ractogateway.rag.stores.in_memory_store

In-memory vector store — pure Python, zero extra dependencies.

Uses brute-force cosine similarity over a list of stored vectors. Suitable for development, testing, and small corpora (< 10k chunks).

class ractogateway.rag.stores.in_memory_store.InMemoryVectorStore(similarity='cosine')[source]

Pure-Python brute-force vector store — no extra dependencies.

This store keeps all chunks and their embeddings in memory. It is not suitable for production-scale corpora but requires no installation.

Parameters:: similarity (str) – Similarity function to use. Currently only "cosine" is supported.

add(chunks)[source]

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)[source]

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)[source]

Remove chunks with the given IDs from the store.

Remove all chunks from the store.

Return the total number of indexed chunks.