What are the exact criteria I should use to benchmark retrieval latency when choosing a vector database for a real-time customer recommendation engine?
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
What are the exact criteria I should use to benchmark retrieval latency when choosing a vector database for a real-time customer recommendation engine?
1 real search · 1 real-search pass
vector database benchmarking retrieval latency p95 p99 recall under load ANN benchmark methodology production workloadGoogle ↗
OBSERVED
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 1 queries…
Searches and sources
ChatGPT1 real searches · gpt-6
vector database benchmarking retrieval latency p95 p99 recall under load ANN benchmark methodology production workload Google ↗
- ANN Benchmark · docs.weaviate.io
- Performance Testing and Analysis · doris.apache.org
- Industry dataset · reinvent.awsevents.com
- VectorDBBench · github.com
- GitHub - zilliztech/VectorDBBench: Benchmark for vector databases. · github.com
- Documentation · qdrant.tech
- Documentation · milvus.io
- Documentation · docs.weaviate.io
- Documentation · docs.pinecone.io
- Documentation · redis.io