viruPVS/langchain-docs huggingface dataset:
RecursiveCharacterTextSplitter -->  <name>TextSplitter
Note: MarkdownHeaderTextSplitter and **HTMLHeaderTextSplitter do not derive from TextSplitter.
Main helpers:
Document, Tokenizer, Language, LineType, HeaderType
Classes¶
text_splitter.CharacterTextSplitter([...])
Splitting text that looks at characters.
text_splitter.ElementType
Element type as typed dict.
text_splitter.HTMLHeaderTextSplitter(...[, ...])
Splitting HTML files based on specified headers.
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, ...)
text_splitter.Tokenizer(chunk_overlap, ...)
Tokenizer data class.
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.retriever.RetrieverInput
Create a new model by parsing and validating input data from keyword arguments.
Functions¶
tools.render.render_text_description(tools)
Render the tool name and description in plain text.
tools.render.render_text_description_and_args(tools)
Render the tool name, description, and args in plain text.
tools.retriever.create_retriever_tool(...)
Create a tool to do retrieval of documents.
langchain.utils¶
Utility functions for LangChain.
These functions do not depend on any other LangChain module.
Classes¶
utils.ernie_functions.FunctionDescription
Representation of a callable function to the Ernie API.
utils.ernie_functions.ToolDescription
Representation of a callable function to the Ernie API.
Functions¶
utils.ernie_functions.convert_pydantic_to_ernie_function(...)
Converts a Pydantic model to a function description for the Ernie API.
utils.ernie_functions.convert_pydantic_to_ernie_tool(...)
Converts a Pydantic model to a function description for the Ernie API.
langchain_community 0.0.3¶
langchain_community.adapters¶
Classes¶
adapters.openai.Chat()
adapters.openai.ChatCompletion()
Chat completion.
adapters.openai.ChatCompletionChunk
Create a new model by parsing and validating input data from keyword arguments.
adapters.openai.ChatCompletions
Create a new model by parsing and validating input data from keyword arguments.
adapters.openai.Choice
Create a new model by parsing and validating input data from keyword arguments.
adapters.openai.ChoiceChunk
Create a new model by parsing and validating input data from keyword arguments.
adapters.openai.Completions()
Completion.
adapters.openai.IndexableBaseModel
Allows a BaseModel to return its fields by string variable indexing
Functions¶
adapters.openai.aenumerate(iterable[, start])
Async version of enumerate function.
adapters.openai.convert_dict_to_message(_dict)
Convert a dictionary to a LangChain message.
adapters.openai.convert_message_to_dict(message)
Convert a LangChain message to a dictionary.
adapters.openai.convert_messages_for_finetuning(...)
Convert messages to a list of lists of dictionaries for fine-tuning.
adapters.openai.convert_openai_messages(messages)
Convert dictionaries representing OpenAI messages to LangChain format.
langchain_community.agent_toolkits¶
Agent toolkits contain integrations with various resources and services.
LangChain has a large ecosystem of integrations with various external resources
like local and remote file systems, APIs and databases.
These integrations allow developers to create versatile applications that combine the
power of LLMs with the ability to access, interact with and manipulate external
resources.
When developing an application, developers should inspect the capabilities and
permissions of the tools that underlie the given agent toolkit, and determine
whether permissions of the given toolkit are appropriate for the application.
whether permissions of the given toolkit are appropriate for the application.
See [Security](https://python.langchain.com/docs/security) for more information.
Classes¶
agent_toolkits.ainetwork.toolkit.AINetworkToolkit
Toolkit for interacting with AINetwork Blockchain.
agent_toolkits.amadeus.toolkit.AmadeusToolkit
Toolkit for interacting with Amadeus which offers APIs for travel.
agent_toolkits.azure_cognitive_services.AzureCognitiveServicesToolkit
Toolkit for Azure Cognitive Services.
agent_toolkits.base.BaseToolkit
Base Toolkit representing a collection of related tools.
agent_toolkits.clickup.toolkit.ClickupToolkit
Clickup Toolkit.
agent_toolkits.file_management.toolkit.FileManagementToolkit
Toolkit for interacting with local files.
agent_toolkits.github.toolkit.BranchName
Create a new model by parsing and validating input data from keyword arguments.
agent_toolkits.github.toolkit.CommentOnIssue
Create a new model by parsing and validating input data from keyword arguments.
agent_toolkits.github.toolkit.CreateFile
Create a new model by parsing and validating input data from keyword arguments.
agent_toolkits.github.toolkit.CreatePR
Create a new model by parsing and validating input data from keyword arguments.
agent_toolkits.github.toolkit.CreateReviewRequest
Create a new model by parsing and validating input data from keyword arguments.
agent_toolkits.github.toolkit.DeleteFile
Create a new model by parsing and validating input data from keyword arguments.
agent_toolkits.github.toolkit.DirectoryPath
Create a new model by parsing and validating input data from keyword arguments.
agent_toolkits.github.toolkit.GetIssue
Create a new model by parsing and validating input data from keyword arguments.
agent_toolkits.github.toolkit.GetPR
Create a new model by parsing and validating input data from keyword arguments.
agent_toolkits.github.toolkit.GitHubToolkit
GitHub Toolkit.
agent_toolkits.github.toolkit.NoInput
GitHub Toolkit.
agent_toolkits.github.toolkit.NoInput
Create a new model by parsing and validating input data from keyword arguments.
agent_toolkits.github.toolkit.ReadFile
Create a new model by parsing and validating input data from keyword arguments.
agent_toolkits.github.toolkit.SearchCode
Create a new model by parsing and validating input data from keyword arguments.
agent_toolkits.github.toolkit.SearchIssuesAndPRs
Create a new model by parsing and validating input data from keyword arguments.
agent_toolkits.github.toolkit.UpdateFile
Create a new model by parsing and validating input data from keyword arguments.
agent_toolkits.gitlab.toolkit.GitLabToolkit
GitLab Toolkit.
agent_toolkits.gmail.toolkit.GmailToolkit
Toolkit for interacting with Gmail.
agent_toolkits.jira.toolkit.JiraToolkit
Jira Toolkit.
agent_toolkits.json.toolkit.JsonToolkit
Toolkit for interacting with a JSON spec.
agent_toolkits.multion.toolkit.MultionToolkit
Toolkit for interacting with the Browser Agent.
agent_toolkits.nasa.toolkit.NasaToolkit
Nasa Toolkit.
agent_toolkits.nla.tool.NLATool
Natural Language API Tool.
agent_toolkits.nla.toolkit.NLAToolkit
Natural Language API Toolkit.
agent_toolkits.office365.toolkit.O365Toolkit
Toolkit for interacting with Office 365.
agent_toolkits.openapi.planner.RequestsDeleteToolWithParsing
A tool that sends a DELETE request and parses the response.
agent_toolkits.openapi.planner.RequestsGetToolWithParsing
Requests GET tool with LLM-instructed extraction of truncated responses.
agent_toolkits.openapi.planner.RequestsPatchToolWithParsing
Requests PATCH tool with LLM-instructed extraction of truncated responses.
agent_toolkits.openapi.planner.RequestsPostToolWithParsing
Requests POST tool with LLM-instructed extraction of truncated responses.
Requests POST tool with LLM-instructed extraction of truncated responses.
agent_toolkits.openapi.planner.RequestsPutToolWithParsing
Requests PUT tool with LLM-instructed extraction of truncated responses.
agent_toolkits.openapi.spec.ReducedOpenAPISpec(...)
A reduced OpenAPI spec.
agent_toolkits.openapi.toolkit.OpenAPIToolkit
Toolkit for interacting with an OpenAPI API.
agent_toolkits.openapi.toolkit.RequestsToolkit
Toolkit for making REST requests.
agent_toolkits.playwright.toolkit.PlayWrightBrowserToolkit
Toolkit for PlayWright browser tools.
agent_toolkits.powerbi.toolkit.PowerBIToolkit
Toolkit for interacting with Power BI dataset.
agent_toolkits.slack.toolkit.SlackToolkit
Toolkit for interacting with Slack.
agent_toolkits.spark_sql.toolkit.SparkSQLToolkit
Toolkit for interacting with Spark SQL.
agent_toolkits.sql.toolkit.SQLDatabaseToolkit
Toolkit for interacting with SQL databases.
agent_toolkits.steam.toolkit.SteamToolkit
Steam Toolkit.
agent_toolkits.zapier.toolkit.ZapierToolkit
Zapier Toolkit.
Functions¶
agent_toolkits.json.base.create_json_agent(...)
Construct a json agent from an LLM and tools.
agent_toolkits.openapi.base.create_openapi_agent(...)
Construct an OpenAPI agent from an LLM and tools.
agent_toolkits.openapi.planner.create_openapi_agent(...)
Instantiate OpenAI API planner and controller for a given spec.
agent_toolkits.openapi.spec.reduce_openapi_spec(spec)
Simplify/distill/minify a spec somehow.
agent_toolkits.powerbi.base.create_pbi_agent(llm)
Construct a Power BI agent from an LLM and tools.
agent_toolkits.powerbi.chat_base.create_pbi_chat_agent(llm)
Construct a Power BI agent from a Chat LLM and tools.
Construct a Power BI agent from a Chat LLM and tools.
agent_toolkits.spark_sql.base.create_spark_sql_agent(...)
Construct a Spark SQL agent from an LLM and tools.
agent_toolkits.sql.base.create_sql_agent(...)
Construct an SQL agent from an LLM and tools.
langchain_community.cache¶
Warning
Beta Feature!
Cache provides an optional caching layer for LLMs.
Cache is useful for two reasons:
It can save you money by reducing the number of API calls you make to the LLM
provider if you’re often requesting the same completion multiple times.
It can speed up your application by reducing the number of API calls you make
to the LLM provider.
Cache directly competes with Memory. See documentation for Pros and Cons.
Class hierarchy:
BaseCache --> <name>Cache  # Examples: InMemoryCache, RedisCache, GPTCache
Classes¶
cache.AstraDBCache(*[, collection_name, ...])
Cache that uses Astra DB as a backend.
cache.AstraDBSemanticCache(*[, ...])
Cache that uses Astra DB as a vector-store backend for semantic (i.e.
cache.CassandraCache([session, keyspace, ...])
Cache that uses Cassandra / Astra DB as a backend.
cache.CassandraSemanticCache(session, ...[, ...])
Cache that uses Cassandra as a vector-store backend for semantic (i.e.
cache.FullLLMCache(**kwargs)
SQLite table for full LLM Cache (all generations).
cache.FullMd5LLMCache(**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.MomentoCache(cache_client, cache_name, *)
Cache that uses Momento as a backend.
cache.RedisCache(redis_, *[, ttl])
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.SQLAlchemyMd5Cache(engine, cache_schema)
Cache that uses SQAlchemy as a backend.
cache.SQLiteCache([database_path])
Cache that uses SQLite as a backend.
cache.UpstashRedisCache(redis_, *[, ttl])
Cache that uses Upstash Redis as a backend.
Functions¶
langchain_community.callbacks¶
Callback handlers allow listening to events in LangChain.
Class hierarchy:
BaseCallbackHandler --> <name>CallbackHandler  # Example: AimCallbackHandler
Classes¶
callbacks.aim_callback.AimCallbackHandler([...])
Callback Handler that logs to Aim.
callbacks.aim_callback.BaseMetadataCallbackHandler()
This class handles the metadata and associated function states for callbacks.
callbacks.argilla_callback.ArgillaCallbackHandler(...)
Callback Handler that logs into Argilla.
callbacks.arize_callback.ArizeCallbackHandler([...])
Callback Handler that logs to Arize.
callbacks.arthur_callback.ArthurCallbackHandler(...)
Callback Handler that logs to Arthur platform.
callbacks.clearml_callback.ClearMLCallbackHandler([...])
Callback Handler that logs to ClearML.
callbacks.comet_ml_callback.CometCallbackHandler([...])
Callback Handler that logs to Comet.
callbacks.confident_callback.DeepEvalCallbackHandler(metrics)
Callback Handler that logs into deepeval.
callbacks.context_callback.ContextCallbackHandler([...])
Callback Handler that records transcripts to the Context service.
callbacks.flyte_callback.FlyteCallbackHandler()
callbacks.flyte_callback.FlyteCallbackHandler()
This callback handler that is used within a Flyte task.
callbacks.human.AsyncHumanApprovalCallbackHandler(...)
Asynchronous callback for manually validating values.
callbacks.human.HumanApprovalCallbackHandler(...)
Callback for manually validating values.
callbacks.human.HumanRejectedException
Exception to raise when a person manually review and rejects a value.
callbacks.infino_callback.InfinoCallbackHandler([...])
Callback Handler that logs to Infino.
callbacks.labelstudio_callback.LabelStudioCallbackHandler([...])
Label Studio callback handler.
callbacks.labelstudio_callback.LabelStudioMode(value)
Label Studio mode enumerator.
callbacks.llmonitor_callback.LLMonitorCallbackHandler([...])
Callback Handler for LLMonitor`.
callbacks.llmonitor_callback.UserContextManager(user_id)
Context manager for LLMonitor user context.
callbacks.mlflow_callback.MlflowCallbackHandler([...])
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.openai_info.OpenAICallbackHandler()
Callback Handler that tracks OpenAI info.
callbacks.promptlayer_callback.PromptLayerCallbackHandler([...])
Callback handler for promptlayer.
callbacks.sagemaker_callback.SageMakerCallbackHandler(run)
Callback Handler that logs prompt artifacts and metrics to SageMaker Experiments.
callbacks.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()
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.comet.CometTracer(**kwargs)
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.trubrics_callback.TrubricsCallbackHandler([...])
Callback handler for Trubrics.
callbacks.utils.BaseMetadataCallbackHandler()
This class handles the metadata and associated function states for callbacks.
callbacks.wandb_callback.WandbCallbackHandler([...])
Callback Handler that logs to Weights and Biases.
callbacks.whylabs_callback.WhyLabsCallbackHandler(...)
Callback Handler for logging to WhyLabs.
Functions¶
callbacks.aim_callback.import_aim()
Import the aim python package and raise an error if it is not installed.
callbacks.clearml_callback.import_clearml()
Import the clearml python package and raise an error if it is not installed.
callbacks.comet_ml_callback.import_comet_ml()
Import comet_ml and raise an error if it is not installed.
callbacks.context_callback.import_context()
Import the getcontext package.
callbacks.flyte_callback.analyze_text(text)
Analyze text using textstat and spacy.
callbacks.flyte_callback.import_flytekit()
callbacks.flyte_callback.import_flytekit()
Import flytekit and flytekitplugins-deck-standard.
callbacks.infino_callback.get_num_tokens(...)
Calculate num tokens for OpenAI with tiktoken package.
callbacks.infino_callback.import_infino()
Import the infino client.
callbacks.infino_callback.import_tiktoken()
Import tiktoken for counting tokens for OpenAI models.
callbacks.labelstudio_callback.get_default_label_configs(mode)
Get default Label Studio configs for the given mode.
callbacks.llmonitor_callback.identify(user_id)
Builds an LLMonitor UserContextManager
callbacks.manager.get_openai_callback()
Get the OpenAI callback handler in a context manager.
callbacks.manager.wandb_tracing_enabled([...])
Get the WandbTracer in a context manager.
callbacks.mlflow_callback.analyze_text(text)
Analyze text using textstat and spacy.
callbacks.mlflow_callback.construct_html_from_prompt_and_generation(...)
Construct an html element from a prompt and a generation.
callbacks.mlflow_callback.import_mlflow()
Import the mlflow python package and raise an error if it is not installed.
callbacks.openai_info.get_openai_token_cost_for_model(...)
Get the cost in USD for a given model and number of tokens.
callbacks.openai_info.standardize_model_name(...)
Standardize the model name to a format that can be used in the OpenAI API.
callbacks.sagemaker_callback.save_json(data, ...)
Save dict to local file path.
callbacks.tracers.comet.import_comet_llm_api()
Import comet_llm api and raise an error if it is not installed.
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()
Hash a string using sha1.
callbacks.utils.import_pandas()
Import the pandas python package and raise an error if it is not installed.
callbacks.utils.import_spacy()
Import the spacy python package and raise an error if it is not installed.
callbacks.utils.import_textstat()
Import the textstat python package and raise an error if it is not installed.
callbacks.utils.load_json(json_path)
Load json file to a string.
callbacks.wandb_callback.analyze_text(text)
Analyze text using textstat and spacy.
callbacks.wandb_callback.construct_html_from_prompt_and_generation(...)
Construct an html element from a prompt and a generation.
callbacks.wandb_callback.import_wandb()
Import the wandb python package and raise an error if it is not installed.
callbacks.wandb_callback.load_json_to_dict(...)
Load json file to a dictionary.
callbacks.whylabs_callback.import_langkit([...])
Import the langkit python package and raise an error if it is not installed.
langchain_community.chat_loaders¶
Chat Loaders load chat messages from common communications platforms.
Load chat messages from various
communications platforms such as Facebook Messenger, Telegram, and
WhatsApp. The loaded chat messages can be used for fine-tuning models.
Class hierarchy:
BaseChatLoader --> <name>ChatLoader  # Examples: WhatsAppChatLoader, IMessageChatLoader
Main helpers:
ChatSession
Classes¶
chat_loaders.base.BaseChatLoader()
Base class for chat loaders.
chat_loaders.facebook_messenger.FolderFacebookMessengerChatLoader(path)
Load Facebook Messenger chat data from a folder.
chat_loaders.facebook_messenger.SingleFileFacebookMessengerChatLoader(path)
Load Facebook Messenger chat data from a single file.
chat_loaders.gmail.GMailLoader(creds[, n, ...])
Load data from GMail.
Load data from GMail.
chat_loaders.imessage.IMessageChatLoader([path])
Load chat sessions from the iMessage chat.db SQLite file.
chat_loaders.langsmith.LangSmithDatasetChatLoader(*, ...)
Load chat sessions from a LangSmith dataset with the "chat" data type.
chat_loaders.langsmith.LangSmithRunChatLoader(runs)
Load chat sessions from a list of LangSmith "llm" runs.
chat_loaders.slack.SlackChatLoader(path)
Load Slack conversations from a dump zip file.
chat_loaders.telegram.TelegramChatLoader(path)
Load telegram conversations to LangChain chat messages.
chat_loaders.whatsapp.WhatsAppChatLoader(path)
Load WhatsApp conversations from a dump zip file or directory.
Functions¶
chat_loaders.utils.map_ai_messages(...)
Convert messages from the specified 'sender' to AI messages.
chat_loaders.utils.map_ai_messages_in_session(...)
Convert messages from the specified 'sender' to AI messages.
chat_loaders.utils.merge_chat_runs(chat_sessions)
Merge chat runs together.
chat_loaders.utils.merge_chat_runs_in_session(...)
Merge chat runs together in a chat session.
langchain_community.chat_message_histories¶
Classes¶
chat_message_histories.astradb.AstraDBChatMessageHistory(*, ...)
Chat message history that stores history in Astra DB.
chat_message_histories.cassandra.CassandraChatMessageHistory(...)
Chat message history that stores history in Cassandra.
chat_message_histories.cosmos_db.CosmosDBChatMessageHistory(...)
Chat message history backed by Azure CosmosDB.
chat_message_histories.dynamodb.DynamoDBChatMessageHistory(...)
Chat message history that stores history in AWS DynamoDB.
chat_message_histories.elasticsearch.ElasticsearchChatMessageHistory(...)
Chat message history that stores history in Elasticsearch.