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    Pythonlanggraph.storebaseIndexConfig
    Class●Since v2.0

    IndexConfig

    Configuration for indexing documents for semantic search in the store.

    If not provided to the store, the store will not support vector search. In that case, all index arguments to put() and aput() operations will be ignored.

    Copy
    IndexConfig()

    Bases

    TypedDict

    Used in Docs

    • How to use a custom store

    Constructors

    constructor
    __init__
    NameType
    dimsint
    embedEmbeddings | EmbeddingsFunc | AEmbeddingsFunc | str
    fieldslist[str] | None

    Attributes

    attribute
    dims: int

    Number of dimensions in the embedding vectors.

    attribute
    embed: Embeddings | EmbeddingsFunc | AEmbeddingsFunc | str

    Optional function to generate embeddings from text.

    attribute
    fields: list[str] | None

    Fields to extract text from for embedding generation.

    Controls which parts of stored items are embedded for semantic search. Follows JSON path syntax:

    • ["$"]: Embeds the entire JSON object as one vector (default)
    • ["field1", "field2"]: Embeds specific top-level fields
    • ["parent.child"]: Embeds nested fields using dot notation
    • ["array[*].field"]: Embeds field from each array element separately
    View source on GitHub