`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.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.reduce.AsyncCombineDocsProtocol`(...)	Interface for the combine_docs method.
`chains.combine_documents.reduce.CombineDocsProtocol`(...)	Interface for the combine_docs method.
`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`
`chains.combine_documents.base.AnalyzeDocumentChain`
`chains.combine_documents.map_reduce.MapReduceDocumentsChain`
`chains.combine_documents.map_rerank.MapRerankDocumentsChain`
`chains.combine_documents.reduce.ReduceDocumentsChain`
`chains.combine_documents.refine.RefineDocumentsChain`
`chains.combine_documents.stuff.StuffDocumentsChain`
`chains.constitutional_ai.base.ConstitutionalChain`
`chains.conversation.base.ConversationChain`
`chains.conversational_retrieval.base.ConversationalRetrievalChain`
`chains.llm.LLMChain`
`chains.llm_checker.base.LLMCheckerChain`
`chains.llm_math.base.LLMMathChain`
`chains.llm_summarization_checker.base.LLMSummarizationCheckerChain`
`chains.mapreduce.MapReduceChain`
`chains.natbot.base.NatBotChain`
`chains.qa_generation.base.QAGenerationChain`
`chains.qa_with_sources.base.BaseQAWithSourcesChain`
`chains.qa_with_sources.base.QAWithSourcesChain`
`chains.retrieval_qa.base.BaseRetrievalQA`
`chains.retrieval_qa.base.RetrievalQA`
`chains.retrieval_qa.base.VectorDBQA`
`chains.router.llm_router.LLMRouterChain`
`chains.router.multi_prompt.MultiPromptChain`

Deprecated functions

`chains.loading.load_chain`(path, **kwargs)
`chains.loading.load_chain_from_config`(...)
`chains.openai_functions.base.create_openai_fn_chain`(...)
`chains.openai_functions.base.create_structured_output_chain`(...)
`chains.openai_functions.citation_fuzzy_match.create_citation_fuzzy_match_chain`(llm)
`chains.openai_functions.extraction.create_extraction_chain`(...)
`chains.openai_functions.extraction.create_extraction_chain_pydantic`(...)
`chains.openai_functions.openapi.get_openapi_chain`(spec)
`chains.openai_functions.qa_with_structure.create_qa_with_sources_chain`(llm)
`chains.openai_functions.qa_with_structure.create_qa_with_structure_chain`(...)
`chains.openai_functions.tagging.create_tagging_chain`(...)
`chains.openai_functions.tagging.create_tagging_chain_pydantic`(...)
`chains.openai_tools.extraction.create_extraction_chain_pydantic`(...)
`chains.qa_with_sources.loading.load_qa_with_sources_chain`(llm)
`chains.query_constructor.base.load_query_constructor_chain`(...)
`chains.question_answering.chain.load_qa_chain`(llm)
`chains.structured_output.base.create_openai_fn_runnable`(...)
`chains.structured_output.base.create_structured_output_runnable`(...)

chains#

`chains`#