vector-index-tuning - Optimize Vector Index Performance
Optimize vector index latency, recall, and memory by tuning HNSW parameters, selecting quantization strategies, and scaling search infrastructure.
Tags
Updated: 2026-09-29Capabilities
Typical Inputs
Typical Outputs
What this skill does
- Tune HNSW parameters
- Select index types
- Apply vector quantization
- Estimate memory usage
- Benchmark search performance
- Configure Qdrant collections
Inputs
- Vector datasets
- Query vectors
- Ground-truth results
- Index parameters
- Recall targets
- Latency limits
- Memory limits
- Qdrant client
Outputs
- HNSW parameter recommendations
- Benchmark results
- Quantized vectors
- Quantization parameters
- Memory usage estimates
- Qdrant collection configurations
Requirements
- Python environment
- NumPy
- hnswlib for HNSW benchmarks
- scikit-learn for product quantization
- qdrant-client for Qdrant configuration