ractogateway.rag.stores.pgvector_store

PostgreSQL + pgvector store (lazy import).

Install with: pip install ractogateway[rag-pgvector]

class ractogateway.rag.stores.pgvector_store.PGVectorStore(dsn, *, table='rag_chunks', dimension=None, distance='cosine', batch_size=100)

Bases: BaseVectorStore

Vector store backed by PostgreSQL with the pgvector extension.

Parameters:

• dsn (str) – PostgreSQL connection string (e.g. "postgresql://user:pass@localhost/mydb").

• table (str) – Table name (default "rag_chunks").

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

• distance (str) – "cosine", "l2", or "inner".

• batch_size (int) – Rows per INSERT 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