LangChain Reference home pageLangChain ReferenceLangChain Reference
  • GitHub
  • Main Docs
Deep Agents
LangChain
LangGraph
Integrations
LangSmith
  • Overview
  • MCP Adapters
    • Overview
    • Agents
    • Callbacks
    • Chains
    • Chat models
    • Embeddings
    • Evaluation
    • Globals
    • Hub
    • Memory
    • Output parsers
    • Retrievers
    • Runnables
    • LangSmith
    • Storage
    Standard Tests
    Text Splitters
    ⌘I

    LangChain Assistant

    Ask a question to get started

    Enter to send•Shift+Enter new line

    Menu

    MCP Adapters
    OverviewAgentsCallbacksChainsChat modelsEmbeddingsEvaluationGlobalsHubMemoryOutput parsersRetrieversRunnablesLangSmithStorage
    Standard Tests
    Text Splitters
    Language
    Theme
    Pythonlangchain-classicvectorstores
    Module●Since v1.0

    vectorstores

    Vector store stores embedded data and performs vector search.

    One of the most common ways to store and search over unstructured data is to embed it and store the resulting embedding vectors, and then query the store and retrieve the data that are 'most similar' to the embedded query.

    Attributes

    attribute
    DEPRECATED_LOOKUP: dict

    Functions

    function
    create_importer

    Create a function that helps retrieve objects from their new locations.

    The goal of this function is to help users transition from deprecated imports to new imports.

    The function will raise deprecation warning on loops using deprecated_lookups or fallback_module.

    Module lookups will import without deprecation warnings (used to speed up imports from large namespaces like llms or chat models).

    This function should ideally only be used with deprecated imports not with existing imports that are valid, as in addition to raising deprecation warnings the dynamic imports can create other issues for developers (e.g., loss of type information, IDE support for going to definition etc).

    Modules

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