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b2499a3c04c8-0 | langchain API Reference¶
langchain.agents¶
Agent is a class that uses an LLM to choose a sequence of actions to take.
In Chains, a sequence of actions is hardcoded. In Agents,
a language model is used as a reasoning engine to determine which actions
to take and in which order.
Agents select and use Tools and Toolkits for actions.
Class hierarchy:
BaseSingleActionAgent --> LLMSingleActionAgent
OpenAIFunctionsAgent
XMLAgent
Agent --> <name>Agent # Examples: ZeroShotAgent, ChatAgent
BaseMultiActionAgent --> OpenAIMultiFunctionsAgent
Main helpers:
AgentType, AgentExecutor, AgentOutputParser, AgentExecutorIterator,
AgentAction, AgentFinish
Classes¶
agents.agent.Agent
Agent that calls the language model and deciding the action.
agents.agent.AgentExecutor
Agent that is using tools.
agents.agent.AgentOutputParser
Base class for parsing agent output into agent action/finish.
agents.agent.BaseMultiActionAgent
Base Multi Action Agent class.
agents.agent.BaseSingleActionAgent
Base Single Action Agent class.
agents.agent.ExceptionTool
Tool that just returns the query.
agents.agent.LLMSingleActionAgent
Base class for single action agents.
agents.agent_iterator.AgentExecutorIterator(...)
Iterator for AgentExecutor.
agents.agent_iterator.BaseAgentExecutorIterator()
Base class for AgentExecutorIterator.
agents.agent_toolkits.amadeus.toolkit.AmadeusToolkit
Toolkit for interacting with Office365.
agents.agent_toolkits.azure_cognitive_services.AzureCognitiveServicesToolkit
Toolkit for Azure Cognitive Services.
agents.agent_toolkits.base.BaseToolkit
Base Toolkit representing a collection of related tools.
agents.agent_toolkits.file_management.toolkit.FileManagementToolkit
Toolkit for interacting with a Local Files.
agents.agent_toolkits.github.toolkit.GitHubToolkit
GitHub Toolkit. | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-1 | agents.agent_toolkits.github.toolkit.GitHubToolkit
GitHub Toolkit.
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.
agents.agent_toolkits.office365.toolkit.O365Toolkit
Toolkit for interacting with Office 365.
agents.agent_toolkits.openapi.planner.RequestsDeleteToolWithParsing
A tool that sends a DELETE request and parses the response.
agents.agent_toolkits.openapi.planner.RequestsGetToolWithParsing
Requests GET tool with LLM-instructed extraction of truncated responses.
agents.agent_toolkits.openapi.planner.RequestsPatchToolWithParsing
Requests PATCH tool with LLM-instructed extraction of truncated responses.
agents.agent_toolkits.openapi.planner.RequestsPostToolWithParsing
Requests POST tool with LLM-instructed extraction of truncated responses.
agents.agent_toolkits.openapi.toolkit.OpenAPIToolkit
Toolkit for interacting with an OpenAPI API.
agents.agent_toolkits.openapi.toolkit.RequestsToolkit
Toolkit for making REST requests.
agents.agent_toolkits.playwright.toolkit.PlayWrightBrowserToolkit
Toolkit for PlayWright browser tools.
agents.agent_toolkits.powerbi.toolkit.PowerBIToolkit
Toolkit for interacting with Power BI 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 | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-2 | 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
Chat Agent.
agents.chat.output_parser.ChatOutputParser
Output parser for the chat agent.
agents.conversational.base.ConversationalAgent
An agent that holds a conversation in addition to using tools.
agents.conversational.output_parser.ConvoOutputParser
Output parser for the conversational agent.
agents.conversational_chat.base.ConversationalChatAgent
An agent designed to hold a conversation in addition to using tools.
agents.conversational_chat.output_parser.ConvoOutputParser
Output parser for the conversational agent.
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
MRKL Output parser for the chat agent.
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 | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-3 | Agent for the ReAct TextWorld chain.
agents.react.output_parser.ReActOutputParser
Output parser for the ReAct agent.
agents.schema.AgentScratchPadChatPromptTemplate
Chat prompt template for the agent scratchpad.
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
Output parser for the self-ask agent.
agents.structured_chat.base.StructuredChatAgent
Structured Chat Agent.
agents.structured_chat.output_parser.StructuredChatOutputParser
Output parser for the structured chat agent.
agents.structured_chat.output_parser.StructuredChatOutputParserWithRetries
Output parser with retries for the structured chat agent.
agents.tools.InvalidTool
Tool that is run when invalid tool name is encountered by agent.
agents.xml.base.XMLAgent
Agent that uses XML tags.
agents.xml.base.XMLAgentOutputParser
Create a new model by parsing and validating input data from keyword arguments.
Functions¶
agents.agent_iterator.rebuild_callback_manager_on_set(...)
Decorator to force setters to rebuild callback mgr
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.multion.base.create_multion_agent(...)
Construct a multion agent from an LLM and tool.
agents.agent_toolkits.openapi.base.create_openapi_agent(...)
Construct an OpenAPI agent from an LLM and tools.
agents.agent_toolkits.openapi.planner.create_openapi_agent(...)
Instantiate OpenAI API planner and controller for a given spec.
agents.agent_toolkits.openapi.spec.dereference_refs(...) | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-4 | 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(llm)
Construct a Power BI agent from an LLM and tools.
agents.agent_toolkits.powerbi.chat_base.create_pbi_chat_agent(llm)
Construct a Power BI agent from a 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 Spark SQL agent from an LLM and tools.
agents.agent_toolkits.sql.base.create_sql_agent(...)
Construct an 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.agent_toolkits.xorbits.base.create_xorbits_agent(...)
Construct a xorbits agent from an LLM and dataframe.
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. | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-5 | agents.load_tools.load_tools(tool_names[, ...])
Load tools based on their name.
agents.loading.load_agent(path, **kwargs)
Unified method for loading an 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.cache¶
Warning
Beta Feature!
Cache provides an optional caching layer for LLMs.
Cache is useful for two reasons:
It can save you money by reducing the number of API calls you make to the LLM
provider if you’re often requesting the same completion multiple times.
It can speed up your application by reducing the number of API calls you make
to the LLM provider.
Cache directly competes with Memory. See documentation for Pros and Cons.
Class hierarchy:
BaseCache --> <name>Cache # Examples: InMemoryCache, RedisCache, GPTCache
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.
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¶
Callback handlers allow listening to events in LangChain.
Class hierarchy: | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-6 | langchain.callbacks¶
Callback handlers allow listening to events in LangChain.
Class hierarchy:
BaseCallbackHandler --> <name>CallbackHandler # Example: AimCallbackHandler
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 handles 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.context_callback.ContextCallbackHandler([...])
Callback Handler that records transcripts to the Context service.
callbacks.file.FileCallbackHandler(filename)
Callback Handler that writes to a file.
callbacks.flyte_callback.FlyteCallbackHandler()
This callback handler that is used 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)
Async callback manager that handles callbacks from LangChain.
callbacks.manager.AsyncCallbackManagerForChainRun(*, ...)
Async callback manager for chain run.
callbacks.manager.AsyncCallbackManagerForLLMRun(*, ...) | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-7 | 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.AsyncParentRunManager(*, ...)
Async Parent Run Manager.
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 handles 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.ParentRunManager(*, ...[, ...])
Sync Parent Run Manager.
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.sagemaker_callback.SageMakerCallbackHandler(run)
Callback Handler that logs prompt artifacts and metrics to SageMaker Experiments.
callbacks.stdout.StdOutCallbackHandler([color])
Callback Handler that prints to std out.
callbacks.streaming_aiter.AsyncIteratorCallbackHandler()
Callback handler that returns an async iterator. | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-8 | 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(...)
The child record as a NamedTuple.
callbacks.streamlit.mutable_expander.ChildType(value)
The enumerator of the child type.
callbacks.streamlit.streamlit_callback_handler.LLMThoughtState(value)
Enumerator of the LLMThought state.
callbacks.streamlit.streamlit_callback_handler.StreamlitCallbackHandler(...)
A callback handler that writes to a Streamlit app.
callbacks.streamlit.streamlit_callback_handler.ToolRecord(...)
The tool record as a NamedTuple.
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. | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-9 | callbacks.tracers.schemas.Run
Run schema for the V2 API in the Tracer.
callbacks.tracers.schemas.ToolRun
Class for ToolRun.
callbacks.tracers.schemas.TracerSession
TracerSessionV1 schema for the V2 API.
callbacks.tracers.schemas.TracerSessionBase
Base 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.stdout.FunctionCallbackHandler(...)
Tracer that calls a function with a single str parameter.
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(...)
Callback Handler for logging to WhyLabs.
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()
Import comet_ml and raise an error if it is not installed.
callbacks.context_callback.import_context()
Import the getcontext package.
callbacks.flyte_callback.analyze_text(text)
Analyze text using textstat and spacy. | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-10 | Analyze text using textstat and spacy.
callbacks.flyte_callback.import_flytekit()
Import flytekit and flytekitplugins-deck-standard.
callbacks.infino_callback.import_infino()
Import the infino client.
callbacks.manager.atrace_as_chain_group(...)
Get an async callback manager for a chain group in a context manager.
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.
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.
callbacks.sagemaker_callback.save_json(data, ...)
Save dict to local file path.
callbacks.streamlit.__init__.StreamlitCallbackHandler(...)
Callback Handler that writes to a Streamlit app.
callbacks.tracers.evaluation.wait_for_all_evaluators() | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-11 | callbacks.tracers.evaluation.wait_for_all_evaluators()
Wait for all tracers to finish.
callbacks.tracers.langchain.log_error_once(...)
Log an error once.
callbacks.tracers.langchain.wait_for_all_tracers()
Wait for all tracers to finish.
callbacks.tracers.langchain_v1.get_headers()
Get the headers for the LangChain API.
callbacks.tracers.schemas.RunTypeEnum()
RunTypeEnum.
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()
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 are easily reusable components linked together. | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-12 | langchain.chains¶
Chains are easily reusable components linked together.
Chains encode a sequence of calls to components like models, document retrievers,
other Chains, etc., and provide a simple interface to this sequence.
The Chain interface makes it easy to create apps that are:
Stateful: add Memory to any Chain to give it state,
Observable: pass Callbacks to a Chain to execute additional functionality,
like logging, outside the main sequence of component calls,
Composable: combine Chains with other components, including other Chains.
Class hierarchy:
Chain --> <name>Chain # Examples: LLMChain, MapReduceChain, RouterChain
Classes¶
chains.api.base.APIChain
Chain that makes API calls and summarizes the responses to answer a question.
chains.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
Abstract base class for creating structured sequences of calls to components.
chains.combine_documents.base.AnalyzeDocumentChain
Chain that splits documents, then analyzes it in pieces.
chains.combine_documents.base.BaseCombineDocumentsChain
Base interface for chains combining documents.
chains.combine_documents.map_reduce.MapReduceDocumentsChain
Combining documents by mapping a chain over them, then combining results.
chains.combine_documents.map_rerank.MapRerankDocumentsChain
Combining documents by mapping a chain over them, then reranking results.
chains.combine_documents.reduce.AsyncCombineDocsProtocol(...)
Interface for the combine_docs method.
chains.combine_documents.reduce.CombineDocsProtocol(...)
Interface for the combine_docs method. | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-13 | chains.combine_documents.reduce.CombineDocsProtocol(...)
Interface for the combine_docs method.
chains.combine_documents.reduce.ReduceDocumentsChain
Combine documents by recursively reducing them.
chains.combine_documents.refine.RefineDocumentsChain
Combine documents by doing a first pass and then refining on more documents.
chains.combine_documents.stuff.StuffDocumentsChain
Chain that combines documents by stuffing into context.
chains.constitutional_ai.base.ConstitutionalChain
Chain for applying constitutional principles.
chains.constitutional_ai.models.ConstitutionalPrinciple
Class for a constitutional principle.
chains.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 having a conversation based on retrieved documents.
chains.elasticsearch_database.base.ElasticsearchDatabaseChain
Chain for interacting with Elasticsearch Database.
chains.flare.base.FlareChain
Chain that combines a retriever, a question generator, and a response generator.
chains.flare.base.QuestionGeneratorChain
Chain that generates questions from uncertain spans.
chains.flare.prompts.FinishedOutputParser
Output parser that checks if the output is finished.
chains.graph_qa.arangodb.ArangoGraphQAChain
Chain for question-answering against a graph by generating AQL statements.
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.hugegraph.HugeGraphQAChain | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-14 | chains.graph_qa.hugegraph.HugeGraphQAChain
Chain for question-answering against a graph by generating gremlin statements.
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.graph_qa.neptune_cypher.NeptuneOpenCypherQAChain
Chain for question-answering against a Neptune graph by generating openCypher statements.
chains.graph_qa.sparql.GraphSparqlQAChain
Chain for question-answering against an RDF or OWL graph by generating SPARQL 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 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 requests a URL and then uses an LLM to parse results.
chains.llm_summarization_checker.base.LLMSummarizationCheckerChain
Chain for question-answering with self-verification.
chains.llm_symbolic_math.base.LLMSymbolicMathChain
Chain that interprets a prompt and executes python code to do symbolic math.
chains.mapreduce.MapReduceChain
Map-reduce chain. | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-15 | 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 a single statement.
chains.openai_functions.citation_fuzzy_match.QuestionAnswer
A question and its answer as a list of facts each one should have a source.
chains.openai_functions.openapi.SimpleRequestChain
Chain for making a simple request to an API endpoint.
chains.openai_functions.qa_with_structure.AnswerWithSources
An answer to the question, with sources.
chains.prompt_selector.BasePromptSelector
Base class for prompt selectors.
chains.prompt_selector.ConditionalPromptSelector
Prompt collection that goes through conditionals.
chains.qa_generation.base.QAGenerationChain
Base class for question-answer generation chains.
chains.qa_with_sources.base.BaseQAWithSourcesChain
Question answering chain with sources over documents.
chains.qa_with_sources.base.QAWithSourcesChain
Question answering with sources over documents.
chains.qa_with_sources.loading.LoadingCallable(...)
Interface for loading the combine documents chain.
chains.qa_with_sources.retrieval.RetrievalQAWithSourcesChain
Question-answering with sources over an index.
chains.qa_with_sources.vector_db.VectorDBQAWithSourcesChain
Question-answering with sources over a vector database.
chains.query_constructor.base.StructuredQueryOutputParser
Output parser that parses a structured query.
chains.query_constructor.ir.Comparator(value)
Enumerator of the comparison operators.
chains.query_constructor.ir.Comparison
A comparison to a value.
chains.query_constructor.ir.Expr
Base class for all expressions.
chains.query_constructor.ir.FilterDirective | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-16 | chains.query_constructor.ir.Expr
Base class for all expressions.
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
A structured query.
chains.query_constructor.ir.Visitor()
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
Base class for question-answering chains.
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
Chain that uses embeddings to route between options.
chains.router.llm_router.LLMRouterChain
A router chain that uses an LLM chain to perform routing.
chains.router.llm_router.RouterOutputParser
Parser for output of router chain 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 | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-17 | chains.sequential.SequentialChain
Chain where the outputs of one chain feed directly into next.
chains.sequential.SimpleSequentialChain
Simple chain where the outputs of one step feed directly into next.
chains.sql_database.query.SQLInput
Input for a SQL Chain.
chains.sql_database.query.SQLInputWithTables
Input for a SQL Chain.
chains.summarize.__init__.LoadingCallable(...)
Interface for loading the combine documents chain.
chains.transform.TransformChain
Chain that transforms the chain output.
Functions¶
chains.example_generator.generate_example(...)
Return another example given a list of examples for a prompt.
chains.graph_qa.cypher.extract_cypher(text)
Extract Cypher code from a text.
chains.graph_qa.neptune_cypher.extract_cypher(text)
Extract Cypher code from text using Regex.
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.base.convert_python_function_to_openai_function(...)
Convert a Python function to an OpenAI function-calling API compatible dict.
chains.openai_functions.base.convert_to_openai_function(...)
Convert a raw function/class to an OpenAI function.
chains.openai_functions.base.create_openai_fn_chain(...)
Create an LLM chain that uses OpenAI functions.
chains.openai_functions.base.create_structured_output_chain(...)
Create an LLMChain that uses an OpenAI function to get a structured output.
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(...) | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-18 | 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.
chains.prompt_selector.is_chat_model(llm)
Check if the language model is a chat model.
chains.prompt_selector.is_llm(llm)
Check if the language model is a LLM.
chains.qa_with_sources.loading.load_qa_with_sources_chain(llm)
Load a question answering with sources chain.
chains.query_constructor.base.load_query_constructor_chain(...)
Load a query constructor chain.
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.sql_database.query.create_sql_query_chain(llm, db)
Create a chain that generates SQL queries.
chains.summarize.__init__.load_summarize_chain(llm)
Load summarizing chain.
langchain.chat_models¶
Chat Models are a variation on language models. | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-19 | langchain.chat_models¶
Chat Models are a variation on language models.
While Chat Models use language models under the hood, the interface they expose
is a bit different. Rather than expose a “text in, text out” API, they expose
an interface where “chat messages” are the inputs and outputs.
Class hierarchy:
BaseLanguageModel --> BaseChatModel --> <name> # Examples: ChatOpenAI, ChatGooglePalm
Main helpers:
AIMessage, BaseMessage, HumanMessage
Classes¶
chat_models.anthropic.ChatAnthropic
Anthropic's large language chat model.
chat_models.azure_openai.AzureChatOpenAI
Wrapper around Azure OpenAI Chat Completion API.
chat_models.azureml_endpoint.AzureMLChatOnlineEndpoint
Azure ML Chat Online Endpoint models.
chat_models.azureml_endpoint.LlamaContentFormatter()
Content formatter for LLaMa
chat_models.base.BaseChatModel
Create a new model by parsing and validating input data from keyword arguments.
chat_models.base.SimpleChatModel
Simple Chat Model.
chat_models.fake.FakeListChatModel
Fake ChatModel for testing purposes.
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.human.HumanInputChatModel
ChatModel which returns user input as the response.
chat_models.jinachat.JinaChat
Wrapper for Jina AI's LLM service, providing cost-effective image chat capabilities.
chat_models.mlflow_ai_gateway.ChatMLflowAIGateway
Wrapper around chat LLMs in the MLflow AI Gateway.
chat_models.mlflow_ai_gateway.ChatParams
Parameters for the MLflow AI Gateway LLM.
chat_models.openai.ChatOpenAI
Wrapper around OpenAI Chat large language models. | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-20 | 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.achat_with_retry(...)
Use tenacity to retry the async completion call.
chat_models.google_palm.chat_with_retry(llm, ...)
Use tenacity to retry the completion call.
chat_models.jinachat.acompletion_with_retry(...)
Use tenacity to retry the async completion call.
chat_models.openai.acompletion_with_retry(llm)
Use tenacity to retry the async completion call.
chat_models.openai.convert_openai_messages(...)
Convert dictionaries representing OpenAI messages to LangChain format.
langchain.docstore¶
Docstores are classes to store and load Documents.
The Docstore is a simplified version of the Document Loader.
Class hierarchy:
Docstore --> <name> # Examples: InMemoryDocstore, Wikipedia
Main helpers:
Document, AddableMixin
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.
langchain.document_loaders¶
Document Loaders are classes to load Documents.
Document Loaders are usually used to load a lot of Documents in a single run.
Class hierarchy:
BaseLoader --> <name>Loader # Examples: TextLoader, UnstructuredFileLoader
Main helpers: | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-21 | Main helpers:
Document, <name>TextSplitter
Classes¶
document_loaders.acreom.AcreomLoader(path[, ...])
Loader that loads acreom vault from a directory.
document_loaders.airbyte_json.AirbyteJSONLoader(...)
Loads local airbyte json files.
document_loaders.airtable.AirtableLoader(...)
Loader for Airtable tables.
document_loaders.apify_dataset.ApifyDatasetLoader
Loads datasets from Apify-a web scraping, crawling, and data extraction platform.
document_loaders.arxiv.ArxivLoader(query[, ...])
Loads a query result from arxiv.org into a list of Documents.
document_loaders.async_html.AsyncHtmlLoader(...)
Loads HTML asynchronously.
document_loaders.azlyrics.AZLyricsLoader(...)
Loads AZLyrics webpages.
document_loaders.azure_blob_storage_container.AzureBlobStorageContainerLoader(...)
Loading Documents from Azure Blob Storage.
document_loaders.azure_blob_storage_file.AzureBlobStorageFileLoader(...)
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(...)
Loads bilibili transcripts.
document_loaders.blackboard.BlackboardLoader(...)
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. | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-22 | 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.brave_search.BraveSearchLoader(...)
Loads a query result from Brave Search engine into a list of Documents.
document_loaders.browserless.BrowserlessLoader(...)
Loads the content of webpages using Browserless' /content endpoint
document_loaders.chatgpt.ChatGPTLoader(log_file)
Load conversations from exported ChatGPT data.
document_loaders.college_confidential.CollegeConfidentialLoader(...)
Loads College Confidential webpages.
document_loaders.concurrent.ConcurrentLoader(...)
A generic document loader that loads and parses documents concurrently.
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.cube_semantic.CubeSemanticLoader(...)
Load Cube semantic layer metadata.
document_loaders.datadog_logs.DatadogLogsLoader(...)
Loads a query result from Datadog into a list of documents.
document_loaders.dataframe.DataFrameLoader(...)
Load Pandas DataFrame.
document_loaders.diffbot.DiffbotLoader(...) | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-23 | Load Pandas DataFrame.
document_loaders.diffbot.DiffbotLoader(...)
Loads Diffbot file json.
document_loaders.directory.DirectoryLoader(...)
Load documents from a directory.
document_loaders.discord.DiscordChatLoader(...)
Load Discord chat logs.
document_loaders.docugami.DocugamiLoader
Loads processed docs from Docugami.
document_loaders.dropbox.DropboxLoader
Loads files from Dropbox.
document_loaders.duckdb_loader.DuckDBLoader(query)
Loads a query result from DuckDB into a list of documents.
document_loaders.email.OutlookMessageLoader(...)
Loads Outlook Message files using extract_msg.
document_loaders.email.UnstructuredEmailLoader(...)
Loader that uses unstructured to load email files.
document_loaders.embaas.BaseEmbaasLoader
Base class for embedding a model into an Embaas document extraction API.
document_loaders.embaas.EmbaasBlobLoader
Embaas's document byte loader.
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
Embaas's document loader.
document_loaders.epub.UnstructuredEPubLoader(...)
Loader that uses Unstructured to load EPUB files.
document_loaders.etherscan.EtherscanLoader(...)
Load transactions from an account on Ethereum mainnet.
document_loaders.evernote.EverNoteLoader(...)
EverNote Loader.
document_loaders.excel.UnstructuredExcelLoader(...)
Loader that uses unstructured to load Excel files.
document_loaders.facebook_chat.FacebookChatLoader(path)
Loads Facebook messages json directory dump. | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-24 | document_loaders.facebook_chat.FacebookChatLoader(path)
Loads Facebook messages json directory dump.
document_loaders.fauna.FaunaLoader(query, ...)
FaunaDB Loader.
document_loaders.figma.FigmaFileLoader(...)
Loads Figma file json.
document_loaders.gcs_directory.GCSDirectoryLoader(...)
Loads Documents from GCS.
document_loaders.gcs_file.GCSFileLoader(...)
Load Documents from a GCS file.
document_loaders.generic.GenericLoader(...)
A generic document loader.
document_loaders.geodataframe.GeoDataFrameLoader(...)
Load geopandas Dataframe.
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
Load issues of a GitHub repository.
document_loaders.github.GitHubIssuesLoader
Load issues of a GitHub repository.
document_loaders.googledrive.GoogleDriveLoader
Loads Google Docs from Google Drive.
document_loaders.gutenberg.GutenbergLoader(...)
Loader that uses urllib to load .txt web files.
document_loaders.helpers.FileEncoding(...)
A file encoding as the NamedTuple.
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)
Load Documents from the Hugging Face Hub.
document_loaders.ifixit.IFixitLoader(web_path)
Load iFixit repair guides, device wikis and answers. | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-25 | Load iFixit repair guides, device wikis and answers.
document_loaders.image.UnstructuredImageLoader(...)
Loader that uses Unstructured to load PNG and JPG files.
document_loaders.image_captions.ImageCaptionLoader(...)
Loads the captions of an image
document_loaders.imsdb.IMSDbLoader(web_path)
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 using a jq schema.
document_loaders.larksuite.LarkSuiteDocLoader(...)
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)
Loads .ipynb notebook files.
document_loaders.notion.NotionDirectoryLoader(path)
Loads Notion directory dump.
document_loaders.notiondb.NotionDBLoader(...)
Notion DB Loader.
document_loaders.obs_directory.OBSDirectoryLoader(...) | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-26 | Notion DB Loader.
document_loaders.obs_directory.OBSDirectoryLoader(...)
Loading logic for loading documents from Huawei OBS.
document_loaders.obs_file.OBSFileLoader(...)
Loader for Huawei OBS file.
document_loaders.obsidian.ObsidianLoader(path)
Loads Obsidian files from disk.
document_loaders.odt.UnstructuredODTLoader(...)
Loader that uses unstructured to load OpenOffice ODT files.
document_loaders.onedrive.OneDriveLoader
Loads data from OneDrive.
document_loaders.onedrive_file.OneDriveFileLoader
Loads a file from OneDrive.
document_loaders.open_city_data.OpenCityDataLoader(...)
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
Exception raised when the GROBID server is unavailable.
document_loaders.parsers.html.bs4.BS4HTMLParser(*)
Parser that uses beautiful soup to parse HTML files.
document_loaders.parsers.language.code_segmenter.CodeSegmenter(code)
The abstract class for the code segmenter.
document_loaders.parsers.language.javascript.JavaScriptSegmenter(code)
The code segmenter for JavaScript.
document_loaders.parsers.language.language_parser.LanguageParser([...])
Language parser that split code using the respective language syntax.
document_loaders.parsers.language.python.PythonSegmenter(code)
The code segmenter for Python.
document_loaders.parsers.pdf.PDFMinerParser() | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-27 | The code segmenter for Python.
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)
This class uses Mathpix service to load PDF files.
document_loaders.pdf.OnlinePDFLoader(file_path)
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(...) | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-28 | 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(...)
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)
Loads ReadTheDocs documentation directory dump.
document_loaders.recursive_url_loader.RecursiveUrlLoader(url)
Loads all child links from a given url.
document_loaders.reddit.RedditPostsLoader(...)
Reddit posts loader.
document_loaders.roam.RoamLoader(path)
Loads Roam files from disk.
document_loaders.rocksetdb.ColumnNotFoundError(...)
Column not found error.
document_loaders.rocksetdb.RocksetLoader(...)
Wrapper around Rockset db
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 an AWS S3.
document_loaders.s3_file.S3FileLoader(...)
Loading logic for loading documents from an AWS S3 file.
document_loaders.sitemap.SitemapLoader(web_path)
Loader that fetches a sitemap and loads those URLs.
document_loaders.slack_directory.SlackDirectoryLoader(...)
Loads documents from a Slack directory dump.
document_loaders.snowflake_loader.SnowflakeLoader(...)
Loads a query result from Snowflake into a list of documents. | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-29 | 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([...])
Loads Telegram chat json directory dump.
document_loaders.telegram.TelegramChatFileLoader(path)
Loads Telegram chat json directory dump.
document_loaders.tencent_cos_directory.TencentCOSDirectoryLoader(...)
Loader for Tencent Cloud COS directory.
document_loaders.tencent_cos_file.TencentCOSFileLoader(...)
Loader for Tencent Cloud COS file.
document_loaders.text.TextLoader(file_path)
Load text files.
document_loaders.tomarkdown.ToMarkdownLoader(...)
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.tsv.UnstructuredTSVLoader(...)
Loader that uses unstructured to load TSV files.
document_loaders.twitter.TwitterTweetLoader(...)
Twitter tweets loader.
document_loaders.unstructured.UnstructuredAPIFileIOLoader(file)
Loader that uses the Unstructured API to load files.
document_loaders.unstructured.UnstructuredAPIFileLoader([...])
Loader that uses the Unstructured API to load files.
document_loaders.unstructured.UnstructuredBaseLoader([...])
Loader that uses Unstructured to load files.
document_loaders.unstructured.UnstructuredFileIOLoader(file)
Loader that uses Unstructured to load files.
document_loaders.unstructured.UnstructuredFileLoader(...) | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-30 | document_loaders.unstructured.UnstructuredFileLoader(...)
Loader that uses Unstructured to load files.
document_loaders.url.UnstructuredURLLoader(urls)
Loader that use Unstructured to load files from remote URLs.
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)
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.
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.xorbits.XorbitsLoader(...)
Load Xorbits DataFrame.
document_loaders.youtube.GoogleApiYoutubeLoader(...)
Loads all Videos from a Channel
document_loaders.youtube.YoutubeLoader(video_id)
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(...) | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-31 | 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)
Recursively removes newlines, no matter the data structure they are stored in.
document_loaders.parsers.registry.get_parser(...)
Get a parser by parser name.
document_loaders.rocksetdb.default_joiner(docs)
Default joiner for content columns.
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 are classes to transform Documents.
Document Transformers usually used to transform a lot of Documents in a single run.
Class hierarchy:
BaseDocumentTransformer --> <name> # Examples: DoctranQATransformer, DoctranTextTranslator
Main helpers:
Document
Classes¶
document_transformers.doctran_text_extract.DoctranPropertyExtractor(...)
Extract properties from text documents using doctran.
document_transformers.doctran_text_qa.DoctranQATransformer([...])
Extract QA from text documents using doctran.
document_transformers.doctran_text_translate.DoctranTextTranslator([...]) | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-32 | document_transformers.doctran_text_translate.DoctranTextTranslator([...])
Translate text documents using doctran.
document_transformers.embeddings_redundant_filter.EmbeddingsClusteringFilter
Perform K-means clustering on document vectors.
document_transformers.embeddings_redundant_filter.EmbeddingsRedundantFilter
Filter that drops redundant documents by comparing their embeddings.
document_transformers.html2text.Html2TextTransformer()
Replace occurrences of a particular search pattern with a replacement string .
document_transformers.long_context_reorder.LongContextReorder
Lost in the middle: Performance degrades when models must access relevant information in the middle of long contexts.
document_transformers.openai_functions.OpenAIMetadataTagger
Extract metadata tags from document contents using OpenAI functions.
Functions¶
document_transformers.embeddings_redundant_filter.get_stateful_documents(...)
Convert a list of documents to a list of documents with state.
document_transformers.openai_functions.create_metadata_tagger(...)
Create a DocumentTransformer that uses an OpenAI function chain to automatically
langchain.embeddings¶
Embedding models are wrappers around embedding models
from different APIs and services.
Embedding models can be LLMs or not.
Class hierarchy:
Embeddings --> <name>Embeddings # Examples: OpenAIEmbeddings, HuggingFaceEmbeddings
Classes¶
embeddings.aleph_alpha.AlephAlphaAsymmetricSemanticEmbedding
Aleph Alpha's asymmetric semantic embedding.
embeddings.aleph_alpha.AlephAlphaSymmetricSemanticEmbedding
The symmetric version of the Aleph Alpha's semantic embeddings.
embeddings.awa.AwaEmbeddings
Create a new model by parsing and validating input data from keyword arguments.
embeddings.base.Embeddings()
Interface for embedding models.
embeddings.bedrock.BedrockEmbeddings
Bedrock embedding models. | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-33 | embeddings.bedrock.BedrockEmbeddings
Bedrock embedding models.
embeddings.clarifai.ClarifaiEmbeddings
Clarifai embedding models.
embeddings.cohere.CohereEmbeddings
Cohere embedding models.
embeddings.dashscope.DashScopeEmbeddings
DashScope embedding models.
embeddings.deepinfra.DeepInfraEmbeddings
Deep Infra's embedding inference service.
embeddings.elasticsearch.ElasticsearchEmbeddings(...)
Elasticsearch embedding models.
embeddings.embaas.EmbaasEmbeddings
Embaas's embedding service.
embeddings.embaas.EmbaasEmbeddingsPayload
Payload for the embaas embeddings API.
embeddings.fake.FakeEmbeddings
Fake embedding model.
embeddings.google_palm.GooglePalmEmbeddings
Google's PaLM Embeddings APIs.
embeddings.gpt4all.GPT4AllEmbeddings
GPT4All embedding models.
embeddings.huggingface.HuggingFaceEmbeddings
HuggingFace sentence_transformers embedding models.
embeddings.huggingface.HuggingFaceInstructEmbeddings
Wrapper around sentence_transformers embedding models.
embeddings.huggingface_hub.HuggingFaceHubEmbeddings
HuggingFaceHub embedding models.
embeddings.jina.JinaEmbeddings
Jina embedding models.
embeddings.llamacpp.LlamaCppEmbeddings
llama.cpp embedding models.
embeddings.localai.LocalAIEmbeddings
LocalAI embedding models.
embeddings.minimax.MiniMaxEmbeddings
MiniMax's embedding service.
embeddings.mlflow_gateway.MlflowAIGatewayEmbeddings
Wrapper around embeddings LLMs in the MLflow AI Gateway.
embeddings.modelscope_hub.ModelScopeEmbeddings
ModelScopeHub embedding models.
embeddings.mosaicml.MosaicMLInstructorEmbeddings
MosaicML embedding service. | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-34 | embeddings.mosaicml.MosaicMLInstructorEmbeddings
MosaicML embedding service.
embeddings.nlpcloud.NLPCloudEmbeddings
NLP Cloud embedding models.
embeddings.octoai_embeddings.OctoAIEmbeddings
OctoAI Compute Service embedding models.
embeddings.openai.OpenAIEmbeddings
OpenAI embedding models.
embeddings.sagemaker_endpoint.EmbeddingsContentHandler()
Content handler for LLM class.
embeddings.sagemaker_endpoint.SagemakerEndpointEmbeddings
Custom Sagemaker Inference Endpoints.
embeddings.self_hosted.SelfHostedEmbeddings
Custom embedding models on self-hosted remote hardware.
embeddings.self_hosted_hugging_face.SelfHostedHuggingFaceEmbeddings
HuggingFace embedding models on self-hosted remote hardware.
embeddings.self_hosted_hugging_face.SelfHostedHuggingFaceInstructEmbeddings
HuggingFace InstructEmbedding models on self-hosted remote hardware.
embeddings.spacy_embeddings.SpacyEmbeddings
Embeddings by SpaCy models.
embeddings.tensorflow_hub.TensorflowHubEmbeddings
TensorflowHub embedding models.
embeddings.vertexai.VertexAIEmbeddings
Google Cloud VertexAI embedding models.
embeddings.xinference.XinferenceEmbeddings([...])
Wrapper around xinference embedding models.
Functions¶
embeddings.dashscope.embed_with_retry(...)
Use tenacity to retry the embedding call.
embeddings.google_palm.embed_with_retry(...)
Use tenacity to retry the completion call.
embeddings.localai.async_embed_with_retry(...)
Use tenacity to retry the embedding call.
embeddings.localai.embed_with_retry(...)
Use tenacity to retry the embedding call.
embeddings.minimax.embed_with_retry(...)
Use tenacity to retry the completion call.
embeddings.openai.async_embed_with_retry(...) | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-35 | Use tenacity to retry the completion call.
embeddings.openai.async_embed_with_retry(...)
Use tenacity to retry the embedding 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¶
Functions¶
env.get_runtime_environment()
Get information about the LangChain runtime environment.
langchain.evaluation¶
Evaluation chains for grading LLM and Chain outputs.
This module contains off-the-shelf evaluation chains for grading the output of
LangChain primitives such as language models and chains.
Loading an evaluator
To load an evaluator, you can use the load_evaluators or
load_evaluator functions with the
names of the evaluators to load.
from langchain.evaluation import load_evaluator
evaluator = load_evaluator("qa")
evaluator.evaluate_strings(
prediction="We sold more than 40,000 units last week",
input="How many units did we sell last week?",
reference="We sold 32,378 units",
)
The evaluator must be one of EvaluatorType.
Datasets
To load one of the LangChain HuggingFace datasets, you can use the load_dataset function with the
name of the dataset to load.
from langchain.evaluation import load_dataset
ds = load_dataset("llm-math")
Some common use cases for evaluation include:
Grading the accuracy of a response against ground truth answers: QAEvalChain
Comparing the output of two models: PairwiseStringEvalChain or LabeledPairwiseStringEvalChain when there is additionally a reference label.
Judging the efficacy of an agent’s tool usage: TrajectoryEvalChain | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-36 | Judging the efficacy of an agent’s tool usage: TrajectoryEvalChain
Checking whether an output complies with a set of criteria: CriteriaEvalChain or LabeledCriteriaEvalChain when there is additionally a reference label.
Computing semantic difference between a prediction and reference: EmbeddingDistanceEvalChain or between two predictions: PairwiseEmbeddingDistanceEvalChain
Measuring the string distance between a prediction and reference StringDistanceEvalChain or between two predictions PairwiseStringDistanceEvalChain
Low-level API
These evaluators implement one of the following interfaces:
StringEvaluator: Evaluate a prediction string against a reference label and/or input context.
PairwiseStringEvaluator: Evaluate two prediction strings against each other. Useful for scoring preferences, measuring similarity between two chain or llm agents, or comparing outputs on similar inputs.
AgentTrajectoryEvaluator Evaluate the full sequence of actions taken by an agent.
These interfaces enable easier composability and usage within a higher level evaluation framework.
Classes¶
evaluation.agents.trajectory_eval_chain.TrajectoryEval
A named tuple containing the score and reasoning for a trajectory.
evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain
A chain for evaluating ReAct style agents.
evaluation.agents.trajectory_eval_chain.TrajectoryOutputParser
Trajectory output parser.
evaluation.comparison.eval_chain.LabeledPairwiseStringEvalChain
A chain for comparing two outputs, such as the outputs
evaluation.comparison.eval_chain.PairwiseStringEvalChain
A chain for comparing two outputs, such as the outputs
evaluation.comparison.eval_chain.PairwiseStringResultOutputParser
A parser for the output of the PairwiseStringEvalChain.
evaluation.criteria.eval_chain.Criteria(value)
A Criteria to evaluate.
evaluation.criteria.eval_chain.CriteriaEvalChain
LLM Chain for evaluating runs against criteria.
evaluation.criteria.eval_chain.CriteriaResultOutputParser
A parser for the output of the CriteriaEvalChain. | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-37 | A parser for the output of the CriteriaEvalChain.
evaluation.criteria.eval_chain.LabeledCriteriaEvalChain
Criteria evaluation chain that requires references.
evaluation.embedding_distance.base.EmbeddingDistance(value)
Embedding Distance Metric.
evaluation.embedding_distance.base.EmbeddingDistanceEvalChain
Use embedding distances to score semantic difference between a prediction and reference.
evaluation.embedding_distance.base.PairwiseEmbeddingDistanceEvalChain
Use embedding distances to score semantic difference between two predictions.
evaluation.qa.eval_chain.ContextQAEvalChain
LLM Chain for evaluating QA w/o GT based on context
evaluation.qa.eval_chain.CotQAEvalChain
LLM Chain for evaluating QA using chain of thought reasoning.
evaluation.qa.eval_chain.QAEvalChain
LLM Chain for evaluating question answering.
evaluation.qa.generate_chain.QAGenerateChain
LLM Chain for generating examples for question answering.
evaluation.schema.AgentTrajectoryEvaluator()
Interface for evaluating agent trajectories.
evaluation.schema.EvaluatorType(value[, ...])
The types of the evaluators.
evaluation.schema.LLMEvalChain
A base class for evaluators that use an LLM.
evaluation.schema.PairwiseStringEvaluator()
Compare the output of two models (or two outputs of the same model).
evaluation.schema.StringEvaluator()
Grade, tag, or otherwise evaluate predictions relative to their inputs and/or reference labels.
evaluation.string_distance.base.PairwiseStringDistanceEvalChain
Compute string edit distances between two predictions.
evaluation.string_distance.base.StringDistance(value)
Distance metric to use.
evaluation.string_distance.base.StringDistanceEvalChain
Compute string distances between the prediction and the reference.
Functions¶
evaluation.comparison.eval_chain.resolve_pairwise_criteria(...)
Resolve the criteria for the pairwise evaluator.
evaluation.criteria.eval_chain.resolve_criteria(...)
Resolve the criteria to evaluate.
evaluation.loading.load_dataset(uri) | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-38 | Resolve the criteria to evaluate.
evaluation.loading.load_dataset(uri)
Load a dataset from the LangChainDatasets HuggingFace org.
evaluation.loading.load_evaluator(evaluator, *)
Load the requested evaluation chain specified by a string.
evaluation.loading.load_evaluators(evaluators, *)
Load evaluators specified by a list of evaluator types.
langchain.graphs¶
Graphs provide a natural language interface to graph databases.
Classes¶
graphs.neptune_graph.NeptuneQueryException(...)
A class to handle queries that fail to execute
graphs.networkx_graph.KnowledgeTriple(...)
A triple in the graph.
Functions¶
graphs.arangodb_graph.get_arangodb_client([...])
Get the Arango DB client from credentials.
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¶
Index utilities.
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.llms¶
LLM classes provide
access to the large language model (LLM) APIs and services.
Class hierarchy:
BaseLanguageModel --> BaseLLM --> LLM --> <name> # Examples: AI21, HuggingFaceHub, OpenAI
Main helpers:
LLMResult, PromptValue,
CallbackManagerForLLMRun, AsyncCallbackManagerForLLMRun,
CallbackManager, AsyncCallbackManager,
AIMessage, BaseMessage
Classes¶
llms.ai21.AI21
AI21 large language models.
llms.ai21.AI21PenaltyData
Parameters for AI21 penalty data.
llms.aleph_alpha.AlephAlpha | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-39 | Parameters for AI21 penalty data.
llms.aleph_alpha.AlephAlpha
Aleph Alpha large language models.
llms.amazon_api_gateway.AmazonAPIGateway
Amazon API Gateway to access LLM models hosted on AWS.
llms.anthropic.Anthropic
Anthropic large language models.
llms.anyscale.Anyscale
Anyscale Service models.
llms.aviary.Aviary
Aviary hosted models.
llms.azureml_endpoint.AzureMLEndpointClient(...)
AzureML Managed Endpoint client.
llms.azureml_endpoint.AzureMLOnlineEndpoint
Azure ML Online Endpoint models.
llms.azureml_endpoint.DollyContentFormatter()
Content handler for the Dolly-v2-12b model
llms.azureml_endpoint.GPT2ContentFormatter()
Content handler for GPT2
llms.azureml_endpoint.HFContentFormatter()
Content handler for LLMs from the HuggingFace catalog.
llms.azureml_endpoint.LlamaContentFormatter()
Content formatter for LLaMa
llms.azureml_endpoint.OSSContentFormatter()
Deprecated: Kept for backwards compatibility
llms.bananadev.Banana
Banana large language models.
llms.base.BaseLLM
Base LLM abstract interface.
llms.base.LLM
Base LLM abstract class.
llms.baseten.Baseten
Baseten models.
llms.beam.Beam
Beam API for gpt2 large language model.
llms.bedrock.Bedrock
Bedrock models.
llms.cerebriumai.CerebriumAI
CerebriumAI large language models.
llms.chatglm.ChatGLM
ChatGLM LLM service.
llms.clarifai.Clarifai
Clarifai large language models.
llms.cohere.Cohere | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-40 | Clarifai large language models.
llms.cohere.Cohere
Cohere large language models.
llms.ctransformers.CTransformers
C Transformers LLM models.
llms.databricks.Databricks
Databricks serving endpoint or a cluster driver proxy app for LLM.
llms.deepinfra.DeepInfra
DeepInfra models.
llms.fake.FakeListLLM
Fake LLM for testing purposes.
llms.forefrontai.ForefrontAI
ForefrontAI large language models.
llms.google_palm.GooglePalm
Google PaLM models.
llms.gooseai.GooseAI
GooseAI large language models.
llms.gpt4all.GPT4All
GPT4All language models.
llms.huggingface_endpoint.HuggingFaceEndpoint
HuggingFace Endpoint models.
llms.huggingface_hub.HuggingFaceHub
HuggingFaceHub models.
llms.huggingface_pipeline.HuggingFacePipeline
HuggingFace Pipeline API.
llms.huggingface_text_gen_inference.HuggingFaceTextGenInference
HuggingFace text generation API.
llms.human.HumanInputLLM
It returns user input as the response.
llms.koboldai.KoboldApiLLM
Kobold API language model.
llms.llamacpp.LlamaCpp
llama.cpp model.
llms.manifest.ManifestWrapper
HazyResearch's Manifest library.
llms.minimax.Minimax
Wrapper around Minimax large language models.
llms.mlflow_ai_gateway.MlflowAIGateway
Wrapper around completions LLMs in the MLflow AI Gateway.
llms.mlflow_ai_gateway.Params
Parameters for the MLflow AI Gateway LLM.
llms.modal.Modal
Modal large language models.
llms.mosaicml.MosaicML | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-41 | Modal large language models.
llms.mosaicml.MosaicML
MosaicML LLM service.
llms.nlpcloud.NLPCloud
NLPCloud large language models.
llms.octoai_endpoint.OctoAIEndpoint
OctoAI LLM Endpoints.
llms.openai.AzureOpenAI
Azure-specific OpenAI large language models.
llms.openai.BaseOpenAI
Base OpenAI large language model class.
llms.openai.OpenAI
OpenAI large language models.
llms.openai.OpenAIChat
OpenAI Chat large language models.
llms.openllm.IdentifyingParams
Parameters for identifying a model as a typed dict.
llms.openllm.OpenLLM
OpenLLM, supporting both in-process model instance and remote OpenLLM servers.
llms.openlm.OpenLM
OpenLM models.
llms.petals.Petals
Petals Bloom models.
llms.pipelineai.PipelineAI
PipelineAI large language models.
llms.predibase.Predibase
Use your Predibase models with Langchain.
llms.predictionguard.PredictionGuard
Prediction Guard large language models.
llms.promptlayer_openai.PromptLayerOpenAI
PromptLayer OpenAI large language models.
llms.promptlayer_openai.PromptLayerOpenAIChat
Wrapper around OpenAI large language models.
llms.replicate.Replicate
Replicate models.
llms.rwkv.RWKV
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
Sagemaker Inference Endpoint models. | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-42 | llms.sagemaker_endpoint.SagemakerEndpoint
Sagemaker Inference Endpoint models.
llms.self_hosted.SelfHostedPipeline
Model inference on self-hosted remote hardware.
llms.self_hosted_hugging_face.SelfHostedHuggingFaceLLM
HuggingFace Pipeline API to run on self-hosted remote hardware.
llms.stochasticai.StochasticAI
StochasticAI large language models.
llms.textgen.TextGen
text-generation-webui models.
llms.tongyi.Tongyi
Tongyi Qwen large language models.
llms.vertexai.VertexAI
Google Vertex AI large language models.
llms.writer.Writer
Writer large language models.
llms.xinference.Xinference
Wrapper for accessing Xinference's large-scale model inference service.
Functions¶
llms.aviary.get_completions(model, prompt[, ...])
Get completions from Aviary models.
llms.aviary.get_models()
List available models
llms.base.create_base_retry_decorator(...[, ...])
Create a retry decorator for a given LLM and provided list of error types.
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.acompletion_with_retry(llm, **kwargs)
Use tenacity to retry the completion call.
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() | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-43 | 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, ...)
Use tenacity to retry the completion call.
llms.koboldai.clean_url(url)
Remove trailing slash and /api from url if present.
llms.loading.load_llm(file)
Load LLM from file.
llms.loading.load_llm_from_config(config)
Load LLM from Config Dict.
llms.openai.acompletion_with_retry(llm[, ...])
Use tenacity to retry the async completion call.
llms.openai.completion_with_retry(llm[, ...])
Use tenacity to retry the completion call.
llms.openai.update_token_usage(keys, ...)
Update token usage.
llms.tongyi.generate_with_retry(llm, **kwargs)
Use tenacity to retry the completion call.
llms.tongyi.stream_generate_with_retry(llm, ...)
Use tenacity to retry the completion call.
llms.utils.enforce_stop_tokens(text, stop)
Cut off the text as soon as any stop words occur.
llms.vertexai.completion_with_retry(llm, ...)
Use tenacity to retry the completion call.
llms.vertexai.is_codey_model(model_name)
Returns True if the model name is a Codey model.
langchain.load¶
Serialization and deserialization.
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) | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-44 | 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 a JSON object from a string.
load.serializable.to_json_not_implemented(obj)
Serialize a "not implemented" object.
langchain.memory¶
Memory maintains Chain state, incorporating context from past runs.
Class hierarchy for Memory:
BaseMemory --> BaseChatMemory --> <name>Memory # Examples: ZepMemory, MotorheadMemory
Main helpers:
BaseChatMessageHistory
Chat Message History stores the chat message history in different stores.
Class hierarchy for ChatMessageHistory:
BaseChatMessageHistory --> <name>ChatMessageHistory # Example: ZepChatMessageHistory
Main helpers:
AIMessage, BaseMessage, HumanMessage
Classes¶
memory.buffer.ConversationBufferMemory
Buffer for storing conversation memory.
memory.buffer.ConversationStringBufferMemory
Buffer for storing conversation memory.
memory.buffer_window.ConversationBufferWindowMemory
Buffer for storing conversation memory inside a limited size window.
memory.chat_memory.BaseChatMemory
Abstract base class for chat memory.
memory.chat_message_histories.cassandra.CassandraChatMessageHistory(...)
Chat message history that stores history in Cassandra.
memory.chat_message_histories.cosmos_db.CosmosDBChatMessageHistory(...)
Chat message 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. | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-45 | Chat message history that stores history in a local file.
memory.chat_message_histories.firestore.FirestoreChatMessageHistory(...)
Chat message history backed by Google Firestore.
memory.chat_message_histories.in_memory.ChatMessageHistory
In memory implementation of chat message history.
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(...)
Chat message history that uses Zep as a backend.
memory.combined.CombinedMemory
Combining multiple memories' data together.
memory.entity.BaseEntityStore
Abstract base class for Entity store.
memory.entity.ConversationEntityMemory
Entity extractor & summarizer memory.
memory.entity.InMemoryEntityStore
In-memory Entity store.
memory.entity.RedisEntityStore
Redis-backed Entity store.
memory.entity.SQLiteEntityStore
SQLite-backed Entity store
memory.kg.ConversationKGMemory
Knowledge graph conversation memory.
memory.motorhead_memory.MotorheadMemory
Chat message memory backed by Motorhead service.
memory.readonly.ReadOnlySharedMemory
A memory wrapper that is read-only and cannot be changed.
memory.simple.SimpleMemory
Simple memory for storing context or other information that shouldn't ever change between prompts.
memory.summary.ConversationSummaryMemory
Conversation summarizer to chat memory.
memory.summary.SummarizerMixin
Mixin for summarizer.
memory.summary_buffer.ConversationSummaryBufferMemory | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-46 | Mixin for summarizer.
memory.summary_buffer.ConversationSummaryBufferMemory
Buffer with summarizer for storing conversation memory.
memory.token_buffer.ConversationTokenBufferMemory
Conversation chat memory with token limit.
memory.vectorstore.VectorStoreRetrieverMemory
VectorStoreRetriever-backed memory.
memory.zep_memory.ZepMemory
Persist your chain history to the Zep Memory Server.
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¶
OutputParser classes parse the output of an LLM call.
Class hierarchy:
BaseLLMOutputParser --> BaseOutputParser --> <name>OutputParser # ListOutputParser, PydanticOutputParser
Main helpers:
Serializable, Generation, PromptValue
Classes¶
output_parsers.boolean.BooleanOutputParser
Parse the output of an LLM call to a boolean.
output_parsers.combining.CombiningOutputParser
Combine multiple output parsers into one.
output_parsers.datetime.DatetimeOutputParser
Parse the output of an LLM call to a datetime.
output_parsers.enum.EnumOutputParser
Parse an output that is one of a set of values.
output_parsers.fix.OutputFixingParser
Wraps a parser and tries to fix parsing errors.
output_parsers.json.SimpleJsonOutputParser
Parse the output of an LLM call to a JSON object.
output_parsers.list.CommaSeparatedListOutputParser
Parse the output of an LLM call to a comma-separated list.
output_parsers.list.ListOutputParser
Parse the output of an LLM call to a list.
output_parsers.openai_functions.JsonKeyOutputFunctionsParser
Parse an output as the element of the Json object.
output_parsers.openai_functions.JsonOutputFunctionsParser | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-47 | output_parsers.openai_functions.JsonOutputFunctionsParser
Parse an output as the Json object.
output_parsers.openai_functions.OutputFunctionsParser
Parse an output that is one of sets of values.
output_parsers.openai_functions.PydanticAttrOutputFunctionsParser
Parse an output as an attribute of a pydantic object.
output_parsers.openai_functions.PydanticOutputFunctionsParser
Parse an output as a pydantic object.
output_parsers.pydantic.PydanticOutputParser
Parse an output using a pydantic model.
output_parsers.rail_parser.GuardrailsOutputParser
Parse the output of an LLM call using Guardrails.
output_parsers.regex.RegexParser
Parse the output of an LLM call using a regex.
output_parsers.regex_dict.RegexDictParser
Parse the output of an LLM call into a Dictionary using a regex.
output_parsers.retry.RetryOutputParser
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
A schema for a response from a structured output parser.
output_parsers.structured.StructuredOutputParser
Parse the output of an LLM call to a structured output.
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 an output parser.
langchain.prompts¶
Prompt is the input to the model.
Prompt is often constructed
from multiple components. Prompt classes and functions make constructing
and working with prompts easy.
Class hierarchy:
BasePromptTemplate --> PipelinePromptTemplate | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-48 | and working with prompts easy.
Class hierarchy:
BasePromptTemplate --> PipelinePromptTemplate
StringPromptTemplate --> PromptTemplate
FewShotPromptTemplate
FewShotPromptWithTemplates
BaseChatPromptTemplate --> AutoGPTPrompt
ChatPromptTemplate --> AgentScratchPadChatPromptTemplate
BaseMessagePromptTemplate --> MessagesPlaceholder
BaseStringMessagePromptTemplate --> ChatMessagePromptTemplate
HumanMessagePromptTemplate
AIMessagePromptTemplate
SystemMessagePromptTemplate
PromptValue --> StringPromptValue
ChatPromptValue
Classes¶
prompts.base.StringPromptTemplate
String prompt that exposes the format method, returning a prompt.
prompts.base.StringPromptValue
String prompt value.
prompts.chat.AIMessagePromptTemplate
AI message prompt template.
prompts.chat.BaseChatPromptTemplate
Base class for chat prompt templates.
prompts.chat.BaseMessagePromptTemplate
Base class for message prompt templates.
prompts.chat.BaseStringMessagePromptTemplate
Base class for message prompt templates that use a string prompt template.
prompts.chat.ChatMessagePromptTemplate
Chat message prompt template.
prompts.chat.ChatPromptTemplate
A prompt template for chat models.
prompts.chat.ChatPromptValue
Chat prompt value.
prompts.chat.HumanMessagePromptTemplate
Human message prompt template.
prompts.chat.MessagesPlaceholder
Prompt template that assumes variable is already list of messages.
prompts.chat.SystemMessagePromptTemplate
System message prompt template.
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 | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-49 | 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.FewShotChatMessagePromptTemplate
Chat prompt template that supports few-shot examples.
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 prompt templates together.
prompts.prompt.PromptTemplate
A prompt template for a language model.
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.retrievers¶
Retriever class returns Documents given a text query.
It is more general than a vector store. A retriever does not need to be able to
store documents, only to return (or retrieve) it. Vector stores can be used as
the backbone of a retriever, but there are other types of retrievers as well.
Class hierarchy: | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-50 | Class hierarchy:
BaseRetriever --> <name>Retriever # Examples: ArxivRetriever, MergerRetriever
Main helpers:
Document, Serializable, Callbacks,
CallbackManagerForRetrieverRun, AsyncCallbackManagerForRetrieverRun
Classes¶
retrievers.arxiv.ArxivRetriever
Retriever for Arxiv.
retrievers.azure_cognitive_search.AzureCognitiveSearchRetriever
Retriever for the Azure Cognitive Search service.
retrievers.bm25.BM25Retriever
BM25 Retriever without elastic search.
retrievers.chaindesk.ChaindeskRetriever
Retriever for the Chaindesk API.
retrievers.chatgpt_plugin_retriever.ChatGPTPluginRetriever
Retrieves documents from a ChatGPT plugin.
retrievers.contextual_compression.ContextualCompressionRetriever
Retriever that wraps a base retriever and compresses the results.
retrievers.databerry.DataberryRetriever
Retriever for the Databerry API.
retrievers.docarray.DocArrayRetriever
Retriever 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
DocumentCompressor that uses an LLM chain to extract the relevant parts of documents.
retrievers.document_compressors.chain_extract.NoOutputParser
Parse outputs that could return a null string of some sort.
retrievers.document_compressors.chain_filter.LLMChainFilter | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-51 | retrievers.document_compressors.chain_filter.LLMChainFilter
Filter that drops documents that aren't relevant to the query.
retrievers.document_compressors.cohere_rerank.CohereRerank
DocumentCompressor that uses Cohere's rerank API to compress documents.
retrievers.document_compressors.embeddings_filter.EmbeddingsFilter
Document compressor that uses embeddings to drop documents unrelated to the query.
retrievers.elastic_search_bm25.ElasticSearchBM25Retriever
Retriever for the Elasticsearch using BM25 as a retrieval method.
retrievers.ensemble.EnsembleRetriever
This class ensemble the results of multiple retrievers by using rank fusion.
retrievers.google_cloud_enterprise_search.GoogleCloudEnterpriseSearchRetriever
Retriever for the Google Cloud Enterprise Search Service API.
retrievers.kendra.AdditionalResultAttribute
An additional result attribute.
retrievers.kendra.AdditionalResultAttributeValue
The value of an additional result attribute.
retrievers.kendra.AmazonKendraRetriever
Retriever for the Amazon Kendra Index.
retrievers.kendra.DocumentAttribute
A document attribute.
retrievers.kendra.DocumentAttributeValue
The value of a document attribute.
retrievers.kendra.Highlight
Represents the information that can be used to highlight key words in the excerpt.
retrievers.kendra.QueryResult
A Query API result.
retrievers.kendra.QueryResultItem
A Query API result item.
retrievers.kendra.ResultItem
Abstract class that represents a result item.
retrievers.kendra.RetrieveResult
A Retrieve API result.
retrievers.kendra.RetrieveResultItem
A Retrieve API result item.
retrievers.kendra.TextWithHighLights
Text with highlights.
retrievers.knn.KNNRetriever
KNN Retriever. | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-52 | retrievers.knn.KNNRetriever
KNN Retriever.
retrievers.llama_index.LlamaIndexGraphRetriever
Retriever for question-answering with sources over an LlamaIndex graph data structure.
retrievers.llama_index.LlamaIndexRetriever
Retriever for the question-answering with sources over an LlamaIndex data structure.
retrievers.merger_retriever.MergerRetriever
Retriever that merges the results of multiple retrievers.
retrievers.metal.MetalRetriever
Retriever that uses the Metal API.
retrievers.milvus.MilvusRetriever
Retriever that uses the Milvus API.
retrievers.multi_query.LineList
List of lines.
retrievers.multi_query.LineListOutputParser
Output parser for a list of lines.
retrievers.multi_query.MultiQueryRetriever
Given a user query, use an LLM to write a set of queries.
retrievers.pinecone_hybrid_search.PineconeHybridSearchRetriever
Pinecone Hybrid Search Retriever.
retrievers.pubmed.PubMedRetriever
Retriever for PubMed API.
retrievers.remote_retriever.RemoteLangChainRetriever
Retriever for remote LangChain API.
retrievers.self_query.base.SelfQueryRetriever
Retriever that uses a vector store and an LLM to generate the vector store queries.
retrievers.self_query.chroma.ChromaTranslator()
Translate internal query language elements to valid filters.
retrievers.self_query.deeplake.DeepLakeTranslator()
Logic for converting internal query language elements to valid filters.
retrievers.self_query.myscale.MyScaleTranslator([...]) | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-53 | retrievers.self_query.myscale.MyScaleTranslator([...])
Translate internal query language elements to valid filters.
retrievers.self_query.pinecone.PineconeTranslator()
Translate the internal query language elements to valid filters.
retrievers.self_query.qdrant.QdrantTranslator(...)
Translate the internal query language elements to valid filters.
retrievers.self_query.weaviate.WeaviateTranslator()
Translate the internal query language elements to valid filters.
retrievers.svm.SVMRetriever
SVM Retriever.
retrievers.tfidf.TFIDFRetriever
TF-IDF Retriever.
retrievers.time_weighted_retriever.TimeWeightedVectorStoreRetriever
Retriever that combines embedding similarity with recency in retrieving values.
retrievers.vespa_retriever.VespaRetriever
Retriever that uses Vespa.
retrievers.weaviate_hybrid_search.WeaviateHybridSearchRetriever
Retriever for the Weaviate's hybrid search.
retrievers.web_research.LineList
List of questions.
retrievers.web_research.QuestionListOutputParser
Output parser for a list of numbered questions.
retrievers.web_research.SearchQueries
Search queries to run to research for the user's goal.
retrievers.web_research.WebResearchRetriever
Retriever for web research based on the Google Search API.
retrievers.wikipedia.WikipediaRetriever
Retriever for Wikipedia API.
retrievers.zep.ZepRetriever
Retriever for the Zep long-term memory store.
retrievers.zilliz.ZillizRetriever
Retriever for the Zilliz API.
Functions¶
retrievers.bm25.default_preprocessing_func(text) | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-54 | Functions¶
retrievers.bm25.default_preprocessing_func(text)
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)
Cleans an excerpt from Kendra.
retrievers.kendra.combined_text(title, excerpt)
Combines a title and an excerpt into a single string.
retrievers.knn.create_index(contexts, embeddings)
Create an index of embeddings for a list of contexts.
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.deeplake.can_cast_to_float(string)
Check if a string can be cast to a float.
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¶
Schemas are the LangChain Base Classes and Interfaces.
Classes¶
schema.agent.AgentFinish(return_values, log)
The final return value of an ActionAgent.
schema.document.BaseDocumentTransformer()
Abstract base class for document transformation systems.
schema.document.Document | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-55 | schema.document.BaseDocumentTransformer()
Abstract base class for document transformation systems.
schema.document.Document
Class for storing a piece of text and associated metadata.
schema.language_model.BaseLanguageModel
Abstract base class for interfacing with language models.
schema.memory.BaseChatMessageHistory()
Abstract base class for storing chat message history.
schema.memory.BaseMemory
Abstract base class for memory in Chains.
schema.messages.AIMessage
A Message from an AI.
schema.messages.AIMessageChunk
Create a new model by parsing and validating input data from keyword arguments.
schema.messages.BaseMessage
The base abstract Message class.
schema.messages.BaseMessageChunk
Create a new model by parsing and validating input data from keyword arguments.
schema.messages.ChatMessage
A Message that can be assigned an arbitrary speaker (i.e.
schema.messages.ChatMessageChunk
Create a new model by parsing and validating input data from keyword arguments.
schema.messages.FunctionMessage
A Message for passing the result of executing a function back to a model.
schema.messages.FunctionMessageChunk
Create a new model by parsing and validating input data from keyword arguments.
schema.messages.HumanMessage
A Message from a human.
schema.messages.HumanMessageChunk
Create a new model by parsing and validating input data from keyword arguments.
schema.messages.SystemMessage
A Message for priming AI behavior, usually passed in as the first of a sequence of input messages.
schema.messages.SystemMessageChunk
Create a new model by parsing and validating input data from keyword arguments.
schema.output.ChatGeneration
A single chat generation output.
schema.output.ChatGenerationChunk
Create a new model by parsing and validating input data from keyword arguments.
schema.output.ChatResult
Class that contains all results for a single chat model call.
schema.output.Generation
A single text generation output.
schema.output.GenerationChunk
Create a new model by parsing and validating input data from keyword arguments. | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-56 | schema.output.GenerationChunk
Create a new model by parsing and validating input data from keyword arguments.
schema.output.LLMResult
Class that contains all results for a batched LLM call.
schema.output.RunInfo
Class that contains metadata for a single execution of a Chain or model.
schema.output_parser.BaseGenerationOutputParser
Create a new model by parsing and validating input data from keyword arguments.
schema.output_parser.BaseLLMOutputParser
Abstract base class for parsing the outputs of a model.
schema.output_parser.BaseOutputParser
Base class to parse the output of an LLM call.
schema.output_parser.OutputParserException(error)
Exception that output parsers should raise to signify a parsing error.
schema.output_parser.StrOutputParser
OutputParser that parses LLMResult into the top likely string..
schema.prompt.PromptValue
Base abstract class for inputs to any language model.
schema.prompt_template.BasePromptTemplate
Base class for all prompt templates, returning a prompt.
schema.retriever.BaseRetriever
Abstract base class for a Document retrieval system.
schema.runnable.RouterInput
schema.runnable.RouterRunnable
Create a new model by parsing and validating input data from keyword arguments.
schema.runnable.Runnable()
schema.runnable.RunnableBinding
Create a new model by parsing and validating input data from keyword arguments.
schema.runnable.RunnableConfig
schema.runnable.RunnableLambda(func)
schema.runnable.RunnableMap
Create a new model by parsing and validating input data from keyword arguments.
schema.runnable.RunnablePassthrough
Create a new model by parsing and validating input data from keyword arguments.
schema.runnable.RunnableSequence
Create a new model by parsing and validating input data from keyword arguments.
Functions¶
schema.messages.get_buffer_string(messages)
Convert sequence of Messages to strings and concatenate them into one string.
schema.messages.messages_from_dict(messages) | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-57 | schema.messages.messages_from_dict(messages)
Convert a sequence of messages from dicts to Message objects.
schema.messages.messages_to_dict(messages)
Convert a sequence of Messages to a list of dictionaries.
schema.prompt_template.format_document(doc, ...)
Format a document into a string based on a prompt template.
langchain.server¶
Script to run langchain-server locally using docker-compose.
Functions¶
server.main()
Run the langchain server locally.
langchain.smith¶
LangSmith utilities.
This module provides utilities for connecting to LangSmith. For more information on LangSmith, see the LangSmith documentation.
Evaluation
LangSmith helps you evaluate Chains and other language model application components using a number of LangChain evaluators.
An example of this is shown below, assuming you’ve created a LangSmith dataset called <my_dataset_name>:
from langsmith import Client
from langchain.chat_models import ChatOpenAI
from langchain.chains import LLMChain
from langchain.smith import RunEvalConfig, run_on_dataset
# Chains may have memory. Passing in a constructor function lets the
# evaluation framework avoid cross-contamination between runs.
def construct_chain():
llm = ChatOpenAI(temperature=0)
chain = LLMChain.from_string(
llm,
"What's the answer to {your_input_key}"
)
return chain
# Load off-the-shelf evaluators via config or the EvaluatorType (string or enum)
evaluation_config = RunEvalConfig(
evaluators=[
"qa", # "Correctness" against a reference answer
"embedding_distance",
RunEvalConfig.Criteria("helpfulness"),
RunEvalConfig.Criteria({
"fifth-grader-score": "Do you have to be smarter than a fifth grader to answer this question?"
}),
] | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-58 | }),
]
)
client = Client()
run_on_dataset(
client,
"<my_dataset_name>",
construct_chain,
evaluation=evaluation_config,
)
You can also create custom evaluators by subclassing the
StringEvaluator
or LangSmith’s RunEvaluator classes.
from typing import Optional
from langchain.evaluation import StringEvaluator
class MyStringEvaluator(StringEvaluator):
@property
def requires_input(self) -> bool:
return False
@property
def requires_reference(self) -> bool:
return True
@property
def evaluation_name(self) -> str:
return "exact_match"
def _evaluate_strings(self, prediction, reference=None, input=None, **kwargs) -> dict:
return {"score": prediction == reference}
evaluation_config = RunEvalConfig(
custom_evaluators = [MyStringEvaluator()],
)
run_on_dataset(
client,
"<my_dataset_name>",
construct_chain,
evaluation=evaluation_config,
)
Primary Functions
arun_on_dataset: Asynchronous function to evaluate a chain, agent, or other LangChain component over a dataset.
run_on_dataset: Function to evaluate a chain, agent, or other LangChain component over a dataset.
RunEvalConfig: Class representing the configuration for running evaluation. You can select evaluators by EvaluatorType or config, or you can pass in custom_evaluators
Classes¶
smith.evaluation.config.EvalConfig
Configuration for a given run evaluator.
smith.evaluation.config.RunEvalConfig
Configuration for a run evaluation.
smith.evaluation.runner_utils.InputFormatError
Raised when the input format is invalid.
smith.evaluation.string_run_evaluator.ChainStringRunMapper
Extract items to evaluate from the run object from a chain. | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-59 | Extract items to evaluate from the run object from a chain.
smith.evaluation.string_run_evaluator.LLMStringRunMapper
Extract items to evaluate from the run object.
smith.evaluation.string_run_evaluator.StringExampleMapper
Map an example, or row in the dataset, to the inputs of an evaluation.
smith.evaluation.string_run_evaluator.StringRunEvaluatorChain
Evaluate Run and optional examples.
smith.evaluation.string_run_evaluator.StringRunMapper
Extract items to evaluate from the run object.
smith.evaluation.string_run_evaluator.ToolStringRunMapper
Map an input to the tool.
Functions¶
smith.evaluation.runner_utils.arun_on_dataset(...)
Asynchronously run the Chain or language model on a dataset and store traces to the specified project name.
smith.evaluation.runner_utils.run_on_dataset(...)
Run the Chain or language model on a dataset and store traces to the specified project name.
langchain.text_splitter¶
Text Splitters are classes for splitting text.
Class hierarchy:
BaseDocumentTransformer --> TextSplitter --> <name>TextSplitter # Example: CharacterTextSplitter
RecursiveCharacterTextSplitter --> <name>TextSplitter
Note: MarkdownHeaderTextSplitter does not derive from TextSplitter.
Main helpers:
Document, Tokenizer, Language, LineType, HeaderType
Classes¶
text_splitter.CharacterTextSplitter([separator])
Splitting text that looks at characters.
text_splitter.HeaderType
Header type as typed dict.
text_splitter.Language(value[, names, ...])
Enum of the programming languages.
text_splitter.LatexTextSplitter(**kwargs)
Attempts to split the text along Latex-formatted layout elements.
text_splitter.LineType
Line type as typed dict.
text_splitter.MarkdownTextSplitter(**kwargs)
Attempts to split the text along Markdown-formatted headings. | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-60 | Attempts to split the text along Markdown-formatted headings.
text_splitter.NLTKTextSplitter([separator])
Splitting text using NLTK package.
text_splitter.PythonCodeTextSplitter(**kwargs)
Attempts to split the text along Python syntax.
text_splitter.RecursiveCharacterTextSplitter([...])
Splitting text by recursively look at characters.
text_splitter.SentenceTransformersTokenTextSplitter([...])
Splitting text to tokens using sentence model tokenizer.
text_splitter.SpacyTextSplitter([separator, ...])
Splitting text using Spacy package.
text_splitter.TextSplitter(chunk_size, ...)
Interface for splitting text into chunks.
text_splitter.TokenTextSplitter([...])
Splitting text to tokens using model tokenizer.
Functions¶
text_splitter.split_text_on_tokens(*, text, ...)
Split incoming text and return chunks using tokenizer.
langchain.tools¶
Tools are classes that an Agent uses to interact with the world.
Each tool has a description. Agent uses the description to choose the right
tool for the job.
Class hierarchy:
ToolMetaclass --> BaseTool --> <name>Tool # Examples: AIPluginTool, BaseGraphQLTool
<name> # Examples: BraveSearch, HumanInputRun
Main helpers:
CallbackManagerForToolRun, AsyncCallbackManagerForToolRun
Classes¶
tools.amadeus.base.AmadeusBaseTool
Base Tool for Amadeus.
tools.amadeus.closest_airport.AmadeusClosestAirport
Tool for finding the closest airport to a particular location.
tools.amadeus.closest_airport.ClosestAirportSchema
Schema for the AmadeusClosestAirport tool.
tools.amadeus.flight_search.AmadeusFlightSearch
Tool for searching for a single flight between two airports. | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-61 | Tool for searching for a single flight between two airports.
tools.amadeus.flight_search.FlightSearchSchema
Schema for the AmadeusFlightSearch tool.
tools.arxiv.tool.ArxivQueryRun
Tool that searches 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
Raised when 'args_schema' is missing or has an incorrect type annotation.
tools.base.StructuredTool
Tool that can operate on any number of inputs.
tools.base.Tool
Tool that takes in function or coroutine directly.
tools.base.ToolException
An optional exception that tool throws when execution error occurs.
tools.base.ToolMetaclass(name, bases, dct)
Metaclass for BaseTool to ensure the provided args_schema
tools.bing_search.tool.BingSearchResults
Tool that queries the Bing Search API and gets back json.
tools.bing_search.tool.BingSearchRun
Tool that queries the Bing search API.
tools.brave_search.tool.BraveSearch
Tool that queries the BraveSearch.
tools.convert_to_openai.FunctionDescription
Representation of a callable function to the OpenAI API.
tools.dataforseo_api_search.tool.DataForSeoAPISearchResults
Tool that queries the DataForSeo Google Search API and get back json. | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-62 | Tool that queries the DataForSeo Google Search API and get back json.
tools.dataforseo_api_search.tool.DataForSeoAPISearchRun
Tool that queries the DataForSeo Google search API.
tools.ddg_search.tool.DuckDuckGoSearchResults
Tool that queries the DuckDuckGo search API and gets back json.
tools.ddg_search.tool.DuckDuckGoSearchRun
Tool that queries the DuckDuckGo search API.
tools.file_management.copy.CopyFileTool
Tool that copies a file.
tools.file_management.copy.FileCopyInput
Input for CopyFileTool.
tools.file_management.delete.DeleteFileTool
Tool that deletes a file.
tools.file_management.delete.FileDeleteInput
Input for DeleteFileTool.
tools.file_management.file_search.FileSearchInput
Input for FileSearchTool.
tools.file_management.file_search.FileSearchTool
Tool that searches for files in a subdirectory that match a regex pattern.
tools.file_management.list_dir.DirectoryListingInput
Input for ListDirectoryTool.
tools.file_management.list_dir.ListDirectoryTool
Tool that lists files and directories in a specified folder.
tools.file_management.move.FileMoveInput
Input for MoveFileTool.
tools.file_management.move.MoveFileTool
Tool that moves a file.
tools.file_management.read.ReadFileInput
Input for ReadFileTool.
tools.file_management.read.ReadFileTool
Tool that reads a file.
tools.file_management.utils.BaseFileToolMixin
Mixin for file system tools.
tools.file_management.utils.FileValidationError
Error for paths outside the root directory.
tools.file_management.write.WriteFileInput
Input for WriteFileTool.
tools.file_management.write.WriteFileTool
Tool that writes a file to disk.
tools.github.tool.GitHubAction
Tool for interacting with the GitHub API.
tools.gmail.base.GmailBaseTool
Base class for Gmail tools.
tools.gmail.create_draft.CreateDraftSchema | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-63 | Base class for Gmail tools.
tools.gmail.create_draft.CreateDraftSchema
Input for CreateDraftTool.
tools.gmail.create_draft.GmailCreateDraft
Tool that creates a draft email for Gmail.
tools.gmail.get_message.GmailGetMessage
Tool that gets a message by ID from Gmail.
tools.gmail.get_message.SearchArgsSchema
Input for GetMessageTool.
tools.gmail.get_thread.GetThreadSchema
Input for GetMessageTool.
tools.gmail.get_thread.GmailGetThread
Tool that gets a thread by ID from Gmail.
tools.gmail.search.GmailSearch
Tool that searches for messages or threads in Gmail.
tools.gmail.search.Resource(value[, names, ...])
Enumerator of Resources to search.
tools.gmail.search.SearchArgsSchema
Input for SearchGmailTool.
tools.gmail.send_message.GmailSendMessage
Tool that sends a message to Gmail.
tools.gmail.send_message.SendMessageSchema
Input for SendMessageTool.
tools.golden_query.tool.GoldenQueryRun
Tool that adds the capability to query using the Golden API and get back JSON.
tools.google_places.tool.GooglePlacesSchema
Input for GooglePlacesTool.
tools.google_places.tool.GooglePlacesTool
Tool that queries the Google places API.
tools.google_search.tool.GoogleSearchResults
Tool that queries the Google Search API and gets back json.
tools.google_search.tool.GoogleSearchRun
Tool that queries the Google search API.
tools.google_serper.tool.GoogleSerperResults
Tool that queries the Serper.dev Google Search API and get back json.
tools.google_serper.tool.GoogleSerperRun
Tool that queries the Serper.dev Google search API.
tools.graphql.tool.BaseGraphQLTool
Base tool for querying a GraphQL API.
tools.human.tool.HumanInputRun
Tool that asks user for input.
tools.ifttt.IFTTTWebhook
IFTTT Webhook.
tools.jira.tool.JiraAction | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-64 | IFTTT Webhook.
tools.jira.tool.JiraAction
Tool that queries the Atlassian Jira API.
tools.json.tool.JsonGetValueTool
Tool for getting a value in a JSON spec.
tools.json.tool.JsonListKeysTool
Tool for listing keys in a JSON spec.
tools.json.tool.JsonSpec
Base class for JSON spec.
tools.metaphor_search.tool.MetaphorSearchResults
Tool that queries the Metaphor Search API and gets back json.
tools.multion.tool.MultionClientTool
Simulates a Browser interacting agent.
tools.office365.base.O365BaseTool
Base class for the Office 365 tools.
tools.office365.create_draft_message.CreateDraftMessageSchema
Input for SendMessageTool.
tools.office365.create_draft_message.O365CreateDraftMessage
Tool for creating a draft email in Office 365.
tools.office365.events_search.O365SearchEvents
Class for searching calendar events in Office 365
tools.office365.events_search.SearchEventsInput
Input for SearchEmails Tool.
tools.office365.messages_search.O365SearchEmails
Class for searching email messages in Office 365
tools.office365.messages_search.SearchEmailsInput
Input for SearchEmails Tool.
tools.office365.send_event.O365SendEvent
Tool for sending calendar events in Office 365.
tools.office365.send_event.SendEventSchema
Input for CreateEvent Tool.
tools.office365.send_message.O365SendMessage
Tool for sending an email in Office 365.
tools.office365.send_message.SendMessageSchema
Input for SendMessageTool.
tools.openapi.utils.api_models.APIOperation
A model for a single API operation.
tools.openapi.utils.api_models.APIProperty
A model for a property in the query, path, header, or cookie params.
tools.openapi.utils.api_models.APIPropertyBase
Base model for an API property.
tools.openapi.utils.api_models.APIPropertyLocation(value) | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-65 | Base model for an API property.
tools.openapi.utils.api_models.APIPropertyLocation(value)
The location of the property.
tools.openapi.utils.api_models.APIRequestBody
A model for a request body.
tools.openapi.utils.api_models.APIRequestBodyProperty
A model for a request body property.
tools.openweathermap.tool.OpenWeatherMapQueryRun
Tool that queries the OpenWeatherMap API.
tools.playwright.base.BaseBrowserTool
Base class for browser tools.
tools.playwright.click.ClickTool
Tool for clicking on an element with the given CSS selector.
tools.playwright.click.ClickToolInput
Input for ClickTool.
tools.playwright.current_page.CurrentWebPageTool
Tool for getting the URL of the current webpage.
tools.playwright.extract_hyperlinks.ExtractHyperlinksTool
Extract all hyperlinks on the page.
tools.playwright.extract_hyperlinks.ExtractHyperlinksToolInput
Input for ExtractHyperlinksTool.
tools.playwright.extract_text.ExtractTextTool
Tool for extracting all the text on the current webpage.
tools.playwright.get_elements.GetElementsTool
Tool for getting elements in the current web page matching a CSS selector.
tools.playwright.get_elements.GetElementsToolInput
Input for GetElementsTool.
tools.playwright.navigate.NavigateTool
Tool for navigating a browser to a URL.
tools.playwright.navigate.NavigateToolInput
Input for NavigateToolInput.
tools.playwright.navigate_back.NavigateBackTool
Navigate back to the previous page in the browser history.
tools.plugin.AIPlugin
AI Plugin Definition.
tools.plugin.AIPluginTool
Tool for getting the OpenAPI spec for an AI Plugin.
tools.plugin.AIPluginToolSchema
Schema for AIPluginTool.
tools.plugin.ApiConfig
API Configuration.
tools.powerbi.tool.InfoPowerBITool
Tool for getting metadata about a PowerBI Dataset.
tools.powerbi.tool.ListPowerBITool
Tool for getting tables names. | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-66 | tools.powerbi.tool.ListPowerBITool
Tool for getting tables names.
tools.powerbi.tool.QueryPowerBITool
Tool for querying a Power BI Dataset.
tools.pubmed.tool.PubmedQueryRun
Tool that searches the PubMed API.
tools.python.tool.PythonAstREPLTool
A tool for running python code in a REPL.
tools.python.tool.PythonREPLTool
A tool for running python code in a REPL.
tools.requests.tool.BaseRequestsTool
Base class for requests tools.
tools.requests.tool.RequestsDeleteTool
Tool for making a DELETE request to an API endpoint.
tools.requests.tool.RequestsGetTool
Tool for making a GET request to an API endpoint.
tools.requests.tool.RequestsPatchTool
Tool for making a PATCH request to an API endpoint.
tools.requests.tool.RequestsPostTool
Tool for making a POST request to an API endpoint.
tools.requests.tool.RequestsPutTool
Tool for making a PUT request to an API endpoint.
tools.scenexplain.tool.SceneXplainInput
Input for SceneXplain.
tools.scenexplain.tool.SceneXplainTool
Tool that explains images.
tools.searx_search.tool.SearxSearchResults
Tool that queries a Searx instance and gets back json.
tools.searx_search.tool.SearxSearchRun
Tool that queries a Searx instance.
tools.shell.tool.ShellInput
Commands for the Bash Shell tool.
tools.shell.tool.ShellTool
Tool to run shell commands.
tools.sleep.tool.SleepInput
Input for CopyFileTool.
tools.sleep.tool.SleepTool
Tool that adds the capability to sleep.
tools.spark_sql.tool.BaseSparkSQLTool
Base tool for interacting with Spark SQL.
tools.spark_sql.tool.InfoSparkSQLTool
Tool for getting metadata about a Spark SQL.
tools.spark_sql.tool.ListSparkSQLTool
Tool for getting tables names. | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-67 | tools.spark_sql.tool.ListSparkSQLTool
Tool for getting tables names.
tools.spark_sql.tool.QueryCheckerTool
Use an LLM to check if a query is correct.
tools.spark_sql.tool.QuerySparkSQLTool
Tool for querying a Spark SQL.
tools.sql_database.tool.BaseSQLDatabaseTool
Base tool for interacting with a SQL database.
tools.sql_database.tool.InfoSQLDatabaseTool
Tool for getting metadata about a SQL database.
tools.sql_database.tool.ListSQLDatabaseTool
Tool for getting tables names.
tools.sql_database.tool.QuerySQLCheckerTool
Use an LLM to check if a query is correct.
tools.sql_database.tool.QuerySQLDataBaseTool
Tool for querying a SQL database.
tools.steamship_image_generation.tool.ModelName(value)
Supported Image Models for generation.
tools.steamship_image_generation.tool.SteamshipImageGenerationTool
Tool used to generate images from a text-prompt.
tools.vectorstore.tool.BaseVectorStoreTool
Base class for tools that use a VectorStore.
tools.vectorstore.tool.VectorStoreQATool
Tool for the VectorDBQA chain.
tools.vectorstore.tool.VectorStoreQAWithSourcesTool
Tool for the VectorDBQAWithSources chain.
tools.wikipedia.tool.WikipediaQueryRun
Tool that searches the Wikipedia API.
tools.wolfram_alpha.tool.WolframAlphaQueryRun
Tool that queries using the Wolfram Alpha SDK.
tools.youtube.search.YouTubeSearchTool
Tool that queries YouTube.
tools.zapier.tool.ZapierNLAListActions
Returns a list of all exposed (enabled) actions associated with
tools.zapier.tool.ZapierNLARunAction
Executes an action that is identified by action_id, must be exposed
Functions¶
tools.amadeus.utils.authenticate()
Authenticate using the Amadeus API
tools.azure_cognitive_services.utils.detect_file_src_type(...) | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-68 | Authenticate using the Amadeus API
tools.azure_cognitive_services.utils.detect_file_src_type(...)
Detect if the file is local or remote.
tools.azure_cognitive_services.utils.download_audio_from_url(...)
Download audio from url to local.
tools.base.create_schema_from_function(...)
Create a pydantic schema from a function's signature.
tools.base.tool(*args[, return_direct, ...])
Make tools out of functions, can be used with or without arguments.
tools.convert_to_openai.format_tool_to_openai_function(tool)
Format tool into the OpenAI function API.
tools.ddg_search.tool.DuckDuckGoSearchTool(...)
Deprecated.
tools.file_management.utils.get_validated_relative_path(...)
Resolve a relative path, raising an error if not within the root directory.
tools.file_management.utils.is_relative_to(...)
Check if path is relative to root.
tools.gmail.utils.build_resource_service([...])
Build a Gmail service.
tools.gmail.utils.clean_email_body(body)
Clean email body.
tools.gmail.utils.get_gmail_credentials([...])
Get credentials.
tools.gmail.utils.import_google()
Import google libraries.
tools.gmail.utils.import_googleapiclient_resource_builder()
Import googleapiclient.discovery.build function.
tools.gmail.utils.import_installed_app_flow()
Import InstalledAppFlow class.
tools.interaction.tool.StdInInquireTool(...)
Tool for asking the user for input.
tools.office365.utils.authenticate()
Authenticate using the Microsoft Grah API
tools.office365.utils.clean_body(body)
Clean body of a message or event.
tools.playwright.base.lazy_import_playwright_browsers()
Lazy import playwright browsers.
tools.playwright.utils.aget_current_page(browser)
Asynchronously get the current page of the browser.
tools.playwright.utils.create_async_playwright_browser([...])
Create an async playwright browser. | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-69 | tools.playwright.utils.create_async_playwright_browser([...])
Create an async playwright browser.
tools.playwright.utils.create_sync_playwright_browser([...])
Create a playwright browser.
tools.playwright.utils.get_current_page(browser)
Get the current page of the browser.
tools.playwright.utils.run_async(coro)
Run an async coroutine.
tools.plugin.marshal_spec(txt)
Convert the yaml or json serialized spec to a dict.
tools.python.tool.sanitize_input(query)
Sanitize input to the python REPL.
tools.steamship_image_generation.utils.make_image_public(...)
Upload a block to a signed URL and return the public URL.
langchain.utilities¶
Utilities are the integrations with third-part systems and packages.
Other LangChain classes use Utilities to interact with third-part systems
and packages.
Classes¶
utilities.arxiv.ArxivAPIWrapper
Wrapper around ArxivAPI.
utilities.awslambda.LambdaWrapper
Wrapper for AWS Lambda SDK.
utilities.bibtex.BibtexparserWrapper
Wrapper around bibtexparser.
utilities.bing_search.BingSearchAPIWrapper
Wrapper for Bing Search API.
utilities.brave_search.BraveSearchWrapper
Wrapper around the Brave search engine.
utilities.dataforseo_api_search.DataForSeoAPIWrapper
Wrapper around the DataForSeo API.
utilities.duckduckgo_search.DuckDuckGoSearchAPIWrapper
Wrapper for DuckDuckGo Search API.
utilities.github.GitHubAPIWrapper
Wrapper for GitHub API.
utilities.golden_query.GoldenQueryAPIWrapper
Wrapper for Golden.
utilities.google_places_api.GooglePlacesAPIWrapper
Wrapper around Google Places API.
utilities.google_search.GoogleSearchAPIWrapper
Wrapper for Google Search API.
utilities.google_serper.GoogleSerperAPIWrapper
Wrapper around the Serper.dev Google Search API.
utilities.graphql.GraphQLAPIWrapper | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-70 | Wrapper around the Serper.dev Google Search API.
utilities.graphql.GraphQLAPIWrapper
Wrapper around GraphQL API.
utilities.jira.JiraAPIWrapper
Wrapper for Jira API.
utilities.metaphor_search.MetaphorSearchAPIWrapper
Wrapper for Metaphor Search API.
utilities.multion.MultionClientAPIWrapper
Wrapper for Multion Client API.
utilities.openapi.HTTPVerb(value[, names, ...])
Enumerator of the HTTP verbs.
utilities.openapi.OpenAPISpec
OpenAPI Model that removes misformatted parts of the spec.
utilities.openweathermap.OpenWeatherMapAPIWrapper
Wrapper for OpenWeatherMap API using PyOWM.
utilities.powerbi.PowerBIDataset
Create PowerBI engine from dataset ID and credential or token.
utilities.pupmed.PubMedAPIWrapper
Wrapper around PubMed API.
utilities.python.PythonREPL
Simulates a standalone Python REPL.
utilities.requests.Requests
Wrapper around requests to handle auth and async.
utilities.requests.TextRequestsWrapper
Lightweight wrapper around requests library.
utilities.scenexplain.SceneXplainAPIWrapper
Wrapper for SceneXplain API.
utilities.searx_search.SearxResults(data)
Dict like wrapper around search api results.
utilities.searx_search.SearxSearchWrapper
Wrapper for Searx API.
utilities.serpapi.SerpAPIWrapper
Wrapper around SerpAPI.
utilities.twilio.TwilioAPIWrapper
Messaging Client using Twilio.
utilities.wikipedia.WikipediaAPIWrapper
Wrapper around WikipediaAPI.
utilities.wolfram_alpha.WolframAlphaAPIWrapper
Wrapper for Wolfram Alpha.
utilities.zapier.ZapierNLAWrapper
Wrapper for Zapier NLA.
Functions¶
utilities.loading.try_load_from_hub(path, ...)
Load configuration from hub.
utilities.powerbi.fix_table_name(table) | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-71 | Load configuration from hub.
utilities.powerbi.fix_table_name(table)
Add single quotes around table names that contain spaces.
utilities.powerbi.json_to_md(json_contents)
Converts a JSON object to a markdown table.
utilities.python.warn_once()
Warn once about the dangers of PythonREPL.
utilities.redis.get_client(redis_url, **kwargs)
Get a redis client from the connection url given.
utilities.sql_database.truncate_word(...[, ...])
Truncate a string to a certain number of words, based on the max string length.
utilities.vertexai.init_vertexai([project, ...])
Init vertexai.
utilities.vertexai.raise_vertex_import_error()
Raise ImportError related to Vertex SDK being not available.
langchain.utils¶
Utility functions for LangChain.
These functions do not depend on any other LangChain module.
Classes¶
utils.formatting.StrictFormatter()
A subclass of formatter that checks for extra keys.
Functions¶
utils.env.get_from_dict_or_env(data, key, ...)
Get a value from a dictionary or an environment variable.
utils.env.get_from_env(key, env_key[, default])
Get a value from a dictionary or an environment variable.
utils.input.get_bolded_text(text)
Get bolded text.
utils.input.get_color_mapping(items[, ...])
Get mapping for items to a support color.
utils.input.get_colored_text(text, color)
Get colored text.
utils.input.print_text(text[, color, end, file])
Print text with highlighting and no end characters.
utils.math.cosine_similarity(X, Y)
Row-wise cosine similarity between two equal-width matrices.
utils.math.cosine_similarity_top_k(X, Y[, ...])
Row-wise cosine similarity with optional top-k and score threshold filtering.
utils.strings.comma_list(items) | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-72 | utils.strings.comma_list(items)
Convert a list to a comma-separated string.
utils.strings.stringify_dict(data)
Stringify a dictionary.
utils.strings.stringify_value(val)
Stringify a value.
utils.utils.check_package_version(package[, ...])
Check the version of a package.
utils.utils.get_pydantic_field_names(...)
Get field names, including aliases, for a pydantic class.
utils.utils.guard_import(module_name, *[, ...])
Dynamically imports a module and raises a helpful exception if the module is not installed.
utils.utils.mock_now(dt_value)
Context manager for mocking out datetime.now() in unit tests.
utils.utils.raise_for_status_with_text(response)
Raise an error with the response text.
utils.utils.xor_args(*arg_groups)
Validate specified keyword args are mutually exclusive.
langchain.vectorstores¶
Vector store stores embedded data and performs vector search.
One of the most common ways to store and search over unstructured data is to
embed it and store the resulting embedding vectors, and then query the store
and retrieve the data that are ‘most similar’ to the embedded query.
Class hierarchy:
VectorStore --> <name> # Examples: Annoy, FAISS, Milvus
BaseRetriever --> VectorStoreRetriever --> <name>Retriever # Example: VespaRetriever
Main helpers:
Embeddings, Document
Classes¶
vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearch(...)
Alibaba Cloud OpenSearch Vector Store
vectorstores.analyticdb.AnalyticDB(...[, ...])
VectorStore implementation using AnalyticDB.
vectorstores.annoy.Annoy(embedding_function, ...)
Wrapper around Annoy vector database.
vectorstores.atlas.AtlasDB(name[, ...]) | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-73 | vectorstores.atlas.AtlasDB(name[, ...])
Wrapper around Atlas: Nomic's neural database and rhizomatic instrument.
vectorstores.awadb.AwaDB([table_name, ...])
Interface implemented by AwaDB vector stores.
vectorstores.azuresearch.AzureSearch(...[, ...])
Azure Cognitive Search vector store.
vectorstores.azuresearch.AzureSearchVectorStoreRetriever
Retriever that uses Azure Search to find similar documents.
vectorstores.base.VectorStore()
Interface for vector stores.
vectorstores.base.VectorStoreRetriever
Retriever class for VectorStore.
vectorstores.cassandra.Cassandra(embedding, ...)
Wrapper around Cassandra embeddings platform.
vectorstores.chroma.Chroma([...])
Wrapper around ChromaDB embeddings platform.
vectorstores.clarifai.Clarifai([user_id, ...])
Wrapper around Clarifai AI platform's vector store.
vectorstores.clickhouse.Clickhouse(embedding)
Wrapper around ClickHouse vector database
vectorstores.clickhouse.ClickhouseSettings
ClickHouse Client Configuration
vectorstores.deeplake.DeepLake([...])
Wrapper around Deep Lake, a data lake for deep learning applications.
vectorstores.docarray.base.DocArrayIndex(...)
Initialize a vector store from DocArray's DocIndex.
vectorstores.docarray.hnsw.DocArrayHnswSearch(...)
Wrapper around HnswLib storage.
vectorstores.docarray.in_memory.DocArrayInMemorySearch(...)
Wrapper around in-memory storage for exact search.
vectorstores.elastic_vector_search.ElasticKnnSearch(...)
ElasticKnnSearch is a class for performing k-nearest neighbor (k-NN) searches on text data using Elasticsearch.
vectorstores.elastic_vector_search.ElasticVectorSearch(...)
Wrapper around Elasticsearch as a vector database.
vectorstores.faiss.FAISS(embedding_function, ...) | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-74 | vectorstores.faiss.FAISS(embedding_function, ...)
Wrapper around FAISS vector database.
vectorstores.hologres.Hologres(...[, ndims, ...])
VectorStore implementation using Hologres.
vectorstores.lancedb.LanceDB(connection, ...)
Wrapper around LanceDB vector database.
vectorstores.marqo.Marqo(client, index_name)
Wrapper around Marqo database.
vectorstores.matching_engine.MatchingEngine(...)
Vertex Matching Engine implementation of the vector store.
vectorstores.meilisearch.Meilisearch(embedding)
Initialize wrapper around Meilisearch vector database.
vectorstores.milvus.Milvus(embedding_function)
Initialize wrapper around the milvus vector database.
vectorstores.mongodb_atlas.MongoDBAtlasVectorSearch(...)
Wrapper around MongoDB Atlas Vector Search.
vectorstores.myscale.MyScale(embedding[, config])
Wrapper around MyScale vector database
vectorstores.myscale.MyScaleSettings
MyScale Client Configuration
vectorstores.opensearch_vector_search.OpenSearchVectorSearch(...)
Wrapper around OpenSearch as a vector database.
vectorstores.pgembedding.BaseModel(**kwargs)
A simple constructor that allows initialization from kwargs.
vectorstores.pgembedding.CollectionStore(...)
A simple constructor that allows initialization from kwargs.
vectorstores.pgembedding.EmbeddingStore(**kwargs)
A simple constructor that allows initialization from kwargs.
vectorstores.pgembedding.PGEmbedding(...[, ...])
VectorStore implementation using Postgres and the pg_embedding extension.
vectorstores.pgvector.BaseModel(**kwargs)
A simple constructor that allows initialization from kwargs.
vectorstores.pgvector.DistanceStrategy(value)
Enumerator of the Distance strategies.
vectorstores.pgvector.PGVector(...[, ...])
VectorStore implementation using Postgres and pgvector. | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-75 | VectorStore implementation using Postgres and pgvector.
vectorstores.pinecone.Pinecone(index, ...[, ...])
Wrapper around Pinecone vector database.
vectorstores.qdrant.Qdrant(client, ...[, ...])
Wrapper around Qdrant vector database.
vectorstores.qdrant.QdrantException
Base class for all the Qdrant related exceptions
vectorstores.redis.Redis(redis_url, ...[, ...])
Wrapper around Redis vector database.
vectorstores.redis.RedisVectorStoreRetriever
Retriever for Redis VectorStore.
vectorstores.rocksetdb.Rockset(client, ...)
Wrapper arpund Rockset vector database.
vectorstores.singlestoredb.SingleStoreDB(...)
This class serves as a Pythonic interface to the SingleStore DB database.
vectorstores.singlestoredb.SingleStoreDBRetriever
Retriever for SingleStoreDB vector stores.
vectorstores.sklearn.BaseSerializer(persist_path)
Abstract base class for saving and loading data.
vectorstores.sklearn.BsonSerializer(persist_path)
Serializes data in binary json using the bson python package.
vectorstores.sklearn.JsonSerializer(persist_path)
Serializes data in json using the json package from python standard library.
vectorstores.sklearn.ParquetSerializer(...)
Serializes data in Apache Parquet format using the pyarrow package.
vectorstores.sklearn.SKLearnVectorStore(...)
A simple in-memory vector store based on the scikit-learn library NearestNeighbors implementation.
vectorstores.sklearn.SKLearnVectorStoreException
Exception raised by SKLearnVectorStore.
vectorstores.starrocks.StarRocks(embedding)
Wrapper around StarRocks vector database
vectorstores.starrocks.StarRocksSettings
StarRocks Client Configuration
vectorstores.supabase.SupabaseVectorStore(...) | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-76 | StarRocks Client Configuration
vectorstores.supabase.SupabaseVectorStore(...)
VectorStore for a Supabase postgres database.
vectorstores.tair.Tair(embedding_function, ...)
Wrapper around Tair Vector store.
vectorstores.tigris.Tigris(client, ...)
Initialize Tigris vector store
vectorstores.typesense.Typesense(...[, ...])
Wrapper around Typesense vector search.
vectorstores.utils.DistanceStrategy(value[, ...])
Enumerator of the Distance strategies for calculating distances between vectors.
vectorstores.vectara.Vectara([...])
Implementation of Vector Store using Vectara.
vectorstores.vectara.VectaraRetriever
Retriever class for Vectara.
vectorstores.weaviate.Weaviate(client, ...)
Wrapper around Weaviate vector database.
vectorstores.zilliz.Zilliz(embedding_function)
Initialize wrapper around the Zilliz vector database.
Functions¶
vectorstores.alibabacloud_opensearch.create_metadata(fields)
Create metadata from fields.
vectorstores.annoy.dependable_annoy_import()
Import annoy if available, otherwise raise error.
vectorstores.clickhouse.has_mul_sub_str(s, *args)
Check if a string contains multiple substrings.
vectorstores.faiss.dependable_faiss_import([...])
Import faiss if available, otherwise raise error.
vectorstores.myscale.has_mul_sub_str(s, *args)
Check if a string contains multiple substrings.
vectorstores.qdrant.sync_call_fallback(method)
Decorator to call the synchronous method of the class if the async method is not implemented.
vectorstores.starrocks.debug_output(s)
Print a debug message if DEBUG is True.
vectorstores.starrocks.get_named_result(...)
Get a named result from a query. | https://api.python.langchain.com/en/latest/api_reference.html |
b2499a3c04c8-77 | vectorstores.starrocks.get_named_result(...)
Get a named result from a query.
vectorstores.starrocks.has_mul_sub_str(s, *args)
Check if a string has multiple substrings.
vectorstores.utils.maximal_marginal_relevance(...)
Calculate maximal marginal relevance. | https://api.python.langchain.com/en/latest/api_reference.html |
a9cc6dfdfc9e-0 | langchain_experimental API Reference¶
langchain_experimental.autonomous_agents¶
Classes¶
autonomous_agents.autogpt.memory.AutoGPTMemory
Memory for AutoGPT.
autonomous_agents.autogpt.output_parser.AutoGPTAction(...)
Action returned by AutoGPTOutputParser.
autonomous_agents.autogpt.output_parser.AutoGPTOutputParser
Output parser for AutoGPT.
autonomous_agents.autogpt.output_parser.BaseAutoGPTOutputParser
Base Output parser for AutoGPT.
autonomous_agents.autogpt.prompt.AutoGPTPrompt
Prompt for AutoGPT.
autonomous_agents.baby_agi.baby_agi.BabyAGI
Controller model for the BabyAGI agent.
autonomous_agents.baby_agi.task_creation.TaskCreationChain
Chain generating tasks.
autonomous_agents.baby_agi.task_execution.TaskExecutionChain
Chain to execute tasks.
autonomous_agents.baby_agi.task_prioritization.TaskPrioritizationChain
Chain to prioritize tasks.
autonomous_agents.hugginggpt.repsonse_generator.ResponseGenerationChain
Chain to execute tasks.
autonomous_agents.hugginggpt.task_planner.BasePlanner
Create a new model by parsing and validating input data from keyword arguments.
autonomous_agents.hugginggpt.task_planner.PlanningOutputParser
Create a new model by parsing and validating input data from keyword arguments.
autonomous_agents.hugginggpt.task_planner.TaskPlaningChain
Chain to execute tasks.
autonomous_agents.hugginggpt.task_planner.TaskPlanner
Create a new model by parsing and validating input data from keyword arguments.
Functions¶
autonomous_agents.autogpt.output_parser.preprocess_json_input(...)
Preprocesses a string to be parsed as json.
autonomous_agents.autogpt.prompt_generator.get_prompt(tools)
Generates a prompt string. | https://api.python.langchain.com/en/latest/experimental_api_reference.html |
a9cc6dfdfc9e-1 | Generates a prompt string.
autonomous_agents.hugginggpt.repsonse_generator.load_response_generator(llm)
autonomous_agents.hugginggpt.task_planner.load_chat_planner(llm)
langchain_experimental.cpal¶
Classes¶
cpal.constants.Constant(value[, names, ...])
Enum for constants used in the CPAL.
langchain_experimental.generative_agents¶
Generative Agents primitives.
Classes¶
generative_agents.generative_agent.GenerativeAgent
An Agent as a character with memory and innate characteristics.
generative_agents.memory.GenerativeAgentMemory
Memory for the generative agent.
langchain_experimental.llms¶
Experimental LLM wrappers.
Classes¶
llms.anthropic_functions.AnthropicFunctions
Create a new model by parsing and validating input data from keyword arguments.
llms.anthropic_functions.TagParser()
A heavy-handed solution, but it's fast for prototyping.
llms.jsonformer_decoder.JsonFormer
Jsonformer wrapped LLM using HuggingFace Pipeline API.
llms.llamaapi.ChatLlamaAPI
Create a new model by parsing and validating input data from keyword arguments.
llms.rellm_decoder.RELLM
RELLM wrapped LLM using HuggingFace Pipeline API.
Functions¶
llms.jsonformer_decoder.import_jsonformer()
Lazily import jsonformer.
llms.rellm_decoder.import_rellm()
Lazily import rellm.
langchain_experimental.pal_chain¶
Implements Program-Aided Language Models.
As in https://arxiv.org/pdf/2211.10435.pdf.
This is vulnerable to arbitrary code execution:
https://github.com/hwchase17/langchain/issues/5872
Classes¶
pal_chain.base.PALChain
Implements Program-Aided Language Models (PAL).
Functions¶ | https://api.python.langchain.com/en/latest/experimental_api_reference.html |
a9cc6dfdfc9e-2 | Implements Program-Aided Language Models (PAL).
Functions¶
langchain_experimental.plan_and_execute¶
Classes¶
plan_and_execute.agent_executor.PlanAndExecute
Plan and execute a chain of steps.
plan_and_execute.executors.base.BaseExecutor
Base executor.
plan_and_execute.executors.base.ChainExecutor
Chain executor.
plan_and_execute.planners.base.BasePlanner
Base planner.
plan_and_execute.planners.base.LLMPlanner
LLM planner.
plan_and_execute.planners.chat_planner.PlanningOutputParser
Planning output parser.
plan_and_execute.schema.BaseStepContainer
Base step container.
plan_and_execute.schema.ListStepContainer
List step container.
plan_and_execute.schema.Plan
Plan.
plan_and_execute.schema.PlanOutputParser
Plan output parser.
plan_and_execute.schema.Step
Step.
plan_and_execute.schema.StepResponse
Step response.
Functions¶
plan_and_execute.executors.agent_executor.load_agent_executor(...)
Load an agent executor.
plan_and_execute.planners.chat_planner.load_chat_planner(llm)
Load a chat planner.
langchain_experimental.prompts¶
Functions¶
langchain_experimental.sql¶
Chain for interacting with SQL Database.
Classes¶
sql.base.SQLDatabaseChain
Chain for interacting with SQL Database.
sql.base.SQLDatabaseSequentialChain
Chain for querying SQL database that is a sequential chain.
langchain_experimental.tot¶
Classes¶
tot.base.ToTChain
A Chain implementing the Tree of Thought (ToT).
tot.checker.ToTChecker
Tree of Thought (ToT) checker.
tot.prompts.CheckerOutputParser
Create a new model by parsing and validating input data from keyword arguments.
tot.prompts.JSONListOutputParser
Class to parse the output of a PROPOSE_PROMPT response.
tot.thought.Thought | https://api.python.langchain.com/en/latest/experimental_api_reference.html |
a9cc6dfdfc9e-3 | Class to parse the output of a PROPOSE_PROMPT response.
tot.thought.Thought
Create a new model by parsing and validating input data from keyword arguments.
tot.thought.ThoughtValidity(value[, names, ...])
tot.thought_generation.BaseThoughtGenerationStrategy
Base class for a thought generation strategy.
tot.thought_generation.ProposePromptStrategy
Propose thoughts sequentially using a "propose prompt".
tot.thought_generation.SampleCoTStrategy
Sample thoughts from a Chain-of-Thought (CoT) prompt. | https://api.python.langchain.com/en/latest/experimental_api_reference.html |
3277559fb4d4-0 | langchain_experimental.autonomous_agents.autogpt.output_parser.preprocess_json_input¶
langchain_experimental.autonomous_agents.autogpt.output_parser.preprocess_json_input(input_str: str) → str[source]¶
Preprocesses a string to be parsed as json.
Replace single backslashes with double backslashes,
while leaving already escaped ones intact.
Parameters
input_str – String to be preprocessed
Returns
Preprocessed string | https://api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.autogpt.output_parser.preprocess_json_input.html |
049d85f7f584-0 | langchain_experimental.autonomous_agents.autogpt.memory.AutoGPTMemory¶
class langchain_experimental.autonomous_agents.autogpt.memory.AutoGPTMemory(*, chat_memory: BaseChatMessageHistory = None, output_key: Optional[str] = None, input_key: Optional[str] = None, return_messages: bool = False, retriever: VectorStoreRetriever)[source]¶
Bases: BaseChatMemory
Memory for AutoGPT.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param chat_memory: BaseChatMessageHistory [Optional]¶
param input_key: Optional[str] = None¶
param output_key: Optional[str] = None¶
param retriever: langchain.vectorstores.base.VectorStoreRetriever [Required]¶
VectorStoreRetriever object to connect to.
param return_messages: bool = False¶
clear() → None¶
Clear memory contents.
load_memory_variables(inputs: Dict[str, Any]) → Dict[str, Any][source]¶
Return key-value pairs given the text input to the chain.
save_context(inputs: Dict[str, Any], outputs: Dict[str, str]) → None¶
Save context from this conversation to buffer.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids. | https://api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.autogpt.memory.AutoGPTMemory.html |
049d85f7f584-1 | Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
property memory_variables: List[str]¶
The string keys this memory class will add to chain inputs.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶ | https://api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.autogpt.memory.AutoGPTMemory.html |
1fe8d1421e59-0 | langchain_experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain¶
class langchain_experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain(*, memory: Optional[BaseMemory] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, verbose: bool = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, prompt: BasePromptTemplate, llm: BaseLanguageModel, output_key: str = 'text', output_parser: BaseLLMOutputParser = None, return_final_only: bool = True, llm_kwargs: dict = None)[source]¶
Bases: LLMChain
Chain to execute tasks.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated, use callbacks instead.
param callbacks: Callbacks = None¶
Optional list of callback handlers (or callback manager). Defaults to None.
Callback handlers are called throughout the lifecycle of a call to a chain,
starting with on_chain_start, ending with on_chain_end or on_chain_error.
Each custom chain can optionally call additional callback methods, see Callback docs
for full details.
param llm: BaseLanguageModel [Required]¶
Language model to call.
param llm_kwargs: dict [Optional]¶
param memory: Optional[BaseMemory] = None¶
Optional memory object. Defaults to None.
Memory is a class that gets called at the start
and at the end of every chain. At the start, memory loads variables and passes
them along in the chain. At the end, it saves any returned variables. | https://api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain.html |
1fe8d1421e59-1 | them along in the chain. At the end, it saves any returned variables.
There are many different types of memory - please see memory docs
for the full catalog.
param metadata: Optional[Dict[str, Any]] = None¶
Optional metadata associated with the chain. Defaults to None.
This metadata will be associated with each call to this chain,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a chain with its use case.
param output_key: str = 'text'¶
param output_parser: BaseLLMOutputParser [Optional]¶
Output parser to use.
Defaults to one that takes the most likely string but does not change it
otherwise.
param prompt: BasePromptTemplate [Required]¶
Prompt object to use.
param return_final_only: bool = True¶
Whether to return only the final parsed result. Defaults to True.
If false, will return a bunch of extra information about the generation.
param tags: Optional[List[str]] = None¶
Optional list of tags associated with the chain. Defaults to None.
These tags will be associated with each call to this chain,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a chain with its use case.
param verbose: bool [Optional]¶
Whether or not run in verbose mode. In verbose mode, some intermediate logs
will be printed to the console. Defaults to langchain.verbose value.
__call__(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, include_run_info: bool = False) → Dict[str, Any]¶ | https://api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain.html |
1fe8d1421e59-2 | Execute the chain.
Parameters
inputs – Dictionary of inputs, or single input if chain expects
only one param. Should contain all inputs specified in
Chain.input_keys except for inputs that will be set by the chain’s
memory.
return_only_outputs – Whether to return only outputs in the
response. If True, only new keys generated by this chain will be
returned. If False, both input keys and new keys generated by this
chain will be returned. Defaults to False.
callbacks – Callbacks to use for this chain run. These will be called in
addition to callbacks passed to the chain during construction, but only
these runtime callbacks will propagate to calls to other objects.
tags – List of string tags to pass to all callbacks. These will be passed in
addition to tags passed to the chain during construction, but only
these runtime tags will propagate to calls to other objects.
metadata – Optional metadata associated with the chain. Defaults to None
include_run_info – Whether to include run info in the response. Defaults
to False.
Returns
A dict of named outputs. Should contain all outputs specified inChain.output_keys.
async aapply(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → List[Dict[str, str]]¶
Utilize the LLM generate method for speed gains.
async aapply_and_parse(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → Sequence[Union[str, List[str], Dict[str, str]]]¶
Call apply and then parse the results. | https://api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain.html |
1fe8d1421e59-3 | Call apply and then parse the results.
async acall(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, include_run_info: bool = False) → Dict[str, Any]¶
Asynchronously execute the chain.
Parameters
inputs – Dictionary of inputs, or single input if chain expects
only one param. Should contain all inputs specified in
Chain.input_keys except for inputs that will be set by the chain’s
memory.
return_only_outputs – Whether to return only outputs in the
response. If True, only new keys generated by this chain will be
returned. If False, both input keys and new keys generated by this
chain will be returned. Defaults to False.
callbacks – Callbacks to use for this chain run. These will be called in
addition to callbacks passed to the chain during construction, but only
these runtime callbacks will propagate to calls to other objects.
tags – List of string tags to pass to all callbacks. These will be passed in
addition to tags passed to the chain during construction, but only
these runtime tags will propagate to calls to other objects.
metadata – Optional metadata associated with the chain. Defaults to None
include_run_info – Whether to include run info in the response. Defaults
to False.
Returns
A dict of named outputs. Should contain all outputs specified inChain.output_keys.
async agenerate(input_list: List[Dict[str, Any]], run_manager: Optional[AsyncCallbackManagerForChainRun] = None) → LLMResult¶
Generate LLM result from inputs.
async ainvoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None) → Dict[str, Any]¶ | https://api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain.html |
1fe8d1421e59-4 | apply(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → List[Dict[str, str]]¶
Utilize the LLM generate method for speed gains.
apply_and_parse(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → Sequence[Union[str, List[str], Dict[str, str]]]¶
Call apply and then parse the results.
async apredict(callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → str¶
Format prompt with kwargs and pass to LLM.
Parameters
callbacks – Callbacks to pass to LLMChain
**kwargs – Keys to pass to prompt template.
Returns
Completion from LLM.
Example
completion = llm.predict(adjective="funny")
async apredict_and_parse(callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Union[str, List[str], Dict[str, str]]¶
Call apredict and then parse the results.
async aprep_prompts(input_list: List[Dict[str, Any]], run_manager: Optional[AsyncCallbackManagerForChainRun] = None) → Tuple[List[PromptValue], Optional[List[str]]]¶
Prepare prompts from inputs.
async arun(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Convenience method for executing chain.
The main difference between this method and Chain.__call__ is that this
method expects inputs to be passed directly in as positional arguments or | https://api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain.html |
1fe8d1421e59-5 | method expects inputs to be passed directly in as positional arguments or
keyword arguments, whereas Chain.__call__ expects a single input dictionary
with all the inputs
Parameters
*args – If the chain expects a single input, it can be passed in as the
sole positional argument.
callbacks – Callbacks to use for this chain run. These will be called in
addition to callbacks passed to the chain during construction, but only
these runtime callbacks will propagate to calls to other objects.
tags – List of string tags to pass to all callbacks. These will be passed in
addition to tags passed to the chain during construction, but only
these runtime tags will propagate to calls to other objects.
**kwargs – If the chain expects multiple inputs, they can be passed in
directly as keyword arguments.
Returns
The chain output.
Example
# Suppose we have a single-input chain that takes a 'question' string:
await chain.arun("What's the temperature in Boise, Idaho?")
# -> "The temperature in Boise is..."
# Suppose we have a multi-input chain that takes a 'question' string
# and 'context' string:
question = "What's the temperature in Boise, Idaho?"
context = "Weather report for Boise, Idaho on 07/03/23..."
await chain.arun(question=question, context=context)
# -> "The temperature in Boise is..."
create_outputs(llm_result: LLMResult) → List[Dict[str, Any]]¶
Create outputs from response.
dict(**kwargs: Any) → Dict¶
Dictionary representation of chain.
Expects Chain._chain_type property to be implemented and for memory to benull.
Parameters
**kwargs – Keyword arguments passed to default pydantic.BaseModel.dict
method.
Returns
A dictionary representation of the chain.
Example
..code-block:: python | https://api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain.html |
1fe8d1421e59-6 | Returns
A dictionary representation of the chain.
Example
..code-block:: python
chain.dict(exclude_unset=True)
# -> {“_type”: “foo”, “verbose”: False, …}
classmethod from_llm(llm: BaseLanguageModel, verbose: bool = True) → LLMChain[source]¶
Get the response parser.
classmethod from_string(llm: BaseLanguageModel, template: str) → LLMChain¶
Create LLMChain from LLM and template.
generate(input_list: List[Dict[str, Any]], run_manager: Optional[CallbackManagerForChainRun] = None) → LLMResult¶
Generate LLM result from inputs.
invoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None) → Dict[str, Any]¶
predict(callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → str¶
Format prompt with kwargs and pass to LLM.
Parameters
callbacks – Callbacks to pass to LLMChain
**kwargs – Keys to pass to prompt template.
Returns
Completion from LLM.
Example
completion = llm.predict(adjective="funny")
predict_and_parse(callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Union[str, List[str], Dict[str, Any]]¶
Call predict and then parse the results.
prep_inputs(inputs: Union[Dict[str, Any], Any]) → Dict[str, str]¶
Validate and prepare chain inputs, including adding inputs from memory.
Parameters
inputs – Dictionary of raw inputs, or single input if chain expects
only one param. Should contain all inputs specified in
Chain.input_keys except for inputs that will be set by the chain’s
memory.
Returns | https://api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain.html |
1fe8d1421e59-7 | Chain.input_keys except for inputs that will be set by the chain’s
memory.
Returns
A dictionary of all inputs, including those added by the chain’s memory.
prep_outputs(inputs: Dict[str, str], outputs: Dict[str, str], return_only_outputs: bool = False) → Dict[str, str]¶
Validate and prepare chain outputs, and save info about this run to memory.
Parameters
inputs – Dictionary of chain inputs, including any inputs added by chain
memory.
outputs – Dictionary of initial chain outputs.
return_only_outputs – Whether to only return the chain outputs. If False,
inputs are also added to the final outputs.
Returns
A dict of the final chain outputs.
prep_prompts(input_list: List[Dict[str, Any]], run_manager: Optional[CallbackManagerForChainRun] = None) → Tuple[List[PromptValue], Optional[List[str]]]¶
Prepare prompts from inputs.
validator raise_callback_manager_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Convenience method for executing chain.
The main difference between this method and Chain.__call__ is that this
method expects inputs to be passed directly in as positional arguments or
keyword arguments, whereas Chain.__call__ expects a single input dictionary
with all the inputs
Parameters
*args – If the chain expects a single input, it can be passed in as the
sole positional argument.
callbacks – Callbacks to use for this chain run. These will be called in
addition to callbacks passed to the chain during construction, but only
these runtime callbacks will propagate to calls to other objects. | https://api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain.html |
1fe8d1421e59-8 | these runtime callbacks will propagate to calls to other objects.
tags – List of string tags to pass to all callbacks. These will be passed in
addition to tags passed to the chain during construction, but only
these runtime tags will propagate to calls to other objects.
**kwargs – If the chain expects multiple inputs, they can be passed in
directly as keyword arguments.
Returns
The chain output.
Example
# Suppose we have a single-input chain that takes a 'question' string:
chain.run("What's the temperature in Boise, Idaho?")
# -> "The temperature in Boise is..."
# Suppose we have a multi-input chain that takes a 'question' string
# and 'context' string:
question = "What's the temperature in Boise, Idaho?"
context = "Weather report for Boise, Idaho on 07/03/23..."
chain.run(question=question, context=context)
# -> "The temperature in Boise is..."
save(file_path: Union[Path, str]) → None¶
Save the chain.
Expects Chain._chain_type property to be implemented and for memory to benull.
Parameters
file_path – Path to file to save the chain to.
Example
chain.save(file_path="path/chain.yaml")
validator set_verbose » verbose¶
Set the chain verbosity.
Defaults to the global setting if not specified by the user.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”] | https://api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain.html |
1fe8d1421e59-9 | eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶ | https://api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain.html |
037090486a45-0 | langchain_experimental.autonomous_agents.autogpt.output_parser.AutoGPTOutputParser¶
class langchain_experimental.autonomous_agents.autogpt.output_parser.AutoGPTOutputParser[source]¶
Bases: BaseAutoGPTOutputParser
Output parser for AutoGPT.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
dict(**kwargs: Any) → Dict¶
Return dictionary representation of output parser.
get_format_instructions() → str¶
Instructions on how the LLM output should be formatted.
invoke(input: str | langchain.schema.messages.BaseMessage, config: langchain.schema.runnable.RunnableConfig | None = None) → T¶
parse(text: str) → AutoGPTAction[source]¶
Return AutoGPTAction
parse_result(result: List[Generation]) → T¶
Parse a list of candidate model Generations into a specific format.
The return value is parsed from only the first Generation in the result, whichis assumed to be the highest-likelihood Generation.
Parameters
result – A list of Generations to be parsed. The Generations are assumed
to be different candidate outputs for a single model input.
Returns
Structured output.
parse_with_prompt(completion: str, prompt: PromptValue) → Any¶
Parse the output of an LLM call with the input prompt for context.
The prompt is largely provided in the event the OutputParser wants
to retry or fix the output in some way, and needs information from
the prompt to do so.
Parameters
completion – String output of a language model.
prompt – Input PromptValue.
Returns
Structured output
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶ | https://api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.autogpt.output_parser.AutoGPTOutputParser.html |
037090486a45-1 | to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶ | https://api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.autogpt.output_parser.AutoGPTOutputParser.html |
510458d00d6c-0 | langchain_experimental.autonomous_agents.baby_agi.task_creation.TaskCreationChain¶
class langchain_experimental.autonomous_agents.baby_agi.task_creation.TaskCreationChain(*, memory: Optional[BaseMemory] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, verbose: bool = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, prompt: BasePromptTemplate, llm: BaseLanguageModel, output_key: str = 'text', output_parser: BaseLLMOutputParser = None, return_final_only: bool = True, llm_kwargs: dict = None)[source]¶
Bases: LLMChain
Chain generating tasks.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated, use callbacks instead.
param callbacks: Callbacks = None¶
Optional list of callback handlers (or callback manager). Defaults to None.
Callback handlers are called throughout the lifecycle of a call to a chain,
starting with on_chain_start, ending with on_chain_end or on_chain_error.
Each custom chain can optionally call additional callback methods, see Callback docs
for full details.
param llm: BaseLanguageModel [Required]¶
Language model to call.
param llm_kwargs: dict [Optional]¶
param memory: Optional[BaseMemory] = None¶
Optional memory object. Defaults to None.
Memory is a class that gets called at the start
and at the end of every chain. At the start, memory loads variables and passes
them along in the chain. At the end, it saves any returned variables. | https://api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_creation.TaskCreationChain.html |
510458d00d6c-1 | them along in the chain. At the end, it saves any returned variables.
There are many different types of memory - please see memory docs
for the full catalog.
param metadata: Optional[Dict[str, Any]] = None¶
Optional metadata associated with the chain. Defaults to None.
This metadata will be associated with each call to this chain,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a chain with its use case.
param output_key: str = 'text'¶
param output_parser: BaseLLMOutputParser [Optional]¶
Output parser to use.
Defaults to one that takes the most likely string but does not change it
otherwise.
param prompt: BasePromptTemplate [Required]¶
Prompt object to use.
param return_final_only: bool = True¶
Whether to return only the final parsed result. Defaults to True.
If false, will return a bunch of extra information about the generation.
param tags: Optional[List[str]] = None¶
Optional list of tags associated with the chain. Defaults to None.
These tags will be associated with each call to this chain,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a chain with its use case.
param verbose: bool [Optional]¶
Whether or not run in verbose mode. In verbose mode, some intermediate logs
will be printed to the console. Defaults to langchain.verbose value.
__call__(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, include_run_info: bool = False) → Dict[str, Any]¶ | https://api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_creation.TaskCreationChain.html |
510458d00d6c-2 | Execute the chain.
Parameters
inputs – Dictionary of inputs, or single input if chain expects
only one param. Should contain all inputs specified in
Chain.input_keys except for inputs that will be set by the chain’s
memory.
return_only_outputs – Whether to return only outputs in the
response. If True, only new keys generated by this chain will be
returned. If False, both input keys and new keys generated by this
chain will be returned. Defaults to False.
callbacks – Callbacks to use for this chain run. These will be called in
addition to callbacks passed to the chain during construction, but only
these runtime callbacks will propagate to calls to other objects.
tags – List of string tags to pass to all callbacks. These will be passed in
addition to tags passed to the chain during construction, but only
these runtime tags will propagate to calls to other objects.
metadata – Optional metadata associated with the chain. Defaults to None
include_run_info – Whether to include run info in the response. Defaults
to False.
Returns
A dict of named outputs. Should contain all outputs specified inChain.output_keys.
async aapply(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → List[Dict[str, str]]¶
Utilize the LLM generate method for speed gains.
async aapply_and_parse(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → Sequence[Union[str, List[str], Dict[str, str]]]¶
Call apply and then parse the results. | https://api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_creation.TaskCreationChain.html |
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