# RAG & Vector Stores

> The 14 vector stores Divinci can put a RAG corpus in — Cloudflare Vectorize, Turso, Pinecone, Qdrant, Redis, MongoDB Atlas, Vertex AI, Neo4j, Couchbase, PageIndex, RAPTOR and LightRAG.

A RAG vector's **store** is chosen when the vector is created and cannot be
changed afterwards. It decides where chunks live, what a query costs, and — for
the graph and tree stores — what "retrieval" even means.

<ToolCatalogTable type="RAG/Vector" />

## Three families, not one list

The table above mixes three genuinely different things:

**Plain vector stores** — Cloudflare Vectorize, Qdrant, Pinecone, Redis,
MongoDB Atlas, Vertex AI Vector Search, Turso, Couchbase. Chunks in, nearest
neighbours out. Pick on where your data is allowed to live and whose bill it
lands on.

**Tree / hierarchical** — RAPTOR builds a summary tree over your chunks and can
retrieve at a level rather than a chunk, which helps on questions that span a
whole document. PageIndex is *vectorless*: it reasons over a document tree
instead of embedding, so there is no similarity threshold to tune.

**Graph-enhanced** — Neo4j Hybrid and LightRAG extract entities and relations
and blend a graph traversal into retrieval. Worth it when the answer depends on
how things relate rather than on which passage is most similar. Expect a slower,
more expensive ingest in exchange.

<Aside type="caution" title="A store choice is a one-way door for that vector">
Changing `vectorIndexTool` on an existing vector does not move a single chunk.
The supported migration is: create a new vector on the new store, re-ingest, and
swap the release over — which is what a connector's draft-and-swap does for you.
</Aside>

## Choosing

| You want | Reach for |
| --- | --- |
| the default that needs no account of your own | Cloudflare Vectorize |
| your data to stay in infrastructure you own | Qdrant, Redis, MongoDB Atlas, Couchbase (all BYOK) |
| questions that span whole documents | RAPTOR or PageIndex |
| answers about relationships between entities | Neo4j Hybrid or LightRAG |
| an existing vector database you already pay for | Pinecone, Qdrant, Vertex AI Vector Search |

## Retrieval is not only the vector leg

Group retrieval runs a **keyword (full-text) leg** alongside the vector leg and
fuses the results. That matters more than it sounds: on our own corpora lexical
retrieval has measured markedly higher recall than the vector leg alone on
questions containing exact terms, names or numbers. If a corpus answers well on
paraphrases but badly on precise product names, the keyword leg is what you are
missing.

## See also

- [Embeddings](/vendors/embeddings/) — the other half of a vector's identity.
- [RAG Knowledge Base](/server/rag/) — creating and filling vectors.
- [Knowledge (RAG) Settings](/server/knowledge/) — how a release resolves which vector to search.