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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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https://langchain.readthedocs.io/en/latest/api_reference.html
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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https://langchain.readthedocs.io/en/latest/api_reference.html
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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https://langchain.readthedocs.io/en/latest/api_reference.html
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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https://langchain.readthedocs.io/en/latest/api_reference.html
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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https://langchain.readthedocs.io/en/latest/api_reference.html
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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https://langchain.readthedocs.io/en/latest/api_reference.html
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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https://langchain.readthedocs.io/en/latest/api_reference.html
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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https://langchain.readthedocs.io/en/latest/api_reference.html
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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https://langchain.readthedocs.io/en/latest/api_reference.html
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msxgpt

Description

This dataset, "msxgpt," is designed for training the GPT-3.5-turbo/ GPT-4 based language model for a task. The data consists of JSON lines, each representing an individual example for the model.

The dataset has been created with an emphasis on encoding, which is pivotal to the functionality of Memory Features, Security, and API Endpoints. It is designed to process and store documents from various data sources continuously, using incoming webhooks to the upsert and delete endpoints.

Potential applications of this dataset could range from natural language understanding tasks to more specialized uses. For instance, tools like Zapier or Make can help configure the webhooks based on events or schedule, enabling sophisticated automation and workflow configuration capabilities.

Data Structure

Each line in the train.jsonl file is a JSON object with the following structure:

{
  "input": "string",
  "target": "string"
}

Usage 

import json

with open('train.jsonl', 'r') as f:
    for line in f:
        obj = json.loads(line)
        print(obj['input'], obj['target'])

License 

This dataset is made available under the Creative Commons CC0 4.0 Universal (CC0 4.0) Public Domain Dedication. You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission.

The above example states that anyone can use the dataset for any purpose without needing to ask for permission, which maximizes the dataset's usability.
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