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281c3974db09-0 | .rst
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SerpAPI
SerpAPI#
For backwards compatiblity.
pydantic model langchain.serpapi.SerpAPIWrapper[source]#
Wrapper around SerpAPI.
To use, you should have the google-search-results python package installed,
and the environment variable SERPAPI_API_KEY set with your API key, or pass
serpapi_api_key as a named param... | /content/https://python.langchain.com/en/latest/reference/modules/serpapi.html |
b784a0b2f8ab-0 | .rst
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Docstore
Docstore#
Wrappers on top of docstores.
class langchain.docstore.InMemoryDocstore(_dict: Dict[str, langchain.schema.Document])[source]#
Simple in memory docstore in the form of a dict.
add(texts: Dict[str, langchain.schema.Document]) → None[source]#
Add texts to in memory dictionary.
search(search: s... | /content/https://python.langchain.com/en/latest/reference/modules/docstore.html |
a34233c6d0ce-0 | .rst
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Document Compressors
Document Compressors#
pydantic model langchain.retrievers.document_compressors.DocumentCompressorPipeline[source]#
Document compressor that uses a pipeline of transformers.
field transformers: List[Union[langchain.schema.BaseDocumentTransformer, langchain.retrievers.document_compressors.b... | /content/https://python.langchain.com/en/latest/reference/modules/document_compressors.html |
a34233c6d0ce-1 | Similarity function for comparing documents. Function expected to take as input
two matrices (List[List[float]]) and return a matrix of scores where higher values
indicate greater similarity.
field similarity_threshold: Optional[float] = None#
Threshold for determining when two documents are similar enough
to be consid... | /content/https://python.langchain.com/en/latest/reference/modules/document_compressors.html |
a34233c6d0ce-2 | Compress retrieved documents given the query context.
compress_documents(documents: Sequence[langchain.schema.Document], query: str) → Sequence[langchain.schema.Document][source]#
Compress page content of raw documents.
classmethod from_llm(llm: langchain.schema.BaseLanguageModel, prompt: Optional[langchain.prompts.pro... | /content/https://python.langchain.com/en/latest/reference/modules/document_compressors.html |
a34233c6d0ce-3 | Filter down documents.
compress_documents(documents: Sequence[langchain.schema.Document], query: str) → Sequence[langchain.schema.Document][source]#
Filter down documents based on their relevance to the query.
classmethod from_llm(llm: langchain.schema.BaseLanguageModel, prompt: Optional[langchain.prompts.base.BaseProm... | /content/https://python.langchain.com/en/latest/reference/modules/document_compressors.html |
f9e722af2602-0 | .rst
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Output Parsers
Output Parsers#
pydantic model langchain.output_parsers.CommaSeparatedListOutputParser[source]#
Parse out comma separated lists.
get_format_instructions() → str[source]#
Instructions on how the LLM output should be formatted.
parse(text: str) → List[str][source]#
Parse the output of an LLM call... | /content/https://python.langchain.com/en/latest/reference/modules/output_parsers.html |
f9e722af2602-1 | Parse the output of an LLM call.
pydantic model langchain.output_parsers.OutputFixingParser[source]#
Wraps a parser and tries to fix parsing errors.
field parser: langchain.schema.BaseOutputParser[langchain.output_parsers.fix.T] [Required]#
field retry_chain: langchain.chains.llm.LLMChain [Required]# | /content/https://python.langchain.com/en/latest/reference/modules/output_parsers.html |
f9e722af2602-2 | classmethod from_llm(llm: langchain.schema.BaseLanguageModel, parser: langchain.schema.BaseOutputParser[langchain.output_parsers.fix.T], prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['completion', 'error', 'instructions'], output_parser=None, partial_variables={}, template='Instruc... | /content/https://python.langchain.com/en/latest/reference/modules/output_parsers.html |
f9e722af2602-3 | and parses it into some structure.
Parameters
text – output of language model
Returns
structured output
pydantic model langchain.output_parsers.PydanticOutputParser[source]#
field pydantic_object: Type[langchain.output_parsers.pydantic.T] [Required]#
get_format_instructions() → str[source]#
Instructions on how the LLM ... | /content/https://python.langchain.com/en/latest/reference/modules/output_parsers.html |
f9e722af2602-4 | field regex: str [Required]#
parse(text: str) → Dict[str, str][source]#
Parse the output of an LLM call.
pydantic model langchain.output_parsers.ResponseSchema[source]#
field description: str [Required]#
field name: str [Required]#
pydantic model langchain.output_parsers.RetryOutputParser[source]#
Wraps a parser and tr... | /content/https://python.langchain.com/en/latest/reference/modules/output_parsers.html |
f9e722af2602-5 | classmethod from_llm(llm: langchain.schema.BaseLanguageModel, parser: langchain.schema.BaseOutputParser[langchain.output_parsers.retry.T], prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['completion', 'prompt'], output_parser=None, partial_variables={}, template='Prompt:\n{prompt}\nC... | /content/https://python.langchain.com/en/latest/reference/modules/output_parsers.html |
f9e722af2602-6 | Optional method to parse the output of an LLM call with a prompt.
The prompt is largely provided in the event the OutputParser wants
to retry or fix the output in some way, and needs information from
the prompt to do so.
Parameters
completion – output of language model
prompt – prompt value
Returns
structured output
py... | /content/https://python.langchain.com/en/latest/reference/modules/output_parsers.html |
f9e722af2602-7 | classmethod from_llm(llm: langchain.schema.BaseLanguageModel, parser: langchain.schema.BaseOutputParser[langchain.output_parsers.retry.T], prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['completion', 'error', 'prompt'], output_parser=None, partial_variables={}, template='Prompt:\n{p... | /content/https://python.langchain.com/en/latest/reference/modules/output_parsers.html |
f9e722af2602-8 | Parameters
text – output of language model
Returns
structured output
parse_with_prompt(completion: str, prompt_value: langchain.schema.PromptValue) → langchain.output_parsers.retry.T[source]#
Optional method to parse the output of an LLM call with a prompt.
The prompt is largely provided in the event the OutputParser w... | /content/https://python.langchain.com/en/latest/reference/modules/output_parsers.html |
a6412d66a7f3-0 | .rst
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Text Splitter
Text Splitter#
Functionality for splitting text.
class langchain.text_splitter.CharacterTextSplitter(separator: str = '\n\n', **kwargs: Any)[source]#
Implementation of splitting text that looks at characters.
split_text(text: str) → List[str][source]#
Split incoming text and return chunks.
class... | /content/https://python.langchain.com/en/latest/reference/modules/text_splitter.html |
a6412d66a7f3-1 | Recursively tries to split by different characters to find one
that works.
split_text(text: str) → List[str][source]#
Split incoming text and return chunks.
class langchain.text_splitter.SpacyTextSplitter(separator: str = '\n\n', pipeline: str = 'en_core_web_sm', **kwargs: Any)[source]#
Implementation of splitting text... | /content/https://python.langchain.com/en/latest/reference/modules/text_splitter.html |
a6412d66a7f3-2 | Text splitter that uses HuggingFace tokenizer to count length.
classmethod from_tiktoken_encoder(encoding_name: str = 'gpt2', model_name: Optional[str] = None, allowed_special: Union[Literal['all'], AbstractSet[str]] = {}, disallowed_special: Union[Literal['all'], Collection[str]] = 'all', **kwargs: Any) → langchain.te... | /content/https://python.langchain.com/en/latest/reference/modules/text_splitter.html |
a6412d66a7f3-3 | split_text(text: str) → List[str][source]#
Split incoming text and return chunks.
previous
Docstore
next
Document Loaders
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/reference/modules/text_splitter.html |
a68d7de27bc2-0 | .rst
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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... | /content/https://python.langchain.com/en/latest/reference/modules/embeddings.html |
a68d7de27bc2-1 | Optional parameter that specifies which datacenters may process the request.
field model: Optional[str] = 'luminous-base'#
Model name to use.
field normalize: Optional[bool] = True#
Should returned embeddings be normalized
embed_documents(texts: List[str]) → List[List[float]][source]#
Call out to Aleph Alpha’s asymmetr... | /content/https://python.langchain.com/en/latest/reference/modules/embeddings.html |
a68d7de27bc2-2 | Returns
Embeddings for the text.
pydantic model langchain.embeddings.CohereEmbeddings[source]#
Wrapper around Cohere embedding models.
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.
Exampl... | /content/https://python.langchain.com/en/latest/reference/modules/embeddings.html |
a68d7de27bc2-3 | Embed query text.
pydantic model langchain.embeddings.HuggingFaceEmbeddings[source]#
Wrapper around sentence_transformers embedding models.
To use, you should have the sentence_transformers python package installed.
Example
from langchain.embeddings import HuggingFaceEmbeddings
model_name = "sentence-transformers/all-m... | /content/https://python.langchain.com/en/latest/reference/modules/embeddings.html |
a68d7de27bc2-4 | text – The text to embed.
Returns
Embeddings for the text.
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 t... | /content/https://python.langchain.com/en/latest/reference/modules/embeddings.html |
a68d7de27bc2-5 | Parameters
text – The text to embed.
Returns
Embeddings for the text.
pydantic model langchain.embeddings.HuggingFaceInstructEmbeddings[source]#
Wrapper around sentence_transformers embedding models.
To use, you should have the sentence_transformers
and InstructorEmbedding python package installed.
Example
from langcha... | /content/https://python.langchain.com/en/latest/reference/modules/embeddings.html |
a68d7de27bc2-6 | Parameters
texts – The list of texts to embed.
Returns
List of embeddings, one for each text.
embed_query(text: str) → List[float][source]#
Compute query embeddings using a HuggingFace instruct model.
Parameters
text – The text to embed.
Returns
Embeddings for the text.
pydantic model langchain.embeddings.LlamaCppEmbed... | /content/https://python.langchain.com/en/latest/reference/modules/embeddings.html |
a68d7de27bc2-7 | of threads is automatically determined.
field seed: int = -1#
Seed. If -1, a random seed is used.
field use_mlock: bool = False#
Force system to keep model in RAM.
field vocab_only: bool = False#
Only load the vocabulary, no weights.
embed_documents(texts: List[str]) → List[List[float]][source]#
Embed a list of documen... | /content/https://python.langchain.com/en/latest/reference/modules/embeddings.html |
a68d7de27bc2-8 | the properties of your endpoint.
In addition, the deployment name must be passed as the model parameter.
Example
import os
os.environ["OPENAI_API_TYPE"] = "azure"
os.environ["OPENAI_API_BASE"] = "https://<your-endpoint.openai.azure.com/"
os.environ["OPENAI_API_KEY"] = "your AzureOpenAI key"
from langchain.embeddings.op... | /content/https://python.langchain.com/en/latest/reference/modules/embeddings.html |
a68d7de27bc2-9 | Parameters
text – The text to embed.
Returns
Embedding for the text.
pydantic model langchain.embeddings.SagemakerEndpointEmbeddings[source]#
Wrapper around custom Sagemaker Inference Endpoints.
To use, you must supply the endpoint name from your deployed
Sagemaker model & the region where it is deployed.
To authentica... | /content/https://python.langchain.com/en/latest/reference/modules/embeddings.html |
a68d7de27bc2-10 | credentials from IMDS will be used.
See: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html
field endpoint_kwargs: Optional[Dict] = None#
Optional attributes passed to the invoke_endpoint
function. See `boto3`_. docs for more info.
.. _boto3: <https://boto3.amazonaws.com/v1/documentation/api... | /content/https://python.langchain.com/en/latest/reference/modules/embeddings.html |
a68d7de27bc2-11 | 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 hardware includes auto-launched instances on AWS, GCP, Azure,
and Lambda, as well as servers specified
by IP address and SSH... | /content/https://python.langchain.com/en/latest/reference/modules/embeddings.html |
a68d7de27bc2-12 | import runhouse as rh
from transformers import pipeline
gpu = rh.cluster(name="rh-a10x", instance_type="A100:1")
pipeline = pipeline(model="bert-base-uncased", task="feature-extraction")
rh.blob(pickle.dumps(pipeline),
path="models/pipeline.pkl").save().to(gpu, path="models")
embeddings = SelfHostedHFEmbeddings.fro... | /content/https://python.langchain.com/en/latest/reference/modules/embeddings.html |
a68d7de27bc2-13 | text – The text to embed.
Returns
Embeddings for the text.
pydantic model langchain.embeddings.SelfHostedHuggingFaceEmbeddings[source]#
Runs sentence_transformers embedding models on self-hosted remote hardware.
Supported hardware includes auto-launched instances on AWS, GCP, Azure,
and Lambda, as well as servers speci... | /content/https://python.langchain.com/en/latest/reference/modules/embeddings.html |
a68d7de27bc2-14 | field model_load_fn: Callable = <function load_embedding_model>#
Function to load the model remotely on the server.
field model_reqs: List[str] = ['./', 'sentence_transformers', 'torch']#
Requirements to install on hardware to inference the model.
pydantic model langchain.embeddings.SelfHostedHuggingFaceInstructEmbeddi... | /content/https://python.langchain.com/en/latest/reference/modules/embeddings.html |
a68d7de27bc2-15 | Model name to use.
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]) → ... | /content/https://python.langchain.com/en/latest/reference/modules/embeddings.html |
a68d7de27bc2-16 | tf = TensorflowHubEmbeddings(model_url=url)
field model_url: str = 'https://tfhub.dev/google/universal-sentence-encoder-multilingual/3'#
Model name to use.
embed_documents(texts: List[str]) → List[List[float]][source]#
Compute doc embeddings using a TensorflowHub embedding model.
Parameters
texts – The list of texts to... | /content/https://python.langchain.com/en/latest/reference/modules/embeddings.html |
74fa2602e439-0 | .rst
.pdf
Chat Models
Chat Models#
pydantic model langchain.chat_models.AzureChatOpenAI[source]#
Wrapper around Azure OpenAI Chat Completion API. To use this class you
must have a deployed model on Azure OpenAI. Use deployment_name in the
constructor to refer to the “Model deployment name” in the Azure portal.
In addit... | /content/https://python.langchain.com/en/latest/reference/modules/chat_models.html |
74fa2602e439-1 | Wrapper around Anthropic’s large language model.
To use, you should have the anthropic python package installed, and the
environment variable ANTHROPIC_API_KEY set with your API key, or pass
it as a named parameter to the constructor.
Example
Validators
set_callback_manager » callback_manager
validate_environment » all... | /content/https://python.langchain.com/en/latest/reference/modules/chat_models.html |
74fa2602e439-2 | Model name to use.
field n: int = 1#
Number of chat completions to generate for each prompt.
field openai_api_key: Optional[str] = None#
field openai_organization: Optional[str] = None#
field request_timeout: int = 60#
Timeout in seconds for the OpenAPI request.
field streaming: bool = False#
Whether to stream the resu... | /content/https://python.langchain.com/en/latest/reference/modules/chat_models.html |
74fa2602e439-3 | All parameters that can be passed to the OpenAI LLM can also
be passed here. The PromptLayerChatOpenAI adds to optional
:param pl_tags: List of strings to tag the request with.
:param return_pl_id: If True, the PromptLayer request ID will be
returned in the generation_info field of the
Generation object.
Example
from l... | /content/https://python.langchain.com/en/latest/reference/modules/chat_models.html |
49b61cd47e96-0 | .rst
.pdf
Agent Toolkits
Agent Toolkits#
Agent toolkits.
pydantic model langchain.agents.agent_toolkits.JiraToolkit[source]#
Jira Toolkit.
field tools: List[langchain.tools.base.BaseTool] = []#
classmethod from_jira_api_wrapper(jira_api_wrapper: langchain.utilities.jira.JiraAPIWrapper) → langchain.agents.agent_toolkits... | /content/https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
49b61cd47e96-1 | List of API Endpoint Tools.
classmethod from_llm_and_ai_plugin(llm: langchain.llms.base.BaseLLM, ai_plugin: langchain.tools.plugin.AIPlugin, requests: Optional[langchain.requests.Requests] = None, verbose: bool = False, **kwargs: Any) → langchain.agents.agent_toolkits.nla.toolkit.NLAToolkit[source]#
Instantiate the too... | /content/https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
49b61cd47e96-2 | Instantiate the toolkit by creating tools for each operation.
classmethod from_llm_and_url(llm: langchain.llms.base.BaseLLM, open_api_url: str, requests: Optional[langchain.requests.Requests] = None, verbose: bool = False, **kwargs: Any) → langchain.agents.agent_toolkits.nla.toolkit.NLAToolkit[source]#
Instantiate the ... | /content/https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
49b61cd47e96-3 | Get the tools in the toolkit.
pydantic model langchain.agents.agent_toolkits.PowerBIToolkit[source]#
Toolkit for interacting with PowerBI dataset.
field callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None#
field examples: Optional[str] = None#
field llm: langchain.schema.BaseLanguageModel [R... | /content/https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
49b61cd47e96-4 | Toolkit for routing between vectorstores.
field llm: langchain.llms.base.BaseLLM [Optional]#
field vectorstores: List[langchain.agents.agent_toolkits.vectorstore.toolkit.VectorStoreInfo] [Required]#
get_tools() → List[langchain.tools.base.BaseTool][source]#
Get the tools in the toolkit.
pydantic model langchain.agents.... | /content/https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
49b61cd47e96-5 | Get the tools in the toolkit.
langchain.agents.agent_toolkits.create_csv_agent(llm: langchain.llms.base.BaseLLM, path: str, pandas_kwargs: Optional[dict] = None, **kwargs: Any) → langchain.agents.agent.AgentExecutor[source]#
Create csv agent by loading to a dataframe and using pandas agent. | /content/https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
49b61cd47e96-6 | langchain.agents.agent_toolkits.create_json_agent(llm: langchain.llms.base.BaseLLM, toolkit: langchain.agents.agent_toolkits.json.toolkit.JsonToolkit, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = 'You are an agent designed to interact with JSON.\nYour goal is to return ... | /content/https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
49b61cd47e96-7 | don\'t know" as the answer.\nAlways begin your interaction with the `json_spec_list_keys` tool with input "data" to see what keys exist in the JSON.\n\nNote that sometimes the value at a given path is large. In this case, you will get an error "Value is a large dictionary, should explore its keys directly".\nIn this ca... | /content/https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
49b61cd47e96-8 | None, verbose: bool = False, **kwargs: Any) → langchain.agents.agent.AgentExecutor[source]# | /content/https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
49b61cd47e96-9 | Construct a json agent from an LLM and tools. | /content/https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
49b61cd47e96-10 | langchain.agents.agent_toolkits.create_openapi_agent(llm: langchain.llms.base.BaseLLM, toolkit: langchain.agents.agent_toolkits.openapi.toolkit.OpenAPIToolkit, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = "You are an agent designed to answer questions by making web requ... | /content/https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
49b61cd47e96-11 | are using a path that actually exists in the spec.\n", suffix: str = 'Begin!\n\nQuestion: {input}\nThought: I should explore the spec to find the base url for the API.\n{agent_scratchpad}', format_instructions: str = 'Use the following format:\n\nQuestion: the input question you must answer\nThought: you should always ... | /content/https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
49b61cd47e96-12 | Construct a json agent from an LLM and tools.
langchain.agents.agent_toolkits.create_pandas_dataframe_agent(llm: langchain.llms.base.BaseLLM, df: Any, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = '\nYou are working with a pandas dataframe in Python. The name of the data... | /content/https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
49b61cd47e96-13 | langchain.agents.agent_toolkits.create_pbi_agent(llm: langchain.llms.base.BaseLLM, toolkit: Optional[langchain.agents.agent_toolkits.powerbi.toolkit.PowerBIToolkit], powerbi: Optional[langchain.utilities.powerbi.PowerBIDataset] = None, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, pre... | /content/https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
49b61cd47e96-14 | wrong try to phrase the question differently and get a new query from the question to query tool.\n\nIf the question does not seem related to the dataset, just return "I don\'t know" as the answer.\n', suffix: str = 'Begin!\n\nQuestion: {input}\nThought: I should first ask which tables I have, then how each table is de... | /content/https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
49b61cd47e96-15 | Construct a pbi agent from an LLM and tools. | /content/https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
49b61cd47e96-16 | langchain.agents.agent_toolkits.create_pbi_chat_agent(llm: langchain.chat_models.base.BaseChatModel, toolkit: Optional[langchain.agents.agent_toolkits.powerbi.toolkit.PowerBIToolkit], powerbi: Optional[langchain.utilities.powerbi.PowerBIDataset] = None, callback_manager: Optional[langchain.callbacks.base.BaseCallbackMa... | /content/https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
49b61cd47e96-17 | and return the answer. Unless the user specifies a specific number of examples they wish to obtain, always limit your query to at most {top_k} results. You can order the results by a relevant column to return the most interesting examples in the database.\n\nOverall, Assistant is a powerful system that can help with a ... | /content/https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
49b61cd47e96-18 | input_variables: Optional[List[str]] = None, memory: Optional[langchain.memory.chat_memory.BaseChatMemory] = None, top_k: int = 10, verbose: bool = False, agent_kwargs: Optional[Dict[str, Any]] = None, **kwargs: Dict[str, Any]) → langchain.agents.agent.AgentExecutor[source]# | /content/https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
49b61cd47e96-19 | Construct a pbi agent from an Chat LLM and tools.
If you supply only a toolkit and no powerbi dataset, the same LLM is used for both.
langchain.agents.agent_toolkits.create_python_agent(llm: langchain.llms.base.BaseLLM, tool: langchain.tools.python.tool.PythonREPLTool, callback_manager: Optional[langchain.callbacks.bas... | /content/https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
49b61cd47e96-20 | langchain.agents.agent_toolkits.create_sql_agent(llm: langchain.llms.base.BaseLLM, toolkit: langchain.agents.agent_toolkits.sql.toolkit.SQLDatabaseToolkit, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = 'You are an agent designed to interact with a SQL database.\nGiven an... | /content/https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
49b61cd47e96-21 | {input}\nThought: I should look at the tables in the database to see what I can query.\n{agent_scratchpad}', format_instructions: str = 'Use the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names... | /content/https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
49b61cd47e96-22 | Construct a sql agent from an LLM and tools.
langchain.agents.agent_toolkits.create_vectorstore_agent(llm: langchain.llms.base.BaseLLM, toolkit: langchain.agents.agent_toolkits.vectorstore.toolkit.VectorStoreToolkit, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = 'You are... | /content/https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
49b61cd47e96-23 | Construct a vectorstore agent from an LLM and tools.
langchain.agents.agent_toolkits.create_vectorstore_router_agent(llm: langchain.llms.base.BaseLLM, toolkit: langchain.agents.agent_toolkits.vectorstore.toolkit.VectorStoreRouterToolkit, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, p... | /content/https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
ddf8cd975bdf-0 | .rst
.pdf
PromptTemplates
PromptTemplates#
Prompt template classes.
pydantic model langchain.prompts.BaseChatPromptTemplate[source]#
format(**kwargs: Any) → str[source]#
Format the prompt with the inputs.
Parameters
kwargs – Any arguments to be passed to the prompt template.
Returns
A formatted string.
Example:
prompt.... | /content/https://python.langchain.com/en/latest/reference/modules/prompts.html |
ddf8cd975bdf-1 | A formatted string.
Example:
prompt.format(variable1="foo")
abstract format_prompt(**kwargs: Any) → langchain.schema.PromptValue[source]#
Create Chat Messages.
partial(**kwargs: Union[str, Callable[[], str]]) → langchain.prompts.base.BasePromptTemplate[source]#
Return a partial of the prompt template.
save(file_path: U... | /content/https://python.langchain.com/en/latest/reference/modules/prompts.html |
ddf8cd975bdf-2 | Parameters
file_path – Path to directory to save prompt to.
Example:
.. code-block:: python
prompt.save(file_path=”path/prompt.yaml”)
pydantic model langchain.prompts.FewShotPromptTemplate[source]#
Prompt template that contains few shot examples.
field example_prompt: langchain.prompts.prompt.PromptTemplate [Required]#... | /content/https://python.langchain.com/en/latest/reference/modules/prompts.html |
ddf8cd975bdf-3 | dict(**kwargs: Any) → Dict[source]#
Return a dictionary of the prompt.
format(**kwargs: Any) → str[source]#
Format the prompt with the inputs.
Parameters
kwargs – Any arguments to be passed to the prompt template.
Returns
A formatted string.
Example:
prompt.format(variable1="foo")
pydantic model langchain.prompts.FewSh... | /content/https://python.langchain.com/en/latest/reference/modules/prompts.html |
ddf8cd975bdf-4 | A PromptTemplate to put after the examples.
field template_format: str = 'f-string'#
The format of the prompt template. Options are: ‘f-string’, ‘jinja2’.
field validate_template: bool = True#
Whether or not to try validating the template.
dict(**kwargs: Any) → Dict[source]#
Return a dictionary of the prompt.
format(**... | /content/https://python.langchain.com/en/latest/reference/modules/prompts.html |
ddf8cd975bdf-5 | field template: str [Required]#
The prompt template.
field template_format: str = 'f-string'#
The format of the prompt template. Options are: ‘f-string’, ‘jinja2’.
field validate_template: bool = True#
Whether or not to try validating the template.
format(**kwargs: Any) → str[source]#
Format the prompt with the inputs.... | /content/https://python.langchain.com/en/latest/reference/modules/prompts.html |
ddf8cd975bdf-6 | examples. Default to an empty string.
Returns
The final prompt generated.
classmethod from_file(template_file: Union[str, pathlib.Path], input_variables: List[str], **kwargs: Any) → langchain.prompts.prompt.PromptTemplate[source]#
Load a prompt from a file.
Parameters
template_file – The path to the file containing the... | /content/https://python.langchain.com/en/latest/reference/modules/prompts.html |
61c95a6bea55-0 | .rst
.pdf
Experimental Modules
Contents
Autonomous Agents
Generative Agents
Experimental Modules#
This module contains experimental modules and reproductions of existing work using LangChain primitives.
Autonomous Agents#
Here, we document the BabyAGI and AutoGPT classes from the langchain.experimental module.
class ... | /content/https://python.langchain.com/en/latest/reference/modules/experimental.html |
61c95a6bea55-1 | Execute a task.
classmethod from_llm(llm: langchain.schema.BaseLanguageModel, vectorstore: langchain.vectorstores.base.VectorStore, verbose: bool = False, task_execution_chain: Optional[langchain.chains.base.Chain] = None, **kwargs: Dict[str, Any]) → langchain.experimental.autonomous_agents.baby_agi.baby_agi.BabyAGI[so... | /content/https://python.langchain.com/en/latest/reference/modules/experimental.html |
61c95a6bea55-2 | Agent class for interacting with Auto-GPT.
Generative Agents#
Here, we document the GenerativeAgent and GenerativeAgentMemory classes from the langchain.experimental module.
class langchain.experimental.GenerativeAgent(*, name: str, age: Optional[int] = None, traits: str = 'N/A', status: str, memory: langchain.experime... | /content/https://python.langchain.com/en/latest/reference/modules/experimental.html |
61c95a6bea55-3 | Return a full header of the agent’s status, summary, and current time.
get_summary(force_refresh: bool = False) → str[source]#
Return a descriptive summary of the agent.
field last_refreshed: datetime.datetime [Optional]#
The last time the character’s summary was regenerated.
field llm: langchain.schema.BaseLanguageMod... | /content/https://python.langchain.com/en/latest/reference/modules/experimental.html |
61c95a6bea55-4 | Permanent traits to ascribe to the character.
class langchain.experimental.GenerativeAgentMemory(*, llm: langchain.schema.BaseLanguageModel, memory_retriever: langchain.retrievers.time_weighted_retriever.TimeWeightedVectorStoreRetriever, verbose: bool = False, reflection_threshold: Optional[float] = None, current_plan:... | /content/https://python.langchain.com/en/latest/reference/modules/experimental.html |
61c95a6bea55-5 | Fetch related memories.
field importance_weight: float = 0.15#
How much weight to assign the memory importance.
field llm: langchain.schema.BaseLanguageModel [Required]#
The core language model.
load_memory_variables(inputs: Dict[str, Any]) → Dict[str, str][source]#
Return key-value pairs given the text input to the ch... | /content/https://python.langchain.com/en/latest/reference/modules/experimental.html |
3a6668428edb-0 | .rst
.pdf
Retrievers
Retrievers#
pydantic model langchain.retrievers.ChatGPTPluginRetriever[source]#
field aiosession: Optional[aiohttp.client.ClientSession] = None#
field bearer_token: str [Required]#
field filter: Optional[dict] = None#
field top_k: int = 3#
field url: str [Required]#
async aget_relevant_documents(qu... | /content/https://python.langchain.com/en/latest/reference/modules/retrievers.html |
3a6668428edb-1 | Parameters
query – string to find relevant documents for
Returns
List of relevant documents
get_relevant_documents(query: str) → List[langchain.schema.Document][source]#
Get documents relevant for a query.
Parameters
query – string to find relevant documents for
Returns
Sequence of relevant documents
class langchain.re... | /content/https://python.langchain.com/en/latest/reference/modules/retrievers.html |
3a6668428edb-2 | elasticsearch_url.
You can obtain your Elastic Cloud URL and login credentials by logging in to the
Elastic Cloud console at https://cloud.elastic.co, selecting your deployment, and
navigating to the “Deployments” page.
To obtain your Elastic Cloud password for the default “elastic” user:
Log in to the Elastic Cloud co... | /content/https://python.langchain.com/en/latest/reference/modules/retrievers.html |
3a6668428edb-3 | get_relevant_documents(query: str) → List[langchain.schema.Document][source]#
Get documents relevant for a query.
Parameters
query – string to find relevant documents for
Returns
List of relevant documents
class langchain.retrievers.MetalRetriever(client: Any, params: Optional[dict] = None)[source]#
async aget_relevant... | /content/https://python.langchain.com/en/latest/reference/modules/retrievers.html |
3a6668428edb-4 | Get documents relevant for a query.
Parameters
query – string to find relevant documents for
Returns
List of relevant documents
pydantic model langchain.retrievers.RemoteLangChainRetriever[source]#
field headers: Optional[dict] = None#
field input_key: str = 'message'#
field metadata_key: str = 'metadata'#
field page_c... | /content/https://python.langchain.com/en/latest/reference/modules/retrievers.html |
3a6668428edb-5 | Parameters
query – string to find relevant documents for
Returns
List of relevant documents
classmethod from_texts(texts: List[str], embeddings: langchain.embeddings.base.Embeddings, **kwargs: Any) → langchain.retrievers.svm.SVMRetriever[source]#
get_relevant_documents(query: str) → List[langchain.schema.Document][sour... | /content/https://python.langchain.com/en/latest/reference/modules/retrievers.html |
3a6668428edb-6 | Parameters
query – string to find relevant documents for
Returns
List of relevant documents
pydantic model langchain.retrievers.TimeWeightedVectorStoreRetriever[source]#
Retriever combining embededing similarity with recency.
field decay_rate: float = 0.01#
The exponential decay factor used as (1.0-decay_rate)**(hrs_pa... | /content/https://python.langchain.com/en/latest/reference/modules/retrievers.html |
3a6668428edb-7 | Add documents to vectorstore.
async aget_relevant_documents(query: str) → List[langchain.schema.Document][source]#
Return documents that are relevant to the query.
get_relevant_documents(query: str) → List[langchain.schema.Document][source]#
Return documents that are relevant to the query.
get_salient_docs(query: str) ... | /content/https://python.langchain.com/en/latest/reference/modules/retrievers.html |
3a6668428edb-8 | Look up similar documents in Weaviate.
previous
Vector Stores
next
Document Compressors
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/reference/modules/retrievers.html |
eeda90637871-0 | .md
.pdf
Llama.cpp
Contents
Installation and Setup
Wrappers
LLM
Embeddings
Llama.cpp#
This page covers how to use llama.cpp within LangChain.
It is broken into two parts: installation and setup, and then references to specific Llama-cpp wrappers.
Installation and Setup#
Install the Python package with pip install lla... | /content/https://python.langchain.com/en/latest/ecosystem/llamacpp.html |
74e12f0eb073-0 | .md
.pdf
Pinecone
Contents
Installation and Setup
Wrappers
VectorStore
Pinecone#
This page covers how to use the Pinecone ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific Pinecone wrappers.
Installation and Setup#
Install the Python SDK with pip install ... | /content/https://python.langchain.com/en/latest/ecosystem/pinecone.html |
e85cb5755044-0 | .md
.pdf
Hugging Face
Contents
Installation and Setup
Wrappers
LLM
Embeddings
Tokenizer
Datasets
Hugging Face#
This page covers how to use the Hugging Face ecosystem (including the Hugging Face Hub) within LangChain.
It is broken into two parts: installation and setup, and then references to specific Hugging Face wra... | /content/https://python.langchain.com/en/latest/ecosystem/huggingface.html |
e85cb5755044-1 | To use the local pipeline wrapper:
from langchain.embeddings import HuggingFaceEmbeddings
To use a the wrapper for a model hosted on Hugging Face Hub:
from langchain.embeddings import HuggingFaceHubEmbeddings
For a more detailed walkthrough of this, see this notebook
Tokenizer#
There are several places you can use toke... | /content/https://python.langchain.com/en/latest/ecosystem/huggingface.html |
47f33361edbc-0 | .ipynb
.pdf
Comet
Contents
Install Comet and Dependencies
Initialize Comet and Set your Credentials
Set OpenAI and SerpAPI credentials
Scenario 1: Using just an LLM
Scenario 2: Using an LLM in a Chain
Scenario 3: Using An Agent with Tools
Scenario 4: Using Custom Evaluation Metrics
Comet#
In this guide we will demons... | /content/https://python.langchain.com/en/latest/ecosystem/comet_tracking.html |
47f33361edbc-1 | from langchain.llms import OpenAI
comet_callback = CometCallbackHandler(
project_name="comet-example-langchain",
complexity_metrics=True,
stream_logs=True,
tags=["llm"],
visualizations=["dep"],
)
manager = CallbackManager([StdOutCallbackHandler(), comet_callback])
llm = OpenAI(temperature=0.9, callb... | /content/https://python.langchain.com/en/latest/ecosystem/comet_tracking.html |
47f33361edbc-2 | template = """You are a playwright. Given the title of play, it is your job to write a synopsis for that title.
Title: {title}
Playwright: This is a synopsis for the above play:"""
prompt_template = PromptTemplate(input_variables=["title"], template=template)
synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, c... | /content/https://python.langchain.com/en/latest/ecosystem/comet_tracking.html |
47f33361edbc-3 | agent = initialize_agent(
tools,
llm,
agent="zero-shot-react-description",
callback_manager=manager,
verbose=True,
)
agent.run(
"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?"
)
comet_callback.flush_tracker(agent, finish=True)
Scenario 4: Using Custom Evalua... | /content/https://python.langchain.com/en/latest/ecosystem/comet_tracking.html |
47f33361edbc-4 | "reference": self.reference,
}
reference = """
The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building.
It was the first structure to reach a height of 300 metres.
It is now taller than the Chrysler Building in New York City by 5.2 metres (17 ft)
Excluding transmitters, the Eiffe... | /content/https://python.langchain.com/en/latest/ecosystem/comet_tracking.html |
47f33361edbc-5 | measuring 125 metres (410 ft) on each side.
During its construction, the Eiffel Tower surpassed the
Washington Monument to become the tallest man-made structure in the world,
a title it held for 41 years until the Chrysler Building
in New York City was... | /content/https://python.langchain.com/en/latest/ecosystem/comet_tracking.html |
94a318b4dfc7-0 | .md
.pdf
Zilliz
Contents
Installation and Setup
Wrappers
VectorStore
Zilliz#
This page covers how to use the Zilliz Cloud ecosystem within LangChain.
Zilliz uses the Milvus integration.
It is broken into two parts: installation and setup, and then references to specific Milvus wrappers.
Installation and Setup#
Instal... | /content/https://python.langchain.com/en/latest/ecosystem/zilliz.html |
fbcb799e3586-0 | .md
.pdf
Google Serper Wrapper
Contents
Setup
Wrappers
Utility
Output
Tool
Google Serper Wrapper#
This page covers how to use the Serper Google Search API within LangChain. Serper is a low-cost Google Search API that can be used to add answer box, knowledge graph, and organic results data from Google Search.
It is br... | /content/https://python.langchain.com/en/latest/ecosystem/google_serper.html |
fbcb799e3586-1 | self_ask_with_search.run("What is the hometown of the reigning men's U.S. Open champion?")
Output#
Entering new AgentExecutor chain...
Yes.
Follow up: Who is the reigning men's U.S. Open champion?
Intermediate answer: Current champions Carlos Alcaraz, 2022 men's singles champion.
Follow up: Where is Carlos Alcaraz fro... | /content/https://python.langchain.com/en/latest/ecosystem/google_serper.html |
7030d5237eb3-0 | .md
.pdf
Qdrant
Contents
Installation and Setup
Wrappers
VectorStore
Qdrant#
This page covers how to use the Qdrant ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific Qdrant wrappers.
Installation and Setup#
Install the Python SDK with pip install qdrant-c... | /content/https://python.langchain.com/en/latest/ecosystem/qdrant.html |
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