentication, you need to change the auth_with_token parameter to True in the instantiation of the loader.from langchain.document_loaders.onedrive import OneDriveLoaderloader = OneDriveLoader(drive_id="YOUR DRIVE ID", auth_with_token=True)🗂️ Documents loader​📑 Loading documents from a OneDrive Directory​OneDriveLoader can load documents from a specific folder within your OneDrive. For instance, you want to load all documents that are stored at Documents/clients folder within your OneDrive.from langchain.document_loaders.onedrive import OneDriveLoaderloader = OneDriveLoader(drive_id="YOUR DRIVE ID", folder_path="Documents/clients", auth_with_token=True)documents = loader.load()📑 Loading documents from a list of Documents IDs​Another possibility is to provide a list of object_id for each document you want to load. For that, you will need to query the Microsoft Graph API to find all the documents ID that you are interested in. This link provides a list of endpoints that will be helpful to retrieve the documents ID.For instance, to retrieve information about all objects that are stored at the root of the Documents folder, you need make a request to: https://graph.microsoft.com/v1.0/drives/{YOUR DRIVE

Microsoft OneDrive (formerly SkyDrive) is a file hosting service operated by Microsoft.

Microsoft OneDrive (formerly SkyDrive) is a file hosting service operated by Microsoft. ->: a user. It is a 2 step authentication with user consent. When you instantiate the loader, it will call will print a url that the user must visit to give consent to the app on the required permissions. The user must then visit this url and give consent to the application. Then the user must copy the resulting page url and paste it back on the console. The method will then return True if the login attempt was successful.from langchain.document_loaders.onedrive import OneDriveLoaderloader = OneDriveLoader(drive_id="YOUR DRIVE ID")Once the authentication has been done, the loader will store a token (o365_token.txt) at ~/.credentials/ folder. This token could be used later to authenticate without the copy/paste steps explained earlier. To use this token for authentication, you need to change the auth_with_token parameter to True in the instantiation of the loader.from langchain.document_loaders.onedrive import OneDriveLoaderloader = OneDriveLoader(drive_id="YOUR DRIVE ID", auth_with_token=True)🗂️ Documents loader​📑 Loading documents from a OneDrive Directory​OneDriveLoader can load documents from a specific folder within your OneDrive. For instance, you want to load all documents that are stored at Documents/clients folder within your OneDrive.from langchain.document_loaders.onedrive import OneDriveLoaderloader = OneDriveLoader(drive_id="YOUR DRIVE ID", folder_path="Documents/clients", auth_with_token=True)documents = loader.load()📑 Loading documents from a list of Documents IDs​Another possibility is to provide a list of object_id for each document you want to load. For that, you will need to query the Microsoft Graph API to find all the documents ID that you are interested in. This link provides a list of endpoints that will be helpful to retrieve the documents ID.For instance, to retrieve information about all objects that are stored at the root of the Documents folder, you need make a request to: https://graph.microsoft.com/v1.0/drives/{YOUR DRIVE

DRIVE ID}/root/children. Once you have the list of IDs that you are interested in, then you can instantiate the loader with the following parameters.from langchain.document_loaders.onedrive import OneDriveLoaderloader = OneDriveLoader(drive_id="YOUR DRIVE ID", object_ids=["ID_1", "ID_2"], auth_with_token=True)documents = loader.load()PreviousmhtmlNextMicrosoft PowerPointPrerequisites🧑 Instructions for ingesting your documents from OneDrive🔑 Authentication🗂️ Documents loaderCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.

Microsoft OneDrive (formerly SkyDrive) is a file hosting service operated by Microsoft.

Microsoft OneDrive (formerly SkyDrive) is a file hosting service operated by Microsoft. ->: DRIVE ID}/root/children. Once you have the list of IDs that you are interested in, then you can instantiate the loader with the following parameters.from langchain.document_loaders.onedrive import OneDriveLoaderloader = OneDriveLoader(drive_id="YOUR DRIVE ID", object_ids=["ID_1", "ID_2"], auth_with_token=True)documents = loader.load()PreviousmhtmlNextMicrosoft PowerPointPrerequisites🧑 Instructions for ingesting your documents from OneDrive🔑 Authentication🗂️ Documents loaderCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.

Microsoft Excel | 🦜️🔗 Langchain

The UnstructuredExcelLoader is used to load Microsoft Excel files. The loader works with both .xlsx and .xls files. The page content will be the raw text of the Excel file. If you use the loader in "elements" mode, an HTML representation of the Excel file will be available in the document metadata under the textashtml key.

The UnstructuredExcelLoader is used to load Microsoft Excel files. The loader works with both .xlsx and .xls files. The page content will be the raw text of the Excel file. If you use the loader in "elements" mode, an HTML representation of the Excel file will be available in the document metadata under the textashtml key. ->: Microsoft Excel | 🦜️🔗 Langchain

Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersMicrosoft ExcelMicrosoft ExcelThe UnstructuredExcelLoader is used to load Microsoft Excel

The UnstructuredExcelLoader is used to load Microsoft Excel files. The loader works with both .xlsx and .xls files. The page content will be the raw text of the Excel file. If you use the loader in "elements" mode, an HTML representation of the Excel file will be available in the document metadata under the textashtml key.

The UnstructuredExcelLoader is used to load Microsoft Excel files. The loader works with both .xlsx and .xls files. The page content will be the raw text of the Excel file. If you use the loader in "elements" mode, an HTML representation of the Excel file will be available in the document metadata under the textashtml key. ->: Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersMicrosoft ExcelMicrosoft ExcelThe UnstructuredExcelLoader is used to load Microsoft Excel

is used to load Microsoft Excel files. The loader works with both .xlsx and .xls files. The page content will be the raw text of the Excel file. If you use the loader in "elements" mode, an HTML representation of the Excel file will be available in the document metadata under the text_as_html key.from langchain.document_loaders import UnstructuredExcelLoaderloader = UnstructuredExcelLoader("example_data/stanley-cups.xlsx", mode="elements")docs = loader.load()docs[0] Document(page_content='\n \n \n Team\n Location\n Stanley Cups\n \n \n Blues\n STL\n 1\n \n \n Flyers\n PHI\n 2\n \n \n Maple Leafs\n TOR\n 13\n \n \n', metadata={'source': 'example_data/stanley-cups.xlsx', 'filename': 'stanley-cups.xlsx', 'file_directory': 'example_data', 'filetype': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'page_number': 1, 'page_name': 'Stanley Cups', 'text_as_html': '<table border="1" class="dataframe">\n <tbody>\n <tr>\n <td>Team</td>\n <td>Location</td>\n <td>Stanley Cups</td>\n </tr>\n <tr>\n <td>Blues</td>\n <td>STL</td>\n <td>1</td>\n </tr>\n <tr>\n <td>Flyers</td>\n <td>PHI</td>\n <td>2</td>\n </tr>\n <tr>\n <td>Maple Leafs</td>\n <td>TOR</td>\n <td>13</td>\n </tr>\n </tbody>\n</table>', 'category': 'Table'})PreviousNotebookNextFacebook ChatCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.

The UnstructuredExcelLoader is used to load Microsoft Excel files. The loader works with both .xlsx and .xls files. The page content will be the raw text of the Excel file. If you use the loader in "elements" mode, an HTML representation of the Excel file will be available in the document metadata under the textashtml key.

The UnstructuredExcelLoader is used to load Microsoft Excel files. The loader works with both .xlsx and .xls files. The page content will be the raw text of the Excel file. If you use the loader in "elements" mode, an HTML representation of the Excel file will be available in the document metadata under the textashtml key. ->: is used to load Microsoft Excel files. The loader works with both .xlsx and .xls files. The page content will be the raw text of the Excel file. If you use the loader in "elements" mode, an HTML representation of the Excel file will be available in the document metadata under the text_as_html key.from langchain.document_loaders import UnstructuredExcelLoaderloader = UnstructuredExcelLoader("example_data/stanley-cups.xlsx", mode="elements")docs = loader.load()docs[0] Document(page_content='\n \n \n Team\n Location\n Stanley Cups\n \n \n Blues\n STL\n 1\n \n \n Flyers\n PHI\n 2\n \n \n Maple Leafs\n TOR\n 13\n \n \n', metadata={'source': 'example_data/stanley-cups.xlsx', 'filename': 'stanley-cups.xlsx', 'file_directory': 'example_data', 'filetype': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'page_number': 1, 'page_name': 'Stanley Cups', 'text_as_html': '<table border="1" class="dataframe">\n <tbody>\n <tr>\n <td>Team</td>\n <td>Location</td>\n <td>Stanley Cups</td>\n </tr>\n <tr>\n <td>Blues</td>\n <td>STL</td>\n <td>1</td>\n </tr>\n <tr>\n <td>Flyers</td>\n <td>PHI</td>\n <td>2</td>\n </tr>\n <tr>\n <td>Maple Leafs</td>\n <td>TOR</td>\n <td>13</td>\n </tr>\n </tbody>\n</table>', 'category': 'Table'})PreviousNotebookNextFacebook ChatCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.

CSV | 🦜️🔗 Langchain

A 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.

A 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. ->: CSV | 🦜️🔗 Langchain

Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersCSVOn this pageCSVA comma-separated values (CSV) file is a delimited text file that uses a

A 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.

A 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. ->: Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersCSVOn this pageCSVA comma-separated values (CSV) file is a delimited text file that uses a

(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}, 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},

A 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.

A 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. ->: (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}, 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},

'./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': 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': './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='',

A 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.

A 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. ->: './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': 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': './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='',

(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', 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‚ÄãSee the csv module

A 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.

A 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. ->: (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', 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‚ÄãSee the csv module

the csv parsing and loading‚ÄãSee 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 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': './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},

A 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.

A 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. ->: the csv parsing and loading‚ÄãSee 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 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': './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},

'./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': './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='',

A 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.

A 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. ->: './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': './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='',

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': './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': './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

A 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.

A 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. ->: 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': './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': './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

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‚ÄãUse 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), 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),

A 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.

A 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. ->: 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‚ÄãUse 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), 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),

'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}, 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': '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',

A 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.

A 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. ->: '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}, 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': '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',

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', 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)]UnstructuredCSVLoader‚ÄãYou can also load the table using the UnstructuredCSVLoader. One advantage of using UnstructuredCSVLoader is that if you use it in "elements" mode, an HTML representation of the table will be available in the metadata.from langchain.document_loaders.csv_loader import UnstructuredCSVLoaderloader = UnstructuredCSVLoader( file_path="example_data/mlb_teams_2012.csv", mode="elements")docs = loader.load()print(docs[0].metadata["text_as_html"]) <table border="1" class="dataframe"> <tbody> <tr> <td>Nationals</td> <td>81.34</td> <td>98</td> </tr> <tr>

A 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.

A 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. ->: 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', 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)]UnstructuredCSVLoader‚ÄãYou can also load the table using the UnstructuredCSVLoader. One advantage of using UnstructuredCSVLoader is that if you use it in "elements" mode, an HTML representation of the table will be available in the metadata.from langchain.document_loaders.csv_loader import UnstructuredCSVLoaderloader = UnstructuredCSVLoader( file_path="example_data/mlb_teams_2012.csv", mode="elements")docs = loader.load()print(docs[0].metadata["text_as_html"]) <table border="1" class="dataframe"> <tbody> <tr> <td>Nationals</td> <td>81.34</td> <td>98</td> </tr> <tr>

<td>98</td> </tr> <tr> <td>Reds</td> <td>82.20</td> <td>97</td> </tr> <tr> <td>Yankees</td> <td>197.96</td> <td>95</td> </tr> <tr> <td>Giants</td> <td>117.62</td> <td>94</td> </tr> <tr> <td>Braves</td> <td>83.31</td> <td>94</td> </tr> <tr> <td>Athletics</td> <td>55.37</td> <td>94</td> </tr> <tr> <td>Rangers</td> <td>120.51</td> <td>93</td> </tr> <tr> <td>Orioles</td> <td>81.43</td> <td>93</td> </tr> <tr> <td>Rays</td> <td>64.17</td> <td>90</td> </tr> <tr> <td>Angels</td> <td>154.49</td> <td>89</td> </tr> <tr> <td>Tigers</td> <td>132.30</td> <td>88</td> </tr> <tr> <td>Cardinals</td> <td>110.30</td> <td>88</td> </tr> <tr> <td>Dodgers</td> <td>95.14</td> <td>86</td> </tr> <tr> <td>White Sox</td> <td>96.92</td> <td>85</td> </tr> <tr> <td>Brewers</td> <td>97.65</td> <td>83</td> </tr> <tr> <td>Phillies</td> <td>174.54</td> <td>81</td> </tr> <tr> <td>Diamondbacks</td> <td>74.28</td> <td>81</td> </tr> <tr> <td>Pirates</td> <td>63.43</td> <td>79</td> </tr> <tr> <td>Padres</td> <td>55.24</td> <td>76</td> </tr> <tr> <td>Mariners</td> <td>81.97</td> <td>75</td> </tr> <tr> <td>Mets</td> <td>93.35</td> <td>74</td> </tr> <tr> <td>Blue Jays</td>

A 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.

A 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. ->: <td>98</td> </tr> <tr> <td>Reds</td> <td>82.20</td> <td>97</td> </tr> <tr> <td>Yankees</td> <td>197.96</td> <td>95</td> </tr> <tr> <td>Giants</td> <td>117.62</td> <td>94</td> </tr> <tr> <td>Braves</td> <td>83.31</td> <td>94</td> </tr> <tr> <td>Athletics</td> <td>55.37</td> <td>94</td> </tr> <tr> <td>Rangers</td> <td>120.51</td> <td>93</td> </tr> <tr> <td>Orioles</td> <td>81.43</td> <td>93</td> </tr> <tr> <td>Rays</td> <td>64.17</td> <td>90</td> </tr> <tr> <td>Angels</td> <td>154.49</td> <td>89</td> </tr> <tr> <td>Tigers</td> <td>132.30</td> <td>88</td> </tr> <tr> <td>Cardinals</td> <td>110.30</td> <td>88</td> </tr> <tr> <td>Dodgers</td> <td>95.14</td> <td>86</td> </tr> <tr> <td>White Sox</td> <td>96.92</td> <td>85</td> </tr> <tr> <td>Brewers</td> <td>97.65</td> <td>83</td> </tr> <tr> <td>Phillies</td> <td>174.54</td> <td>81</td> </tr> <tr> <td>Diamondbacks</td> <td>74.28</td> <td>81</td> </tr> <tr> <td>Pirates</td> <td>63.43</td> <td>79</td> </tr> <tr> <td>Padres</td> <td>55.24</td> <td>76</td> </tr> <tr> <td>Mariners</td> <td>81.97</td> <td>75</td> </tr> <tr> <td>Mets</td> <td>93.35</td> <td>74</td> </tr> <tr> <td>Blue Jays</td>

<tr> <td>Blue Jays</td> <td>75.48</td> <td>73</td> </tr> <tr> <td>Royals</td> <td>60.91</td> <td>72</td> </tr> <tr> <td>Marlins</td> <td>118.07</td> <td>69</td> </tr> <tr> <td>Red Sox</td> <td>173.18</td> <td>69</td> </tr> <tr> <td>Indians</td> <td>78.43</td> <td>68</td> </tr> <tr> <td>Twins</td> <td>94.08</td> <td>66</td> </tr> <tr> <td>Rockies</td> <td>78.06</td> <td>64</td> </tr> <tr> <td>Cubs</td> <td>88.19</td> <td>61</td> </tr> <tr> <td>Astros</td> <td>60.65</td> <td>55</td> </tr> </tbody> </table>PreviousCopy PasteNextCube Semantic LayerCustomizing the csv parsing and loadingSpecify a column to identify the document sourceUnstructuredCSVLoaderCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.

A 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.

A 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. ->: <tr> <td>Blue Jays</td> <td>75.48</td> <td>73</td> </tr> <tr> <td>Royals</td> <td>60.91</td> <td>72</td> </tr> <tr> <td>Marlins</td> <td>118.07</td> <td>69</td> </tr> <tr> <td>Red Sox</td> <td>173.18</td> <td>69</td> </tr> <tr> <td>Indians</td> <td>78.43</td> <td>68</td> </tr> <tr> <td>Twins</td> <td>94.08</td> <td>66</td> </tr> <tr> <td>Rockies</td> <td>78.06</td> <td>64</td> </tr> <tr> <td>Cubs</td> <td>88.19</td> <td>61</td> </tr> <tr> <td>Astros</td> <td>60.65</td> <td>55</td> </tr> </tbody> </table>PreviousCopy PasteNextCube Semantic LayerCustomizing the csv parsing and loadingSpecify a column to identify the document sourceUnstructuredCSVLoaderCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.

Blockchain | 🦜️🔗 Langchain

Overview

Overview ->: Blockchain | 🦜️🔗 Langchain

Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersBlockchainOn this pageBlockchainOverview​The intention of this notebook is to provide a

Overview

Overview ->: Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersBlockchainOn this pageBlockchainOverview​The intention of this notebook is to provide a

intention of this notebook is to provide a means of testing functionality in the Langchain Document Loader for Blockchain.Initially this Loader supports:Loading NFTs as Documents from NFT Smart Contracts (ERC721 and ERC1155)Ethereum Mainnnet, Ethereum Testnet, Polygon Mainnet, Polygon Testnet (default is eth-mainnet)Alchemy's getNFTsForCollection APIIt can be extended if the community finds value in this loader. Specifically:Additional APIs can be added (e.g. Tranction-related APIs)This Document Loader Requires:A free Alchemy API KeyThe output takes the following format:pageContent= Individual NFTmetadata={'source': '0x1a92f7381b9f03921564a437210bb9396471050c', 'blockchain': 'eth-mainnet', 'tokenId': '0x15'})Load NFTs into Document Loader​# get ALCHEMY_API_KEY from https://www.alchemy.com/alchemyApiKey = "..."Option 1: Ethereum Mainnet (default BlockchainType)​from langchain.document_loaders.blockchain import ( BlockchainDocumentLoader, BlockchainType,)contractAddress = "0xbc4ca0eda7647a8ab7c2061c2e118a18a936f13d" # Bored Ape Yacht Club contract addressblockchainType = BlockchainType.ETH_MAINNET # default value, optional parameterblockchainLoader = BlockchainDocumentLoader( contract_address=contractAddress, api_key=alchemyApiKey)nfts = blockchainLoader.load()nfts[:2]Option 2: Polygon Mainnet​contractAddress = ( "0x448676ffCd0aDf2D85C1f0565e8dde6924A9A7D9" # Polygon Mainnet contract address)blockchainType = BlockchainType.POLYGON_MAINNETblockchainLoader = BlockchainDocumentLoader( contract_address=contractAddress, blockchainType=blockchainType, api_key=alchemyApiKey,)nfts = blockchainLoader.load()nfts[:2]PreviousBlackboardNextBrave SearchOverviewLoad NFTs into Document LoaderOption 1: Ethereum Mainnet (default BlockchainType)Option 2: Polygon MainnetCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.

Overview

Overview ->: intention of this notebook is to provide a means of testing functionality in the Langchain Document Loader for Blockchain.Initially this Loader supports:Loading NFTs as Documents from NFT Smart Contracts (ERC721 and ERC1155)Ethereum Mainnnet, Ethereum Testnet, Polygon Mainnet, Polygon Testnet (default is eth-mainnet)Alchemy's getNFTsForCollection APIIt can be extended if the community finds value in this loader. Specifically:Additional APIs can be added (e.g. Tranction-related APIs)This Document Loader Requires:A free Alchemy API KeyThe output takes the following format:pageContent= Individual NFTmetadata={'source': '0x1a92f7381b9f03921564a437210bb9396471050c', 'blockchain': 'eth-mainnet', 'tokenId': '0x15'})Load NFTs into Document Loader​# get ALCHEMY_API_KEY from https://www.alchemy.com/alchemyApiKey = "..."Option 1: Ethereum Mainnet (default BlockchainType)​from langchain.document_loaders.blockchain import ( BlockchainDocumentLoader, BlockchainType,)contractAddress = "0xbc4ca0eda7647a8ab7c2061c2e118a18a936f13d" # Bored Ape Yacht Club contract addressblockchainType = BlockchainType.ETH_MAINNET # default value, optional parameterblockchainLoader = BlockchainDocumentLoader( contract_address=contractAddress, api_key=alchemyApiKey)nfts = blockchainLoader.load()nfts[:2]Option 2: Polygon Mainnet​contractAddress = ( "0x448676ffCd0aDf2D85C1f0565e8dde6924A9A7D9" # Polygon Mainnet contract address)blockchainType = BlockchainType.POLYGON_MAINNETblockchainLoader = BlockchainDocumentLoader( contract_address=contractAddress, blockchainType=blockchainType, api_key=alchemyApiKey,)nfts = blockchainLoader.load()nfts[:2]PreviousBlackboardNextBrave SearchOverviewLoad NFTs into Document LoaderOption 1: Ethereum Mainnet (default BlockchainType)Option 2: Polygon MainnetCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.

ReadTheDocs Documentation | 🦜️🔗 Langchain

Read the Docs is an open-sourced free software documentation hosting platform. It generates documentation written with the Sphinx documentation generator.

Read the Docs is an open-sourced free software documentation hosting platform. It generates documentation written with the Sphinx documentation generator. ->: ReadTheDocs Documentation | 🦜️🔗 Langchain

Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersReadTheDocs DocumentationReadTheDocs DocumentationRead the Docs is an open-sourced free

Read the Docs is an open-sourced free software documentation hosting platform. It generates documentation written with the Sphinx documentation generator.

Read the Docs is an open-sourced free software documentation hosting platform. It generates documentation written with the Sphinx documentation generator. ->: Skip to main content🦜️🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersReadTheDocs DocumentationReadTheDocs DocumentationRead the Docs is an open-sourced free