---

说明

LangChain 发展越来越大，但从范例难以窥全貌，这样学起来云里雾里。
这里整理了它的类，方便查看使用。

基于 0.1.13 版本

---

官方文档：https://python.langchain.com/docs/get_started/introduction

---

langchain

help(langchain) 

---

PACKAGE CONTENTS

• _api (package)
• adapters (package) w
• agents (package)
• base_language
• cache
• callbacks (package)
• chains (package)
• chat_loaders (package)
• chat_models (package)
• docstore (package)
• document_loaders (package)
• document_transformers (package)
• embeddings (package)
• env
• evaluation (package)
• example_generator
• formatting
• globals (package)
• graphs (package)
• hub
• indexes (package)
• input
• llms (package)
• load (package)
• memory (package)
• model_laboratory
• output_parsers (package)
• prompts (package)
• pydantic_v1 (package)
• python
• requests
• retrievers (package)
• runnables (package)
• schema (package)
• serpapi
• smith (package)
• sql_database
• storage (package)
• text_splitter
• tools (package)
• utilities (package)
• utils (package)
• vectorstores (package)

---

agents

---

PACKAGE CONTENTS

• agent
• agent_iterator
• agent_toolkits (package)
• agent_types
• chat (package)
• conversational (package)
• conversational_chat (package)
• format_scratchpad (package)
• initialize
• json_chat (package)
• load_tools
• loading
• mrkl (package)
• openai_assistant (package)
• openai_functions_agent (package)
• openai_functions_multi_agent (package)
• openai_tools (package)
• output_parsers (package)
• react (package)
• schema
• self_ask_with_search (package)
• structured_chat (package)
• tools
• types
• utils
• xml (package)

---

CLASSES

• builtins.object
  • langchain.agents.agent_iterator.AgentExecutorIterator
• builtins.str(builtins.object)
  • langchain.agents.agent_types.AgentType(builtins.str, enum.Enum)
• enum.Enum(builtins.object)
  • langchain.agents.agent_types.AgentType(builtins.str, enum.Enum)
• langchain.agents.react.base.ReActDocstoreAgent(langchain.agents.agent.Agent)
  • langchain.agents.react.base.ReActTextWorldAgent
• langchain.chains.base.Chain(langchain_core.runnables.base.RunnableSerializable, abc.ABC)
  • langchain.agents.agent.AgentExecutor
    • langchain.agents.mrkl.base.MRKLChain
    • langchain.agents.react.base.ReActChain
    • langchain.agents.self_ask_with_search.base.SelfAskWithSearchChain
• langchain_core.output_parsers.base.BaseOutputParser(langchain_core.output_parsers.base.BaseLLMOutputParser, langchain_core.runnables.base.RunnableSerializable)
  • langchain.agents.agent.AgentOutputParser
• langchain_core.tools.BaseTool(langchain_core.runnables.base.RunnableSerializable)
  • langchain_core.tools.Tool
• pydantic.v1.main.BaseModel(pydantic.v1.utils.Representation)
  • langchain.agents.agent.BaseMultiActionAgent
    • langchain.agents.openai_functions_multi_agent.base.OpenAIMultiFunctionsAgent
  • langchain.agents.agent.BaseSingleActionAgent
    • langchain.agents.agent.Agent
      • langchain.agents.conversational.base.ConversationalAgent
      • langchain.agents.conversational_chat.base.ConversationalChatAgent
      • langchain.agents.mrkl.base.ZeroShotAgent
      • langchain.agents.structured_chat.base.StructuredChatAgent
    • langchain.agents.agent.LLMSingleActionAgent
    • langchain.agents.openai_functions_agent.base.OpenAIFunctionsAgent
    • langchain.agents.xml.base.XMLAgent

---

cache

Help on module langchain.cache in langchain:

---

CLASSES

• langchain_community.cache._RedisCacheBase(langchain_core.caches.BaseCache, abc.ABC)
  • langchain_community.cache.RedisCache
• langchain_core.caches.BaseCache(abc.ABC)
  • langchain_community.cache.AstraDBCache
  • langchain_community.cache.AstraDBSemanticCache
  • langchain_community.cache.CassandraCache
  • langchain_community.cache.CassandraSemanticCache
  • langchain_community.cache.GPTCache
  • langchain_community.cache.InMemoryCache
  • langchain_community.cache.MomentoCache
  • langchain_community.cache.RedisSemanticCache
  • langchain_community.cache.SQLAlchemyCache
    • langchain_community.cache.SQLiteCache
  • langchain_community.cache.SQLAlchemyMd5Cache
  • langchain_community.cache.UpstashRedisCache
• sqlalchemy.orm.decl_api.Base(builtins.object)
  • langchain_community.cache.FullLLMCache
  • langchain_community.cache.FullMd5LLMCache

---

callbacks

Help on package langchain.callbacks in langchain:

---

NAME

langchain.callbacks -Callback handlers allow listening to events in LangChain.

---

DESCRIPTION

---

Class hierarchy:

… code-block::

​ BaseCallbackHandler --> CallbackHandler # Example: AimCallbackHandler

---

PACKAGE CONTENTS

• aim_callback
• argilla_callback
• arize_callback
• arthur_callback
• base
• clearml_callback
• comet_ml_callback
• confident_callback
• context_callback
• file
• flyte_callback
• human
• infino_callback
• labelstudio_callback
• llmonitor_callback
• manager
• mlflow_callback
• openai_info
• promptlayer_callback
• sagemaker_callback
• stdout
• streaming_aiter
• streaming_aiter_final_only
• streaming_stdout
• streaming_stdout_final_only
• streamlit (package)
• tracers (package)
• trubrics_callback
• utils
• wandb_callback
• whylabs_callback

---

CLASSES

• langchain_core.callbacks.base.AsyncCallbackHandler(langchain_core.callbacks.base.BaseCallbackHandler)
  • langchain.callbacks.streaming_aiter.AsyncIteratorCallbackHandler
• langchain_core.callbacks.base.BaseCallbackHandler(langchain_core.callbacks.base.LLMManagerMixin, langchain_core.callbacks.base.ChainManagerMixin, langchain_core.callbacks.base.ToolManagerMixin, langchain_core.callbacks.base.RetrieverManagerMixin, langchain_core.callbacks.base.CallbackManagerMixin, langchain_core.callbacks.base.RunManagerMixin)
  • langchain.callbacks.file.FileCallbackHandler
  • langchain_core.callbacks.stdout.StdOutCallbackHandler
  • langchain_core.callbacks.streaming_stdout.StreamingStdOutCallbackHandler
  • • langchain.callbacks.streaming_stdout_final_only.FinalStreamingStdOutCallbackHandler
• langchain_core.tracers.base.BaseTracer(langchain_core.callbacks.base.BaseCallbackHandler, abc.ABC)
  • langchain_core.tracers.langchain.LangChainTracer

---

memory

---

PACKAGE CONTENTS

• buffer
• buffer_window
• chat_memory
• chat_message_histories (package)
• combined
• entity
• kg
• motorhead_memory
• prompt
• readonly
• simple
• summary
• summary_buffer
• token_buffer
• utils
• vectorstore
• zep_memory

---

CLASSES

• langchain.memory.chat_memory.BaseChatMemory(langchain_core.memory.BaseMemory, abc.ABC)
  • langchain.memory.buffer.ConversationBufferMemory
    • langchain.memory.zep_memory.ZepMemory
  • langchain.memory.buffer_window.ConversationBufferWindowMemory
  • langchain.memory.entity.ConversationEntityMemory
  • langchain.memory.kg.ConversationKGMemory
  • langchain.memory.motorhead_memory.MotorheadMemory
  • langchain.memory.summary.ConversationSummaryMemory(langchain.memory.chat_memory.BaseChatMemory, langchain.memory.summary.SummarizerMixin)
  • langchain.memory.summary_buffer.ConversationSummaryBufferMemory(langchain.memory.chat_memory.BaseChatMemory, langchain.memory.summary.SummarizerMixin)
  • langchain.memory.token_buffer.ConversationTokenBufferMemory
• langchain.memory.entity.BaseEntityStore(pydantic.v1.main.BaseModel, abc.ABC)
  • langchain.memory.entity.InMemoryEntityStore
  • langchain.memory.entity.RedisEntityStore
  • langchain.memory.entity.SQLiteEntityStore
  • langchain.memory.entity.UpstashRedisEntityStore
• langchain.memory.summary.SummarizerMixin(pydantic.v1.main.BaseModel)
  • langchain.memory.summary.ConversationSummaryMemory(langchain.memory.chat_memory.BaseChatMemory, langchain.memory.summary.SummarizerMixin)
  • langchain.memory.summary_buffer.ConversationSummaryBufferMemory(langchain.memory.chat_memory.BaseChatMemory, langchain.memory.summary.SummarizerMixin)
• langchain_core.chat_history.BaseChatMessageHistory(abc.ABC)
  • langchain_community.chat_message_histories.astradb.AstraDBChatMessageHistory
  • langchain_community.chat_message_histories.cassandra.CassandraChatMessageHistory
  • langchain_community.chat_message_histories.cosmos_db.CosmosDBChatMessageHistory
  • langchain_community.chat_message_histories.dynamodb.DynamoDBChatMessageHistory
  • langchain_community.chat_message_histories.elasticsearch.ElasticsearchChatMessageHistory
  • langchain_community.chat_message_histories.file.FileChatMessageHistory
  • langchain_community.chat_message_histories.in_memory.ChatMessageHistory(langchain_core.chat_history.BaseChatMessageHistory, pydantic.v1.main.BaseModel)
  • langchain_community.chat_message_histories.momento.MomentoChatMessageHistory
  • langchain_community.chat_message_histories.mongodb.MongoDBChatMessageHistory
  • langchain_community.chat_message_histories.postgres.PostgresChatMessageHistory
  • langchain_community.chat_message_histories.redis.RedisChatMessageHistory
  • langchain_community.chat_message_histories.singlestoredb.SingleStoreDBChatMessageHistory
  • langchain_community.chat_message_histories.sql.SQLChatMessageHistory
  • langchain_community.chat_message_histories.streamlit.StreamlitChatMessageHistory
  • langchain_community.chat_message_histories.upstash_redis.UpstashRedisChatMessageHistory
  • langchain_community.chat_message_histories.xata.XataChatMessageHistory
  • langchain_community.chat_message_histories.zep.ZepChatMessageHistory
• langchain_core.memory.BaseMemory(langchain_core.load.serializable.Serializable, abc.ABC)
  • langchain.memory.buffer.ConversationStringBufferMemory
  • langchain.memory.combined.CombinedMemory
  • langchain.memory.readonly.ReadOnlySharedMemory
  • langchain.memory.simple.SimpleMemory
  • langchain.memory.vectorstore.VectorStoreRetrieverMemory
• pydantic.v1.main.BaseModel(pydantic.v1.utils.Representation)
  • langchain_community.chat_message_histories.in_memory.ChatMessageHistory(langchain_core.chat_history.BaseChatMessageHistory, pydantic.v1.main.BaseModel)

---

---

PACKAGE CONTENTS

• amadeus (package)
• arxiv (package)
• base
• bearly (package)
• bing_search (package)
• brave_search (package)
• clickup (package)
• convert_to_openai
• dataforseo_api_search (package)
• ddg_search (package)
• e2b_data_analysis (package)
• edenai (package)
• eleven_labs (package)
• file_management (package)
• github (package)
• gitlab (package)
• gmail (package)
• golden_query (package)
• google_cloud (package)
• google_finance (package)
• google_jobs (package)
• google_lens (package)
• google_places (package)
• google_scholar (package)
• google_search (package)
• google_trends (package)
• graphql (package)
• human (package)
• ifttt
• interaction (package)
• jira (package)
• json (package)
• memorize (package)
• merriam_webster (package)
• metaphor_search (package)
• multion (package)
• nasa (package)
• nuclia (package)
• office365 (package)
• openapi (package)
• openweathermap (package)
• playwright (package)
• plugin
• powerbi (package)
• pubmed (package)
• python (package)
• reddit_search (package)
• render
• requests (package)
• retriever
• scenexplain (package)
• searchapi (package)
• searx_search (package)
• shell (package)
• slack (package)
• sleep (package)
• spark_sql (package)
• sql_database (package)
• stackexchange (package)
• steam (package)
• steamship_image_generation (package)
• tavily_search (package)
• vectorstore (package)
• wikipedia (package)
• wolfram_alpha (package)
• yahoo_finance_news
• youtube (package)
• zapier (package)

---

CLASSES

• langchain_core.runnables.base.RunnableSerializable(langchain_core.load.serializable.Serializable, langchain_core.runnables.base.Runnable)
  • langchain_core.tools.BaseTool
    • langchain_core.tools.StructuredTool
    • langchain_core.tools.Tool

---

chat_loaders

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:

… code-block::

​ BaseChatLoader --> ChatLoader # Examples: WhatsAppChatLoader, IMessageChatLoader

---

Main helpers:

… code-block::

​ ChatSession

---

PACKAGE CONTENTS

• base
• facebook_messenger
• gmail
• imessage
• langsmith
• slack
• telegram
• utils
• whatsapp

---

chat_models

---

NAME

langchain.chat_models -Chat Models are a variation on language models.

---

DESCRIPTION

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:

… code-block::

​ BaseLanguageModel --> BaseChatModel --> # Examples: ChatOpenAI, ChatGooglePalm

---

Main helpers:

… code-block::

​ AIMessage, BaseMessage, HumanMessage

---

PACKAGE CONTENTS

• anthropic
• anyscale
• azure_openai
• azureml_endpoint
• baichuan
• baidu_qianfan_endpoint
• base
• bedrock
• cohere
• databricks
• ernie
• everlyai
• fake
• fireworks
• gigachat
• google_palm
• human
• hunyuan
• javelin_ai_gateway
• jinachat
• konko
• litellm
• meta
• minimax
• mlflow
• mlflow_ai_gateway
• ollama
• openai
• pai_eas_endpoint
• promptlayer_openai
• tongyi
• vertexai
• volcengine_maas
• yandex

---

docstore

Help on package langchain.docstore in langchain:

---

NAME

langchain.docstore -Docstores are classes to store and load Documents.

---

DESCRIPTION

The Docstore is a simplified version of the Document Loader.

---

Class hierarchy:

… code-block::

​ Docstore --> # Examples: InMemoryDocstore, Wikipedia

---

Main helpers:

… code-block::

​ Document, AddableMixin

---

PACKAGE CONTENTS

• arbitrary_fn
• base
• document
• in_memory
• wikipedia

---

document_loaders

help(langchain_community.document_loaders) 

---

PACKAGE CONTENTS

• acreom
• airbyte
• airbyte_json
• airtable
• apify_dataset
• arcgis_loader
• arxiv
• assemblyai
• astradb
• async_html
• athena
• azlyrics
• azure_ai_data
• azure_blob_storage_container
• azure_blob_storage_file
• baiducloud_bos_directory
• baiducloud_bos_file
• base
• base_o365
• bibtex
• bigquery
• bilibili
• blackboard
• blob_loaders (package)
• blockchain
• brave_search
• browserless
• cassandra
• chatgpt
• chm
• chromium
• college_confidential
• concurrent
• confluence
• conllu
• couchbase
• csv_loader
• cube_semantic
• datadog_logs
• dataframe
• diffbot
• directory
• discord
• doc_intelligence
• docugami
• docusaurus
• dropbox
• duckdb_loader
• email
• epub
• etherscan
• evernote
• excel
• facebook_chat
• fauna
• figma
• gcs_directory
• gcs_file
• generic
• geodataframe
• git
• gitbook
• github
• google_speech_to_text
• googledrive
• gutenberg
• helpers
• hn
• html
• html_bs
• hugging_face_dataset
• hugging_face_model
• ifixit
• image
• image_captions
• imsdb
• iugu
• joplin
• json_loader
• lakefs
• larksuite
• markdown
• mastodon
• max_compute
• mediawikidump
• merge
• mhtml
• modern_treasury
• mongodb
• news
• notebook
• notion
• notiondb
• nuclia
• obs_directory
• obs_file
• obsidian
• odt
• onedrive
• onedrive_file
• onenote
• open_city_data
• org_mode
• parsers (package)
• pdf
• pebblo
• polars_dataframe
• powerpoint
• psychic
• pubmed
• pyspark_dataframe
• python
• quip
• readthedocs
• recursive_url_loader
• reddit
• roam
• rocksetdb
• rspace
• rss
• rst
• rtf
• s3_directory
• s3_file
• sharepoint
• sitemap
• slack_directory
• snowflake_loader
• spreedly
• sql_database
• srt
• stripe
• surrealdb
• telegram
• tencent_cos_directory
• tencent_cos_file
• tensorflow_datasets
• text
• tidb
• tomarkdown
• toml
• trello
• tsv
• twitter
• unstructured
• url
• url_playwright
• url_selenium
• vsdx
• weather
• web_base
• whatsapp_chat
• wikipedia
• word_document
• xml
• xorbits
• youtube
• yuque

---

document_transformers

---

PACKAGE CONTENTS

• beautiful_soup_transformer
• doctran_text_extract
• doctran_text_qa
• doctran_text_translate
• embeddings_redundant_filter
• google_translate
• html2text
• long_context_reorder
• nuclia_text_transform
• openai_functions

---

embeddings

---

PACKAGE CONTENTS

• aleph_alpha
• awa
• azure_openai
• baidu_qianfan_endpoint
• base
• bedrock
• bookend
• cache
• clarifai
• cloudflare_workersai
• cohere
• dashscope
• databricks
• deepinfra
• edenai
• elasticsearch
• embaas
• ernie
• fake
• fastembed
• google_palm
• gpt4all
• gradient_ai
• huggingface
• huggingface_hub
• infinity
• javelin_ai_gateway
• jina
• johnsnowlabs
• llamacpp
• llm_rails
• localai
• minimax
• mlflow
• mlflow_gateway
• modelscope_hub
• mosaicml
• nlpcloud
• octoai_embeddings
• ollama
• openai
• sagemaker_endpoint
• self_hosted
• self_hosted_hugging_face
• sentence_transformer
• spacy_embeddings
• tensorflow_hub
• vertexai
• voyageai
• xinference

---

CLASSES

• langchain_core.embeddings.Embeddings(abc.ABC)
  • langchain.embeddings.cache.CacheBackedEmbeddings

---

evaluation

---

NAME

langchain.evaluation -Evaluation chains for grading LLM and Chain outputs.

---

DESCRIPTION

This module contains off-the-shelf evaluation chains for grading the output of LangChain primitives such as language models and chains.

Loading an evaluator to load an evaluator, you can use the :func:load_evaluators <langchain.evaluation.loading.load_evaluators> or

:func:load_evaluator <langchain.evaluation.loading.load_evaluator> functions with the

names of the evaluators to load.

… code-block:: python

​ 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 :class:EvaluatorType <langchain.evaluation.schema.EvaluatorType>.

Datasets to load one of the LangChain HuggingFace datasets, you can use the :func:load_dataset <langchain.evaluation.loading.load_dataset> function with the name of the dataset to load.

… code-block:: python

​ 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: :class:QAEvalChain <langchain.evaluation.qa.eval_chain.QAEvalChain>
• Comparing the output of two models: :class:PairwiseStringEvalChain <langchain.evaluation.comparison.eval_chain.PairwiseStringEvalChain> or :class:LabeledPairwiseStringEvalChain <langchain.evaluation.comparison.eval_chain.LabeledPairwiseStringEvalChain> when there is additionally a reference label.
• Judging the efficacy of an agent’s tool usage: :class:TrajectoryEvalChain <langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain>
• Checking whether an output complies with a set of criteria: :class:CriteriaEvalChain <langchain.evaluation.criteria.eval_chain.CriteriaEvalChain> or :class:LabeledCriteriaEvalChain <langchain.evaluation.criteria.eval_chain.LabeledCriteriaEvalChain> when there is additionally a reference label.
• Computing semantic difference between a prediction and reference: :class:EmbeddingDistanceEvalChain <langchain.evaluation.embedding_distance.base.EmbeddingDistanceEvalChain> or between two predictions: :class:PairwiseEmbeddingDistanceEvalChain <langchain.evaluation.embedding_distance.base.PairwiseEmbeddingDistanceEvalChain>
• Measuring the string distance between a prediction and reference :class:StringDistanceEvalChain <langchain.evaluation.string_distance.base.StringDistanceEvalChain> or between two predictions :class:PairwiseStringDistanceEvalChain <langchain.evaluation.string_distance.base.PairwiseStringDistanceEvalChain>

---

Low-level API

These evaluators implement one of the following interfaces:

• :class:StringEvaluator <langchain.evaluation.schema.StringEvaluator>: Evaluate a prediction string against a reference label and/or input context.
• :class:PairwiseStringEvaluator <langchain.evaluation.schema.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.
• :class:AgentTrajectoryEvaluator <langchain.evaluation.schema.AgentTrajectoryEvaluator> Evaluate the full sequence of actions taken by an agent.

These interfaces enable easier composability and usage within a higher level evaluation framework.

---

PACKAGE CONTENTS

• agents (package)
• comparison (package)
• criteria (package)
• embedding_distance (package)
• exact_match (package)
• loading
• parsing (package)
• qa (package)
• regex_match (package)
• schema
• scoring (package)
• string_distance (package)

---

CLASSES

• abc.ABC(builtins.object)
  • langchain.evaluation.schema.AgentTrajectoryEvaluator(langchain.evaluation.schema._EvalArgsMixin, abc.ABC)
    • langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain(langchain.evaluation.schema.AgentTrajectoryEvaluator, langchain.evaluation.schema.LLMEvalChain)
  • langchain.evaluation.schema.PairwiseStringEvaluator(langchain.evaluation.schema._EvalArgsMixin, abc.ABC)
    • langchain.evaluation.comparison.eval_chain.PairwiseStringEvalChain(langchain.evaluation.schema.PairwiseStringEvaluator, langchain.evaluation.schema.LLMEvalChain, langchain.chains.llm.LLMChain)
      • langchain.evaluation.comparison.eval_chain.LabeledPairwiseStringEvalChain
    • langchain.evaluation.embedding_distance.base.PairwiseEmbeddingDistanceEvalChain(langchain.evaluation.embedding_distance.base._EmbeddingDistanceChainMixin, langchain.evaluation.schema.PairwiseStringEvaluator)
    • langchain.evaluation.string_distance.base.PairwiseStringDistanceEvalChain(langchain.evaluation.schema.PairwiseStringEvaluator, langchain.evaluation.string_distance.base._RapidFuzzChainMixin)
  • langchain.evaluation.schema.StringEvaluator(langchain.evaluation.schema._EvalArgsMixin, abc.ABC)
    • langchain.evaluation.criteria.eval_chain.CriteriaEvalChain(langchain.evaluation.schema.StringEvaluator, langchain.evaluation.schema.LLMEvalChain, langchain.chains.llm.LLMChain)
      • langchain.evaluation.criteria.eval_chain.LabeledCriteriaEvalChain
    • langchain.evaluation.embedding_distance.base.EmbeddingDistanceEvalChain(langchain.evaluation.embedding_distance.base._EmbeddingDistanceChainMixin, langchain.evaluation.schema.StringEvaluator)
    • langchain.evaluation.exact_match.base.ExactMatchStringEvaluator
    • langchain.evaluation.parsing.base.JsonEqualityEvaluator
    • langchain.evaluation.parsing.base.JsonValidityEvaluator
    • langchain.evaluation.parsing.json_distance.JsonEditDistanceEvaluator
    • langchain.evaluation.parsing.json_schema.JsonSchemaEvaluator
    • langchain.evaluation.qa.eval_chain.ContextQAEvalChain(langchain.chains.llm.LLMChain, langchain.evaluation.schema.StringEvaluator, langchain.evaluation.schema.LLMEvalChain)
      • ​ langchain.evaluation.qa.eval_chain.CotQAEvalChain
    • langchain.evaluation.qa.eval_chain.QAEvalChain(langchain.chains.llm.LLMChain, langchain.evaluation.schema.StringEvaluator, langchain.evaluation.schema.LLMEvalChain)
    • langchain.evaluation.regex_match.base.RegexMatchStringEvaluator
    • langchain.evaluation.scoring.eval_chain.ScoreStringEvalChain(langchain.evaluation.schema.StringEvaluator, langchain.evaluation.schema.LLMEvalChain, langchain.chains.llm.LLMChain)
      • langchain.evaluation.scoring.eval_chain.LabeledScoreStringEvalChain
    • langchain.evaluation.string_distance.base.StringDistanceEvalChain(langchain.evaluation.schema.StringEvaluator, langchain.evaluation.string_distance.base._RapidFuzzChainMixin)
• builtins.str(builtins.object)
  • langchain.evaluation.criteria.eval_chain.Criteria(builtins.str, enum.Enum)
  • langchain.evaluation.embedding_distance.base.EmbeddingDistance(builtins.str, enum.Enum)
  • langchain.evaluation.schema.EvaluatorType(builtins.str, enum.Enum)
  • langchain.evaluation.string_distance.base.StringDistance(builtins.str, enum.Enum)
• enum.Enum(builtins.object)
  • langchain.evaluation.criteria.eval_chain.Criteria(builtins.str, enum.Enum)
  • langchain.evaluation.embedding_distance.base.EmbeddingDistance(builtins.str, enum.Enum)
  • langchain.evaluation.schema.EvaluatorType(builtins.str, enum.Enum)
  • langchain.evaluation.string_distance.base.StringDistance(builtins.str, enum.Enum)
• langchain.chains.llm.LLMChain(langchain.chains.base.Chain)
  • langchain.evaluation.qa.eval_chain.ContextQAEvalChain(langchain.chains.llm.LLMChain, langchain.evaluation.schema.StringEvaluator, langchain.evaluation.schema.LLMEvalChain)
    • langchain.evaluation.qa.eval_chain.CotQAEvalChain
  • langchain.evaluation.qa.eval_chain.QAEvalChain(langchain.chains.llm.LLMChain, langchain.evaluation.schema.StringEvaluator, langchain.evaluation.schema.LLMEvalChain)
• langchain.evaluation.embedding_distance.base._EmbeddingDistanceChainMixin(langchain.chains.base.Chain)
  • langchain.evaluation.embedding_distance.base.EmbeddingDistanceEvalChain(langchain.evaluation.embedding_distance.base._EmbeddingDistanceChainMixin, langchain.evaluation.schema.StringEvaluator)
  • langchain.evaluation.embedding_distance.base.PairwiseEmbeddingDistanceEvalChain(langchain.evaluation.embedding_distance.base._EmbeddingDistanceChainMixin, langchain.evaluation.schema.PairwiseStringEvaluator)
• langchain.evaluation.schema._EvalArgsMixin(builtins.object)
  • langchain.evaluation.schema.AgentTrajectoryEvaluator(langchain.evaluation.schema._EvalArgsMixin, abc.ABC)
    • langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain(langchain.evaluation.schema.AgentTrajectoryEvaluator, langchain.evaluation.schema.LLMEvalChain)
  • langchain.evaluation.schema.PairwiseStringEvaluator(langchain.evaluation.schema._EvalArgsMixin, abc.ABC)
    • langchain.evaluation.comparison.eval_chain.PairwiseStringEvalChain(langchain.evaluation.schema.PairwiseStringEvaluator, langchain.evaluation.schema.LLMEvalChain, langchain.chains.llm.LLMChain)
      • langchain.evaluation.comparison.eval_chain.LabeledPairwiseStringEvalChain
    • langchain.evaluation.embedding_distance.base.PairwiseEmbeddingDistanceEvalChain(langchain.evaluation.embedding_distance.base._EmbeddingDistanceChainMixin, langchain.evaluation.schema.PairwiseStringEvaluator)
    • langchain.evaluation.string_distance.base.PairwiseStringDistanceEvalChain(langchain.evaluation.schema.PairwiseStringEvaluator, langchain.evaluation.string_distance.base._RapidFuzzChainMixin)
  • langchain.evaluation.schema.StringEvaluator(langchain.evaluation.schema._EvalArgsMixin, abc.ABC)
    • langchain.evaluation.criteria.eval_chain.CriteriaEvalChain(langchain.evaluation.schema.StringEvaluator, langchain.evaluation.schema.LLMEvalChain, langchain.chains.llm.LLMChain)
      • langchain.evaluation.criteria.eval_chain.LabeledCriteriaEvalChain
    • langchain.evaluation.embedding_distance.base.EmbeddingDistanceEvalChain(langchain.evaluation.embedding_distance.base._EmbeddingDistanceChainMixin, langchain.evaluation.schema.StringEvaluator)
    • langchain.evaluation.exact_match.base.ExactMatchStringEvaluator
    • langchain.evaluation.parsing.base.JsonEqualityEvaluator
    • langchain.evaluation.parsing.base.JsonValidityEvaluator
    • langchain.evaluation.parsing.json_distance.JsonEditDistanceEvaluator
    • langchain.evaluation.parsing.json_schema.JsonSchemaEvaluator
    • langchain.evaluation.qa.eval_chain.ContextQAEvalChain(langchain.chains.llm.LLMChain, langchain.evaluation.schema.StringEvaluator, langchain.evaluation.schema.LLMEvalChain)
      • langchain.evaluation.qa.eval_chain.CotQAEvalChain
    • langchain.evaluation.qa.eval_chain.QAEvalChain(langchain.chains.llm.LLMChain, langchain.evaluation.schema.StringEvaluator, langchain.evaluation.schema.LLMEvalChain)
    • langchain.evaluation.regex_match.base.RegexMatchStringEvaluator
    • langchain.evaluation.scoring.eval_chain.ScoreStringEvalChain(langchain.evaluation.schema.StringEvaluator, langchain.evaluation.schema.LLMEvalChain, langchain.chains.llm.LLMChain)
      • langchain.evaluation.scoring.eval_chain.LabeledScoreStringEvalChain
    • langchain.evaluation.string_distance.base.StringDistanceEvalChain(langchain.evaluation.schema.StringEvaluator, langchain.evaluation.string_distance.base._RapidFuzzChainMixin)

---

graphs

Help on package langchain.graphs in langchain:

---

NAME

langchain.graphs -Graphs provide a natural language interface to graph databases.

---

PACKAGE CONTENTS

• arangodb_graph
• falkordb_graph
• graph_document
• graph_store
• hugegraph
• kuzu_graph
• memgraph_graph
• nebula_graph
• neo4j_graph
• neptune_graph
• networkx_graph
• rdf_graph

---

indexes

Help on package langchain.indexes in langchain:

---

NAME

langchain.indexes

---

DESCRIPTION

Index is used to avoid writing duplicated content into the vectostore and to avoid over-writing content if it’s unchanged.

Indexes also :

• Create knowledge graphs from data.

• Support indexing workflows from LangChain data loaders to vectorstores.

  Importantly, Index keeps on working even if the content being written is derived via a set of transformations from some source content (e.g., indexing children

  documents that were derived from parent documents by chunking.)

---

PACKAGE CONTENTS

• _api
• _sql_record_manager
• base
• graph
• prompts (package)
• vectorstore

---

CLASSES

• builtins.dict(builtins.object)
  • langchain.indexes._api.IndexingResult
• langchain.indexes.base.RecordManager(abc.ABC)
  • langchain.indexes._sql_record_manager.SQLRecordManager
• pydantic.v1.main.BaseModel(pydantic.v1.utils.Representation)
  • langchain.indexes.graph.GraphIndexCreator
  • langchain.indexes.vectorstore.VectorstoreIndexCreator

---

llms

Help on package langchain.llms in langchain:

---

NAME

langchain.llms

---

DESCRIPTION

LLM classes provide access to the large language model (LLM) APIs and services.

---

Class hierarchy:

… code-block::

​ BaseLanguageModel --> BaseLLM --> LLM --> # Examples: AI21, HuggingFaceHub, OpenAI

---

Main helpers:

… code-block::

​ LLMResult, PromptValue,

​ CallbackManagerForLLMRun, AsyncCallbackManagerForLLMRun,

​ CallbackManager, AsyncCallbackManager,

​ AIMessage, BaseMessage

---

PACKAGE CONTENTS

• ai21
• aleph_alpha
• amazon_api_gateway
• anthropic
• anyscale
• arcee
• aviary
• azureml_endpoint
• baidu_qianfan_endpoint
• bananadev
• base
• baseten
• beam
• bedrock
• bittensor
• cerebriumai
• chatglm
• clarifai
• cloudflare_workersai
• cohere
• ctransformers
• ctranslate2
• databricks
• deepinfra
• deepsparse
• edenai
• fake
• fireworks
• forefrontai
• gigachat
• google_palm
• gooseai
• gpt4all
• gradient_ai
• huggingface_endpoint
• huggingface_hub
• huggingface_pipeline
• huggingface_text_gen_inference
• human
• javelin_ai_gateway
• koboldai
• llamacpp
• loading
• manifest
• minimax
• mlflow
• mlflow_ai_gateway
• modal
• mosaicml
• nlpcloud
• octoai_endpoint
• ollama
• opaqueprompts
• openai
• openllm
• openlm
• pai_eas_endpoint
• petals
• pipelineai
• predibase
• predictionguard
• promptlayer_openai
• replicate
• rwkv
• sagemaker_endpoint
• self_hosted
• self_hosted_hugging_face
• stochasticai
• symblai_nebula
• textgen
• titan_takeoff
• titan_takeoff_pro
• together
• tongyi
• utils
• vertexai
• vllm
• volcengine_maas
• watsonxllm
• writer
• xinference
• yandex

---

load

Help on package langchain.load in langchain:

---

NAME

langchain.load - Serialization and deserialization.

---

PACKAGE CONTENTS

• dump
• load
• serializable

---

memory

Help on package langchain.memory in langchain:

NAME
langchain.memory -Memory maintains Chain state, incorporating context from
past runs.

DESCRIPTION
Class hierarchy for Memory:

.. code-block::BaseMemory --> BaseChatMemory --> <name>Memory  # Examples: ZepMemory, MotorheadMemory

---

​Main helpers:
​
​ … code-block::
​
​ BaseChatMessageHistory
​
​Chat Message History stores the chat message history in different stores.
​
​Class hierarchy for ChatMessageHistory:
​
… code-block::

BaseChatMessageHistory --> ChatMessageHistory # Example: ZepChatMessageHistory

---

​Main helpers:
​
​ … code-block::
​
​ AIMessage, BaseMessage, HumanMessage

---

PACKAGE CONTENTS

• buffer
• buffer_window
• chat_memory
• chat_message_histories (package)
• combined
• entity
• kg
• motorhead_memory
• prompt
• readonly
• simple
• summary
• summary_buffer
• token_buffer
• utils
• vectorstore
• zep_memory

---

CLASSES

• langchain.memory.chat_memory.BaseChatMemory(langchain_core.memory.BaseMemory, abc.ABC)
  • langchain.memory.buffer.ConversationBufferMemory
    • langchain.memory.zep_memory.ZepMemory
  • langchain.memory.buffer_window.ConversationBufferWindowMemory
  • langchain.memory.entity.ConversationEntityMemory
  • langchain.memory.kg.ConversationKGMemory
  • langchain.memory.motorhead_memory.MotorheadMemory
  • langchain.memory.summary.ConversationSummaryMemory(langchain.memory.chat_memory.BaseChatMemory, langchain.memory.summary.SummarizerMixin)
  • langchain.memory.summary_buffer.ConversationSummaryBufferMemory(langchain.memory.chat_memory.BaseChatMemory, langchain.memory.summary.SummarizerMixin)
  • langchain.memory.token_buffer.ConversationTokenBufferMemory
• langchain.memory.entity.BaseEntityStore(pydantic.v1.main.BaseModel, abc.ABC)
  • langchain.memory.entity.InMemoryEntityStore
  • langchain.memory.entity.RedisEntityStore
  • langchain.memory.entity.SQLiteEntityStore
  • langchain.memory.entity.UpstashRedisEntityStore
• langchain.memory.summary.SummarizerMixin(pydantic.v1.main.BaseModel)
  • langchain.memory.summary.ConversationSummaryMemory(langchain.memory.chat_memory.BaseChatMemory, langchain.memory.summary.SummarizerMixin)
  • langchain.memory.summary_buffer.ConversationSummaryBufferMemory(langchain.memory.chat_memory.BaseChatMemory, langchain.memory.summary.SummarizerMixin)
• langchain_core.chat_history.BaseChatMessageHistory(abc.ABC)
  • langchain_community.chat_message_histories.astradb.AstraDBChatMessageHistory
  • langchain_community.chat_message_histories.cassandra.CassandraChatMessageHistory
  • langchain_community.chat_message_histories.cosmos_db.CosmosDBChatMessageHistory
  • langchain_community.chat_message_histories.dynamodb.DynamoDBChatMessageHistory
  • langchain_community.chat_message_histories.elasticsearch.ElasticsearchChatMessageHistory
  • langchain_community.chat_message_histories.file.FileChatMessageHistory
  • langchain_community.chat_message_histories.in_memory.ChatMessageHistory(langchain_core.chat_history.BaseChatMessageHistory, pydantic.v1.main.BaseModel)
  • langchain_community.chat_message_histories.momento.MomentoChatMessageHistory
  • langchain_community.chat_message_histories.mongodb.MongoDBChatMessageHistory
  • langchain_community.chat_message_histories.postgres.PostgresChatMessageHistory
  • langchain_community.chat_message_histories.redis.RedisChatMessageHistory
  • langchain_community.chat_message_histories.singlestoredb.SingleStoreDBChatMessageHistory
  • langchain_community.chat_message_histories.sql.SQLChatMessageHistory
  • langchain_community.chat_message_histories.streamlit.StreamlitChatMessageHistory
  • langchain_community.chat_message_histories.upstash_redis.UpstashRedisChatMessageHistory
  • langchain_community.chat_message_histories.xata.XataChatMessageHistory
  • langchain_community.chat_message_histories.zep.ZepChatMessageHistory
• langchain_core.memory.BaseMemory(langchain_core.load.serializable.Serializable, abc.ABC)
  • langchain.memory.buffer.ConversationStringBufferMemory
  • langchain.memory.combined.CombinedMemory
  • langchain.memory.readonly.ReadOnlySharedMemory
  • langchain.memory.simple.SimpleMemory
  • langchain.memory.vectorstore.VectorStoreRetrieverMemory
• pydantic.v1.main.BaseModel(pydantic.v1.utils.Representation)
  • langchain_community.chat_message_histories.in_memory.ChatMessageHistory(langchain_core.chat_history.BaseChatMessageHistory, pydantic.v1.main.BaseModel)

---

output_parsers

Help on package langchain.output_parsers in langchain:

---

NAME

langchain.output_parsers -OutputParser classes parse the output of an LLM call.

---

DESCRIPTION

Class hierarchy:

… code-block::

​ BaseLLMOutputParser --> BaseOutputParser --> OutputParser # ListOutputParser, PydanticOutputParser

Main helpers:

… code-block::

​ Serializable, Generation, PromptValue

---

PACKAGE CONTENTS

• boolean
• combining
• datetime
• enum
• ernie_functions
• fix
• format_instructions
• json
• list
• loading
• openai_functions
• openai_tools
• pandas_dataframe
• prompts
• pydantic
• rail_parser
• regex
• regex_dict
• retry
• structured
• xml
• yaml

---

CLASSES

• langchain_core.output_parsers.base.BaseOutputParser(langchain_core.output_parsers.base.BaseLLMOutputParser, langchain_core.runnables.base.RunnableSerializable)
  • langchain.output_parsers.boolean.BooleanOutputParser
  • langchain.output_parsers.combining.CombiningOutputParser
  • langchain.output_parsers.datetime.DatetimeOutputParser
  • langchain.output_parsers.enum.EnumOutputParser
  • langchain.output_parsers.fix.OutputFixingParser
  • langchain.output_parsers.pandas_dataframe.PandasDataFrameOutputParser
  • langchain.output_parsers.regex.RegexParser
  • langchain.output_parsers.regex_dict.RegexDictParser
  • langchain.output_parsers.retry.RetryOutputParser
  • langchain.output_parsers.retry.RetryWithErrorOutputParser
  • langchain.output_parsers.structured.StructuredOutputParser
  • langchain.output_parsers.yaml.YamlOutputParser
  • langchain_community.output_parsers.rail_parser.GuardrailsOutputParser
• langchain_core.output_parsers.json.JsonOutputParser(langchain_core.output_parsers.transform.BaseCumulativeTransformOutputParser)
  • langchain_core.output_parsers.pydantic.PydanticOutputParser(langchain_core.output_parsers.json.JsonOutputParser, typing.Generic)
• langchain_core.output_parsers.transform.BaseCumulativeTransformOutputParser(langchain_core.output_parsers.transform.BaseTransformOutputParser)
  • langchain_core.output_parsers.openai_tools.JsonOutputToolsParser
    • langchain_core.output_parsers.openai_tools.JsonOutputKeyToolsParser
    • langchain_core.output_parsers.openai_tools.PydanticToolsParser
• langchain_core.output_parsers.transform.BaseTransformOutputParser(langchain_core.output_parsers.base.BaseOutputParser)
  • langchain_core.output_parsers.list.ListOutputParser
    • langchain_core.output_parsers.list.CommaSeparatedListOutputParser
    • langchain_core.output_parsers.list.MarkdownListOutputParser
    • langchain_core.output_parsers.list.NumberedListOutputParser
  • langchain_core.output_parsers.xml.XMLOutputParser
• pydantic.v1.main.BaseModel(pydantic.v1.utils.Representation)
  • langchain.output_parsers.structured.ResponseSchema
• typing.Generic(builtins.object)
  • langchain_core.output_parsers.pydantic.PydanticOutputParser(langchain_core.output_parsers.json.JsonOutputParser, typing.Generic)

---

prompts

Help on package langchain.prompts in langchain:

---

NAME

langchain.prompts -Prompt is the input to the model.

---

DESCRIPTION

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

---

PACKAGE CONTENTS

• base
• chat
• example_selector (package)
• few_shot
• few_shot_with_templates
• loading
• pipeline
• prompt

---

CLASSES

• abc.ABC(builtins.object)
  • langchain_core.prompts.base.BasePromptTemplate(langchain_core.runnables.base.RunnableSerializable, typing.Generic, abc.ABC)
    • langchain_core.prompts.chat.BaseChatPromptTemplate(langchain_core.prompts.base.BasePromptTemplate, abc.ABC)
      • langchain_core.prompts.chat.ChatPromptTemplate
      • langchain_core.prompts.few_shot.FewShotChatMessagePromptTemplate(langchain_core.prompts.chat.BaseChatPromptTemplate, langchain_core.prompts.few_shot._FewShotPromptTemplateMixin)
    • langchain_core.prompts.pipeline.PipelinePromptTemplate
    • langchain_core.prompts.string.StringPromptTemplate(langchain_core.prompts.base.BasePromptTemplate, abc.ABC)
      • langchain_core.prompts.few_shot.FewShotPromptTemplate(langchain_core.prompts.few_shot._FewShotPromptTemplateMixin, langchain_core.prompts.string.StringPromptTemplate)
      • langchain_core.prompts.few_shot_with_templates.FewShotPromptWithTemplates
      • langchain_core.prompts.prompt.PromptTemplate
• langchain_core.example_selectors.base.BaseExampleSelector(abc.ABC)
  • langchain_community.example_selectors.ngram_overlap.NGramOverlapExampleSelector(langchain_core.example_selectors.base.BaseExampleSelector, pydantic.v1.main.BaseModel)
  • langchain_core.example_selectors.length_based.LengthBasedExampleSelector(langchain_core.example_selectors.base.BaseExampleSelector, pydantic.v1.main.BaseModel)
  • langchain_core.example_selectors.semantic_similarity.SemanticSimilarityExampleSelector(langchain_core.example_selectors.base.BaseExampleSelector, pydantic.v1.main.BaseModel)
    • langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector
• langchain_core.prompts.chat.BaseMessagePromptTemplate(langchain_core.load.serializable.Serializable, abc.ABC)
  • langchain_core.prompts.chat.MessagesPlaceholder
• langchain_core.prompts.chat.BaseStringMessagePromptTemplate(langchain_core.prompts.chat.BaseMessagePromptTemplate, abc.ABC)
  • langchain_core.prompts.chat.ChatMessagePromptTemplate
• langchain_core.prompts.chat._StringImageMessagePromptTemplate(langchain_core.prompts.chat.BaseMessagePromptTemplate)
  • langchain_core.prompts.chat.AIMessagePromptTemplate
  • langchain_core.prompts.chat.HumanMessagePromptTemplate
  • langchain_core.prompts.chat.SystemMessagePromptTemplate
• langchain_core.prompts.few_shot._FewShotPromptTemplateMixin(pydantic.v1.main.BaseModel)
  • langchain_core.prompts.few_shot.FewShotPromptTemplate(langchain_core.prompts.few_shot._FewShotPromptTemplateMixin, langchain_core.prompts.string.StringPromptTemplate)
• langchain_core.runnables.base.RunnableSerializable(langchain_core.load.serializable.Serializable, langchain_core.runnables.base.Runnable)
  • langchain_core.prompts.base.BasePromptTemplate(langchain_core.runnables.base.RunnableSerializable, typing.Generic, abc.ABC)
    • langchain_core.prompts.chat.BaseChatPromptTemplate(langchain_core.prompts.base.BasePromptTemplate, abc.ABC)
      • langchain_core.prompts.chat.ChatPromptTemplate
      • langchain_core.prompts.few_shot.FewShotChatMessagePromptTemplate(langchain_core.prompts.chat.BaseChatPromptTemplate, langchain_core.prompts.few_shot._FewShotPromptTemplateMixin)
    • langchain_core.prompts.pipeline.PipelinePromptTemplate
    • langchain_core.prompts.string.StringPromptTemplate(langchain_core.prompts.base.BasePromptTemplate, abc.ABC)
      • langchain_core.prompts.few_shot.FewShotPromptTemplate(langchain_core.prompts.few_shot._FewShotPromptTemplateMixin, langchain_core.prompts.string.StringPromptTemplate)
      • langchain_core.prompts.few_shot_with_templates.FewShotPromptWithTemplates
      • langchain_core.prompts.prompt.PromptTemplate
• pydantic.v1.main.BaseModel(pydantic.v1.utils.Representation)
  • langchain_community.example_selectors.ngram_overlap.NGramOverlapExampleSelector(langchain_core.example_selectors.base.BaseExampleSelector, pydantic.v1.main.BaseModel)
  • langchain_core.example_selectors.length_based.LengthBasedExampleSelector(langchain_core.example_selectors.base.BaseExampleSelector, pydantic.v1.main.BaseModel)
  • langchain_core.example_selectors.semantic_similarity.SemanticSimilarityExampleSelector(langchain_core.example_selectors.base.BaseExampleSelector, pydantic.v1.main.BaseModel)
    • langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector
• typing.Generic(builtins.object)
  • langchain_core.prompts.base.BasePromptTemplate(langchain_core.runnables.base.RunnableSerializable, typing.Generic, abc.ABC)
    • langchain_core.prompts.chat.BaseChatPromptTemplate(langchain_core.prompts.base.BasePromptTemplate, abc.ABC)
      • langchain_core.prompts.chat.ChatPromptTemplate
      • langchain_core.prompts.few_shot.FewShotChatMessagePromptTemplate(langchain_core.prompts.chat.BaseChatPromptTemplate, langchain_core.prompts.few_shot._FewShotPromptTemplateMixin)
    • langchain_core.prompts.pipeline.PipelinePromptTemplate
    • langchain_core.prompts.string.StringPromptTemplate(langchain_core.prompts.base.BasePromptTemplate, abc.ABC)
      • langchain_core.prompts.few_shot.FewShotPromptTemplate(langchain_core.prompts.few_shot._FewShotPromptTemplateMixin, langchain_core.prompts.string.StringPromptTemplate)
      • langchain_core.prompts.few_shot_with_templates.FewShotPromptWithTemplates
      • langchain_core.prompts.prompt.PromptTemplate

---

retrievers

Help on package langchain.retrievers in langchain:

---

NAME

langchain.retrievers -Retriever class returns Documents given a textquery.

---

DESCRIPTION

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:

… code-block::

​ BaseRetriever --> Retriever # Examples: ArxivRetriever, MergerRetriever

Main helpers:

… code-block::

​ Document, Serializable, Callbacks, CallbackManagerForRetrieverRun, AsyncCallbackManagerForRetrieverRun

---

PACKAGE CONTENTS

• arcee
• arxiv
• azure_cognitive_search
• bedrock
• bm25
• chaindesk
• chatgpt_plugin_retriever
• cohere_rag_retriever
• contextual_compression
• databerry
• docarray
• document_compressors (package)
• elastic_search_bm25
• embedchain
• ensemble
• google_cloud_documentai_warehouse
• google_vertex_ai_search
• kay
• kendra
• knn
• llama_index
• merger_retriever
• metal
• milvus
• multi_query
• multi_vector
• outline
• parent_document_retriever
• pinecone_hybrid_search
• pubmed
• pupmed
• re_phraser
• remote_retriever
• self_query (package)
• svm
• tavily_search_api
• tfidf
• time_weighted_retriever
• vespa_retriever
• weaviate_hybrid_search
• web_research
• wikipedia
• you
• zep
• zilliz

---

CLASSES

• langchain_community.utilities.outline.OutlineAPIWrapper(pydantic.v1.main.BaseModel)
  • langchain_community.retrievers.outline.OutlineRetriever(langchain_core.retrievers.BaseRetriever, langchain_community.utilities.outline.OutlineAPIWrapper)
• langchain_core.retrievers.BaseRetriever(langchain_core.runnables.base.RunnableSerializable, abc.ABC)
  • langchain.retrievers.contextual_compression.ContextualCompressionRetriever
  • langchain.retrievers.ensemble.EnsembleRetriever
  • langchain.retrievers.merger_retriever.MergerRetriever
  • langchain.retrievers.multi_query.MultiQueryRetriever
  • langchain.retrievers.multi_vector.MultiVectorRetriever
  • • langchain.retrievers.parent_document_retriever.ParentDocumentRetriever
  • langchain.retrievers.re_phraser.RePhraseQueryRetriever
  • langchain.retrievers.self_query.base.SelfQueryRetriever
  • langchain.retrievers.time_weighted_retriever.TimeWeightedVectorStoreRetriever
  • langchain.retrievers.web_research.WebResearchRetriever
  • langchain_community.retrievers.outline.OutlineRetriever(langchain_core.retrievers.BaseRetriever, langchain_community.utilities.outline.OutlineAPIWrapper)

---

runnables

Help on package langchain.runnables in langchain:

---

NAME

langchain.runnables - LangChainRunnable and the LangChain Expression Language (LCEL).

---

DESCRIPTION

The LangChain Expression Language (LCEL) offers a declarative method to build production-grade programs that harness the power of LLMs.

Programs created using LCEL and LangChain Runnables inherently support synchronous, asynchronous, batch, and streaming operations.

Support forasync allows servers hosting the LCEL based programs to scale better for higher concurrent loads.

---

Batch operations allow for processing multiple inputs in parallel.

Streaming of intermediate outputs, as they’re being generated, allows for creating more responsive UX.

This module contains non-core Runnable classes.

---

PACKAGE CONTENTS

• hub
• openai_functions

---

schema

Help on package langchain.schema in langchain:

---

NAME

langchain.schema -Schemas are the LangChain Base Classes and Interfaces.

---

PACKAGE CONTENTS

• agent
• cache
• callbacks (package)
• chat
• chat_history
• document
• embeddings
• exceptions
• language_model
• memory
• messages
• output
• output_parser
• prompt
• prompt_template
• retriever
• runnable (package)
• storage
• vectorstore

---

CLASSES

• abc.ABC(builtins.object)
  • langchain_core.caches.BaseCache
  • langchain_core.chat_history.BaseChatMessageHistory
  • langchain_core.documents.transformers.BaseDocumentTransformer
  • langchain_core.memory.BaseMemory(langchain_core.load.serializable.Serializable, abc.ABC)
  • langchain_core.output_parsers.base.BaseLLMOutputParser(typing.Generic, abc.ABC)
    • langchain_core.output_parsers.base.BaseOutputParser(langchain_core.output_parsers.base.BaseLLMOutputParser, langchain_core.runnables.base.RunnableSerializable)
  • langchain_core.prompt_values.PromptValue(langchain_core.load.serializable.Serializable, abc.ABC)
  • langchain_core.prompts.base.BasePromptTemplate(langchain_core.runnables.base.RunnableSerializable, typing.Generic, abc.ABC)
  • langchain_core.retrievers.BaseRetriever(langchain_core.runnables.base.RunnableSerializable, abc.ABC)
  • langchain_core.stores.BaseStore(typing.Generic, abc.ABC)
• builtins.Exception(builtins.BaseException)
  • langchain_core.exceptions.LangChainException
    • langchain_core.exceptions.OutputParserException(builtins.ValueError, langchain_core.exceptions.LangChainException)
• builtins.ValueError(builtins.Exception)
  • langchain_core.exceptions.OutputParserException(builtins.ValueError, langchain_core.exceptions.LangChainException)
• langchain_core.load.serializable.Serializable(pydantic.v1.main.BaseModel, abc.ABC)
  • langchain_core.agents.AgentAction
  • langchain_core.agents.AgentFinish
  • langchain_core.documents.base.Document
  • langchain_core.memory.BaseMemory(langchain_core.load.serializable.Serializable, abc.ABC)
  • langchain_core.messages.base.BaseMessage
    • langchain_core.messages.ai.AIMessage
    • langchain_core.messages.chat.ChatMessage
    • langchain_core.messages.function.FunctionMessage
    • langchain_core.messages.human.HumanMessage
    • langchain_core.messages.system.SystemMessage
  • langchain_core.outputs.generation.Generation
    • langchain_core.outputs.chat_generation.ChatGeneration
  • langchain_core.prompt_values.PromptValue(langchain_core.load.serializable.Serializable, abc.ABC)
• langchain_core.output_parsers.transform.BaseTransformOutputParser(langchain_core.output_parsers.base.BaseOutputParser)
  • langchain_core.output_parsers.string.StrOutputParser
• langchain_core.runnables.base.RunnableSerializable(langchain_core.load.serializable.Serializable, langchain_core.runnables.base.Runnable)
  • langchain_core.prompts.base.BasePromptTemplate(langchain_core.runnables.base.RunnableSerializable, typing.Generic, abc.ABC)
  • langchain_core.retrievers.BaseRetriever(langchain_core.runnables.base.RunnableSerializable, abc.ABC)
• pydantic.v1.main.BaseModel(pydantic.v1.utils.Representation)
  • langchain_core.outputs.chat_result.ChatResult
  • langchain_core.outputs.llm_result.LLMResult
  • langchain_core.outputs.run_info.RunInfo
• typing.Generic(builtins.object)
  • langchain_core.output_parsers.base.BaseLLMOutputParser(typing.Generic, abc.ABC)
    • langchain_core.output_parsers.base.BaseOutputParser(langchain_core.output_parsers.base.BaseLLMOutputParser, langchain_core.runnables.base.RunnableSerializable)
  • langchain_core.prompts.base.BasePromptTemplate(langchain_core.runnables.base.RunnableSerializable, typing.Generic, abc.ABC)
  • langchain_core.stores.BaseStore(typing.Generic, abc.ABC)

---

smith

Help on package langchain.smith in langchain:

---

NAME

langchain.smith -LangSmith utilities.

---

DESCRIPTION

This module provides utilities for connecting to LangSmith <https://smith.langchain.com/>.

For more information on LangSmith, see the LangSmith documentation <https://docs.smith.langchain.com/>_.

---

PACKAGE CONTENTS

• evaluation (package)

---

ClassES

• pydantic.v1.main.BaseModel(pydantic.v1.utils.Representation)
  • langchain.smith.evaluation.config.RunEvalConfig

---

storage

Help on package langchain.storage in langchain:

---

NAME

langchain.storage - Implementations of key-value stores and storage helpers.

---

DESCRIPTION

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.

---

PACKAGE CONTENTS

• _lc_store
• encoder_backed
• exceptions
• file_system
• in_memory
• redis
• upstash_redis

---

CLASSES

• langchain_core.stores.BaseStore(typing.Generic, abc.ABC)
  • langchain.storage.encoder_backed.EncoderBackedStore
  • langchain.storage.file_system.LocalFileStore

---

text_splitter

Help on module langchain.text_splitter in langchain:

---

NAME

langchain.text_splitter - Kept for backwards compatibility.

---

CLASSES

• abc.ABC(builtins.object)
  • langchain_text_splitters.base.TextSplitter(langchain_core.documents.transformers.BaseDocumentTransformer, abc.ABC)
    • langchain_text_splitters.base.TokenTextSplitter
    • langchain_text_splitters.character.CharacterTextSplitter
    • langchain_text_splitters.character.RecursiveCharacterTextSplitter
      • langchain_text_splitters.latex.LatexTextSplitter
      • langchain_text_splitters.markdown.MarkdownTextSplitter
      • langchain_text_splitters.python.PythonCodeTextSplitter
    • langchain_text_splitters.konlpy.KonlpyTextSplitter
    • langchain_text_splitters.nltk.NLTKTextSplitter
    • langchain_text_splitters.sentence_transformers.SentenceTransformersTokenTextSplitter
    • langchain_text_splitters.spacy.SpacyTextSplitter
• builtins.dict(builtins.object)
  • langchain_text_splitters.html.ElementType
  • langchain_text_splitters.markdown.HeaderType
  • langchain_text_splitters.markdown.LineType
• builtins.object
  • langchain_text_splitters.base.Tokenizer
  • langchain_text_splitters.html.HTMLHeaderTextSplitter
  • langchain_text_splitters.json.RecursiveJsonSplitter
  • langchain_text_splitters.markdown.MarkdownHeaderTextSplitter
• builtins.str(builtins.object)
  • langchain_text_splitters.base.Language(builtins.str, enum.Enum)
• enum.Enum(builtins.object)
  • langchain_text_splitters.base.Language(builtins.str, enum.Enum)
• langchain_core.documents.transformers.BaseDocumentTransformer(abc.ABC)
  • langchain_text_splitters.base.TextSplitter(langchain_core.documents.transformers.BaseDocumentTransformer, abc.ABC)
    • langchain_text_splitters.base.TokenTextSplitter
    • langchain_text_splitters.character.CharacterTextSplitter
    • langchain_text_splitters.character.RecursiveCharacterTextSplitter
      • langchain_text_splitters.latex.LatexTextSplitter
      • langchain_text_splitters.markdown.MarkdownTextSplitter
      • langchain_text_splitters.python.PythonCodeTextSplitter
    • langchain_text_splitters.konlpy.KonlpyTextSplitter
    • langchain_text_splitters.nltk.NLTKTextSplitter
    • langchain_text_splitters.sentence_transformers.SentenceTransformersTokenTextSplitter
    • langchain_text_splitters.spacy.SpacyTextSplitter

---

tools

Help on package langchain.tools in langchain:

---

NAME

langchain.tools -Tools are classes that an Agent uses to interact with the world.

---

DESCRIPTION

Each tool has adescription. Agent uses the description to choose the right tool for the job.

---

Class hierarchy:

… code-block::

​ ToolMetaclass --> BaseTool --> Tool # Examples: AIPluginTool, BaseGraphQLTool

​ # Examples: BraveSearch, HumanInputRun

Main helpers:

… code-block::

​ CallbackManagerForToolRun, AsyncCallbackManagerForToolRun

---

PACKAGE CONTENTS

• amadeus (package)
• arxiv (package)
• azure_cognitive_services (package)
• base
• bearly (package)
• bing_search (package)
• brave_search (package)
• clickup (package)
• convert_to_openai
• dataforseo_api_search (package)
• ddg_search (package)
• e2b_data_analysis (package)
• edenai (package)
• eleven_labs (package)
• file_management (package)
• github (package)
• gitlab (package)
• gmail (package)
• golden_query (package)
• google_cloud (package)
• google_finance (package)
• google_jobs (package)
• google_lens (package)
• google_places (package)
• google_scholar (package)
• google_search (package)
• google_serper (package)
• google_trends (package)
• graphql (package)
• human (package)
• ifttt
• interaction (package)
• jira (package)
• json (package)
• memorize (package)
• merriam_webster (package)
• metaphor_search (package)
• multion (package)
• nasa (package)
• nuclia (package)
• office365 (package)
• openapi (package)
• openweathermap (package)
• playwright (package)
• plugin
• powerbi (package)
• pubmed (package)
• python (package)
• reddit_search (package)
• render
• requests (package)
• retriever
• scenexplain (package)
• searchapi (package)
• searx_search (package)
• shell (package)
• slack (package)
• sleep (package)
• spark_sql (package)
• sql_database (package)
• stackexchange (package)
• steam (package)
• steamship_image_generation (package)
• tavily_search (package)
• vectorstore (package)
• wikipedia (package)
• wolfram_alpha (package)
• yahoo_finance_news
• youtube (package)
• zapier (package)

---

CLASSES

• langchain_core.runnables.base.RunnableSerializable(langchain_core.load.serializable.Serializable, langchain_core.runnables.base.Runnable)
  • langchain_core.tools.BaseTool
    • langchain_core.tools.StructuredTool
    • langchain_core.tools.Tool

---

utilities

Help on package langchain.utilities in langchain:

---

NAME

langchain.utilities -Utilities are the integrations with third-part systems and packages.

---

DESCRIPTION

Other LangChain classes useUtilities to interact with third-part systems

---

PACKAGE CONTENTS

• alpha_vantage
• anthropic
• apify
• arcee
• arxiv
• asyncio
• awslambda
• bibtex
• bing_search
• brave_search
• clickup
• dalle_image_generator
• dataforseo_api_search
• duckduckgo_search
• github
• gitlab
• golden_query
• google_finance
• google_jobs
• google_lens
• google_places_api
• google_scholar
• google_search
• google_serper
• google_trends
• graphql
• jira
• loading
• max_compute
• merriam_webster
• metaphor_search
• nasa
• opaqueprompts
• openapi
• openweathermap
• outline
• portkey
• powerbi
• pubmed
• python
• reddit_search
• redis
• requests
• scenexplain
• searchapi
• searx_search
• serpapi
• spark_sql
• sql_database
• stackexchange
• steam
• tavily_search
• tensorflow_datasets
• twilio
• vertexai
• wikipedia
• wolfram_alpha
• zapier

---

CLASSES

• langchain_community.utilities.requests.GenericRequestsWrapper(pydantic.v1.main.BaseModel)
  • langchain_community.utilities.requests.TextRequestsWrapper
• pydantic.v1.main.BaseModel(pydantic.v1.utils.Representation)
  • langchain_community.utilities.requests.Requests

---

utils

Help on package langchain.utils in langchain:

---

NAME

langchain.utils -Utility functions for LangChain.

---

DESCRIPTION

These functions do not depend on any other LangChain module.

---

PACKAGE CONTENTS

• aiter
• env
• ernie_functions
• formatting
• html
• input
• interactive_env
• iter
• json_schema
• loading
• math
• openai
• openai_functions
• pydantic
• strings
• utils

---

CLASSES

• string.Formatter(builtins.object)
  • langchain_core.utils.formatting.StrictFormatter

---

vectorstores

Help on package langchain.vectorstores in langchain:

---

NAME

langchain.vectorstores -Vector store stores embedded data and performs vector search.

---

DESCRIPTION

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:

… code-block::

VectorStore --> # Examples: Annoy, FAISS, Milvus

BaseRetriever --> VectorStoreRetriever --> Retriever # Example: VespaRetriever

---

Main helpers:

… code-block::

Embeddings, Document

---

PACKAGE CONTENTS

• alibabacloud_opensearch
• analyticdb
• annoy
• astradb
• atlas
• awadb
• azure_cosmos_db
• azuresearch
• bageldb
• baiducloud_vector_search
• base
• cassandra
• chroma
• clarifai
• clickhouse
• dashvector
• databricks_vector_search
• deeplake
• dingo
• docarray (package)
• elastic_vector_search
• elasticsearch
• epsilla
• faiss
• hippo
• hologres
• lancedb
• llm_rails
• marqo
• matching_engine
• meilisearch
• milvus
• momento_vector_index
• mongodb_atlas
• myscale
• neo4j_vector
• nucliadb
• opensearch_vector_search
• pgembedding
• pgvecto_rs
• pgvector
• pinecone
• qdrant
• redis (package)
• rocksetdb
• scann
• semadb
• singlestoredb
• sklearn
• sqlitevss
• starrocks
• supabase
• tair
• tencentvectordb
• tigris
• tiledb
• timescalevector
• typesense
• usearch
• utils
• vald
• vearch
• vectara
• vespa
• weaviate
• xata
• yellowbrick
• zep
• zilliz

---

CLASSES

• abc.ABC(builtins.object)
  • langchain_core.vectorstores.VectorStore

---

2014-03-27（三）