Real ChatGPT answer · index seedgOeY2w1pJqIcompleteB2B software · AI and automation toolsinformational
How much does vector search accuracy improve when switching from basic keyword matching to advanced embedding models today?
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
How much does vector search accuracy improve when switching from basic keyword matching to advanced embedding models today?
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).
Searches and sources
ChatGPT2 real searches · gpt-6
2026 embedding models retrieval benchmark BM25 dense retrieval hybrid search accuracy comparison Google ↗
2025 2026 benchmark lexical BM25 vs dense embedding retrieval nDCG recall MTEB BEIR results hybrid search Google ↗
- GitHub - abhisheklakhani-it/retrieval-eval-bench: Benchmark of TF-IDF, BM25, dense (FAISS) and hybrid retrieval for RAG on BEIR SciFact, with recall@k, MRR, nDCG and significance tests · github.com
- Hybrid model · docs.mteb.org
- Embedding Models: 25 Models Benchmarked for Retrieval · aimultiple.com
- NVIDIA Nemotron embeddings for agentic search in OpenSearch · opensearch.org
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
2026 embedding models retrieval benchmark BM25 dense retrieval hybrid search accuracy comparison OBSERVED | Google ↗ AI Mode ↗ | — | Comparison | — | — | 1x of 1 | |
2025 2026 benchmark lexical BM25 vs dense embedding retrieval nDCG recall MTEB BEIR results hybrid search OBSERVED | Google ↗ AI Mode ↗ | — | Comparison | — | — | 1x of 1 |