ontocast.tool.vector_store.factory

Factory for vector store backend selection.

Backend modules are imported inside the selected branch rather than at module scope: importing :mod:ontocast.tool.vector_store.qdrant pulls the Qdrant SDK (and, through it, gRPC and an ONNX runtime), and OntoCast's base install ships neither. Only the backend actually configured is loaded.

create_vector_store_manager(tool_config, embedding, sparse_embedding=None)

Return a vector store manager for the configured backend.

Selection is driven by VectorStoreConfig.backend. The default, :attr:~ontocast.onto.enum.VectorStoreBackend.AUTO, infers the backend from whichever connection setting is populated and otherwise resolves to :attr:~ontocast.onto.enum.VectorStoreBackend.NONE, returning None. A deployment that configures neither Qdrant nor LanceDB has no vector retrieval: ontology context comes from a single working ontology, which is the default :class:~ontocast.onto.enum.OntologyContextMode.

Parameters:

Name	Type	Description	Default
tool_config	ToolConfig	The resolved tool configuration.	required
embedding	EmbeddingTool	Dense embedding provider.	required
sparse_embedding	FastembedBm25SparseTool | None	BM25 sparse provider, required by both backends.	None

Returns:

Type	Description
VectorStoreManager | None	A manager for the selected backend, or None when the backend is
VectorStoreManager | None	explicitly disabled.

Raises:

Type	Description
ValueError	If an explicitly requested backend is not configured, or if Qdrant's vector_size contradicts the embedding dimension.

Source code in ontocast/tool/vector_store/factory.py

def create_vector_store_manager(
    tool_config: ToolConfig,
    embedding: EmbeddingTool,
    sparse_embedding: FastembedBm25SparseTool | None = None,
) -> VectorStoreManager | None:
    """Return a vector store manager for the configured backend.

    Selection is driven by ``VectorStoreConfig.backend``. The default,
    :attr:`~ontocast.onto.enum.VectorStoreBackend.AUTO`, infers the backend from
    whichever connection setting is populated and otherwise resolves to
    :attr:`~ontocast.onto.enum.VectorStoreBackend.NONE`, returning ``None``.
    A deployment that configures neither Qdrant nor LanceDB has **no** vector
    retrieval: ontology context comes from a single working ontology, which is
    the default :class:`~ontocast.onto.enum.OntologyContextMode`.

    Args:
        tool_config: The resolved tool configuration.
        embedding: Dense embedding provider.
        sparse_embedding: BM25 sparse provider, required by both backends.

    Returns:
        A manager for the selected backend, or ``None`` when the backend is
        explicitly disabled.

    Raises:
        ValueError: If an explicitly requested backend is not configured, or if
            Qdrant's ``vector_size`` contradicts the embedding dimension.
    """
    backend = _resolve_backend(tool_config)

    if backend is VectorStoreBackend.NONE:
        return None

    if backend is VectorStoreBackend.QDRANT:
        q_vs = tool_config.qdrant.vector_size
        emb_dim = tool_config.embedding.dimension
        if q_vs is not None and q_vs != emb_dim:
            raise ValueError(
                "QdrantConfig.vector_size must match "
                "EmbeddingConfig.dimension when set "
                f"(got vector_size={q_vs}, embedding.dimension={emb_dim})"
            )
        from ontocast.tool.vector_store.qdrant import QdrantVectorStoreManager

        return QdrantVectorStoreManager(
            store_config=tool_config.vector_store,
            qdrant_config=tool_config.qdrant,
            embedding=embedding,
            sparse_embedding=sparse_embedding,
        )

    from ontocast.tool.vector_store.lancedb import LanceDBVectorStoreManager

    return LanceDBVectorStoreManager(
        store_config=tool_config.vector_store,
        lancedb_config=tool_config.lancedb,
        embedding=embedding,
        sparse_embedding=sparse_embedding,
    )