Can you show me how to implement hybrid search combining BM25 keyword matching with dense vector retrieval in Weaviate for better context recall
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
Can you show me how to implement hybrid search combining BM25 keyword matching with dense vector retrieval in Weaviate for better context recall
1 real search · 1 real-search pass
Weaviate hybrid search BM25 vector search alpha relativeScoreFusion rankedFusion Python client v4 documentationGoogle ↗
OBSERVEDOther / navigational
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 (1)
Classes come from the query-classifier (a local Laya model). 1 of 1 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT1 real searches · gpt-6
Weaviate hybrid search BM25 vector search alpha relativeScoreFusion rankedFusion Python client v4 documentation Google ↗
- Hybrid search · docs.weaviate.io
- Hybrid search · docs.weaviate.io
- docs/docs/weaviate/search/hybrid.md at main · weaviate/docs · github.com
- Query and search · docs.weaviate.io