Tony20100/langchain_full huggingface dataset:
Toolkit for interacting with Amadeus which offers APIs for travel.
agent_toolkits.azure_ai_services.AzureAiServicesToolkit
Toolkit for Azure AI Services.
agent_toolkits.azure_cognitive_services.AzureCognitiveServicesToolkit
Toolkit for Azure Cognitive Services.
agent_toolkits.cassandra_database.toolkit.CassandraDatabaseToolkit
Toolkit for interacting with an Apache Cassandra database.
agent_toolkits.clickup.toolkit.ClickupToolkit
Clickup Toolkit.
agent_toolkits.cogniswitch.toolkit.CogniswitchToolkit
Toolkit for CogniSwitch.
agent_toolkits.connery.toolkit.ConneryToolkit
Toolkit with a list of Connery Actions as tools.
agent_toolkits.file_management.toolkit.FileManagementToolkit
Toolkit for interacting with local files.
agent_toolkits.github.toolkit.BranchName
Schema for operations that require a branch name as input.
agent_toolkits.github.toolkit.CommentOnIssue
Schema for operations that require a comment as input.
agent_toolkits.github.toolkit.CreateFile
Schema for operations that require a file path and content as input.
agent_toolkits.github.toolkit.CreatePR
Schema for operations that require a PR title and body as input.
agent_toolkits.github.toolkit.CreateReviewRequest
Schema for operations that require a username as input.
agent_toolkits.github.toolkit.DeleteFile
Schema for operations that require a file path as input.
agent_toolkits.github.toolkit.DirectoryPath
Schema for operations that require a directory path as input.
agent_toolkits.github.toolkit.GetIssue
Schema for operations that require an issue number as input.
agent_toolkits.github.toolkit.GetPR
Schema for operations that require a PR number as input.
agent_toolkits.github.toolkit.GitHubToolkit
GitHub Toolkit.
agent_toolkits.github.toolkit.NoInput
Schema for operations that do not require any input.
agent_toolkits.github.toolkit.ReadFile
Schema for operations that do not require any input.
agent_toolkits.github.toolkit.ReadFile
Schema for operations that require a file path as input.
agent_toolkits.github.toolkit.SearchCode
Schema for operations that require a search query as input.
agent_toolkits.github.toolkit.SearchIssuesAndPRs
Schema for operations that require a search query as input.
agent_toolkits.github.toolkit.UpdateFile
Schema for operations that require a file path and content as input.
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
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.
agent_toolkits.openapi.planner.RequestsPutToolWithParsing
Requests PUT tool with LLM-instructed extraction of truncated responses.
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.polygon.toolkit.PolygonToolkit
Polygon Toolkit.
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.load_tools.get_all_tool_names()
Get a list of all possible tool names.
agent_toolkits.load_tools.load_huggingface_tool(...)
Loads a tool from the HuggingFace Hub.
agent_toolkits.load_tools.load_tools(tool_names)
Load tools based on their name.
agent_toolkits.openapi.base.create_openapi_agent(...)
Construct an OpenAPI agent from an LLM and tools.
agent_toolkits.openapi.planner.create_openapi_agent(...)
Construct an 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)
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.
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(llm)
Construct a SQL agent from an LLM and toolkit or database.
langchain_community.agents¶
Classes¶
agents.openai_assistant.base.OpenAIAssistantV2Runnable
[Beta] Run an OpenAI Assistant.
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, ...])
[Deprecated]
cache.AstraDBSemanticCache(*[, ...])
[Deprecated]
cache.AsyncRedisCache(redis_, *[, ttl])
Cache that uses Redis as a backend.
cache.AzureCosmosDBSemanticCache(...[, ...])
Cache that uses Cosmos DB Mongo vCore vector-store backend
cache.CassandraCache([session, keyspace, ...])
Cache that uses Cassandra / Astra DB as a backend.
cache.CassandraSemanticCache([session, ...])
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 that uses Momento as a backend.
cache.OpenSearchSemanticCache(...[, ...])
Cache that uses OpenSearch vector store 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.
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()
Callback handler for 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 into Argilla.
callbacks.arize_callback.ArizeCallbackHandler([...])
Callback Handler that logs to Arize.
callbacks.arthur_callback.ArthurCallbackHandler(...)
Callback Handler that logs to Arthur platform.
callbacks.bedrock_anthropic_callback.BedrockAnthropicTokenUsageCallbackHandler()
Callback Handler that tracks bedrock anthropic info.
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.fiddler_callback.FiddlerCallbackHandler(...)
Initialize Fiddler callback handler.
callbacks.flyte_callback.FlyteCallbackHandler()
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)
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(...)
Child record as a NamedTuple.
callbacks.streamlit.mutable_expander.ChildType(value)
Enumerator of the child type.
callbacks.streamlit.mutable_expander.MutableExpander(...)
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(...)
Callback handler that writes to a Streamlit app.
callbacks.streamlit.streamlit_callback_handler.ToolRecord(...)
Tool record as a NamedTuple.
callbacks.tracers.comet.CometTracer(**kwargs)
Comet Tracer.
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.upstash_ratelimit_callback.UpstashRatelimitError(...)
callbacks.upstash_ratelimit_callback.UpstashRatelimitError(...)
Upstash Ratelimit Error
callbacks.upstash_ratelimit_callback.UpstashRatelimitHandler(...)
Callback to handle rate limiting based on the number of requests or the number of tokens in the input.
callbacks.uptrain_callback.UpTrainCallbackHandler(*)
Callback Handler that logs evaluation results to uptrain and the console.
callbacks.uptrain_callback.UpTrainDataSchema(...)
The UpTrain data schema for tracking evaluation results.
callbacks.utils.BaseMetadataCallbackHandler()
Handle 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.fiddler_callback.import_fiddler()
Import the fiddler python package and raise an error if it is not installed.
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.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.
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_bedrock_anthropic_callback()
Get the Bedrock anthropic callback handler in a context manager.
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.get_text_complexity_metrics()
Get the text complexity metrics from textstat.
callbacks.mlflow_callback.import_mlflow()
Import the mlflow python package and raise an error if it is not installed.
callbacks.mlflow_callback.mlflow_callback_metrics()
Get the metrics to log to MLFlow.
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.uptrain_callback.import_uptrain()
Import the uptrain package.
callbacks.utils.flatten_dict(nested_dict[, ...])
Flatten a nested dictionary into a flat dictionary.
callbacks.utils.hash_string(s)
Hash a string using sha1.
callbacks.utils.hash_string(s)
Hash a string using sha1.
callbacks.utils.import_pandas()
Import the pandas python package and raise an error if it is not installed.
callbacks.utils.import_spacy()
Import the spacy python package and raise an error if it is not installed.
callbacks.utils.import_textstat()
Import the textstat python package and raise an error if it is not installed.
callbacks.utils.load_json(json_path)
Load json file to a string.
callbacks.wandb_callback.analyze_text(text)
Analyze text using textstat and spacy.
callbacks.wandb_callback.construct_html_from_prompt_and_generation(...)
Construct an html element from a prompt and a generation.
callbacks.wandb_callback.import_wandb()
Import the wandb python package and raise an error if it is not installed.
callbacks.wandb_callback.load_json_to_dict(...)
Load json file to a dictionary.
callbacks.whylabs_callback.import_langkit([...])
Import the langkit python package and raise an error if it is not installed.
langchain_community.chains¶
Chains module for langchain_community
This module contains the community chains.
Classes¶
chains.graph_qa.arangodb.ArangoGraphQAChain
Chain for question-answering against a graph by generating AQL statements.
chains.graph_qa.base.GraphQAChain
Chain for question-answering against a graph.
chains.graph_qa.cypher.GraphCypherQAChain
Chain for question-answering against a graph by generating Cypher statements.
chains.graph_qa.cypher_utils.CypherQueryCorrector(schemas)
Used to correct relationship direction in generated Cypher statements.
chains.graph_qa.cypher_utils.Schema(...)
Create new instance of Schema(left_node, relation, right_node)
chains.graph_qa.falkordb.FalkorDBQAChain
chains.graph_qa.falkordb.FalkorDBQAChain
Chain for question-answering against a graph by generating Cypher statements.
chains.graph_qa.gremlin.GremlinQAChain
Chain for question-answering against a graph by generating gremlin statements.
chains.graph_qa.hugegraph.HugeGraphQAChain
Chain for question-answering against a graph by generating gremlin statements.
chains.graph_qa.kuzu.KuzuQAChain
Question-answering against a graph by generating Cypher statements for Kùzu.
chains.graph_qa.nebulagraph.NebulaGraphQAChain
Chain for question-answering against a graph by generating nGQL statements.
chains.graph_qa.neptune_cypher.NeptuneOpenCypherQAChain
Chain for question-answering against a Neptune graph by generating openCypher statements.
chains.graph_qa.neptune_sparql.NeptuneSparqlQAChain
Chain for question-answering against a Neptune graph by generating SPARQL statements.
chains.graph_qa.ontotext_graphdb.OntotextGraphDBQAChain
Question-answering against Ontotext GraphDB
chains.graph_qa.sparql.GraphSparqlQAChain
Question-answering against an RDF or OWL graph by generating SPARQL statements.
chains.llm_requests.LLMRequestsChain
Chain that requests a URL and then uses an LLM to parse results.
chains.openapi.chain.OpenAPIEndpointChain
Chain interacts with an OpenAPI endpoint using natural language.
chains.openapi.requests_chain.APIRequesterChain
Get the request parser.
chains.openapi.requests_chain.APIRequesterOutputParser
Parse the request and error tags.
chains.openapi.response_chain.APIResponderChain
Get the response parser.
chains.openapi.response_chain.APIResponderOutputParser
Get the response parser.
chains.openapi.response_chain.APIResponderOutputParser
Parse the response and error tags.
chains.pebblo_retrieval.base.PebbloRetrievalQA
Retrieval Chain with Identity & Semantic Enforcement for question-answering against a vector database.
chains.pebblo_retrieval.models.App
Create a new model by parsing and validating input data from keyword arguments.
chains.pebblo_retrieval.models.AuthContext
Class for an authorization context.
chains.pebblo_retrieval.models.ChainInput
Input for PebbloRetrievalQA chain.
chains.pebblo_retrieval.models.Chains
Create a new model by parsing and validating input data from keyword arguments.
chains.pebblo_retrieval.models.Context
Create a new model by parsing and validating input data from keyword arguments.
chains.pebblo_retrieval.models.Framework
Langchain framework details
chains.pebblo_retrieval.models.Model
Create a new model by parsing and validating input data from keyword arguments.
chains.pebblo_retrieval.models.PkgInfo
Create a new model by parsing and validating input data from keyword arguments.
chains.pebblo_retrieval.models.Prompt
Create a new model by parsing and validating input data from keyword arguments.
chains.pebblo_retrieval.models.Qa
Create a new model by parsing and validating input data from keyword arguments.
chains.pebblo_retrieval.models.Runtime
OS, language details
chains.pebblo_retrieval.models.SemanticContext
Class for a semantic context.
chains.pebblo_retrieval.models.SemanticEntities
Class for a semantic entity filter.
chains.pebblo_retrieval.models.SemanticTopics
Class for a semantic topic filter.
chains.pebblo_retrieval.models.VectorDB
Create a new model by parsing and validating input data from keyword arguments.
Create a new model by parsing and validating input data from keyword arguments.
Functions¶
chains.ernie_functions.base.convert_python_function_to_ernie_function(...)
Convert a Python function to an Ernie function-calling API compatible dict.
chains.ernie_functions.base.convert_to_ernie_function(...)
Convert a raw function/class to an Ernie function.
chains.ernie_functions.base.create_ernie_fn_chain(...)
[Legacy] Create an LLM chain that uses Ernie functions.
chains.ernie_functions.base.create_ernie_fn_runnable(...)
Create a runnable sequence that uses Ernie functions.
chains.ernie_functions.base.create_structured_output_chain(...)
[Legacy] Create an LLMChain that uses an Ernie function to get a structured output.
chains.ernie_functions.base.create_structured_output_runnable(...)
Create a runnable that uses an Ernie function to get a structured output.
chains.ernie_functions.base.get_ernie_output_parser(...)
Get the appropriate function output parser given the user functions.
chains.graph_qa.cypher.construct_schema(...)
Filter the schema based on included or excluded types
chains.graph_qa.cypher.extract_cypher(text)
Extract Cypher code from a text.
chains.graph_qa.falkordb.extract_cypher(text)
Extract Cypher code from a text.
chains.graph_qa.gremlin.extract_gremlin(text)
Extract Gremlin code from a text.
chains.graph_qa.kuzu.extract_cypher(text)
Extract Cypher code from a text.
chains.graph_qa.kuzu.remove_prefix(text, prefix)
Remove a prefix from a text.
chains.graph_qa.neptune_cypher.extract_cypher(text)
Extract Cypher code from text using Regex.
chains.graph_qa.neptune_cypher.trim_query(query)
chains.graph_qa.neptune_cypher.trim_query(query)
Trim the query to only include Cypher keywords.
chains.graph_qa.neptune_cypher.use_simple_prompt(llm)
Decides whether to use the simple prompt
chains.graph_qa.neptune_sparql.extract_sparql(query)
Extract SPARQL code from a text.
chains.pebblo_retrieval.enforcement_filters.set_enforcement_filters(...)
Set identity and semantic enforcement filters in the retriever.
chains.pebblo_retrieval.utilities.get_ip()
Fetch local runtime ip address.
chains.pebblo_retrieval.utilities.get_runtime()
Fetch the current Framework and Runtime details.
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.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, ...])
[Deprecated] 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.