API Reference

MatrixOneVectorStore

class langchain_matrixone.vectorstores.MatrixOneVectorStore(embedding: Embeddings, connection_args: Dict[str, Any] | None = None, *, client: Client | None = None, table_name: str = 'langchain_vectors', content_column: str = 'content', metadata_column: str = 'metadata', vector_column: str = 'embedding', drop_old: bool = False, distance: str = 'l2')[source]

Bases: VectorStore

MatrixOne vector store integration compatible with LangChain.

add_texts(texts: Iterable[str], metadatas: List[dict] | None = None, ids: List[str] | None = None, **kwargs: Any) → List[str][source]

Run more texts through the embeddings and add to the VectorStore.

Parameters:

  • texts – Iterable of strings to add to the VectorStore.

  • metadatas – Optional list of metadatas associated with the texts.

  • ids – Optional list of IDs associated with the texts.

  • **kwargs – VectorStore specific parameters. One of the kwargs should be ids which is a list of ids associated with the texts.

Returns:

List of IDs from adding the texts into the VectorStore.

Raises:

  • ValueError – If the number of metadatas does not match the number of texts.

  • ValueError – If the number of IDs does not match the number of texts.

delete(ids: List[str] | None = None, **kwargs: Any) → bool | None[source]

Delete by vector ID or other criteria.

Parameters:

  • ids – List of IDs to delete. If None, delete all.

  • **kwargs – Other keyword arguments that subclasses might use.

Returns:

True if deletion is successful, False otherwise, None if not

implemented.

classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: List[dict] | None = None, connection_args: Dict[str, Any] | None = None, client: Client | None = None, **kwargs: Any) → MatrixOneVectorStore[source]

Return VectorStore initialized from texts and embeddings.

Parameters:

  • texts – Texts to add to the VectorStore.

  • embedding – Embedding function to use.

  • metadatas – Optional list of metadatas associated with the texts.

  • ids – Optional list of IDs associated with the texts.

  • **kwargs – Additional keyword arguments.

Returns:

VectorStore initialized from texts and embeddings.

Return docs most similar to query.

Parameters:

  • query – Input text.

  • k – Number of Document objects to return.

  • **kwargs – Arguments to pass to the search method.

Returns:

List of Document objects most similar to the query.

similarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document][source]

Return docs most similar to embedding vector.

Parameters:

  • embedding – Embedding to look up documents similar to.

  • k – Number of Document objects to return.

  • **kwargs – Arguments to pass to the search method.

Returns:

List of Document objects most similar to the query vector.