langchain: 0.2.16

Main entrypoint into package.

agents

Classes

agents.agent.AgentExecutor	Agent that is using tools.
agents.agent.AgentOutputParser	Base class for parsing agent output into agent action/finish.
agents.agent.BaseMultiActionAgent	Base Multi Action Agent class.
agents.agent.BaseSingleActionAgent	Base Single Action Agent class.
agents.agent.ExceptionTool	Tool that just returns the query.
agents.agent.MultiActionAgentOutputParser	Base class for parsing agent output into agent actions/finish.
agents.agent.RunnableAgent	Agent powered by Runnables.
agents.agent.RunnableMultiActionAgent	Agent powered by Runnables.
agents.agent_iterator.AgentExecutorIterator(...)	Iterator for AgentExecutor.
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.chat.output_parser.ChatOutputParser	Output parser for the chat agent.
agents.conversational.output_parser.ConvoOutputParser	Output parser for the conversational agent.
agents.conversational_chat.output_parser.ConvoOutputParser	Output parser for the conversational agent.
agents.mrkl.base.ChainConfig(action_name, ...)	Configuration for a chain to use in MRKL system.
agents.mrkl.output_parser.MRKLOutputParser	MRKL Output parser for the chat agent.
agents.openai_assistant.base.OpenAIAssistantAction	AgentAction with info needed to submit custom tool output to existing run.
agents.openai_assistant.base.OpenAIAssistantFinish	AgentFinish with run and thread metadata.
agents.openai_assistant.base.OpenAIAssistantRunnable	Run an OpenAI Assistant.
agents.openai_functions_agent.agent_token_buffer_memory.AgentTokenBufferMemory	Memory used to save agent output AND intermediate steps.
agents.output_parsers.json.JSONAgentOutputParser	Parses tool invocations and final answers in JSON format.
agents.output_parsers.openai_functions.OpenAIFunctionsAgentOutputParser	Parses a message into agent action/finish.
agents.output_parsers.openai_tools.OpenAIToolsAgentOutputParser	Parses a message into agent actions/finish.
agents.output_parsers.react_json_single_input.ReActJsonSingleInputOutputParser	Parses ReAct-style LLM calls that have a single tool input in json format.
agents.output_parsers.react_single_input.ReActSingleInputOutputParser	Parses ReAct-style LLM calls that have a single tool input.
agents.output_parsers.self_ask.SelfAskOutputParser	Parses self-ask style LLM calls.
agents.output_parsers.tools.ToolAgentAction	Fields:
agents.output_parsers.tools.ToolsAgentOutputParser	Parses a message into agent actions/finish.
agents.output_parsers.xml.XMLAgentOutputParser	Parses tool invocations and final answers in XML format.
agents.react.output_parser.ReActOutputParser	Output parser for the ReAct agent.
agents.schema.AgentScratchPadChatPromptTemplate	Chat prompt template for the agent scratchpad.
agents.structured_chat.output_parser.StructuredChatOutputParser	Output parser for the structured chat agent.
agents.structured_chat.output_parser.StructuredChatOutputParserWithRetries	Output parser with retries for the structured chat agent.
agents.tools.InvalidTool	Tool that is run when invalid tool name is encountered by agent.

Functions

agents.agent_toolkits.conversational_retrieval.openai_functions.create_conversational_retrieval_agent(...)	A convenience method for creating a conversational retrieval agent.
agents.format_scratchpad.log.format_log_to_str(...)	Construct the scratchpad that lets the agent continue its thought process.
agents.format_scratchpad.log_to_messages.format_log_to_messages(...)	Construct the scratchpad that lets the agent continue its thought process.
agents.format_scratchpad.openai_functions.format_to_openai_function_messages(...)	Convert (AgentAction, tool output) tuples into FunctionMessages.
agents.format_scratchpad.openai_functions.format_to_openai_functions(...)	Convert (AgentAction, tool output) tuples into FunctionMessages.
agents.format_scratchpad.tools.format_to_tool_messages(...)	Convert (AgentAction, tool output) tuples into ToolMessages.
agents.format_scratchpad.xml.format_xml(...)	Format the intermediate steps as XML.
agents.json_chat.base.create_json_chat_agent(...)	Create an agent that uses JSON to format its logic, build for Chat Models.
agents.openai_functions_agent.base.create_openai_functions_agent(...)	Create an agent that uses OpenAI function calling.
agents.openai_tools.base.create_openai_tools_agent(...)	Create an agent that uses OpenAI tools.
agents.output_parsers.openai_tools.parse_ai_message_to_openai_tool_action(message)	Parse an AI message potentially containing tool_calls.
agents.output_parsers.tools.parse_ai_message_to_tool_action(message)	Parse an AI message potentially containing tool_calls.
agents.react.agent.create_react_agent(llm, ...)	Create an agent that uses ReAct prompting.
agents.self_ask_with_search.base.create_self_ask_with_search_agent(...)	Create an agent that uses self-ask with search prompting.
agents.structured_chat.base.create_structured_chat_agent(...)	Create an agent aimed at supporting tools with multiple inputs.
agents.tool_calling_agent.base.create_tool_calling_agent(...)	Create an agent that uses tools.
agents.utils.validate_tools_single_input(...)	Validate tools for single input.
agents.xml.base.create_xml_agent(llm, tools, ...)	Create an agent that uses XML to format its logic.

Deprecated classes

agents.agent.Agent	Deprecated since version 0.1.0: Use new agent constructor methods like create_react_agent, create_json_agent, create_structured_chat_agent, etc.
agents.agent.LLMSingleActionAgent	Deprecated since version 0.1.0: Use new agent constructor methods like create_react_agent, create_json_agent, create_structured_chat_agent, etc.
agents.agent_types.AgentType(value[, names, ...])	Deprecated since version 0.1.0: Use new agent constructor methods like create_react_agent, create_json_agent, create_structured_chat_agent, etc.
agents.chat.base.ChatAgent	Deprecated since version 0.1.0: Use create_react_agent instead.
agents.conversational.base.ConversationalAgent	Deprecated since version 0.1.0: Use create_react_agent instead.
agents.conversational_chat.base.ConversationalChatAgent	Deprecated since version 0.1.0: Use create_json_chat_agent instead.
agents.mrkl.base.MRKLChain	Deprecated since version 0.1.0.
agents.mrkl.base.ZeroShotAgent	Deprecated since version 0.1.0: Use create_react_agent instead.
agents.openai_functions_agent.base.OpenAIFunctionsAgent	Deprecated since version 0.1.0: Use create_openai_functions_agent instead.
agents.openai_functions_multi_agent.base.OpenAIMultiFunctionsAgent	Deprecated since version 0.1.0: Use create_openai_tools_agent instead.
agents.react.base.DocstoreExplorer(docstore)	Deprecated since version 0.1.0.
agents.react.base.ReActChain	Deprecated since version 0.1.0.
agents.react.base.ReActDocstoreAgent	Deprecated since version 0.1.0.
agents.react.base.ReActTextWorldAgent	Deprecated since version 0.1.0.
agents.self_ask_with_search.base.SelfAskWithSearchAgent	Deprecated since version 0.1.0: Use create_self_ask_with_search instead.
agents.self_ask_with_search.base.SelfAskWithSearchChain	Deprecated since version 0.1.0.
agents.structured_chat.base.StructuredChatAgent	Deprecated since version 0.1.0: Use create_structured_chat_agent instead.
agents.xml.base.XMLAgent	Deprecated since version 0.1.0: Use create_xml_agent instead.

Deprecated functions

agents.agent_toolkits.vectorstore.base.create_vectorstore_agent(...)	Deprecated since version 0.2.13: See API reference for this function for a replacement implementation: https://api.python.langchain.com/en/latest/agents/langchain.agents.agent_toolkits.vectorstore.base.create_vectorstore_agent.html Read more here on how to create agents that query vector stores: https://python.langchain.com/v0.2/docs/how_to/qa_chat_history_how_to/#agents
agents.agent_toolkits.vectorstore.base.create_vectorstore_router_agent(...)	Deprecated since version 0.2.13: See API reference for this function for a replacement implementation: https://api.python.langchain.com/en/latest/agents/langchain.agents.agent_toolkits.vectorstore.base.create_vectorstore_router_agent.html Read more here on how to create agents that query vector stores: https://python.langchain.com/v0.2/docs/how_to/qa_chat_history_how_to/#agents
agents.initialize.initialize_agent(tools, llm)	Deprecated since version 0.1.0: Use Use new agent constructor methods like create_react_agent, create_json_agent, create_structured_chat_agent, etc. instead.
agents.loading.load_agent(path, **kwargs)	Deprecated since version 0.1.0.
agents.loading.load_agent_from_config(config)	Deprecated since version 0.1.0.

callbacks

Classes

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_final_only.FinalStreamingStdOutCallbackHandler(*)	Callback handler for streaming in agents.
callbacks.tracers.logging.LoggingCallbackHandler(logger)	Tracer that logs via the input Logger.

chains

Classes

chains.base.Chain	Abstract base class for creating structured sequences of calls to components.
chains.combine_documents.base.BaseCombineDocumentsChain	Base interface for chains combining documents.
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.reduce.AsyncCombineDocsProtocol(...)	Interface for the combine_docs method.
chains.combine_documents.reduce.CombineDocsProtocol(...)	Interface for the combine_docs method.
chains.combine_documents.reduce.ReduceDocumentsChain	Combine documents by recursively reducing them.
chains.combine_documents.refine.RefineDocumentsChain	Combine documents by doing a first pass and then refining on more documents.
chains.constitutional_ai.models.ConstitutionalPrinciple	Class for a constitutional principle.
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.InputType	Input type for ConversationalRetrievalChain.
chains.elasticsearch_database.base.ElasticsearchDatabaseChain	Chain for interacting with Elasticsearch Database.
chains.flare.base.FlareChain	Chain that combines a retriever, a question generator, and a response generator.
chains.flare.base.QuestionGeneratorChain	Chain that generates questions from uncertain spans.
chains.flare.prompts.FinishedOutputParser	Output parser that checks if the output is finished.
chains.hyde.base.HypotheticalDocumentEmbedder	Generate hypothetical document for query, and then embed that.
chains.moderation.OpenAIModerationChain	Pass input through a moderation endpoint.
chains.natbot.crawler.Crawler()	A crawler for web pages.
chains.natbot.crawler.ElementInViewPort	A typed dictionary containing information about elements in the viewport.
chains.openai_functions.citation_fuzzy_match.FactWithEvidence	Class representing a 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	Chain for making a simple request to an API endpoint.
chains.openai_functions.qa_with_structure.AnswerWithSources	An answer to the question, with sources.
chains.prompt_selector.BasePromptSelector	Base class for prompt selectors.
chains.prompt_selector.ConditionalPromptSelector	Prompt collection that goes through conditionals.
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	Output parser that parses a structured query.
chains.query_constructor.parser.ISO8601Date	A date in ISO 8601 format (YYYY-MM-DD).
chains.query_constructor.parser.ISO8601DateTime	A datetime in ISO 8601 format (YYYY-MM-DDTHH:MM:SS).
chains.query_constructor.schema.AttributeInfo	Information about a data source attribute.
chains.question_answering.chain.LoadingCallable(...)	Interface for loading the combine documents chain.
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	Chain that uses embeddings to route between options.
chains.router.llm_router.RouterOutputParser	Parser for output of router chain in the multi-prompt chain.
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.query.SQLInput	Input for a SQL Chain.
chains.sql_database.query.SQLInputWithTables	Input for a SQL Chain.
chains.summarize.chain.LoadingCallable(...)	Interface for loading the combine documents chain.
chains.transform.TransformChain	Chain that transforms the chain output.

Functions

chains.combine_documents.reduce.acollapse_docs(...)	Execute a collapse function on a set of documents and merge their metadatas.
chains.combine_documents.reduce.collapse_docs(...)	Execute a collapse function on a set of documents and merge their metadatas.
chains.combine_documents.reduce.split_list_of_docs(...)	Split Documents into subsets that each meet a cumulative length constraint.
chains.combine_documents.stuff.create_stuff_documents_chain(...)	Create a chain for passing a list of Documents to a model.
chains.example_generator.generate_example(...)	Return another example given a list of examples for a prompt.
chains.history_aware_retriever.create_history_aware_retriever(...)	Create a chain that takes conversation history and returns documents.
chains.openai_functions.citation_fuzzy_match.create_citation_fuzzy_match_runnable(llm)	Create a citation fuzzy match Runnable.
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.utils.get_llm_kwargs(...)	Return 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.query_constructor.base.construct_examples(...)	Construct examples from input-output pairs.
chains.query_constructor.base.fix_filter_directive(...)	Fix invalid filter directive.
chains.query_constructor.base.get_query_constructor_prompt(...)	Create query construction prompt.
chains.query_constructor.base.load_query_constructor_runnable(...)	Load a query constructor runnable chain.
chains.query_constructor.parser.get_parser([...])	Return a parser for the query language.
chains.query_constructor.parser.v_args(...)	Dummy decorator for when lark is not installed.
chains.retrieval.create_retrieval_chain(...)	Create retrieval chain that retrieves documents and then passes them on.
chains.sql_database.query.create_sql_query_chain(llm, db)	Create a chain that generates SQL queries.
chains.structured_output.base.get_openai_output_parser(...)	Get the appropriate function output parser given the user functions.
chains.summarize.chain.load_summarize_chain(llm)	Load summarizing chain.

Deprecated classes

chains.api.base.APIChain	Deprecated since version 0.2.13: This class is deprecated and will be removed in langchain 1.0. See API reference for replacement: https://api.python.langchain.com/en/latest/chains/langchain.chains.api.base.APIChain.html
chains.combine_documents.base.AnalyzeDocumentChain	Deprecated since version 0.2.7: Use example in API reference with more detail: https://api.python.langchain.com/en/latest/chains/langchain.chains.combine_documents.base.AnalyzeDocumentChain.html instead.
chains.combine_documents.stuff.StuffDocumentsChain	Deprecated since version 0.2.13: This class is deprecated. Use the create_stuff_documents_chain constructor instead. See migration guide here: https://python.langchain.com/v0.2/docs/versions/migrating_chains/stuff_docs_chain/
chains.constitutional_ai.base.ConstitutionalChain	Deprecated since version 0.2.13: This class is deprecated and will be removed in langchain 1.0. See API reference for replacement: https://api.python.langchain.com/en/latest/chains/langchain.chains.constitutional_ai.base.ConstitutionalChain.html
chains.conversation.base.ConversationChain	Deprecated since version 0.2.7: Use RunnableWithMessageHistory: https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.history.RunnableWithMessageHistory.html instead.
chains.conversational_retrieval.base.ConversationalRetrievalChain	Deprecated since version 0.1.17: Use create_history_aware_retriever together with create_retrieval_chain (see example in docstring) instead.
chains.llm.LLMChain	Deprecated since version 0.1.17: Use RunnableSequence, e.g., `prompt | llm` instead.
chains.llm_checker.base.LLMCheckerChain	Deprecated since version 0.2.13: See LangGraph guides for a variety of self-reflection and corrective strategies for question-answering and other tasks: https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_self_rag/
chains.llm_math.base.LLMMathChain	Deprecated since version 0.2.13: This class is deprecated and will be removed in langchain 1.0. See API reference for replacement: https://api.python.langchain.com/en/latest/chains/langchain.chains.llm_math.base.LLMMathChain.html
chains.llm_summarization_checker.base.LLMSummarizationCheckerChain	Deprecated since version 0.2.13: See LangGraph guides for a variety of self-reflection and corrective strategies for question-answering and other tasks: https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_self_rag/
chains.mapreduce.MapReduceChain	Deprecated since version 0.2.13: Refer here for a recommended map-reduce implementation using langgraph: https://langchain-ai.github.io/langgraph/how-tos/map-reduce/. See also migration guide: https://python.langchain.com/v0.2/docs/versions/migrating_chains/map_reduce_chain/
chains.natbot.base.NatBotChain	Deprecated since version 0.2.13: Importing NatBotChain from langchain is deprecated and will be removed in langchain 1.0. Please import from langchain_community instead: from langchain_community.chains.natbot import NatBotChain. You may need to pip install -U langchain-community.
chains.qa_generation.base.QAGenerationChain	Deprecated since version 0.2.7: Use example in API reference with more detail: https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_generation.base.QAGenerationChain.html instead.
chains.qa_with_sources.base.BaseQAWithSourcesChain	Deprecated since version 0.2.13: This class is deprecated. Refer to this guide on retrieval and question answering with sources: https://python.langchain.com/v0.2/docs/how_to/qa_sources/
chains.qa_with_sources.base.QAWithSourcesChain	Deprecated since version 0.2.13: This class is deprecated. Refer to this guide on retrieval and question answering with sources: https://python.langchain.com/v0.2/docs/how_to/qa_sources/
chains.retrieval_qa.base.BaseRetrievalQA	Deprecated since version 0.2.13: This class is deprecated. Use the create_retrieval_chain constructor instead. See migration guide here: https://python.langchain.com/v0.2/docs/versions/migrating_chains/retrieval_qa/
chains.retrieval_qa.base.RetrievalQA	Deprecated since version 0.1.17: This class is deprecated. Use the create_retrieval_chain constructor instead. See migration guide here: https://python.langchain.com/v0.2/docs/versions/migrating_chains/retrieval_qa/
chains.retrieval_qa.base.VectorDBQA	Deprecated since version 0.2.13: This class is deprecated. Use the create_retrieval_chain constructor instead. See migration guide here: https://python.langchain.com/v0.2/docs/versions/migrating_chains/retrieval_qa/
chains.router.llm_router.LLMRouterChain	Deprecated since version 0.2.12: Use RunnableLambda to select from multiple prompt templates. See example in API reference: https://api.python.langchain.com/en/latest/chains/langchain.chains.router.llm_router.LLMRouterChain.html
chains.router.multi_prompt.MultiPromptChain	Deprecated since version 0.2.12: Use RunnableLambda to select from multiple prompt templates. See example in API reference: https://api.python.langchain.com/en/latest/chains/langchain.chains.router.multi_prompt.MultiPromptChain.html

Deprecated functions

chains.loading.load_chain(path, **kwargs)	Deprecated since version 0.2.13: This function is deprecated and will be removed in langchain 1.0. At that point chains must be imported from their respective modules.
chains.loading.load_chain_from_config(...)	Deprecated since version 0.2.13: This function is deprecated and will be removed in langchain 1.0. At that point chains must be imported from their respective modules.
chains.openai_functions.base.create_openai_fn_chain(...)	Deprecated since version 0.1.1: Use create_openai_fn_runnable instead.
chains.openai_functions.base.create_structured_output_chain(...)	Deprecated since version 0.1.1: Use ChatOpenAI.with_structured_output instead.
chains.openai_functions.citation_fuzzy_match.create_citation_fuzzy_match_chain(llm)	Deprecated since version 0.2.13: Use create_citation_fuzzy_match_runnable instead.
chains.openai_functions.extraction.create_extraction_chain(...)	Deprecated since version 0.1.14: LangChain has introduced a method called with_structured_output thatis available on ChatModels capable of tool calling.You can read more about the method here: <https://python.langchain.com/docs/modules/model_io/chat/structured_output/>. Please follow our extraction use case documentation for more guidelineson how to do information extraction with LLMs.<https://python.langchain.com/docs/use_cases/extraction/>. If you notice other issues, please provide feedback here:<langchain-ai/langchain#18154> Use ``
chains.openai_functions.extraction.create_extraction_chain_pydantic(...)	Deprecated since version 0.1.14: LangChain has introduced a method called with_structured_output thatis available on ChatModels capable of tool calling.You can read more about the method here: <https://python.langchain.com/docs/modules/model_io/chat/structured_output/>. Please follow our extraction use case documentation for more guidelineson how to do information extraction with LLMs.<https://python.langchain.com/docs/use_cases/extraction/>. If you notice other issues, please provide feedback here:<langchain-ai/langchain#18154> Use ``
chains.openai_functions.openapi.get_openapi_chain(spec)	Deprecated since version 0.2.13: This function is deprecated and will be removed in langchain 1.0. See API reference for replacement: https://api.python.langchain.com/en/latest/chains/langchain.chains.openai_functions.openapi.get_openapi_chain.html
chains.openai_functions.qa_with_structure.create_qa_with_sources_chain(llm)	Deprecated since version 0.2.13: This function is deprecated. Refer to this guide on retrieval and question answering with sources: https://python.langchain.com/v0.2/docs/how_to/qa_sources/#structure-sources-in-model-response
chains.openai_functions.qa_with_structure.create_qa_with_structure_chain(...)	Deprecated since version 0.2.13: This function is deprecated. Refer to this guide on retrieval and question answering with structured responses: https://python.langchain.com/v0.2/docs/how_to/qa_sources/#structure-sources-in-model-response
chains.openai_functions.tagging.create_tagging_chain(...)	Deprecated since version 0.2.13: LangChain has introduced a method called with_structured_output that is available on ChatModels capable of tool calling. See API reference for this function for replacement: <https://api.python.langchain.com/en/latest/chains/langchain.chains.openai_functions.tagging.create_tagging_chain.html> You can read more about with_structured_output here: <https://python.langchain.com/v0.2/docs/how_to/structured_output/>. If you notice other issues, please provide feedback here: <langchain-ai/langchain#18154>
chains.openai_functions.tagging.create_tagging_chain_pydantic(...)	Deprecated since version 0.2.13: LangChain has introduced a method called with_structured_output that is available on ChatModels capable of tool calling. See API reference for this function for replacement: <https://api.python.langchain.com/en/latest/chains/langchain.chains.openai_functions.tagging.create_tagging_chain_pydantic.html> You can read more about with_structured_output here: <https://python.langchain.com/v0.2/docs/how_to/structured_output/>. If you notice other issues, please provide feedback here: <langchain-ai/langchain#18154>
chains.openai_tools.extraction.create_extraction_chain_pydantic(...)	Deprecated since version 0.1.14: LangChain has introduced a method called with_structured_output thatis available on ChatModels capable of tool calling.You can read more about the method here: <https://python.langchain.com/docs/modules/model_io/chat/structured_output/>. Please follow our extraction use case documentation for more guidelineson how to do information extraction with LLMs.<https://python.langchain.com/docs/use_cases/extraction/>. with_structured_output does not currently support a list of pydantic schemas. If this is a blocker or if you notice other issues, please provide feedback here:<langchain-ai/langchain#18154> Use ``
chains.qa_with_sources.loading.load_qa_with_sources_chain(llm)	Deprecated since version 0.2.13: This function is deprecated. Refer to this guide on retrieval and question answering with sources: https://python.langchain.com/v0.2/docs/how_to/qa_sources/
chains.query_constructor.base.load_query_constructor_chain(...)	Deprecated since version 0.2.13: Use load_query_constructor_runnable instead.
chains.question_answering.chain.load_qa_chain(llm)	Deprecated since version 0.2.13: This class is deprecated. See the following migration guides for replacements based on chain_type:
chains.structured_output.base.create_openai_fn_runnable(...)	Deprecated since version 0.1.14: LangChain has introduced a method called with_structured_output that is available on ChatModels capable of tool calling. You can read more about the method here: <https://python.langchain.com/docs/modules/model_io/chat/structured_output/>. Please follow our extraction use case documentation for more guidelines on how to do information extraction with LLMs. <https://python.langchain.com/docs/use_cases/extraction/>. If you notice other issues, please provide feedback here: <langchain-ai/langchain#18154> Use ``
chains.structured_output.base.create_structured_output_runnable(...)	Deprecated since version 0.1.17: LangChain has introduced a method called with_structured_output that is available on ChatModels capable of tool calling. You can read more about the method here: <https://python.langchain.com/docs/modules/model_io/chat/structured_output/>.Please follow our extraction use case documentation for more guidelines on how to do information extraction with LLMs. <https://python.langchain.com/docs/use_cases/extraction/>. If you notice other issues, please provide feedback here: <langchain-ai/langchain#18154> Use ``

chat_models

Functions

chat_models.base.init_chat_model()

embeddings

Classes

embeddings.cache.CacheBackedEmbeddings(...)

Interface for caching results from embedding models.

evaluation

Classes

evaluation.agents.trajectory_eval_chain.TrajectoryEval	A named tuple containing the score and reasoning for a trajectory.
evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain	A chain for evaluating ReAct style agents.
evaluation.agents.trajectory_eval_chain.TrajectoryOutputParser	Trajectory output parser.
evaluation.comparison.eval_chain.LabeledPairwiseStringEvalChain	A chain for comparing two outputs, such as the outputs
evaluation.comparison.eval_chain.PairwiseStringEvalChain	A chain for comparing two outputs, such as the outputs
evaluation.comparison.eval_chain.PairwiseStringResultOutputParser	A parser for the output of the PairwiseStringEvalChain.
evaluation.criteria.eval_chain.Criteria(value)	A Criteria to evaluate.
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.criteria.eval_chain.LabeledCriteriaEvalChain	Criteria evaluation chain that requires references.
evaluation.embedding_distance.base.EmbeddingDistance(value)	Embedding Distance Metric.
evaluation.embedding_distance.base.EmbeddingDistanceEvalChain	Use embedding distances to score semantic difference between a prediction and reference.
evaluation.embedding_distance.base.PairwiseEmbeddingDistanceEvalChain	Use embedding distances to score semantic difference between two predictions.
evaluation.exact_match.base.ExactMatchStringEvaluator(*)	Compute an exact match between the prediction and the reference.
evaluation.parsing.base.JsonEqualityEvaluator([...])	Evaluate whether the prediction is equal to the reference after
evaluation.parsing.base.JsonValidityEvaluator(...)	Evaluate whether the prediction is valid JSON.
evaluation.parsing.json_distance.JsonEditDistanceEvaluator([...])	An evaluator that calculates the edit distance between JSON strings.
evaluation.parsing.json_schema.JsonSchemaEvaluator(...)	An evaluator that validates a JSON prediction against a JSON schema reference.
evaluation.qa.eval_chain.ContextQAEvalChain	LLM Chain for evaluating QA w/o GT based on context
evaluation.qa.eval_chain.CotQAEvalChain	LLM Chain for evaluating QA using chain of thought reasoning.
evaluation.qa.eval_chain.QAEvalChain	LLM Chain for evaluating question answering.
evaluation.qa.generate_chain.QAGenerateChain	LLM Chain for generating examples for question answering.
evaluation.regex_match.base.RegexMatchStringEvaluator(*)	Compute a regex match between the prediction and the reference.
evaluation.schema.AgentTrajectoryEvaluator()	Interface for evaluating agent trajectories.
evaluation.schema.EvaluatorType(value[, ...])	The types of the evaluators.
evaluation.schema.LLMEvalChain	A base class for evaluators that use an LLM.
evaluation.schema.PairwiseStringEvaluator()	Compare the output of two models (or two outputs of the same model).
evaluation.schema.StringEvaluator()	Grade, tag, or otherwise evaluate predictions relative to their inputs and/or reference labels.
evaluation.scoring.eval_chain.LabeledScoreStringEvalChain	A chain for scoring the output of a model on a scale of 1-10.
evaluation.scoring.eval_chain.ScoreStringEvalChain	A chain for scoring on a scale of 1-10 the output of a model.
evaluation.scoring.eval_chain.ScoreStringResultOutputParser	A parser for the output of the ScoreStringEvalChain.
evaluation.string_distance.base.PairwiseStringDistanceEvalChain	Compute string edit distances between two predictions.
evaluation.string_distance.base.StringDistance(value)	Distance metric to use.
evaluation.string_distance.base.StringDistanceEvalChain	Compute string distances between the prediction and the reference.

Functions

evaluation.comparison.eval_chain.resolve_pairwise_criteria(...)	Resolve the criteria for the pairwise evaluator.
evaluation.criteria.eval_chain.resolve_criteria(...)	Resolve the criteria to evaluate.
evaluation.loading.load_dataset(uri)	Load a dataset from the LangChainDatasets on HuggingFace.
evaluation.loading.load_evaluator(evaluator, *)	Load the requested evaluation chain specified by a string.
evaluation.loading.load_evaluators(evaluators, *)	Load evaluators specified by a list of evaluator types.
evaluation.scoring.eval_chain.resolve_criteria(...)	Resolve the criteria for the pairwise evaluator.

globals

Functions

globals.get_debug()	Get the value of the debug global setting.
globals.get_llm_cache()	Get the value of the llm_cache global setting.
globals.get_verbose()	Get the value of the verbose global setting.
globals.set_debug(value)	Set a new value for the debug global setting.
globals.set_llm_cache(value)	Set a new LLM cache, overwriting the previous value, if any.
globals.set_verbose(value)	Set a new value for the verbose global setting.

hub

Functions

hub.pull(owner_repo_commit, *[, ...])	Pull an object from the hub and returns it as a LangChain object.
hub.push(repo_full_name, object, *[, ...])	Push an object to the hub and returns the URL it can be viewed at in a browser.

indexes

Classes

indexes.vectorstore.VectorStoreIndexWrapper	Wrapper around a vectorstore for easy access.
indexes.vectorstore.VectorstoreIndexCreator	Logic for creating indexes.

memory

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 inside a limited size window.
memory.chat_memory.BaseChatMemory	Abstract base class for chat memory.
memory.combined.CombinedMemory	Combining multiple memories' data together.
memory.entity.BaseEntityStore	Abstract base class for Entity store.
memory.entity.ConversationEntityMemory	Entity extractor & summarizer memory.
memory.entity.InMemoryEntityStore	In-memory Entity store.
memory.entity.RedisEntityStore	Redis-backed Entity store.
memory.entity.SQLiteEntityStore	SQLite-backed Entity store
memory.entity.UpstashRedisEntityStore	Upstash Redis backed Entity store.
memory.readonly.ReadOnlySharedMemory	Memory wrapper that is read-only and cannot be changed.
memory.simple.SimpleMemory	Simple memory for storing context or other information that shouldn't ever change between prompts.
memory.summary.ConversationSummaryMemory	Conversation summarizer to chat memory.
memory.summary_buffer.ConversationSummaryBufferMemory	Buffer with summarizer for storing conversation memory.
memory.token_buffer.ConversationTokenBufferMemory	Conversation chat memory with token limit.
memory.vectorstore.VectorStoreRetrieverMemory	VectorStoreRetriever-backed memory.
memory.vectorstore_token_buffer_memory.ConversationVectorStoreTokenBufferMemory	Conversation chat memory with token limit and vectordb backing.

Functions

memory.utils.get_prompt_input_key(inputs, ...)

Get the prompt input key.

Deprecated classes

memory.summary.SummarizerMixin

Deprecated since version 0.2.12: Refer here for how to incorporate summaries of conversation history: https://langchain-ai.github.io/langgraph/how-tos/memory/add-summary-conversation-history/

model_laboratory

Classes

model_laboratory.ModelLaboratory(chains[, names])

Experiment with different models.

output_parsers

Classes

output_parsers.boolean.BooleanOutputParser	Parse the output of an LLM call to a boolean.
output_parsers.combining.CombiningOutputParser	Combine multiple output parsers into one.
output_parsers.datetime.DatetimeOutputParser	Parse the output of an LLM call to a datetime.
output_parsers.enum.EnumOutputParser	Parse an output that is one of a set of values.
output_parsers.fix.OutputFixingParser	Wrap a parser and try to fix parsing errors.
output_parsers.fix.OutputFixingParserRetryChainInput
output_parsers.pandas_dataframe.PandasDataFrameOutputParser	Parse an output using Pandas DataFrame format.
output_parsers.regex.RegexParser	Parse the output of an LLM call using a regex.
output_parsers.regex_dict.RegexDictParser	Parse the output of an LLM call into a Dictionary using a regex.
output_parsers.retry.RetryOutputParser	Wrap a parser and try to fix parsing errors.
output_parsers.retry.RetryOutputParserRetryChainInput
output_parsers.retry.RetryWithErrorOutputParser	Wrap a parser and try to fix parsing errors.
output_parsers.retry.RetryWithErrorOutputParserRetryChainInput
output_parsers.structured.ResponseSchema	Schema for a response from a structured output parser.
output_parsers.structured.StructuredOutputParser	Parse the output of an LLM call to a structured output.
output_parsers.yaml.YamlOutputParser	Parse YAML output using a pydantic model.

Functions

output_parsers.loading.load_output_parser(config)

Load an output parser.

retrievers

Classes

retrievers.contextual_compression.ContextualCompressionRetriever	Retriever that wraps a base retriever and compresses the results.
retrievers.document_compressors.base.DocumentCompressorPipeline	Document compressor that uses a pipeline of Transformers.
retrievers.document_compressors.chain_extract.LLMChainExtractor	Document compressor that uses an LLM chain to extract the relevant parts of documents.
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.cross_encoder.BaseCrossEncoder()	Interface for cross encoder models.
retrievers.document_compressors.cross_encoder_rerank.CrossEncoderReranker	Document compressor that uses CrossEncoder for reranking.
retrievers.document_compressors.embeddings_filter.EmbeddingsFilter	Document compressor that uses embeddings to drop documents unrelated to the query.
retrievers.document_compressors.listwise_rerank.LLMListwiseRerank	Document compressor that uses Zero-Shot Listwise Document Reranking.
retrievers.ensemble.EnsembleRetriever	Retriever that ensembles the multiple retrievers.
retrievers.merger_retriever.MergerRetriever	Retriever that merges the results of multiple retrievers.
retrievers.multi_query.LineListOutputParser	Output parser for a list of lines.
retrievers.multi_query.MultiQueryRetriever	Given a query, use an LLM to write a set of queries.
retrievers.multi_vector.MultiVectorRetriever	Retrieve from a set of multiple embeddings for the same document.
retrievers.multi_vector.SearchType(value[, ...])	Enumerator of the types of search to perform.
retrievers.parent_document_retriever.ParentDocumentRetriever	Retrieve small chunks then retrieve their parent documents.
retrievers.re_phraser.RePhraseQueryRetriever	Given a query, use an LLM to re-phrase it.
retrievers.self_query.base.SelfQueryRetriever	Retriever that uses a vector store and an LLM to generate the vector store queries.
retrievers.time_weighted_retriever.TimeWeightedVectorStoreRetriever	Retriever that combines embedding similarity with recency in retrieving values.

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.ensemble.unique_by_key(iterable, key)	Yield unique elements of an iterable based on a key function.

Deprecated classes

retrievers.document_compressors.cohere_rerank.CohereRerank

Deprecated since version 0.0.30: Use langchain_cohere.CohereRerank instead.

runnables

Classes

runnables.hub.HubRunnable	An instance of a runnable stored in the LangChain Hub.
runnables.openai_functions.OpenAIFunction	A function description for ChatOpenAI
runnables.openai_functions.OpenAIFunctionsRouter	A runnable that routes to the selected function.

smith

Classes

smith.evaluation.config.EvalConfig	Configuration for a given run evaluator.
smith.evaluation.config.RunEvalConfig	Configuration for a run evaluation.
smith.evaluation.config.SingleKeyEvalConfig	Configuration for a run evaluator that only requires a single key.
smith.evaluation.progress.ProgressBarCallback(total)	A simple progress bar for the console.
smith.evaluation.runner_utils.ChatModelInput	Input for a chat model.
smith.evaluation.runner_utils.EvalError(...)	Your architecture raised an error.
smith.evaluation.runner_utils.InputFormatError	Raised when the input format is invalid.
smith.evaluation.runner_utils.TestResult	A dictionary of the results of a single test run.
smith.evaluation.string_run_evaluator.ChainStringRunMapper	Extract items to evaluate from the run object from a chain.
smith.evaluation.string_run_evaluator.LLMStringRunMapper	Extract items to evaluate from the run object.
smith.evaluation.string_run_evaluator.StringExampleMapper	Map an example, or row in the dataset, to the inputs of an evaluation.
smith.evaluation.string_run_evaluator.StringRunEvaluatorChain	Evaluate Run and optional examples.
smith.evaluation.string_run_evaluator.StringRunMapper	Extract items to evaluate from the run object.
smith.evaluation.string_run_evaluator.ToolStringRunMapper	Map an input to the tool.

Functions

smith.evaluation.name_generation.random_name()	Generate a random name.
smith.evaluation.runner_utils.arun_on_dataset(...)	Run the Chain or language model on a dataset and store traces to the specified project name.
smith.evaluation.runner_utils.run_on_dataset(...)	Run the Chain or language model on a dataset and store traces to the specified project name.

storage

Classes

storage.encoder_backed.EncoderBackedStore(...)	Wraps a store with key and value encoders/decoders.
storage.file_system.LocalFileStore(root_path, *)	BaseStore interface that works on the local file system.