vector-index-tuning - Tune vector indexes for production performance
Optimize vector index latency, recall, and memory through HNSW tuning, quantization, and scalable search configurations.
Tags
Updated: 2026-09-29Capabilities
Typical Inputs
Typical Outputs
What this skill does
- Tune HNSW parameters
- Benchmark index configurations
- Apply vector quantization
- Estimate memory usage
- Recommend HNSW settings
- Configure Qdrant collections
Inputs
- Vector data
- Query vectors
- Ground-truth neighbors
- Index requirements
- Qdrant client
- Collection settings
Outputs
- Benchmark results
- HNSW recommendations
- Quantized vectors
- Quantization parameters
- Memory estimates
- Qdrant configuration
Requirements
- Python environment
- NumPy
- hnswlib
- scikit-learn
- qdrant-client