langchain: 0.3.9#

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`
`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`
`agents.agent.LLMSingleActionAgent`
`agents.agent_types.AgentType`(value[, names, ...])
`agents.chat.base.ChatAgent`
`agents.conversational.base.ConversationalAgent`
`agents.conversational_chat.base.ConversationalChatAgent`
`agents.mrkl.base.MRKLChain`
`agents.mrkl.base.ZeroShotAgent`
`agents.openai_functions_agent.base.OpenAIFunctionsAgent`
`agents.openai_functions_multi_agent.base.OpenAIMultiFunctionsAgent`
`agents.react.base.DocstoreExplorer`(docstore)
`agents.react.base.ReActChain`
`agents.react.base.ReActDocstoreAgent`
`agents.react.base.ReActTextWorldAgent`
`agents.self_ask_with_search.base.SelfAskWithSearchAgent`
`agents.self_ask_with_search.base.SelfAskWithSearchChain`
`agents.structured_chat.base.StructuredChatAgent`
`agents.xml.base.XMLAgent`

Deprecated functions

`agents.agent_toolkits.vectorstore.base.create_vectorstore_agent`(...)
`agents.agent_toolkits.vectorstore.base.create_vectorstore_router_agent`(...)
`agents.initialize.initialize_agent`(tools, llm)
`agents.loading.load_agent`(path, **kwargs)
`agents.loading.load_agent_from_config`(config)

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.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`(...)

chat_models #

Functions

chat_models.base.init_chat_model()

Initialize a ChatModel from the model name and provider.

embeddings #

Classes

embeddings.cache.CacheBackedEmbeddings(...)

Interface for caching results from embedding models.

Functions

embeddings.base.init_embeddings(model, *[, ...])

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.combined.CombinedMemory`	Combining multiple memories' data together.
`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.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.buffer.ConversationBufferMemory`
`memory.buffer.ConversationStringBufferMemory`
`memory.buffer_window.ConversationBufferWindowMemory`
`memory.chat_memory.BaseChatMemory`
`memory.entity.BaseEntityStore`
`memory.entity.ConversationEntityMemory`
`memory.entity.InMemoryEntityStore`
`memory.entity.RedisEntityStore`
`memory.entity.SQLiteEntityStore`
`memory.entity.UpstashRedisEntityStore`
`memory.summary.ConversationSummaryMemory`
`memory.summary.SummarizerMixin`
`memory.summary_buffer.ConversationSummaryBufferMemory`
`memory.token_buffer.ConversationTokenBufferMemory`
`memory.vectorstore.VectorStoreRetrieverMemory`

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

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.