LangChain

文章目录

- 说明
- langchain
- agents
- cache
- callbacks
- memory
- chat_loaders
- chat_models
- docstore
- document_loaders
- document_transformers
- embeddings
- evaluation
- graphs
- indexes
- llms
- load
- memory
- output_parsers
- prompts
- retrievers
- runnables
- schema
- smith
- storage
- text_splitter
- tools
- utilities
- utils
- vectorstores

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#

python 复制代码

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

shell 复制代码

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:

复制代码

.. 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）