# Hybrid search: dense plus sparse in one index
## The pattern
On the Vectors API, each record carries both a dense vector (meaning) and a sparse vector (keyword signal). One query sends both; Pinecone combines them. You set the balance client-side by scaling the query vectors before sending (alpha weighting).
## Steps
1. **Create the index** with `metric="dotproduct"`. Hybrid on the Vectors API uses dotproduct.
2. **Produce both vectors per record.** Dense from your embedding model; sparse from a sparse model (e.g. pinecone-sparse-english-v0) or BM25 weights over your text. Records without sparse values silently degrade to dense-only.
3. **Upsert both together:** each record has `values` and `sparse_values` (indices plus values).
4. **Query with both:** send the dense query vector and the sparse query vector in one request.
5. **Tune alpha.** Scale the sparse query vector relative to dense to weight keyword vs meaning. There is no universal alpha; evaluate on your queries.
6. **Evaluate properly.** Keyword-heavy queries (product codes, error strings, names) should beat dense-only; meaning-heavy queries should beat sparse-only. If hybrid loses on both, the sparse vectors are bad, not the idea.
## Traps
1. Upserting dense only and expecting hybrid: the sparse side contributes nothing and you paid the complexity for zero gain.
2. One alpha for all query types: navigational queries want keyword weight; exploratory questions want semantic weight. Consider per-query-type alpha.
3. Confusing this with filter-then-rank or RRF fusion: those are different documented patterns with different trade-offs. Pick one deliberately.