How do we evaluate vector database providers when our primary use case is hybrid search combining keyword and semantic queries
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
How do we evaluate vector database providers when our primary use case is hybrid search combining keyword and semantic queries
2 real searches · 1 real-search pass
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
ChatGPT real searches (2)
Classes come from the query-classifier (a local Laya model). 2 of 2 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT2 real searches · gpt-6
vector database hybrid search BM25 dense vector fusion documentation Qdrant Weaviate Pinecone Elasticsearch 2026 Google ↗
hybrid search vector database evaluation benchmark BM25 reciprocal rank fusion score normalization documentation Google ↗
- Hybrid Queries · qdrant.tech
- Hybrid Search · qdrant.tech
- Hybrid search · docs.weaviate.io
- Hybrid search · docs.weaviate.io
- Hybrid search · docs.opensearch.org
- Reciprocal rank fusion · docs.opensearch.org
- Hybrid search · elastic.co
- Elasticsearch Vector Database project overview · elastic.co
- Hybrid search documentation · docs.opensearch.org
- Pinecone documentation · docs.pinecone.io
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
vector database hybrid search BM25 dense vector fusion documentation Qdrant Weaviate Pinecone Elasticsearch 2026 OBSERVED | Google ↗ AI Mode ↗ | — | Other / navigational | — | — | 1x of 1 | |
hybrid search vector database evaluation benchmark BM25 reciprocal rank fusion score normalization documentation OBSERVED | Google ↗ AI Mode ↗ | — | Instruction | — | — | 1x of 1 |