What are the benchmarked latency and recall rates for hybrid keyword and vector search in Milvus versus Qdrant for large-scale enterprise data?
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The searches the AI actually ran for this prompt.
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What are the benchmarked latency and recall rates for hybrid keyword and vector search in Milvus versus Qdrant for large-scale enterprise data?
3 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.
Classifying 3 queries…
Searches and sources
ChatGPT3 real searches · gpt-6
Milvus Qdrant hybrid search benchmark latency recall keyword vector large scale dataset Google ↗
Milvus benchmark hybrid search BM25 dense vector recall latency billion scale Google ↗
Qdrant hybrid search benchmark latency recall sparse dense vector benchmark Google ↗
- Vector Database Benchmark: 7 Open-Source Engines for RAG · aimultiple.com
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- Milvus vs Qdrant: Scalability, Performance, Features · milvus.io
- Hybrid Search in Qdrant · qdrant.tech
- Hybrid Search Vector Database: BM25 + Vectors · milvus.io
- How to Tune Hybrid Search in Qdrant · qdrant.tech
- Milvus 2.2 Benchmark Test Report · milvus.io
- Hybrid Queries · qdrant.tech
Queries
3 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
Milvus Qdrant hybrid search benchmark latency recall keyword vector large scale dataset OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 | |
Milvus benchmark hybrid search BM25 dense vector recall latency billion scale OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 | |
Qdrant hybrid search benchmark latency recall sparse dense vector benchmark OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 |