id stringlengths 14 16 | text stringlengths 31 2.41k | source stringlengths 53 121 |
|---|---|---|
a1409c1b5e0f-126 | process_doc() (langchain.document_loaders.ConfluenceLoader method)
process_image() (langchain.document_loaders.ConfluenceLoader method)
process_index_results() (langchain.vectorstores.Annoy method)
process_output() (langchain.utilities.BashProcess method)
process_page() (langchain.document_loaders.ConfluenceLoader method)
process_pages() (langchain.document_loaders.ConfluenceLoader method)
process_pdf() (langchain.document_loaders.ConfluenceLoader method)
process_svg() (langchain.document_loaders.ConfluenceLoader method)
process_xls() (langchain.document_loaders.ConfluenceLoader method)
project (langchain.llms.VertexAI attribute)
project() (langchain.utilities.JiraAPIWrapper method)
Prompt (in module langchain.prompts)
prompt (langchain.agents.OpenAIFunctionsAgent attribute)
(langchain.chains.ConversationChain attribute)
(langchain.chains.LLMBashChain attribute)
(langchain.chains.LLMChain attribute)
(langchain.chains.LLMMathChain attribute)
(langchain.chains.PALChain attribute)
(langchain.chains.SQLDatabaseChain attribute)
prompt_func (langchain.tools.HumanInputRun attribute)
prompt_length() (langchain.chains.MapReduceDocumentsChain method)
(langchain.chains.MapRerankDocumentsChain method)
(langchain.chains.RefineDocumentsChain method)
(langchain.chains.StuffDocumentsChain method)
prompt_tokens (langchain.callbacks.OpenAICallbackHandler attribute)
properties (langchain.tools.APIOperation attribute)
PROTO (langchain.text_splitter.Language attribute)
prune() (langchain.memory.ConversationSummaryBufferMemory method)
PsychicLoader (class in langchain.document_loaders)
put() (langchain.utilities.TextRequestsWrapper method) | https://api.python.langchain.com/en/stable/genindex.html |
a1409c1b5e0f-127 | put() (langchain.utilities.TextRequestsWrapper method)
pydantic_object (langchain.output_parsers.PydanticOutputParser attribute)
PyMuPDFLoader (class in langchain.document_loaders)
PyPDFDirectoryLoader (class in langchain.document_loaders)
PyPDFium2Loader (class in langchain.document_loaders)
PyPDFLoader (class in langchain.document_loaders)
PySparkDataFrameLoader (class in langchain.document_loaders)
PYTHON (langchain.text_splitter.Language attribute)
python_globals (langchain.chains.PALChain attribute)
python_locals (langchain.chains.PALChain attribute)
python_repl (langchain.tools.PythonREPLTool attribute)
PythonCodeTextSplitter (class in langchain.text_splitter)
PythonLoader (class in langchain.document_loaders)
Q
qa_chain (langchain.chains.GraphCypherQAChain attribute)
(langchain.chains.GraphQAChain attribute)
(langchain.chains.KuzuQAChain attribute)
(langchain.chains.NebulaGraphQAChain attribute)
Qdrant (class in langchain.vectorstores)
query (langchain.document_loaders.FaunaLoader attribute)
query() (langchain.utilities.MaxComputeAPIWrapper method)
query_checker_prompt (langchain.chains.SQLDatabaseChain attribute)
query_configs (langchain.retrievers.LlamaIndexGraphRetriever attribute)
query_instruction (langchain.embeddings.DeepInfraEmbeddings attribute)
(langchain.embeddings.HuggingFaceInstructEmbeddings attribute)
(langchain.embeddings.MosaicMLInstructorEmbeddings attribute)
(langchain.embeddings.SelfHostedHuggingFaceInstructEmbeddings attribute)
query_kwargs (langchain.retrievers.LlamaIndexRetriever attribute)
query_name (langchain.vectorstores.SupabaseVectorStore attribute) | https://api.python.langchain.com/en/stable/genindex.html |
a1409c1b5e0f-128 | query_name (langchain.vectorstores.SupabaseVectorStore attribute)
query_params (langchain.document_loaders.GitHubIssuesLoader property)
(langchain.tools.APIOperation property)
query_suffix (langchain.utilities.SearxSearchWrapper attribute)
question_generator (langchain.chains.ChatVectorDBChain attribute)
(langchain.chains.ConversationalRetrievalChain attribute)
question_generator_chain (langchain.chains.FlareChain attribute)
question_to_checked_assertions_chain (langchain.chains.LLMCheckerChain attribute)
queue (langchain.callbacks.AsyncIteratorCallbackHandler attribute)
R
raise_error (langchain.callbacks.HumanApprovalCallbackHandler attribute)
raise_for_status (langchain.document_loaders.WebBaseLoader attribute)
rank_key (langchain.chains.MapRerankDocumentsChain attribute)
raw_completion (langchain.llms.AlephAlpha attribute)
REACT_DOCSTORE (langchain.agents.AgentType attribute)
ReadTheDocsLoader (class in langchain.document_loaders)
recall_ttl (langchain.memory.RedisEntityStore attribute)
recursive (langchain.document_loaders.GoogleDriveLoader attribute)
RecursiveCharacterTextSplitter (class in langchain.text_splitter)
RecursiveUrlLoader (class in langchain.document_loaders)
RedditPostsLoader (class in langchain.document_loaders)
Redis (class in langchain.vectorstores)
redis_client (langchain.memory.RedisEntityStore attribute)
RedisChatMessageHistory (class in langchain.memory)
reduce_k_below_max_tokens (langchain.chains.RetrievalQAWithSourcesChain attribute)
(langchain.chains.VectorDBQAWithSourcesChain attribute)
refine_llm_chain (langchain.chains.RefineDocumentsChain attribute)
reflection_threshold (langchain.experimental.GenerativeAgentMemory attribute)
regex (langchain.output_parsers.RegexParser attribute) | https://api.python.langchain.com/en/stable/genindex.html |
a1409c1b5e0f-129 | regex (langchain.output_parsers.RegexParser attribute)
regex_pattern (langchain.output_parsers.RegexDictParser attribute)
region (langchain.utilities.DuckDuckGoSearchAPIWrapper attribute)
region_name (langchain.embeddings.BedrockEmbeddings attribute)
(langchain.embeddings.SagemakerEndpointEmbeddings attribute)
(langchain.llms.Bedrock attribute)
(langchain.llms.SagemakerEndpoint attribute)
relevancy_threshold (langchain.retrievers.KNNRetriever attribute)
(langchain.retrievers.SVMRetriever attribute)
remove_end_sequence (langchain.llms.NLPCloud attribute)
remove_input (langchain.llms.NLPCloud attribute)
repeat_last_n (langchain.llms.GPT4All attribute)
repeat_penalty (langchain.llms.GPT4All attribute)
(langchain.llms.LlamaCpp attribute)
repetition_penalties_include_completion (langchain.llms.AlephAlpha attribute)
repetition_penalties_include_prompt (langchain.llms.AlephAlpha attribute)
repetition_penalty (langchain.llms.ForefrontAI attribute)
(langchain.llms.NLPCloud attribute)
(langchain.llms.TextGen attribute)
(langchain.llms.Writer attribute)
repo_id (langchain.embeddings.HuggingFaceHubEmbeddings attribute)
(langchain.llms.HuggingFaceHub attribute)
request_body (langchain.tools.APIOperation attribute)
request_parallelism (langchain.llms.VertexAI attribute)
request_timeout (langchain.chat_models.ChatOpenAI attribute)
(langchain.embeddings.OpenAIEmbeddings attribute)
(langchain.llms.AzureOpenAI attribute)
(langchain.llms.OpenAI attribute)
(langchain.llms.OpenLM attribute)
request_url (langchain.utilities.PowerBIDataset property)
requests (langchain.chains.OpenAPIEndpointChain attribute) | https://api.python.langchain.com/en/stable/genindex.html |
a1409c1b5e0f-130 | requests (langchain.chains.OpenAPIEndpointChain attribute)
(langchain.utilities.TextRequestsWrapper property)
requests_kwargs (langchain.document_loaders.WebBaseLoader attribute)
requests_per_second (langchain.document_loaders.WebBaseLoader attribute)
requests_wrapper (langchain.agents.agent_toolkits.OpenAPIToolkit attribute)
(langchain.chains.APIChain attribute)
(langchain.chains.LLMRequestsChain attribute)
(langchain.tools.BaseRequestsTool attribute)
response_chain (langchain.chains.FlareChain attribute)
response_key (langchain.retrievers.RemoteLangChainRetriever attribute)
response_schemas (langchain.output_parsers.StructuredOutputParser attribute)
responses (langchain.chat_models.FakeListChatModel attribute)
results() (langchain.utilities.BingSearchAPIWrapper method)
(langchain.utilities.DuckDuckGoSearchAPIWrapper method)
(langchain.utilities.GoogleSearchAPIWrapper method)
(langchain.utilities.GoogleSerperAPIWrapper method)
(langchain.utilities.MetaphorSearchAPIWrapper method)
(langchain.utilities.SearxSearchWrapper method)
(langchain.utilities.SerpAPIWrapper method)
results_async() (langchain.utilities.MetaphorSearchAPIWrapper method)
retrieve_article() (langchain.utilities.PubMedAPIWrapper method)
retrieve_documents() (langchain.retrievers.MultiQueryRetriever method)
retriever (langchain.chains.ConversationalRetrievalChain attribute)
(langchain.chains.FlareChain attribute)
(langchain.chains.RetrievalQA attribute)
(langchain.chains.RetrievalQAWithSourcesChain attribute)
(langchain.memory.VectorStoreRetrieverMemory attribute)
retry_chain (langchain.output_parsers.OutputFixingParser attribute)
(langchain.output_parsers.RetryOutputParser attribute)
(langchain.output_parsers.RetryWithErrorOutputParser attribute) | https://api.python.langchain.com/en/stable/genindex.html |
a1409c1b5e0f-131 | (langchain.output_parsers.RetryWithErrorOutputParser attribute)
retry_sleep (langchain.embeddings.MosaicMLInstructorEmbeddings attribute)
(langchain.llms.MosaicML attribute)
return_all (langchain.chains.SequentialChain attribute)
return_direct (langchain.chains.GraphCypherQAChain attribute)
(langchain.chains.SQLDatabaseChain attribute)
(langchain.tools.BaseTool attribute)
(langchain.tools.Tool attribute)
return_docs (langchain.memory.VectorStoreRetrieverMemory attribute)
return_final_only (langchain.chains.ConversationChain attribute)
(langchain.chains.LLMChain attribute)
return_generated_question (langchain.chains.ChatVectorDBChain attribute)
(langchain.chains.ConversationalRetrievalChain attribute)
return_intermediate_steps (langchain.agents.AgentExecutor attribute)
(langchain.chains.ConstitutionalChain attribute)
(langchain.chains.GraphCypherQAChain attribute)
(langchain.chains.MapReduceDocumentsChain attribute)
(langchain.chains.MapRerankDocumentsChain attribute)
(langchain.chains.OpenAPIEndpointChain attribute)
(langchain.chains.PALChain attribute)
(langchain.chains.RefineDocumentsChain attribute)
(langchain.chains.SQLDatabaseChain attribute)
(langchain.chains.SQLDatabaseSequentialChain attribute)
return_pl_id (langchain.chat_models.PromptLayerChatOpenAI attribute)
return_source_documents (langchain.chains.ChatVectorDBChain attribute)
(langchain.chains.ConversationalRetrievalChain attribute)
(langchain.chains.QAWithSourcesChain attribute)
(langchain.chains.RetrievalQA attribute)
(langchain.chains.RetrievalQAWithSourcesChain attribute)
(langchain.chains.VectorDBQA attribute)
(langchain.chains.VectorDBQAWithSourcesChain attribute)
return_stopped_response() (langchain.agents.Agent method) | https://api.python.langchain.com/en/stable/genindex.html |
a1409c1b5e0f-132 | return_stopped_response() (langchain.agents.Agent method)
(langchain.agents.BaseMultiActionAgent method)
(langchain.agents.BaseSingleActionAgent method)
return_urls (langchain.tools.SteamshipImageGenerationTool attribute)
return_values (langchain.agents.Agent property)
(langchain.agents.BaseMultiActionAgent property)
(langchain.agents.BaseSingleActionAgent property)
(langchain.schema.AgentFinish attribute)
revised_answer_prompt (langchain.chains.LLMCheckerChain attribute)
revised_summary_prompt (langchain.chains.LLMSummarizationCheckerChain attribute)
revision_chain (langchain.chains.ConstitutionalChain attribute)
RoamLoader (class in langchain.document_loaders)
Rockset (class in langchain.vectorstores)
Rockset.DistanceFunction (class in langchain.vectorstores)
role (langchain.prompts.ChatMessagePromptTemplate attribute)
root_dir (langchain.agents.agent_toolkits.FileManagementToolkit attribute)
route() (langchain.chains.LLMRouterChain method)
(langchain.chains.RouterChain method)
router_chain (langchain.chains.MultiPromptChain attribute)
(langchain.chains.MultiRetrievalQAChain attribute)
(langchain.chains.MultiRouteChain attribute)
RST (langchain.text_splitter.Language attribute)
RUBY (langchain.text_splitter.Language attribute)
run (langchain.schema.LLMResult attribute)
run() (langchain.chains.AnalyzeDocumentChain method)
(langchain.chains.APIChain method)
(langchain.chains.ChatVectorDBChain method)
(langchain.chains.ConstitutionalChain method)
(langchain.chains.ConversationalRetrievalChain method)
(langchain.chains.ConversationChain method)
(langchain.chains.FlareChain method)
(langchain.chains.GraphCypherQAChain method) | https://api.python.langchain.com/en/stable/genindex.html |
a1409c1b5e0f-133 | (langchain.chains.GraphCypherQAChain method)
(langchain.chains.GraphQAChain method)
(langchain.chains.HypotheticalDocumentEmbedder method)
(langchain.chains.KuzuQAChain method)
(langchain.chains.LLMBashChain method)
(langchain.chains.LLMChain method)
(langchain.chains.LLMCheckerChain method)
(langchain.chains.LLMMathChain method)
(langchain.chains.LLMRequestsChain method)
(langchain.chains.LLMRouterChain method)
(langchain.chains.LLMSummarizationCheckerChain method)
(langchain.chains.MapReduceChain method)
(langchain.chains.MapReduceDocumentsChain method)
(langchain.chains.MapRerankDocumentsChain method)
(langchain.chains.MultiPromptChain method)
(langchain.chains.MultiRetrievalQAChain method)
(langchain.chains.MultiRouteChain method)
(langchain.chains.NatBotChain method)
(langchain.chains.NebulaGraphQAChain method)
(langchain.chains.OpenAIModerationChain method)
(langchain.chains.OpenAPIEndpointChain method)
(langchain.chains.PALChain method)
(langchain.chains.QAGenerationChain method)
(langchain.chains.QAWithSourcesChain method)
(langchain.chains.RefineDocumentsChain method)
(langchain.chains.RetrievalQA method)
(langchain.chains.RetrievalQAWithSourcesChain method)
(langchain.chains.RouterChain method)
(langchain.chains.SequentialChain method)
(langchain.chains.SimpleSequentialChain method)
(langchain.chains.SQLDatabaseChain method)
(langchain.chains.SQLDatabaseSequentialChain method)
(langchain.chains.StuffDocumentsChain method)
(langchain.chains.TransformChain method)
(langchain.chains.VectorDBQA method) | https://api.python.langchain.com/en/stable/genindex.html |
a1409c1b5e0f-134 | (langchain.chains.TransformChain method)
(langchain.chains.VectorDBQA method)
(langchain.chains.VectorDBQAWithSourcesChain method)
(langchain.tools.BaseTool method)
(langchain.utilities.ArxivAPIWrapper method)
(langchain.utilities.BashProcess method)
(langchain.utilities.BingSearchAPIWrapper method)
(langchain.utilities.BraveSearchWrapper method)
(langchain.utilities.DuckDuckGoSearchAPIWrapper method)
(langchain.utilities.GooglePlacesAPIWrapper method)
(langchain.utilities.GoogleSearchAPIWrapper method)
(langchain.utilities.GoogleSerperAPIWrapper method)
(langchain.utilities.GraphQLAPIWrapper method)
(langchain.utilities.JiraAPIWrapper method)
(langchain.utilities.LambdaWrapper method)
(langchain.utilities.OpenWeatherMapAPIWrapper method)
(langchain.utilities.PowerBIDataset method)
(langchain.utilities.PubMedAPIWrapper method)
(langchain.utilities.PythonREPL method)
(langchain.utilities.SceneXplainAPIWrapper method)
(langchain.utilities.SearxSearchWrapper method)
(langchain.utilities.SerpAPIWrapper method)
(langchain.utilities.SparkSQL method)
(langchain.utilities.TwilioAPIWrapper method)
(langchain.utilities.WikipediaAPIWrapper method)
(langchain.utilities.WolframAlphaAPIWrapper method)
(langchain.utilities.ZapierNLAWrapper method)
run_as_str() (langchain.utilities.ZapierNLAWrapper method)
run_creation() (langchain.llms.Beam method)
run_no_throw() (langchain.utilities.SparkSQL method)
runner (langchain.llms.OpenLLM property)
RUST (langchain.text_splitter.Language attribute)
rwkv_verbose (langchain.llms.RWKV attribute)
S
S3DirectoryLoader (class in langchain.document_loaders)
S3FileLoader (class in langchain.document_loaders) | https://api.python.langchain.com/en/stable/genindex.html |
a1409c1b5e0f-135 | S3FileLoader (class in langchain.document_loaders)
safesearch (langchain.utilities.DuckDuckGoSearchAPIWrapper attribute)
sample_rows_in_table_info (langchain.utilities.PowerBIDataset attribute)
sanitize_input (langchain.tools.PythonAstREPLTool attribute)
(langchain.tools.PythonREPLTool attribute)
save() (langchain.agents.AgentExecutor method)
(langchain.agents.BaseMultiActionAgent method)
(langchain.agents.BaseSingleActionAgent method)
(langchain.chains.AnalyzeDocumentChain method)
(langchain.chains.APIChain method)
(langchain.chains.ChatVectorDBChain method)
(langchain.chains.ConstitutionalChain method)
(langchain.chains.ConversationalRetrievalChain method)
(langchain.chains.ConversationChain method)
(langchain.chains.FlareChain method)
(langchain.chains.GraphCypherQAChain method)
(langchain.chains.GraphQAChain method)
(langchain.chains.HypotheticalDocumentEmbedder method)
(langchain.chains.KuzuQAChain method)
(langchain.chains.LLMBashChain method)
(langchain.chains.LLMChain method)
(langchain.chains.LLMCheckerChain method)
(langchain.chains.LLMMathChain method)
(langchain.chains.LLMRequestsChain method)
(langchain.chains.LLMRouterChain method)
(langchain.chains.LLMSummarizationCheckerChain method)
(langchain.chains.MapReduceChain method)
(langchain.chains.MapReduceDocumentsChain method)
(langchain.chains.MapRerankDocumentsChain method)
(langchain.chains.MultiPromptChain method)
(langchain.chains.MultiRetrievalQAChain method)
(langchain.chains.MultiRouteChain method)
(langchain.chains.NatBotChain method) | https://api.python.langchain.com/en/stable/genindex.html |
a1409c1b5e0f-136 | (langchain.chains.NatBotChain method)
(langchain.chains.NebulaGraphQAChain method)
(langchain.chains.OpenAIModerationChain method)
(langchain.chains.OpenAPIEndpointChain method)
(langchain.chains.PALChain method)
(langchain.chains.QAGenerationChain method)
(langchain.chains.QAWithSourcesChain method)
(langchain.chains.RefineDocumentsChain method)
(langchain.chains.RetrievalQA method)
(langchain.chains.RetrievalQAWithSourcesChain method)
(langchain.chains.RouterChain method)
(langchain.chains.SequentialChain method)
(langchain.chains.SimpleSequentialChain method)
(langchain.chains.SQLDatabaseChain method)
(langchain.chains.SQLDatabaseSequentialChain method)
(langchain.chains.StuffDocumentsChain method)
(langchain.chains.TransformChain method)
(langchain.chains.VectorDBQA method)
(langchain.chains.VectorDBQAWithSourcesChain method)
(langchain.llms.AI21 method)
(langchain.llms.AlephAlpha method)
(langchain.llms.AmazonAPIGateway method)
(langchain.llms.Anthropic method)
(langchain.llms.Anyscale method)
(langchain.llms.Aviary method)
(langchain.llms.AzureMLOnlineEndpoint method)
(langchain.llms.AzureOpenAI method)
(langchain.llms.Banana method)
(langchain.llms.Baseten method)
(langchain.llms.Beam method)
(langchain.llms.Bedrock method)
(langchain.llms.CerebriumAI method)
(langchain.llms.Clarifai method)
(langchain.llms.Cohere method)
(langchain.llms.CTransformers method)
(langchain.llms.Databricks method)
(langchain.llms.DeepInfra method) | https://api.python.langchain.com/en/stable/genindex.html |
a1409c1b5e0f-137 | (langchain.llms.Databricks method)
(langchain.llms.DeepInfra method)
(langchain.llms.FakeListLLM method)
(langchain.llms.ForefrontAI method)
(langchain.llms.GooglePalm method)
(langchain.llms.GooseAI method)
(langchain.llms.GPT4All method)
(langchain.llms.HuggingFaceEndpoint method)
(langchain.llms.HuggingFaceHub method)
(langchain.llms.HuggingFacePipeline method)
(langchain.llms.HuggingFaceTextGenInference method)
(langchain.llms.HumanInputLLM method)
(langchain.llms.LlamaCpp method)
(langchain.llms.ManifestWrapper method)
(langchain.llms.Modal method)
(langchain.llms.MosaicML method)
(langchain.llms.NLPCloud method)
(langchain.llms.OctoAIEndpoint method)
(langchain.llms.OpenAI method)
(langchain.llms.OpenAIChat method)
(langchain.llms.OpenLLM method)
(langchain.llms.OpenLM method)
(langchain.llms.Petals method)
(langchain.llms.PipelineAI method)
(langchain.llms.PredictionGuard method)
(langchain.llms.PromptLayerOpenAI method)
(langchain.llms.PromptLayerOpenAIChat method)
(langchain.llms.Replicate method)
(langchain.llms.RWKV method)
(langchain.llms.SagemakerEndpoint method)
(langchain.llms.SelfHostedHuggingFaceLLM method)
(langchain.llms.SelfHostedPipeline method)
(langchain.llms.StochasticAI method)
(langchain.llms.TextGen method)
(langchain.llms.VertexAI method)
(langchain.llms.Writer method)
(langchain.prompts.BasePromptTemplate method) | https://api.python.langchain.com/en/stable/genindex.html |
a1409c1b5e0f-138 | (langchain.llms.Writer method)
(langchain.prompts.BasePromptTemplate method)
(langchain.prompts.ChatPromptTemplate method)
save_agent() (langchain.agents.AgentExecutor method)
save_context() (langchain.experimental.GenerativeAgentMemory method)
(langchain.memory.CombinedMemory method)
(langchain.memory.ConversationEntityMemory method)
(langchain.memory.ConversationKGMemory method)
(langchain.memory.ConversationStringBufferMemory method)
(langchain.memory.ConversationSummaryBufferMemory method)
(langchain.memory.ConversationSummaryMemory method)
(langchain.memory.ConversationTokenBufferMemory method)
(langchain.memory.MotorheadMemory method)
(langchain.memory.ReadOnlySharedMemory method)
(langchain.memory.SimpleMemory method)
(langchain.memory.VectorStoreRetrieverMemory method)
(langchain.schema.BaseMemory method)
save_local() (langchain.vectorstores.Annoy method)
(langchain.vectorstores.FAISS method)
SCALA (langchain.text_splitter.Language attribute)
scenex_api_key (langchain.utilities.SceneXplainAPIWrapper attribute)
scenex_api_url (langchain.utilities.SceneXplainAPIWrapper attribute)
schemas (langchain.utilities.PowerBIDataset attribute)
scrape() (langchain.document_loaders.WebBaseLoader method)
scrape_all() (langchain.document_loaders.WebBaseLoader method)
scrape_page() (langchain.tools.ExtractHyperlinksTool static method)
search() (langchain.memory.ZepChatMessageHistory method)
(langchain.utilities.JiraAPIWrapper method)
(langchain.vectorstores.VectorStore method)
search_field (langchain.retrievers.DocArrayRetriever attribute), [1]
search_index (langchain.vectorstores.Tigris property)
search_kwargs (langchain.chains.ChatVectorDBChain attribute)
(langchain.chains.VectorDBQA attribute) | https://api.python.langchain.com/en/stable/genindex.html |
a1409c1b5e0f-139 | (langchain.chains.VectorDBQA attribute)
(langchain.chains.VectorDBQAWithSourcesChain attribute)
(langchain.retrievers.SelfQueryRetriever attribute)
(langchain.retrievers.TimeWeightedVectorStoreRetriever attribute)
(langchain.utilities.BraveSearchWrapper attribute)
search_type (langchain.chains.VectorDBQA attribute)
(langchain.retrievers.DocArrayRetriever attribute), [1]
(langchain.retrievers.SelfQueryRetriever attribute)
search_wrapper (langchain.tools.BraveSearch attribute)
searx_host (langchain.utilities.SearxSearchWrapper attribute)
secret (langchain.document_loaders.FaunaLoader attribute)
seed (langchain.embeddings.LlamaCppEmbeddings attribute)
(langchain.llms.GPT4All attribute)
(langchain.llms.LlamaCpp attribute)
(langchain.llms.TextGen attribute)
select_examples() (langchain.prompts.example_selector.LengthBasedExampleSelector method)
(langchain.prompts.example_selector.MaxMarginalRelevanceExampleSelector method)
(langchain.prompts.example_selector.NGramOverlapExampleSelector method)
(langchain.prompts.example_selector.SemanticSimilarityExampleSelector method)
(langchain.prompts.LengthBasedExampleSelector method)
(langchain.prompts.MaxMarginalRelevanceExampleSelector method)
(langchain.prompts.NGramOverlapExampleSelector method)
(langchain.prompts.SemanticSimilarityExampleSelector method)
selected_tools (langchain.agents.agent_toolkits.FileManagementToolkit attribute)
SeleniumURLLoader (class in langchain.document_loaders)
SELF_ASK_WITH_SEARCH (langchain.agents.AgentType attribute)
semantic_hybrid_search() (langchain.vectorstores.AzureSearch method)
semantic_hybrid_search_with_score() (langchain.vectorstores.AzureSearch method)
send_pdf() (langchain.document_loaders.MathpixPDFLoader method) | https://api.python.langchain.com/en/stable/genindex.html |
a1409c1b5e0f-140 | send_pdf() (langchain.document_loaders.MathpixPDFLoader method)
SentenceTransformerEmbeddings (in module langchain.embeddings)
SentenceTransformersTokenTextSplitter (class in langchain.text_splitter)
sequential_chain (langchain.chains.LLMSummarizationCheckerChain attribute)
serpapi_api_key (langchain.utilities.SerpAPIWrapper attribute)
serper_api_key (langchain.utilities.GoogleSerperAPIWrapper attribute)
server_type (langchain.llms.OpenLLM attribute)
server_url (langchain.llms.OpenLLM attribute)
service_account_key (langchain.document_loaders.GoogleDriveLoader attribute)
service_account_path (langchain.document_loaders.GoogleApiClient attribute)
service_name (langchain.retrievers.AzureCognitiveSearchRetriever attribute)
session_cache (langchain.tools.QueryPowerBITool attribute)
session_id (langchain.memory.MotorheadMemory attribute)
(langchain.memory.RedisEntityStore attribute)
(langchain.memory.SQLiteEntityStore attribute)
set() (langchain.memory.InMemoryEntityStore method)
(langchain.memory.RedisEntityStore method)
(langchain.memory.SQLiteEntityStore method)
settings (langchain.document_loaders.OneDriveLoader attribute)
setup() (langchain.callbacks.AimCallbackHandler method)
silent_errors (langchain.chains.MultiPromptChain attribute)
(langchain.chains.MultiRetrievalQAChain attribute)
(langchain.chains.MultiRouteChain attribute)
similarity_fn (langchain.document_transformers.EmbeddingsRedundantFilter attribute)
(langchain.retrievers.document_compressors.EmbeddingsFilter attribute)
similarity_search() (langchain.vectorstores.AlibabaCloudOpenSearch method)
(langchain.vectorstores.AnalyticDB method)
(langchain.vectorstores.Annoy method)
(langchain.vectorstores.AtlasDB method)
(langchain.vectorstores.AwaDB method) | https://api.python.langchain.com/en/stable/genindex.html |
a1409c1b5e0f-141 | (langchain.vectorstores.AwaDB method)
(langchain.vectorstores.AzureSearch method)
(langchain.vectorstores.Cassandra method)
(langchain.vectorstores.Chroma method)
(langchain.vectorstores.Clarifai method)
(langchain.vectorstores.Clickhouse method)
(langchain.vectorstores.DeepLake method)
(langchain.vectorstores.ElasticVectorSearch method)
(langchain.vectorstores.FAISS method)
(langchain.vectorstores.Hologres method)
(langchain.vectorstores.LanceDB method)
(langchain.vectorstores.MatchingEngine method)
(langchain.vectorstores.Milvus method)
(langchain.vectorstores.MongoDBAtlasVectorSearch method)
(langchain.vectorstores.MyScale method)
(langchain.vectorstores.OpenSearchVectorSearch method)
(langchain.vectorstores.Pinecone method)
(langchain.vectorstores.Qdrant method)
(langchain.vectorstores.Redis method)
(langchain.vectorstores.Rockset method)
(langchain.vectorstores.SingleStoreDB method)
(langchain.vectorstores.SKLearnVectorStore method)
(langchain.vectorstores.StarRocks method)
(langchain.vectorstores.SupabaseVectorStore method)
(langchain.vectorstores.Tair method)
(langchain.vectorstores.Tigris method)
(langchain.vectorstores.Typesense method)
(langchain.vectorstores.Vectara method)
(langchain.vectorstores.VectorStore method)
(langchain.vectorstores.Weaviate method)
similarity_search_by_index() (langchain.vectorstores.Annoy method)
similarity_search_by_text() (langchain.vectorstores.Weaviate method)
similarity_search_by_vector() (langchain.vectorstores.AlibabaCloudOpenSearch method)
(langchain.vectorstores.AnalyticDB method)
(langchain.vectorstores.Annoy method)
(langchain.vectorstores.AwaDB method)
(langchain.vectorstores.Cassandra method) | https://api.python.langchain.com/en/stable/genindex.html |
a1409c1b5e0f-142 | (langchain.vectorstores.AwaDB method)
(langchain.vectorstores.Cassandra method)
(langchain.vectorstores.Chroma method)
(langchain.vectorstores.Clickhouse method)
(langchain.vectorstores.DeepLake method)
(langchain.vectorstores.FAISS method)
(langchain.vectorstores.Hologres method)
(langchain.vectorstores.Milvus method)
(langchain.vectorstores.MyScale method)
(langchain.vectorstores.Qdrant method)
(langchain.vectorstores.Rockset method)
(langchain.vectorstores.StarRocks method)
(langchain.vectorstores.SupabaseVectorStore method)
(langchain.vectorstores.VectorStore method)
(langchain.vectorstores.Weaviate method)
similarity_search_by_vector_returning_embeddings() (langchain.vectorstores.SupabaseVectorStore method)
similarity_search_by_vector_with_relevance_scores() (langchain.vectorstores.Rockset method)
(langchain.vectorstores.SupabaseVectorStore method)
similarity_search_limit_score() (langchain.vectorstores.Redis method)
similarity_search_with_relevance_scores() (langchain.vectorstores.AlibabaCloudOpenSearch method)
(langchain.vectorstores.AwaDB method)
(langchain.vectorstores.Clickhouse method)
(langchain.vectorstores.MyScale method)
(langchain.vectorstores.Rockset method)
(langchain.vectorstores.StarRocks method)
(langchain.vectorstores.SupabaseVectorStore method)
(langchain.vectorstores.VectorStore method)
similarity_search_with_score() (langchain.vectorstores.AnalyticDB method)
(langchain.vectorstores.Annoy method)
(langchain.vectorstores.AwaDB method)
(langchain.vectorstores.Cassandra method)
(langchain.vectorstores.Chroma method)
(langchain.vectorstores.Clarifai method)
(langchain.vectorstores.DeepLake method)
(langchain.vectorstores.ElasticVectorSearch method) | https://api.python.langchain.com/en/stable/genindex.html |
a1409c1b5e0f-143 | (langchain.vectorstores.DeepLake method)
(langchain.vectorstores.ElasticVectorSearch method)
(langchain.vectorstores.FAISS method)
(langchain.vectorstores.Hologres method)
(langchain.vectorstores.Milvus method)
(langchain.vectorstores.MongoDBAtlasVectorSearch method)
(langchain.vectorstores.OpenSearchVectorSearch method)
(langchain.vectorstores.Pinecone method)
(langchain.vectorstores.Qdrant method)
(langchain.vectorstores.Redis method)
(langchain.vectorstores.SingleStoreDB method)
(langchain.vectorstores.SKLearnVectorStore method)
(langchain.vectorstores.Tigris method)
(langchain.vectorstores.Typesense method)
(langchain.vectorstores.Vectara method)
(langchain.vectorstores.Weaviate method)
similarity_search_with_score_by_index() (langchain.vectorstores.Annoy method)
similarity_search_with_score_by_vector() (langchain.vectorstores.AnalyticDB method)
(langchain.vectorstores.Annoy method)
(langchain.vectorstores.Cassandra method)
(langchain.vectorstores.FAISS method)
(langchain.vectorstores.Hologres method)
(langchain.vectorstores.Milvus method)
(langchain.vectorstores.Qdrant method)
similarity_search_with_score_id() (langchain.vectorstores.Cassandra method)
similarity_search_with_score_id_by_vector() (langchain.vectorstores.Cassandra method)
similarity_threshold (langchain.document_transformers.EmbeddingsRedundantFilter attribute)
(langchain.retrievers.document_compressors.EmbeddingsFilter attribute)
since (langchain.document_loaders.GitHubIssuesLoader attribute)
SingleStoreDB (class in langchain.vectorstores)
SitemapLoader (class in langchain.document_loaders)
siterestrict (langchain.utilities.GoogleSearchAPIWrapper attribute)
size (langchain.tools.SteamshipImageGenerationTool attribute) | https://api.python.langchain.com/en/stable/genindex.html |
a1409c1b5e0f-144 | size (langchain.tools.SteamshipImageGenerationTool attribute)
skip_special_tokens (langchain.llms.TextGen attribute)
SKLearnVectorStore (class in langchain.vectorstores)
SlackDirectoryLoader (class in langchain.document_loaders)
SnowflakeLoader (class in langchain.document_loaders)
SOL (langchain.text_splitter.Language attribute)
sort (langchain.document_loaders.GitHubIssuesLoader attribute)
source (langchain.document_loaders.Blob property)
SpacyTextSplitter (class in langchain.text_splitter)
SparkSQL (class in langchain.utilities)
sparse_encoder (langchain.retrievers.PineconeHybridSearchRetriever attribute)
spec (langchain.agents.agent_toolkits.JsonToolkit attribute)
(langchain.tools.JsonGetValueTool attribute)
(langchain.tools.JsonListKeysTool attribute)
split_documents() (langchain.text_splitter.TextSplitter method)
split_text() (langchain.text_splitter.CharacterTextSplitter method)
(langchain.text_splitter.MarkdownHeaderTextSplitter method)
(langchain.text_splitter.NLTKTextSplitter method)
(langchain.text_splitter.RecursiveCharacterTextSplitter method)
(langchain.text_splitter.SentenceTransformersTokenTextSplitter method)
(langchain.text_splitter.SpacyTextSplitter method)
(langchain.text_splitter.TextSplitter method)
(langchain.text_splitter.TokenTextSplitter method)
split_text_on_tokens() (in module langchain.text_splitter)
SpreedlyLoader (class in langchain.document_loaders)
sql_chain (langchain.chains.SQLDatabaseSequentialChain attribute)
SQLChatMessageHistory (class in langchain.memory)
SRTLoader (class in langchain.document_loaders)
StarRocks (class in langchain.vectorstores) | https://api.python.langchain.com/en/stable/genindex.html |
a1409c1b5e0f-145 | StarRocks (class in langchain.vectorstores)
start_with_retrieval (langchain.chains.FlareChain attribute)
state (langchain.document_loaders.GitHubIssuesLoader attribute)
status (langchain.experimental.GenerativeAgent attribute)
StdInInquireTool() (in module langchain.tools)
StdOutCallbackHandler (class in langchain.callbacks)
steamship (langchain.tools.SteamshipImageGenerationTool attribute)
stop (langchain.agents.LLMSingleActionAgent attribute)
(langchain.chains.PALChain attribute)
(langchain.llms.GPT4All attribute)
(langchain.llms.LlamaCpp attribute)
(langchain.llms.VertexAI attribute)
(langchain.llms.Writer attribute)
stop_sequences (langchain.llms.AlephAlpha attribute)
stopping_strings (langchain.llms.TextGen attribute)
store (langchain.memory.InMemoryEntityStore attribute)
strategy (langchain.llms.RWKV attribute)
stream() (langchain.llms.Anthropic method)
(langchain.llms.AzureOpenAI method)
(langchain.llms.LlamaCpp method)
(langchain.llms.OpenAI method)
(langchain.llms.OpenLM method)
(langchain.llms.PromptLayerOpenAI method)
streaming (langchain.chat_models.ChatOpenAI attribute)
(langchain.llms.Anthropic attribute)
(langchain.llms.AzureOpenAI attribute)
(langchain.llms.GPT4All attribute)
(langchain.llms.LlamaCpp attribute)
(langchain.llms.OpenAI attribute)
(langchain.llms.OpenAIChat attribute)
(langchain.llms.OpenLM attribute)
(langchain.llms.PromptLayerOpenAIChat attribute)
(langchain.llms.TextGen attribute)
StreamingStdOutCallbackHandler (class in langchain.callbacks) | https://api.python.langchain.com/en/stable/genindex.html |
a1409c1b5e0f-146 | StreamingStdOutCallbackHandler (class in langchain.callbacks)
StreamlitCallbackHandler() (in module langchain.callbacks)
strip_outputs (langchain.chains.SimpleSequentialChain attribute)
StripeLoader (class in langchain.document_loaders)
STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION (langchain.agents.AgentType attribute)
structured_query_translator (langchain.retrievers.SelfQueryRetriever attribute)
successful_requests (langchain.callbacks.OpenAICallbackHandler attribute)
suffix (langchain.llms.LlamaCpp attribute)
(langchain.prompts.FewShotPromptTemplate attribute)
(langchain.prompts.FewShotPromptWithTemplates attribute)
summarize_related_memories() (langchain.experimental.GenerativeAgent method)
summary (langchain.experimental.GenerativeAgent attribute)
summary_message_cls (langchain.memory.ConversationKGMemory attribute)
summary_refresh_seconds (langchain.experimental.GenerativeAgent attribute)
SupabaseVectorStore (class in langchain.vectorstores)
SWIFT (langchain.text_splitter.Language attribute)
sync_browser (langchain.agents.agent_toolkits.PlayWrightBrowserToolkit attribute)
T
table (langchain.vectorstores.ClickhouseSettings attribute)
(langchain.vectorstores.MyScaleSettings attribute)
table_info (langchain.utilities.PowerBIDataset property)
table_name (langchain.memory.SQLiteEntityStore attribute)
(langchain.vectorstores.SupabaseVectorStore attribute)
table_names (langchain.utilities.PowerBIDataset attribute)
tags (langchain.chains.AnalyzeDocumentChain attribute)
(langchain.chains.APIChain attribute)
(langchain.chains.ChatVectorDBChain attribute)
(langchain.chains.ConstitutionalChain attribute)
(langchain.chains.ConversationalRetrievalChain attribute)
(langchain.chains.ConversationChain attribute)
(langchain.chains.FlareChain attribute) | https://api.python.langchain.com/en/stable/genindex.html |
a1409c1b5e0f-147 | (langchain.chains.ConversationChain attribute)
(langchain.chains.FlareChain attribute)
(langchain.chains.GraphCypherQAChain attribute)
(langchain.chains.GraphQAChain attribute)
(langchain.chains.HypotheticalDocumentEmbedder attribute)
(langchain.chains.KuzuQAChain attribute)
(langchain.chains.LLMBashChain attribute)
(langchain.chains.LLMChain attribute)
(langchain.chains.LLMCheckerChain attribute)
(langchain.chains.LLMMathChain attribute)
(langchain.chains.LLMRequestsChain attribute)
(langchain.chains.LLMRouterChain attribute)
(langchain.chains.LLMSummarizationCheckerChain attribute)
(langchain.chains.MapReduceChain attribute)
(langchain.chains.MapReduceDocumentsChain attribute)
(langchain.chains.MapRerankDocumentsChain attribute)
(langchain.chains.MultiPromptChain attribute)
(langchain.chains.MultiRetrievalQAChain attribute)
(langchain.chains.MultiRouteChain attribute)
(langchain.chains.NatBotChain attribute)
(langchain.chains.NebulaGraphQAChain attribute)
(langchain.chains.OpenAIModerationChain attribute)
(langchain.chains.OpenAPIEndpointChain attribute)
(langchain.chains.PALChain attribute)
(langchain.chains.QAGenerationChain attribute)
(langchain.chains.QAWithSourcesChain attribute)
(langchain.chains.RefineDocumentsChain attribute)
(langchain.chains.RetrievalQA attribute)
(langchain.chains.RetrievalQAWithSourcesChain attribute)
(langchain.chains.RouterChain attribute)
(langchain.chains.SequentialChain attribute)
(langchain.chains.SimpleSequentialChain attribute)
(langchain.chains.SQLDatabaseChain attribute)
(langchain.chains.SQLDatabaseSequentialChain attribute)
(langchain.chains.StuffDocumentsChain attribute) | https://api.python.langchain.com/en/stable/genindex.html |
a1409c1b5e0f-148 | (langchain.chains.StuffDocumentsChain attribute)
(langchain.chains.TransformChain attribute)
(langchain.chains.VectorDBQA attribute)
(langchain.chains.VectorDBQAWithSourcesChain attribute)
(langchain.llms.AI21 attribute)
(langchain.llms.AlephAlpha attribute)
(langchain.llms.AmazonAPIGateway attribute)
(langchain.llms.Anthropic attribute)
(langchain.llms.Anyscale attribute)
(langchain.llms.Aviary attribute)
(langchain.llms.AzureMLOnlineEndpoint attribute)
(langchain.llms.AzureOpenAI attribute)
(langchain.llms.Banana attribute)
(langchain.llms.Baseten attribute)
(langchain.llms.Beam attribute)
(langchain.llms.Bedrock attribute)
(langchain.llms.CerebriumAI attribute)
(langchain.llms.Clarifai attribute)
(langchain.llms.Cohere attribute)
(langchain.llms.CTransformers attribute)
(langchain.llms.Databricks attribute)
(langchain.llms.DeepInfra attribute)
(langchain.llms.FakeListLLM attribute)
(langchain.llms.ForefrontAI attribute)
(langchain.llms.GooglePalm attribute)
(langchain.llms.GooseAI attribute)
(langchain.llms.GPT4All attribute)
(langchain.llms.HuggingFaceEndpoint attribute)
(langchain.llms.HuggingFaceHub attribute)
(langchain.llms.HuggingFacePipeline attribute)
(langchain.llms.HuggingFaceTextGenInference attribute)
(langchain.llms.HumanInputLLM attribute)
(langchain.llms.LlamaCpp attribute)
(langchain.llms.ManifestWrapper attribute)
(langchain.llms.Modal attribute)
(langchain.llms.MosaicML attribute)
(langchain.llms.NLPCloud attribute) | https://api.python.langchain.com/en/stable/genindex.html |
a1409c1b5e0f-149 | (langchain.llms.NLPCloud attribute)
(langchain.llms.OctoAIEndpoint attribute)
(langchain.llms.OpenAI attribute)
(langchain.llms.OpenAIChat attribute)
(langchain.llms.OpenLLM attribute)
(langchain.llms.OpenLM attribute)
(langchain.llms.Petals attribute)
(langchain.llms.PipelineAI attribute)
(langchain.llms.PredictionGuard attribute)
(langchain.llms.Replicate attribute)
(langchain.llms.RWKV attribute)
(langchain.llms.SagemakerEndpoint attribute)
(langchain.llms.SelfHostedHuggingFaceLLM attribute)
(langchain.llms.SelfHostedPipeline attribute)
(langchain.llms.StochasticAI attribute)
(langchain.llms.TextGen attribute)
(langchain.llms.VertexAI attribute)
(langchain.llms.Writer attribute)
Tair (class in langchain.vectorstores)
task (langchain.embeddings.HuggingFaceHubEmbeddings attribute)
(langchain.llms.HuggingFaceEndpoint attribute)
(langchain.llms.HuggingFaceHub attribute)
(langchain.llms.SelfHostedHuggingFaceLLM attribute)
tbs (langchain.utilities.GoogleSerperAPIWrapper attribute)
TelegramChatApiLoader (class in langchain.document_loaders)
TelegramChatFileLoader (class in langchain.document_loaders)
TelegramChatLoader (in module langchain.document_loaders)
temp (langchain.llms.GPT4All attribute)
temperature (langchain.chat_models.ChatGooglePalm attribute)
(langchain.chat_models.ChatOpenAI attribute)
(langchain.llms.AI21 attribute)
(langchain.llms.AlephAlpha attribute)
(langchain.llms.Anthropic attribute)
(langchain.llms.AzureOpenAI attribute)
(langchain.llms.Cohere attribute) | https://api.python.langchain.com/en/stable/genindex.html |
a1409c1b5e0f-150 | (langchain.llms.AzureOpenAI attribute)
(langchain.llms.Cohere attribute)
(langchain.llms.ForefrontAI attribute)
(langchain.llms.GooglePalm attribute)
(langchain.llms.GooseAI attribute)
(langchain.llms.LlamaCpp attribute)
(langchain.llms.NLPCloud attribute)
(langchain.llms.OpenAI attribute)
(langchain.llms.OpenLM attribute)
(langchain.llms.Petals attribute)
(langchain.llms.PredictionGuard attribute)
(langchain.llms.RWKV attribute)
(langchain.llms.TextGen attribute)
(langchain.llms.VertexAI attribute)
(langchain.llms.Writer attribute)
template (langchain.prompts.PromptTemplate attribute)
(langchain.tools.QueryCheckerTool attribute)
(langchain.tools.QueryPowerBITool attribute)
(langchain.tools.QuerySQLCheckerTool attribute)
template_format (langchain.prompts.FewShotPromptTemplate attribute)
(langchain.prompts.FewShotPromptWithTemplates attribute)
(langchain.prompts.PromptTemplate attribute)
template_tool_response (langchain.agents.ConversationalChatAgent attribute)
TencentCOSDirectoryLoader (class in langchain.document_loaders)
TencentCOSFileLoader (class in langchain.document_loaders)
text (langchain.schema.ChatGeneration attribute)
(langchain.schema.Generation attribute)
text_length (langchain.chains.LLMRequestsChain attribute)
text_splitter (langchain.chains.AnalyzeDocumentChain attribute)
(langchain.chains.MapReduceChain attribute)
(langchain.chains.QAGenerationChain attribute)
TextLoader (class in langchain.document_loaders)
texts (langchain.retrievers.KNNRetriever attribute)
(langchain.retrievers.SVMRetriever attribute)
TextSplitter (class in langchain.text_splitter) | https://api.python.langchain.com/en/stable/genindex.html |
a1409c1b5e0f-151 | TextSplitter (class in langchain.text_splitter)
tfidf_array (langchain.retrievers.TFIDFRetriever attribute)
threshold (langchain.prompts.example_selector.NGramOverlapExampleSelector attribute)
(langchain.prompts.NGramOverlapExampleSelector attribute)
Tigris (class in langchain.vectorstores)
tiktoken_model_name (langchain.chat_models.ChatOpenAI attribute)
(langchain.embeddings.OpenAIEmbeddings attribute)
(langchain.llms.AzureOpenAI attribute)
(langchain.llms.OpenAI attribute)
(langchain.llms.OpenLM attribute)
time (langchain.utilities.DuckDuckGoSearchAPIWrapper attribute)
to_json() (langchain.chains.AnalyzeDocumentChain method)
(langchain.chains.APIChain method)
(langchain.chains.ChatVectorDBChain method)
(langchain.chains.ConstitutionalChain method)
(langchain.chains.ConversationalRetrievalChain method)
(langchain.chains.ConversationChain method)
(langchain.chains.FlareChain method)
(langchain.chains.GraphCypherQAChain method)
(langchain.chains.GraphQAChain method)
(langchain.chains.HypotheticalDocumentEmbedder method)
(langchain.chains.KuzuQAChain method)
(langchain.chains.LLMBashChain method)
(langchain.chains.LLMChain method)
(langchain.chains.LLMCheckerChain method)
(langchain.chains.LLMMathChain method)
(langchain.chains.LLMRequestsChain method)
(langchain.chains.LLMRouterChain method)
(langchain.chains.LLMSummarizationCheckerChain method)
(langchain.chains.MapReduceChain method)
(langchain.chains.MapReduceDocumentsChain method)
(langchain.chains.MapRerankDocumentsChain method)
(langchain.chains.MultiPromptChain method) | https://api.python.langchain.com/en/stable/genindex.html |
a1409c1b5e0f-152 | (langchain.chains.MultiPromptChain method)
(langchain.chains.MultiRetrievalQAChain method)
(langchain.chains.MultiRouteChain method)
(langchain.chains.NatBotChain method)
(langchain.chains.NebulaGraphQAChain method)
(langchain.chains.OpenAIModerationChain method)
(langchain.chains.OpenAPIEndpointChain method)
(langchain.chains.PALChain method)
(langchain.chains.QAGenerationChain method)
(langchain.chains.QAWithSourcesChain method)
(langchain.chains.RefineDocumentsChain method)
(langchain.chains.RetrievalQA method)
(langchain.chains.RetrievalQAWithSourcesChain method)
(langchain.chains.RouterChain method)
(langchain.chains.SequentialChain method)
(langchain.chains.SimpleSequentialChain method)
(langchain.chains.SQLDatabaseChain method)
(langchain.chains.SQLDatabaseSequentialChain method)
(langchain.chains.StuffDocumentsChain method)
(langchain.chains.TransformChain method)
(langchain.chains.VectorDBQA method)
(langchain.chains.VectorDBQAWithSourcesChain method)
to_json_not_implemented() (langchain.chains.AnalyzeDocumentChain method)
(langchain.chains.APIChain method)
(langchain.chains.ChatVectorDBChain method)
(langchain.chains.ConstitutionalChain method)
(langchain.chains.ConversationalRetrievalChain method)
(langchain.chains.ConversationChain method)
(langchain.chains.FlareChain method)
(langchain.chains.GraphCypherQAChain method)
(langchain.chains.GraphQAChain method)
(langchain.chains.HypotheticalDocumentEmbedder method)
(langchain.chains.KuzuQAChain method)
(langchain.chains.LLMBashChain method)
(langchain.chains.LLMChain method) | https://api.python.langchain.com/en/stable/genindex.html |
a1409c1b5e0f-153 | (langchain.chains.LLMChain method)
(langchain.chains.LLMCheckerChain method)
(langchain.chains.LLMMathChain method)
(langchain.chains.LLMRequestsChain method)
(langchain.chains.LLMRouterChain method)
(langchain.chains.LLMSummarizationCheckerChain method)
(langchain.chains.MapReduceChain method)
(langchain.chains.MapReduceDocumentsChain method)
(langchain.chains.MapRerankDocumentsChain method)
(langchain.chains.MultiPromptChain method)
(langchain.chains.MultiRetrievalQAChain method)
(langchain.chains.MultiRouteChain method)
(langchain.chains.NatBotChain method)
(langchain.chains.NebulaGraphQAChain method)
(langchain.chains.OpenAIModerationChain method)
(langchain.chains.OpenAPIEndpointChain method)
(langchain.chains.PALChain method)
(langchain.chains.QAGenerationChain method)
(langchain.chains.QAWithSourcesChain method)
(langchain.chains.RefineDocumentsChain method)
(langchain.chains.RetrievalQA method)
(langchain.chains.RetrievalQAWithSourcesChain method)
(langchain.chains.RouterChain method)
(langchain.chains.SequentialChain method)
(langchain.chains.SimpleSequentialChain method)
(langchain.chains.SQLDatabaseChain method)
(langchain.chains.SQLDatabaseSequentialChain method)
(langchain.chains.StuffDocumentsChain method)
(langchain.chains.TransformChain method)
(langchain.chains.VectorDBQA method)
(langchain.chains.VectorDBQAWithSourcesChain method)
to_messages() (langchain.schema.PromptValue method)
to_string() (langchain.schema.PromptValue method)
to_typescript() (langchain.tools.APIOperation method)
token (langchain.llms.PredictionGuard attribute) | https://api.python.langchain.com/en/stable/genindex.html |
a1409c1b5e0f-154 | token (langchain.llms.PredictionGuard attribute)
(langchain.utilities.PowerBIDataset attribute)
token_path (langchain.document_loaders.GoogleApiClient attribute)
(langchain.document_loaders.GoogleDriveLoader attribute)
Tokenizer (class in langchain.text_splitter)
tokenizer (langchain.llms.Petals attribute)
tokens (langchain.llms.AlephAlpha attribute)
tokens_path (langchain.llms.RWKV attribute)
tokens_per_chunk (langchain.text_splitter.Tokenizer attribute)
TokenTextSplitter (class in langchain.text_splitter)
ToMarkdownLoader (class in langchain.document_loaders)
TomlLoader (class in langchain.document_loaders)
tool() (in module langchain.agents)
(in module langchain.tools)
tool_run_logging_kwargs() (langchain.agents.Agent method)
(langchain.agents.BaseMultiActionAgent method)
(langchain.agents.BaseSingleActionAgent method)
(langchain.agents.LLMSingleActionAgent method)
tools (langchain.agents.agent_toolkits.JiraToolkit attribute)
(langchain.agents.agent_toolkits.ZapierToolkit attribute)
(langchain.agents.AgentExecutor attribute)
(langchain.agents.OpenAIFunctionsAgent attribute)
top_k (langchain.chains.GraphCypherQAChain attribute)
(langchain.chains.SQLDatabaseChain attribute)
(langchain.chat_models.ChatGooglePalm attribute)
(langchain.llms.AlephAlpha attribute)
(langchain.llms.Anthropic attribute)
(langchain.llms.ForefrontAI attribute)
(langchain.llms.GooglePalm attribute)
(langchain.llms.GPT4All attribute)
(langchain.llms.LlamaCpp attribute)
(langchain.llms.NLPCloud attribute)
(langchain.llms.Petals attribute)
(langchain.llms.TextGen attribute) | https://api.python.langchain.com/en/stable/genindex.html |
a1409c1b5e0f-155 | (langchain.llms.Petals attribute)
(langchain.llms.TextGen attribute)
(langchain.llms.VertexAI attribute)
(langchain.retrievers.ChatGPTPluginRetriever attribute)
(langchain.retrievers.DataberryRetriever attribute)
(langchain.retrievers.DocArrayRetriever attribute), [1]
(langchain.retrievers.PineconeHybridSearchRetriever attribute)
top_k_docs_for_context (langchain.chains.ChatVectorDBChain attribute)
top_k_results (langchain.utilities.ArxivAPIWrapper attribute)
(langchain.utilities.GooglePlacesAPIWrapper attribute)
(langchain.utilities.PubMedAPIWrapper attribute)
(langchain.utilities.WikipediaAPIWrapper attribute)
top_n (langchain.retrievers.document_compressors.CohereRerank attribute)
top_p (langchain.chat_models.ChatGooglePalm attribute)
(langchain.llms.AlephAlpha attribute)
(langchain.llms.Anthropic attribute)
(langchain.llms.AzureOpenAI attribute)
(langchain.llms.ForefrontAI attribute)
(langchain.llms.GooglePalm attribute)
(langchain.llms.GooseAI attribute)
(langchain.llms.GPT4All attribute)
(langchain.llms.LlamaCpp attribute)
(langchain.llms.NLPCloud attribute)
(langchain.llms.OpenAI attribute)
(langchain.llms.OpenLM attribute)
(langchain.llms.Petals attribute)
(langchain.llms.RWKV attribute)
(langchain.llms.TextGen attribute)
(langchain.llms.VertexAI attribute)
(langchain.llms.Writer attribute)
topP (langchain.llms.AI21 attribute)
total_cost (langchain.callbacks.OpenAICallbackHandler attribute)
total_tokens (langchain.callbacks.OpenAICallbackHandler attribute)
tracing_enabled() (in module langchain.callbacks) | https://api.python.langchain.com/en/stable/genindex.html |
a1409c1b5e0f-156 | tracing_enabled() (in module langchain.callbacks)
traits (langchain.experimental.GenerativeAgent attribute)
transform (langchain.chains.TransformChain attribute)
transform_documents() (langchain.document_transformers.EmbeddingsRedundantFilter method)
(langchain.schema.BaseDocumentTransformer method)
(langchain.text_splitter.TextSplitter method)
transform_input_fn (langchain.llms.Databricks attribute)
transform_output_fn (langchain.llms.Databricks attribute)
transformers (langchain.retrievers.document_compressors.DocumentCompressorPipeline attribute)
TrelloLoader (class in langchain.document_loaders)
true_val (langchain.output_parsers.BooleanOutputParser attribute)
truncate (langchain.embeddings.CohereEmbeddings attribute)
(langchain.llms.Cohere attribute)
truncation_length (langchain.llms.TextGen attribute)
ts_type_from_python() (langchain.tools.APIOperation static method)
ttl (langchain.memory.RedisEntityStore attribute)
tuned_model_name (langchain.llms.VertexAI attribute)
TwitterTweetLoader (class in langchain.document_loaders)
type (langchain.output_parsers.ResponseSchema attribute)
(langchain.schema.AIMessage property)
(langchain.schema.BaseMessage property)
(langchain.schema.ChatMessage property)
(langchain.schema.FunctionMessage property)
(langchain.schema.HumanMessage property)
(langchain.schema.SystemMessage property)
(langchain.utilities.GoogleSerperAPIWrapper attribute)
Typesense (class in langchain.vectorstores)
typical_p (langchain.llms.TextGen attribute)
U
unique_union() (langchain.retrievers.MultiQueryRetriever method)
unsecure (langchain.utilities.SearxSearchWrapper attribute)
UnstructuredAPIFileIOLoader (class in langchain.document_loaders)
UnstructuredAPIFileLoader (class in langchain.document_loaders) | https://api.python.langchain.com/en/stable/genindex.html |
a1409c1b5e0f-157 | UnstructuredAPIFileLoader (class in langchain.document_loaders)
UnstructuredCSVLoader (class in langchain.document_loaders)
UnstructuredEmailLoader (class in langchain.document_loaders)
UnstructuredEPubLoader (class in langchain.document_loaders)
UnstructuredExcelLoader (class in langchain.document_loaders)
UnstructuredFileIOLoader (class in langchain.document_loaders)
UnstructuredFileLoader (class in langchain.document_loaders)
UnstructuredHTMLLoader (class in langchain.document_loaders)
UnstructuredImageLoader (class in langchain.document_loaders)
UnstructuredMarkdownLoader (class in langchain.document_loaders)
UnstructuredODTLoader (class in langchain.document_loaders)
UnstructuredOrgModeLoader (class in langchain.document_loaders)
UnstructuredPDFLoader (class in langchain.document_loaders)
UnstructuredPowerPointLoader (class in langchain.document_loaders)
UnstructuredRSTLoader (class in langchain.document_loaders)
UnstructuredRTFLoader (class in langchain.document_loaders)
UnstructuredURLLoader (class in langchain.document_loaders)
UnstructuredWordDocumentLoader (class in langchain.document_loaders)
UnstructuredXMLLoader (class in langchain.document_loaders)
update_document() (langchain.vectorstores.Chroma method)
update_forward_refs() (langchain.llms.AI21 class method)
(langchain.llms.AlephAlpha class method)
(langchain.llms.AmazonAPIGateway class method)
(langchain.llms.Anthropic class method)
(langchain.llms.Anyscale class method)
(langchain.llms.Aviary class method)
(langchain.llms.AzureMLOnlineEndpoint class method)
(langchain.llms.AzureOpenAI class method)
(langchain.llms.Banana class method) | https://api.python.langchain.com/en/stable/genindex.html |
a1409c1b5e0f-158 | (langchain.llms.Banana class method)
(langchain.llms.Baseten class method)
(langchain.llms.Beam class method)
(langchain.llms.Bedrock class method)
(langchain.llms.CerebriumAI class method)
(langchain.llms.Clarifai class method)
(langchain.llms.Cohere class method)
(langchain.llms.CTransformers class method)
(langchain.llms.Databricks class method)
(langchain.llms.DeepInfra class method)
(langchain.llms.FakeListLLM class method)
(langchain.llms.ForefrontAI class method)
(langchain.llms.GooglePalm class method)
(langchain.llms.GooseAI class method)
(langchain.llms.GPT4All class method)
(langchain.llms.HuggingFaceEndpoint class method)
(langchain.llms.HuggingFaceHub class method)
(langchain.llms.HuggingFacePipeline class method)
(langchain.llms.HuggingFaceTextGenInference class method)
(langchain.llms.HumanInputLLM class method)
(langchain.llms.LlamaCpp class method)
(langchain.llms.ManifestWrapper class method)
(langchain.llms.Modal class method)
(langchain.llms.MosaicML class method)
(langchain.llms.NLPCloud class method)
(langchain.llms.OctoAIEndpoint class method)
(langchain.llms.OpenAI class method)
(langchain.llms.OpenAIChat class method)
(langchain.llms.OpenLLM class method)
(langchain.llms.OpenLM class method)
(langchain.llms.Petals class method)
(langchain.llms.PipelineAI class method)
(langchain.llms.PredictionGuard class method)
(langchain.llms.PromptLayerOpenAI class method) | https://api.python.langchain.com/en/stable/genindex.html |
a1409c1b5e0f-159 | (langchain.llms.PromptLayerOpenAI class method)
(langchain.llms.PromptLayerOpenAIChat class method)
(langchain.llms.Replicate class method)
(langchain.llms.RWKV class method)
(langchain.llms.SagemakerEndpoint class method)
(langchain.llms.SelfHostedHuggingFaceLLM class method)
(langchain.llms.SelfHostedPipeline class method)
(langchain.llms.StochasticAI class method)
(langchain.llms.TextGen class method)
(langchain.llms.VertexAI class method)
(langchain.llms.Writer class method)
(langchain.schema.AIMessage class method)
(langchain.schema.BaseLLMOutputParser class method)
(langchain.schema.BaseMemory class method)
(langchain.schema.BaseMessage class method)
(langchain.schema.BaseOutputParser class method)
(langchain.schema.ChatGeneration class method)
(langchain.schema.ChatMessage class method)
(langchain.schema.ChatResult class method)
(langchain.schema.Document class method)
(langchain.schema.FunctionMessage class method)
(langchain.schema.Generation class method)
(langchain.schema.HumanMessage class method)
(langchain.schema.LLMResult class method)
(langchain.schema.NoOpOutputParser class method)
(langchain.schema.PromptValue class method)
(langchain.schema.RunInfo class method)
(langchain.schema.SystemMessage class method)
upsert_messages() (langchain.memory.CosmosDBChatMessageHistory method)
url (langchain.document_loaders.GitHubIssuesLoader property)
(langchain.document_loaders.MathpixPDFLoader property)
(langchain.llms.Beam attribute)
(langchain.memory.MotorheadMemory attribute)
(langchain.retrievers.ChatGPTPluginRetriever attribute)
(langchain.retrievers.RemoteLangChainRetriever attribute)
(langchain.tools.IFTTTWebhook attribute) | https://api.python.langchain.com/en/stable/genindex.html |
a1409c1b5e0f-160 | (langchain.tools.IFTTTWebhook attribute)
urls (langchain.document_loaders.PlaywrightURLLoader attribute)
(langchain.document_loaders.SeleniumURLLoader attribute)
use() (langchain.vectorstores.AwaDB method)
use_mlock (langchain.embeddings.LlamaCppEmbeddings attribute)
(langchain.llms.GPT4All attribute)
(langchain.llms.LlamaCpp attribute)
use_mmap (langchain.llms.LlamaCpp attribute)
use_multiplicative_presence_penalty (langchain.llms.AlephAlpha attribute)
use_original_query (langchain.retrievers.SelfQueryRetriever attribute)
use_query_checker (langchain.chains.SQLDatabaseChain attribute)
user_id (langchain.llms.Clarifai attribute)
username (langchain.vectorstores.AlibabaCloudOpenSearchSettings attribute)
(langchain.vectorstores.ClickhouseSettings attribute)
(langchain.vectorstores.MyScaleSettings attribute)
V
validate_channel_or_videoIds_is_set() (langchain.document_loaders.GoogleApiClient class method)
(langchain.document_loaders.GoogleApiYoutubeLoader class method)
validate_environment() (langchain.utilities.SceneXplainAPIWrapper class method)
validate_init_args() (langchain.document_loaders.ConfluenceLoader static method)
validate_template (langchain.prompts.FewShotPromptTemplate attribute)
(langchain.prompts.FewShotPromptWithTemplates attribute)
(langchain.prompts.PromptTemplate attribute)
variable_name (langchain.prompts.MessagesPlaceholder attribute)
Vectara (class in langchain.vectorstores)
vector_field (langchain.vectorstores.SingleStoreDB attribute)
vector_search() (langchain.vectorstores.AzureSearch method)
vector_search_with_score() (langchain.vectorstores.AzureSearch method)
vectorizer (langchain.retrievers.TFIDFRetriever attribute)
VectorStore (class in langchain.vectorstores) | https://api.python.langchain.com/en/stable/genindex.html |
a1409c1b5e0f-161 | VectorStore (class in langchain.vectorstores)
vectorstore (langchain.agents.agent_toolkits.VectorStoreInfo attribute)
(langchain.chains.ChatVectorDBChain attribute)
(langchain.chains.VectorDBQA attribute)
(langchain.chains.VectorDBQAWithSourcesChain attribute)
(langchain.prompts.example_selector.SemanticSimilarityExampleSelector attribute)
(langchain.prompts.MaxMarginalRelevanceExampleSelector attribute)
(langchain.prompts.SemanticSimilarityExampleSelector attribute)
(langchain.retrievers.SelfQueryRetriever attribute)
(langchain.retrievers.TimeWeightedVectorStoreRetriever attribute)
vectorstore_info (langchain.agents.agent_toolkits.VectorStoreToolkit attribute)
vectorstores (langchain.agents.agent_toolkits.VectorStoreRouterToolkit attribute)
verbose (langchain.chains.AnalyzeDocumentChain attribute)
(langchain.chains.APIChain attribute)
(langchain.chains.ChatVectorDBChain attribute)
(langchain.chains.ConstitutionalChain attribute)
(langchain.chains.ConversationalRetrievalChain attribute)
(langchain.chains.ConversationChain attribute)
(langchain.chains.FlareChain attribute)
(langchain.chains.GraphCypherQAChain attribute)
(langchain.chains.GraphQAChain attribute)
(langchain.chains.HypotheticalDocumentEmbedder attribute)
(langchain.chains.KuzuQAChain attribute)
(langchain.chains.LLMBashChain attribute)
(langchain.chains.LLMChain attribute)
(langchain.chains.LLMCheckerChain attribute)
(langchain.chains.LLMMathChain attribute)
(langchain.chains.LLMRequestsChain attribute)
(langchain.chains.LLMRouterChain attribute)
(langchain.chains.LLMSummarizationCheckerChain attribute)
(langchain.chains.MapReduceChain attribute)
(langchain.chains.MapReduceDocumentsChain attribute) | https://api.python.langchain.com/en/stable/genindex.html |
a1409c1b5e0f-162 | (langchain.chains.MapReduceDocumentsChain attribute)
(langchain.chains.MapRerankDocumentsChain attribute)
(langchain.chains.MultiPromptChain attribute)
(langchain.chains.MultiRetrievalQAChain attribute)
(langchain.chains.MultiRouteChain attribute)
(langchain.chains.NatBotChain attribute)
(langchain.chains.NebulaGraphQAChain attribute)
(langchain.chains.OpenAIModerationChain attribute)
(langchain.chains.OpenAPIEndpointChain attribute)
(langchain.chains.PALChain attribute)
(langchain.chains.QAGenerationChain attribute)
(langchain.chains.QAWithSourcesChain attribute)
(langchain.chains.RefineDocumentsChain attribute)
(langchain.chains.RetrievalQA attribute)
(langchain.chains.RetrievalQAWithSourcesChain attribute)
(langchain.chains.RouterChain attribute)
(langchain.chains.SequentialChain attribute)
(langchain.chains.SimpleSequentialChain attribute)
(langchain.chains.SQLDatabaseChain attribute)
(langchain.chains.SQLDatabaseSequentialChain attribute)
(langchain.chains.StuffDocumentsChain attribute)
(langchain.chains.TransformChain attribute)
(langchain.chains.VectorDBQA attribute)
(langchain.chains.VectorDBQAWithSourcesChain attribute)
(langchain.llms.AI21 attribute)
(langchain.llms.AlephAlpha attribute)
(langchain.llms.AmazonAPIGateway attribute)
(langchain.llms.Anthropic attribute)
(langchain.llms.Anyscale attribute)
(langchain.llms.Aviary attribute)
(langchain.llms.AzureMLOnlineEndpoint attribute)
(langchain.llms.AzureOpenAI attribute)
(langchain.llms.Banana attribute)
(langchain.llms.Baseten attribute)
(langchain.llms.Beam attribute)
(langchain.llms.Bedrock attribute)
(langchain.llms.CerebriumAI attribute) | https://api.python.langchain.com/en/stable/genindex.html |
a1409c1b5e0f-163 | (langchain.llms.CerebriumAI attribute)
(langchain.llms.Clarifai attribute)
(langchain.llms.Cohere attribute)
(langchain.llms.CTransformers attribute)
(langchain.llms.Databricks attribute)
(langchain.llms.DeepInfra attribute)
(langchain.llms.FakeListLLM attribute)
(langchain.llms.ForefrontAI attribute)
(langchain.llms.GooglePalm attribute)
(langchain.llms.GooseAI attribute)
(langchain.llms.GPT4All attribute)
(langchain.llms.HuggingFaceEndpoint attribute)
(langchain.llms.HuggingFaceHub attribute)
(langchain.llms.HuggingFacePipeline attribute)
(langchain.llms.HuggingFaceTextGenInference attribute)
(langchain.llms.HumanInputLLM attribute)
(langchain.llms.LlamaCpp attribute)
(langchain.llms.ManifestWrapper attribute)
(langchain.llms.Modal attribute)
(langchain.llms.MosaicML attribute)
(langchain.llms.NLPCloud attribute)
(langchain.llms.OctoAIEndpoint attribute)
(langchain.llms.OpenAI attribute)
(langchain.llms.OpenAIChat attribute)
(langchain.llms.OpenLLM attribute)
(langchain.llms.OpenLM attribute)
(langchain.llms.Petals attribute)
(langchain.llms.PipelineAI attribute)
(langchain.llms.PredictionGuard attribute)
(langchain.llms.Replicate attribute)
(langchain.llms.RWKV attribute)
(langchain.llms.SagemakerEndpoint attribute)
(langchain.llms.SelfHostedHuggingFaceLLM attribute)
(langchain.llms.SelfHostedPipeline attribute)
(langchain.llms.StochasticAI attribute)
(langchain.llms.TextGen attribute)
(langchain.llms.VertexAI attribute)
(langchain.llms.Writer attribute) | https://api.python.langchain.com/en/stable/genindex.html |
a1409c1b5e0f-164 | (langchain.llms.VertexAI attribute)
(langchain.llms.Writer attribute)
(langchain.retrievers.SelfQueryRetriever attribute)
(langchain.tools.BaseTool attribute)
(langchain.tools.Tool attribute)
VespaRetriever (class in langchain.retrievers)
video_ids (langchain.document_loaders.GoogleApiYoutubeLoader attribute)
visible_only (langchain.tools.ClickTool attribute)
vocab_only (langchain.embeddings.LlamaCppEmbeddings attribute)
(langchain.llms.GPT4All attribute)
(langchain.llms.LlamaCpp attribute)
W
wait_for_processing() (langchain.document_loaders.MathpixPDFLoader method)
wandb_tracing_enabled() (in module langchain.callbacks)
WandbCallbackHandler (class in langchain.callbacks)
WeatherDataLoader (class in langchain.document_loaders)
Weaviate (class in langchain.vectorstores)
WeaviateHybridSearchRetriever (class in langchain.retrievers)
WeaviateHybridSearchRetriever.Config (class in langchain.retrievers)
web_path (langchain.document_loaders.WebBaseLoader property)
web_paths (langchain.document_loaders.WebBaseLoader attribute)
WebBaseLoader (class in langchain.document_loaders)
WhatsAppChatLoader (class in langchain.document_loaders)
WhyLabsCallbackHandler (class in langchain.callbacks)
WikipediaLoader (class in langchain.document_loaders)
with_traceback() (langchain.schema.OutputParserException method)
wolfram_alpha_appid (langchain.utilities.WolframAlphaAPIWrapper attribute)
wrapper (langchain.tools.SearxSearchResults attribute)
(langchain.tools.SearxSearchRun attribute)
writer_api_key (langchain.llms.Writer attribute)
writer_org_id (langchain.llms.Writer attribute)
Y | https://api.python.langchain.com/en/stable/genindex.html |
a1409c1b5e0f-165 | writer_org_id (langchain.llms.Writer attribute)
Y
yield_blobs() (langchain.document_loaders.BlobLoader method)
(langchain.document_loaders.FileSystemBlobLoader method)
(langchain.document_loaders.YoutubeAudioLoader method)
YoutubeAudioLoader (class in langchain.document_loaders)
YoutubeLoader (class in langchain.document_loaders)
Z
zapier_description (langchain.tools.ZapierNLARunAction attribute)
zapier_nla_api_base (langchain.utilities.ZapierNLAWrapper attribute)
zapier_nla_api_key (langchain.utilities.ZapierNLAWrapper attribute)
zapier_nla_oauth_access_token (langchain.utilities.ZapierNLAWrapper attribute)
zep_messages (langchain.memory.ZepChatMessageHistory property)
zep_summary (langchain.memory.ZepChatMessageHistory property)
ZepChatMessageHistory (class in langchain.memory)
ZepRetriever (class in langchain.retrievers)
ZERO_SHOT_REACT_DESCRIPTION (langchain.agents.AgentType attribute)
Zilliz (class in langchain.vectorstores)
ZillizRetriever (class in langchain.retrievers) | https://api.python.langchain.com/en/stable/genindex.html |
17154dc0c4bf-0 | Agentsο
Reference guide for Agents and associated abstractions.
Agents
Tools
Agent Toolkits | https://api.python.langchain.com/en/stable/agents.html |
c73826eb6edd-0 | Memoryο
class langchain.memory.CassandraChatMessageHistory(contact_points, session_id, port=9042, username='cassandra', password='cassandra', keyspace_name='chat_history', table_name='message_store')[source]ο
Bases: langchain.schema.BaseChatMessageHistory
Chat message history that stores history in Cassandra.
Parameters
contact_points (List[str]) β list of ips to connect to Cassandra cluster
session_id (str) β arbitrary key that is used to store the messages
of a single chat session.
port (int) β port to connect to Cassandra cluster
username (str) β username to connect to Cassandra cluster
password (str) β password to connect to Cassandra cluster
keyspace_name (str) β name of the keyspace to use
table_name (str) β name of the table to use
property messages: List[langchain.schema.BaseMessage]ο
Retrieve the messages from Cassandra
add_message(message)[source]ο
Append the message to the record in Cassandra
Parameters
message (langchain.schema.BaseMessage) β
Return type
None
clear()[source]ο
Clear session memory from Cassandra
Return type
None
class langchain.memory.ChatMessageHistory(*, messages=[])[source]ο
Bases: langchain.schema.BaseChatMessageHistory, pydantic.main.BaseModel
Parameters
messages (List[langchain.schema.BaseMessage]) β
Return type
None
attribute messages: List[langchain.schema.BaseMessage] = []ο
add_message(message)[source]ο
Add a self-created message to the store
Parameters
message (langchain.schema.BaseMessage) β
Return type
None
clear()[source]ο
Remove all messages from the store
Return type
None
class langchain.memory.CombinedMemory(*, memories)[source]ο | https://api.python.langchain.com/en/stable/modules/memory.html |
c73826eb6edd-1 | None
class langchain.memory.CombinedMemory(*, memories)[source]ο
Bases: langchain.schema.BaseMemory
Class for combining multiple memoriesβ data together.
Parameters
memories (List[langchain.schema.BaseMemory]) β
Return type
None
attribute memories: List[langchain.schema.BaseMemory] [Required]ο
For tracking all the memories that should be accessed.
clear()[source]ο
Clear context from this session for every memory.
Return type
None
load_memory_variables(inputs)[source]ο
Load all vars from sub-memories.
Parameters
inputs (Dict[str, Any]) β
Return type
Dict[str, str]
save_context(inputs, outputs)[source]ο
Save context from this session for every memory.
Parameters
inputs (Dict[str, Any]) β
outputs (Dict[str, str]) β
Return type
None
property memory_variables: List[str]ο
All the memory variables that this instance provides.
class langchain.memory.ConversationBufferMemory(*, chat_memory=None, output_key=None, input_key=None, return_messages=False, human_prefix='Human', ai_prefix='AI', memory_key='history')[source]ο
Bases: langchain.memory.chat_memory.BaseChatMemory
Buffer for storing conversation memory.
Parameters
chat_memory (langchain.schema.BaseChatMessageHistory) β
output_key (Optional[str]) β
input_key (Optional[str]) β
return_messages (bool) β
human_prefix (str) β
ai_prefix (str) β
memory_key (str) β
Return type
None
attribute ai_prefix: str = 'AI'ο
attribute human_prefix: str = 'Human'ο
load_memory_variables(inputs)[source]ο
Return history buffer.
Parameters | https://api.python.langchain.com/en/stable/modules/memory.html |
c73826eb6edd-2 | load_memory_variables(inputs)[source]ο
Return history buffer.
Parameters
inputs (Dict[str, Any]) β
Return type
Dict[str, Any]
property buffer: Anyο
String buffer of memory.
class langchain.memory.ConversationBufferWindowMemory(*, chat_memory=None, output_key=None, input_key=None, return_messages=False, human_prefix='Human', ai_prefix='AI', memory_key='history', k=5)[source]ο
Bases: langchain.memory.chat_memory.BaseChatMemory
Buffer for storing conversation memory.
Parameters
chat_memory (langchain.schema.BaseChatMessageHistory) β
output_key (Optional[str]) β
input_key (Optional[str]) β
return_messages (bool) β
human_prefix (str) β
ai_prefix (str) β
memory_key (str) β
k (int) β
Return type
None
attribute ai_prefix: str = 'AI'ο
attribute human_prefix: str = 'Human'ο
attribute k: int = 5ο
load_memory_variables(inputs)[source]ο
Return history buffer.
Parameters
inputs (Dict[str, Any]) β
Return type
Dict[str, str]
property buffer: List[langchain.schema.BaseMessage]ο
String buffer of memory. | https://api.python.langchain.com/en/stable/modules/memory.html |
c73826eb6edd-3 | class langchain.memory.ConversationEntityMemory(*, chat_memory=None, output_key=None, input_key=None, return_messages=False, human_prefix='Human', ai_prefix='AI', llm, entity_extraction_prompt=PromptTemplate(input_variables=['history', 'input'], output_parser=None, partial_variables={}, template='You are an AI assistant reading the transcript of a conversation between an AI and a human. Extract all of the proper nouns from the last line of conversation. As a guideline, a proper noun is generally capitalized. You should definitely extract all names and places.\n\nThe conversation history is provided just in case of a coreference (e.g. "What do you know about him" where "him" is defined in a previous line) -- ignore items mentioned there that are not in the last line.\n\nReturn the output as a single comma-separated list, or NONE if there is nothing of note to return (e.g. the user is just issuing a greeting or having a simple conversation).\n\nEXAMPLE\nConversation history:\nPerson #1: how\'s it going today?\nAI: "It\'s going great! How about you?"\nPerson #1: good! busy working on Langchain. lots to do.\nAI: "That sounds like a lot of work! What kind of things are you doing to make Langchain better?"\nLast line:\nPerson #1: i\'m trying to improve Langchain\'s interfaces, the UX, its integrations with various products the user might want ... a lot of stuff.\nOutput: Langchain\nEND OF EXAMPLE\n\nEXAMPLE\nConversation history:\nPerson #1: how\'s it going today?\nAI: "It\'s going great! How about you?"\nPerson #1: good! busy working on Langchain. lots to do.\nAI: "That sounds like a lot of work! What kind of things are you doing to | https://api.python.langchain.com/en/stable/modules/memory.html |
c73826eb6edd-4 | "That sounds like a lot of work! What kind of things are you doing to make Langchain better?"\nLast line:\nPerson #1: i\'m trying to improve Langchain\'s interfaces, the UX, its integrations with various products the user might want ... a lot of stuff. I\'m working with Person #2.\nOutput: Langchain, Person #2\nEND OF EXAMPLE\n\nConversation history (for reference only):\n{history}\nLast line of conversation (for extraction):\nHuman: {input}\n\nOutput:', template_format='f-string', validate_template=True), entity_summarization_prompt=PromptTemplate(input_variables=['entity', 'summary', 'history', 'input'], output_parser=None, partial_variables={}, template='You are an AI assistant helping a human keep track of facts about relevant people, places, and concepts in their life. Update the summary of the provided entity in the "Entity" section based on the last line of your conversation with the human. If you are writing the summary for the first time, return a single sentence.\nThe update should only include facts that are relayed in the last line of conversation about the provided entity, and should only contain facts about the provided entity.\n\nIf there is no new information about the provided entity or the information is not worth noting (not an important or relevant fact to remember long-term), return the existing summary unchanged.\n\nFull conversation history (for context):\n{history}\n\nEntity to summarize:\n{entity}\n\nExisting summary of {entity}:\n{summary}\n\nLast line of conversation:\nHuman: {input}\nUpdated summary:', template_format='f-string', validate_template=True), entity_cache=[], k=3, chat_history_key='history', entity_store=None)[source]ο | https://api.python.langchain.com/en/stable/modules/memory.html |
c73826eb6edd-5 | Bases: langchain.memory.chat_memory.BaseChatMemory
Entity extractor & summarizer memory.
Extracts named entities from the recent chat history and generates summaries.
With a swapable entity store, persisting entities across conversations.
Defaults to an in-memory entity store, and can be swapped out for a Redis,
SQLite, or other entity store.
Parameters
chat_memory (langchain.schema.BaseChatMessageHistory) β
output_key (Optional[str]) β
input_key (Optional[str]) β
return_messages (bool) β
human_prefix (str) β
ai_prefix (str) β
llm (langchain.base_language.BaseLanguageModel) β
entity_extraction_prompt (langchain.prompts.base.BasePromptTemplate) β
entity_summarization_prompt (langchain.prompts.base.BasePromptTemplate) β
entity_cache (List[str]) β
k (int) β
chat_history_key (str) β
entity_store (langchain.memory.entity.BaseEntityStore) β
Return type
None
attribute ai_prefix: str = 'AI'ο
attribute chat_history_key: str = 'history'ο
attribute entity_cache: List[str] = []ο | https://api.python.langchain.com/en/stable/modules/memory.html |
c73826eb6edd-6 | attribute entity_extraction_prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['history', 'input'], output_parser=None, partial_variables={}, template='You are an AI assistant reading the transcript of a conversation between an AI and a human. Extract all of the proper nouns from the last line of conversation. As a guideline, a proper noun is generally capitalized. You should definitely extract all names and places.\n\nThe conversation history is provided just in case of a coreference (e.g. "What do you know about him" where "him" is defined in a previous line) -- ignore items mentioned there that are not in the last line.\n\nReturn the output as a single comma-separated list, or NONE if there is nothing of note to return (e.g. the user is just issuing a greeting or having a simple conversation).\n\nEXAMPLE\nConversation history:\nPerson #1: how\'s it going today?\nAI: "It\'s going great! How about you?"\nPerson #1: good! busy working on Langchain. lots to do.\nAI: "That sounds like a lot of work! What kind of things are you doing to make Langchain better?"\nLast line:\nPerson #1: i\'m trying to improve Langchain\'s interfaces, the UX, its integrations with various products the user might want ... a lot of stuff.\nOutput: Langchain\nEND OF EXAMPLE\n\nEXAMPLE\nConversation history:\nPerson #1: how\'s it going today?\nAI: "It\'s going great! How about you?"\nPerson #1: good! busy working on Langchain. lots to do.\nAI: "That sounds like a lot of work! What kind of things are you doing to make Langchain better?"\nLast line:\nPerson #1: i\'m trying to improve Langchain\'s interfaces, the | https://api.python.langchain.com/en/stable/modules/memory.html |
c73826eb6edd-7 | line:\nPerson #1: i\'m trying to improve Langchain\'s interfaces, the UX, its integrations with various products the user might want ... a lot of stuff. I\'m working with Person #2.\nOutput: Langchain, Person #2\nEND OF EXAMPLE\n\nConversation history (for reference only):\n{history}\nLast line of conversation (for extraction):\nHuman: {input}\n\nOutput:', template_format='f-string', validate_template=True)ο | https://api.python.langchain.com/en/stable/modules/memory.html |
c73826eb6edd-8 | attribute entity_store: langchain.memory.entity.BaseEntityStore [Optional]ο
attribute entity_summarization_prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['entity', 'summary', 'history', 'input'], output_parser=None, partial_variables={}, template='You are an AI assistant helping a human keep track of facts about relevant people, places, and concepts in their life. Update the summary of the provided entity in the "Entity" section based on the last line of your conversation with the human. If you are writing the summary for the first time, return a single sentence.\nThe update should only include facts that are relayed in the last line of conversation about the provided entity, and should only contain facts about the provided entity.\n\nIf there is no new information about the provided entity or the information is not worth noting (not an important or relevant fact to remember long-term), return the existing summary unchanged.\n\nFull conversation history (for context):\n{history}\n\nEntity to summarize:\n{entity}\n\nExisting summary of {entity}:\n{summary}\n\nLast line of conversation:\nHuman: {input}\nUpdated summary:', template_format='f-string', validate_template=True)ο
attribute human_prefix: str = 'Human'ο
attribute k: int = 3ο
attribute llm: langchain.base_language.BaseLanguageModel [Required]ο
clear()[source]ο
Clear memory contents.
Return type
None
load_memory_variables(inputs)[source]ο
Returns chat history and all generated entities with summaries if available,
and updates or clears the recent entity cache.
New entity name can be found when calling this method, before the entity
summaries are generated, so the entity cache values may be empty if no entity
descriptions are generated yet.
Parameters
inputs (Dict[str, Any]) β
Return type | https://api.python.langchain.com/en/stable/modules/memory.html |
c73826eb6edd-9 | Parameters
inputs (Dict[str, Any]) β
Return type
Dict[str, Any]
save_context(inputs, outputs)[source]ο
Save context from this conversation history to the entity store.
Generates a summary for each entity in the entity cache by prompting
the model, and saves these summaries to the entity store.
Parameters
inputs (Dict[str, Any]) β
outputs (Dict[str, str]) β
Return type
None
property buffer: List[langchain.schema.BaseMessage]ο
Access chat memory messages. | https://api.python.langchain.com/en/stable/modules/memory.html |
c73826eb6edd-10 | class langchain.memory.ConversationKGMemory(*, chat_memory=None, output_key=None, input_key=None, return_messages=False, k=2, human_prefix='Human', ai_prefix='AI', kg=None, knowledge_extraction_prompt=PromptTemplate(input_variables=['history', 'input'], output_parser=None, partial_variables={}, template="You are a networked intelligence helping a human track knowledge triples about all relevant people, things, concepts, etc. and integrating them with your knowledge stored within your weights as well as that stored in a knowledge graph. Extract all of the knowledge triples from the last line of conversation. A knowledge triple is a clause that contains a subject, a predicate, and an object. The subject is the entity being described, the predicate is the property of the subject that is being described, and the object is the value of the property.\n\nEXAMPLE\nConversation history:\nPerson #1: Did you hear aliens landed in Area 51?\nAI: No, I didn't hear that. What do you know about Area 51?\nPerson #1: It's a secret military base in Nevada.\nAI: What do you know about Nevada?\nLast line of conversation:\nPerson #1: It's a state in the US. It's also the number 1 producer of gold in the US.\n\nOutput: (Nevada, is a, state)<|>(Nevada, is in, US)<|>(Nevada, is the number 1 producer of, gold)\nEND OF EXAMPLE\n\nEXAMPLE\nConversation history:\nPerson #1: Hello.\nAI: Hi! How are you?\nPerson #1: I'm good. How are you?\nAI: I'm good too.\nLast line of conversation:\nPerson #1: I'm going to the store.\n\nOutput: NONE\nEND OF EXAMPLE\n\nEXAMPLE\nConversation history:\nPerson #1: What | https://api.python.langchain.com/en/stable/modules/memory.html |
c73826eb6edd-11 | NONE\nEND OF EXAMPLE\n\nEXAMPLE\nConversation history:\nPerson #1: What do you know about Descartes?\nAI: Descartes was a French philosopher, mathematician, and scientist who lived in the 17th century.\nPerson #1: The Descartes I'm referring to is a standup comedian and interior designer from Montreal.\nAI: Oh yes, He is a comedian and an interior designer. He has been in the industry for 30 years. His favorite food is baked bean pie.\nLast line of conversation:\nPerson #1: Oh huh. I know Descartes likes to drive antique scooters and play the mandolin.\nOutput: (Descartes, likes to drive, antique scooters)<|>(Descartes, plays, mandolin)\nEND OF EXAMPLE\n\nConversation history (for reference only):\n{history}\nLast line of conversation (for extraction):\nHuman: {input}\n\nOutput:", template_format='f-string', validate_template=True), entity_extraction_prompt=PromptTemplate(input_variables=['history', 'input'], output_parser=None, partial_variables={}, template='You are an AI assistant reading the transcript of a conversation between an AI and a human. Extract all of the proper nouns from the last line of conversation. As a guideline, a proper noun is generally capitalized. You should definitely extract all names and places.\n\nThe conversation history is provided just in case of a coreference (e.g. "What do you know about him" where "him" is defined in a previous line) -- ignore items mentioned there that are not in the last line.\n\nReturn the output as a single comma-separated list, or NONE if there is nothing of note to return (e.g. the user is just issuing a greeting or having a simple conversation).\n\nEXAMPLE\nConversation history:\nPerson #1: how\'s it going today?\nAI: | https://api.python.langchain.com/en/stable/modules/memory.html |
c73826eb6edd-12 | history:\nPerson #1: how\'s it going today?\nAI: "It\'s going great! How about you?"\nPerson #1: good! busy working on Langchain. lots to do.\nAI: "That sounds like a lot of work! What kind of things are you doing to make Langchain better?"\nLast line:\nPerson #1: i\'m trying to improve Langchain\'s interfaces, the UX, its integrations with various products the user might want ... a lot of stuff.\nOutput: Langchain\nEND OF EXAMPLE\n\nEXAMPLE\nConversation history:\nPerson #1: how\'s it going today?\nAI: "It\'s going great! How about you?"\nPerson #1: good! busy working on Langchain. lots to do.\nAI: "That sounds like a lot of work! What kind of things are you doing to make Langchain better?"\nLast line:\nPerson #1: i\'m trying to improve Langchain\'s interfaces, the UX, its integrations with various products the user might want ... a lot of stuff. I\'m working with Person #2.\nOutput: Langchain, Person #2\nEND OF EXAMPLE\n\nConversation history (for reference only):\n{history}\nLast line of conversation (for extraction):\nHuman: {input}\n\nOutput:', template_format='f-string', validate_template=True), llm, summary_message_cls=<class 'langchain.schema.SystemMessage'>, memory_key='history')[source]ο | https://api.python.langchain.com/en/stable/modules/memory.html |
c73826eb6edd-13 | Bases: langchain.memory.chat_memory.BaseChatMemory
Knowledge graph memory for storing conversation memory.
Integrates with external knowledge graph to store and retrieve
information about knowledge triples in the conversation.
Parameters
chat_memory (langchain.schema.BaseChatMessageHistory) β
output_key (Optional[str]) β
input_key (Optional[str]) β
return_messages (bool) β
k (int) β
human_prefix (str) β
ai_prefix (str) β
kg (langchain.graphs.networkx_graph.NetworkxEntityGraph) β
knowledge_extraction_prompt (langchain.prompts.base.BasePromptTemplate) β
entity_extraction_prompt (langchain.prompts.base.BasePromptTemplate) β
llm (langchain.base_language.BaseLanguageModel) β
summary_message_cls (Type[langchain.schema.BaseMessage]) β
memory_key (str) β
Return type
None
attribute ai_prefix: str = 'AI'ο | https://api.python.langchain.com/en/stable/modules/memory.html |
c73826eb6edd-14 | attribute entity_extraction_prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['history', 'input'], output_parser=None, partial_variables={}, template='You are an AI assistant reading the transcript of a conversation between an AI and a human. Extract all of the proper nouns from the last line of conversation. As a guideline, a proper noun is generally capitalized. You should definitely extract all names and places.\n\nThe conversation history is provided just in case of a coreference (e.g. "What do you know about him" where "him" is defined in a previous line) -- ignore items mentioned there that are not in the last line.\n\nReturn the output as a single comma-separated list, or NONE if there is nothing of note to return (e.g. the user is just issuing a greeting or having a simple conversation).\n\nEXAMPLE\nConversation history:\nPerson #1: how\'s it going today?\nAI: "It\'s going great! How about you?"\nPerson #1: good! busy working on Langchain. lots to do.\nAI: "That sounds like a lot of work! What kind of things are you doing to make Langchain better?"\nLast line:\nPerson #1: i\'m trying to improve Langchain\'s interfaces, the UX, its integrations with various products the user might want ... a lot of stuff.\nOutput: Langchain\nEND OF EXAMPLE\n\nEXAMPLE\nConversation history:\nPerson #1: how\'s it going today?\nAI: "It\'s going great! How about you?"\nPerson #1: good! busy working on Langchain. lots to do.\nAI: "That sounds like a lot of work! What kind of things are you doing to make Langchain better?"\nLast line:\nPerson #1: i\'m trying to improve Langchain\'s interfaces, the | https://api.python.langchain.com/en/stable/modules/memory.html |
c73826eb6edd-15 | line:\nPerson #1: i\'m trying to improve Langchain\'s interfaces, the UX, its integrations with various products the user might want ... a lot of stuff. I\'m working with Person #2.\nOutput: Langchain, Person #2\nEND OF EXAMPLE\n\nConversation history (for reference only):\n{history}\nLast line of conversation (for extraction):\nHuman: {input}\n\nOutput:', template_format='f-string', validate_template=True)ο | https://api.python.langchain.com/en/stable/modules/memory.html |
c73826eb6edd-16 | attribute human_prefix: str = 'Human'ο
attribute k: int = 2ο
attribute kg: langchain.graphs.networkx_graph.NetworkxEntityGraph [Optional]ο | https://api.python.langchain.com/en/stable/modules/memory.html |
c73826eb6edd-17 | attribute knowledge_extraction_prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['history', 'input'], output_parser=None, partial_variables={}, template="You are a networked intelligence helping a human track knowledge triples about all relevant people, things, concepts, etc. and integrating them with your knowledge stored within your weights as well as that stored in a knowledge graph. Extract all of the knowledge triples from the last line of conversation. A knowledge triple is a clause that contains a subject, a predicate, and an object. The subject is the entity being described, the predicate is the property of the subject that is being described, and the object is the value of the property.\n\nEXAMPLE\nConversation history:\nPerson #1: Did you hear aliens landed in Area 51?\nAI: No, I didn't hear that. What do you know about Area 51?\nPerson #1: It's a secret military base in Nevada.\nAI: What do you know about Nevada?\nLast line of conversation:\nPerson #1: It's a state in the US. It's also the number 1 producer of gold in the US.\n\nOutput: (Nevada, is a, state)<|>(Nevada, is in, US)<|>(Nevada, is the number 1 producer of, gold)\nEND OF EXAMPLE\n\nEXAMPLE\nConversation history:\nPerson #1: Hello.\nAI: Hi! How are you?\nPerson #1: I'm good. How are you?\nAI: I'm good too.\nLast line of conversation:\nPerson #1: I'm going to the store.\n\nOutput: NONE\nEND OF EXAMPLE\n\nEXAMPLE\nConversation history:\nPerson #1: What do you know about Descartes?\nAI: Descartes was a French philosopher, mathematician, and scientist who lived in the 17th | https://api.python.langchain.com/en/stable/modules/memory.html |
c73826eb6edd-18 | Descartes was a French philosopher, mathematician, and scientist who lived in the 17th century.\nPerson #1: The Descartes I'm referring to is a standup comedian and interior designer from Montreal.\nAI: Oh yes, He is a comedian and an interior designer. He has been in the industry for 30 years. His favorite food is baked bean pie.\nLast line of conversation:\nPerson #1: Oh huh. I know Descartes likes to drive antique scooters and play the mandolin.\nOutput: (Descartes, likes to drive, antique scooters)<|>(Descartes, plays, mandolin)\nEND OF EXAMPLE\n\nConversation history (for reference only):\n{history}\nLast line of conversation (for extraction):\nHuman: {input}\n\nOutput:", template_format='f-string', validate_template=True)ο | https://api.python.langchain.com/en/stable/modules/memory.html |
c73826eb6edd-19 | attribute llm: langchain.base_language.BaseLanguageModel [Required]ο
attribute summary_message_cls: Type[langchain.schema.BaseMessage] = <class 'langchain.schema.SystemMessage'>ο
Number of previous utterances to include in the context.
clear()[source]ο
Clear memory contents.
Return type
None
get_current_entities(input_string)[source]ο
Parameters
input_string (str) β
Return type
List[str]
get_knowledge_triplets(input_string)[source]ο
Parameters
input_string (str) β
Return type
List[langchain.graphs.networkx_graph.KnowledgeTriple]
load_memory_variables(inputs)[source]ο
Return history buffer.
Parameters
inputs (Dict[str, Any]) β
Return type
Dict[str, Any]
save_context(inputs, outputs)[source]ο
Save context from this conversation to buffer.
Parameters
inputs (Dict[str, Any]) β
outputs (Dict[str, str]) β
Return type
None
class langchain.memory.ConversationStringBufferMemory(*, human_prefix='Human', ai_prefix='AI', buffer='', output_key=None, input_key=None, memory_key='history')[source]ο
Bases: langchain.schema.BaseMemory
Buffer for storing conversation memory.
Parameters
human_prefix (str) β
ai_prefix (str) β
buffer (str) β
output_key (Optional[str]) β
input_key (Optional[str]) β
memory_key (str) β
Return type
None
attribute ai_prefix: str = 'AI'ο
Prefix to use for AI generated responses.
attribute buffer: str = ''ο
attribute human_prefix: str = 'Human'ο
attribute input_key: Optional[str] = Noneο | https://api.python.langchain.com/en/stable/modules/memory.html |
c73826eb6edd-20 | attribute input_key: Optional[str] = Noneο
attribute output_key: Optional[str] = Noneο
clear()[source]ο
Clear memory contents.
Return type
None
load_memory_variables(inputs)[source]ο
Return history buffer.
Parameters
inputs (Dict[str, Any]) β
Return type
Dict[str, str]
save_context(inputs, outputs)[source]ο
Save context from this conversation to buffer.
Parameters
inputs (Dict[str, Any]) β
outputs (Dict[str, str]) β
Return type
None
property memory_variables: List[str]ο
Will always return list of memory variables.
:meta private: | https://api.python.langchain.com/en/stable/modules/memory.html |
c73826eb6edd-21 | Will always return list of memory variables.
:meta private:
class langchain.memory.ConversationSummaryBufferMemory(*, human_prefix='Human', ai_prefix='AI', llm, prompt=PromptTemplate(input_variables=['summary', 'new_lines'], output_parser=None, partial_variables={}, template='Progressively summarize the lines of conversation provided, adding onto the previous summary returning a new summary.\n\nEXAMPLE\nCurrent summary:\nThe human asks what the AI thinks of artificial intelligence. The AI thinks artificial intelligence is a force for good.\n\nNew lines of conversation:\nHuman: Why do you think artificial intelligence is a force for good?\nAI: Because artificial intelligence will help humans reach their full potential.\n\nNew summary:\nThe human asks what the AI thinks of artificial intelligence. The AI thinks artificial intelligence is a force for good because it will help humans reach their full potential.\nEND OF EXAMPLE\n\nCurrent summary:\n{summary}\n\nNew lines of conversation:\n{new_lines}\n\nNew summary:', template_format='f-string', validate_template=True), summary_message_cls=<class 'langchain.schema.SystemMessage'>, chat_memory=None, output_key=None, input_key=None, return_messages=False, max_token_limit=2000, moving_summary_buffer='', memory_key='history')[source]ο
Bases: langchain.memory.chat_memory.BaseChatMemory, langchain.memory.summary.SummarizerMixin
Buffer with summarizer for storing conversation memory.
Parameters
human_prefix (str) β
ai_prefix (str) β
llm (langchain.base_language.BaseLanguageModel) β
prompt (langchain.prompts.base.BasePromptTemplate) β
summary_message_cls (Type[langchain.schema.BaseMessage]) β
chat_memory (langchain.schema.BaseChatMessageHistory) β
output_key (Optional[str]) β
input_key (Optional[str]) β | https://api.python.langchain.com/en/stable/modules/memory.html |
c73826eb6edd-22 | output_key (Optional[str]) β
input_key (Optional[str]) β
return_messages (bool) β
max_token_limit (int) β
moving_summary_buffer (str) β
memory_key (str) β
Return type
None
attribute max_token_limit: int = 2000ο
attribute memory_key: str = 'history'ο
attribute moving_summary_buffer: str = ''ο
clear()[source]ο
Clear memory contents.
Return type
None
load_memory_variables(inputs)[source]ο
Return history buffer.
Parameters
inputs (Dict[str, Any]) β
Return type
Dict[str, Any]
prune()[source]ο
Prune buffer if it exceeds max token limit
Return type
None
save_context(inputs, outputs)[source]ο
Save context from this conversation to buffer.
Parameters
inputs (Dict[str, Any]) β
outputs (Dict[str, str]) β
Return type
None
property buffer: List[langchain.schema.BaseMessage]ο | https://api.python.langchain.com/en/stable/modules/memory.html |
c73826eb6edd-23 | Return type
None
property buffer: List[langchain.schema.BaseMessage]ο
class langchain.memory.ConversationSummaryMemory(*, human_prefix='Human', ai_prefix='AI', llm, prompt=PromptTemplate(input_variables=['summary', 'new_lines'], output_parser=None, partial_variables={}, template='Progressively summarize the lines of conversation provided, adding onto the previous summary returning a new summary.\n\nEXAMPLE\nCurrent summary:\nThe human asks what the AI thinks of artificial intelligence. The AI thinks artificial intelligence is a force for good.\n\nNew lines of conversation:\nHuman: Why do you think artificial intelligence is a force for good?\nAI: Because artificial intelligence will help humans reach their full potential.\n\nNew summary:\nThe human asks what the AI thinks of artificial intelligence. The AI thinks artificial intelligence is a force for good because it will help humans reach their full potential.\nEND OF EXAMPLE\n\nCurrent summary:\n{summary}\n\nNew lines of conversation:\n{new_lines}\n\nNew summary:', template_format='f-string', validate_template=True), summary_message_cls=<class 'langchain.schema.SystemMessage'>, chat_memory=None, output_key=None, input_key=None, return_messages=False, buffer='', memory_key='history')[source]ο
Bases: langchain.memory.chat_memory.BaseChatMemory, langchain.memory.summary.SummarizerMixin
Conversation summarizer to memory.
Parameters
human_prefix (str) β
ai_prefix (str) β
llm (langchain.base_language.BaseLanguageModel) β
prompt (langchain.prompts.base.BasePromptTemplate) β
summary_message_cls (Type[langchain.schema.BaseMessage]) β
chat_memory (langchain.schema.BaseChatMessageHistory) β
output_key (Optional[str]) β
input_key (Optional[str]) β
return_messages (bool) β | https://api.python.langchain.com/en/stable/modules/memory.html |
c73826eb6edd-24 | input_key (Optional[str]) β
return_messages (bool) β
buffer (str) β
memory_key (str) β
Return type
None
attribute buffer: str = ''ο
clear()[source]ο
Clear memory contents.
Return type
None
classmethod from_messages(llm, chat_memory, *, summarize_step=2, **kwargs)[source]ο
Parameters
llm (langchain.base_language.BaseLanguageModel) β
chat_memory (langchain.schema.BaseChatMessageHistory) β
summarize_step (int) β
kwargs (Any) β
Return type
langchain.memory.summary.ConversationSummaryMemory
load_memory_variables(inputs)[source]ο
Return history buffer.
Parameters
inputs (Dict[str, Any]) β
Return type
Dict[str, Any]
save_context(inputs, outputs)[source]ο
Save context from this conversation to buffer.
Parameters
inputs (Dict[str, Any]) β
outputs (Dict[str, str]) β
Return type
None
class langchain.memory.ConversationTokenBufferMemory(*, chat_memory=None, output_key=None, input_key=None, return_messages=False, human_prefix='Human', ai_prefix='AI', llm, memory_key='history', max_token_limit=2000)[source]ο
Bases: langchain.memory.chat_memory.BaseChatMemory
Buffer for storing conversation memory.
Parameters
chat_memory (langchain.schema.BaseChatMessageHistory) β
output_key (Optional[str]) β
input_key (Optional[str]) β
return_messages (bool) β
human_prefix (str) β
ai_prefix (str) β
llm (langchain.base_language.BaseLanguageModel) β
memory_key (str) β
max_token_limit (int) β
Return type
None | https://api.python.langchain.com/en/stable/modules/memory.html |
c73826eb6edd-25 | max_token_limit (int) β
Return type
None
attribute ai_prefix: str = 'AI'ο
attribute human_prefix: str = 'Human'ο
attribute llm: langchain.base_language.BaseLanguageModel [Required]ο
attribute max_token_limit: int = 2000ο
attribute memory_key: str = 'history'ο
load_memory_variables(inputs)[source]ο
Return history buffer.
Parameters
inputs (Dict[str, Any]) β
Return type
Dict[str, Any]
save_context(inputs, outputs)[source]ο
Save context from this conversation to buffer. Pruned.
Parameters
inputs (Dict[str, Any]) β
outputs (Dict[str, str]) β
Return type
None
property buffer: List[langchain.schema.BaseMessage]ο
String buffer of memory.
class langchain.memory.CosmosDBChatMessageHistory(cosmos_endpoint, cosmos_database, cosmos_container, session_id, user_id, credential=None, connection_string=None, ttl=None, cosmos_client_kwargs=None)[source]ο
Bases: langchain.schema.BaseChatMessageHistory
Chat history backed by Azure CosmosDB.
Parameters
cosmos_endpoint (str) β
cosmos_database (str) β
cosmos_container (str) β
session_id (str) β
user_id (str) β
credential (Any) β
connection_string (Optional[str]) β
ttl (Optional[int]) β
cosmos_client_kwargs (Optional[dict]) β
prepare_cosmos()[source]ο
Prepare the CosmosDB client.
Use this function or the context manager to make sure your database is ready.
Return type
None
load_messages()[source]ο
Retrieve the messages from Cosmos
Return type
None
add_message(message)[source]ο | https://api.python.langchain.com/en/stable/modules/memory.html |
c73826eb6edd-26 | Retrieve the messages from Cosmos
Return type
None
add_message(message)[source]ο
Add a self-created message to the store
Parameters
message (langchain.schema.BaseMessage) β
Return type
None
upsert_messages()[source]ο
Update the cosmosdb item.
Return type
None
clear()[source]ο
Clear session memory from this memory and cosmos.
Return type
None
class langchain.memory.DynamoDBChatMessageHistory(table_name, session_id, endpoint_url=None)[source]ο
Bases: langchain.schema.BaseChatMessageHistory
Chat message history that stores history in AWS DynamoDB.
This class expects that a DynamoDB table with name table_name
and a partition Key of SessionId is present.
Parameters
table_name (str) β name of the DynamoDB table
session_id (str) β arbitrary key that is used to store the messages
of a single chat session.
endpoint_url (Optional[str]) β URL of the AWS endpoint to connect to. This argument
is optional and useful for test purposes, like using Localstack.
If you plan to use AWS cloud service, you normally donβt have to
worry about setting the endpoint_url.
property messages: List[langchain.schema.BaseMessage]ο
Retrieve the messages from DynamoDB
add_message(message)[source]ο
Append the message to the record in DynamoDB
Parameters
message (langchain.schema.BaseMessage) β
Return type
None
clear()[source]ο
Clear session memory from DynamoDB
Return type
None
class langchain.memory.FileChatMessageHistory(file_path)[source]ο
Bases: langchain.schema.BaseChatMessageHistory
Chat message history that stores history in a local file.
Parameters
file_path (str) β path of the local file to store the messages. | https://api.python.langchain.com/en/stable/modules/memory.html |
c73826eb6edd-27 | Parameters
file_path (str) β path of the local file to store the messages.
property messages: List[langchain.schema.BaseMessage]ο
Retrieve the messages from the local file
add_message(message)[source]ο
Append the message to the record in the local file
Parameters
message (langchain.schema.BaseMessage) β
Return type
None
clear()[source]ο
Clear session memory from the local file
Return type
None
class langchain.memory.InMemoryEntityStore(*, store={})[source]ο
Bases: langchain.memory.entity.BaseEntityStore
Basic in-memory entity store.
Parameters
store (Dict[str, Optional[str]]) β
Return type
None
attribute store: Dict[str, Optional[str]] = {}ο
clear()[source]ο
Delete all entities from store.
Return type
None
delete(key)[source]ο
Delete entity value from store.
Parameters
key (str) β
Return type
None
exists(key)[source]ο
Check if entity exists in store.
Parameters
key (str) β
Return type
bool
get(key, default=None)[source]ο
Get entity value from store.
Parameters
key (str) β
default (Optional[str]) β
Return type
Optional[str]
set(key, value)[source]ο
Set entity value in store.
Parameters
key (str) β
value (Optional[str]) β
Return type
None
class langchain.memory.MomentoChatMessageHistory(session_id, cache_client, cache_name, *, key_prefix='message_store:', ttl=None, ensure_cache_exists=True)[source]ο
Bases: langchain.schema.BaseChatMessageHistory
Chat message history cache that uses Momento as a backend.
See https://gomomento.com/ | https://api.python.langchain.com/en/stable/modules/memory.html |
c73826eb6edd-28 | See https://gomomento.com/
Parameters
session_id (str) β
cache_client (momento.CacheClient) β
cache_name (str) β
key_prefix (str) β
ttl (Optional[timedelta]) β
ensure_cache_exists (bool) β
classmethod from_client_params(session_id, cache_name, ttl, *, configuration=None, auth_token=None, **kwargs)[source]ο
Construct cache from CacheClient parameters.
Parameters
session_id (str) β
cache_name (str) β
ttl (timedelta) β
configuration (Optional[momento.config.Configuration]) β
auth_token (Optional[str]) β
kwargs (Any) β
Return type
MomentoChatMessageHistory
property messages: list[langchain.schema.BaseMessage]ο
Retrieve the messages from Momento.
Raises
SdkException β Momento service or network error
Exception β Unexpected response
Returns
List of cached messages
Return type
list[BaseMessage]
add_message(message)[source]ο
Store a message in the cache.
Parameters
message (BaseMessage) β The message object to store.
Raises
SdkException β Momento service or network error.
Exception β Unexpected response.
Return type
None
clear()[source]ο
Remove the sessionβs messages from the cache.
Raises
SdkException β Momento service or network error.
Exception β Unexpected response.
Return type
None
class langchain.memory.MongoDBChatMessageHistory(connection_string, session_id, database_name='chat_history', collection_name='message_store')[source]ο
Bases: langchain.schema.BaseChatMessageHistory
Chat message history that stores history in MongoDB.
Parameters
connection_string (str) β connection string to connect to MongoDB
session_id (str) β arbitrary key that is used to store the messages | https://api.python.langchain.com/en/stable/modules/memory.html |
c73826eb6edd-29 | session_id (str) β arbitrary key that is used to store the messages
of a single chat session.
database_name (str) β name of the database to use
collection_name (str) β name of the collection to use
property messages: List[langchain.schema.BaseMessage]ο
Retrieve the messages from MongoDB
add_message(message)[source]ο
Append the message to the record in MongoDB
Parameters
message (langchain.schema.BaseMessage) β
Return type
None
clear()[source]ο
Clear session memory from MongoDB
Return type
None
class langchain.memory.MotorheadMemory(*, chat_memory=None, output_key=None, input_key=None, return_messages=False, url='https://api.getmetal.io/v1/motorhead', session_id, context=None, api_key=None, client_id=None, timeout=3000, memory_key='history')[source]ο
Bases: langchain.memory.chat_memory.BaseChatMemory
Parameters
chat_memory (langchain.schema.BaseChatMessageHistory) β
output_key (Optional[str]) β
input_key (Optional[str]) β
return_messages (bool) β
url (str) β
session_id (str) β
context (Optional[str]) β
api_key (Optional[str]) β
client_id (Optional[str]) β
timeout (int) β
memory_key (str) β
Return type
None
attribute api_key: Optional[str] = Noneο
attribute client_id: Optional[str] = Noneο
attribute context: Optional[str] = Noneο
attribute session_id: str [Required]ο
attribute url: str = 'https://api.getmetal.io/v1/motorhead'ο
delete_session()[source]ο
Delete a session
Return type
None
async init()[source]ο | https://api.python.langchain.com/en/stable/modules/memory.html |
c73826eb6edd-30 | Delete a session
Return type
None
async init()[source]ο
Return type
None
load_memory_variables(values)[source]ο
Return key-value pairs given the text input to the chain.
If None, return all memories
Parameters
values (Dict[str, Any]) β
Return type
Dict[str, Any]
save_context(inputs, outputs)[source]ο
Save context from this conversation to buffer.
Parameters
inputs (Dict[str, Any]) β
outputs (Dict[str, str]) β
Return type
None
property memory_variables: List[str]ο
Input keys this memory class will load dynamically.
class langchain.memory.PostgresChatMessageHistory(session_id, connection_string='postgresql://postgres:mypassword@localhost/chat_history', table_name='message_store')[source]ο
Bases: langchain.schema.BaseChatMessageHistory
Chat message history stored in a Postgres database.
Parameters
session_id (str) β
connection_string (str) β
table_name (str) β
property messages: List[langchain.schema.BaseMessage]ο
Retrieve the messages from PostgreSQL
add_message(message)[source]ο
Append the message to the record in PostgreSQL
Parameters
message (langchain.schema.BaseMessage) β
Return type
None
clear()[source]ο
Clear session memory from PostgreSQL
Return type
None
class langchain.memory.ReadOnlySharedMemory(*, memory)[source]ο
Bases: langchain.schema.BaseMemory
A memory wrapper that is read-only and cannot be changed.
Parameters
memory (langchain.schema.BaseMemory) β
Return type
None
attribute memory: langchain.schema.BaseMemory [Required]ο
clear()[source]ο
Nothing to clear, got a memory like a vault.
Return type
None | https://api.python.langchain.com/en/stable/modules/memory.html |
c73826eb6edd-31 | Nothing to clear, got a memory like a vault.
Return type
None
load_memory_variables(inputs)[source]ο
Load memory variables from memory.
Parameters
inputs (Dict[str, Any]) β
Return type
Dict[str, str]
save_context(inputs, outputs)[source]ο
Nothing should be saved or changed
Parameters
inputs (Dict[str, Any]) β
outputs (Dict[str, str]) β
Return type
None
property memory_variables: List[str]ο
Return memory variables.
class langchain.memory.RedisChatMessageHistory(session_id, url='redis://localhost:6379/0', key_prefix='message_store:', ttl=None)[source]ο
Bases: langchain.schema.BaseChatMessageHistory
Chat message history stored in a Redis database.
Parameters
session_id (str) β
url (str) β
key_prefix (str) β
ttl (Optional[int]) β
property key: strο
Construct the record key to use
property messages: List[langchain.schema.BaseMessage]ο
Retrieve the messages from Redis
add_message(message)[source]ο
Append the message to the record in Redis
Parameters
message (langchain.schema.BaseMessage) β
Return type
None
clear()[source]ο
Clear session memory from Redis
Return type
None
class langchain.memory.RedisEntityStore(session_id='default', url='redis://localhost:6379/0', key_prefix='memory_store', ttl=86400, recall_ttl=259200, *args, redis_client=None)[source]ο
Bases: langchain.memory.entity.BaseEntityStore
Redis-backed Entity store. Entities get a TTL of 1 day by default, and
that TTL is extended by 3 days every time the entity is read back.
Parameters | https://api.python.langchain.com/en/stable/modules/memory.html |
c73826eb6edd-32 | that TTL is extended by 3 days every time the entity is read back.
Parameters
session_id (str) β
url (str) β
key_prefix (str) β
ttl (Optional[int]) β
recall_ttl (Optional[int]) β
args (Any) β
redis_client (Any) β
Return type
None
attribute key_prefix: str = 'memory_store'ο
attribute recall_ttl: Optional[int] = 259200ο
attribute redis_client: Any = Noneο
attribute session_id: str = 'default'ο
attribute ttl: Optional[int] = 86400ο
clear()[source]ο
Delete all entities from store.
Return type
None
delete(key)[source]ο
Delete entity value from store.
Parameters
key (str) β
Return type
None
exists(key)[source]ο
Check if entity exists in store.
Parameters
key (str) β
Return type
bool
get(key, default=None)[source]ο
Get entity value from store.
Parameters
key (str) β
default (Optional[str]) β
Return type
Optional[str]
set(key, value)[source]ο
Set entity value in store.
Parameters
key (str) β
value (Optional[str]) β
Return type
None
property full_key_prefix: strο
class langchain.memory.SQLChatMessageHistory(session_id, connection_string, table_name='message_store')[source]ο
Bases: langchain.schema.BaseChatMessageHistory
Chat message history stored in an SQL database.
Parameters
session_id (str) β
connection_string (str) β
table_name (str) β
property messages: List[langchain.schema.BaseMessage]ο | https://api.python.langchain.com/en/stable/modules/memory.html |
c73826eb6edd-33 | property messages: List[langchain.schema.BaseMessage]ο
Retrieve all messages from db
add_message(message)[source]ο
Append the message to the record in db
Parameters
message (langchain.schema.BaseMessage) β
Return type
None
clear()[source]ο
Clear session memory from db
Return type
None
class langchain.memory.SQLiteEntityStore(session_id='default', db_file='entities.db', table_name='memory_store', *args)[source]ο
Bases: langchain.memory.entity.BaseEntityStore
SQLite-backed Entity store
Parameters
session_id (str) β
db_file (str) β
table_name (str) β
args (Any) β
Return type
None
attribute session_id: str = 'default'ο
attribute table_name: str = 'memory_store'ο
clear()[source]ο
Delete all entities from store.
Return type
None
delete(key)[source]ο
Delete entity value from store.
Parameters
key (str) β
Return type
None
exists(key)[source]ο
Check if entity exists in store.
Parameters
key (str) β
Return type
bool
get(key, default=None)[source]ο
Get entity value from store.
Parameters
key (str) β
default (Optional[str]) β
Return type
Optional[str]
set(key, value)[source]ο
Set entity value in store.
Parameters
key (str) β
value (Optional[str]) β
Return type
None
property full_table_name: strο
class langchain.memory.SimpleMemory(*, memories={})[source]ο
Bases: langchain.schema.BaseMemory
Simple memory for storing context or other bits of information that shouldnβt
ever change between prompts. | https://api.python.langchain.com/en/stable/modules/memory.html |
c73826eb6edd-34 | Simple memory for storing context or other bits of information that shouldnβt
ever change between prompts.
Parameters
memories (Dict[str, Any]) β
Return type
None
attribute memories: Dict[str, Any] = {}ο
clear()[source]ο
Nothing to clear, got a memory like a vault.
Return type
None
load_memory_variables(inputs)[source]ο
Return key-value pairs given the text input to the chain.
If None, return all memories
Parameters
inputs (Dict[str, Any]) β
Return type
Dict[str, str]
save_context(inputs, outputs)[source]ο
Nothing should be saved or changed, my memory is set in stone.
Parameters
inputs (Dict[str, Any]) β
outputs (Dict[str, str]) β
Return type
None
property memory_variables: List[str]ο
Input keys this memory class will load dynamically.
class langchain.memory.VectorStoreRetrieverMemory(*, retriever, memory_key='history', input_key=None, return_docs=False)[source]ο
Bases: langchain.schema.BaseMemory
Class for a VectorStore-backed memory object.
Parameters
retriever (langchain.vectorstores.base.VectorStoreRetriever) β
memory_key (str) β
input_key (Optional[str]) β
return_docs (bool) β
Return type
None
attribute input_key: Optional[str] = Noneο
Key name to index the inputs to load_memory_variables.
attribute memory_key: str = 'history'ο
Key name to locate the memories in the result of load_memory_variables.
attribute retriever: langchain.vectorstores.base.VectorStoreRetriever [Required]ο
VectorStoreRetriever object to connect to.
attribute return_docs: bool = Falseο | https://api.python.langchain.com/en/stable/modules/memory.html |
c73826eb6edd-35 | attribute return_docs: bool = Falseο
Whether or not to return the result of querying the database directly.
clear()[source]ο
Nothing to clear.
Return type
None
load_memory_variables(inputs)[source]ο
Return history buffer.
Parameters
inputs (Dict[str, Any]) β
Return type
Dict[str, Union[List[langchain.schema.Document], str]]
save_context(inputs, outputs)[source]ο
Save context from this conversation to buffer.
Parameters
inputs (Dict[str, Any]) β
outputs (Dict[str, str]) β
Return type
None
property memory_variables: List[str]ο
The list of keys emitted from the load_memory_variables method.
class langchain.memory.ZepChatMessageHistory(session_id, url='http://localhost:8000')[source]ο
Bases: langchain.schema.BaseChatMessageHistory
A ChatMessageHistory implementation that uses Zep as a backend.
Recommended usage:
# Set up Zep Chat History
zep_chat_history = ZepChatMessageHistory(
session_id=session_id,
url=ZEP_API_URL,
)
# Use a standard ConversationBufferMemory to encapsulate the Zep chat history
memory = ConversationBufferMemory(
memory_key="chat_history", chat_memory=zep_chat_history
)
Zep provides long-term conversation storage for LLM apps. The server stores,
summarizes, embeds, indexes, and enriches conversational AI chat
histories, and exposes them via simple, low-latency APIs.
For server installation instructions and more, see: https://getzep.github.io/
This class is a thin wrapper around the zep-python package. Additional
Zep functionality is exposed via the zep_summary and zep_messages
properties.
For more information on the zep-python package, see: | https://api.python.langchain.com/en/stable/modules/memory.html |
c73826eb6edd-36 | properties.
For more information on the zep-python package, see:
https://github.com/getzep/zep-python
Parameters
session_id (str) β
url (str) β
Return type
None
property messages: List[langchain.schema.BaseMessage]ο
Retrieve messages from Zep memory
property zep_messages: List[Message]ο
Retrieve summary from Zep memory
property zep_summary: Optional[str]ο
Retrieve summary from Zep memory
add_message(message)[source]ο
Append the message to the Zep memory history
Parameters
message (langchain.schema.BaseMessage) β
Return type
None
search(query, metadata=None, limit=None)[source]ο
Search Zep memory for messages matching the query
Parameters
query (str) β
metadata (Optional[Dict]) β
limit (Optional[int]) β
Return type
List[MemorySearchResult]
clear()[source]ο
Clear session memory from Zep. Note that Zep is long-term storage for memory
and this is not advised unless you have specific data retention requirements.
Return type
None | https://api.python.langchain.com/en/stable/modules/memory.html |
2b28b54605b7-0 | Vector Storesο
Wrappers on top of vector stores.
class langchain.vectorstores.AlibabaCloudOpenSearch(embedding, config, **kwargs)[source]ο
Bases: langchain.vectorstores.base.VectorStore
Alibaba Cloud OpenSearch Vector Store
Parameters
embedding (langchain.embeddings.base.Embeddings) β
config (langchain.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearchSettings) β
kwargs (Any) β
Return type
None
add_texts(texts, metadatas=None, **kwargs)[source]ο
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts (Iterable[str]) β Iterable of strings to add to the vectorstore.
metadatas (Optional[List[dict]]) β Optional list of metadatas associated with the texts.
kwargs (Any) β vectorstore specific parameters
Returns
List of ids from adding the texts into the vectorstore.
Return type
List[str]
similarity_search(query, k=4, search_filter=None, **kwargs)[source]ο
Return docs most similar to query.
Parameters
query (str) β
k (int) β
search_filter (Optional[Dict[str, Any]]) β
kwargs (Any) β
Return type
List[langchain.schema.Document]
similarity_search_with_relevance_scores(query, k=4, search_filter=None, **kwargs)[source]ο
Return docs and relevance scores in the range [0, 1].
0 is dissimilar, 1 is most similar.
Parameters
query (str) β input text
k (int) β Number of Documents to return. Defaults to 4.
**kwargs β kwargs to be passed to similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 to | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-1 | score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
search_filter (Optional[dict]) β
kwargs (Any) β
Returns
List of Tuples of (doc, similarity_score)
Return type
List[Tuple[langchain.schema.Document, float]]
similarity_search_by_vector(embedding, k=4, search_filter=None, **kwargs)[source]ο
Return docs most similar to embedding vector.
Parameters
embedding (List[float]) β Embedding to look up documents similar to.
k (int) β Number of Documents to return. Defaults to 4.
search_filter (Optional[dict]) β
kwargs (Any) β
Returns
List of Documents most similar to the query vector.
Return type
List[langchain.schema.Document]
inner_embedding_query(embedding, search_filter=None, k=4)[source]ο
Parameters
embedding (List[float]) β
search_filter (Optional[Dict[str, Any]]) β
k (int) β
Return type
Dict[str, Any]
create_results(json_result)[source]ο
Parameters
json_result (Dict[str, Any]) β
Return type
List[langchain.schema.Document]
create_results_with_score(json_result)[source]ο
Parameters
json_result (Dict[str, Any]) β
Return type
List[Tuple[langchain.schema.Document, float]]
classmethod from_texts(texts, embedding, metadatas=None, config=None, **kwargs)[source]ο
Return VectorStore initialized from texts and embeddings.
Parameters
texts (List[str]) β
embedding (langchain.embeddings.base.Embeddings) β
metadatas (Optional[List[dict]]) β | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-2 | metadatas (Optional[List[dict]]) β
config (Optional[langchain.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearchSettings]) β
kwargs (Any) β
Return type
langchain.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearch
classmethod from_documents(documents, embedding, ids=None, config=None, **kwargs)[source]ο
Return VectorStore initialized from documents and embeddings.
Parameters
documents (List[langchain.schema.Document]) β
embedding (langchain.embeddings.base.Embeddings) β
ids (Optional[List[str]]) β
config (Optional[langchain.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearchSettings]) β
kwargs (Any) β
Return type
langchain.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearch
class langchain.vectorstores.AlibabaCloudOpenSearchSettings(endpoint, instance_id, username, password, datasource_name, embedding_index_name, field_name_mapping)[source]ο
Bases: object
Opensearch Client Configuration
Attribute:
endpoint (str) : The endpoint of opensearch instance, You can find it
from the console of Alibaba Cloud OpenSearch.
instance_id (str) : The identify of opensearch instance, You can find
it from the console of Alibaba Cloud OpenSearch.
datasource_name (str): The name of the data source specified when creating it.
username (str) : The username specified when purchasing the instance.
password (str) : The password specified when purchasing the instance.
embedding_index_name (str) : The name of the vector attribute specified
when configuring the instance attributes.
field_name_mapping (Dict) : Using field name mapping between opensearch
vector store and opensearch instance configuration table field names:
{ | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-3 | vector store and opensearch instance configuration table field names:
{
βidβ: βThe id field name map of index document.β,
βdocumentβ: βThe text field name map of index document.β,
βembeddingβ: βIn the embedding field of the opensearch instance,
the values must be in float16 multivalue type and separated by commas.β,
βmetadata_field_xβ: βMetadata field mapping includes the mapped
field name and operator in the mapping value, separated by a comma
between the mapped field name and the operator.β,
}
Parameters
endpoint (str) β
instance_id (str) β
username (str) β
password (str) β
datasource_name (str) β
embedding_index_name (str) β
field_name_mapping (Dict[str, str]) β
Return type
None
endpoint: strο
instance_id: strο
username: strο
password: strο
datasource_name: strο
embedding_index_name: strο
field_name_mapping: Dict[str, str] = {'document': 'document', 'embedding': 'embedding', 'id': 'id', 'metadata_field_x': 'metadata_field_x,operator'}ο
class langchain.vectorstores.AnalyticDB(connection_string, embedding_function, embedding_dimension=1536, collection_name='langchain_document', pre_delete_collection=False, logger=None)[source]ο
Bases: langchain.vectorstores.base.VectorStore
VectorStore implementation using AnalyticDB.
AnalyticDB is a distributed full PostgresSQL syntax cloud-native database.
- connection_string is a postgres connection string.
- embedding_function any embedding function implementing
langchain.embeddings.base.Embeddings interface.
collection_name is the name of the collection to use. (default: langchain) | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-4 | collection_name is the name of the collection to use. (default: langchain)
NOTE: This is not the name of the table, but the name of the collection.The tables will be created when initializing the store (if not exists)
So, make sure the user has the right permissions to create tables.
pre_delete_collection if True, will delete the collection if it exists.(default: False)
- Useful for testing.
Parameters
connection_string (str) β
embedding_function (Embeddings) β
embedding_dimension (int) β
collection_name (str) β
pre_delete_collection (bool) β
logger (Optional[logging.Logger]) β
Return type
None
create_table_if_not_exists()[source]ο
Return type
None
create_collection()[source]ο
Return type
None
delete_collection()[source]ο
Return type
None
add_texts(texts, metadatas=None, ids=None, batch_size=500, **kwargs)[source]ο
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts (Iterable[str]) β Iterable of strings to add to the vectorstore.
metadatas (Optional[List[dict]]) β Optional list of metadatas associated with the texts.
kwargs (Any) β vectorstore specific parameters
ids (Optional[List[str]]) β
batch_size (int) β
Returns
List of ids from adding the texts into the vectorstore.
Return type
List[str]
similarity_search(query, k=4, filter=None, **kwargs)[source]ο
Run similarity search with AnalyticDB with distance.
Parameters
query (str) β Query text to search for.
k (int) β Number of results to return. Defaults to 4. | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-5 | k (int) β Number of results to return. Defaults to 4.
filter (Optional[Dict[str, str]]) β Filter by metadata. Defaults to None.
kwargs (Any) β
Returns
List of Documents most similar to the query.
Return type
List[langchain.schema.Document]
similarity_search_with_score(query, k=4, filter=None)[source]ο
Return docs most similar to query.
Parameters
query (str) β Text to look up documents similar to.
k (int) β Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]) β Filter by metadata. Defaults to None.
Returns
List of Documents most similar to the query and score for each
Return type
List[Tuple[langchain.schema.Document, float]]
similarity_search_with_score_by_vector(embedding, k=4, filter=None)[source]ο
Parameters
embedding (List[float]) β
k (int) β
filter (Optional[dict]) β
Return type
List[Tuple[langchain.schema.Document, float]]
similarity_search_by_vector(embedding, k=4, filter=None, **kwargs)[source]ο
Return docs most similar to embedding vector.
Parameters
embedding (List[float]) β Embedding to look up documents similar to.
k (int) β Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]) β Filter by metadata. Defaults to None.
kwargs (Any) β
Returns
List of Documents most similar to the query vector.
Return type
List[langchain.schema.Document]
classmethod from_texts(texts, embedding, metadatas=None, embedding_dimension=1536, collection_name='langchain_document', ids=None, pre_delete_collection=False, **kwargs)[source]ο | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-6 | Return VectorStore initialized from texts and embeddings.
Postgres Connection string is required
Either pass it as a parameter
or set the PG_CONNECTION_STRING environment variable.
Parameters
texts (List[str]) β
embedding (langchain.embeddings.base.Embeddings) β
metadatas (Optional[List[dict]]) β
embedding_dimension (int) β
collection_name (str) β
ids (Optional[List[str]]) β
pre_delete_collection (bool) β
kwargs (Any) β
Return type
langchain.vectorstores.analyticdb.AnalyticDB
classmethod get_connection_string(kwargs)[source]ο
Parameters
kwargs (Dict[str, Any]) β
Return type
str
classmethod from_documents(documents, embedding, embedding_dimension=1536, collection_name='langchain_document', ids=None, pre_delete_collection=False, **kwargs)[source]ο
Return VectorStore initialized from documents and embeddings.
Postgres Connection string is required
Either pass it as a parameter
or set the PG_CONNECTION_STRING environment variable.
Parameters
documents (List[langchain.schema.Document]) β
embedding (langchain.embeddings.base.Embeddings) β
embedding_dimension (int) β
collection_name (str) β
ids (Optional[List[str]]) β
pre_delete_collection (bool) β
kwargs (Any) β
Return type
langchain.vectorstores.analyticdb.AnalyticDB
classmethod connection_string_from_db_params(driver, host, port, database, user, password)[source]ο
Return connection string from database parameters.
Parameters
driver (str) β
host (str) β
port (int) β
database (str) β
user (str) β
password (str) β
Return type
str | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-7 | user (str) β
password (str) β
Return type
str
class langchain.vectorstores.Annoy(embedding_function, index, metric, docstore, index_to_docstore_id)[source]ο
Bases: langchain.vectorstores.base.VectorStore
Wrapper around Annoy vector database.
To use, you should have the annoy python package installed.
Example
from langchain import Annoy
db = Annoy(embedding_function, index, docstore, index_to_docstore_id)
Parameters
embedding_function (Callable) β
index (Any) β
metric (str) β
docstore (Docstore) β
index_to_docstore_id (Dict[int, str]) β
add_texts(texts, metadatas=None, **kwargs)[source]ο
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts (Iterable[str]) β Iterable of strings to add to the vectorstore.
metadatas (Optional[List[dict]]) β Optional list of metadatas associated with the texts.
kwargs (Any) β vectorstore specific parameters
Returns
List of ids from adding the texts into the vectorstore.
Return type
List[str]
process_index_results(idxs, dists)[source]ο
Turns annoy results into a list of documents and scores.
Parameters
idxs (List[int]) β List of indices of the documents in the index.
dists (List[float]) β List of distances of the documents in the index.
Returns
List of Documents and scores.
Return type
List[Tuple[langchain.schema.Document, float]]
similarity_search_with_score_by_vector(embedding, k=4, search_k=- 1)[source]ο
Return docs most similar to query.
Parameters
query β Text to look up documents similar to. | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-8 | Parameters
query β Text to look up documents similar to.
k (int) β Number of Documents to return. Defaults to 4.
search_k (int) β inspect up to search_k nodes which defaults
to n_trees * n if not provided
embedding (List[float]) β
Returns
List of Documents most similar to the query and score for each
Return type
List[Tuple[langchain.schema.Document, float]]
similarity_search_with_score_by_index(docstore_index, k=4, search_k=- 1)[source]ο
Return docs most similar to query.
Parameters
query β Text to look up documents similar to.
k (int) β Number of Documents to return. Defaults to 4.
search_k (int) β inspect up to search_k nodes which defaults
to n_trees * n if not provided
docstore_index (int) β
Returns
List of Documents most similar to the query and score for each
Return type
List[Tuple[langchain.schema.Document, float]]
similarity_search_with_score(query, k=4, search_k=- 1)[source]ο
Return docs most similar to query.
Parameters
query (str) β Text to look up documents similar to.
k (int) β Number of Documents to return. Defaults to 4.
search_k (int) β inspect up to search_k nodes which defaults
to n_trees * n if not provided
Returns
List of Documents most similar to the query and score for each
Return type
List[Tuple[langchain.schema.Document, float]]
similarity_search_by_vector(embedding, k=4, search_k=- 1, **kwargs)[source]ο
Return docs most similar to embedding vector.
Parameters
embedding (List[float]) β Embedding to look up documents similar to. | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-9 | Parameters
embedding (List[float]) β Embedding to look up documents similar to.
k (int) β Number of Documents to return. Defaults to 4.
search_k (int) β inspect up to search_k nodes which defaults
to n_trees * n if not provided
kwargs (Any) β
Returns
List of Documents most similar to the embedding.
Return type
List[langchain.schema.Document]
similarity_search_by_index(docstore_index, k=4, search_k=- 1, **kwargs)[source]ο
Return docs most similar to docstore_index.
Parameters
docstore_index (int) β Index of document in docstore
k (int) β Number of Documents to return. Defaults to 4.
search_k (int) β inspect up to search_k nodes which defaults
to n_trees * n if not provided
kwargs (Any) β
Returns
List of Documents most similar to the embedding.
Return type
List[langchain.schema.Document]
similarity_search(query, k=4, search_k=- 1, **kwargs)[source]ο
Return docs most similar to query.
Parameters
query (str) β Text to look up documents similar to.
k (int) β Number of Documents to return. Defaults to 4.
search_k (int) β inspect up to search_k nodes which defaults
to n_trees * n if not provided
kwargs (Any) β
Returns
List of Documents most similar to the query.
Return type
List[langchain.schema.Document]
max_marginal_relevance_search_by_vector(embedding, k=4, fetch_k=20, lambda_mult=0.5, **kwargs)[source]ο
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-10 | Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
embedding (List[float]) β Embedding to look up documents similar to.
fetch_k (int) β Number of Documents to fetch to pass to MMR algorithm.
k (int) β Number of Documents to return. Defaults to 4.
lambda_mult (float) β Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
kwargs (Any) β
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[langchain.schema.Document]
max_marginal_relevance_search(query, k=4, fetch_k=20, lambda_mult=0.5, **kwargs)[source]ο
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
query (str) β Text to look up documents similar to.
k (int) β Number of Documents to return. Defaults to 4.
fetch_k (int) β Number of Documents to fetch to pass to MMR algorithm.
lambda_mult (float) β Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
kwargs (Any) β
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[langchain.schema.Document]
classmethod from_texts(texts, embedding, metadatas=None, metric='angular', trees=100, n_jobs=- 1, **kwargs)[source]ο
Construct Annoy wrapper from raw documents.
Parameters
texts (List[str]) β List of documents to index. | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-11 | Parameters
texts (List[str]) β List of documents to index.
embedding (langchain.embeddings.base.Embeddings) β Embedding function to use.
metadatas (Optional[List[dict]]) β List of metadata dictionaries to associate with documents.
metric (str) β Metric to use for indexing. Defaults to βangularβ.
trees (int) β Number of trees to use for indexing. Defaults to 100.
n_jobs (int) β Number of jobs to use for indexing. Defaults to -1.
kwargs (Any) β
Return type
langchain.vectorstores.annoy.Annoy
This is a user friendly interface that:
Embeds documents.
Creates an in memory docstore
Initializes the Annoy database
This is intended to be a quick way to get started.
Example
from langchain import Annoy
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
index = Annoy.from_texts(texts, embeddings)
classmethod from_embeddings(text_embeddings, embedding, metadatas=None, metric='angular', trees=100, n_jobs=- 1, **kwargs)[source]ο
Construct Annoy wrapper from embeddings.
Parameters
text_embeddings (List[Tuple[str, List[float]]]) β List of tuples of (text, embedding)
embedding (langchain.embeddings.base.Embeddings) β Embedding function to use.
metadatas (Optional[List[dict]]) β List of metadata dictionaries to associate with documents.
metric (str) β Metric to use for indexing. Defaults to βangularβ.
trees (int) β Number of trees to use for indexing. Defaults to 100.
n_jobs (int) β Number of jobs to use for indexing. Defaults to -1
kwargs (Any) β
Return type
langchain.vectorstores.annoy.Annoy | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-12 | Return type
langchain.vectorstores.annoy.Annoy
This is a user friendly interface that:
Creates an in memory docstore with provided embeddings
Initializes the Annoy database
This is intended to be a quick way to get started.
Example
from langchain import Annoy
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
text_embeddings = embeddings.embed_documents(texts)
text_embedding_pairs = list(zip(texts, text_embeddings))
db = Annoy.from_embeddings(text_embedding_pairs, embeddings)
save_local(folder_path, prefault=False)[source]ο
Save Annoy index, docstore, and index_to_docstore_id to disk.
Parameters
folder_path (str) β folder path to save index, docstore,
and index_to_docstore_id to.
prefault (bool) β Whether to pre-load the index into memory.
Return type
None
classmethod load_local(folder_path, embeddings)[source]ο
Load Annoy index, docstore, and index_to_docstore_id to disk.
Parameters
folder_path (str) β folder path to load index, docstore,
and index_to_docstore_id from.
embeddings (langchain.embeddings.base.Embeddings) β Embeddings to use when generating queries.
Return type
langchain.vectorstores.annoy.Annoy
class langchain.vectorstores.AtlasDB(name, embedding_function=None, api_key=None, description='A description for your project', is_public=True, reset_project_if_exists=False)[source]ο
Bases: langchain.vectorstores.base.VectorStore
Wrapper around Atlas: Nomicβs neural database and rhizomatic instrument.
To use, you should have the nomic python package installed.
Example
from langchain.vectorstores import AtlasDB | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-13 | Example
from langchain.vectorstores import AtlasDB
from langchain.embeddings.openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
vectorstore = AtlasDB("my_project", embeddings.embed_query)
Parameters
name (str) β
embedding_function (Optional[Embeddings]) β
api_key (Optional[str]) β
description (str) β
is_public (bool) β
reset_project_if_exists (bool) β
Return type
None
add_texts(texts, metadatas=None, ids=None, refresh=True, **kwargs)[source]ο
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts (Iterable[str]) β Texts to add to the vectorstore.
metadatas (Optional[List[dict]], optional) β Optional list of metadatas.
ids (Optional[List[str]]) β An optional list of ids.
refresh (bool) β Whether or not to refresh indices with the updated data.
Default True.
kwargs (Any) β
Returns
List of IDs of the added texts.
Return type
List[str]
create_index(**kwargs)[source]ο
Creates an index in your project.
See
https://docs.nomic.ai/atlas_api.html#nomic.project.AtlasProject.create_index
for full detail.
Parameters
kwargs (Any) β
Return type
Any
similarity_search(query, k=4, **kwargs)[source]ο
Run similarity search with AtlasDB
Parameters
query (str) β Query text to search for.
k (int) β Number of results to return. Defaults to 4.
kwargs (Any) β
Returns
List of documents most similar to the query text.
Return type
List[Document] | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-14 | List of documents most similar to the query text.
Return type
List[Document]
classmethod from_texts(texts, embedding=None, metadatas=None, ids=None, name=None, api_key=None, description='A description for your project', is_public=True, reset_project_if_exists=False, index_kwargs=None, **kwargs)[source]ο
Create an AtlasDB vectorstore from a raw documents.
Parameters
texts (List[str]) β The list of texts to ingest.
name (str) β Name of the project to create.
api_key (str) β Your nomic API key,
embedding (Optional[Embeddings]) β Embedding function. Defaults to None.
metadatas (Optional[List[dict]]) β List of metadatas. Defaults to None.
ids (Optional[List[str]]) β Optional list of document IDs. If None,
ids will be auto created
description (str) β A description for your project.
is_public (bool) β Whether your project is publicly accessible.
True by default.
reset_project_if_exists (bool) β Whether to reset this project if it
already exists. Default False.
Generally userful during development and testing.
index_kwargs (Optional[dict]) β Dict of kwargs for index creation.
See https://docs.nomic.ai/atlas_api.html
kwargs (Any) β
Returns
Nomicβs neural database and finest rhizomatic instrument
Return type
AtlasDB
classmethod from_documents(documents, embedding=None, ids=None, name=None, api_key=None, persist_directory=None, description='A description for your project', is_public=True, reset_project_if_exists=False, index_kwargs=None, **kwargs)[source]ο
Create an AtlasDB vectorstore from a list of documents.
Parameters
name (str) β Name of the collection to create.
api_key (str) β Your nomic API key, | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-15 | api_key (str) β Your nomic API key,
documents (List[Document]) β List of documents to add to the vectorstore.
embedding (Optional[Embeddings]) β Embedding function. Defaults to None.
ids (Optional[List[str]]) β Optional list of document IDs. If None,
ids will be auto created
description (str) β A description for your project.
is_public (bool) β Whether your project is publicly accessible.
True by default.
reset_project_if_exists (bool) β Whether to reset this project if
it already exists. Default False.
Generally userful during development and testing.
index_kwargs (Optional[dict]) β Dict of kwargs for index creation.
See https://docs.nomic.ai/atlas_api.html
persist_directory (Optional[str]) β
kwargs (Any) β
Returns
Nomicβs neural database and finest rhizomatic instrument
Return type
AtlasDB
class langchain.vectorstores.AwaDB(table_name='langchain_awadb', embedding=None, log_and_data_dir=None, client=None)[source]ο
Bases: langchain.vectorstores.base.VectorStore
Interface implemented by AwaDB vector stores.
Parameters
table_name (str) β
embedding (Optional[Embeddings]) β
log_and_data_dir (Optional[str]) β
client (Optional[awadb.Client]) β
Return type
None
add_texts(texts, metadatas=None, is_duplicate_texts=None, **kwargs)[source]ο
Run more texts through the embeddings and add to the vectorstore.
:param texts: Iterable of strings to add to the vectorstore.
:param metadatas: Optional list of metadatas associated with the texts.
:param is_duplicate_texts: Optional whether to duplicate texts.
:param kwargs: vectorstore specific parameters.
Returns | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-16 | :param kwargs: vectorstore specific parameters.
Returns
List of ids from adding the texts into the vectorstore.
Parameters
texts (Iterable[str]) β
metadatas (Optional[List[dict]]) β
is_duplicate_texts (Optional[bool]) β
kwargs (Any) β
Return type
List[str]
load_local(table_name, **kwargs)[source]ο
Parameters
table_name (str) β
kwargs (Any) β
Return type
bool
similarity_search(query, k=4, **kwargs)[source]ο
Return docs most similar to query.
Parameters
query (str) β
k (int) β
kwargs (Any) β
Return type
List[langchain.schema.Document]
similarity_search_with_score(query, k=4, **kwargs)[source]ο
Return docs and relevance scores, normalized on a scale from 0 to 1.
0 is dissimilar, 1 is most similar.
Parameters
query (str) β
k (int) β
kwargs (Any) β
Return type
List[Tuple[langchain.schema.Document, float]]
similarity_search_with_relevance_scores(query, k=4, **kwargs)[source]ο
Return docs and relevance scores, normalized on a scale from 0 to 1.
0 is dissimilar, 1 is most similar.
Parameters
query (str) β
k (int) β
kwargs (Any) β
Return type
List[Tuple[langchain.schema.Document, float]]
similarity_search_by_vector(embedding=None, k=4, scores=None, **kwargs)[source]ο
Return docs most similar to embedding vector.
Parameters
embedding (Optional[List[float]]) β Embedding to look up documents similar to. | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-17 | Parameters
embedding (Optional[List[float]]) β Embedding to look up documents similar to.
k (int) β Number of Documents to return. Defaults to 4.
scores (Optional[list]) β
kwargs (Any) β
Returns
List of Documents most similar to the query vector.
Return type
List[langchain.schema.Document]
create_table(table_name, **kwargs)[source]ο
Create a new table.
Parameters
table_name (str) β
kwargs (Any) β
Return type
bool
use(table_name, **kwargs)[source]ο
Use the specified table. Donβt know the tables, please invoke list_tables.
Parameters
table_name (str) β
kwargs (Any) β
Return type
bool
list_tables(**kwargs)[source]ο
List all the tables created by the client.
Parameters
kwargs (Any) β
Return type
List[str]
get_current_table(**kwargs)[source]ο
Get the current table.
Parameters
kwargs (Any) β
Return type
str
classmethod from_texts(texts, embedding=None, metadatas=None, table_name='langchain_awadb', log_and_data_dir=None, client=None, **kwargs)[source]ο
Create an AwaDB vectorstore from a raw documents.
Parameters
texts (List[str]) β List of texts to add to the table.
embedding (Optional[Embeddings]) β Embedding function. Defaults to None.
metadatas (Optional[List[dict]]) β List of metadatas. Defaults to None.
table_name (str) β Name of the table to create.
log_and_data_dir (Optional[str]) β Directory of logging and persistence.
client (Optional[awadb.Client]) β AwaDB client
kwargs (Any) β
Returns | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-18 | kwargs (Any) β
Returns
AwaDB vectorstore.
Return type
AwaDB
classmethod from_documents(documents, embedding=None, table_name='langchain_awadb', log_and_data_dir=None, client=None, **kwargs)[source]ο
Create an AwaDB vectorstore from a list of documents.
If a log_and_data_dir specified, the table will be persisted there.
Parameters
documents (List[Document]) β List of documents to add to the vectorstore.
embedding (Optional[Embeddings]) β Embedding function. Defaults to None.
table_name (str) β Name of the table to create.
log_and_data_dir (Optional[str]) β Directory to persist the table.
client (Optional[awadb.Client]) β AwaDB client
kwargs (Any) β
Returns
AwaDB vectorstore.
Return type
AwaDB
class langchain.vectorstores.AzureSearch(azure_search_endpoint, azure_search_key, index_name, embedding_function, search_type='hybrid', semantic_configuration_name=None, semantic_query_language='en-us', **kwargs)[source]ο
Bases: langchain.vectorstores.base.VectorStore
Parameters
azure_search_endpoint (str) β
azure_search_key (str) β
index_name (str) β
embedding_function (Callable) β
search_type (str) β
semantic_configuration_name (Optional[str]) β
semantic_query_language (str) β
kwargs (Any) β
add_texts(texts, metadatas=None, **kwargs)[source]ο
Add texts data to an existing index.
Parameters
texts (Iterable[str]) β
metadatas (Optional[List[dict]]) β
kwargs (Any) β
Return type
List[str]
similarity_search(query, k=4, **kwargs)[source]ο | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-19 | similarity_search(query, k=4, **kwargs)[source]ο
Return docs most similar to query.
Parameters
query (str) β
k (int) β
kwargs (Any) β
Return type
List[langchain.schema.Document]
vector_search(query, k=4, **kwargs)[source]ο
Returns the most similar indexed documents to the query text.
Parameters
query (str) β The query text for which to find similar documents.
k (int) β The number of documents to return. Default is 4.
kwargs (Any) β
Returns
A list of documents that are most similar to the query text.
Return type
List[Document]
vector_search_with_score(query, k=4, filters=None)[source]ο
Return docs most similar to query.
Parameters
query (str) β Text to look up documents similar to.
k (int) β Number of Documents to return. Defaults to 4.
filters (Optional[str]) β
Returns
List of Documents most similar to the query and score for each
Return type
List[Tuple[langchain.schema.Document, float]]
hybrid_search(query, k=4, **kwargs)[source]ο
Returns the most similar indexed documents to the query text.
Parameters
query (str) β The query text for which to find similar documents.
k (int) β The number of documents to return. Default is 4.
kwargs (Any) β
Returns
A list of documents that are most similar to the query text.
Return type
List[Document]
hybrid_search_with_score(query, k=4, filters=None)[source]ο
Return docs most similar to query with an hybrid query.
Parameters
query (str) β Text to look up documents similar to. | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-20 | Parameters
query (str) β Text to look up documents similar to.
k (int) β Number of Documents to return. Defaults to 4.
filters (Optional[str]) β
Returns
List of Documents most similar to the query and score for each
Return type
List[Tuple[langchain.schema.Document, float]]
semantic_hybrid_search(query, k=4, **kwargs)[source]ο
Returns the most similar indexed documents to the query text.
Parameters
query (str) β The query text for which to find similar documents.
k (int) β The number of documents to return. Default is 4.
kwargs (Any) β
Returns
A list of documents that are most similar to the query text.
Return type
List[Document]
semantic_hybrid_search_with_score(query, k=4, filters=None)[source]ο
Return docs most similar to query with an hybrid query.
Parameters
query (str) β Text to look up documents similar to.
k (int) β Number of Documents to return. Defaults to 4.
filters (Optional[str]) β
Returns
List of Documents most similar to the query and score for each
Return type
List[Tuple[langchain.schema.Document, float]]
classmethod from_texts(texts, embedding, metadatas=None, azure_search_endpoint='', azure_search_key='', index_name='langchain-index', **kwargs)[source]ο
Return VectorStore initialized from texts and embeddings.
Parameters
texts (List[str]) β
embedding (langchain.embeddings.base.Embeddings) β
metadatas (Optional[List[dict]]) β
azure_search_endpoint (str) β
azure_search_key (str) β
index_name (str) β
kwargs (Any) β
Return type
langchain.vectorstores.azuresearch.AzureSearch | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-21 | kwargs (Any) β
Return type
langchain.vectorstores.azuresearch.AzureSearch
class langchain.vectorstores.Cassandra(embedding, session, keyspace, table_name, ttl_seconds=None)[source]ο
Bases: langchain.vectorstores.base.VectorStore
Wrapper around Cassandra embeddings platform.
There is no notion of a default table name, since each embedding
function implies its own vector dimension, which is part of the schema.
Example
from langchain.vectorstores import Cassandra
from langchain.embeddings.openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
session = ...
keyspace = 'my_keyspace'
vectorstore = Cassandra(embeddings, session, keyspace, 'my_doc_archive')
Parameters
embedding (Embeddings) β
session (Session) β
keyspace (str) β
table_name (str) β
ttl_seconds (int | None) β
Return type
None
delete_collection()[source]ο
Just an alias for clear
(to better align with other VectorStore implementations).
Return type
None
clear()[source]ο
Empty the collection.
Return type
None
delete_by_document_id(document_id)[source]ο
Parameters
document_id (str) β
Return type
None
add_texts(texts, metadatas=None, ids=None, **kwargs)[source]ο
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts (Iterable[str]) β Texts to add to the vectorstore.
metadatas (Optional[List[dict]], optional) β Optional list of metadatas.
ids (Optional[List[str]], optional) β Optional list of IDs.
kwargs (Any) β
Returns
List of IDs of the added texts.
Return type
List[str] | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
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