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20e9b0d32d50-0 | API Reference¶
langchain.agents: Agents¶
Interface for agents.
Classes¶
agents.agent.Agent
Class responsible for calling the language model and deciding the action.
agents.agent.AgentExecutor
Consists of an agent using tools.
agents.agent.AgentOutputParser
Create a new model by parsing and validating input data from keyword arguments.
agents.agent.BaseMultiActionAgent
Base Agent class.
agents.agent.BaseSingleActionAgent
Base Agent class.
agents.agent.ExceptionTool
Create a new model by parsing and validating input data from keyword arguments.
agents.agent.LLMSingleActionAgent
Create a new model by parsing and validating input data from keyword arguments.
agents.agent_toolkits.azure_cognitive_services.toolkit.AzureCognitiveServicesToolkit
Toolkit for Azure Cognitive Services.
agents.agent_toolkits.base.BaseToolkit
Class responsible for defining a collection of related tools.
agents.agent_toolkits.file_management.toolkit.FileManagementToolkit
Toolkit for interacting with a Local Files.
agents.agent_toolkits.gmail.toolkit.GmailToolkit
Toolkit for interacting with Gmail.
agents.agent_toolkits.jira.toolkit.JiraToolkit
Jira Toolkit.
agents.agent_toolkits.json.toolkit.JsonToolkit
Toolkit for interacting with a JSON spec.
agents.agent_toolkits.nla.tool.NLATool
Natural Language API Tool.
agents.agent_toolkits.nla.toolkit.NLAToolkit
Natural Language API Toolkit Definition.
agents.agent_toolkits.office365.toolkit.O365Toolkit
Toolkit for interacting with Office365.
agents.agent_toolkits.openapi.planner.RequestsDeleteToolWithParsing
Create a new model by parsing and validating input data from keyword arguments.
agents.agent_toolkits.openapi.planner.RequestsGetToolWithParsing
Create a new model by parsing and validating input data from keyword arguments.
agents.agent_toolkits.openapi.planner.RequestsPatchToolWithParsing | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-1 | agents.agent_toolkits.openapi.planner.RequestsPatchToolWithParsing
Create a new model by parsing and validating input data from keyword arguments.
agents.agent_toolkits.openapi.planner.RequestsPostToolWithParsing
Create a new model by parsing and validating input data from keyword arguments.
agents.agent_toolkits.openapi.toolkit.OpenAPIToolkit
Toolkit for interacting with a OpenAPI api.
agents.agent_toolkits.openapi.toolkit.RequestsToolkit
Toolkit for making requests.
agents.agent_toolkits.playwright.toolkit.PlayWrightBrowserToolkit
Toolkit for web browser tools.
agents.agent_toolkits.powerbi.toolkit.PowerBIToolkit
Toolkit for interacting with PowerBI dataset.
agents.agent_toolkits.spark_sql.toolkit.SparkSQLToolkit
Toolkit for interacting with Spark SQL.
agents.agent_toolkits.sql.toolkit.SQLDatabaseToolkit
Toolkit for interacting with SQL databases.
agents.agent_toolkits.vectorstore.toolkit.VectorStoreInfo
Information about a vectorstore.
agents.agent_toolkits.vectorstore.toolkit.VectorStoreRouterToolkit
Toolkit for routing between vector stores.
agents.agent_toolkits.vectorstore.toolkit.VectorStoreToolkit
Toolkit for interacting with a vector store.
agents.agent_toolkits.zapier.toolkit.ZapierToolkit
Zapier Toolkit.
agents.agent_types.AgentType(value[, names, ...])
Enumerator with the Agent types.
agents.chat.base.ChatAgent
Create a new model by parsing and validating input data from keyword arguments.
agents.chat.output_parser.ChatOutputParser
Create a new model by parsing and validating input data from keyword arguments.
agents.conversational.base.ConversationalAgent
An agent designed to hold a conversation in addition to using tools.
agents.conversational.output_parser.ConvoOutputParser
Create a new model by parsing and validating input data from keyword arguments.
agents.conversational_chat.base.ConversationalChatAgent | [
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20e9b0d32d50-2 | agents.conversational_chat.base.ConversationalChatAgent
An agent designed to hold a conversation in addition to using tools.
agents.conversational_chat.output_parser.ConvoOutputParser
Create a new model by parsing and validating input data from keyword arguments.
agents.mrkl.base.ChainConfig(action_name, ...)
Configuration for chain to use in MRKL system.
agents.mrkl.base.MRKLChain
Chain that implements the MRKL system.
agents.mrkl.base.ZeroShotAgent
Agent for the MRKL chain.
agents.mrkl.output_parser.MRKLOutputParser
Create a new model by parsing and validating input data from keyword arguments.
agents.openai_functions_agent.base.OpenAIFunctionsAgent
An Agent driven by OpenAIs function powered API.
agents.openai_functions_multi_agent.base.OpenAIMultiFunctionsAgent
An Agent driven by OpenAIs function powered API.
agents.react.base.ReActChain
Chain that implements the ReAct paper.
agents.react.base.ReActDocstoreAgent
Agent for the ReAct chain.
agents.react.base.ReActTextWorldAgent
Agent for the ReAct TextWorld chain.
agents.react.output_parser.ReActOutputParser
Create a new model by parsing and validating input data from keyword arguments.
agents.schema.AgentScratchPadChatPromptTemplate
Create a new model by parsing and validating input data from keyword arguments.
agents.self_ask_with_search.base.SelfAskWithSearchAgent
Agent for the self-ask-with-search paper.
agents.self_ask_with_search.base.SelfAskWithSearchChain
Chain that does self ask with search.
agents.self_ask_with_search.output_parser.SelfAskOutputParser
Create a new model by parsing and validating input data from keyword arguments.
agents.structured_chat.base.StructuredChatAgent
Create a new model by parsing and validating input data from keyword arguments. | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-3 | Create a new model by parsing and validating input data from keyword arguments.
agents.structured_chat.output_parser.StructuredChatOutputParser
Create a new model by parsing and validating input data from keyword arguments.
agents.structured_chat.output_parser.StructuredChatOutputParserWithRetries
Create a new model by parsing and validating input data from keyword arguments.
agents.tools.InvalidTool
Tool that is run when invalid tool name is encountered by agent.
Functions¶
agents.agent_toolkits.csv.base.create_csv_agent(...)
Create csv agent by loading to a dataframe and using pandas agent.
agents.agent_toolkits.json.base.create_json_agent(...)
Construct a json agent from an LLM and tools.
agents.agent_toolkits.openapi.base.create_openapi_agent(...)
Construct a json agent from an LLM and tools.
agents.agent_toolkits.openapi.planner.create_openapi_agent(...)
Instantiate API planner and controller for a given spec.
agents.agent_toolkits.openapi.spec.dereference_refs(...)
Try to substitute $refs.
agents.agent_toolkits.openapi.spec.reduce_openapi_spec(spec)
Simplify/distill/minify a spec somehow.
agents.agent_toolkits.pandas.base.create_pandas_dataframe_agent(llm, df)
Construct a pandas agent from an LLM and dataframe.
agents.agent_toolkits.powerbi.base.create_pbi_agent(...)
Construct a pbi agent from an LLM and tools.
agents.agent_toolkits.powerbi.chat_base.create_pbi_chat_agent(...)
Construct a pbi agent from an Chat LLM and tools.
agents.agent_toolkits.python.base.create_python_agent(...)
Construct a python agent from an LLM and tool.
agents.agent_toolkits.spark.base.create_spark_dataframe_agent(llm, df)
Construct a spark agent from an LLM and dataframe.
agents.agent_toolkits.spark_sql.base.create_spark_sql_agent(...)
Construct a sql agent from an LLM and tools. | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-4 | Construct a sql agent from an LLM and tools.
agents.agent_toolkits.sql.base.create_sql_agent(...)
Construct a sql agent from an LLM and tools.
agents.agent_toolkits.vectorstore.base.create_vectorstore_agent(...)
Construct a vectorstore agent from an LLM and tools.
agents.agent_toolkits.vectorstore.base.create_vectorstore_router_agent(...)
Construct a vectorstore router agent from an LLM and tools.
agents.initialize.initialize_agent(tools, llm)
Load an agent executor given tools and LLM.
agents.load_tools.get_all_tool_names()
Get a list of all possible tool names.
agents.load_tools.load_huggingface_tool(...)
Loads a tool from the HuggingFace Hub.
agents.load_tools.load_tools(tool_names[, ...])
Load tools based on their name.
agents.loading.load_agent(path, **kwargs)
Unified method for loading a agent from LangChainHub or local fs.
agents.loading.load_agent_from_config(config)
Load agent from Config Dict.
agents.utils.validate_tools_single_input(...)
Validate tools for single input.
langchain.base_language: Base Language¶
Base class for all language models.
Classes¶
base_language.BaseLanguageModel
Create a new model by parsing and validating input data from keyword arguments.
langchain.cache: Cache¶
Beta Feature: base interface for cache.
Classes¶
cache.BaseCache()
Base interface for cache.
cache.FullLLMCache(**kwargs)
SQLite table for full LLM Cache (all generations).
cache.GPTCache([init_func])
Cache that uses GPTCache as a backend.
cache.InMemoryCache()
Cache that stores things in memory.
cache.MomentoCache(cache_client, cache_name, *)
Cache that uses Momento as a backend.
cache.RedisCache(redis_)
Cache that uses Redis as a backend. | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-5 | cache.RedisCache(redis_)
Cache that uses Redis as a backend.
cache.RedisSemanticCache(redis_url, embedding)
Cache that uses Redis as a vector-store backend.
cache.SQLAlchemyCache(engine, cache_schema)
Cache that uses SQAlchemy as a backend.
cache.SQLiteCache([database_path])
Cache that uses SQLite as a backend.
langchain.callbacks: Callbacks¶
Callback handlers that allow listening to events in LangChain.
Classes¶
callbacks.aim_callback.AimCallbackHandler([...])
Callback Handler that logs to Aim.
callbacks.argilla_callback.ArgillaCallbackHandler(...)
Callback Handler that logs into Argilla.
callbacks.arize_callback.ArizeCallbackHandler([...])
Callback Handler that logs to Arize.
callbacks.arthur_callback.ArthurCallbackHandler(...)
Callback Handler that logs to Arthur platform.
callbacks.base.AsyncCallbackHandler()
Async callback handler that can be used to handle callbacks from langchain.
callbacks.base.BaseCallbackHandler()
Base callback handler that can be used to handle callbacks from langchain.
callbacks.base.BaseCallbackManager(handlers)
Base callback manager that can be used to handle callbacks from LangChain.
callbacks.clearml_callback.ClearMLCallbackHandler([...])
Callback Handler that logs to ClearML.
callbacks.comet_ml_callback.CometCallbackHandler([...])
Callback Handler that logs to Comet.
callbacks.file.FileCallbackHandler(filename)
Callback Handler that writes to a file.
callbacks.flyte_callback.FlyteCallbackHandler()
This callback handler is designed specifically for usage within a Flyte task.
callbacks.human.HumanApprovalCallbackHandler(...)
Callback for manually validating values.
callbacks.human.HumanRejectedException
Exception to raise when a person manually review and rejects a value.
callbacks.infino_callback.InfinoCallbackHandler([...])
Callback Handler that logs to Infino.
callbacks.manager.AsyncCallbackManager(handlers) | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-6 | Callback Handler that logs to Infino.
callbacks.manager.AsyncCallbackManager(handlers)
Async callback manager that can be used to handle callbacks from LangChain.
callbacks.manager.AsyncCallbackManagerForChainRun(*, ...)
Async callback manager for chain run.
callbacks.manager.AsyncCallbackManagerForLLMRun(*, ...)
Async callback manager for LLM run.
callbacks.manager.AsyncCallbackManagerForRetrieverRun(*, ...)
Async callback manager for retriever run.
callbacks.manager.AsyncCallbackManagerForToolRun(*, ...)
Async callback manager for tool run.
callbacks.manager.AsyncRunManager(*, run_id, ...)
Async Run Manager.
callbacks.manager.BaseRunManager(*, run_id, ...)
Base class for run manager (a bound callback manager).
callbacks.manager.CallbackManager(handlers)
Callback manager that can be used to handle callbacks from langchain.
callbacks.manager.CallbackManagerForChainRun(*, ...)
Callback manager for chain run.
callbacks.manager.CallbackManagerForLLMRun(*, ...)
Callback manager for LLM run.
callbacks.manager.CallbackManagerForRetrieverRun(*, ...)
Callback manager for retriever run.
callbacks.manager.CallbackManagerForToolRun(*, ...)
Callback manager for tool run.
callbacks.manager.RunManager(*, run_id, ...)
Sync Run Manager.
callbacks.mlflow_callback.MlflowCallbackHandler([...])
Callback Handler that logs metrics and artifacts to mlflow server.
callbacks.openai_info.OpenAICallbackHandler()
Callback Handler that tracks OpenAI info.
callbacks.promptlayer_callback.PromptLayerCallbackHandler([...])
Callback handler for promptlayer.
callbacks.stdout.StdOutCallbackHandler([color])
Callback Handler that prints to std out.
callbacks.streaming_aiter.AsyncIteratorCallbackHandler()
Callback handler that returns an async iterator. | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-7 | callbacks.streaming_aiter.AsyncIteratorCallbackHandler()
Callback handler that returns an async iterator.
callbacks.streaming_aiter_final_only.AsyncFinalIteratorCallbackHandler(*)
Callback handler that returns an async iterator.
callbacks.streaming_stdout.StreamingStdOutCallbackHandler()
Callback handler for streaming.
callbacks.streaming_stdout_final_only.FinalStreamingStdOutCallbackHandler(*)
Callback handler for streaming in agents.
callbacks.streamlit.mutable_expander.ChildRecord(...)
Create new instance of ChildRecord(type, kwargs, dg)
callbacks.streamlit.mutable_expander.ChildType(value)
callbacks.streamlit.streamlit_callback_handler.LLMThoughtState(value)
callbacks.streamlit.streamlit_callback_handler.StreamlitCallbackHandler(...)
Create a StreamlitCallbackHandler instance.
callbacks.streamlit.streamlit_callback_handler.ToolRecord(...)
Create new instance of ToolRecord(name, input_str)
callbacks.tracers.base.BaseTracer(**kwargs)
Base interface for tracers.
callbacks.tracers.base.TracerException
Base class for exceptions in tracers module.
callbacks.tracers.evaluation.EvaluatorCallbackHandler(...)
A tracer that runs a run evaluator whenever a run is persisted.
callbacks.tracers.langchain.LangChainTracer([...])
An implementation of the SharedTracer that POSTS to the langchain endpoint.
callbacks.tracers.langchain_v1.LangChainTracerV1(...)
An implementation of the SharedTracer that POSTS to the langchain endpoint.
callbacks.tracers.run_collector.RunCollectorCallbackHandler([...])
A tracer that collects all nested runs in a list.
callbacks.tracers.schemas.BaseRun
Base class for Run.
callbacks.tracers.schemas.ChainRun
Class for ChainRun.
callbacks.tracers.schemas.LLMRun
Class for LLMRun.
callbacks.tracers.schemas.Run
Run schema for the V2 API in the Tracer.
callbacks.tracers.schemas.ToolRun
Class for ToolRun. | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-8 | callbacks.tracers.schemas.ToolRun
Class for ToolRun.
callbacks.tracers.schemas.TracerSession
TracerSessionV1 schema for the V2 API.
callbacks.tracers.schemas.TracerSessionBase
A creation class for TracerSession.
callbacks.tracers.schemas.TracerSessionV1
TracerSessionV1 schema.
callbacks.tracers.schemas.TracerSessionV1Base
Base class for TracerSessionV1.
callbacks.tracers.schemas.TracerSessionV1Create
Create class for TracerSessionV1.
callbacks.tracers.stdout.ConsoleCallbackHandler(...)
Tracer that prints to the console.
callbacks.tracers.wandb.WandbRunArgs
Arguments for the WandbTracer.
callbacks.tracers.wandb.WandbTracer([run_args])
Callback Handler that logs to Weights and Biases.
callbacks.wandb_callback.WandbCallbackHandler([...])
Callback Handler that logs to Weights and Biases.
callbacks.whylabs_callback.WhyLabsCallbackHandler(logger)
WhyLabs CallbackHandler.
Functions¶
callbacks.aim_callback.import_aim()
Import the aim python package and raise an error if it is not installed.
callbacks.clearml_callback.import_clearml()
Import the clearml python package and raise an error if it is not installed.
callbacks.comet_ml_callback.import_comet_ml()
callbacks.flyte_callback.analyze_text(text)
Analyze text using textstat and spacy.
callbacks.flyte_callback.import_flytekit()
callbacks.infino_callback.import_infino()
callbacks.manager.env_var_is_set(env_var)
Check if an environment variable is set.
callbacks.manager.get_openai_callback()
Get the OpenAI callback handler in a context manager.
callbacks.manager.trace_as_chain_group(...)
Get a callback manager for a chain group in a context manager. | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-9 | Get a callback manager for a chain group in a context manager.
callbacks.manager.tracing_enabled([session_name])
Get the Deprecated LangChainTracer in a context manager.
callbacks.manager.tracing_v2_enabled([...])
Instruct LangChain to log all runs in context to LangSmith.
callbacks.manager.wandb_tracing_enabled([...])
Get the WandbTracer in a context manager.
callbacks.mlflow_callback.analyze_text(text)
Analyze text using textstat and spacy.
callbacks.mlflow_callback.construct_html_from_prompt_and_generation(...)
Construct an html element from a prompt and a generation.
callbacks.mlflow_callback.import_mlflow()
Import the mlflow python package and raise an error if it is not installed.
callbacks.openai_info.get_openai_token_cost_for_model(...)
Get the cost in USD for a given model and number of tokens.
callbacks.openai_info.standardize_model_name(...)
Standardize the model name to a format that can be used in the OpenAI API. :param model_name: Model name to standardize. :param is_completion: Whether the model is used for completion or not. Defaults to False.
callbacks.streamlit.__init__.StreamlitCallbackHandler(...)
Construct a new StreamlitCallbackHandler.
callbacks.tracers.langchain.log_error_once(...)
Log an error once.
callbacks.tracers.langchain.wait_for_all_tracers()
callbacks.tracers.langchain_v1.get_headers()
Get the headers for the LangChain API.
callbacks.tracers.stdout.elapsed(run)
Get the elapsed time of a run.
callbacks.tracers.stdout.try_json_stringify(...)
Try to stringify an object to JSON.
callbacks.utils.flatten_dict(nested_dict[, ...])
Flattens a nested dictionary into a flat dictionary.
callbacks.utils.hash_string(s)
Hash a string using sha1.
callbacks.utils.import_pandas() | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-10 | Hash a string using sha1.
callbacks.utils.import_pandas()
Import the pandas python package and raise an error if it is not installed.
callbacks.utils.import_spacy()
Import the spacy python package and raise an error if it is not installed.
callbacks.utils.import_textstat()
Import the textstat python package and raise an error if it is not installed.
callbacks.utils.load_json(json_path)
Load json file to a string.
callbacks.wandb_callback.analyze_text(text)
Analyze text using textstat and spacy.
callbacks.wandb_callback.construct_html_from_prompt_and_generation(...)
Construct an html element from a prompt and a generation.
callbacks.wandb_callback.import_wandb()
Import the wandb python package and raise an error if it is not installed.
callbacks.wandb_callback.load_json_to_dict(...)
Load json file to a dictionary.
callbacks.whylabs_callback.import_langkit([...])
Import the langkit python package and raise an error if it is not installed.
langchain.chains: Chains¶
Chains are easily reusable components which can be linked together.
Classes¶
chains.api.base.APIChain
Chain that makes API calls and summarizes the responses to answer a question.
chains.api.openapi.chain.OpenAPIEndpointChain
Chain interacts with an OpenAPI endpoint using natural language.
chains.api.openapi.requests_chain.APIRequesterChain
Get the request parser.
chains.api.openapi.requests_chain.APIRequesterOutputParser
Parse the request and error tags.
chains.api.openapi.response_chain.APIResponderChain
Get the response parser.
chains.api.openapi.response_chain.APIResponderOutputParser
Parse the response and error tags.
chains.base.Chain
Base interface that all chains should implement.
chains.combine_documents.base.AnalyzeDocumentChain
Chain that splits documents, then analyzes it in pieces.
chains.combine_documents.base.BaseCombineDocumentsChain | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-11 | chains.combine_documents.base.BaseCombineDocumentsChain
Base interface for chains combining documents.
chains.combine_documents.map_reduce.CombineDocsProtocol(...)
Interface for the combine_docs method.
chains.combine_documents.map_reduce.MapReduceDocumentsChain
Combining documents by mapping a chain over them, then combining results.
chains.combine_documents.map_rerank.MapRerankDocumentsChain
Combining documents by mapping a chain over them, then reranking results.
chains.combine_documents.refine.RefineDocumentsChain
Combine documents by doing a first pass and then refining on more documents.
chains.combine_documents.stuff.StuffDocumentsChain
Chain that combines documents by stuffing into context.
chains.constitutional_ai.base.ConstitutionalChain
Chain for applying constitutional principles.
chains.constitutional_ai.models.ConstitutionalPrinciple
Class for a constitutional principle.
chains.conversation.base.ConversationChain
Chain to have a conversation and load context from memory.
chains.conversational_retrieval.base.BaseConversationalRetrievalChain
Chain for chatting with an index.
chains.conversational_retrieval.base.ChatVectorDBChain
Chain for chatting with a vector database.
chains.conversational_retrieval.base.ConversationalRetrievalChain
Chain for chatting with an index.
chains.flare.base.FlareChain
Create a new model by parsing and validating input data from keyword arguments.
chains.flare.base.QuestionGeneratorChain
Create a new model by parsing and validating input data from keyword arguments.
chains.flare.prompts.FinishedOutputParser
Create a new model by parsing and validating input data from keyword arguments.
chains.graph_qa.base.GraphQAChain
Chain for question-answering against a graph.
chains.graph_qa.cypher.GraphCypherQAChain
Chain for question-answering against a graph by generating Cypher statements.
chains.graph_qa.kuzu.KuzuQAChain | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-12 | chains.graph_qa.kuzu.KuzuQAChain
Chain for question-answering against a graph by generating Cypher statements for Kùzu.
chains.graph_qa.nebulagraph.NebulaGraphQAChain
Chain for question-answering against a graph by generating nGQL statements.
chains.hyde.base.HypotheticalDocumentEmbedder
Generate hypothetical document for query, and then embed that.
chains.llm.LLMChain
Chain to run queries against LLMs.
chains.llm_bash.base.LLMBashChain
Chain that interprets a prompt and executes bash code to perform bash operations.
chains.llm_bash.prompt.BashOutputParser
Parser for bash output.
chains.llm_checker.base.LLMCheckerChain
Chain for question-answering with self-verification.
chains.llm_math.base.LLMMathChain
Chain that interprets a prompt and executes python code to do math.
chains.llm_requests.LLMRequestsChain
Chain that hits a URL and then uses an LLM to parse results.
chains.llm_summarization_checker.base.LLMSummarizationCheckerChain
Chain for question-answering with self-verification.
chains.mapreduce.MapReduceChain
Map-reduce chain.
chains.moderation.OpenAIModerationChain
Pass input through a moderation endpoint.
chains.natbot.base.NatBotChain
Implement an LLM driven browser.
chains.natbot.crawler.ElementInViewPort
A typed dictionary containing information about elements in the viewport.
chains.openai_functions.citation_fuzzy_match.FactWithEvidence
Class representing single statement.
chains.openai_functions.citation_fuzzy_match.QuestionAnswer
A question and its answer as a list of facts each one should have a source.
chains.openai_functions.openapi.SimpleRequestChain
Create a new model by parsing and validating input data from keyword arguments. | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-13 | Create a new model by parsing and validating input data from keyword arguments.
chains.openai_functions.qa_with_structure.AnswerWithSources
An answer to the question being asked, with sources.
chains.pal.base.PALChain
Implements Program-Aided Language Models.
chains.prompt_selector.BasePromptSelector
Create a new model by parsing and validating input data from keyword arguments.
chains.prompt_selector.ConditionalPromptSelector
Prompt collection that goes through conditionals.
chains.qa_generation.base.QAGenerationChain
Create a new model by parsing and validating input data from keyword arguments.
chains.qa_with_sources.base.BaseQAWithSourcesChain
Question answering with sources over documents.
chains.qa_with_sources.base.QAWithSourcesChain
Question answering with sources over documents.
chains.qa_with_sources.loading.LoadingCallable(...)
Interface for loading the combine documents chain.
chains.qa_with_sources.retrieval.RetrievalQAWithSourcesChain
Question-answering with sources over an index.
chains.qa_with_sources.vector_db.VectorDBQAWithSourcesChain
Question-answering with sources over a vector database.
chains.query_constructor.base.StructuredQueryOutputParser
Create a new model by parsing and validating input data from keyword arguments.
chains.query_constructor.ir.Comparator(value)
Enumerator of the comparison operators.
chains.query_constructor.ir.Comparison
A comparison to a value.
chains.query_constructor.ir.Expr
Create a new model by parsing and validating input data from keyword arguments.
chains.query_constructor.ir.FilterDirective
A filtering expression.
chains.query_constructor.ir.Operation
A logical operation over other directives.
chains.query_constructor.ir.Operator(value)
Enumerator of the operations.
chains.query_constructor.ir.StructuredQuery
Create a new model by parsing and validating input data from keyword arguments.
chains.query_constructor.ir.Visitor()
Defines interface for IR translation using visitor pattern.
chains.query_constructor.parser.QueryTransformer | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-14 | Defines interface for IR translation using visitor pattern.
chains.query_constructor.parser.QueryTransformer
chains.query_constructor.schema.AttributeInfo
Information about a data source attribute.
chains.question_answering.__init__.LoadingCallable(...)
Interface for loading the combine documents chain.
chains.retrieval_qa.base.BaseRetrievalQA
Create a new model by parsing and validating input data from keyword arguments.
chains.retrieval_qa.base.RetrievalQA
Chain for question-answering against an index.
chains.retrieval_qa.base.VectorDBQA
Chain for question-answering against a vector database.
chains.router.base.MultiRouteChain
Use a single chain to route an input to one of multiple candidate chains.
chains.router.base.Route(destination, ...)
Create new instance of Route(destination, next_inputs)
chains.router.base.RouterChain
Chain that outputs the name of a destination chain and the inputs to it.
chains.router.embedding_router.EmbeddingRouterChain
Class that uses embeddings to route between options.
chains.router.llm_router.LLMRouterChain
A router chain that uses an LLM chain to perform routing.
chains.router.llm_router.RouterOutputParser
Parser for output of router chain int he multi-prompt chain.
chains.router.multi_prompt.MultiPromptChain
A multi-route chain that uses an LLM router chain to choose amongst prompts.
chains.router.multi_retrieval_qa.MultiRetrievalQAChain
A multi-route chain that uses an LLM router chain to choose amongst retrieval qa chains.
chains.sequential.SequentialChain
Chain where the outputs of one chain feed directly into next.
chains.sequential.SimpleSequentialChain
Simple chain where the outputs of one step feed directly into next.
chains.sql_database.base.SQLDatabaseChain
Chain for interacting with SQL Database.
chains.sql_database.base.SQLDatabaseSequentialChain
Chain for querying SQL database that is a sequential chain. | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-15 | Chain for querying SQL database that is a sequential chain.
chains.summarize.__init__.LoadingCallable(...)
Interface for loading the combine documents chain.
chains.transform.TransformChain
Chain transform chain output.
Functions¶
chains.combine_documents.base.format_document(...)
Format a document into a string based on a prompt template.
chains.graph_qa.cypher.extract_cypher(text)
Extract Cypher code from a text.
chains.loading.load_chain(path, **kwargs)
Unified method for loading a chain from LangChainHub or local fs.
chains.loading.load_chain_from_config(...)
Load chain from Config Dict.
chains.openai_functions.citation_fuzzy_match.create_citation_fuzzy_match_chain(llm)
Create a citation fuzzy match chain.
chains.openai_functions.extraction.create_extraction_chain(...)
Creates a chain that extracts information from a passage.
chains.openai_functions.extraction.create_extraction_chain_pydantic(...)
Creates a chain that extracts information from a passage using pydantic schema.
chains.openai_functions.openapi.get_openapi_chain(spec)
Create a chain for querying an API from a OpenAPI spec.
chains.openai_functions.openapi.openapi_spec_to_openai_fn(spec)
Convert a valid OpenAPI spec to the JSON Schema format expected for OpenAI
chains.openai_functions.qa_with_structure.create_qa_with_sources_chain(...)
Create a question answering chain that returns an answer with sources.
chains.openai_functions.qa_with_structure.create_qa_with_structure_chain(...)
Create a question answering chain that returns an answer with sources.
chains.openai_functions.tagging.create_tagging_chain(...)
Creates a chain that extracts information from a passage.
chains.openai_functions.tagging.create_tagging_chain_pydantic(...)
Creates a chain that extracts information from a passage.
chains.openai_functions.utils.get_llm_kwargs(...)
Returns the kwargs for the LLMChain constructor. | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-16 | Returns the kwargs for the LLMChain constructor.
chains.prompt_selector.is_chat_model(llm)
Check if the language model is a chat model.
chains.prompt_selector.is_llm(llm)
Check if the language model is a LLM.
chains.qa_with_sources.loading.load_qa_with_sources_chain(llm)
Load question answering with sources chain.
chains.query_constructor.base.load_query_constructor_chain(...)
Load a query constructor chain. :param llm: BaseLanguageModel to use for the chain. :param document_contents: The contents of the document to be queried. :param attribute_info: A list of AttributeInfo objects describing the attributes of the document. :param examples: Optional list of examples to use for the chain. :param allowed_comparators: An optional list of allowed comparators. :param allowed_operators: An optional list of allowed operators. :param enable_limit: Whether to enable the limit operator. Defaults to False. :param **kwargs:.
chains.query_constructor.parser.get_parser([...])
Returns a parser for the query language.
chains.question_answering.__init__.load_qa_chain(llm)
Load question answering chain.
chains.summarize.__init__.load_summarize_chain(llm)
Load summarizing chain.
langchain.chat_models: Chat Models¶
Classes¶
chat_models.anthropic.ChatAnthropic
Wrapper around Anthropic's large language model.
chat_models.azure_openai.AzureChatOpenAI
Wrapper around Azure OpenAI Chat Completion API.
chat_models.base.BaseChatModel
Create a new model by parsing and validating input data from keyword arguments.
chat_models.base.SimpleChatModel
Create a new model by parsing and validating input data from keyword arguments.
chat_models.fake.FakeListChatModel
Fake ChatModel for testing purposes.
chat_models.google_palm.ChatGooglePalm
Wrapper around Google's PaLM Chat API. | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-17 | chat_models.google_palm.ChatGooglePalm
Wrapper around Google's PaLM Chat API.
chat_models.google_palm.ChatGooglePalmError
Error raised when there is an issue with the Google PaLM API.
chat_models.openai.ChatOpenAI
Wrapper around OpenAI Chat large language models.
chat_models.promptlayer_openai.PromptLayerChatOpenAI
Wrapper around OpenAI Chat large language models and PromptLayer.
chat_models.vertexai.ChatVertexAI
Wrapper around Vertex AI large language models.
Functions¶
chat_models.google_palm.chat_with_retry(llm, ...)
Use tenacity to retry the completion call.
langchain.client: Client¶
LangChain+ Client.
Classes¶
client.runner_utils.InputFormatError
Raised when the input format is invalid.
Functions¶
client.runner_utils.run_llm(llm, inputs, ...)
Run the language model on the example.
client.runner_utils.run_llm_or_chain(...[, ...])
Run the Chain or language model synchronously.
client.runner_utils.run_on_dataset(...[, ...])
Run the Chain or language model on a dataset and store traces to the specified project name.
client.runner_utils.run_on_examples(...[, ...])
Run the Chain or language model on examples and store traces to the specified project name.
langchain.docstore: Docstore¶
Wrappers on top of docstores.
Classes¶
docstore.arbitrary_fn.DocstoreFn(lookup_fn)
Langchain Docstore via arbitrary lookup function.
docstore.base.AddableMixin()
Mixin class that supports adding texts.
docstore.base.Docstore()
Interface to access to place that stores documents.
docstore.in_memory.InMemoryDocstore(_dict)
Simple in memory docstore in the form of a dict.
docstore.wikipedia.Wikipedia()
Wrapper around wikipedia API. | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-18 | docstore.wikipedia.Wikipedia()
Wrapper around wikipedia API.
langchain.document_loaders: Document Loaders¶
All different types of document loaders.
Classes¶
document_loaders.acreom.AcreomLoader(path[, ...])
Initialize with path.
document_loaders.airbyte_json.AirbyteJSONLoader(...)
Loader that loads local airbyte json files.
document_loaders.airtable.AirtableLoader(...)
Loader for Airtable tables.
document_loaders.apify_dataset.ApifyDatasetLoader
Logic for loading documents from Apify datasets.
document_loaders.arxiv.ArxivLoader(query[, ...])
Loads a query result from arxiv.org into a list of Documents.
document_loaders.azlyrics.AZLyricsLoader(...)
Loader that loads AZLyrics webpages.
document_loaders.azure_blob_storage_container.AzureBlobStorageContainerLoader(...)
Loading logic for loading documents from Azure Blob Storage.
document_loaders.azure_blob_storage_file.AzureBlobStorageFileLoader(...)
Loading logic for loading documents from Azure Blob Storage.
document_loaders.base.BaseBlobParser()
Abstract interface for blob parsers.
document_loaders.base.BaseLoader()
Interface for loading documents.
document_loaders.bibtex.BibtexLoader(...[, ...])
Loads a bibtex file into a list of Documents.
document_loaders.bigquery.BigQueryLoader(query)
Loads a query result from BigQuery into a list of documents.
document_loaders.bilibili.BiliBiliLoader(...)
Loader that loads bilibili transcripts.
document_loaders.blackboard.BlackboardLoader(...)
Loader that loads all documents from a Blackboard course.
document_loaders.blob_loaders.file_system.FileSystemBlobLoader(path, *)
Blob loader for the local file system.
document_loaders.blob_loaders.schema.Blob
A blob is used to represent raw data by either reference or value. | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-19 | A blob is used to represent raw data by either reference or value.
document_loaders.blob_loaders.schema.BlobLoader()
Abstract interface for blob loaders implementation.
document_loaders.blob_loaders.youtube_audio.YoutubeAudioLoader(...)
Load YouTube urls as audio file(s).
document_loaders.blockchain.BlockchainDocumentLoader(...)
Loads elements from a blockchain smart contract into Langchain documents.
document_loaders.blockchain.BlockchainType(value)
Enumerator of the supported blockchains.
document_loaders.chatgpt.ChatGPTLoader(log_file)
Loader that loads conversations from exported ChatGPT data.
document_loaders.college_confidential.CollegeConfidentialLoader(...)
Loader that loads College Confidential webpages.
document_loaders.confluence.ConfluenceLoader(url)
Load Confluence pages.
document_loaders.confluence.ContentFormat(value)
Enumerator of the content formats of Confluence page.
document_loaders.conllu.CoNLLULoader(file_path)
Load CoNLL-U files.
document_loaders.csv_loader.CSVLoader(file_path)
Loads a CSV file into a list of documents.
document_loaders.csv_loader.UnstructuredCSVLoader(...)
Loader that uses unstructured to load CSV files.
document_loaders.dataframe.DataFrameLoader(...)
Load Pandas DataFrames.
document_loaders.diffbot.DiffbotLoader(...)
Loader that loads Diffbot file json.
document_loaders.directory.DirectoryLoader(...)
Loading logic for loading documents from a directory.
document_loaders.discord.DiscordChatLoader(...)
Load Discord chat logs.
document_loaders.docugami.DocugamiLoader
Loader that loads processed docs from Docugami.
document_loaders.duckdb_loader.DuckDBLoader(query)
Loads a query result from DuckDB into a list of documents.
document_loaders.email.OutlookMessageLoader(...)
Loader that loads Outlook Message files using extract_msg. | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-20 | Loader that loads Outlook Message files using extract_msg.
document_loaders.email.UnstructuredEmailLoader(...)
Loader that uses unstructured to load email files.
document_loaders.embaas.BaseEmbaasLoader
Create a new model by parsing and validating input data from keyword arguments.
document_loaders.embaas.EmbaasBlobLoader
Wrapper around embaas's document byte loader service.
document_loaders.embaas.EmbaasDocumentExtractionParameters
Parameters for the embaas document extraction API.
document_loaders.embaas.EmbaasDocumentExtractionPayload
Payload for the Embaas document extraction API.
document_loaders.embaas.EmbaasLoader
Wrapper around embaas's document loader service.
document_loaders.epub.UnstructuredEPubLoader(...)
Loader that uses unstructured to load epub files.
document_loaders.evernote.EverNoteLoader(...)
EverNote Loader.
document_loaders.excel.UnstructuredExcelLoader(...)
Loader that uses unstructured to load Microsoft Excel files.
document_loaders.facebook_chat.FacebookChatLoader(path)
Loader that loads Facebook messages json directory dump.
document_loaders.fauna.FaunaLoader(query, ...)
FaunaDB Loader.
document_loaders.figma.FigmaFileLoader(...)
Loader that loads Figma file json.
document_loaders.gcs_directory.GCSDirectoryLoader(...)
Loading logic for loading documents from GCS.
document_loaders.gcs_file.GCSFileLoader(...)
Loading logic for loading documents from GCS.
document_loaders.generic.GenericLoader(...)
A generic document loader.
document_loaders.git.GitLoader(repo_path[, ...])
Loads files from a Git repository into a list of documents.
document_loaders.gitbook.GitbookLoader(web_page)
Load GitBook data.
document_loaders.github.BaseGitHubLoader | [
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20e9b0d32d50-21 | Load GitBook data.
document_loaders.github.BaseGitHubLoader
Load issues of a GitHub repository.
document_loaders.github.GitHubIssuesLoader
Create a new model by parsing and validating input data from keyword arguments.
document_loaders.googledrive.GoogleDriveLoader
Loader that loads Google Docs from Google Drive.
document_loaders.gutenberg.GutenbergLoader(...)
Loader that uses urllib to load .txt web files.
document_loaders.helpers.FileEncoding(...)
Create new instance of FileEncoding(encoding, confidence, language)
document_loaders.hn.HNLoader(web_path[, ...])
Load Hacker News data from either main page results or the comments page.
document_loaders.html.UnstructuredHTMLLoader(...)
Loader that uses unstructured to load HTML files.
document_loaders.html_bs.BSHTMLLoader(file_path)
Loader that uses beautiful soup to parse HTML files.
document_loaders.hugging_face_dataset.HuggingFaceDatasetLoader(path)
Loading logic for loading documents from the Hugging Face Hub.
document_loaders.ifixit.IFixitLoader(web_path)
Load iFixit repair guides, device wikis and answers.
document_loaders.image.UnstructuredImageLoader(...)
Loader that uses unstructured to load image files, such as PNGs and JPGs.
document_loaders.image_captions.ImageCaptionLoader(...)
Loader that loads the captions of an image
document_loaders.imsdb.IMSDbLoader(web_path)
Loader that loads IMSDb webpages.
document_loaders.iugu.IuguLoader(resource[, ...])
Loader that fetches data from IUGU.
document_loaders.joplin.JoplinLoader([...])
Loader that fetches notes from Joplin.
document_loaders.json_loader.JSONLoader(...)
Loads a JSON file and references a jq schema provided to load the text into documents. | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-22 | Loads a JSON file and references a jq schema provided to load the text into documents.
document_loaders.larksuite.LarkSuiteDocLoader(...)
Loader that loads LarkSuite (FeiShu) document.
document_loaders.markdown.UnstructuredMarkdownLoader(...)
Loader that uses unstructured to load markdown files.
document_loaders.mastodon.MastodonTootsLoader(...)
Mastodon toots loader.
document_loaders.max_compute.MaxComputeLoader(...)
Loads a query result from Alibaba Cloud MaxCompute table into documents.
document_loaders.mediawikidump.MWDumpLoader(...)
Load MediaWiki dump from XML file .
document_loaders.merge.MergedDataLoader(loaders)
Merge documents from a list of loaders
document_loaders.mhtml.MHTMLLoader(file_path)
Loader that uses beautiful soup to parse HTML files.
document_loaders.modern_treasury.ModernTreasuryLoader(...)
Loader that fetches data from Modern Treasury.
document_loaders.notebook.NotebookLoader(path)
Loader that loads .ipynb notebook files.
document_loaders.notion.NotionDirectoryLoader(path)
Loader that loads Notion directory dump.
document_loaders.notiondb.NotionDBLoader(...)
Notion DB Loader.
document_loaders.obsidian.ObsidianLoader(path)
Loader that loads Obsidian files from disk.
document_loaders.odt.UnstructuredODTLoader(...)
Loader that uses unstructured to load open office ODT files.
document_loaders.onedrive.OneDriveLoader
Create a new model by parsing and validating input data from keyword arguments.
document_loaders.onedrive_file.OneDriveFileLoader
Create a new model by parsing and validating input data from keyword arguments.
document_loaders.open_city_data.OpenCityDataLoader(...)
Loader that loads Open city data.
document_loaders.org_mode.UnstructuredOrgModeLoader(...) | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-23 | Loader that loads Open city data.
document_loaders.org_mode.UnstructuredOrgModeLoader(...)
Loader that uses unstructured to load Org-Mode files.
document_loaders.parsers.audio.OpenAIWhisperParser()
Transcribe and parse audio files.
document_loaders.parsers.generic.MimeTypeBasedParser(...)
A parser that uses mime-types to determine how to parse a blob.
document_loaders.parsers.grobid.GrobidParser(...)
Loader that uses Grobid to load article PDF files.
document_loaders.parsers.grobid.ServerUnavailableException
document_loaders.parsers.html.bs4.BS4HTMLParser(*)
Parser that uses beautiful soup to parse HTML files.
document_loaders.parsers.language.code_segmenter.CodeSegmenter(code)
document_loaders.parsers.language.javascript.JavaScriptSegmenter(code)
document_loaders.parsers.language.language_parser.LanguageParser([...])
Language parser that split code using the respective language syntax.
document_loaders.parsers.language.python.PythonSegmenter(code)
document_loaders.parsers.pdf.PDFMinerParser()
Parse PDFs with PDFMiner.
document_loaders.parsers.pdf.PDFPlumberParser([...])
Parse PDFs with PDFPlumber.
document_loaders.parsers.pdf.PyMuPDFParser([...])
Parse PDFs with PyMuPDF.
document_loaders.parsers.pdf.PyPDFParser([...])
Loads a PDF with pypdf and chunks at character level.
document_loaders.parsers.pdf.PyPDFium2Parser()
Parse PDFs with PyPDFium2.
document_loaders.parsers.txt.TextParser()
Parser for text blobs.
document_loaders.pdf.BasePDFLoader(file_path)
Base loader class for PDF files.
document_loaders.pdf.MathpixPDFLoader(file_path)
Initialize with file path.
document_loaders.pdf.OnlinePDFLoader(file_path)
Loader that loads online PDFs. | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-24 | document_loaders.pdf.OnlinePDFLoader(file_path)
Loader that loads online PDFs.
document_loaders.pdf.PDFMinerLoader(file_path)
Loader that uses PDFMiner to load PDF files.
document_loaders.pdf.PDFMinerPDFasHTMLLoader(...)
Loader that uses PDFMiner to load PDF files as HTML content.
document_loaders.pdf.PDFPlumberLoader(file_path)
Loader that uses pdfplumber to load PDF files.
document_loaders.pdf.PyMuPDFLoader(file_path)
Loader that uses PyMuPDF to load PDF files.
document_loaders.pdf.PyPDFDirectoryLoader(path)
Loads a directory with PDF files with pypdf and chunks at character level.
document_loaders.pdf.PyPDFLoader(file_path)
Loads a PDF with pypdf and chunks at character level.
document_loaders.pdf.PyPDFium2Loader(file_path)
Loads a PDF with pypdfium2 and chunks at character level.
document_loaders.pdf.UnstructuredPDFLoader(...)
Loader that uses unstructured to load PDF files.
document_loaders.powerpoint.UnstructuredPowerPointLoader(...)
Loader that uses unstructured to load powerpoint files.
document_loaders.psychic.PsychicLoader(...)
Loader that loads documents from Psychic.dev.
document_loaders.pyspark_dataframe.PySparkDataFrameLoader([...])
Load PySpark DataFrames
document_loaders.python.PythonLoader(file_path)
Load Python files, respecting any non-default encoding if specified.
document_loaders.readthedocs.ReadTheDocsLoader(path)
Loader that loads ReadTheDocs documentation directory dump.
document_loaders.recursive_url_loader.RecursiveUrlLoader(url)
Loader that loads all child links from a given url.
document_loaders.reddit.RedditPostsLoader(...)
Reddit posts loader.
document_loaders.roam.RoamLoader(path) | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-25 | Reddit posts loader.
document_loaders.roam.RoamLoader(path)
Loader that loads Roam files from disk.
document_loaders.rst.UnstructuredRSTLoader(...)
Loader that uses unstructured to load RST files.
document_loaders.rtf.UnstructuredRTFLoader(...)
Loader that uses unstructured to load rtf files.
document_loaders.s3_directory.S3DirectoryLoader(bucket)
Loading logic for loading documents from s3.
document_loaders.s3_file.S3FileLoader(...)
Loading logic for loading documents from s3.
document_loaders.sitemap.SitemapLoader(web_path)
Loader that fetches a sitemap and loads those URLs.
document_loaders.slack_directory.SlackDirectoryLoader(...)
Loader for loading documents from a Slack directory dump.
document_loaders.snowflake_loader.SnowflakeLoader(...)
Loads a query result from Snowflake into a list of documents.
document_loaders.spreedly.SpreedlyLoader(...)
Loader that fetches data from Spreedly API.
document_loaders.srt.SRTLoader(file_path)
Loader for .srt (subtitle) files.
document_loaders.stripe.StripeLoader(resource)
Loader that fetches data from Stripe.
document_loaders.telegram.TelegramChatApiLoader([...])
Loader that loads Telegram chat json directory dump.
document_loaders.telegram.TelegramChatFileLoader(path)
Loader that loads Telegram chat json directory dump.
document_loaders.tencent_cos_directory.TencentCOSDirectoryLoader(...)
Loading logic for loading documents from Tencent Cloud COS.
document_loaders.tencent_cos_file.TencentCOSFileLoader(...)
Loading logic for loading documents from Tencent Cloud COS.
document_loaders.text.TextLoader(file_path)
Load text files.
document_loaders.tomarkdown.ToMarkdownLoader(...)
Loader that loads HTML to markdown using 2markdown. | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-26 | Loader that loads HTML to markdown using 2markdown.
document_loaders.toml.TomlLoader(source)
A TOML document loader that inherits from the BaseLoader class.
document_loaders.trello.TrelloLoader(client, ...)
Trello loader.
document_loaders.twitter.TwitterTweetLoader(...)
Twitter tweets loader.
document_loaders.unstructured.UnstructuredAPIFileIOLoader(file)
Loader that uses the unstructured web API to load file IO objects.
document_loaders.unstructured.UnstructuredAPIFileLoader([...])
Loader that uses the unstructured web API to load files.
document_loaders.unstructured.UnstructuredBaseLoader([mode])
Loader that uses unstructured to load files.
document_loaders.unstructured.UnstructuredFileIOLoader(file)
Loader that uses unstructured to load file IO objects.
document_loaders.unstructured.UnstructuredFileLoader(...)
Loader that uses unstructured to load files.
document_loaders.url.UnstructuredURLLoader(urls)
Loader that uses unstructured to load HTML files.
document_loaders.url_playwright.PlaywrightURLLoader(urls)
Loader that uses Playwright and to load a page and unstructured to load the html.
document_loaders.url_selenium.SeleniumURLLoader(urls)
Loader that uses Selenium and to load a page and unstructured to load the html.
document_loaders.weather.WeatherDataLoader(...)
Weather Reader.
document_loaders.web_base.WebBaseLoader(web_path)
Loader that uses urllib and beautiful soup to load webpages.
document_loaders.whatsapp_chat.WhatsAppChatLoader(path)
Loader that loads WhatsApp messages text file.
document_loaders.wikipedia.WikipediaLoader(query)
Loads a query result from www.wikipedia.org into a list of Documents.
document_loaders.word_document.Docx2txtLoader(...)
Loads a DOCX with docx2txt and chunks at character level. | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-27 | Loads a DOCX with docx2txt and chunks at character level.
document_loaders.word_document.UnstructuredWordDocumentLoader(...)
Loader that uses unstructured to load word documents.
document_loaders.xml.UnstructuredXMLLoader(...)
Loader that uses unstructured to load XML files.
document_loaders.youtube.GoogleApiYoutubeLoader(...)
Loader that loads all Videos from a Channel
document_loaders.youtube.YoutubeLoader(video_id)
Loader that loads Youtube transcripts.
Functions¶
document_loaders.chatgpt.concatenate_rows(...)
Combine message information in a readable format ready to be used.
document_loaders.facebook_chat.concatenate_rows(row)
Combine message information in a readable format ready to be used.
document_loaders.helpers.detect_file_encodings(...)
Try to detect the file encoding.
document_loaders.notebook.concatenate_cells(...)
Combine cells information in a readable format ready to be used.
document_loaders.notebook.remove_newlines(x)
Remove recursively newlines, no matter the data structure they are stored in.
document_loaders.parsers.registry.get_parser(...)
Get a parser by parser name.
document_loaders.telegram.concatenate_rows(row)
Combine message information in a readable format ready to be used.
document_loaders.telegram.text_to_docs(text)
Converts a string or list of strings to a list of Documents with metadata.
document_loaders.unstructured.get_elements_from_api([...])
Retrieves a list of elements from the Unstructured API.
document_loaders.unstructured.satisfies_min_unstructured_version(...)
Checks to see if the installed unstructured version exceeds the minimum version for the feature in question.
document_loaders.unstructured.validate_unstructured_version(...)
Raises an error if the unstructured version does not exceed the specified minimum.
document_loaders.whatsapp_chat.concatenate_rows(...)
Combine message information in a readable format ready to be used.
langchain.document_transformers: Document Transformers¶ | [
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20e9b0d32d50-28 | langchain.document_transformers: Document Transformers¶
Transform documents
Classes¶
document_transformers.EmbeddingsRedundantFilter
Filter that drops redundant documents by comparing their embeddings.
Functions¶
document_transformers.get_stateful_documents(...)
Convert a list of documents to a list of documents with state.
langchain.embeddings: Embeddings¶
Wrappers around embedding modules.
Classes¶
embeddings.aleph_alpha.AlephAlphaAsymmetricSemanticEmbedding
Wrapper for Aleph Alpha's Asymmetric Embeddings AA provides you with an endpoint to embed a document and a query.
embeddings.aleph_alpha.AlephAlphaSymmetricSemanticEmbedding
The symmetric version of the Aleph Alpha's semantic embeddings.
embeddings.base.Embeddings()
Interface for embedding models.
embeddings.bedrock.BedrockEmbeddings
Embeddings provider to invoke Bedrock embedding models.
embeddings.cohere.CohereEmbeddings
Wrapper around Cohere embedding models.
embeddings.dashscope.DashScopeEmbeddings
Wrapper around DashScope embedding models.
embeddings.deepinfra.DeepInfraEmbeddings
Wrapper around Deep Infra's embedding inference service.
embeddings.elasticsearch.ElasticsearchEmbeddings(...)
Wrapper around Elasticsearch embedding models.
embeddings.embaas.EmbaasEmbeddings
Wrapper around embaas's embedding service.
embeddings.embaas.EmbaasEmbeddingsPayload
Payload for the embaas embeddings API.
embeddings.fake.FakeEmbeddings
Create a new model by parsing and validating input data from keyword arguments.
embeddings.google_palm.GooglePalmEmbeddings
Create a new model by parsing and validating input data from keyword arguments.
embeddings.huggingface.HuggingFaceEmbeddings
Wrapper around sentence_transformers embedding models.
embeddings.huggingface.HuggingFaceInstructEmbeddings
Wrapper around sentence_transformers embedding models. | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-29 | Wrapper around sentence_transformers embedding models.
embeddings.huggingface_hub.HuggingFaceHubEmbeddings
Wrapper around HuggingFaceHub embedding models.
embeddings.jina.JinaEmbeddings
Create a new model by parsing and validating input data from keyword arguments.
embeddings.llamacpp.LlamaCppEmbeddings
Wrapper around llama.cpp embedding models.
embeddings.minimax.MiniMaxEmbeddings
Wrapper around MiniMax's embedding inference service.
embeddings.modelscope_hub.ModelScopeEmbeddings
Wrapper around modelscope_hub embedding models.
embeddings.mosaicml.MosaicMLInstructorEmbeddings
Wrapper around MosaicML's embedding inference service.
embeddings.octoai_embeddings.OctoAIEmbeddings
Wrapper around OctoAI Compute Service embedding models.
embeddings.openai.OpenAIEmbeddings
Wrapper around OpenAI embedding models.
embeddings.sagemaker_endpoint.EmbeddingsContentHandler()
Content handler for LLM class.
embeddings.sagemaker_endpoint.SagemakerEndpointEmbeddings
Wrapper around custom Sagemaker Inference Endpoints.
embeddings.self_hosted.SelfHostedEmbeddings
Runs custom embedding models on self-hosted remote hardware.
embeddings.self_hosted_hugging_face.SelfHostedHuggingFaceEmbeddings
Runs sentence_transformers embedding models on self-hosted remote hardware.
embeddings.self_hosted_hugging_face.SelfHostedHuggingFaceInstructEmbeddings
Runs InstructorEmbedding embedding models on self-hosted remote hardware.
embeddings.tensorflow_hub.TensorflowHubEmbeddings
Wrapper around tensorflow_hub embedding models.
embeddings.vertexai.VertexAIEmbeddings
Create a new model by parsing and validating input data from keyword arguments.
Functions¶
embeddings.dashscope.embed_with_retry(...)
Use tenacity to retry the embedding call.
embeddings.google_palm.embed_with_retry(...) | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-30 | Use tenacity to retry the embedding call.
embeddings.google_palm.embed_with_retry(...)
Use tenacity to retry the completion call.
embeddings.minimax.embed_with_retry(...)
Use tenacity to retry the completion call.
embeddings.openai.embed_with_retry(...)
Use tenacity to retry the embedding call.
embeddings.self_hosted_hugging_face.load_embedding_model(...)
Load the embedding model.
langchain.env: Env¶
Functions¶
env.get_runtime_environment()
Get information about the environment.
langchain.evaluation: Evaluation¶
Functionality relating to evaluation.
This module contains off-the-shelf evaluation chains for
grading the output of LangChain primitives such as LLMs and Chains.
Some common use cases for evaluation include:
Grading accuracy of a response against ground truth answers: QAEvalChain
Comparing the output of two models: PairwiseStringEvalChain
Judging the efficacy of an agent’s tool usage: TrajectoryEvalChain
Checking whether an output complies with a set of criteria: CriteriaEvalChain
This module also contains low level APIs for making more evaluators for your
custom evaluation task. These include:
- StringEvaluator: Evaluates an output string against a reference and/or
with input context.
PairwiseStringEvaluator: Evaluates two strings against each other.
Classes¶
evaluation.agents.trajectory_eval_chain.TrajectoryEval(...)
Create new instance of TrajectoryEval(score, reasoning)
evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain
A chain for evaluating ReAct style agents.
evaluation.agents.trajectory_eval_chain.TrajectoryOutputParser
Create a new model by parsing and validating input data from keyword arguments.
evaluation.comparison.eval_chain.PairwiseStringEvalChain
A chain for comparing the output of two models.
evaluation.comparison.eval_chain.PairwiseStringResultOutputParser | [
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20e9b0d32d50-31 | evaluation.comparison.eval_chain.PairwiseStringResultOutputParser
A parser for the output of the PairwiseStringEvalChain.
evaluation.criteria.eval_chain.CriteriaEvalChain
LLM Chain for evaluating runs against criteria.
evaluation.criteria.eval_chain.CriteriaResultOutputParser
A parser for the output of the CriteriaEvalChain.
evaluation.qa.eval_chain.ContextQAEvalChain
LLM Chain specifically for evaluating QA w/o GT based on context
evaluation.qa.eval_chain.CotQAEvalChain
LLM Chain specifically for evaluating QA using chain of thought reasoning.
evaluation.qa.eval_chain.QAEvalChain
LLM Chain specifically for evaluating question answering.
evaluation.qa.generate_chain.QAGenerateChain
LLM Chain specifically for generating examples for question answering.
evaluation.run_evaluators.base.RunEvaluatorChain
Evaluate Run and optional examples.
evaluation.run_evaluators.base.RunEvaluatorOutputParser
Parse the output of a run.
evaluation.run_evaluators.implementations.ChoicesOutputParser
Parse a feedback run with optional choices.
evaluation.run_evaluators.implementations.CriteriaOutputParser
Parse a criteria results into an evaluation result.
evaluation.run_evaluators.implementations.StringRunEvaluatorInputMapper
Maps the Run and Optional[Example] to a dictionary.
evaluation.run_evaluators.implementations.TrajectoryEvalOutputParser
Create a new model by parsing and validating input data from keyword arguments.
evaluation.run_evaluators.implementations.TrajectoryInputMapper
Maps the Run and Optional[Example] to a dictionary.
evaluation.schema.PairwiseStringEvaluator(...)
A protocol for comparing the output of two models.
evaluation.schema.StringEvaluator(*args, ...)
Protocol for evaluating strings.
Functions¶
evaluation.loading.load_dataset(uri)
evaluation.run_evaluators.implementations.get_criteria_evaluator(...)
Get an eval chain for grading a model's response against a map of criteria. | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-32 | Get an eval chain for grading a model's response against a map of criteria.
evaluation.run_evaluators.implementations.get_qa_evaluator(llm, *)
Get an eval chain that compares response against ground truth.
evaluation.run_evaluators.implementations.get_trajectory_evaluator(...)
Get an eval chain for grading a model's response against a map of criteria.
langchain.example_generator: Example Generator¶
Utility functions for working with prompts.
Functions¶
example_generator.generate_example(examples, ...)
Return another example given a list of examples for a prompt.
langchain.experimental: Experimental¶
Classes¶
experimental.autonomous_agents.autogpt.memory.AutoGPTMemory
Create a new model by parsing and validating input data from keyword arguments.
experimental.autonomous_agents.autogpt.output_parser.AutoGPTAction(...)
Create new instance of AutoGPTAction(name, args)
experimental.autonomous_agents.autogpt.output_parser.AutoGPTOutputParser
Create a new model by parsing and validating input data from keyword arguments.
experimental.autonomous_agents.autogpt.output_parser.BaseAutoGPTOutputParser
Create a new model by parsing and validating input data from keyword arguments.
experimental.autonomous_agents.autogpt.prompt.AutoGPTPrompt
Create a new model by parsing and validating input data from keyword arguments.
experimental.autonomous_agents.baby_agi.baby_agi.BabyAGI
Controller model for the BabyAGI agent.
experimental.autonomous_agents.baby_agi.task_creation.TaskCreationChain
Chain to generates tasks.
experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain
Chain to execute tasks.
experimental.autonomous_agents.baby_agi.task_prioritization.TaskPrioritizationChain
Chain to prioritize tasks.
experimental.generative_agents.generative_agent.GenerativeAgent
A character with memory and innate characteristics.
experimental.generative_agents.memory.GenerativeAgentMemory | [
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20e9b0d32d50-33 | A character with memory and innate characteristics.
experimental.generative_agents.memory.GenerativeAgentMemory
Create a new model by parsing and validating input data from keyword arguments.
experimental.llms.jsonformer_decoder.JsonFormer
Create a new model by parsing and validating input data from keyword arguments.
experimental.llms.rellm_decoder.RELLM
Create a new model by parsing and validating input data from keyword arguments.
experimental.plan_and_execute.agent_executor.PlanAndExecute
Create a new model by parsing and validating input data from keyword arguments.
experimental.plan_and_execute.executors.base.BaseExecutor
Create a new model by parsing and validating input data from keyword arguments.
experimental.plan_and_execute.executors.base.ChainExecutor
Create a new model by parsing and validating input data from keyword arguments.
experimental.plan_and_execute.planners.base.BasePlanner
Create a new model by parsing and validating input data from keyword arguments.
experimental.plan_and_execute.planners.base.LLMPlanner
Create a new model by parsing and validating input data from keyword arguments.
experimental.plan_and_execute.planners.chat_planner.PlanningOutputParser
Create a new model by parsing and validating input data from keyword arguments.
experimental.plan_and_execute.schema.BaseStepContainer
Create a new model by parsing and validating input data from keyword arguments.
experimental.plan_and_execute.schema.ListStepContainer
Create a new model by parsing and validating input data from keyword arguments.
experimental.plan_and_execute.schema.Plan
Create a new model by parsing and validating input data from keyword arguments.
experimental.plan_and_execute.schema.PlanOutputParser
Create a new model by parsing and validating input data from keyword arguments.
experimental.plan_and_execute.schema.Step
Create a new model by parsing and validating input data from keyword arguments.
experimental.plan_and_execute.schema.StepResponse
Create a new model by parsing and validating input data from keyword arguments.
Functions¶ | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-34 | Create a new model by parsing and validating input data from keyword arguments.
Functions¶
experimental.autonomous_agents.autogpt.output_parser.preprocess_json_input(...)
Preprocesses a string to be parsed as json.
experimental.autonomous_agents.autogpt.prompt_generator.get_prompt(tools)
This function generates a prompt string.
experimental.llms.jsonformer_decoder.import_jsonformer()
Lazily import jsonformer.
experimental.llms.rellm_decoder.import_rellm()
Lazily import rellm.
experimental.plan_and_execute.executors.agent_executor.load_agent_executor(...)
Load an agent executor.
experimental.plan_and_execute.planners.chat_planner.load_chat_planner(llm)
Load a chat planner.
langchain.formatting: Formatting¶
Utilities for formatting strings.
Classes¶
formatting.StrictFormatter()
A subclass of formatter that checks for extra keys.
langchain.graphs: Graphs¶
Graph implementations.
Classes¶
graphs.networkx_graph.KnowledgeTriple(...)
A triple in the graph.
Functions¶
graphs.networkx_graph.get_entities(entity_str)
Extract entities from entity string.
graphs.networkx_graph.parse_triples(...)
Parse knowledge triples from the knowledge string.
langchain.indexes: Indexes¶
All index utils.
Classes¶
indexes.graph.GraphIndexCreator
Functionality to create graph index.
indexes.vectorstore.VectorStoreIndexWrapper
Wrapper around a vectorstore for easy access.
indexes.vectorstore.VectorstoreIndexCreator
Logic for creating indexes.
langchain.input: Input¶
Handle chained inputs.
Functions¶
input.get_bolded_text(text)
Get bolded text.
input.get_color_mapping(items[, excluded_colors])
Get mapping for items to a support color.
input.get_colored_text(text, color)
Get colored text.
input.print_text(text[, color, end, file]) | [
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20e9b0d32d50-35 | Get colored text.
input.print_text(text[, color, end, file])
Print text with highlighting and no end characters.
langchain.llms: LLMs¶
Wrappers on top of large language models APIs.
Classes¶
llms.ai21.AI21
Wrapper around AI21 large language models.
llms.ai21.AI21PenaltyData
Parameters for AI21 penalty data.
llms.aleph_alpha.AlephAlpha
Wrapper around Aleph Alpha large language models.
llms.amazon_api_gateway.AmazonAPIGateway
Wrapper around custom Amazon API Gateway
llms.anthropic.Anthropic
Wrapper around Anthropic's large language models.
llms.anyscale.Anyscale
Wrapper around Anyscale Services.
llms.aviary.Aviary
Allow you to use an Aviary.
llms.azureml_endpoint.AzureMLEndpointClient(...)
Wrapper around AzureML Managed Online Endpoint Client.
llms.azureml_endpoint.AzureMLOnlineEndpoint
Wrapper around Azure ML Hosted models using Managed Online Endpoints.
llms.azureml_endpoint.DollyContentFormatter()
Content handler for the Dolly-v2-12b model
llms.azureml_endpoint.HFContentFormatter()
Content handler for LLMs from the HuggingFace catalog.
llms.azureml_endpoint.OSSContentFormatter()
Content handler for LLMs from the OSS catalog.
llms.bananadev.Banana
Wrapper around Banana large language models.
llms.base.BaseLLM
LLM wrapper should take in a prompt and return a string.
llms.base.LLM
LLM class that expect subclasses to implement a simpler call method.
llms.baseten.Baseten
Use your Baseten models in Langchain
llms.beam.Beam
Wrapper around Beam API for gpt2 large language model. | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-36 | llms.beam.Beam
Wrapper around Beam API for gpt2 large language model.
llms.bedrock.Bedrock
LLM provider to invoke Bedrock models.
llms.cerebriumai.CerebriumAI
Wrapper around CerebriumAI large language models.
llms.clarifai.Clarifai
Wrapper around Clarifai's large language models.
llms.cohere.Cohere
Wrapper around Cohere large language models.
llms.ctransformers.CTransformers
Wrapper around the C Transformers LLM interface.
llms.databricks.Databricks
LLM wrapper around a Databricks serving endpoint or a cluster driver proxy app.
llms.deepinfra.DeepInfra
Wrapper around DeepInfra deployed models.
llms.fake.FakeListLLM
Fake LLM wrapper for testing purposes.
llms.forefrontai.ForefrontAI
Wrapper around ForefrontAI large language models.
llms.google_palm.GooglePalm
Create a new model by parsing and validating input data from keyword arguments.
llms.gooseai.GooseAI
Wrapper around OpenAI large language models.
llms.gpt4all.GPT4All
Wrapper around GPT4All language models.
llms.huggingface_endpoint.HuggingFaceEndpoint
Wrapper around HuggingFaceHub Inference Endpoints.
llms.huggingface_hub.HuggingFaceHub
Wrapper around HuggingFaceHub models.
llms.huggingface_pipeline.HuggingFacePipeline
Wrapper around HuggingFace Pipeline API.
llms.huggingface_text_gen_inference.HuggingFaceTextGenInference
HuggingFace text generation inference API.
llms.human.HumanInputLLM
A LLM wrapper which returns user input as the response.
llms.llamacpp.LlamaCpp
Wrapper around the llama.cpp model. | [
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20e9b0d32d50-37 | llms.llamacpp.LlamaCpp
Wrapper around the llama.cpp model.
llms.manifest.ManifestWrapper
Wrapper around HazyResearch's Manifest library.
llms.modal.Modal
Wrapper around Modal large language models.
llms.mosaicml.MosaicML
Wrapper around MosaicML's LLM inference service.
llms.nlpcloud.NLPCloud
Wrapper around NLPCloud large language models.
llms.octoai_endpoint.OctoAIEndpoint
Wrapper around OctoAI Inference Endpoints.
llms.openai.AzureOpenAI
Wrapper around Azure-specific OpenAI large language models.
llms.openai.BaseOpenAI
Wrapper around OpenAI large language models.
llms.openai.OpenAI
Wrapper around OpenAI large language models.
llms.openai.OpenAIChat
Wrapper around OpenAI Chat large language models.
llms.openllm.IdentifyingParams
llms.openllm.OpenLLM
Wrapper for accessing OpenLLM, supporting both in-process model instance and remote OpenLLM servers.
llms.openlm.OpenLM
Create a new model by parsing and validating input data from keyword arguments.
llms.petals.Petals
Wrapper around Petals Bloom models.
llms.pipelineai.PipelineAI
Wrapper around PipelineAI large language models.
llms.predictionguard.PredictionGuard
Wrapper around Prediction Guard large language models.
llms.promptlayer_openai.PromptLayerOpenAI
Wrapper around OpenAI large language models.
llms.promptlayer_openai.PromptLayerOpenAIChat
Wrapper around OpenAI large language models.
llms.replicate.Replicate
Wrapper around Replicate models.
llms.rwkv.RWKV
Wrapper around RWKV language models.
llms.sagemaker_endpoint.ContentHandlerBase() | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-38 | Wrapper around RWKV language models.
llms.sagemaker_endpoint.ContentHandlerBase()
A handler class to transform input from LLM to a format that SageMaker endpoint expects.
llms.sagemaker_endpoint.LLMContentHandler()
Content handler for LLM class.
llms.sagemaker_endpoint.SagemakerEndpoint
Wrapper around custom Sagemaker Inference Endpoints.
llms.self_hosted.SelfHostedPipeline
Run model inference on self-hosted remote hardware.
llms.self_hosted_hugging_face.SelfHostedHuggingFaceLLM
Wrapper around HuggingFace Pipeline API to run on self-hosted remote hardware.
llms.stochasticai.StochasticAI
Wrapper around StochasticAI large language models.
llms.textgen.TextGen
Wrapper around the text-generation-webui model.
llms.vertexai.VertexAI
Wrapper around Google Vertex AI large language models.
llms.writer.Writer
Wrapper around Writer large language models.
Functions¶
llms.aviary.get_completions(model, prompt[, ...])
Get completions from Aviary models.
llms.aviary.get_models()
List available models
llms.base.get_prompts(params, prompts)
Get prompts that are already cached.
llms.base.update_cache(existing_prompts, ...)
Update the cache and get the LLM output.
llms.cohere.completion_with_retry(llm, **kwargs)
Use tenacity to retry the completion call.
llms.databricks.get_default_api_token()
Gets the default Databricks personal access token.
llms.databricks.get_default_host()
Gets the default Databricks workspace hostname.
llms.databricks.get_repl_context()
Gets the notebook REPL context if running inside a Databricks notebook.
llms.google_palm.generate_with_retry(llm, ...) | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-39 | llms.google_palm.generate_with_retry(llm, ...)
Use tenacity to retry the completion call.
llms.loading.load_llm(file)
Load LLM from file.
llms.loading.load_llm_from_config(config)
Load LLM from Config Dict.
llms.openai.completion_with_retry(llm, **kwargs)
Use tenacity to retry the completion call.
llms.openai.update_token_usage(keys, ...)
Update token usage.
llms.utils.enforce_stop_tokens(text, stop)
Cut off the text as soon as any stop words occur.
llms.vertexai.is_codey_model(model_name)
langchain.load: Load¶
Classes¶
load.serializable.BaseSerialized
Base class for serialized objects.
load.serializable.Serializable
Serializable base class.
load.serializable.SerializedConstructor
Serialized constructor.
load.serializable.SerializedNotImplemented
Serialized not implemented.
load.serializable.SerializedSecret
Serialized secret.
Functions¶
load.dump.default(obj)
Return a default value for a Serializable object or a SerializedNotImplemented object.
load.dump.dumpd(obj)
Return a json dict representation of an object.
load.dump.dumps(obj, *[, pretty])
Return a json string representation of an object.
load.load.loads(text, *[, secrets_map])
load.serializable.to_json_not_implemented(obj)
Serialize a "not implemented" object.
langchain.math_utils: Math Utils¶
Math utils.
Functions¶
math_utils.cosine_similarity(X, Y)
Row-wise cosine similarity between two equal-width matrices.
math_utils.cosine_similarity_top_k(X, Y[, ...])
Row-wise cosine similarity with optional top-k and score threshold filtering.
langchain.memory: Memory¶
Classes¶
memory.buffer.ConversationBufferMemory
Buffer for storing conversation memory. | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-40 | Classes¶
memory.buffer.ConversationBufferMemory
Buffer for storing conversation memory.
memory.buffer.ConversationStringBufferMemory
Buffer for storing conversation memory.
memory.buffer_window.ConversationBufferWindowMemory
Buffer for storing conversation memory.
memory.chat_memory.BaseChatMemory
Create a new model by parsing and validating input data from keyword arguments.
memory.chat_message_histories.cassandra.CassandraChatMessageHistory(...)
Chat message history that stores history in Cassandra.
memory.chat_message_histories.cosmos_db.CosmosDBChatMessageHistory(...)
Chat history backed by Azure CosmosDB.
memory.chat_message_histories.dynamodb.DynamoDBChatMessageHistory(...)
Chat message history that stores history in AWS DynamoDB.
memory.chat_message_histories.file.FileChatMessageHistory(...)
Chat message history that stores history in a local file.
memory.chat_message_histories.firestore.FirestoreChatMessageHistory(...)
Chat history backed by Google Firestore.
memory.chat_message_histories.in_memory.ChatMessageHistory
Create a new model by parsing and validating input data from keyword arguments.
memory.chat_message_histories.momento.MomentoChatMessageHistory(...)
Chat message history cache that uses Momento as a backend.
memory.chat_message_histories.mongodb.MongoDBChatMessageHistory(...)
Chat message history that stores history in MongoDB.
memory.chat_message_histories.postgres.PostgresChatMessageHistory(...)
Chat message history stored in a Postgres database.
memory.chat_message_histories.redis.RedisChatMessageHistory(...)
Chat message history stored in a Redis database.
memory.chat_message_histories.sql.SQLChatMessageHistory(...)
Chat message history stored in an SQL database.
memory.chat_message_histories.zep.ZepChatMessageHistory(...)
A ChatMessageHistory implementation that uses Zep as a backend.
memory.combined.CombinedMemory
Class for combining multiple memories' data together.
memory.entity.BaseEntityStore | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-41 | Class for combining multiple memories' data together.
memory.entity.BaseEntityStore
Create a new model by parsing and validating input data from keyword arguments.
memory.entity.ConversationEntityMemory
Entity extractor & summarizer memory.
memory.entity.InMemoryEntityStore
Basic in-memory entity store.
memory.entity.RedisEntityStore
Redis-backed Entity store.
memory.entity.SQLiteEntityStore
SQLite-backed Entity store
memory.kg.ConversationKGMemory
Knowledge graph memory for storing conversation memory.
memory.motorhead_memory.MotorheadMemory
Create a new model by parsing and validating input data from keyword arguments.
memory.readonly.ReadOnlySharedMemory
A memory wrapper that is read-only and cannot be changed.
memory.simple.SimpleMemory
Simple memory for storing context or other bits of information that shouldn't ever change between prompts.
memory.summary.ConversationSummaryMemory
Conversation summarizer to memory.
memory.summary.SummarizerMixin
Create a new model by parsing and validating input data from keyword arguments.
memory.summary_buffer.ConversationSummaryBufferMemory
Buffer with summarizer for storing conversation memory.
memory.token_buffer.ConversationTokenBufferMemory
Buffer for storing conversation memory.
memory.vectorstore.VectorStoreRetrieverMemory
Class for a VectorStore-backed memory object.
Functions¶
memory.chat_message_histories.sql.create_message_model(...)
Create a message model for a given table name.
memory.utils.get_prompt_input_key(inputs, ...)
Get the prompt input key.
langchain.output_parsers: Output Parsers¶
Classes¶
output_parsers.boolean.BooleanOutputParser
Create a new model by parsing and validating input data from keyword arguments.
output_parsers.combining.CombiningOutputParser
Class to combine multiple output parsers into one.
output_parsers.datetime.DatetimeOutputParser
Create a new model by parsing and validating input data from keyword arguments.
output_parsers.enum.EnumOutputParser | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-42 | output_parsers.enum.EnumOutputParser
Create a new model by parsing and validating input data from keyword arguments.
output_parsers.fix.OutputFixingParser
Wraps a parser and tries to fix parsing errors.
output_parsers.list.CommaSeparatedListOutputParser
Parse out comma separated lists.
output_parsers.list.ListOutputParser
Class to parse the output of an LLM call to a list.
output_parsers.openai_functions.JsonKeyOutputFunctionsParser
Create a new model by parsing and validating input data from keyword arguments.
output_parsers.openai_functions.JsonOutputFunctionsParser
Create a new model by parsing and validating input data from keyword arguments.
output_parsers.openai_functions.OutputFunctionsParser
Create a new model by parsing and validating input data from keyword arguments.
output_parsers.openai_functions.PydanticAttrOutputFunctionsParser
Create a new model by parsing and validating input data from keyword arguments.
output_parsers.openai_functions.PydanticOutputFunctionsParser
Create a new model by parsing and validating input data from keyword arguments.
output_parsers.pydantic.PydanticOutputParser
Create a new model by parsing and validating input data from keyword arguments.
output_parsers.rail_parser.GuardrailsOutputParser
Create a new model by parsing and validating input data from keyword arguments.
output_parsers.regex.RegexParser
Class to parse the output into a dictionary.
output_parsers.regex_dict.RegexDictParser
Class to parse the output into a dictionary.
output_parsers.retry.RetryOutputParser
Wraps a parser and tries to fix parsing errors.
output_parsers.retry.RetryWithErrorOutputParser
Wraps a parser and tries to fix parsing errors.
output_parsers.structured.ResponseSchema
Create a new model by parsing and validating input data from keyword arguments.
output_parsers.structured.StructuredOutputParser | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-43 | output_parsers.structured.StructuredOutputParser
Create a new model by parsing and validating input data from keyword arguments.
Functions¶
output_parsers.json.parse_and_check_json_markdown(...)
Parse a JSON string from a Markdown string and check that it contains the expected keys.
output_parsers.json.parse_json_markdown(...)
Parse a JSON string from a Markdown string.
output_parsers.loading.load_output_parser(config)
Load output parser.
langchain.prompts: Prompts¶
Prompt template classes.
Classes¶
prompts.base.BasePromptTemplate
Base class for all prompt templates, returning a prompt.
prompts.base.StringPromptTemplate
String prompt should expose the format method, returning a prompt.
prompts.base.StringPromptValue
Create a new model by parsing and validating input data from keyword arguments.
prompts.chat.AIMessagePromptTemplate
Create a new model by parsing and validating input data from keyword arguments.
prompts.chat.BaseChatPromptTemplate
Create a new model by parsing and validating input data from keyword arguments.
prompts.chat.BaseMessagePromptTemplate
Create a new model by parsing and validating input data from keyword arguments.
prompts.chat.BaseStringMessagePromptTemplate
Create a new model by parsing and validating input data from keyword arguments.
prompts.chat.ChatMessagePromptTemplate
Create a new model by parsing and validating input data from keyword arguments.
prompts.chat.ChatPromptTemplate
Create a new model by parsing and validating input data from keyword arguments.
prompts.chat.ChatPromptValue
Create a new model by parsing and validating input data from keyword arguments.
prompts.chat.HumanMessagePromptTemplate
Create a new model by parsing and validating input data from keyword arguments.
prompts.chat.MessagesPlaceholder
Prompt template that assumes variable is already list of messages.
prompts.chat.SystemMessagePromptTemplate
Create a new model by parsing and validating input data from keyword arguments. | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-44 | Create a new model by parsing and validating input data from keyword arguments.
prompts.example_selector.base.BaseExampleSelector()
Interface for selecting examples to include in prompts.
prompts.example_selector.length_based.LengthBasedExampleSelector
Select examples based on length.
prompts.example_selector.ngram_overlap.NGramOverlapExampleSelector
Select and order examples based on ngram overlap score (sentence_bleu score).
prompts.example_selector.semantic_similarity.MaxMarginalRelevanceExampleSelector
ExampleSelector that selects examples based on Max Marginal Relevance.
prompts.example_selector.semantic_similarity.SemanticSimilarityExampleSelector
Example selector that selects examples based on SemanticSimilarity.
prompts.few_shot.FewShotPromptTemplate
Prompt template that contains few shot examples.
prompts.few_shot_with_templates.FewShotPromptWithTemplates
Prompt template that contains few shot examples.
prompts.pipeline.PipelinePromptTemplate
A prompt template for composing multiple prompts together.
prompts.prompt.PromptTemplate
Schema to represent a prompt for an LLM.
Functions¶
prompts.base.check_valid_template(template, ...)
Check that template string is valid.
prompts.base.jinja2_formatter(template, **kwargs)
Format a template using jinja2.
prompts.base.validate_jinja2(template, ...)
Validate that the input variables are valid for the template.
prompts.example_selector.ngram_overlap.ngram_overlap_score(...)
Compute ngram overlap score of source and example as sentence_bleu score.
prompts.example_selector.semantic_similarity.sorted_values(values)
Return a list of values in dict sorted by key.
prompts.loading.load_prompt(path)
Unified method for loading a prompt from LangChainHub or local fs.
prompts.loading.load_prompt_from_config(config)
Load prompt from Config Dict.
langchain.requests: Requests¶
Lightweight wrapper around requests library, with async support.
Classes¶ | [
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20e9b0d32d50-45 | Lightweight wrapper around requests library, with async support.
Classes¶
requests.Requests
Wrapper around requests to handle auth and async.
requests.TextRequestsWrapper
Lightweight wrapper around requests library.
langchain.retrievers: Retrievers¶
Classes¶
retrievers.arxiv.ArxivRetriever
It is effectively a wrapper for ArxivAPIWrapper.
retrievers.azure_cognitive_search.AzureCognitiveSearchRetriever
Wrapper around Azure Cognitive Search.
retrievers.chatgpt_plugin_retriever.ChatGPTPluginRetriever
Create a new model by parsing and validating input data from keyword arguments.
retrievers.contextual_compression.ContextualCompressionRetriever
Retriever that wraps a base retriever and compresses the results.
retrievers.databerry.DataberryRetriever(...)
Retriever that uses the Databerry API.
retrievers.docarray.DocArrayRetriever
Retriever class for DocArray Document Indices.
retrievers.docarray.SearchType(value[, ...])
Enumerator of the types of search to perform.
retrievers.document_compressors.base.BaseDocumentCompressor
Base abstraction interface for document compression.
retrievers.document_compressors.base.DocumentCompressorPipeline
Document compressor that uses a pipeline of transformers.
retrievers.document_compressors.chain_extract.LLMChainExtractor
Create a new model by parsing and validating input data from keyword arguments.
retrievers.document_compressors.chain_extract.NoOutputParser
Parse outputs that could return a null string of some sort.
retrievers.document_compressors.chain_filter.LLMChainFilter
Filter that drops documents that aren't relevant to the query.
retrievers.document_compressors.cohere_rerank.CohereRerank
Create a new model by parsing and validating input data from keyword arguments. | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-46 | Create a new model by parsing and validating input data from keyword arguments.
retrievers.document_compressors.embeddings_filter.EmbeddingsFilter
Create a new model by parsing and validating input data from keyword arguments.
retrievers.elastic_search_bm25.ElasticSearchBM25Retriever(...)
Wrapper around Elasticsearch using BM25 as a retrieval method.
retrievers.kendra.AdditionalResultAttribute
Create a new model by parsing and validating input data from keyword arguments.
retrievers.kendra.AdditionalResultAttributeValue
Create a new model by parsing and validating input data from keyword arguments.
retrievers.kendra.AmazonKendraRetriever(index_id)
Retriever class to query documents from Amazon Kendra Index.
retrievers.kendra.DocumentAttribute
Create a new model by parsing and validating input data from keyword arguments.
retrievers.kendra.DocumentAttributeValue
Create a new model by parsing and validating input data from keyword arguments.
retrievers.kendra.Highlight
Create a new model by parsing and validating input data from keyword arguments.
retrievers.kendra.QueryResult
Create a new model by parsing and validating input data from keyword arguments.
retrievers.kendra.QueryResultItem
Create a new model by parsing and validating input data from keyword arguments.
retrievers.kendra.RetrieveResult
Create a new model by parsing and validating input data from keyword arguments.
retrievers.kendra.RetrieveResultItem
Create a new model by parsing and validating input data from keyword arguments.
retrievers.kendra.TextWithHighLights
Create a new model by parsing and validating input data from keyword arguments.
retrievers.knn.KNNRetriever
KNN Retriever.
retrievers.llama_index.LlamaIndexGraphRetriever
Question-answering with sources over an LlamaIndex graph data structure. | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-47 | Question-answering with sources over an LlamaIndex graph data structure.
retrievers.llama_index.LlamaIndexRetriever
Question-answering with sources over an LlamaIndex data structure.
retrievers.merger_retriever.MergerRetriever(...)
This class merges the results of multiple retrievers.
retrievers.metal.MetalRetriever(client[, params])
Retriever that uses the Metal API.
retrievers.milvus.MilvusRetriever(...[, ...])
Retriever that uses the Milvus API.
retrievers.multi_query.LineList
Create a new model by parsing and validating input data from keyword arguments.
retrievers.multi_query.LineListOutputParser
Create a new model by parsing and validating input data from keyword arguments.
retrievers.multi_query.MultiQueryRetriever(...)
Given a user query, use an LLM to write a set of queries.
retrievers.pinecone_hybrid_search.PineconeHybridSearchRetriever
Create a new model by parsing and validating input data from keyword arguments.
retrievers.pubmed.PubMedRetriever
It is effectively a wrapper for PubMedAPIWrapper.
retrievers.remote_retriever.RemoteLangChainRetriever
Create a new model by parsing and validating input data from keyword arguments.
retrievers.self_query.base.SelfQueryRetriever
Retriever that wraps around a vector store and uses an LLM to generate the vector store queries.
retrievers.self_query.chroma.ChromaTranslator()
Logic for converting internal query language elements to valid filters.
retrievers.self_query.myscale.MyScaleTranslator([...])
Logic for converting internal query language elements to valid filters.
retrievers.self_query.pinecone.PineconeTranslator() | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-48 | retrievers.self_query.pinecone.PineconeTranslator()
Logic for converting internal query language elements to valid filters.
retrievers.self_query.qdrant.QdrantTranslator(...)
Logic for converting internal query language elements to valid filters.
retrievers.self_query.weaviate.WeaviateTranslator()
Logic for converting internal query language elements to valid filters.
retrievers.svm.SVMRetriever
SVM Retriever.
retrievers.tfidf.TFIDFRetriever
Create a new model by parsing and validating input data from keyword arguments.
retrievers.time_weighted_retriever.TimeWeightedVectorStoreRetriever
Retriever combining embedding similarity with recency.
retrievers.vespa_retriever.VespaRetriever(...)
Retriever that uses the Vespa.
retrievers.weaviate_hybrid_search.WeaviateHybridSearchRetriever(...)
retrievers.wikipedia.WikipediaRetriever
It is effectively a wrapper for WikipediaAPIWrapper.
retrievers.zep.ZepRetriever(session_id, url)
A Retriever implementation for the Zep long-term memory store.
retrievers.zilliz.ZillizRetriever(...[, ...])
Retriever that uses the Zilliz API.
Functions¶
retrievers.document_compressors.chain_extract.default_get_input(...)
Return the compression chain input.
retrievers.document_compressors.chain_filter.default_get_input(...)
Return the compression chain input.
retrievers.kendra.clean_excerpt(excerpt)
retrievers.kendra.combined_text(title, excerpt)
retrievers.knn.create_index(contexts, embeddings)
Create an index of embeddings for a list of contexts.
retrievers.milvus.MilvusRetreiver(*args, ...) | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-49 | retrievers.milvus.MilvusRetreiver(*args, ...)
Deprecated MilvusRetreiver.
retrievers.pinecone_hybrid_search.create_index(...)
Create a Pinecone index from a list of contexts.
retrievers.pinecone_hybrid_search.hash_text(text)
Hash a text using SHA256.
retrievers.self_query.myscale.DEFAULT_COMPOSER(op_name)
Default composer for logical operators.
retrievers.self_query.myscale.FUNCTION_COMPOSER(op_name)
Composer for functions.
retrievers.svm.create_index(contexts, embeddings)
Create an index of embeddings for a list of contexts.
retrievers.zilliz.ZillizRetreiver(*args, ...)
Deprecated ZillizRetreiver.
langchain.schema: Schema¶
Common schema objects.
Classes¶
schema.AIMessage
Type of message that is spoken by the AI.
schema.AgentFinish(return_values, log)
Agent's return value.
schema.BaseChatMessageHistory()
Base interface for chat message history See ChatMessageHistory for default implementation.
schema.BaseDocumentTransformer()
Base interface for transforming documents.
schema.BaseLLMOutputParser
Create a new model by parsing and validating input data from keyword arguments.
schema.BaseMemory
Base interface for memory in chains.
schema.BaseMessage
Message object.
schema.BaseOutputParser
Class to parse the output of an LLM call.
schema.BaseRetriever()
Base interface for a retriever.
schema.ChatGeneration
Output of a single generation.
schema.ChatMessage
Type of message with arbitrary speaker.
schema.ChatResult
Class that contains all relevant information for a Chat Result.
schema.Document
Interface for interacting with a document.
schema.FunctionMessage
Create a new model by parsing and validating input data from keyword arguments. | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-50 | schema.FunctionMessage
Create a new model by parsing and validating input data from keyword arguments.
schema.Generation
Output of a single generation.
schema.HumanMessage
Type of message that is spoken by the human.
schema.LLMResult
Class that contains all relevant information for an LLM Result.
schema.NoOpOutputParser
Output parser that just returns the text as is.
schema.OutputParserException(error[, ...])
Exception that output parsers should raise to signify a parsing error.
schema.PromptValue
Create a new model by parsing and validating input data from keyword arguments.
schema.RunInfo
Class that contains all relevant metadata for a Run.
schema.SystemMessage
Type of message that is a system message.
Functions¶
schema.get_buffer_string(messages[, ...])
Get buffer string of messages.
schema.messages_from_dict(messages)
Convert messages from dict.
schema.messages_to_dict(messages)
Convert messages to dict.
langchain.server: Server¶
Script to run langchain-server locally using docker-compose.
Functions¶
server.main()
Run the langchain server locally.
langchain.sql_database: Sql Database¶
SQLAlchemy wrapper around a database.
Functions¶
sql_database.truncate_word(content, *, length)
Truncate a string to a certain number of words, based on the max string length.
langchain.text_splitter: Text Splitter¶
Functionality for splitting text.
Classes¶
text_splitter.CharacterTextSplitter([separator])
Implementation of splitting text that looks at characters.
text_splitter.HeaderType
Header type as typed dict.
text_splitter.Language(value[, names, ...])
text_splitter.LatexTextSplitter(**kwargs)
Attempts to split the text along Latex-formatted layout elements.
text_splitter.LineType
Line type as typed dict. | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-51 | text_splitter.LineType
Line type as typed dict.
text_splitter.MarkdownTextSplitter(**kwargs)
Attempts to split the text along Markdown-formatted headings.
text_splitter.NLTKTextSplitter([separator])
Implementation of splitting text that looks at sentences using NLTK.
text_splitter.PythonCodeTextSplitter(**kwargs)
Attempts to split the text along Python syntax.
text_splitter.RecursiveCharacterTextSplitter([...])
Implementation of splitting text that looks at characters.
text_splitter.SentenceTransformersTokenTextSplitter([...])
Implementation of splitting text that looks at tokens.
text_splitter.SpacyTextSplitter([separator, ...])
Implementation of splitting text that looks at sentences using Spacy.
text_splitter.TextSplitter(chunk_size, ...)
Interface for splitting text into chunks.
text_splitter.TokenTextSplitter([...])
Implementation of splitting text that looks at tokens.
Functions¶
text_splitter.split_text_on_tokens(*, text, ...)
Split incoming text and return chunks.
langchain.tools: Tools¶
Core toolkit implementations.
Classes¶
tools.arxiv.tool.ArxivQueryRun
Tool that adds the capability to search using the Arxiv API.
tools.azure_cognitive_services.form_recognizer.AzureCogsFormRecognizerTool
Tool that queries the Azure Cognitive Services Form Recognizer API.
tools.azure_cognitive_services.image_analysis.AzureCogsImageAnalysisTool
Tool that queries the Azure Cognitive Services Image Analysis API.
tools.azure_cognitive_services.speech2text.AzureCogsSpeech2TextTool
Tool that queries the Azure Cognitive Services Speech2Text API.
tools.azure_cognitive_services.text2speech.AzureCogsText2SpeechTool
Tool that queries the Azure Cognitive Services Text2Speech API.
tools.base.BaseTool
Interface LangChain tools must implement.
tools.base.SchemaAnnotationError | [
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20e9b0d32d50-52 | tools.base.BaseTool
Interface LangChain tools must implement.
tools.base.SchemaAnnotationError
Raised when 'args_schema' is missing or has an incorrect type annotation.
tools.base.StructuredTool
Tool that can operate on any number of inputs.
tools.base.Tool
Tool that takes in function or coroutine directly.
tools.base.ToolException
An optional exception that tool throws when execution error occurs.
tools.base.ToolMetaclass(name, bases, dct)
Metaclass for BaseTool to ensure the provided args_schema
tools.bing_search.tool.BingSearchResults
Tool that has capability to query the Bing Search API and get back json.
tools.bing_search.tool.BingSearchRun
Tool that adds the capability to query the Bing search API.
tools.brave_search.tool.BraveSearch
Create a new model by parsing and validating input data from keyword arguments.
tools.convert_to_openai.FunctionDescription
Representation of a callable function to the OpenAI API.
tools.ddg_search.tool.DuckDuckGoSearchResults
Tool that queries the Duck Duck Go Search API and get back json.
tools.ddg_search.tool.DuckDuckGoSearchRun
Tool that adds the capability to query the DuckDuckGo search API.
tools.file_management.copy.CopyFileTool
Create a new model by parsing and validating input data from keyword arguments.
tools.file_management.copy.FileCopyInput
Input for CopyFileTool.
tools.file_management.delete.DeleteFileTool
Create a new model by parsing and validating input data from keyword arguments.
tools.file_management.delete.FileDeleteInput
Input for DeleteFileTool.
tools.file_management.file_search.FileSearchInput
Input for FileSearchTool.
tools.file_management.file_search.FileSearchTool
Create a new model by parsing and validating input data from keyword arguments.
tools.file_management.list_dir.DirectoryListingInput
Input for ListDirectoryTool. | [
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20e9b0d32d50-53 | tools.file_management.list_dir.DirectoryListingInput
Input for ListDirectoryTool.
tools.file_management.list_dir.ListDirectoryTool
Create a new model by parsing and validating input data from keyword arguments.
tools.file_management.move.FileMoveInput
Input for MoveFileTool.
tools.file_management.move.MoveFileTool
Create a new model by parsing and validating input data from keyword arguments.
tools.file_management.read.ReadFileInput
Input for ReadFileTool.
tools.file_management.read.ReadFileTool
Create a new model by parsing and validating input data from keyword arguments.
tools.file_management.utils.BaseFileToolMixin
Mixin for file system tools.
tools.file_management.utils.FileValidationError
Error for paths outside the root directory.
tools.file_management.write.WriteFileInput
Input for WriteFileTool.
tools.file_management.write.WriteFileTool
Create a new model by parsing and validating input data from keyword arguments.
tools.gmail.base.GmailBaseTool
Create a new model by parsing and validating input data from keyword arguments.
tools.gmail.create_draft.CreateDraftSchema
Create a new model by parsing and validating input data from keyword arguments.
tools.gmail.create_draft.GmailCreateDraft
Create a new model by parsing and validating input data from keyword arguments.
tools.gmail.get_message.GmailGetMessage
Create a new model by parsing and validating input data from keyword arguments.
tools.gmail.get_message.SearchArgsSchema
Create a new model by parsing and validating input data from keyword arguments.
tools.gmail.get_thread.GetThreadSchema
Create a new model by parsing and validating input data from keyword arguments.
tools.gmail.get_thread.GmailGetThread
Create a new model by parsing and validating input data from keyword arguments.
tools.gmail.search.GmailSearch
Create a new model by parsing and validating input data from keyword arguments.
tools.gmail.search.Resource(value[, names, ...])
Enumerator of Resources to search.
tools.gmail.search.SearchArgsSchema | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-54 | Enumerator of Resources to search.
tools.gmail.search.SearchArgsSchema
Create a new model by parsing and validating input data from keyword arguments.
tools.gmail.send_message.GmailSendMessage
Create a new model by parsing and validating input data from keyword arguments.
tools.gmail.send_message.SendMessageSchema
Create a new model by parsing and validating input data from keyword arguments.
tools.google_places.tool.GooglePlacesSchema
Create a new model by parsing and validating input data from keyword arguments.
tools.google_places.tool.GooglePlacesTool
Tool that adds the capability to query the Google places API.
tools.google_search.tool.GoogleSearchResults
Tool that has capability to query the Google Search API and get back json.
tools.google_search.tool.GoogleSearchRun
Tool that adds the capability to query the Google search API.
tools.google_serper.tool.GoogleSerperResults
Tool that has capability to query the Serper.dev Google Search API and get back json.
tools.google_serper.tool.GoogleSerperRun
Tool that adds the capability to query the Serper.dev Google search API.
tools.graphql.tool.BaseGraphQLTool
Base tool for querying a GraphQL API.
tools.human.tool.HumanInputRun
Tool that adds the capability to ask user for input.
tools.ifttt.IFTTTWebhook
IFTTT Webhook.
tools.jira.tool.JiraAction
Create a new model by parsing and validating input data from keyword arguments.
tools.json.tool.JsonGetValueTool
Tool for getting a value in a JSON spec.
tools.json.tool.JsonListKeysTool
Tool for listing keys in a JSON spec.
tools.json.tool.JsonSpec
Base class for JSON spec.
tools.metaphor_search.tool.MetaphorSearchResults
Tool that has capability to query the Metaphor Search API and get back json.
tools.office365.base.O365BaseTool
Create a new model by parsing and validating input data from keyword arguments. | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-55 | Create a new model by parsing and validating input data from keyword arguments.
tools.office365.create_draft_message.CreateDraftMessageSchema
Create a new model by parsing and validating input data from keyword arguments.
tools.office365.create_draft_message.O365CreateDraftMessage
Create a new model by parsing and validating input data from keyword arguments.
tools.office365.events_search.O365SearchEvents
Class for searching calendar events in Office 365
tools.office365.events_search.SearchEventsInput
Input for SearchEmails Tool.
tools.office365.messages_search.O365SearchEmails
Class for searching email messages in Office 365
tools.office365.messages_search.SearchEmailsInput
Input for SearchEmails Tool.
tools.office365.send_event.O365SendEvent
Create a new model by parsing and validating input data from keyword arguments.
tools.office365.send_event.SendEventSchema
Input for CreateEvent Tool.
tools.office365.send_message.O365SendMessage
Create a new model by parsing and validating input data from keyword arguments.
tools.office365.send_message.SendMessageSchema
Create a new model by parsing and validating input data from keyword arguments.
tools.openapi.utils.api_models.APIOperation
A model for a single API operation.
tools.openapi.utils.api_models.APIProperty
A model for a property in the query, path, header, or cookie params.
tools.openapi.utils.api_models.APIPropertyBase
Base model for an API property.
tools.openapi.utils.api_models.APIPropertyLocation(value)
The location of the property.
tools.openapi.utils.api_models.APIRequestBody
A model for a request body.
tools.openapi.utils.api_models.APIRequestBodyProperty
A model for a request body property.
tools.openweathermap.tool.OpenWeatherMapQueryRun
Tool that adds the capability to query using the OpenWeatherMap API.
tools.playwright.base.BaseBrowserTool
Base class for browser tools.
tools.playwright.click.ClickTool | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-56 | Base class for browser tools.
tools.playwright.click.ClickTool
Create a new model by parsing and validating input data from keyword arguments.
tools.playwright.click.ClickToolInput
Input for ClickTool.
tools.playwright.current_page.CurrentWebPageTool
Create a new model by parsing and validating input data from keyword arguments.
tools.playwright.extract_hyperlinks.ExtractHyperlinksTool
Extract all hyperlinks on the page.
tools.playwright.extract_hyperlinks.ExtractHyperlinksToolInput
Input for ExtractHyperlinksTool.
tools.playwright.extract_text.ExtractTextTool
Create a new model by parsing and validating input data from keyword arguments.
tools.playwright.get_elements.GetElementsTool
Create a new model by parsing and validating input data from keyword arguments.
tools.playwright.get_elements.GetElementsToolInput
Input for GetElementsTool.
tools.playwright.navigate.NavigateTool
Create a new model by parsing and validating input data from keyword arguments.
tools.playwright.navigate.NavigateToolInput
Input for NavigateToolInput.
tools.playwright.navigate_back.NavigateBackTool
Navigate back to the previous page in the browser history.
tools.plugin.AIPlugin
AI Plugin Definition.
tools.plugin.AIPluginTool
Create a new model by parsing and validating input data from keyword arguments.
tools.plugin.AIPluginToolSchema
AIPLuginToolSchema.
tools.plugin.ApiConfig
Create a new model by parsing and validating input data from keyword arguments.
tools.powerbi.tool.InfoPowerBITool
Tool for getting metadata about a PowerBI Dataset.
tools.powerbi.tool.ListPowerBITool
Tool for getting tables names.
tools.powerbi.tool.QueryPowerBITool
Tool for querying a Power BI Dataset.
tools.pubmed.tool.PubmedQueryRun
Tool that adds the capability to search using the PubMed API.
tools.python.tool.PythonAstREPLTool
A tool for running python code in a REPL. | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-57 | A tool for running python code in a REPL.
tools.python.tool.PythonREPLTool
A tool for running python code in a REPL.
tools.requests.tool.BaseRequestsTool
Base class for requests tools.
tools.requests.tool.RequestsDeleteTool
Tool for making a DELETE request to an API endpoint.
tools.requests.tool.RequestsGetTool
Tool for making a GET request to an API endpoint.
tools.requests.tool.RequestsPatchTool
Tool for making a PATCH request to an API endpoint.
tools.requests.tool.RequestsPostTool
Tool for making a POST request to an API endpoint.
tools.requests.tool.RequestsPutTool
Tool for making a PUT request to an API endpoint.
tools.scenexplain.tool.SceneXplainInput
Input for SceneXplain.
tools.scenexplain.tool.SceneXplainTool
Tool that adds the capability to explain images.
tools.searx_search.tool.SearxSearchResults
Tool that has the capability to query a Searx instance and get back json.
tools.searx_search.tool.SearxSearchRun
Tool that adds the capability to query a Searx instance.
tools.shell.tool.ShellInput
Commands for the Bash Shell tool.
tools.shell.tool.ShellTool
Tool to run shell commands.
tools.sleep.tool.SleepInput
Input for CopyFileTool.
tools.sleep.tool.SleepTool
Tool that adds the capability to sleep.
tools.spark_sql.tool.BaseSparkSQLTool
Base tool for interacting with Spark SQL.
tools.spark_sql.tool.InfoSparkSQLTool
Tool for getting metadata about a Spark SQL.
tools.spark_sql.tool.ListSparkSQLTool
Tool for getting tables names.
tools.spark_sql.tool.QueryCheckerTool
Use an LLM to check if a query is correct.
tools.spark_sql.tool.QuerySparkSQLTool
Tool for querying a Spark SQL.
tools.sql_database.tool.BaseSQLDatabaseTool | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-58 | Tool for querying a Spark SQL.
tools.sql_database.tool.BaseSQLDatabaseTool
Base tool for interacting with a SQL database.
tools.sql_database.tool.InfoSQLDatabaseTool
Tool for getting metadata about a SQL database.
tools.sql_database.tool.ListSQLDatabaseTool
Tool for getting tables names.
tools.sql_database.tool.QuerySQLCheckerTool
Use an LLM to check if a query is correct.
tools.sql_database.tool.QuerySQLDataBaseTool
Tool for querying a SQL database.
tools.steamship_image_generation.tool.ModelName(value)
Supported Image Models for generation.
tools.steamship_image_generation.tool.SteamshipImageGenerationTool
Create a new model by parsing and validating input data from keyword arguments.
tools.vectorstore.tool.BaseVectorStoreTool
Base class for tools that use a VectorStore.
tools.vectorstore.tool.VectorStoreQATool
Tool for the VectorDBQA chain.
tools.vectorstore.tool.VectorStoreQAWithSourcesTool
Tool for the VectorDBQAWithSources chain.
tools.wikipedia.tool.WikipediaQueryRun
Tool that adds the capability to search using the Wikipedia API.
tools.wolfram_alpha.tool.WolframAlphaQueryRun
Tool that adds the capability to query using the Wolfram Alpha SDK.
tools.youtube.search.YouTubeSearchTool
Create a new model by parsing and validating input data from keyword arguments.
tools.zapier.tool.ZapierNLAListActions
Returns a list of all exposed (enabled) actions associated with
tools.zapier.tool.ZapierNLARunAction
Executes an action that is identified by action_id, must be exposed
Functions¶
tools.azure_cognitive_services.utils.detect_file_src_type(...)
Detect if the file is local or remote.
tools.azure_cognitive_services.utils.download_audio_from_url(...)
Download audio from url to local.
tools.base.create_schema_from_function(...) | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-59 | Download audio from url to local.
tools.base.create_schema_from_function(...)
Create a pydantic schema from a function's signature.
tools.base.tool(*args[, return_direct, ...])
Make tools out of functions, can be used with or without arguments.
tools.convert_to_openai.format_tool_to_openai_function(tool)
Format tool into the OpenAI function API.
tools.ddg_search.tool.DuckDuckGoSearchTool(...)
Deprecated.
tools.file_management.utils.get_validated_relative_path(...)
Resolve a relative path, raising an error if not within the root directory.
tools.file_management.utils.is_relative_to(...)
Check if path is relative to root.
tools.gmail.utils.build_resource_service([...])
Build a Gmail service.
tools.gmail.utils.clean_email_body(body)
Clean email body.
tools.gmail.utils.get_gmail_credentials([...])
Get credentials.
tools.gmail.utils.import_google()
Import google libraries.
tools.gmail.utils.import_googleapiclient_resource_builder()
Import googleapiclient.discovery.build function.
tools.gmail.utils.import_installed_app_flow()
Import InstalledAppFlow class.
tools.interaction.tool.StdInInquireTool(...)
Tool for asking the user for input.
tools.office365.utils.authenticate()
Authenticate using the Microsoft Grah API
tools.office365.utils.clean_body(body)
Clean body of a message or event.
tools.playwright.base.lazy_import_playwright_browsers()
Lazy import playwright browsers.
tools.playwright.utils.create_async_playwright_browser([...])
Create a async playwright browser.
tools.playwright.utils.create_sync_playwright_browser([...])
Create a playwright browser.
tools.playwright.utils.get_current_page(browser)
Get the current page of the browser.
tools.playwright.utils.run_async(coro)
param coro
The coroutine to run. Coroutine[Any, Any, T]
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-60 | tools.plugin.marshal_spec(txt)
Convert the yaml or json serialized spec to a dict.
tools.python.tool.sanitize_input(query)
Sanitize input to the python REPL.
tools.steamship_image_generation.utils.make_image_public(...)
Upload a block to a signed URL and return the public URL.
langchain.utilities: Utilities¶
General utilities.
Classes¶
utilities.apify.ApifyWrapper
Wrapper around Apify.
utilities.arxiv.ArxivAPIWrapper
Wrapper around ArxivAPI.
utilities.awslambda.LambdaWrapper
Wrapper for AWS Lambda SDK.
utilities.bibtex.BibtexparserWrapper
Wrapper around bibtexparser.
utilities.bing_search.BingSearchAPIWrapper
Wrapper for Bing Search API.
utilities.brave_search.BraveSearchWrapper
Create a new model by parsing and validating input data from keyword arguments.
utilities.duckduckgo_search.DuckDuckGoSearchAPIWrapper
Wrapper for DuckDuckGo Search API.
utilities.google_places_api.GooglePlacesAPIWrapper
Wrapper around Google Places API.
utilities.google_search.GoogleSearchAPIWrapper
Wrapper for Google Search API.
utilities.google_serper.GoogleSerperAPIWrapper
Wrapper around the Serper.dev Google Search API.
utilities.graphql.GraphQLAPIWrapper
Wrapper around GraphQL API.
utilities.jira.JiraAPIWrapper
Wrapper for Jira API.
utilities.metaphor_search.MetaphorSearchAPIWrapper
Wrapper for Metaphor Search API.
utilities.openapi.HTTPVerb(value[, names, ...])
HTTP verbs.
utilities.openapi.OpenAPISpec
OpenAPI Model that removes misformatted parts of the spec.
utilities.openweathermap.OpenWeatherMapAPIWrapper
Wrapper for OpenWeatherMap API using PyOWM.
utilities.powerbi.PowerBIDataset
Create PowerBI engine from dataset ID and credential or token.
utilities.pupmed.PubMedAPIWrapper | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-61 | utilities.pupmed.PubMedAPIWrapper
Wrapper around PubMed API.
utilities.python.PythonREPL
Simulates a standalone Python REPL.
utilities.scenexplain.SceneXplainAPIWrapper
Wrapper for SceneXplain API.
utilities.searx_search.SearxResults(data)
Dict like wrapper around search api results.
utilities.searx_search.SearxSearchWrapper
Wrapper for Searx API.
utilities.serpapi.SerpAPIWrapper
Wrapper around SerpAPI.
utilities.twilio.TwilioAPIWrapper
Messaging Client using Twilio.
utilities.wikipedia.WikipediaAPIWrapper
Wrapper around WikipediaAPI.
utilities.wolfram_alpha.WolframAlphaAPIWrapper
Wrapper for Wolfram Alpha.
utilities.zapier.ZapierNLAWrapper
Wrapper for Zapier NLA.
Functions¶
utilities.loading.try_load_from_hub(path, ...)
Load configuration from hub.
utilities.powerbi.fix_table_name(table)
Add single quotes around table names that contain spaces.
utilities.powerbi.json_to_md(json_contents)
Converts a JSON object to a markdown table.
utilities.vertexai.init_vertexai([project, ...])
Init vertexai.
utilities.vertexai.raise_vertex_import_error()
Raise ImportError related to Vertex SDK being not available.
langchain.utils: Utils¶
Generic utility functions.
Functions¶
utils.comma_list(items)
utils.get_from_dict_or_env(data, key, env_key)
Get a value from a dictionary or an environment variable.
utils.get_from_env(key, env_key[, default])
Get a value from a dictionary or an environment variable.
utils.guard_import(module_name, *[, ...])
Dynamically imports a module and raises a helpful exception if the module is not installed.
utils.mock_now(dt_value) | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-62 | utils.mock_now(dt_value)
Context manager for mocking out datetime.now() in unit tests. Example: with mock_now(datetime.datetime(2011, 2, 3, 10, 11)): assert datetime.datetime.now() == datetime.datetime(2011, 2, 3, 10, 11).
utils.raise_for_status_with_text(response)
Raise an error with the response text.
utils.stringify_dict(data)
Stringify a dictionary.
utils.stringify_value(val)
Stringify a value.
utils.xor_args(*arg_groups)
Validate specified keyword args are mutually exclusive.
langchain.vectorstores: Vectorstores¶
Wrappers on top of vector stores.
Classes¶
vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearch(...)
Alibaba Cloud OpenSearch Vector Store
vectorstores.analyticdb.AnalyticDB(...[, ...])
VectorStore implementation using AnalyticDB.
vectorstores.annoy.Annoy(embedding_function, ...)
Wrapper around Annoy vector database.
vectorstores.atlas.AtlasDB(name[, ...])
Wrapper around Atlas: Nomic's neural database and rhizomatic instrument.
vectorstores.awadb.AwaDB([table_name, ...])
Interface implemented by AwaDB vector stores.
vectorstores.azuresearch.AzureSearch(...[, ...])
Initialize with necessary components.
vectorstores.azuresearch.AzureSearchVectorStoreRetriever
Create a new model by parsing and validating input data from keyword arguments.
vectorstores.base.VectorStore()
Interface for vector stores.
vectorstores.base.VectorStoreRetriever
Create a new model by parsing and validating input data from keyword arguments.
vectorstores.cassandra.Cassandra(embedding, ...)
Wrapper around Cassandra embeddings platform.
vectorstores.chroma.Chroma([...])
Wrapper around ChromaDB embeddings platform. | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-63 | vectorstores.chroma.Chroma([...])
Wrapper around ChromaDB embeddings platform.
vectorstores.clarifai.Clarifai([user_id, ...])
Wrapper around Clarifai AI platform's vector store.
vectorstores.clickhouse.Clickhouse(embedding)
Wrapper around ClickHouse vector database
vectorstores.clickhouse.ClickhouseSettings
ClickHouse Client Configuration
vectorstores.deeplake.DeepLake([...])
Wrapper around Deep Lake, a data lake for deep learning applications.
vectorstores.docarray.base.DocArrayIndex(...)
Initialize a vector store from DocArray's DocIndex.
vectorstores.docarray.hnsw.DocArrayHnswSearch(...)
Wrapper around HnswLib storage.
vectorstores.docarray.in_memory.DocArrayInMemorySearch(...)
Wrapper around in-memory storage for exact search.
vectorstores.elastic_vector_search.ElasticKnnSearch(...)
A class for performing k-Nearest Neighbors (k-NN) search on an Elasticsearch index.
vectorstores.elastic_vector_search.ElasticVectorSearch(...)
Wrapper around Elasticsearch as a vector database.
vectorstores.faiss.FAISS(embedding_function, ...)
Wrapper around FAISS vector database.
vectorstores.hologres.Hologres(...[, ndims, ...])
VectorStore implementation using Hologres.
vectorstores.lancedb.LanceDB(connection, ...)
Wrapper around LanceDB vector database.
vectorstores.matching_engine.MatchingEngine(...)
Vertex Matching Engine implementation of the vector store.
vectorstores.milvus.Milvus(embedding_function)
Wrapper around the Milvus vector database.
vectorstores.mongodb_atlas.MongoDBAtlasVectorSearch(...)
Wrapper around MongoDB Atlas Vector Search.
vectorstores.myscale.MyScale(embedding[, config])
Wrapper around MyScale vector database
vectorstores.myscale.MyScaleSettings
MyScale Client Configuration | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-64 | Wrapper around MyScale vector database
vectorstores.myscale.MyScaleSettings
MyScale Client Configuration
vectorstores.opensearch_vector_search.OpenSearchVectorSearch(...)
Wrapper around OpenSearch as a vector database.
vectorstores.pinecone.Pinecone(index, ...[, ...])
Wrapper around Pinecone vector database.
vectorstores.qdrant.Qdrant(client, ...[, ...])
Wrapper around Qdrant vector database.
vectorstores.redis.Redis(redis_url, ...)
Wrapper around Redis vector database.
vectorstores.redis.RedisVectorStoreRetriever
Create a new model by parsing and validating input data from keyword arguments.
vectorstores.rocksetdb.Rockset(client, ...)
Wrapper arpund Rockset vector database.
vectorstores.singlestoredb.DistanceStrategy(value)
Enumerator of the Distance strategies for SingleStoreDB.
vectorstores.singlestoredb.SingleStoreDB(...)
This class serves as a Pythonic interface to the SingleStore DB database.
vectorstores.singlestoredb.SingleStoreDBRetriever
Retriever for SingleStoreDB vector stores.
vectorstores.sklearn.BaseSerializer(persist_path)
Abstract base class for saving and loading data.
vectorstores.sklearn.BsonSerializer(persist_path)
Serializes data in binary json using the bson python package.
vectorstores.sklearn.JsonSerializer(persist_path)
Serializes data in json using the json package from python standard library.
vectorstores.sklearn.ParquetSerializer(...)
Serializes data in Apache Parquet format using the pyarrow package.
vectorstores.sklearn.SKLearnVectorStore(...)
A simple in-memory vector store based on the scikit-learn library NearestNeighbors implementation.
vectorstores.sklearn.SKLearnVectorStoreException
Exception raised by SKLearnVectorStore.
vectorstores.starrocks.StarRocks(embedding)
Wrapper around StarRocks vector database | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-65 | Wrapper around StarRocks vector database
vectorstores.starrocks.StarRocksSettings
StarRocks Client Configuration
vectorstores.supabase.SupabaseVectorStore(...)
VectorStore for a Supabase postgres database.
vectorstores.tair.Tair(embedding_function, ...)
Wrapper around Tair Vector store.
vectorstores.tigris.Tigris(client, ...)
Initialize Tigris vector store
vectorstores.typesense.Typesense(...[, ...])
Wrapper around Typesense vector search.
vectorstores.vectara.Vectara([...])
Implementation of Vector Store using Vectara (https://vectara.com).
vectorstores.vectara.VectaraRetriever
Create a new model by parsing and validating input data from keyword arguments.
vectorstores.weaviate.Weaviate(client, ...)
Wrapper around Weaviate vector database.
vectorstores.zilliz.Zilliz(embedding_function)
Initialize wrapper around the milvus vector database.
Functions¶
vectorstores.alibabacloud_opensearch.create_metadata(fields)
Create metadata from fields.
vectorstores.annoy.dependable_annoy_import()
Import annoy if available, otherwise raise error.
vectorstores.clickhouse.has_mul_sub_str(s, *args)
Check if a string contains multiple substrings.
vectorstores.faiss.dependable_faiss_import([...])
Import faiss if available, otherwise raise error.
vectorstores.myscale.has_mul_sub_str(s, *args)
Check if a string contains multiple substrings.
vectorstores.starrocks.debug_output(s)
Print a debug message if DEBUG is True.
vectorstores.starrocks.get_named_result(...)
Get a named result from a query.
vectorstores.starrocks.has_mul_sub_str(s, *args)
Check if a string has multiple substrings. | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
20e9b0d32d50-66 | Check if a string has multiple substrings.
vectorstores.utils.maximal_marginal_relevance(...)
Calculate maximal marginal relevance. | [
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] | https://langchain.readthedocs.io/en/latest/api_reference.html |
efe6d5187693-0 | langchain.experimental.plan_and_execute.agent_executor.PlanAndExecute¶
class langchain.experimental.plan_and_execute.agent_executor.PlanAndExecute(*, memory: Optional[BaseMemory] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, verbose: bool = None, tags: Optional[List[str]] = None, planner: BasePlanner, executor: BaseExecutor, step_container: BaseStepContainer = None, input_key: str = 'input', output_key: str = 'output')[source]¶
Bases: Chain
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated, use callbacks instead.
param callbacks: Callbacks = None¶
Optional list of callback handlers (or callback manager). Defaults to None.
Callback handlers are called throughout the lifecycle of a call to a chain,
starting with on_chain_start, ending with on_chain_end or on_chain_error.
Each custom chain can optionally call additional callback methods, see Callback docs
for full details.
param executor: langchain.experimental.plan_and_execute.executors.base.BaseExecutor [Required]¶
param input_key: str = 'input'¶
param memory: Optional[BaseMemory] = None¶
Optional memory object. Defaults to None.
Memory is a class that gets called at the start
and at the end of every chain. At the start, memory loads variables and passes
them along in the chain. At the end, it saves any returned variables.
There are many different types of memory - please see memory docs
for the full catalog.
param output_key: str = 'output'¶ | [
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] | https://langchain.readthedocs.io/en/latest/experimental/langchain.experimental.plan_and_execute.agent_executor.PlanAndExecute.html |
efe6d5187693-1 | for the full catalog.
param output_key: str = 'output'¶
param planner: langchain.experimental.plan_and_execute.planners.base.BasePlanner [Required]¶
param step_container: langchain.experimental.plan_and_execute.schema.BaseStepContainer [Optional]¶
param tags: Optional[List[str]] = None¶
Optional list of tags associated with the chain. Defaults to None
These tags will be associated with each call to this chain,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a chain with its use case.
param verbose: bool [Optional]¶
Whether or not run in verbose mode. In verbose mode, some intermediate logs
will be printed to the console. Defaults to langchain.verbose value.
__call__(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, include_run_info: bool = False) → Dict[str, Any]¶
Run the logic of this chain and add to output if desired.
Parameters
inputs – Dictionary of inputs, or single input if chain expects
only one param.
return_only_outputs – boolean for whether to return only outputs in the
response. If True, only new keys generated by this chain will be
returned. If False, both input keys and new keys generated by this
chain will be returned. Defaults to False.
callbacks – Callbacks to use for this chain run. If not provided, will
use the callbacks provided to the chain.
include_run_info – Whether to include run info in the response. Defaults
to False. | [
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] | https://langchain.readthedocs.io/en/latest/experimental/langchain.experimental.plan_and_execute.agent_executor.PlanAndExecute.html |
efe6d5187693-2 | include_run_info – Whether to include run info in the response. Defaults
to False.
async acall(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, include_run_info: bool = False) → Dict[str, Any]¶
Run the logic of this chain and add to output if desired.
Parameters
inputs – Dictionary of inputs, or single input if chain expects
only one param.
return_only_outputs – boolean for whether to return only outputs in the
response. If True, only new keys generated by this chain will be
returned. If False, both input keys and new keys generated by this
chain will be returned. Defaults to False.
callbacks – Callbacks to use for this chain run. If not provided, will
use the callbacks provided to the chain.
include_run_info – Whether to include run info in the response. Defaults
to False.
apply(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → List[Dict[str, str]]¶
Call the chain on all inputs in the list.
async arun(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, **kwargs: Any) → str¶
Run the chain as text in, text out or multiple variables, text out.
dict(**kwargs: Any) → Dict¶
Return dictionary representation of chain.
prep_inputs(inputs: Union[Dict[str, Any], Any]) → Dict[str, str]¶
Validate and prep inputs. | [
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efe6d5187693-3 | Validate and prep inputs.
prep_outputs(inputs: Dict[str, str], outputs: Dict[str, str], return_only_outputs: bool = False) → Dict[str, str]¶
Validate and prep outputs.
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, **kwargs: Any) → str¶
Run the chain as text in, text out or multiple variables, text out.
save(file_path: Union[Path, str]) → None¶
Save the chain.
Parameters
file_path – Path to file to save the chain to.
Example:
.. code-block:: python
chain.save(file_path=”path/chain.yaml”)
validator set_verbose » verbose¶
If verbose is None, set it.
This allows users to pass in None as verbose to access the global setting.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property input_keys: List[str]¶
Input keys this chain expects.
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
property output_keys: List[str]¶ | [
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efe6d5187693-4 | Return whether or not the class is serializable.
property output_keys: List[str]¶
Output keys this chain expects.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶ | [
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d5a20ae1d7c7-0 | langchain.experimental.autonomous_agents.baby_agi.baby_agi.BabyAGI¶
class langchain.experimental.autonomous_agents.baby_agi.baby_agi.BabyAGI(*, memory: Optional[BaseMemory] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, verbose: bool = None, tags: Optional[List[str]] = None, task_list: deque = None, task_creation_chain: Chain, task_prioritization_chain: Chain, execution_chain: Chain, task_id_counter: int = 1, vectorstore: VectorStore, max_iterations: Optional[int] = None)[source]¶
Bases: Chain, BaseModel
Controller model for the BabyAGI agent.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated, use callbacks instead.
param callbacks: Callbacks = None¶
Optional list of callback handlers (or callback manager). Defaults to None.
Callback handlers are called throughout the lifecycle of a call to a chain,
starting with on_chain_start, ending with on_chain_end or on_chain_error.
Each custom chain can optionally call additional callback methods, see Callback docs
for full details.
param execution_chain: langchain.chains.base.Chain [Required]¶
param max_iterations: Optional[int] = None¶
param memory: Optional[BaseMemory] = None¶
Optional memory object. Defaults to None.
Memory is a class that gets called at the start
and at the end of every chain. At the start, memory loads variables and passes
them along in the chain. At the end, it saves any returned variables. | [
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d5a20ae1d7c7-1 | them along in the chain. At the end, it saves any returned variables.
There are many different types of memory - please see memory docs
for the full catalog.
param tags: Optional[List[str]] = None¶
Optional list of tags associated with the chain. Defaults to None
These tags will be associated with each call to this chain,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a chain with its use case.
param task_creation_chain: langchain.chains.base.Chain [Required]¶
param task_id_counter: int = 1¶
param task_list: collections.deque [Optional]¶
param task_prioritization_chain: langchain.chains.base.Chain [Required]¶
param vectorstore: langchain.vectorstores.base.VectorStore [Required]¶
param verbose: bool [Optional]¶
Whether or not run in verbose mode. In verbose mode, some intermediate logs
will be printed to the console. Defaults to langchain.verbose value.
__call__(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, include_run_info: bool = False) → Dict[str, Any]¶
Run the logic of this chain and add to output if desired.
Parameters
inputs – Dictionary of inputs, or single input if chain expects
only one param.
return_only_outputs – boolean for whether to return only outputs in the
response. If True, only new keys generated by this chain will be
returned. If False, both input keys and new keys generated by this
chain will be returned. Defaults to False.
callbacks – Callbacks to use for this chain run. If not provided, will
use the callbacks provided to the chain. | [
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] | https://langchain.readthedocs.io/en/latest/experimental/langchain.experimental.autonomous_agents.baby_agi.baby_agi.BabyAGI.html |
d5a20ae1d7c7-2 | use the callbacks provided to the chain.
include_run_info – Whether to include run info in the response. Defaults
to False.
async acall(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, include_run_info: bool = False) → Dict[str, Any]¶
Run the logic of this chain and add to output if desired.
Parameters
inputs – Dictionary of inputs, or single input if chain expects
only one param.
return_only_outputs – boolean for whether to return only outputs in the
response. If True, only new keys generated by this chain will be
returned. If False, both input keys and new keys generated by this
chain will be returned. Defaults to False.
callbacks – Callbacks to use for this chain run. If not provided, will
use the callbacks provided to the chain.
include_run_info – Whether to include run info in the response. Defaults
to False.
add_task(task: Dict) → None[source]¶
apply(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → List[Dict[str, str]]¶
Call the chain on all inputs in the list.
async arun(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, **kwargs: Any) → str¶
Run the chain as text in, text out or multiple variables, text out.
dict(**kwargs: Any) → Dict¶
Return dictionary representation of chain.
execute_task(objective: str, task: str, k: int = 5) → str[source]¶
Execute a task. | [
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] | https://langchain.readthedocs.io/en/latest/experimental/langchain.experimental.autonomous_agents.baby_agi.baby_agi.BabyAGI.html |
d5a20ae1d7c7-3 | Execute a task.
classmethod from_llm(llm: BaseLanguageModel, vectorstore: VectorStore, verbose: bool = False, task_execution_chain: Optional[Chain] = None, **kwargs: Dict[str, Any]) → BabyAGI[source]¶
Initialize the BabyAGI Controller.
get_next_task(result: str, task_description: str, objective: str) → List[Dict][source]¶
Get the next task.
prep_inputs(inputs: Union[Dict[str, Any], Any]) → Dict[str, str]¶
Validate and prep inputs.
prep_outputs(inputs: Dict[str, str], outputs: Dict[str, str], return_only_outputs: bool = False) → Dict[str, str]¶
Validate and prep outputs.
print_next_task(task: Dict) → None[source]¶
print_task_list() → None[source]¶
print_task_result(result: str) → None[source]¶
prioritize_tasks(this_task_id: int, objective: str) → List[Dict][source]¶
Prioritize tasks.
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, **kwargs: Any) → str¶
Run the chain as text in, text out or multiple variables, text out.
save(file_path: Union[Path, str]) → None¶
Save the chain.
Parameters
file_path – Path to file to save the chain to.
Example:
.. code-block:: python
chain.save(file_path=”path/chain.yaml”)
validator set_verbose » verbose¶
If verbose is None, set it.
This allows users to pass in None as verbose to access the global setting. | [
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] | https://langchain.readthedocs.io/en/latest/experimental/langchain.experimental.autonomous_agents.baby_agi.baby_agi.BabyAGI.html |
d5a20ae1d7c7-4 | This allows users to pass in None as verbose to access the global setting.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property input_keys: List[str]¶
Input keys this chain expects.
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
property output_keys: List[str]¶
Output keys this chain expects.
model Config[source]¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶ | [
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] | https://langchain.readthedocs.io/en/latest/experimental/langchain.experimental.autonomous_agents.baby_agi.baby_agi.BabyAGI.html |
93b92e1120ef-0 | langchain.experimental.llms.jsonformer_decoder.import_jsonformer¶
langchain.experimental.llms.jsonformer_decoder.import_jsonformer() → jsonformer[source]¶
Lazily import jsonformer. | [
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] | https://langchain.readthedocs.io/en/latest/experimental/langchain.experimental.llms.jsonformer_decoder.import_jsonformer.html |
fdfc46867253-0 | langchain.experimental.plan_and_execute.schema.StepResponse¶
class langchain.experimental.plan_and_execute.schema.StepResponse(*, response: str)[source]¶
Bases: BaseModel
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param response: str [Required]¶ | [
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] | https://langchain.readthedocs.io/en/latest/experimental/langchain.experimental.plan_and_execute.schema.StepResponse.html |
0d27f21fa852-0 | langchain.experimental.generative_agents.memory.GenerativeAgentMemory¶
class langchain.experimental.generative_agents.memory.GenerativeAgentMemory(*, llm: BaseLanguageModel, memory_retriever: TimeWeightedVectorStoreRetriever, verbose: bool = False, reflection_threshold: Optional[float] = None, current_plan: List[str] = [], importance_weight: float = 0.15, aggregate_importance: float = 0.0, max_tokens_limit: int = 1200, queries_key: str = 'queries', most_recent_memories_token_key: str = 'recent_memories_token', add_memory_key: str = 'add_memory', relevant_memories_key: str = 'relevant_memories', relevant_memories_simple_key: str = 'relevant_memories_simple', most_recent_memories_key: str = 'most_recent_memories', now_key: str = 'now', reflecting: bool = False)[source]¶
Bases: BaseMemory
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param add_memory_key: str = 'add_memory'¶
param aggregate_importance: float = 0.0¶
Track the sum of the ‘importance’ of recent memories.
Triggers reflection when it reaches reflection_threshold.
param current_plan: List[str] = []¶
The current plan of the agent.
param importance_weight: float = 0.15¶
How much weight to assign the memory importance.
param llm: langchain.base_language.BaseLanguageModel [Required]¶
The core language model.
param max_tokens_limit: int = 1200¶
param memory_retriever: langchain.retrievers.time_weighted_retriever.TimeWeightedVectorStoreRetriever [Required]¶
The retriever to fetch related memories. | [
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] | https://langchain.readthedocs.io/en/latest/experimental/langchain.experimental.generative_agents.memory.GenerativeAgentMemory.html |
0d27f21fa852-1 | The retriever to fetch related memories.
param most_recent_memories_key: str = 'most_recent_memories'¶
param most_recent_memories_token_key: str = 'recent_memories_token'¶
param now_key: str = 'now'¶
param queries_key: str = 'queries'¶
param reflecting: bool = False¶
param reflection_threshold: Optional[float] = None¶
When aggregate_importance exceeds reflection_threshold, stop to reflect.
param relevant_memories_key: str = 'relevant_memories'¶
param relevant_memories_simple_key: str = 'relevant_memories_simple'¶
param verbose: bool = False¶
add_memories(memory_content: str, now: Optional[datetime] = None) → List[str][source]¶
Add an observations or memories to the agent’s memory.
add_memory(memory_content: str, now: Optional[datetime] = None) → List[str][source]¶
Add an observation or memory to the agent’s memory.
chain(prompt: PromptTemplate) → LLMChain[source]¶
clear() → None[source]¶
Clear memory contents.
fetch_memories(observation: str, now: Optional[datetime] = None) → List[Document][source]¶
Fetch related memories.
format_memories_detail(relevant_memories: List[Document]) → str[source]¶
format_memories_simple(relevant_memories: List[Document]) → str[source]¶
load_memory_variables(inputs: Dict[str, Any]) → Dict[str, str][source]¶
Return key-value pairs given the text input to the chain.
pause_to_reflect(now: Optional[datetime] = None) → List[str][source]¶
Reflect on recent observations and generate ‘insights’.
save_context(inputs: Dict[str, Any], outputs: Dict[str, Any]) → None[source]¶ | [
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] | https://langchain.readthedocs.io/en/latest/experimental/langchain.experimental.generative_agents.memory.GenerativeAgentMemory.html |
0d27f21fa852-2 | Save the context of this model run to memory.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
property memory_variables: List[str]¶
Input keys this memory class will load dynamically.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶ | [
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] | https://langchain.readthedocs.io/en/latest/experimental/langchain.experimental.generative_agents.memory.GenerativeAgentMemory.html |
09fe64b1c7e5-0 | langchain.experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain¶
class langchain.experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain(*, memory: Optional[BaseMemory] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, verbose: bool = None, tags: Optional[List[str]] = None, prompt: BasePromptTemplate, llm: BaseLanguageModel, output_key: str = 'text', output_parser: BaseLLMOutputParser = None, return_final_only: bool = True, llm_kwargs: dict = None)[source]¶
Bases: LLMChain
Chain to execute tasks.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated, use callbacks instead.
param callbacks: Callbacks = None¶
Optional list of callback handlers (or callback manager). Defaults to None.
Callback handlers are called throughout the lifecycle of a call to a chain,
starting with on_chain_start, ending with on_chain_end or on_chain_error.
Each custom chain can optionally call additional callback methods, see Callback docs
for full details.
param llm: BaseLanguageModel [Required]¶
Language model to call.
param llm_kwargs: dict [Optional]¶
param memory: Optional[BaseMemory] = None¶
Optional memory object. Defaults to None.
Memory is a class that gets called at the start
and at the end of every chain. At the start, memory loads variables and passes
them along in the chain. At the end, it saves any returned variables.
There are many different types of memory - please see memory docs
for the full catalog. | [
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] | https://langchain.readthedocs.io/en/latest/experimental/langchain.experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain.html |
09fe64b1c7e5-1 | There are many different types of memory - please see memory docs
for the full catalog.
param output_key: str = 'text'¶
param output_parser: BaseLLMOutputParser [Optional]¶
Output parser to use.
Defaults to one that takes the most likely string but does not change it
otherwise.
param prompt: BasePromptTemplate [Required]¶
Prompt object to use.
param return_final_only: bool = True¶
Whether to return only the final parsed result. Defaults to True.
If false, will return a bunch of extra information about the generation.
param tags: Optional[List[str]] = None¶
Optional list of tags associated with the chain. Defaults to None
These tags will be associated with each call to this chain,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a chain with its use case.
param verbose: bool [Optional]¶
Whether or not run in verbose mode. In verbose mode, some intermediate logs
will be printed to the console. Defaults to langchain.verbose value.
__call__(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, include_run_info: bool = False) → Dict[str, Any]¶
Run the logic of this chain and add to output if desired.
Parameters
inputs – Dictionary of inputs, or single input if chain expects
only one param.
return_only_outputs – boolean for whether to return only outputs in the
response. If True, only new keys generated by this chain will be
returned. If False, both input keys and new keys generated by this
chain will be returned. Defaults to False. | [
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] | https://langchain.readthedocs.io/en/latest/experimental/langchain.experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain.html |
09fe64b1c7e5-2 | chain will be returned. Defaults to False.
callbacks – Callbacks to use for this chain run. If not provided, will
use the callbacks provided to the chain.
include_run_info – Whether to include run info in the response. Defaults
to False.
async aapply(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → List[Dict[str, str]]¶
Utilize the LLM generate method for speed gains.
async aapply_and_parse(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → Sequence[Union[str, List[str], Dict[str, str]]]¶
Call apply and then parse the results.
async acall(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, include_run_info: bool = False) → Dict[str, Any]¶
Run the logic of this chain and add to output if desired.
Parameters
inputs – Dictionary of inputs, or single input if chain expects
only one param.
return_only_outputs – boolean for whether to return only outputs in the
response. If True, only new keys generated by this chain will be
returned. If False, both input keys and new keys generated by this
chain will be returned. Defaults to False.
callbacks – Callbacks to use for this chain run. If not provided, will
use the callbacks provided to the chain.
include_run_info – Whether to include run info in the response. Defaults
to False.
async agenerate(input_list: List[Dict[str, Any]], run_manager: Optional[AsyncCallbackManagerForChainRun] = None) → LLMResult¶ | [
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] | https://langchain.readthedocs.io/en/latest/experimental/langchain.experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain.html |
09fe64b1c7e5-3 | Generate LLM result from inputs.
apply(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → List[Dict[str, str]]¶
Utilize the LLM generate method for speed gains.
apply_and_parse(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → Sequence[Union[str, List[str], Dict[str, str]]]¶
Call apply and then parse the results.
async apredict(callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → str¶
Format prompt with kwargs and pass to LLM.
Parameters
callbacks – Callbacks to pass to LLMChain
**kwargs – Keys to pass to prompt template.
Returns
Completion from LLM.
Example
completion = llm.predict(adjective="funny")
async apredict_and_parse(callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Union[str, List[str], Dict[str, str]]¶
Call apredict and then parse the results.
async aprep_prompts(input_list: List[Dict[str, Any]], run_manager: Optional[AsyncCallbackManagerForChainRun] = None) → Tuple[List[PromptValue], Optional[List[str]]]¶
Prepare prompts from inputs.
async arun(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, **kwargs: Any) → str¶
Run the chain as text in, text out or multiple variables, text out.
create_outputs(llm_result: LLMResult) → List[Dict[str, Any]]¶
Create outputs from response. | [
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] | https://langchain.readthedocs.io/en/latest/experimental/langchain.experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain.html |
09fe64b1c7e5-4 | Create outputs from response.
dict(**kwargs: Any) → Dict¶
Return dictionary representation of chain.
classmethod from_llm(llm: BaseLanguageModel, verbose: bool = True) → LLMChain[source]¶
Get the response parser.
classmethod from_string(llm: BaseLanguageModel, template: str) → LLMChain¶
Create LLMChain from LLM and template.
generate(input_list: List[Dict[str, Any]], run_manager: Optional[CallbackManagerForChainRun] = None) → LLMResult¶
Generate LLM result from inputs.
predict(callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → str¶
Format prompt with kwargs and pass to LLM.
Parameters
callbacks – Callbacks to pass to LLMChain
**kwargs – Keys to pass to prompt template.
Returns
Completion from LLM.
Example
completion = llm.predict(adjective="funny")
predict_and_parse(callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Union[str, List[str], Dict[str, Any]]¶
Call predict and then parse the results.
prep_inputs(inputs: Union[Dict[str, Any], Any]) → Dict[str, str]¶
Validate and prep inputs.
prep_outputs(inputs: Dict[str, str], outputs: Dict[str, str], return_only_outputs: bool = False) → Dict[str, str]¶
Validate and prep outputs.
prep_prompts(input_list: List[Dict[str, Any]], run_manager: Optional[CallbackManagerForChainRun] = None) → Tuple[List[PromptValue], Optional[List[str]]]¶
Prepare prompts from inputs.
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used. | [
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] | https://langchain.readthedocs.io/en/latest/experimental/langchain.experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain.html |
09fe64b1c7e5-5 | Raise deprecation warning if callback_manager is used.
run(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, **kwargs: Any) → str¶
Run the chain as text in, text out or multiple variables, text out.
save(file_path: Union[Path, str]) → None¶
Save the chain.
Parameters
file_path – Path to file to save the chain to.
Example:
.. code-block:: python
chain.save(file_path=”path/chain.yaml”)
validator set_verbose » verbose¶
If verbose is None, set it.
This allows users to pass in None as verbose to access the global setting.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶ | [
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40b32f8aa961-0 | langchain.experimental.plan_and_execute.schema.Step¶
class langchain.experimental.plan_and_execute.schema.Step(*, value: str)[source]¶
Bases: BaseModel
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param value: str [Required]¶ | [
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cc52df5c7075-0 | langchain.experimental.plan_and_execute.schema.BaseStepContainer¶
class langchain.experimental.plan_and_execute.schema.BaseStepContainer[source]¶
Bases: BaseModel
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
abstract add_step(step: Step, step_response: StepResponse) → None[source]¶
Add step and step response to the container.
abstract get_final_response() → str[source]¶
Return the final response based on steps taken. | [
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7787d30f75a1-0 | langchain.experimental.generative_agents.generative_agent.GenerativeAgent¶
class langchain.experimental.generative_agents.generative_agent.GenerativeAgent(*, name: str, age: Optional[int] = None, traits: str = 'N/A', status: str, memory: GenerativeAgentMemory, llm: BaseLanguageModel, verbose: bool = False, summary: str = '', summary_refresh_seconds: int = 3600, last_refreshed: datetime = None, daily_summaries: List[str] = None)[source]¶
Bases: BaseModel
A character with memory and innate characteristics.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param age: Optional[int] = None¶
The optional age of the character.
param daily_summaries: List[str] [Optional]¶
Summary of the events in the plan that the agent took.
param last_refreshed: datetime.datetime [Optional]¶
The last time the character’s summary was regenerated.
param llm: langchain.base_language.BaseLanguageModel [Required]¶
The underlying language model.
param memory: langchain.experimental.generative_agents.memory.GenerativeAgentMemory [Required]¶
The memory object that combines relevance, recency, and ‘importance’.
param name: str [Required]¶
The character’s name.
param status: str [Required]¶
The traits of the character you wish not to change.
param summary: str = ''¶
Stateful self-summary generated via reflection on the character’s memory.
param summary_refresh_seconds: int = 3600¶
How frequently to re-generate the summary.
param traits: str = 'N/A'¶
Permanent traits to ascribe to the character.
param verbose: bool = False¶ | [
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] | https://langchain.readthedocs.io/en/latest/experimental/langchain.experimental.generative_agents.generative_agent.GenerativeAgent.html |
7787d30f75a1-1 | Permanent traits to ascribe to the character.
param verbose: bool = False¶
chain(prompt: PromptTemplate) → LLMChain[source]¶
generate_dialogue_response(observation: str, now: Optional[datetime] = None) → Tuple[bool, str][source]¶
React to a given observation.
generate_reaction(observation: str, now: Optional[datetime] = None) → Tuple[bool, str][source]¶
React to a given observation.
get_full_header(force_refresh: bool = False, now: Optional[datetime] = None) → str[source]¶
Return a full header of the agent’s status, summary, and current time.
get_summary(force_refresh: bool = False, now: Optional[datetime] = None) → str[source]¶
Return a descriptive summary of the agent.
summarize_related_memories(observation: str) → str[source]¶
Summarize memories that are most relevant to an observation.
model Config[source]¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶ | [
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] | https://langchain.readthedocs.io/en/latest/experimental/langchain.experimental.generative_agents.generative_agent.GenerativeAgent.html |
b321caf6e58c-0 | langchain.experimental.plan_and_execute.executors.base.ChainExecutor¶
class langchain.experimental.plan_and_execute.executors.base.ChainExecutor(*, chain: Chain)[source]¶
Bases: BaseExecutor
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param chain: langchain.chains.base.Chain [Required]¶
async astep(inputs: dict, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → StepResponse[source]¶
Take step.
step(inputs: dict, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → StepResponse[source]¶
Take step. | [
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] | https://langchain.readthedocs.io/en/latest/experimental/langchain.experimental.plan_and_execute.executors.base.ChainExecutor.html |
b36820d301d4-0 | langchain.experimental.plan_and_execute.schema.Plan¶
class langchain.experimental.plan_and_execute.schema.Plan(*, steps: List[Step])[source]¶
Bases: BaseModel
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param steps: List[langchain.experimental.plan_and_execute.schema.Step] [Required]¶ | [
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] | https://langchain.readthedocs.io/en/latest/experimental/langchain.experimental.plan_and_execute.schema.Plan.html |
a876fa76f275-0 | langchain.experimental.plan_and_execute.planners.base.LLMPlanner¶
class langchain.experimental.plan_and_execute.planners.base.LLMPlanner(*, llm_chain: LLMChain, output_parser: PlanOutputParser, stop: Optional[List] = None)[source]¶
Bases: BasePlanner
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param llm_chain: langchain.chains.llm.LLMChain [Required]¶
param output_parser: langchain.experimental.plan_and_execute.schema.PlanOutputParser [Required]¶
param stop: Optional[List] = None¶
async aplan(inputs: dict, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Plan[source]¶
Given input, decide what to do.
plan(inputs: dict, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Plan[source]¶
Given input, decide what to do. | [
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] | https://langchain.readthedocs.io/en/latest/experimental/langchain.experimental.plan_and_execute.planners.base.LLMPlanner.html |
ca64f3922be2-0 | langchain.experimental.plan_and_execute.planners.base.BasePlanner¶
class langchain.experimental.plan_and_execute.planners.base.BasePlanner[source]¶
Bases: BaseModel
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
abstract async aplan(inputs: dict, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Plan[source]¶
Given input, decide what to do.
abstract plan(inputs: dict, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Plan[source]¶
Given input, decide what to do. | [
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] | https://langchain.readthedocs.io/en/latest/experimental/langchain.experimental.plan_and_execute.planners.base.BasePlanner.html |
dfae51c5061c-0 | langchain.experimental.plan_and_execute.executors.base.BaseExecutor¶
class langchain.experimental.plan_and_execute.executors.base.BaseExecutor[source]¶
Bases: BaseModel
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
abstract async astep(inputs: dict, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → StepResponse[source]¶
Take step.
abstract step(inputs: dict, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → StepResponse[source]¶
Take step. | [
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] | https://langchain.readthedocs.io/en/latest/experimental/langchain.experimental.plan_and_execute.executors.base.BaseExecutor.html |
b23a23bb2588-0 | langchain.experimental.llms.rellm_decoder.import_rellm¶
langchain.experimental.llms.rellm_decoder.import_rellm() → rellm[source]¶
Lazily import rellm. | [
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00a34504a304-0 | langchain.experimental.autonomous_agents.baby_agi.task_creation.TaskCreationChain¶
class langchain.experimental.autonomous_agents.baby_agi.task_creation.TaskCreationChain(*, memory: Optional[BaseMemory] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, verbose: bool = None, tags: Optional[List[str]] = None, prompt: BasePromptTemplate, llm: BaseLanguageModel, output_key: str = 'text', output_parser: BaseLLMOutputParser = None, return_final_only: bool = True, llm_kwargs: dict = None)[source]¶
Bases: LLMChain
Chain to generates tasks.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated, use callbacks instead.
param callbacks: Callbacks = None¶
Optional list of callback handlers (or callback manager). Defaults to None.
Callback handlers are called throughout the lifecycle of a call to a chain,
starting with on_chain_start, ending with on_chain_end or on_chain_error.
Each custom chain can optionally call additional callback methods, see Callback docs
for full details.
param llm: BaseLanguageModel [Required]¶
Language model to call.
param llm_kwargs: dict [Optional]¶
param memory: Optional[BaseMemory] = None¶
Optional memory object. Defaults to None.
Memory is a class that gets called at the start
and at the end of every chain. At the start, memory loads variables and passes
them along in the chain. At the end, it saves any returned variables.
There are many different types of memory - please see memory docs
for the full catalog. | [
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00a34504a304-1 | There are many different types of memory - please see memory docs
for the full catalog.
param output_key: str = 'text'¶
param output_parser: BaseLLMOutputParser [Optional]¶
Output parser to use.
Defaults to one that takes the most likely string but does not change it
otherwise.
param prompt: BasePromptTemplate [Required]¶
Prompt object to use.
param return_final_only: bool = True¶
Whether to return only the final parsed result. Defaults to True.
If false, will return a bunch of extra information about the generation.
param tags: Optional[List[str]] = None¶
Optional list of tags associated with the chain. Defaults to None
These tags will be associated with each call to this chain,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a chain with its use case.
param verbose: bool [Optional]¶
Whether or not run in verbose mode. In verbose mode, some intermediate logs
will be printed to the console. Defaults to langchain.verbose value.
__call__(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, include_run_info: bool = False) → Dict[str, Any]¶
Run the logic of this chain and add to output if desired.
Parameters
inputs – Dictionary of inputs, or single input if chain expects
only one param.
return_only_outputs – boolean for whether to return only outputs in the
response. If True, only new keys generated by this chain will be
returned. If False, both input keys and new keys generated by this
chain will be returned. Defaults to False. | [
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13
] | https://langchain.readthedocs.io/en/latest/experimental/langchain.experimental.autonomous_agents.baby_agi.task_creation.TaskCreationChain.html |
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