What problem does it solve? Vector search systems often suffer from high latency, poor recall, or excessive memory consumption when index parameters are left at defaults. This Skill provides concrete guidance and code templates for tuning HNSW parameters, applying quantization, and monitoring search performance in production. ## Core Features & Use Cases - HNSW Parameter Tuning: Benchmark M, efConstruction, and efSearch values against recall and latency targets with ready-to-use Python templates. - Quantization Strategies: Implement INT8 scalar, product, and binary quantization to reduce memory footprint by up to 32x. - Qdrant Configuration: Create optimized Qdrant collections with preset configurations for recall, speed, balanced, or memory-optimized workloads. - Use Case: When your vector database with 10 million embeddings exceeds memory limits, use the quantization templates to compress vectors with INT8 or product quantization while maintaining 95% recall. ## Quick Start Ask the AI to recommend HNSW parameters for your dataset size, target recall, and latency budget using the vector index tuning guidance.
