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088bd5c75607-0 | langchain API Reference¶
langchain._api¶
Helper functions for managing the LangChain API.
This module is only relevant for LangChain developers, not for users.
Warning
This module and its submodules are for internal use only. Do not use them
in your own code. We may change the API at any time with no warning.
Classes¶
_api.deprecation.LangChainDeprecationWarning
A class for issuing deprecation warnings for LangChain users.
Functions¶
_api.deprecation.deprecated(since, *[, ...])
Decorator to mark a function, a class, or a property as deprecated.
_api.deprecation.suppress_langchain_deprecation_warning()
Context manager to suppress LangChainDeprecationWarning.
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_iterator.AgentExecutorIterator(...)
Iterator for AgentExecutor.
agents.agent_iterator.BaseAgentExecutorIterator()
Base class for AgentExecutorIterator.
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. | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-1 | 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.tools.InvalidTool
Tool that is run when invalid tool name is encountered by agent.
agents.schema.AgentScratchPadChatPromptTemplate
Chat prompt template for the agent scratchpad.
agents.agent_types.AgentType(value[, names, ...])
Enumerator with the Agent types.
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.
agents.conversational_chat.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.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.structured_chat.base.StructuredChatAgent
Structured Chat Agent.
agents.self_ask_with_search.output_parser.SelfAskOutputParser
Output parser for the self-ask agent.
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.conversational.output_parser.ConvoOutputParser
Output parser for the conversational agent.
agents.conversational.base.ConversationalAgent
An agent that holds a conversation in addition to using tools. | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-2 | An agent that holds a conversation in addition to using tools.
agents.react.output_parser.ReActOutputParser
Output parser for the ReAct agent.
agents.react.base.DocstoreExplorer(docstore)
Class to assist with exploration of a document store.
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.chat.output_parser.ChatOutputParser
Output parser for the chat agent.
agents.chat.base.ChatAgent
Chat Agent.
agents.openai_functions_agent.base.OpenAIFunctionsAgent
An Agent driven by OpenAIs function powered API.
agents.openai_functions_agent.agent_token_buffer_memory.AgentTokenBufferMemory
Memory used to save agent output AND intermediate steps.
agents.mrkl.output_parser.MRKLOutputParser
MRKL Output parser for the chat 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.agent_toolkits.base.BaseToolkit
Base Toolkit representing a collection of related tools.
agents.agent_toolkits.azure_cognitive_services.AzureCognitiveServicesToolkit
Toolkit for Azure Cognitive Services.
agents.agent_toolkits.spark_sql.toolkit.SparkSQLToolkit
Toolkit for interacting with Spark SQL.
agents.agent_toolkits.amadeus.toolkit.AmadeusToolkit
Toolkit for interacting with Office365.
agents.agent_toolkits.playwright.toolkit.PlayWrightBrowserToolkit
Toolkit for PlayWright browser tools.
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 |
088bd5c75607-3 | 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_toolkits.office365.toolkit.O365Toolkit
Toolkit for interacting with Office 365.
agents.agent_toolkits.nla.toolkit.NLAToolkit
Natural Language API Toolkit.
agents.agent_toolkits.nla.tool.NLATool
Natural Language API Tool.
agents.agent_toolkits.multion.toolkit.MultionToolkit
Toolkit for interacting with the Browser Agent
agents.agent_toolkits.gmail.toolkit.GmailToolkit
Toolkit for interacting with Gmail.
agents.agent_toolkits.sql.toolkit.SQLDatabaseToolkit
Toolkit for interacting with SQL databases.
agents.agent_toolkits.openapi.spec.ReducedOpenAPISpec(...)
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.file_management.toolkit.FileManagementToolkit
Toolkit for interacting with a Local Files.
agents.agent_toolkits.jira.toolkit.JiraToolkit
Jira Toolkit. | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-4 | agents.agent_toolkits.jira.toolkit.JiraToolkit
Jira Toolkit.
agents.agent_toolkits.github.toolkit.GitHubToolkit
GitHub Toolkit.
agents.agent_toolkits.powerbi.toolkit.PowerBIToolkit
Toolkit for interacting with Power BI dataset.
agents.agent_toolkits.json.toolkit.JsonToolkit
Toolkit for interacting with a JSON spec.
agents.openai_functions_multi_agent.base.OpenAIMultiFunctionsAgent
An Agent driven by OpenAIs function powered API.
Functions¶
agents.agent_iterator.rebuild_callback_manager_on_set(...)
Decorator to force setters to rebuild callback mgr
agents.agent_toolkits.conversational_retrieval.openai_functions.create_conversational_retrieval_agent(...)
A convenience method for creating a conversational retrieval agent.
agents.agent_toolkits.conversational_retrieval.tool.create_retriever_tool(...)
Create a tool to do retrieval of documents.
agents.agent_toolkits.csv.base.create_csv_agent(...)
Create csv agent by loading to a dataframe and using pandas agent.
agents.agent_toolkits.json.base.create_json_agent(...)
Construct a json agent from an LLM and tools.
agents.agent_toolkits.openapi.base.create_openapi_agent(...)
Construct 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(...)
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. | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-5 | 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.
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: | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-6 | 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.
Functions¶
langchain.callbacks¶
Callback handlers allow listening to events in LangChain.
Class hierarchy:
BaseCallbackHandler --> <name>CallbackHandler # Example: AimCallbackHandler
Classes¶
callbacks.sagemaker_callback.SageMakerCallbackHandler(run)
Callback Handler that logs prompt artifacts and metrics to SageMaker Experiments.
callbacks.argilla_callback.ArgillaCallbackHandler(...)
Callback Handler that logs into Argilla.
callbacks.mlflow_callback.MlflowCallbackHandler([...])
Callback Handler that logs metrics and artifacts to mlflow server. | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-7 | Callback Handler that logs metrics and artifacts to mlflow server.
callbacks.mlflow_callback.MlflowLogger(**kwargs)
Callback Handler that logs metrics and artifacts to mlflow server.
callbacks.human.HumanApprovalCallbackHandler(...)
Callback for manually validating values.
callbacks.human.HumanRejectedException
Exception to raise when a person manually review and rejects a value.
callbacks.streaming_aiter_final_only.AsyncFinalIteratorCallbackHandler(*)
Callback handler that returns an async iterator.
callbacks.streaming_aiter.AsyncIteratorCallbackHandler()
Callback handler that returns an async iterator.
callbacks.streaming_stdout_final_only.FinalStreamingStdOutCallbackHandler(*)
Callback handler for streaming in agents.
callbacks.arize_callback.ArizeCallbackHandler([...])
Callback Handler that logs to Arize.
callbacks.promptlayer_callback.PromptLayerCallbackHandler([...])
Callback handler for promptlayer.
callbacks.openai_info.OpenAICallbackHandler()
Callback Handler that tracks OpenAI info.
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.base.CallbackManagerMixin()
Mixin for callback manager.
callbacks.base.ChainManagerMixin()
Mixin for chain callbacks.
callbacks.base.LLMManagerMixin()
Mixin for LLM callbacks.
callbacks.base.RetrieverManagerMixin()
Mixin for Retriever callbacks.
callbacks.base.RunManagerMixin()
Mixin for run manager.
callbacks.base.ToolManagerMixin()
Mixin for tool callbacks.
callbacks.comet_ml_callback.CometCallbackHandler([...])
Callback Handler that logs to Comet.
callbacks.manager.AsyncCallbackManager(handlers)
Async callback manager that handles callbacks from LangChain. | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-8 | callbacks.manager.AsyncCallbackManager(handlers)
Async callback manager that handles callbacks from LangChain.
callbacks.manager.AsyncCallbackManagerForChainRun(*, ...)
Async callback manager for chain run.
callbacks.manager.AsyncCallbackManagerForLLMRun(*, ...)
Async callback manager for LLM run.
callbacks.manager.AsyncCallbackManagerForRetrieverRun(*, ...)
Async callback manager for retriever run.
callbacks.manager.AsyncCallbackManagerForToolRun(*, ...)
Async callback manager for tool run.
callbacks.manager.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.infino_callback.InfinoCallbackHandler([...])
Callback Handler that logs to Infino.
callbacks.clearml_callback.ClearMLCallbackHandler([...])
Callback Handler that logs to ClearML.
callbacks.file.FileCallbackHandler(filename)
Callback Handler that writes to a file.
callbacks.wandb_callback.WandbCallbackHandler([...])
Callback Handler that logs to Weights and Biases. | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-9 | Callback Handler that logs to Weights and Biases.
callbacks.flyte_callback.FlyteCallbackHandler()
This callback handler that is used within a Flyte task.
callbacks.utils.BaseMetadataCallbackHandler()
This class handles the metadata and associated function states for callbacks.
callbacks.streaming_stdout.StreamingStdOutCallbackHandler()
Callback handler for streaming.
callbacks.whylabs_callback.WhyLabsCallbackHandler(...)
Callback Handler for logging to WhyLabs.
callbacks.aim_callback.AimCallbackHandler([...])
Callback Handler that logs to Aim.
callbacks.aim_callback.BaseMetadataCallbackHandler()
This class handles the metadata and associated function states for callbacks.
callbacks.stdout.StdOutCallbackHandler([color])
Callback Handler that prints to std out.
callbacks.arthur_callback.ArthurCallbackHandler(...)
Callback Handler that logs to Arthur platform.
callbacks.context_callback.ContextCallbackHandler([...])
Callback Handler that records transcripts to the Context service.
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.mutable_expander.MutableExpander(...)
A Streamlit expander that can be renamed and dynamically expanded/collapsed.
callbacks.streamlit.streamlit_callback_handler.LLMThought(...)
A thought in the LLM's thought stream.
callbacks.streamlit.streamlit_callback_handler.LLMThoughtLabeler()
Generates markdown labels for LLMThought containers.
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.schemas.BaseRun
Base class for Run. | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-10 | callbacks.tracers.schemas.BaseRun
Base class for Run.
callbacks.tracers.schemas.ChainRun
Class for ChainRun.
callbacks.tracers.schemas.LLMRun
Class for LLMRun.
callbacks.tracers.schemas.Run
Run schema for the V2 API in the Tracer.
callbacks.tracers.schemas.ToolRun
Class for ToolRun.
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.run_collector.RunCollectorCallbackHandler([...])
A tracer that collects all nested runs in a list.
callbacks.tracers.base.BaseTracer(**kwargs)
Base interface for tracers.
callbacks.tracers.base.TracerException
Base class for exceptions in tracers module.
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.wandb.RunProcessor(...)
Handles the conversion of a LangChain Runs into a WBTraceTree.
callbacks.tracers.wandb.WandbRunArgs
Arguments for the WandbTracer.
callbacks.tracers.wandb.WandbTracer([run_args])
Callback Handler that logs to Weights and Biases.
callbacks.tracers.stdout.ConsoleCallbackHandler(...)
Tracer that prints to the console. | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-11 | 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.evaluation.EvaluatorCallbackHandler(...)
A tracer that runs a run evaluator whenever a run is persisted.
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.
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. | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-12 | 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.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. | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-13 | 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.
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.transform.TransformChain
Chain that transforms the chain output.
chains.base.Chain
Abstract base class for creating structured sequences of calls to components.
chains.mapreduce.MapReduceChain
Map-reduce chain.
chains.moderation.OpenAIModerationChain
Pass input through a moderation endpoint.
chains.llm_requests.LLMRequestsChain
Chain that requests a URL and then uses an LLM to parse results.
chains.llm.LLMChain
Chain to run queries against LLMs.
chains.prompt_selector.BasePromptSelector
Base class for prompt selectors. | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-14 | chains.prompt_selector.BasePromptSelector
Base class for prompt selectors.
chains.prompt_selector.ConditionalPromptSelector
Prompt collection that goes through conditionals.
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.llm_summarization_checker.base.LLMSummarizationCheckerChain
Chain for question-answering with self-verification.
chains.openai_functions.openapi.SimpleRequestChain
Chain for making a simple request to an API endpoint.
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.qa_with_structure.AnswerWithSources
An answer to the question, with sources.
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
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.schema.AttributeInfo
Information about a data source attribute.
chains.hyde.base.HypotheticalDocumentEmbedder
Generate hypothetical document for query, and then embed that.
chains.router.multi_prompt.MultiPromptChain
A multi-route chain that uses an LLM router chain to choose amongst prompts.
chains.router.base.MultiRouteChain | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-15 | 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.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_retrieval_qa.MultiRetrievalQAChain
A multi-route chain that uses an LLM router chain to choose amongst retrieval qa chains.
chains.router.embedding_router.EmbeddingRouterChain
Chain that uses embeddings to route between options.
chains.constitutional_ai.base.ConstitutionalChain
Chain for applying constitutional principles.
chains.constitutional_ai.models.ConstitutionalPrinciple
Class for a constitutional principle.
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.vector_db.VectorDBQAWithSourcesChain
Question-answering with sources over a vector database.
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.llm_bash.base.LLMBashChain
Chain that interprets a prompt and executes bash operations.
chains.llm_bash.prompt.BashOutputParser
Parser for bash output.
chains.retrieval_qa.base.BaseRetrievalQA
Base class for question-answering chains.
chains.retrieval_qa.base.RetrievalQA | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-16 | 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.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.response_chain.APIResponderChain
Get the response parser.
chains.api.openapi.response_chain.APIResponderOutputParser
Parse the response and error tags.
chains.api.openapi.requests_chain.APIRequesterChain
Get the request parser.
chains.api.openapi.requests_chain.APIRequesterOutputParser
Parse the request and error tags.
chains.elasticsearch_database.base.ElasticsearchDatabaseChain
Chain for interacting with Elasticsearch Database.
chains.llm_math.base.LLMMathChain
Chain that interprets a prompt and executes python code to do math.
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.reduce.AsyncCombineDocsProtocol(...)
Interface for the combine_docs method.
chains.combine_documents.reduce.CombineDocsProtocol(...)
Interface for the combine_docs method.
chains.combine_documents.reduce.ReduceDocumentsChain
Combine documents by recursively reducing them.
chains.combine_documents.refine.RefineDocumentsChain
Combine documents by doing a first pass and then refining on more documents.
chains.combine_documents.stuff.StuffDocumentsChain
Chain that combines documents by stuffing into context.
chains.combine_documents.map_rerank.MapRerankDocumentsChain
Combining documents by mapping a chain over them, then reranking results.
chains.combine_documents.map_reduce.MapReduceDocumentsChain
Combining documents by mapping a chain over them, then combining results. | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-17 | Combining documents by mapping a chain over them, then combining results.
chains.llm_symbolic_math.base.LLMSymbolicMathChain
Chain that interprets a prompt and executes python code to do symbolic math.
chains.llm_checker.base.LLMCheckerChain
Chain for question-answering with self-verification.
chains.sql_database.query.SQLInput
Input for a SQL Chain.
chains.sql_database.query.SQLInputWithTables
Input for a SQL Chain.
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.natbot.base.NatBotChain
Implement an LLM driven browser.
chains.natbot.crawler.Crawler()
chains.natbot.crawler.ElementInViewPort
A typed dictionary containing information about elements in the viewport.
chains.qa_generation.base.QAGenerationChain
Base class for question-answer generation chains.
chains.graph_qa.cypher.GraphCypherQAChain
Chain for question-answering against a graph by generating Cypher statements.
chains.graph_qa.neptune_cypher.NeptuneOpenCypherQAChain
Chain for question-answering against a Neptune graph by generating openCypher statements.
chains.graph_qa.base.GraphQAChain
Chain for question-answering against a graph.
chains.graph_qa.sparql.GraphSparqlQAChain
Chain for question-answering against an RDF or OWL graph by generating SPARQL statements.
chains.graph_qa.hugegraph.HugeGraphQAChain
Chain for question-answering against a graph by generating gremlin statements. | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-18 | Chain for question-answering against a graph by generating gremlin statements.
chains.graph_qa.arangodb.ArangoGraphQAChain
Chain for question-answering against a graph by generating AQL 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.flare.prompts.FinishedOutputParser
Output parser that checks if the output is finished.
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.conversation.base.ConversationChain
Chain to have a conversation and load context from memory.
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(...) | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-19 | 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(...)
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. | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-20 | chains.query_constructor.parser.get_parser([...])
Returns a parser for the query language.
chains.query_constructor.parser.v_args(...)
chains.sql_database.query.create_sql_query_chain(llm, db)
Create a chain that generates SQL queries.
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.openai.ChatOpenAI
Wrapper around OpenAI Chat large language models.
chat_models.human.HumanInputChatModel
ChatModel which returns user input as the response.
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.vertexai.ChatVertexAI
Wrapper around Vertex AI large language models.
chat_models.azure_openai.AzureChatOpenAI
Wrapper around Azure OpenAI Chat Completion API.
chat_models.jinachat.JinaChat
Wrapper for Jina AI's LLM service, providing cost-effective image chat capabilities.
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.anthropic.ChatAnthropic
Anthropic's large language chat model. | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-21 | chat_models.anthropic.ChatAnthropic
Anthropic's large language chat model.
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.anyscale.ChatAnyscale
Wrapper around Anyscale Chat large language models.
chat_models.fake.FakeListChatModel
Fake ChatModel for testing purposes.
chat_models.promptlayer_openai.PromptLayerChatOpenAI
Wrapper around OpenAI Chat large language models and PromptLayer.
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.base.AddableMixin()
Mixin class that supports adding texts.
docstore.base.Docstore()
Interface to access to place that stores documents.
docstore.wikipedia.Wikipedia()
Wrapper around wikipedia API.
docstore.arbitrary_fn.DocstoreFn(lookup_fn)
Langchain Docstore via arbitrary lookup function.
docstore.in_memory.InMemoryDocstore([_dict]) | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-22 | docstore.in_memory.InMemoryDocstore([_dict])
Simple in memory docstore in the form of a dict.
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:
Document, <name>TextSplitter
Classes¶
document_loaders.docugami.DocugamiLoader
Loads processed docs from Docugami.
document_loaders.git.GitLoader(repo_path[, ...])
Loads files from a Git repository into a list of documents.
document_loaders.url_selenium.SeleniumURLLoader(urls)
Loader that uses Selenium and to load a page and unstructured to load the html.
document_loaders.cube_semantic.CubeSemanticLoader(...)
Load Cube semantic layer metadata.
document_loaders.azure_blob_storage_file.AzureBlobStorageFileLoader(...)
Loading Documents from Azure Blob Storage.
document_loaders.powerpoint.UnstructuredPowerPointLoader(...)
Loader that uses unstructured to load PowerPoint files.
document_loaders.psychic.PsychicLoader(...)
Loads documents from Psychic.dev.
document_loaders.html.UnstructuredHTMLLoader(...)
Loader that uses Unstructured to load HTML files.
document_loaders.spreedly.SpreedlyLoader(...)
Loader that fetches data from Spreedly API.
document_loaders.whatsapp_chat.WhatsAppChatLoader(path)
Loads WhatsApp messages text file.
document_loaders.diffbot.DiffbotLoader(...)
Loads Diffbot file json.
document_loaders.mastodon.MastodonTootsLoader(...)
Mastodon toots loader.
document_loaders.image.UnstructuredImageLoader(...)
Loader that uses Unstructured to load PNG and JPG files. | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-23 | Loader that uses Unstructured to load PNG and JPG files.
document_loaders.roam.RoamLoader(path)
Loads Roam files from disk.
document_loaders.s3_directory.S3DirectoryLoader(bucket)
Loading logic for loading documents from an AWS S3.
document_loaders.iugu.IuguLoader(resource[, ...])
Loader that fetches data from IUGU.
document_loaders.imsdb.IMSDbLoader(web_path)
Loads IMSDb webpages.
document_loaders.gutenberg.GutenbergLoader(...)
Loader that uses urllib to load .txt web files.
document_loaders.larksuite.LarkSuiteDocLoader(...)
Loads LarkSuite (FeiShu) document.
document_loaders.directory.DirectoryLoader(...)
Load documents from a directory.
document_loaders.duckdb_loader.DuckDBLoader(query)
Loads a query result from DuckDB into a list of documents.
document_loaders.conllu.CoNLLULoader(file_path)
Load CoNLL-U files.
document_loaders.snowflake_loader.SnowflakeLoader(...)
Loads a query result from Snowflake into a list of documents.
document_loaders.bigquery.BigQueryLoader(query)
Loads a query result from BigQuery into a list of documents.
document_loaders.datadog_logs.DatadogLogsLoader(...)
Loads a query result from Datadog into a list of documents.
document_loaders.weather.WeatherDataLoader(...)
Weather Reader.
document_loaders.notebook.NotebookLoader(path)
Loads .ipynb notebook files.
document_loaders.pyspark_dataframe.PySparkDataFrameLoader([...])
Load PySpark DataFrames
document_loaders.gcs_directory.GCSDirectoryLoader(...)
Loads Documents from GCS.
document_loaders.notiondb.NotionDBLoader(...)
Notion DB Loader. | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-24 | document_loaders.notiondb.NotionDBLoader(...)
Notion DB Loader.
document_loaders.rss.RSSFeedLoader([urls, ...])
Loader that uses newspaper to load news articles from RSS feeds.
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.onedrive.OneDriveLoader
Loads data from OneDrive.
document_loaders.ifixit.IFixitLoader(web_path)
Load iFixit repair guides, device wikis and answers.
document_loaders.discord.DiscordChatLoader(...)
Load Discord chat logs.
document_loaders.trello.TrelloLoader(client, ...)
Trello loader.
document_loaders.etherscan.EtherscanLoader(...)
Load transactions from an account on Ethereum mainnet.
document_loaders.url.UnstructuredURLLoader(urls)
Loader that use Unstructured to load files from remote URLs.
document_loaders.base.BaseBlobParser()
Abstract interface for blob parsers.
document_loaders.base.BaseLoader()
Interface for loading Documents.
document_loaders.evernote.EverNoteLoader(...)
EverNote Loader.
document_loaders.async_html.AsyncHtmlLoader(...)
Loads HTML asynchronously.
document_loaders.wikipedia.WikipediaLoader(query)
Loads a query result from www.wikipedia.org into a list of Documents.
document_loaders.youtube.GoogleApiClient([...])
A Generic Google Api Client. | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-25 | document_loaders.youtube.GoogleApiClient([...])
A Generic Google Api Client.
document_loaders.youtube.GoogleApiYoutubeLoader(...)
Loads all Videos from a Channel
document_loaders.youtube.YoutubeLoader(video_id)
Loads Youtube transcripts.
document_loaders.python.PythonLoader(file_path)
Load Python files, respecting any non-default encoding if specified.
document_loaders.blackboard.BlackboardLoader(...)
Loads all documents from a Blackboard course.
document_loaders.pubmed.PubMedLoader(query)
Loads a query result from PubMed biomedical library into a list of Documents.
document_loaders.dataframe.DataFrameLoader(...)
Load Pandas DataFrame.
document_loaders.tensorflow_datasets.TensorflowDatasetLoader(...)
Loads from TensorFlow Datasets into a list of Documents.
document_loaders.web_base.WebBaseLoader(web_path)
Loader that uses urllib and beautiful soup to load webpages.
document_loaders.tencent_cos_file.TencentCOSFileLoader(...)
Loader for Tencent Cloud COS file.
document_loaders.browserless.BrowserlessLoader(...)
Loads the content of webpages using Browserless' /content endpoint
document_loaders.helpers.FileEncoding(...)
A file encoding as the NamedTuple.
document_loaders.srt.SRTLoader(file_path)
Loader for .srt (subtitle) files.
document_loaders.azure_blob_storage_container.AzureBlobStorageContainerLoader(...)
Loading Documents from Azure Blob Storage.
document_loaders.open_city_data.OpenCityDataLoader(...)
Loads Open City data.
document_loaders.mhtml.MHTMLLoader(file_path)
Loader that uses beautiful soup to parse HTML files.
document_loaders.html_bs.BSHTMLLoader(file_path)
Loader that uses beautiful soup to parse HTML files.
document_loaders.generic.GenericLoader(...)
A generic document loader.
document_loaders.recursive_url_loader.RecursiveUrlLoader(url)
Loads all child links from a given url. | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-26 | Loads all child links from a given url.
document_loaders.twitter.TwitterTweetLoader(...)
Twitter tweets loader.
document_loaders.markdown.UnstructuredMarkdownLoader(...)
Loader that uses Unstructured to load markdown files.
document_loaders.merge.MergedDataLoader(loaders)
Merge documents from a list of loaders
document_loaders.github.BaseGitHubLoader
Load issues of a GitHub repository.
document_loaders.github.GitHubIssuesLoader
Load issues of a GitHub repository.
document_loaders.bibtex.BibtexLoader(...[, ...])
Loads a bibtex file into a list of Documents.
document_loaders.image_captions.ImageCaptionLoader(...)
Loads the captions of an image
document_loaders.joplin.JoplinLoader([...])
Loader that fetches notes from Joplin.
document_loaders.concurrent.ConcurrentLoader(...)
A generic document loader that loads and parses documents concurrently.
document_loaders.modern_treasury.ModernTreasuryLoader(...)
Loader that fetches data from Modern Treasury.
document_loaders.sitemap.SitemapLoader(web_path)
Loader that fetches a sitemap and loads those URLs.
document_loaders.pdf.AmazonTextractPDFLoader(...)
Loads a PDF document from local file system, HTTP or S3.
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) | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-27 | document_loaders.pdf.PDFPlumberLoader(file_path)
Loader that uses pdfplumber to load PDF files.
document_loaders.pdf.PyMuPDFLoader(file_path)
Loader that uses PyMuPDF to load PDF files.
document_loaders.pdf.PyPDFDirectoryLoader(path)
Loads a directory with PDF files with pypdf and chunks at character level.
document_loaders.pdf.PyPDFLoader(file_path)
Loads a PDF with pypdf and chunks at character level.
document_loaders.pdf.PyPDFium2Loader(file_path)
Loads a PDF with pypdfium2 and chunks at character level.
document_loaders.pdf.UnstructuredPDFLoader(...)
Loader that uses unstructured to load PDF files.
document_loaders.brave_search.BraveSearchLoader(...)
Loads a query result from Brave Search engine into a list of Documents.
document_loaders.tsv.UnstructuredTSVLoader(...)
Loader that uses unstructured to load TSV files.
document_loaders.dropbox.DropboxLoader
Loads files from Dropbox.
document_loaders.s3_file.S3FileLoader(...)
Loading logic for loading documents from an AWS S3 file.
document_loaders.max_compute.MaxComputeLoader(...)
Loads a query result from Alibaba Cloud MaxCompute table into documents.
document_loaders.airbyte.AirbyteCDKLoader(...)
Loads records using an Airbyte source connector implemented using the CDK.
document_loaders.airbyte.AirbyteGongLoader(...)
document_loaders.airbyte.AirbyteHubspotLoader(...)
document_loaders.airbyte.AirbyteSalesforceLoader(...)
document_loaders.airbyte.AirbyteShopifyLoader(...)
document_loaders.airbyte.AirbyteStripeLoader(...)
document_loaders.airbyte.AirbyteTypeformLoader(...)
document_loaders.airbyte.AirbyteZendeskSupportLoader(...)
document_loaders.xml.UnstructuredXMLLoader(...) | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-28 | document_loaders.xml.UnstructuredXMLLoader(...)
Loader that uses unstructured to load XML files.
document_loaders.nuclia.NucliaLoader(path, ...)
Extract text from any file type.
document_loaders.toml.TomlLoader(source)
A TOML document loader that inherits from the BaseLoader class.
document_loaders.url_playwright.PlaywrightURLLoader(urls)
Loader that uses Playwright and to load a page and unstructured to load the html.
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(...)
Loader that uses Unstructured to load files.
document_loaders.gitbook.GitbookLoader(web_page)
Load GitBook data.
document_loaders.reddit.RedditPostsLoader(...)
Reddit posts loader.
document_loaders.slack_directory.SlackDirectoryLoader(...)
Loads documents from a Slack directory dump.
document_loaders.excel.UnstructuredExcelLoader(...)
Loader that uses unstructured to load Excel files.
document_loaders.rst.UnstructuredRSTLoader(...)
Loader that uses unstructured to load RST files.
document_loaders.acreom.AcreomLoader(path[, ...])
Loader that loads acreom vault from a directory.
document_loaders.obs_directory.OBSDirectoryLoader(...)
Loading logic for loading documents from Huawei OBS.
document_loaders.text.TextLoader(file_path)
Load text files.
document_loaders.figma.FigmaFileLoader(...) | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-29 | Load text files.
document_loaders.figma.FigmaFileLoader(...)
Loads Figma file json.
document_loaders.obs_file.OBSFileLoader(...)
Loader for Huawei OBS file.
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.airbyte_json.AirbyteJSONLoader(...)
Loads local airbyte json files.
document_loaders.hugging_face_dataset.HuggingFaceDatasetLoader(path)
Load Documents from the Hugging Face Hub.
document_loaders.arxiv.ArxivLoader(query[, ...])
Loads a query result from arxiv.org into a list of Documents.
document_loaders.news.NewsURLLoader(urls[, ...])
Loader that uses newspaper to load news articles from URLs.
document_loaders.onedrive_file.OneDriveFileLoader
Loads a file from OneDrive.
document_loaders.azlyrics.AZLyricsLoader(...)
Loads AZLyrics webpages.
document_loaders.apify_dataset.ApifyDatasetLoader
Loads datasets from Apify-a web scraping, crawling, and data extraction platform.
document_loaders.facebook_chat.FacebookChatLoader(path)
Loads Facebook messages json directory dump.
document_loaders.tomarkdown.ToMarkdownLoader(...)
Loads HTML to markdown using 2markdown.
document_loaders.tencent_cos_directory.TencentCOSDirectoryLoader(...)
Loader for Tencent Cloud COS directory.
document_loaders.json_loader.JSONLoader(...)
Loads a JSON file using a jq schema.
document_loaders.stripe.StripeLoader(resource)
Loader that fetches data from Stripe.
document_loaders.org_mode.UnstructuredOrgModeLoader(...)
Loader that uses unstructured to load Org-Mode files.
document_loaders.googledrive.GoogleDriveLoader | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-30 | document_loaders.googledrive.GoogleDriveLoader
Loads Google Docs from Google Drive.
document_loaders.odt.UnstructuredODTLoader(...)
Loader that uses unstructured to load OpenOffice ODT files.
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.gcs_file.GCSFileLoader(...)
Load Documents from a GCS file.
document_loaders.epub.UnstructuredEPubLoader(...)
Loader that uses Unstructured to load EPUB files.
document_loaders.notion.NotionDirectoryLoader(path)
Loads Notion directory dump.
document_loaders.geodataframe.GeoDataFrameLoader(...)
Load geopandas Dataframe.
document_loaders.mediawikidump.MWDumpLoader(...)
Load MediaWiki dump from XML file .
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.readthedocs.ReadTheDocsLoader(path)
Loads ReadTheDocs documentation directory dump.
document_loaders.xorbits.XorbitsLoader(...)
Load Xorbits DataFrame.
document_loaders.college_confidential.CollegeConfidentialLoader(...)
Loads College Confidential webpages.
document_loaders.chatgpt.ChatGPTLoader(log_file)
Load conversations from exported ChatGPT data.
document_loaders.fauna.FaunaLoader(query, ...)
FaunaDB Loader.
document_loaders.airtable.AirtableLoader(...)
Loader for Airtable tables.
document_loaders.bilibili.BiliBiliLoader(...)
Loads bilibili transcripts.
document_loaders.rocksetdb.ColumnNotFoundError(...)
Column not found error. | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-31 | document_loaders.rocksetdb.ColumnNotFoundError(...)
Column not found error.
document_loaders.rocksetdb.RocksetLoader(...)
Wrapper around Rockset db
document_loaders.confluence.ConfluenceLoader(url)
Load Confluence pages.
document_loaders.confluence.ContentFormat(value)
Enumerator of the content formats of Confluence page.
document_loaders.hn.HNLoader(web_path[, ...])
Load Hacker News data from either main page results or the comments page.
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.obsidian.ObsidianLoader(path)
Loads Obsidian files from disk.
document_loaders.telegram.TelegramChatApiLoader([...])
Loads Telegram chat json directory dump.
document_loaders.telegram.TelegramChatFileLoader(path)
Loads Telegram chat json directory dump.
document_loaders.rtf.UnstructuredRTFLoader(...)
Loader that uses unstructured to load RTF files.
document_loaders.parsers.generic.MimeTypeBasedParser(...)
A parser that uses mime-types to determine how to parse a blob.
document_loaders.parsers.txt.TextParser()
Parser for text blobs.
document_loaders.parsers.pdf.AmazonTextractPDFParser([...])
Sends PDF files to Amazon Textract and parses them to generate Documents.
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([...]) | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-32 | 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.audio.OpenAIWhisperParser([...])
Transcribe and parse audio files.
document_loaders.parsers.audio.OpenAIWhisperParserLocal([...])
Transcribe and parse audio files. Audio transcription with OpenAI Whisper model locally from transformers Parameters: device - device to use NOTE: By default uses the gpu if available, if you want to use cpu, please set device = "cpu" lang_model - whisper model to use, for example "openai/whisper-medium" forced_decoder_ids - id states for decoder in multilanguage model, usage example: from transformers import WhisperProcessor processor = WhisperProcessor.from_pretrained("openai/whisper-medium") forced_decoder_ids = WhisperProcessor.get_decoder_prompt_ids(language="french", task="transcribe") forced_decoder_ids = WhisperProcessor.get_decoder_prompt_ids(language="french", task="translate").
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.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.language.code_segmenter.CodeSegmenter(code)
The abstract class for the code segmenter.
document_loaders.parsers.language.javascript.JavaScriptSegmenter(code) | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-33 | document_loaders.parsers.language.javascript.JavaScriptSegmenter(code)
The code segmenter for JavaScript.
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.
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).
Functions¶
document_loaders.chatgpt.concatenate_rows(...)
Combine message information in a readable format ready to be used.
document_loaders.facebook_chat.concatenate_rows(row)
Combine message information in a readable format ready to be used.
document_loaders.helpers.detect_file_encodings(...)
Try to detect the file encoding.
document_loaders.notebook.concatenate_cells(...)
Combine cells information in a readable format ready to be used.
document_loaders.notebook.remove_newlines(x)
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(...) | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-34 | 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.long_context_reorder.LongContextReorder
Lost in the middle: Performance degrades when models must access relevant information in the middle of long contexts.
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.doctran_text_qa.DoctranQATransformer([...])
Extract QA from text documents using doctran.
document_transformers.doctran_text_extract.DoctranPropertyExtractor(...)
Extract properties from text documents using doctran.
document_transformers.openai_functions.OpenAIMetadataTagger
Extract metadata tags from document contents using OpenAI functions.
document_transformers.html2text.Html2TextTransformer()
Replace occurrences of a particular search pattern with a replacement string .
document_transformers.nuclia_text_transform.NucliaTextTransformer(nua)
The Nuclia Understanding API splits into paragraphs and sentences, identifies entities, provides a summary of the text and generates embeddings for all the sentences.
Functions¶ | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-35 | 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.jina.JinaEmbeddings
Jina embedding models.
embeddings.openai.OpenAIEmbeddings
OpenAI embedding models.
embeddings.cohere.CohereEmbeddings
Cohere embedding models.
embeddings.gpt4all.GPT4AllEmbeddings
GPT4All embedding models.
embeddings.mosaicml.MosaicMLInstructorEmbeddings
MosaicML embedding service.
embeddings.dashscope.DashScopeEmbeddings
DashScope embedding models.
embeddings.embaas.EmbaasEmbeddings
Embaas's embedding service.
embeddings.embaas.EmbaasEmbeddingsPayload
Payload for the embaas embeddings API.
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.clarifai.ClarifaiEmbeddings
Clarifai embedding models.
embeddings.base.Embeddings()
Interface for embedding models.
embeddings.vertexai.VertexAIEmbeddings
Google Cloud VertexAI embedding models.
embeddings.bedrock.BedrockEmbeddings
Bedrock embedding models. | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-36 | embeddings.bedrock.BedrockEmbeddings
Bedrock embedding models.
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.mlflow_gateway.MlflowAIGatewayEmbeddings
Wrapper around embeddings LLMs in the MLflow AI Gateway.
embeddings.modelscope_hub.ModelScopeEmbeddings
ModelScopeHub embedding models.
embeddings.minimax.MiniMaxEmbeddings
MiniMax's embedding service.
embeddings.tensorflow_hub.TensorflowHubEmbeddings
TensorflowHub embedding models.
embeddings.elasticsearch.ElasticsearchEmbeddings(...)
Elasticsearch embedding models.
embeddings.awa.AwaEmbeddings
Create a new model by parsing and validating input data from keyword arguments.
embeddings.octoai_embeddings.OctoAIEmbeddings
OctoAI Compute Service embedding models.
embeddings.huggingface.HuggingFaceBgeEmbeddings
HuggingFace BGE sentence_transformers embedding models.
embeddings.huggingface.HuggingFaceEmbeddings
HuggingFace sentence_transformers embedding models.
embeddings.huggingface.HuggingFaceInstructEmbeddings
Wrapper around sentence_transformers embedding models.
embeddings.xinference.XinferenceEmbeddings([...])
Wrapper around xinference embedding models.
embeddings.google_palm.GooglePalmEmbeddings
Google's PaLM Embeddings APIs.
embeddings.sagemaker_endpoint.EmbeddingsContentHandler()
Content handler for LLM class.
embeddings.sagemaker_endpoint.SagemakerEndpointEmbeddings
Custom Sagemaker Inference Endpoints. | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-37 | Custom Sagemaker Inference Endpoints.
embeddings.deepinfra.DeepInfraEmbeddings
Deep Infra's embedding inference service.
embeddings.huggingface_hub.HuggingFaceHubEmbeddings
HuggingFaceHub embedding models.
embeddings.edenai.EdenAiEmbeddings
EdenAI embedding.
embeddings.localai.LocalAIEmbeddings
LocalAI embedding models.
embeddings.nlpcloud.NLPCloudEmbeddings
NLP Cloud embedding models.
embeddings.fake.DeterministicFakeEmbedding
Fake embedding model that always returns the same embedding vector for the same text.
embeddings.fake.FakeEmbeddings
Fake embedding model.
embeddings.self_hosted.SelfHostedEmbeddings
Custom embedding models on self-hosted remote hardware.
embeddings.llamacpp.LlamaCppEmbeddings
llama.cpp 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(...)
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.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 | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-38 | 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
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. | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-39 | 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.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.criteria.eval_chain.Criteria(value)
A Criteria to evaluate.
evaluation.criteria.eval_chain.CriteriaEvalChain
LLM Chain for evaluating runs against criteria.
evaluation.criteria.eval_chain.CriteriaResultOutputParser
A parser for the output of the CriteriaEvalChain.
evaluation.criteria.eval_chain.LabeledCriteriaEvalChain
Criteria evaluation chain that requires references.
evaluation.qa.generate_chain.QAGenerateChain
LLM Chain for generating examples for question answering.
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.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. | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-40 | 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.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.string_distance.base.PairwiseStringDistanceEvalChain
Compute string edit distances between two predictions.
evaluation.string_distance.base.StringDistance(value)
Distance metric to use.
evaluation.string_distance.base.StringDistanceEvalChain
Compute string distances between the prediction and the reference.
Functions¶
evaluation.comparison.eval_chain.resolve_pairwise_criteria(...)
Resolve the criteria for the pairwise evaluator.
evaluation.criteria.eval_chain.resolve_criteria(...)
Resolve the criteria to evaluate.
evaluation.loading.load_dataset(uri)
Load a dataset from the LangChainDatasets 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.networkx_graph.KnowledgeTriple(...)
A triple in the graph.
graphs.networkx_graph.NetworkxEntityGraph([graph])
Networkx wrapper for entity graph operations. | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-41 | Networkx wrapper for entity graph operations.
graphs.kuzu_graph.KuzuGraph(db[, database])
Kùzu wrapper for graph operations.
graphs.rdf_graph.RdfGraph([source_file, ...])
RDFlib wrapper for graph operations.
graphs.nebula_graph.NebulaGraph(space[, ...])
NebulaGraph wrapper for graph operations NebulaGraph inherits methods from Neo4jGraph to bring ease to the user space.
graphs.hugegraph.HugeGraph([username, ...])
HugeGraph wrapper for graph operations
graphs.memgraph_graph.MemgraphGraph(url, ...)
Memgraph wrapper for graph operations.
graphs.neo4j_graph.Neo4jGraph(url, username, ...)
Neo4j wrapper for graph operations.
graphs.arangodb_graph.ArangoGraph(db)
ArangoDB wrapper for graph operations.
graphs.neptune_graph.NeptuneGraph(host[, ...])
Neptune wrapper for graph operations.
graphs.neptune_graph.NeptuneQueryException(...)
A class to handle queries that fail to execute
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.vectorstore.VectorStoreIndexWrapper
Wrapper around a vectorstore for easy access.
indexes.vectorstore.VectorstoreIndexCreator
Logic for creating indexes.
indexes.graph.GraphIndexCreator
Functionality to create graph index.
Functions¶
langchain.llms¶
LLM classes provide
access to the large language model (LLM) APIs and services.
Class hierarchy: | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-42 | 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.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.cohere.Cohere
Cohere large language models.
llms.amazon_api_gateway.AmazonAPIGateway
Amazon API Gateway to access LLM models hosted on AWS.
llms.amazon_api_gateway.ContentHandlerAmazonAPIGateway()
Adapter to prepare the inputs from Langchain to a format that LLM model expects.
llms.manifest.ManifestWrapper
HazyResearch's Manifest library.
llms.huggingface_pipeline.HuggingFacePipeline
HuggingFace Pipeline API.
llms.baseten.Baseten
Baseten models.
llms.huggingface_text_gen_inference.HuggingFaceTextGenInference
HuggingFace text generation API.
llms.human.HumanInputLLM
It returns user input as the response.
llms.databricks.Databricks
Databricks serving endpoint or a cluster driver proxy app for LLM.
llms.gpt4all.GPT4All
GPT4All language models.
llms.openlm.OpenLM
OpenLM models.
llms.mosaicml.MosaicML
MosaicML LLM service. | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-43 | llms.mosaicml.MosaicML
MosaicML LLM service.
llms.azureml_endpoint.AzureMLEndpointClient(...)
AzureML Managed Endpoint client.
llms.azureml_endpoint.AzureMLOnlineEndpoint
Azure ML Online Endpoint models.
llms.azureml_endpoint.ContentFormatterBase()
Transform request and response of AzureML endpoint to match with required schema.
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.aleph_alpha.AlephAlpha
Aleph Alpha large language models.
llms.predibase.Predibase
Use your Predibase models with Langchain.
llms.clarifai.Clarifai
Clarifai large language models.
llms.base.BaseLLM
Base LLM abstract interface.
llms.base.LLM
Base LLM abstract class.
llms.vertexai.VertexAI
Google Vertex AI large language models.
llms.bedrock.Bedrock
Bedrock models.
llms.bedrock.LLMInputOutputAdapter()
Adapter class to prepare the inputs from Langchain to a format that LLM model expects.
llms.self_hosted_hugging_face.SelfHostedHuggingFaceLLM
HuggingFace Pipeline API to run on self-hosted remote hardware.
llms.bananadev.Banana
Banana large language models.
llms.minimax.Minimax
Wrapper around Minimax large language models.
llms.beam.Beam | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-44 | Wrapper around Minimax large language models.
llms.beam.Beam
Beam API for gpt2 large language model.
llms.ctransformers.CTransformers
C Transformers LLM models.
llms.xinference.Xinference
Wrapper for accessing Xinference's large-scale model inference service.
llms.google_palm.GooglePalm
Google PaLM models.
llms.predictionguard.PredictionGuard
Prediction Guard large 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.
llms.ai21.AI21
AI21 large language models.
llms.ai21.AI21PenaltyData
Parameters for AI21 penalty data.
llms.forefrontai.ForefrontAI
ForefrontAI large language models.
llms.koboldai.KoboldApiLLM
Kobold API language model.
llms.deepinfra.DeepInfra
DeepInfra models.
llms.anthropic.Anthropic
Anthropic large language models.
llms.modal.Modal
Modal large language models.
llms.symblai_nebula.Nebula
Nebula Service models.
llms.aviary.Aviary
Aviary hosted models.
llms.aviary.AviaryBackend(backend_url, bearer)
llms.huggingface_hub.HuggingFaceHub
HuggingFaceHub models.
llms.huggingface_endpoint.HuggingFaceEndpoint
HuggingFace Endpoint models.
llms.vllm.VLLM
Create a new model by parsing and validating input data from keyword arguments. | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-45 | Create a new model by parsing and validating input data from keyword arguments.
llms.tongyi.Tongyi
Tongyi Qwen large language models.
llms.ollama.Ollama
Ollama locally run large language models.
llms.petals.Petals
Petals Bloom models.
llms.octoai_endpoint.OctoAIEndpoint
OctoAI LLM Endpoints.
llms.edenai.EdenAI
Wrapper around edenai 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.cerebriumai.CerebriumAI
CerebriumAI large language models.
llms.rwkv.RWKV
RWKV 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.anyscale.Anyscale
Anyscale Service models.
llms.writer.Writer
Writer large language models.
llms.textgen.TextGen
text-generation-webui models.
llms.gooseai.GooseAI
GooseAI large language models.
llms.nlpcloud.NLPCloud
NLPCloud large language models.
llms.chatglm.ChatGLM
ChatGLM LLM service.
llms.stochasticai.StochasticAI
StochasticAI large language models.
llms.pipelineai.PipelineAI
PipelineAI large language models.
llms.fireworks.BaseFireworks
Wrapper around Fireworks large language models.
llms.fireworks.Fireworks
Wrapper around Fireworks large language models. | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-46 | llms.fireworks.Fireworks
Wrapper around Fireworks large language models.
llms.fireworks.FireworksChat
Wrapper around Fireworks Chat large language models.
llms.fake.FakeListLLM
Fake LLM for testing purposes.
llms.promptlayer_openai.PromptLayerOpenAI
PromptLayer OpenAI large language models.
llms.promptlayer_openai.PromptLayerOpenAIChat
Wrapper around OpenAI large language models.
llms.self_hosted.SelfHostedPipeline
Model inference on self-hosted remote hardware.
llms.replicate.Replicate
Replicate models.
llms.llamacpp.LlamaCpp
llama.cpp model.
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()
Gets the notebook REPL context if running inside a Databricks notebook.
llms.fireworks.acompletion_with_retry(llm, ...) | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-47 | llms.fireworks.acompletion_with_retry(llm, ...)
Use tenacity to retry the async completion call.
llms.fireworks.completion_with_retry(llm, ...)
Use tenacity to retry the completion call.
llms.fireworks.execute(prompt, model, api_key)
Execute LLM query
llms.fireworks.update_token_usage(keys, ...)
Update token usage.
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 | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-48 | 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.
load.load.Reviver([secrets_map, ...])
Reviver for JSON objects.
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.load(obj, *[, secrets_map, ...])
Revive a LangChain class from a JSON object.
load.load.loads(text, *[, secrets_map, ...])
Revive a LangChain class from a JSON 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.summary.ConversationSummaryMemory
Conversation summarizer to chat memory.
memory.summary.SummarizerMixin
Mixin for summarizer.
memory.vectorstore.VectorStoreRetrieverMemory
VectorStoreRetriever-backed memory.
memory.buffer_window.ConversationBufferWindowMemory | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-49 | VectorStoreRetriever-backed memory.
memory.buffer_window.ConversationBufferWindowMemory
Buffer for storing conversation memory inside a limited size window.
memory.combined.CombinedMemory
Combining multiple memories' data together.
memory.summary_buffer.ConversationSummaryBufferMemory
Buffer with summarizer for storing conversation memory.
memory.kg.ConversationKGMemory
Knowledge graph conversation memory.
memory.motorhead_memory.MotorheadMemory
Chat message memory backed by Motorhead service.
memory.zep_memory.ZepMemory
Persist your chain history to the Zep Memory Server.
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.token_buffer.ConversationTokenBufferMemory
Conversation chat memory with token limit.
memory.buffer.ConversationBufferMemory
Buffer for storing conversation memory.
memory.buffer.ConversationStringBufferMemory
Buffer for storing conversation memory.
memory.chat_memory.BaseChatMemory
Abstract base class for chat memory.
memory.simple.SimpleMemory
Simple memory for storing context or other information that shouldn't ever change between prompts.
memory.readonly.ReadOnlySharedMemory
A memory wrapper that is read-only and cannot be changed.
memory.chat_message_histories.cosmos_db.CosmosDBChatMessageHistory(...)
Chat message history backed by Azure CosmosDB.
memory.chat_message_histories.mongodb.MongoDBChatMessageHistory(...)
Chat message history that stores history in MongoDB.
memory.chat_message_histories.cassandra.CassandraChatMessageHistory(...)
Chat message history that stores history in Cassandra.
memory.chat_message_histories.postgres.PostgresChatMessageHistory(...)
Chat message history stored in a Postgres database. | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-50 | Chat message history stored in a Postgres database.
memory.chat_message_histories.zep.ZepChatMessageHistory(...)
Chat message history that uses Zep as a backend.
memory.chat_message_histories.dynamodb.DynamoDBChatMessageHistory(...)
Chat message history that stores history in AWS DynamoDB.
memory.chat_message_histories.momento.MomentoChatMessageHistory(...)
Chat message history cache that uses Momento as a backend.
memory.chat_message_histories.file.FileChatMessageHistory(...)
Chat message history that stores history in a local file.
memory.chat_message_histories.streamlit.StreamlitChatMessageHistory([key])
Chat message history that stores messages in Streamlit session state.
memory.chat_message_histories.in_memory.ChatMessageHistory
In memory implementation of chat message history.
memory.chat_message_histories.firestore.FirestoreChatMessageHistory(...)
Chat message history backed by Google Firestore.
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.rocksetdb.RocksetChatMessageHistory(...)
Uses Rockset to store chat messages.
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.model_laboratory¶
Experiment with different models.
Classes¶
model_laboratory.ModelLaboratory(chains[, names])
Experiment with different models.
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 | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-51 | Main helpers:
Serializable, Generation, PromptValue
Classes¶
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.boolean.BooleanOutputParser
Parse the output of an LLM call to a boolean.
output_parsers.datetime.DatetimeOutputParser
Parse the output of an LLM call to a datetime.
output_parsers.combining.CombiningOutputParser
Combine multiple output parsers into one.
output_parsers.regex.RegexParser
Parse the output of an LLM call using a regex.
output_parsers.fix.OutputFixingParser
Wraps a parser and tries to fix parsing errors.
output_parsers.regex_dict.RegexDictParser
Parse the output of an LLM call into a Dictionary using a regex.
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.
output_parsers.openai_functions.JsonKeyOutputFunctionsParser
Parse an output as the element of the Json object.
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.rail_parser.GuardrailsOutputParser
Parse the output of an LLM call using Guardrails.
output_parsers.json.SimpleJsonOutputParser | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-52 | output_parsers.json.SimpleJsonOutputParser
Parse the output of an LLM call to a JSON object.
output_parsers.pydantic.PydanticOutputParser
Parse an output using a pydantic model.
output_parsers.enum.EnumOutputParser
Parse an output that is one of a set of values.
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.
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
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.few_shot_with_templates.FewShotPromptWithTemplates
Prompt template that contains few shot examples.
prompts.few_shot.FewShotChatMessagePromptTemplate | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-53 | prompts.few_shot.FewShotChatMessagePromptTemplate
Chat prompt template that supports few-shot examples.
prompts.few_shot.FewShotPromptTemplate
Prompt template that contains few shot examples.
prompts.prompt.Prompt
alias of PromptTemplate
prompts.prompt.PromptTemplate
A prompt template for a language model.
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.pipeline.PipelinePromptTemplate
A prompt template for composing multiple prompt templates together.
prompts.example_selector.base.BaseExampleSelector()
Interface for selecting examples to include in prompts.
prompts.example_selector.ngram_overlap.NGramOverlapExampleSelector
Select and order examples based on ngram overlap score (sentence_bleu score).
prompts.example_selector.semantic_similarity.MaxMarginalRelevanceExampleSelector
ExampleSelector that selects examples based on Max Marginal Relevance.
prompts.example_selector.semantic_similarity.SemanticSimilarityExampleSelector
Example selector that selects examples based on SemanticSimilarity.
prompts.example_selector.length_based.LengthBasedExampleSelector
Select examples based on length.
Functions¶
prompts.base.check_valid_template(template, ...)
Check that template string is valid. | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-54 | 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:
BaseRetriever --> <name>Retriever # Examples: ArxivRetriever, MergerRetriever
Main helpers:
Document, Serializable, Callbacks,
CallbackManagerForRetrieverRun, AsyncCallbackManagerForRetrieverRun
Classes¶
retrievers.docarray.DocArrayRetriever
Retriever for DocArray Document Indices.
retrievers.docarray.SearchType(value[, ...])
Enumerator of the types of search to perform.
retrievers.remote_retriever.RemoteLangChainRetriever
Retriever for remote LangChain API.
retrievers.chatgpt_plugin_retriever.ChatGPTPluginRetriever
Retrieves documents from a ChatGPT plugin. | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-55 | Retrieves documents from a ChatGPT plugin.
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.databerry.DataberryRetriever
Retriever for the Databerry API.
retrievers.bm25.BM25Retriever
BM25 Retriever without elastic search.
retrievers.wikipedia.WikipediaRetriever
Retriever for Wikipedia API.
retrievers.zilliz.ZillizRetriever
Retriever for the Zilliz API.
retrievers.pinecone_hybrid_search.PineconeHybridSearchRetriever
Pinecone Hybrid Search Retriever.
retrievers.pubmed.PubMedRetriever
Retriever for PubMed API.
retrievers.zep.ZepRetriever
Retriever for the Zep long-term memory store.
retrievers.svm.SVMRetriever
SVM 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.time_weighted_retriever.TimeWeightedVectorStoreRetriever
Retriever that combines embedding similarity with recency in retrieving values.
retrievers.knn.KNNRetriever
KNN Retriever. | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-56 | retrievers.knn.KNNRetriever
KNN Retriever.
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.vespa_retriever.VespaRetriever
Retriever that uses Vespa.
retrievers.milvus.MilvusRetriever
Retriever that uses the Milvus API.
retrievers.re_phraser.RePhraseQueryRetriever
Given a user query, use an LLM to re-phrase it.
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
Represents an Amazon Kendra Query API search result, which is composed of:
retrievers.kendra.QueryResultItem
A Query API result item.
retrievers.kendra.ResultItem
Abstract class that represents a result item.
retrievers.kendra.RetrieveResult
Represents an Amazon Kendra Retrieve API search result, which is composed of:
retrievers.kendra.RetrieveResultItem
A Retrieve API result item.
retrievers.kendra.TextWithHighLights
Text with highlights. | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-57 | retrievers.kendra.TextWithHighLights
Text with highlights.
retrievers.weaviate_hybrid_search.WeaviateHybridSearchRetriever
Retriever for the Weaviate's hybrid search.
retrievers.merger_retriever.MergerRetriever
Retriever that merges the results of multiple retrievers.
retrievers.ensemble.EnsembleRetriever
This class ensemble the results of multiple retrievers by using rank fusion.
retrievers.arxiv.ArxivRetriever
Retriever for Arxiv.
retrievers.elastic_search_bm25.ElasticSearchBM25Retriever
Retriever for the Elasticsearch using BM25 as a retrieval method.
retrievers.tfidf.TFIDFRetriever
TF-IDF Retriever.
retrievers.chaindesk.ChaindeskRetriever
Retriever for the Chaindesk API.
retrievers.google_cloud_enterprise_search.GoogleCloudEnterpriseSearchRetriever
Retriever for the Google Cloud Enterprise Search Service API.
retrievers.azure_cognitive_search.AzureCognitiveSearchRetriever
Retriever for the Azure Cognitive Search service.
retrievers.metal.MetalRetriever
Retriever that uses the Metal API.
retrievers.contextual_compression.ContextualCompressionRetriever
Retriever that wraps a base retriever and compresses the results.
retrievers.parent_document_retriever.ParentDocumentRetriever
Fetches small chunks, then fetches their parent documents.
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.embeddings_filter.EmbeddingsFilter | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-58 | retrievers.document_compressors.embeddings_filter.EmbeddingsFilter
Document compressor that uses embeddings to drop documents unrelated to the query.
retrievers.document_compressors.cohere_rerank.CohereRerank
DocumentCompressor that uses Cohere's rerank API to compress documents.
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_filter.LLMChainFilter
Filter that drops documents that aren't relevant to the query.
retrievers.self_query.deeplake.DeepLakeTranslator()
Logic for converting internal query language elements to valid filters.
retrievers.self_query.base.SelfQueryRetriever
Retriever that uses a vector store and an LLM to generate the vector store queries.
retrievers.self_query.qdrant.QdrantTranslator(...)
Translate the internal query language elements to valid filters.
retrievers.self_query.chroma.ChromaTranslator()
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.myscale.MyScaleTranslator([...])
Translate internal query language elements to valid filters.
retrievers.self_query.weaviate.WeaviateTranslator()
Translate the internal query language elements to valid filters.
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(item) | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-59 | retrievers.kendra.combined_text(item)
Combines a ResultItem title and 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.memory.BaseChatMessageHistory()
Abstract base class for storing chat message history.
schema.memory.BaseMemory
Abstract base class for memory in Chains.
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.NoOpOutputParser
alias of StrOutputParser
schema.output_parser.OutputParserException(error) | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-60 | alias of StrOutputParser
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.document.BaseDocumentTransformer()
Abstract base class for document transformation systems.
schema.document.Document
Class for storing a piece of text and associated metadata.
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 |
088bd5c75607-61 | 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.agent.AgentAction(tool, tool_input, log)
A full description of an action for an ActionAgent to execute.
schema.agent.AgentFinish(return_values, log)
The final return value of an ActionAgent.
schema.prompt_template.BasePromptTemplate
Base class for all prompt templates, returning a prompt.
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.
schema.runnable.RunnableWithFallbacks
Create a new model by parsing and validating input data from keyword arguments.
schema.language_model.BaseLanguageModel
Abstract base class for interfacing with language models.
schema.exceptions.LangChainException
General LangChain exception.
schema.storage.BaseStore()
Abstract interface for a key-value store.
schema.retriever.BaseRetriever
Abstract base class for a Document retrieval system.
schema.prompt.PromptValue
Base abstract class for inputs to any language model.
Functions¶
schema.messages.get_buffer_string(messages)
Convert sequence of Messages to strings and concatenate them into one string. | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-62 | Convert sequence of Messages to strings and concatenate them into one string.
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({ | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-63 | RunEvalConfig.Criteria("helpfulness"),
RunEvalConfig.Criteria({
"fifth-grader-score": "Do you have to be smarter than a fifth grader to answer this question?"
}),
]
)
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 | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-64 | 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.
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.storage¶
Implementations of key-value stores and storage helpers.
Module provides implementations of various key-value stores that conform
to a simple key-value interface.
The primary goal of these storages is to support implementation of caching.
Classes¶
storage.encoder_backed.EncoderBackedStore(...)
Wraps a store with key and value encoders/decoders.
storage.exceptions.InvalidKeyException
Raised when a key is invalid; e.g., uses incorrect characters.
storage.in_memory.InMemoryStore()
In-memory implementation of the BaseStore using a dictionary.
storage.file_system.LocalFileStore(root_path)
BaseStore interface that works on the local file system.
langchain.text_splitter¶
Text Splitters are classes for splitting text.
Class hierarchy: | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-65 | 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([...])
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.MarkdownHeaderTextSplitter(...)
Splitting markdown files based on specified headers.
text_splitter.MarkdownTextSplitter(**kwargs)
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.
text_splitter.Tokenizer(chunk_overlap, ...)
Functions¶ | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-66 | text_splitter.Tokenizer(chunk_overlap, ...)
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.ifttt.IFTTTWebhook
IFTTT Webhook.
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.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.convert_to_openai.FunctionDescription
Representation of a callable function to the OpenAI API.
tools.wolfram_alpha.tool.WolframAlphaQueryRun
Tool that queries using the Wolfram Alpha SDK.
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. | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-67 | tools.spark_sql.tool.InfoSparkSQLTool
Tool for getting metadata about a Spark SQL.
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.shell.tool.ShellInput
Commands for the Bash Shell tool.
tools.shell.tool.ShellTool
Tool to run shell commands.
tools.human.tool.HumanInputRun
Tool that asks user for input.
tools.golden_query.tool.GoldenQueryRun
Tool that adds the capability to query using the Golden API and get back JSON.
tools.wikipedia.tool.WikipediaQueryRun
Tool that searches the Wikipedia API.
tools.google_places.tool.GooglePlacesSchema
Input for GooglePlacesTool.
tools.google_places.tool.GooglePlacesTool
Tool that queries the Google places API.
tools.amadeus.base.AmadeusBaseTool
Base Tool for Amadeus.
tools.amadeus.flight_search.AmadeusFlightSearch
Tool for searching for a single flight between two airports.
tools.amadeus.flight_search.FlightSearchSchema
Schema for the AmadeusFlightSearch tool.
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.arxiv.tool.ArxivQueryRun
Tool that searches the Arxiv API.
tools.playwright.extract_text.ExtractTextTool
Tool for extracting all the text on the current webpage.
tools.playwright.base.BaseBrowserTool
Base class for browser tools.
tools.playwright.navigate.NavigateTool
Tool for navigating a browser to a URL.
tools.playwright.navigate.NavigateToolInput
Input for NavigateToolInput. | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-68 | tools.playwright.navigate.NavigateToolInput
Input for NavigateToolInput.
tools.playwright.click.ClickTool
Tool for clicking on an element with the given CSS selector.
tools.playwright.click.ClickToolInput
Input for ClickTool.
tools.playwright.navigate_back.NavigateBackTool
Navigate back to the previous page in the browser history.
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.extract_hyperlinks.ExtractHyperlinksTool
Extract all hyperlinks on the page.
tools.playwright.extract_hyperlinks.ExtractHyperlinksToolInput
Input for ExtractHyperlinksTool.
tools.playwright.current_page.CurrentWebPageTool
Tool for getting the URL of the current webpage.
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.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
tools.youtube.search.YouTubeSearchTool
Tool that queries YouTube.
tools.office365.events_search.O365SearchEvents
Class for searching calendar events in Office 365
tools.office365.events_search.SearchEventsInput
Input for SearchEmails Tool.
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.base.O365BaseTool
Base class for the Office 365 tools. | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-69 | tools.office365.base.O365BaseTool
Base class for the Office 365 tools.
tools.office365.send_message.O365SendMessage
Tool for sending an email in Office 365.
tools.office365.send_message.SendMessageSchema
Input for SendMessageTool.
tools.office365.send_event.O365SendEvent
Tool for sending calendar events in Office 365.
tools.office365.send_event.SendEventSchema
Input for CreateEvent Tool.
tools.office365.messages_search.O365SearchEmails
Class for searching email messages in Office 365
tools.office365.messages_search.SearchEmailsInput
Input for SearchEmails Tool.
tools.multion.update_session.MultionUpdateSession
Create a new model by parsing and validating input data from keyword arguments.
tools.multion.update_session.UpdateSessionSchema
Input for UpdateSessionTool.
tools.multion.create_session.CreateSessionSchema
Input for CreateSessionTool.
tools.multion.create_session.MultionCreateSession
Create a new model by parsing and validating input data from keyword arguments.
tools.gmail.get_message.GmailGetMessage
Tool that gets a message by ID from Gmail.
tools.gmail.get_message.SearchArgsSchema
Input for GetMessageTool.
tools.gmail.create_draft.CreateDraftSchema
Input for CreateDraftTool.
tools.gmail.create_draft.GmailCreateDraft
Tool that creates a draft email for Gmail.
tools.gmail.base.GmailBaseTool
Base class for Gmail tools.
tools.gmail.send_message.GmailSendMessage
Tool that sends a message to Gmail.
tools.gmail.send_message.SendMessageSchema
Input for SendMessageTool.
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.get_thread.GetThreadSchema
Input for GetMessageTool.
tools.gmail.get_thread.GmailGetThread | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-70 | Input for GetMessageTool.
tools.gmail.get_thread.GmailGetThread
Tool that gets a thread by ID from Gmail.
tools.brave_search.tool.BraveSearch
Tool that queries the BraveSearch.
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)
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.metaphor_search.tool.MetaphorSearchResults
Tool that queries the Metaphor Search API and gets back json.
tools.dataforseo_api_search.tool.DataForSeoAPISearchResults
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.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.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. | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-71 | tools.requests.tool.RequestsPutTool
Tool for making a PUT request to an API endpoint.
tools.pubmed.tool.PubmedQueryRun
Tool that searches the PubMed API.
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.read.ReadFileInput
Input for ReadFileTool.
tools.file_management.read.ReadFileTool
Tool that reads a file.
tools.file_management.move.FileMoveInput
Input for MoveFileTool.
tools.file_management.move.MoveFileTool
Tool that moves a file.
tools.file_management.copy.CopyFileTool
Tool that copies a file.
tools.file_management.copy.FileCopyInput
Input for CopyFileTool.
tools.file_management.write.WriteFileInput
Input for WriteFileTool.
tools.file_management.write.WriteFileTool
Tool that writes a file to disk.
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.utils.BaseFileToolMixin
Mixin for file system tools.
tools.file_management.utils.FileValidationError
Error for paths outside the root directory.
tools.file_management.delete.DeleteFileTool
Tool that deletes a file.
tools.file_management.delete.FileDeleteInput
Input for DeleteFileTool.
tools.azure_cognitive_services.text2speech.AzureCogsText2SpeechTool
Tool that queries the Azure Cognitive Services Text2Speech API.
tools.azure_cognitive_services.speech2text.AzureCogsSpeech2TextTool
Tool that queries the Azure Cognitive Services Speech2Text API.
tools.azure_cognitive_services.image_analysis.AzureCogsImageAnalysisTool
Tool that queries the Azure Cognitive Services Image Analysis API.
tools.azure_cognitive_services.form_recognizer.AzureCogsFormRecognizerTool | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-72 | tools.azure_cognitive_services.form_recognizer.AzureCogsFormRecognizerTool
Tool that queries the Azure Cognitive Services Form Recognizer API.
tools.openweathermap.tool.OpenWeatherMapQueryRun
Tool that queries the OpenWeatherMap API.
tools.jira.tool.JiraAction
Tool that queries the Atlassian Jira API.
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.github.tool.GitHubAction
Tool for interacting with the GitHub API.
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.scenexplain.tool.SceneXplainInput
Input for SceneXplain.
tools.scenexplain.tool.SceneXplainTool
Tool that explains images.
tools.sleep.tool.SleepInput
Input for CopyFileTool.
tools.sleep.tool.SleepTool
Tool that adds the capability to sleep.
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.powerbi.tool.InfoPowerBITool
Tool for getting metadata about a PowerBI Dataset.
tools.powerbi.tool.ListPowerBITool
Tool for getting tables names.
tools.powerbi.tool.QueryPowerBITool
Tool for querying a Power BI Dataset.
tools.ddg_search.tool.DuckDuckGoSearchResults | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-73 | 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.graphql.tool.BaseGraphQLTool
Base tool for querying a GraphQL 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.nuclia.tool.NUASchema
Create a new model by parsing and validating input data from keyword arguments.
tools.nuclia.tool.NucliaUnderstandingAPI
Tool to process files with the Nuclia Understanding API.
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.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.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.
Functions¶
tools.amadeus.utils.authenticate()
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. | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-74 | 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.
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) | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-75 | 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.sql_database.SQLDatabase(engine[, ...])
SQLAlchemy wrapper around a database.
utilities.bing_search.BingSearchAPIWrapper
Wrapper for Bing Search API.
utilities.scenexplain.SceneXplainAPIWrapper
Wrapper for SceneXplain API.
utilities.golden_query.GoldenQueryAPIWrapper
Wrapper for Golden.
utilities.searx_search.SearxResults(data)
Dict like wrapper around search api results.
utilities.searx_search.SearxSearchWrapper
Wrapper for Searx API.
utilities.serpapi.HiddenPrints()
Context manager to hide prints.
utilities.serpapi.SerpAPIWrapper
Wrapper around SerpAPI.
utilities.portkey.Portkey()
utilities.requests.Requests
Wrapper around requests to handle auth and async.
utilities.requests.RequestsWrapper
alias of TextRequestsWrapper
utilities.requests.TextRequestsWrapper
Lightweight wrapper around requests library.
utilities.wikipedia.WikipediaAPIWrapper
Wrapper around WikipediaAPI.
utilities.openapi.HTTPVerb(value[, names, ...])
Enumerator of the HTTP verbs.
utilities.openapi.OpenAPISpec
OpenAPI Model that removes misformatted parts of the spec.
utilities.python.PythonREPL
Simulates a standalone Python REPL.
utilities.pubmed.PubMedAPIWrapper
Wrapper around PubMed API.
utilities.tensorflow_datasets.TensorflowDatasets
Access to the TensorFlow Datasets. | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-76 | utilities.tensorflow_datasets.TensorflowDatasets
Access to the TensorFlow Datasets.
utilities.dataforseo_api_search.DataForSeoAPIWrapper
Wrapper around the DataForSeo API.
utilities.metaphor_search.MetaphorSearchAPIWrapper
Wrapper for Metaphor Search API.
utilities.google_search.GoogleSearchAPIWrapper
Wrapper for Google Search API.
utilities.powerbi.PowerBIDataset
Create PowerBI engine from dataset ID and credential or token.
utilities.github.GitHubAPIWrapper
Wrapper for GitHub API.
utilities.bibtex.BibtexparserWrapper
Wrapper around bibtexparser.
utilities.duckduckgo_search.DuckDuckGoSearchAPIWrapper
Wrapper for DuckDuckGo Search API.
utilities.brave_search.BraveSearchWrapper
Wrapper around the Brave search engine.
utilities.google_serper.GoogleSerperAPIWrapper
Wrapper around the Serper.dev Google Search API.
utilities.max_compute.MaxComputeAPIWrapper(client)
Interface for querying Alibaba Cloud MaxCompute tables.
utilities.dalle_image_generator.DallEAPIWrapper
Wrapper for OpenAI's DALL-E Image Generator.
utilities.twilio.TwilioAPIWrapper
Messaging Client using Twilio.
utilities.zapier.ZapierNLAWrapper
Wrapper for Zapier NLA.
utilities.jira.JiraAPIWrapper
Wrapper for Jira API.
utilities.arxiv.ArxivAPIWrapper
Wrapper around ArxivAPI.
utilities.google_places_api.GooglePlacesAPIWrapper
Wrapper around Google Places API.
utilities.spark_sql.SparkSQL([...])
utilities.awslambda.LambdaWrapper
Wrapper for AWS Lambda SDK.
utilities.wolfram_alpha.WolframAlphaAPIWrapper
Wrapper for Wolfram Alpha.
utilities.openweathermap.OpenWeatherMapAPIWrapper
Wrapper for OpenWeatherMap API using PyOWM.
utilities.bash.BashProcess([strip_newlines, ...]) | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-77 | utilities.bash.BashProcess([strip_newlines, ...])
Wrapper class for starting subprocesses.
utilities.graphql.GraphQLAPIWrapper
Wrapper around GraphQL API.
Functions¶
utilities.loading.try_load_from_hub(path, ...)
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.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. | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-78 | 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)
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.build_extra_kwargs(extra_kwargs, ...)
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.xata.XataVectorStore(api_key, ...)
VectorStore for a Xata database.
vectorstores.awadb.AwaDB([table_name, ...]) | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-79 | vectorstores.awadb.AwaDB([table_name, ...])
Interface implemented by AwaDB vector stores.
vectorstores.marqo.Marqo(client, index_name)
Wrapper around Marqo database.
vectorstores.opensearch_vector_search.OpenSearchVectorSearch(...)
Wrapper around OpenSearch as a vector database.
vectorstores.starrocks.StarRocks(embedding)
Wrapper around StarRocks vector database
vectorstores.starrocks.StarRocksSettings
StarRocks Client Configuration
vectorstores.singlestoredb.SingleStoreDB(...)
This class serves as a Pythonic interface to the SingleStore DB database.
vectorstores.singlestoredb.SingleStoreDBRetriever
Retriever for SingleStoreDB vector stores.
vectorstores.deeplake.DeepLake([...])
Wrapper around Deep Lake, a data lake for deep learning applications.
vectorstores.cassandra.Cassandra(embedding, ...)
Wrapper around Cassandra embeddings platform.
vectorstores.tigris.Tigris(client, ...)
Initialize Tigris vector store
vectorstores.scann.ScaNN(embedding, index, ...)
Wrapper around ScaNN vector database.
vectorstores.clarifai.Clarifai([user_id, ...])
Wrapper around Clarifai AI platform's vector store.
vectorstores.base.VectorStore()
Interface for vector stores.
vectorstores.base.VectorStoreRetriever
Retriever class for VectorStore.
vectorstores.typesense.Typesense(...[, ...])
Wrapper around Typesense vector search.
vectorstores.vectara.Vectara([...])
Implementation of Vector Store using Vectara.
vectorstores.vectara.VectaraRetriever
Retriever class for Vectara.
vectorstores.zilliz.Zilliz(embedding_function)
Initialize wrapper around the Zilliz vector database. | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-80 | Initialize wrapper around the Zilliz vector database.
vectorstores.tair.Tair(embedding_function, ...)
Wrapper around Tair Vector store.
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.pgembedding.QueryResult()
vectorstores.meilisearch.Meilisearch(embedding)
Initialize wrapper around Meilisearch vector database.
vectorstores.analyticdb.AnalyticDB(...[, ...])
VectorStore implementation using AnalyticDB.
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.clickhouse.Clickhouse(embedding)
Wrapper around ClickHouse vector database
vectorstores.clickhouse.ClickhouseSettings
ClickHouse Client Configuration
vectorstores.milvus.Milvus(embedding_function)
Initialize wrapper around the milvus vector database.
vectorstores.elastic_vector_search.ElasticKnnSearch(...) | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-81 | 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.qdrant.Qdrant(client, ...[, ...])
Wrapper around Qdrant vector database.
vectorstores.qdrant.QdrantException
Base class for all the Qdrant related exceptions
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.
vectorstores.faiss.FAISS(embedding_function, ...)
Wrapper around FAISS vector database.
vectorstores.azuresearch.AzureSearch(...[, ...])
Azure Cognitive Search vector store.
vectorstores.azuresearch.AzureSearchVectorStoreRetriever
Retriever that uses Azure Search to find similar documents.
vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearch(...)
Alibaba Cloud OpenSearch Vector Store
vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearchSettings(...) | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-82 | vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearchSettings(...)
Opensearch Client Configuration Attribute: endpoint (str) : The endpoint of opensearch instance, You can find it from the console of Alibaba Cloud OpenSearch. instance_id (str) : The identify of opensearch instance, You can find it from the console of Alibaba Cloud OpenSearch. datasource_name (str): The name of the data source specified when creating it. username (str) : The username specified when purchasing the instance. password (str) : The password specified when purchasing the instance. embedding_index_name (str) : The name of the vector attribute specified when configuring the instance attributes. field_name_mapping (Dict) : Using field name mapping between opensearch vector store and opensearch instance configuration table field names: { 'id': 'The id field name map of index document.', 'document': 'The text field name map of index document.', 'embedding': 'In the embedding field of the opensearch instance, the values must be in float16 multivalue type and separated by commas.', 'metadata_field_x': 'Metadata field mapping includes the mapped field name and operator in the mapping value, separated by a comma between the mapped field name and the operator.', }.
vectorstores.redis.Redis(redis_url, ...[, ...])
Wrapper around Redis vector database.
vectorstores.redis.RedisVectorStoreRetriever
Retriever for Redis VectorStore.
vectorstores.chroma.Chroma([...])
Wrapper around ChromaDB embeddings platform.
vectorstores.utils.DistanceStrategy(value[, ...])
Enumerator of the Distance strategies for calculating distances between vectors.
vectorstores.lancedb.LanceDB(connection, ...)
Wrapper around LanceDB vector database.
vectorstores.atlas.AtlasDB(name[, ...]) | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-83 | vectorstores.atlas.AtlasDB(name[, ...])
Wrapper around Atlas: Nomic's neural database and rhizomatic instrument.
vectorstores.hologres.Hologres(...[, ndims, ...])
VectorStore implementation using Hologres.
vectorstores.hologres.HologresWrapper(...)
vectorstores.mongodb_atlas.MongoDBAtlasVectorSearch(...)
Wrapper around MongoDB Atlas Vector Search.
vectorstores.pinecone.Pinecone(index, ...[, ...])
Wrapper around Pinecone vector database.
vectorstores.myscale.MyScale(embedding[, config])
Wrapper around MyScale vector database
vectorstores.myscale.MyScaleSettings
MyScale Client Configuration
vectorstores.matching_engine.MatchingEngine(...)
Vertex Matching Engine implementation of the vector store.
vectorstores.usearch.USearch(embedding, ...)
Wrapper around USearch vector database.
vectorstores.weaviate.Weaviate(client, ...)
Wrapper around Weaviate vector database.
vectorstores.rocksetdb.Rockset(client, ...)
Wrapper arpund Rockset vector database.
vectorstores.supabase.SupabaseVectorStore(...)
VectorStore for a Supabase postgres database.
vectorstores.annoy.Annoy(embedding_function, ...)
Wrapper around Annoy vector database.
vectorstores.docarray.base.DocArrayIndex(...)
Initialize a vector store from DocArray's DocIndex.
vectorstores.docarray.in_memory.DocArrayInMemorySearch(...)
Wrapper around in-memory storage for exact search.
vectorstores.docarray.hnsw.DocArrayHnswSearch(...)
Wrapper around HnswLib storage.
Functions¶
vectorstores.alibabacloud_opensearch.create_metadata(fields)
Create metadata from fields.
vectorstores.annoy.dependable_annoy_import()
Import annoy if available, otherwise raise error. | https://api.python.langchain.com/en/latest/api_reference.html |
088bd5c75607-84 | 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.scann.dependable_scann_import()
Import scann if available, otherwise raise error.
vectorstores.scann.normalize(x)
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.
vectorstores.starrocks.has_mul_sub_str(s, *args)
Check if a string has multiple substrings.
vectorstores.usearch.dependable_usearch_import()
Import usearch if available, otherwise raise error.
vectorstores.utils.maximal_marginal_relevance(...)
Calculate maximal marginal relevance. | https://api.python.langchain.com/en/latest/api_reference.html |
b69aa00ac138-0 | langchain_experimental API Reference¶
langchain_experimental.autonomous_agents¶
Classes¶
autonomous_agents.hugginggpt.hugginggpt.HuggingGPT(...)
autonomous_agents.hugginggpt.repsonse_generator.ResponseGenerationChain
Chain to execute tasks.
autonomous_agents.hugginggpt.repsonse_generator.ResponseGenerator(...)
autonomous_agents.hugginggpt.task_planner.BasePlanner
Create a new model by parsing and validating input data from keyword arguments.
autonomous_agents.hugginggpt.task_planner.Plan(steps)
autonomous_agents.hugginggpt.task_planner.PlanningOutputParser
Create a new model by parsing and validating input data from keyword arguments.
autonomous_agents.hugginggpt.task_planner.Step(...)
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.
autonomous_agents.hugginggpt.task_executor.Task(...)
autonomous_agents.hugginggpt.task_executor.TaskExecutor(plan)
Load tools to execute tasks.
autonomous_agents.baby_agi.task_execution.TaskExecutionChain
Chain to execute tasks.
autonomous_agents.baby_agi.baby_agi.BabyAGI
Controller model for the BabyAGI agent.
autonomous_agents.baby_agi.task_prioritization.TaskPrioritizationChain
Chain to prioritize tasks.
autonomous_agents.baby_agi.task_creation.TaskCreationChain
Chain generating tasks.
autonomous_agents.autogpt.memory.AutoGPTMemory
Memory for AutoGPT.
autonomous_agents.autogpt.prompt_generator.PromptGenerator()
A class for generating custom prompt strings.
autonomous_agents.autogpt.output_parser.AutoGPTAction(...)
Action returned by AutoGPTOutputParser. | https://api.python.langchain.com/en/latest/experimental_api_reference.html |
b69aa00ac138-1 | 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.agent.AutoGPT(...)
Agent class for interacting with Auto-GPT.
autonomous_agents.autogpt.prompt.AutoGPTPrompt
Prompt for AutoGPT.
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.
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.memory.GenerativeAgentMemory
Memory for the generative agent.
generative_agents.generative_agent.GenerativeAgent
An Agent as a character with memory and innate characteristics.
langchain_experimental.llms¶
Experimental LLM wrappers.
Classes¶
llms.rellm_decoder.RELLM
RELLM wrapped LLM using HuggingFace Pipeline API.
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.llamaapi.ChatLlamaAPI
Create a new model by parsing and validating input data from keyword arguments.
llms.jsonformer_decoder.JsonFormer | https://api.python.langchain.com/en/latest/experimental_api_reference.html |
b69aa00ac138-2 | llms.jsonformer_decoder.JsonFormer
Jsonformer 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).
pal_chain.base.PALValidation([...])
Initialize a PALValidation instance.
langchain_experimental.plan_and_execute¶
Classes¶
plan_and_execute.agent_executor.PlanAndExecute
Plan and execute a chain of steps.
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.
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.executors.base.BaseExecutor
Base executor.
plan_and_execute.executors.base.ChainExecutor
Chain executor.
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.sql¶
Chain for interacting with SQL Database.
Classes¶ | https://api.python.langchain.com/en/latest/experimental_api_reference.html |
b69aa00ac138-3 | 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.memory.ToTDFSMemory([stack])
Memory for the Tree of Thought (ToT) chain.
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.checker.ToTChecker
Tree of Thought (ToT) checker.
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.
tot.base.ToTChain
A Chain implementing the Tree of Thought (ToT).
tot.controller.ToTController([c])
Tree of Thought (ToT) controller.
tot.thought.Thought
Create a new model by parsing and validating input data from keyword arguments.
tot.thought.ThoughtValidity(value[, names, ...]) | https://api.python.langchain.com/en/latest/experimental_api_reference.html |
87025ac1244e-0 | langchain.llms.fireworks.completion_with_retry¶
langchain.llms.fireworks.completion_with_retry(llm: Union[BaseFireworks, FireworksChat], **kwargs: Any) → Any[source]¶
Use tenacity to retry the completion call. | https://api.python.langchain.com/en/latest/llms/langchain.llms.fireworks.completion_with_retry.html |
de5bf662f185-0 | langchain.llms.azureml_endpoint.GPT2ContentFormatter¶
class langchain.llms.azureml_endpoint.GPT2ContentFormatter[source]¶
Content handler for GPT2
Attributes
accepts
The MIME type of the response data returned from the endpoint
content_type
The MIME type of the input data passed to the endpoint
Methods
__init__()
escape_special_characters(prompt)
Escapes any special characters in prompt
format_request_payload(prompt, model_kwargs)
Formats the request body according to the input schema of the model.
format_response_payload(output)
Formats the response body according to the output schema of the model.
__init__()¶
static escape_special_characters(prompt: str) → str¶
Escapes any special characters in prompt
format_request_payload(prompt: str, model_kwargs: Dict) → bytes[source]¶
Formats the request body according to the input schema of
the model. Returns bytes or seekable file like object in the
format specified in the content_type request header.
format_response_payload(output: bytes) → str[source]¶
Formats the response body according to the output
schema of the model. Returns the data type that is
received from the response. | https://api.python.langchain.com/en/latest/llms/langchain.llms.azureml_endpoint.GPT2ContentFormatter.html |
4187a9c3b475-0 | langchain_experimental.llms.llamaapi.ChatLlamaAPI¶
class langchain_experimental.llms.llamaapi.ChatLlamaAPI[source]¶
Bases: BaseChatModel
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 cache: Optional[bool] = None¶
Whether to cache the response.
param callback_manager: Optional[BaseCallbackManager] = None¶
Callback manager to add to the run trace.
param callbacks: Callbacks = None¶
Callbacks to add to the run trace.
param metadata: Optional[Dict[str, Any]] = None¶
Metadata to add to the run trace.
param tags: Optional[List[str]] = None¶
Tags to add to the run trace.
param verbose: bool [Optional]¶
Whether to print out response text.
__call__(messages: List[BaseMessage], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → BaseMessage¶
Call self as a function.
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, max_concurrency: Optional[int] = None) → List[Output]¶
async agenerate(messages: List[List[BaseMessage]], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → LLMResult¶
Top Level call | https://api.python.langchain.com/en/latest/llms/langchain_experimental.llms.llamaapi.ChatLlamaAPI.html |
4187a9c3b475-1 | Top Level call
async agenerate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → LLMResult¶
Asynchronously pass a sequence of prompts and return model generations.
This method should make use of batched calls for models that expose a batched
API.
Use this method when you want to:
take advantage of batched calls,
need more output from the model than just the top generated value,
are building chains that are agnostic to the underlying language modeltype (e.g., pure text completion models vs chat models).
Parameters
prompts – List of PromptValues. A PromptValue is an object that can be
converted to match the format of any language model (string for pure
text generation models and BaseMessages for chat models).
stop – Stop words to use when generating. Model output is cut off at the
first occurrence of any of these substrings.
callbacks – Callbacks to pass through. Used for executing additional
functionality, such as logging or streaming, throughout generation.
**kwargs – Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Returns
An LLMResult, which contains a list of candidate Generations for each inputprompt and additional model provider-specific output.
async ainvoke(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → BaseMessageChunk¶
async apredict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → str¶
Asynchronously pass a string to the model and return a string prediction.
Use this method when calling pure text generation models and only the topcandidate generation is needed. | https://api.python.langchain.com/en/latest/llms/langchain_experimental.llms.llamaapi.ChatLlamaAPI.html |
4187a9c3b475-2 | Use this method when calling pure text generation models and only the topcandidate generation is needed.
Parameters
text – String input to pass to the model.
stop – Stop words to use when generating. Model output is cut off at the
first occurrence of any of these substrings.
**kwargs – Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Returns
Top model prediction as a string.
async apredict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → BaseMessage¶
Asynchronously pass messages to the model and return a message prediction.
Use this method when calling chat models and only the topcandidate generation is needed.
Parameters
messages – A sequence of chat messages corresponding to a single model input.
stop – Stop words to use when generating. Model output is cut off at the
first occurrence of any of these substrings.
**kwargs – Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Returns
Top model prediction as a message.
async astream(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → AsyncIterator[BaseMessageChunk]¶
batch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, max_concurrency: Optional[int] = None) → List[Output]¶
bind(**kwargs: Any) → Runnable[Input, Output]¶
Bind arguments to a Runnable, returning a new Runnable.
call_as_llm(message: str, stop: Optional[List[str]] = None, **kwargs: Any) → str¶
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶ | https://api.python.langchain.com/en/latest/llms/langchain_experimental.llms.llamaapi.ChatLlamaAPI.html |
4187a9c3b475-3 | Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) → Dict¶
Return a dictionary of the LLM.
classmethod from_orm(obj: Any) → Model¶
generate(messages: List[List[BaseMessage]], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → LLMResult¶
Top Level call
generate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → LLMResult¶
Pass a sequence of prompts to the model and return model generations.
This method should make use of batched calls for models that expose a batched
API. | https://api.python.langchain.com/en/latest/llms/langchain_experimental.llms.llamaapi.ChatLlamaAPI.html |
4187a9c3b475-4 | This method should make use of batched calls for models that expose a batched
API.
Use this method when you want to:
take advantage of batched calls,
need more output from the model than just the top generated value,
are building chains that are agnostic to the underlying language modeltype (e.g., pure text completion models vs chat models).
Parameters
prompts – List of PromptValues. A PromptValue is an object that can be
converted to match the format of any language model (string for pure
text generation models and BaseMessages for chat models).
stop – Stop words to use when generating. Model output is cut off at the
first occurrence of any of these substrings.
callbacks – Callbacks to pass through. Used for executing additional
functionality, such as logging or streaming, throughout generation.
**kwargs – Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Returns
An LLMResult, which contains a list of candidate Generations for each inputprompt and additional model provider-specific output.
get_num_tokens(text: str) → int¶
Get the number of tokens present in the text.
Useful for checking if an input will fit in a model’s context window.
Parameters
text – The string input to tokenize.
Returns
The integer number of tokens in the text.
get_num_tokens_from_messages(messages: List[BaseMessage]) → int¶
Get the number of tokens in the messages.
Useful for checking if an input will fit in a model’s context window.
Parameters
messages – The message inputs to tokenize.
Returns
The sum of the number of tokens across the messages.
get_token_ids(text: str) → List[int]¶
Return the ordered ids of the tokens in a text.
Parameters
text – The string input to tokenize.
Returns | https://api.python.langchain.com/en/latest/llms/langchain_experimental.llms.llamaapi.ChatLlamaAPI.html |
4187a9c3b475-5 | Parameters
text – The string input to tokenize.
Returns
A list of ids corresponding to the tokens in the text, in order they occurin the text.
invoke(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → BaseMessageChunk¶
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
predict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → str¶
Pass a single string input to the model and return a string prediction.
Use this method when passing in raw text. If you want to pass in specifictypes of chat messages, use predict_messages.
Parameters | https://api.python.langchain.com/en/latest/llms/langchain_experimental.llms.llamaapi.ChatLlamaAPI.html |
4187a9c3b475-6 | Parameters
text – String input to pass to the model.
stop – Stop words to use when generating. Model output is cut off at the
first occurrence of any of these substrings.
**kwargs – Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Returns
Top model prediction as a string.
predict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → BaseMessage¶
Pass a message sequence to the model and return a message prediction.
Use this method when passing in chat messages. If you want to pass in raw text,use predict.
Parameters
messages – A sequence of chat messages corresponding to a single model input.
stop – Stop words to use when generating. Model output is cut off at the
first occurrence of any of these substrings.
**kwargs – Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Returns
Top model prediction as a message.
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
stream(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → Iterator[BaseMessageChunk]¶
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶ | https://api.python.langchain.com/en/latest/llms/langchain_experimental.llms.llamaapi.ChatLlamaAPI.html |
4187a9c3b475-7 | classmethod validate(value: Any) → Model¶
with_fallbacks(fallbacks: ~typing.Sequence[~langchain.schema.runnable.Runnable[~langchain.schema.runnable.Input, ~langchain.schema.runnable.Output]], *, exceptions_to_handle: ~typing.Tuple[~typing.Type[BaseException]] = (<class 'Exception'>,)) → RunnableWithFallbacks[Input, Output]¶
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. | https://api.python.langchain.com/en/latest/llms/langchain_experimental.llms.llamaapi.ChatLlamaAPI.html |
0b29baa3a28c-0 | langchain.llms.loading.load_llm_from_config¶
langchain.llms.loading.load_llm_from_config(config: dict) → BaseLLM[source]¶
Load LLM from Config Dict. | https://api.python.langchain.com/en/latest/llms/langchain.llms.loading.load_llm_from_config.html |
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