Combine dense and sparse vectors in one Qdrant collection and run hybrid search with prefetch + RRF/DBSF fusion via the Query API

Documented steps

1. Create the collection: PUT /collections/{collection_name} with body {"vectors":{"text-dense":{"size":768,"distance":"Cosine"}},"sparse_vectors":{"text-sparse":{}}} — dense and sparse vector names must differ within the collection.
2. For BM25-style sparse scoring, add a modifier when defining the sparse vector: "sparse_vectors":{"text-sparse":{"modifier":"idf"}}. Omitted by default, which means raw dot product is used.
3. Upsert points with both named vectors: PUT /collections/{collection_name}/points where each point's "vector" is {"text-dense":[0.01,0.45,...],"text-sparse":{"indices":[1,42],"values":[0.22,0.8]}}.
4. Run the hybrid query: POST /collections/{collection_name}/points/query with a "prefetch" list, one entry per vector type: {"query":{"indices":[1,42],"values":[0.22,0.8]},"using":"text-sparse","limit":20} and {"query":[0.01,0.45,0.67],"using":"text-dense","limit":20}.
5. Set the outer fusion query to merge prefetch results: "query":{"fusion":"rrf"} or "query":{"fusion":"dbsf"}.
6. Optionally tune RRF: "query":{"rrf":{"k":60,"weights":[3.0,1.0]}} — configurable k available as of v1.16.0, per-prefetch weights as of v1.17.0.
7. Set the outer "limit" (e.g. 10). Each prefetch "limit" must be at least outer limit + offset, otherwise the fused result can come back empty or short.
8. Parse response result.points[]: each item carries id, score, payload, and vector.
9. Reference: https://qdrant.tech/documentation/search/hybrid-queries/
10. Reference: https://api.qdrant.tech/api-reference/search/query-points