id stringlengths 14 16 | source stringlengths 49 117 | text stringlengths 16 2.73k |
|---|---|---|
1ae35bf65e45-149 | https://python.langchain.com/en/latest/reference/modules/llms.html | __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → str#
Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[str]] = N... |
1ae35bf65e45-150 | https://python.langchain.com/en/latest/reference/modules/llms.html | copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fie... |
1ae35bf65e45-151 | https://python.langchain.com/en/latest/reference/modules/llms.html | get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int#
Get the number of tokens in the message.
get_token_ids(text: str) → List[int]#
Get the token present in the text.
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, Map... |
1ae35bf65e45-152 | https://python.langchain.com/en/latest/reference/modules/llms.html | set with your API key.
Example
from langchain.llms import StochasticAI
stochasticai = StochasticAI(api_url="")
Validators
build_extra » all fields
raise_deprecation » all fields
set_verbose » verbose
validate_environment » all fields
field api_url: str = ''#
Model name to use.
field model_kwargs: Dict[str, Any] [Option... |
1ae35bf65e45-153 | https://python.langchain.com/en/latest/reference/modules/llms.html | classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model#
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values... |
1ae35bf65e45-154 | https://python.langchain.com/en/latest/reference/modules/llms.html | get_num_tokens(text: str) → int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int#
Get the number of tokens in the message.
get_token_ids(text: str) → List[int]#
Get the token present in the text.
json(*, include: Optional[Union[AbstractSetIn... |
1ae35bf65e45-155 | https://python.langchain.com/en/latest/reference/modules/llms.html | Wrapper around Google Vertex AI large language models.
Validators
raise_deprecation » all fields
set_verbose » verbose
validate_environment » all fields
field credentials: Any = None#
The default custom credentials (google.auth.credentials.Credentials) to use
field location: str = 'us-central1'#
The default location to... |
1ae35bf65e45-156 | https://python.langchain.com/en/latest/reference/modules/llms.html | async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult... |
1ae35bf65e45-157 | https://python.langchain.com/en/latest/reference/modules/llms.html | generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.Prom... |
1ae35bf65e45-158 | https://python.langchain.com/en/latest/reference/modules/llms.html | predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage#
Predict message from messages.
save(file_path: Union[pathlib.Path, str]) → None#
Save the LLM.
Parameters
file_path – Path to file to save the LLM to.
Example:
.. code-block:: python
l... |
1ae35bf65e45-159 | https://python.langchain.com/en/latest/reference/modules/llms.html | Penalizes repeated tokens according to frequency.
field stop: Optional[List[str]] = None#
Sequences when completion generation will stop.
field temperature: Optional[float] = None#
What sampling temperature to use.
field top_p: Optional[float] = None#
Total probability mass of tokens to consider at each step.
field ver... |
1ae35bf65e45-160 | https://python.langchain.com/en/latest/reference/modules/llms.html | classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model#
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values... |
1ae35bf65e45-161 | https://python.langchain.com/en/latest/reference/modules/llms.html | get_num_tokens(text: str) → int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int#
Get the number of tokens in the message.
get_token_ids(text: str) → List[int]#
Get the token present in the text.
json(*, include: Optional[Union[AbstractSetIn... |
1ae35bf65e45-162 | https://python.langchain.com/en/latest/reference/modules/llms.html | © Copyright 2023, Harrison Chase.
Last updated on Jun 04, 2023. |
9c5eb9c561e7-0 | https://python.langchain.com/en/latest/reference/modules/embeddings.html | .rst
.pdf
Embeddings
Embeddings#
Wrappers around embedding modules.
pydantic model langchain.embeddings.AlephAlphaAsymmetricSemanticEmbedding[source]#
Wrapper for Aleph Alpha’s Asymmetric Embeddings
AA provides you with an endpoint to embed a document and a query.
The models were optimized to make the embeddings of doc... |
9c5eb9c561e7-1 | https://python.langchain.com/en/latest/reference/modules/embeddings.html | Call out to Aleph Alpha’s asymmetric Document endpoint.
Parameters
texts – The list of texts to embed.
Returns
List of embeddings, one for each text.
embed_query(text: str) → List[float][source]#
Call out to Aleph Alpha’s asymmetric, query embedding endpoint
:param text: The text to embed.
Returns
Embeddings for the te... |
9c5eb9c561e7-2 | https://python.langchain.com/en/latest/reference/modules/embeddings.html | Make sure the credentials / roles used have the required policies to
access the Bedrock service.
field credentials_profile_name: Optional[str] = None#
The name of the profile in the ~/.aws/credentials or ~/.aws/config files, which
has either access keys or role information specified.
If not specified, the default crede... |
9c5eb9c561e7-3 | https://python.langchain.com/en/latest/reference/modules/embeddings.html | To use, you should have the cohere python package installed, and the
environment variable COHERE_API_KEY set with your API key or pass it
as a named parameter to the constructor.
Example
from langchain.embeddings import CohereEmbeddings
cohere = CohereEmbeddings(
model="embed-english-light-v2.0", cohere_api_key="my... |
9c5eb9c561e7-4 | https://python.langchain.com/en/latest/reference/modules/embeddings.html | texts (List[str]) – A list of document text strings to generate embeddings
for.
Returns
A list of embeddings, one for each document in the inputlist.
Return type
List[List[float]]
embed_query(text: str) → List[float][source]#
Generate an embedding for a single query text.
Parameters
text (str) – The query text to gener... |
9c5eb9c561e7-5 | https://python.langchain.com/en/latest/reference/modules/embeddings.html | # es_user="bar",
# es_password="baz",
)
documents = [
"This is an example document.",
"Another example document to generate embeddings for.",
]
embeddings_generator.embed_documents(documents)
classmethod from_es_connection(model_id: str, es_connection: Elasticsearch, input_field: str = 'text_field') → Elast... |
9c5eb9c561e7-6 | https://python.langchain.com/en/latest/reference/modules/embeddings.html | pydantic model langchain.embeddings.FakeEmbeddings[source]#
embed_documents(texts: List[str]) → List[List[float]][source]#
Embed search docs.
embed_query(text: str) → List[float][source]#
Embed query text.
pydantic model langchain.embeddings.HuggingFaceEmbeddings[source]#
Wrapper around sentence_transformers embedding ... |
9c5eb9c561e7-7 | https://python.langchain.com/en/latest/reference/modules/embeddings.html | pydantic model langchain.embeddings.HuggingFaceHubEmbeddings[source]#
Wrapper around HuggingFaceHub embedding models.
To use, you should have the huggingface_hub python package installed, and the
environment variable HUGGINGFACEHUB_API_TOKEN set with your API token, or pass
it as a named parameter to the constructor.
E... |
9c5eb9c561e7-8 | https://python.langchain.com/en/latest/reference/modules/embeddings.html | model_kwargs = {'device': 'cpu'}
encode_kwargs = {'normalize_embeddings': True}
hf = HuggingFaceInstructEmbeddings(
model_name=model_name,
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs
)
field cache_folder: Optional[str] = None#
Path to store models.
Can be also set by SENTENCE_TRANSFORMERS_HOME en... |
9c5eb9c561e7-9 | https://python.langchain.com/en/latest/reference/modules/embeddings.html | llama = LlamaCppEmbeddings(model_path="/path/to/model.bin")
field f16_kv: bool = False#
Use half-precision for key/value cache.
field logits_all: bool = False#
Return logits for all tokens, not just the last token.
field n_batch: Optional[int] = 8#
Number of tokens to process in parallel.
Should be a number between 1 a... |
9c5eb9c561e7-10 | https://python.langchain.com/en/latest/reference/modules/embeddings.html | MINIMAX_API_KEY set with your API token, or pass it as a named parameter to
the constructor.
Example
from langchain.embeddings import MiniMaxEmbeddings
embeddings = MiniMaxEmbeddings()
query_text = "This is a test query."
query_result = embeddings.embed_query(query_text)
document_text = "This is a test document."
docum... |
9c5eb9c561e7-11 | https://python.langchain.com/en/latest/reference/modules/embeddings.html | field model_id: str = 'damo/nlp_corom_sentence-embedding_english-base'#
Model name to use.
embed_documents(texts: List[str]) → List[List[float]][source]#
Compute doc embeddings using a modelscope embedding model.
Parameters
texts – The list of texts to embed.
Returns
List of embeddings, one for each text.
embed_query(t... |
9c5eb9c561e7-12 | https://python.langchain.com/en/latest/reference/modules/embeddings.html | Embed documents using a MosaicML deployed instructor embedding model.
Parameters
texts – The list of texts to embed.
Returns
List of embeddings, one for each text.
embed_query(text: str) → List[float][source]#
Embed a query using a MosaicML deployed instructor embedding model.
Parameters
text – The text to embed.
Retur... |
9c5eb9c561e7-13 | https://python.langchain.com/en/latest/reference/modules/embeddings.html | api_base="https://your-endpoint.openai.azure.com/",
api_type="azure",
)
text = "This is a test query."
query_result = embeddings.embed_query(text)
field chunk_size: int = 1000#
Maximum number of texts to embed in each batch
field max_retries: int = 6#
Maximum number of retries to make when generating.
field request... |
9c5eb9c561e7-14 | https://python.langchain.com/en/latest/reference/modules/embeddings.html | See: https://docs.aws.amazon.com/IAM/latest/UserGuide/access_policies.html
field content_handler: langchain.embeddings.sagemaker_endpoint.EmbeddingsContentHandler [Required]#
The content handler class that provides an input and
output transform functions to handle formats between LLM
and the endpoint.
field credentials... |
9c5eb9c561e7-15 | https://python.langchain.com/en/latest/reference/modules/embeddings.html | embed_query(text: str) → List[float][source]#
Compute query embeddings using a SageMaker inference endpoint.
Parameters
text – The text to embed.
Returns
Embeddings for the text.
pydantic model langchain.embeddings.SelfHostedEmbeddings[source]#
Runs custom embedding models on self-hosted remote hardware.
Supported hard... |
9c5eb9c561e7-16 | https://python.langchain.com/en/latest/reference/modules/embeddings.html | embeddings = SelfHostedHFEmbeddings.from_pipeline(
pipeline="models/pipeline.pkl",
hardware=gpu,
model_reqs=["./", "torch", "transformers"],
)
Validators
raise_deprecation » all fields
set_verbose » verbose
field inference_fn: Callable = <function _embed_documents>#
Inference function to extract the embeddi... |
9c5eb9c561e7-17 | https://python.langchain.com/en/latest/reference/modules/embeddings.html | field hardware: Any = None#
Remote hardware to send the inference function to.
field inference_fn: Callable = <function _embed_documents>#
Inference function to extract the embeddings.
field load_fn_kwargs: Optional[dict] = None#
Key word arguments to pass to the model load function.
field model_id: str = 'sentence-tra... |
9c5eb9c561e7-18 | https://python.langchain.com/en/latest/reference/modules/embeddings.html | field model_reqs: List[str] = ['./', 'InstructorEmbedding', 'torch']#
Requirements to install on hardware to inference the model.
field query_instruction: str = 'Represent the question for retrieving supporting documents: '#
Instruction to use for embedding query.
embed_documents(texts: List[str]) → List[List[float]][s... |
9c5eb9c561e7-19 | https://python.langchain.com/en/latest/reference/modules/embeddings.html | Chat Models
next
Indexes
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 04, 2023. |
5adc047fdeb4-0 | https://python.langchain.com/en/latest/getting_started/getting_started.html | .md
.pdf
Quickstart Guide
Contents
Installation
Environment Setup
Building a Language Model Application: LLMs
LLMs: Get predictions from a language model
Prompt Templates: Manage prompts for LLMs
Chains: Combine LLMs and prompts in multi-step workflows
Agents: Dynamically Call Chains Based on User Input
Memory: Add S... |
5adc047fdeb4-1 | https://python.langchain.com/en/latest/getting_started/getting_started.html | LangChain provides many modules that can be used to build language model applications. Modules can be combined to create more complex applications, or be used individually for simple applications.
LLMs: Get predictions from a language model#
The most basic building block of LangChain is calling an LLM on some input.
Le... |
5adc047fdeb4-2 | https://python.langchain.com/en/latest/getting_started/getting_started.html | from langchain.prompts import PromptTemplate
prompt = PromptTemplate(
input_variables=["product"],
template="What is a good name for a company that makes {product}?",
)
Let’s now see how this works! We can call the .format method to format it.
print(prompt.format(product="colorful socks"))
What is a good name f... |
5adc047fdeb4-3 | https://python.langchain.com/en/latest/getting_started/getting_started.html | There we go! There’s the first chain - an LLM Chain.
This is one of the simpler types of chains, but understanding how it works will set you up well for working with more complex chains.
For more details, check out the getting started guide for chains.
Agents: Dynamically Call Chains Based on User Input#
So far the cha... |
5adc047fdeb4-4 | https://python.langchain.com/en/latest/getting_started/getting_started.html | from langchain.agents import initialize_agent
from langchain.agents import AgentType
from langchain.llms import OpenAI
# First, let's load the language model we're going to use to control the agent.
llm = OpenAI(temperature=0)
# Next, let's load some tools to use. Note that the `llm-math` tool uses an LLM, so we need t... |
5adc047fdeb4-5 | https://python.langchain.com/en/latest/getting_started/getting_started.html | So far, all the chains and agents we’ve gone through have been stateless. But often, you may want a chain or agent to have some concept of “memory” so that it may remember information about its previous interactions. The clearest and simple example of this is when designing a chatbot - you want it to remember previous ... |
5adc047fdeb4-6 | https://python.langchain.com/en/latest/getting_started/getting_started.html | The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.
Current conversation:
Human: Hi there!
AI: Hello! How are you today?
Human: I'm doing we... |
5adc047fdeb4-7 | https://python.langchain.com/en/latest/getting_started/getting_started.html | You can also pass in multiple messages for OpenAI’s gpt-3.5-turbo and gpt-4 models.
messages = [
SystemMessage(content="You are a helpful assistant that translates English to French."),
HumanMessage(content="I love programming.")
]
chat(messages)
# -> AIMessage(content="J'aime programmer.", additional_kwargs={}... |
5adc047fdeb4-8 | https://python.langchain.com/en/latest/getting_started/getting_started.html | Similar to LLMs, you can make use of templating by using a MessagePromptTemplate. You can build a ChatPromptTemplate from one or more MessagePromptTemplates. You can use ChatPromptTemplate’s format_prompt – this returns a PromptValue, which you can convert to a string or Message object, depending on whether you want to... |
5adc047fdeb4-9 | https://python.langchain.com/en/latest/getting_started/getting_started.html | system_message_prompt = SystemMessagePromptTemplate.from_template(template)
human_template = "{text}"
human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
chain = LLMChain(llm=chat, prompt=chat_promp... |
5adc047fdeb4-10 | https://python.langchain.com/en/latest/getting_started/getting_started.html | Thought: I need to use a search engine to find Olivia Wilde's boyfriend and a calculator to raise his age to the 0.23 power.
Action:
{
"action": "Search",
"action_input": "Olivia Wilde boyfriend"
}
Observation: Sudeikis and Wilde's relationship ended in November 2020. Wilde was publicly served with court documents ... |
5adc047fdeb4-11 | https://python.langchain.com/en/latest/getting_started/getting_started.html | prompt = ChatPromptTemplate.from_messages([
SystemMessagePromptTemplate.from_template("The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know... |
5adc047fdeb4-12 | https://python.langchain.com/en/latest/getting_started/getting_started.html | Memory: Add State to Chains and Agents
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 04, 2023. |
3d0e4bc9ebee-0 | https://python.langchain.com/en/latest/getting_started/concepts.html | .md
.pdf
Concepts
Contents
Chain of Thought
Action Plan Generation
ReAct
Self-ask
Prompt Chaining
Memetic Proxy
Self Consistency
Inception
MemPrompt
Concepts#
These are concepts and terminology commonly used when developing LLM applications.
It contains reference to external papers or sources where the concept was fi... |
3d0e4bc9ebee-1 | https://python.langchain.com/en/latest/getting_started/concepts.html | will result in that type of response.
For example, as a conversation between a student and a teacher.
Paper
Self Consistency#
Self Consistency is a decoding strategy that samples a diverse set of reasoning paths and then selects the most consistent answer.
Is most effective when combined with Chain-of-thought prompting... |
e3a29f8afabb-0 | https://python.langchain.com/en/latest/getting_started/tutorials.html | .md
.pdf
Tutorials
Contents
DeepLearning.AI course
Handbook
Tutorials
Tutorials#
⛓ icon marks a new addition [last update 2023-05-15]
DeepLearning.AI course#
⛓LangChain for LLM Application Development by Harrison Chase presented by Andrew Ng
Handbook#
LangChain AI Handbook By James Briggs and Francisco Ingham
Tutoria... |
e3a29f8afabb-1 | https://python.langchain.com/en/latest/getting_started/tutorials.html | Connect Google Drive Files To OpenAI
YouTube Transcripts + OpenAI
Question A 300 Page Book (w/ OpenAI + Pinecone)
Workaround OpenAI's Token Limit With Chain Types
Build Your Own OpenAI + LangChain Web App in 23 Minutes
Working With The New ChatGPT API
OpenAI + LangChain Wrote Me 100 Custom Sales Emails
Structured Outpu... |
e3a29f8afabb-2 | https://python.langchain.com/en/latest/getting_started/tutorials.html | ⛓ Using LangChain with DuckDuckGO Wikipedia & PythonREPL Tools
⛓ Building Custom Tools and Agents with LangChain (gpt-3.5-turbo)
⛓ LangChain Retrieval QA Over Multiple Files with ChromaDB
⛓ LangChain Retrieval QA with Instructor Embeddings & ChromaDB for PDFs
⛓ LangChain + Retrieval Local LLMs for Retrieval QA - No Ope... |
e3a29f8afabb-3 | https://python.langchain.com/en/latest/getting_started/tutorials.html | Analyze Custom CSV Data with GPT-4 using Langchain
⛓ Build ChatGPT Chatbots with LangChain Memory: Understanding and Implementing Memory in Conversations
⛓ icon marks a new addition [last update 2023-05-15]
previous
Concepts
next
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Contents
DeepLearning.AI course
Handbook
Tutorials
By Harrison Chase
... |
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