chains

Chains are easily reusable components linked together.

Chains encode a sequence of calls to components like models, document retrievers, other Chains, etc., and provide a simple interface to this sequence.

The Chain interface makes it easy to create apps that are:

• Stateful: add Memory to any Chain to give it state,

• Observable: pass Callbacks to a Chain to execute additional functionality, like logging, outside the main sequence of component calls,

• Composable: combine Chains with other components, including other Chains.

Class hierarchy:

Chain --> <name>Chain  # Examples: LLMChain, MapReduceChain, RouterChain

Classes

chains.api.base.APIChain	Chain that makes API calls and summarizes the responses to answer a question.
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.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.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.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_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.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.base.BaseQAWithSourcesChain	Question answering chain 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	Output parser that parses a structured query.
chains.query_constructor.parser.ISO8601Date	A date in ISO 8601 format (YYYY-MM-DD).
chains.query_constructor.schema.AttributeInfo	Information about a data source attribute.
chains.question_answering.chain.LoadingCallable(...)	Interface for loading the combine documents chain.
chains.retrieval_qa.base.BaseRetrievalQA	Base class for question-answering chains.
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	Chain 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 in the 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.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.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.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(llm)	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(...)	Create a chain that extracts information from a passage
chains.openai_functions.tagging.create_tagging_chain_pydantic(...)	Create a chain that extracts information from a passage
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.qa_with_sources.loading.load_qa_with_sources_chain(llm)	Load a question answering with sources chain.
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_chain(...)	Load a query constructor chain.
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.question_answering.chain.load_qa_chain(llm)	Load question answering chain.
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.combine_documents.base.AnalyzeDocumentChain
chains.conversation.base.ConversationChain
chains.conversational_retrieval.base.ConversationalRetrievalChain
chains.llm.LLMChain
chains.qa_generation.base.QAGenerationChain
chains.retrieval_qa.base.RetrievalQA

Deprecated functions

chains.openai_functions.base.create_openai_fn_chain(...)
chains.openai_functions.base.create_structured_output_chain(...)
chains.openai_functions.extraction.create_extraction_chain(...)
chains.openai_functions.extraction.create_extraction_chain_pydantic(...)
chains.openai_tools.extraction.create_extraction_chain_pydantic(...)
chains.structured_output.base.create_openai_fn_runnable(...)
chains.structured_output.base.create_structured_output_runnable(...)