文章目录
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
API 文档:https://api.python.langchain.com/en/latest/langchain_api_reference.html#
langchain
python
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.agents.agent.AgentExecutor
- 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
- langchain.agents.agent.Agent
- langchain.agents.agent.BaseMultiActionAgent
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.buffer.ConversationBufferMemory
- 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
- langchain_core.tools.BaseTool
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
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
shell
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
- 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)
- pebblo
- polars_dataframe
- powerpoint
- psychic
- pubmed
- pyspark_dataframe
- python
- quip
- readthedocs
- recursive_url_loader
- 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
- 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.comparison.eval_chain.PairwiseStringEvalChain(langchain.evaluation.schema.PairwiseStringEvaluator, langchain.evaluation.schema.LLMEvalChain, langchain.chains.llm.LLMChain)
- 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)
- langchain.evaluation.criteria.eval_chain.CriteriaEvalChain(langchain.evaluation.schema.StringEvaluator, langchain.evaluation.schema.LLMEvalChain, langchain.chains.llm.LLMChain)
- langchain.evaluation.schema.AgentTrajectoryEvaluator(langchain.evaluation.schema._EvalArgsMixin, abc.ABC)
- 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.qa.eval_chain.ContextQAEvalChain(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.comparison.eval_chain.PairwiseStringEvalChain(langchain.evaluation.schema.PairwiseStringEvaluator, langchain.evaluation.schema.LLMEvalChain, langchain.chains.llm.LLMChain)
- 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)
- langchain.evaluation.criteria.eval_chain.CriteriaEvalChain(langchain.evaluation.schema.StringEvaluator, langchain.evaluation.schema.LLMEvalChain, langchain.chains.llm.LLMChain)
- langchain.evaluation.schema.AgentTrajectoryEvaluator(langchain.evaluation.schema._EvalArgsMixin, abc.ABC)
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.buffer.ConversationBufferMemory
- 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.openai_tools.JsonOutputToolsParser
- 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
- langchain_core.output_parsers.list.ListOutputParser
- 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.prompts.chat.BaseChatPromptTemplate(langchain_core.prompts.base.BasePromptTemplate, abc.ABC)
- langchain_core.prompts.base.BasePromptTemplate(langchain_core.runnables.base.RunnableSerializable, typing.Generic, abc.ABC)
- 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
- langchain_core.prompts.chat.BaseChatPromptTemplate(langchain_core.prompts.base.BasePromptTemplate, abc.ABC)
- langchain_core.prompts.base.BasePromptTemplate(langchain_core.runnables.base.RunnableSerializable, typing.Generic, abc.ABC)
- 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
- langchain_core.prompts.chat.BaseChatPromptTemplate(langchain_core.prompts.base.BasePromptTemplate, abc.ABC)
- langchain_core.prompts.base.BasePromptTemplate(langchain_core.runnables.base.RunnableSerializable, typing.Generic, abc.ABC)
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)
- 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)
- langchain_core.output_parsers.base.BaseLLMOutputParser(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
- langchain_text_splitters.base.TextSplitter(langchain_core.documents.transformers.BaseDocumentTransformer, abc.ABC)
- 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
- langchain_text_splitters.base.TextSplitter(langchain_core.documents.transformers.BaseDocumentTransformer, abc.ABC)
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
- langchain_core.tools.BaseTool
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)