ractogateway.rag.stores.qdrant_store

Qdrant vector store (lazy import).

Install with: pip install ractogateway[rag-qdrant]

class ractogateway.rag.stores.qdrant_store.QdrantStore(collection='ractogateway', *, url=None, api_key=None, distance='cosine', dimension=None, batch_size=100)

Bases: BaseVectorStore

Vector store backed by Qdrant.

Parameters:

• collection (str) – Qdrant collection name.

• url (str | None) – Qdrant server URL. None = in-memory.

• api_key (str | None) – Qdrant cloud API key (optional).

• distance (str) – "cosine", "euclid", or "dot".

• dimension (int | None) – Vector dimension. Inferred on first add if None.

• batch_size (int) – Points per upsert batch.

add(chunks)

Add chunks (with embeddings) to the store.

Parameters:

chunks (list[Chunk]) – Chunks to index. Each chunk must have a non-None embedding.

Raises:

ValueError – If any chunk has embedding=None.

Return type:

None

search(embedding, top_k=5, filters=None)

Search for the top_k most similar chunks.

Parameters:

• embedding (list[float]) – Query embedding vector.

• top_k (int) – Number of results to return.

• filters (dict[str, Any] | None) – Optional metadata filters (store-specific format).

Return type:

list[RetrievalResult]

Returns:

list[RetrievalResult] – Ranked list of results (rank 1 = most similar).

delete(chunk_ids)

Remove chunks with the given IDs from the store.

Return type:

None

clear()

Remove all chunks from the store.

Return type:

None

count()

Return the total number of indexed chunks.

Return type:

int