Weaviate hybrid search versus Pinecone sparse dense vectors for optimizing vector search accuracy in e-commerce
Download run
Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
Weaviate hybrid search versus Pinecone sparse dense vectors for optimizing vector search accuracy in e-commerce
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
Weaviate hybrid search documentation BM25 vector alpha relative score fusion 2026 Google ↗
Pinecone hybrid search dense sparse vectors documentation sparse-dense single index 2026 Google ↗
- Hybrid search · docs.weaviate.io
- Query and search · docs.weaviate.io
- Introducing support for sparse-dense embeddings for better search result · pinecone.io
- Don’t be dense: Launching sparse indexes in Pinecone · pinecone.io
- Pinecone hybrid search documentation · docs.pinecone.io
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
Weaviate hybrid search documentation BM25 vector alpha relative score fusion 2026 OBSERVED | Google ↗ AI Mode ↗ | — | Other / navigational | — | — | 1x of 1 | |
Pinecone hybrid search dense sparse vectors documentation sparse-dense single index 2026 OBSERVED | Google ↗ AI Mode ↗ | — | Other / navigational | — | — | 1x of 1 |