aindex() — langchain Function Reference

Source Code

libs/core/langchain_core/indexing/api.py lines 629–948

async def aindex(
    docs_source: BaseLoader | Iterable[Document] | AsyncIterator[Document],
    record_manager: RecordManager,
    vector_store: VectorStore | DocumentIndex,
    *,
    batch_size: int = 100,
    cleanup: Literal["incremental", "full", "scoped_full"] | None = None,
    source_id_key: str | Callable[[Document], str] | None = None,
    cleanup_batch_size: int = 1_000,
    force_update: bool = False,
    key_encoder: Literal["sha1", "sha256", "sha512", "blake2b"]
    | Callable[[Document], str] = "sha1",
    upsert_kwargs: dict[str, Any] | None = None,
) -> IndexingResult:
    """Async index data from the loader into the vector store.

    Indexing functionality uses a manager to keep track of which documents
    are in the vector store.

    This allows us to keep track of which documents were updated, and which
    documents were deleted, which documents should be skipped.

    For the time being, documents are indexed using their hashes, and users
    are not able to specify the uid of the document.

    !!! warning "Behavior changed in `langchain-core` 0.3.25"

        Added `scoped_full` cleanup mode.

    !!! warning

        * In full mode, the loader should be returning
            the entire dataset, and not just a subset of the dataset.
            Otherwise, the auto_cleanup will remove documents that it is not
            supposed to.
        * In incremental mode, if documents associated with a particular
            source id appear across different batches, the indexing API
            will do some redundant work. This will still result in the
            correct end state of the index, but will unfortunately not be
            100% efficient. For example, if a given document is split into 15
            chunks, and we index them using a batch size of 5, we'll have 3 batches
            all with the same source id. In general, to avoid doing too much
            redundant work select as big a batch size as possible.
        * The `scoped_full` mode is suitable if determining an appropriate batch size
            is challenging or if your data loader cannot return the entire dataset at
            once. This mode keeps track of source IDs in memory, which should be fine
            for most use cases. If your dataset is large (10M+ docs), you will likely
            need to parallelize the indexing process regardless.

    Args:
        docs_source: Data loader or iterable of documents to index.
        record_manager: Timestamped set to keep track of which documents were
            updated.
        vector_store: `VectorStore` or DocumentIndex to index the documents into.
        batch_size: Batch size to use when indexing.
        cleanup: How to handle clean up of documents.

            - incremental: Cleans up all documents that haven't been updated AND
                that are associated with source IDs that were seen during indexing.
                Clean up is done continuously during indexing helping to minimize the
                probability of users seeing duplicated content.
            - full: Delete all documents that have not been returned by the loader
                during this run of indexing.
                Clean up runs after all documents have been indexed.
                This means that users may see duplicated content during indexing.
            - scoped_full: Similar to Full, but only deletes all documents
                that haven't been updated AND that are associated with
                source IDs that were seen during indexing.
            - None: Do not delete any documents.
        source_id_key: Optional key that helps identify the original source
            of the document.
        cleanup_batch_size: Batch size to use when cleaning up documents.
        force_update: Force update documents even if they are present in the
            record manager. Useful if you are re-indexing with updated embeddings.
        key_encoder: Hashing algorithm to use for hashing the document content and
            metadata. Options include "blake2b", "sha256", and "sha512".

            !!! version-added "Added in `langchain-core` 0.3.66"

        key_encoder: Hashing algorithm to use for hashing the document.
            If not provided, a default encoder using SHA-1 will be used.