vector-index-tuning

Tune HNSW parameters like M, efConstruction, and efSearch to balance the tradeoff between search latency and recall. Adjusting M increases graph connectivity and memory, while efSearch controls search depth and query accuracy.

Reduce vector database memory footprint by applying quantization strategies such as INT8 scalar quantization, Product Quantization (PQ), or binary quantization. These methods compress embedding representations while maintaining acceptable search recall.

Yes, you can use scikit-learn and qdrant-client to optimize similarity search for millions of embeddings. The skill leverages these dependencies to estimate memory usage and implement quantization for large-scale vector search applications.

Estimate memory requirements for 1 million vectors by calculating HNSW graph size based on the M parameter and applying quantization strategies. Memory estimation helps determine if strict latency and memory constraints are achievable.

INT8 scalar quantization compresses individual vector dimensions into 8-bit integers, while Product Quantization divides vectors into subvectors and compresses each independently. Both reduce memory footprint but impact recall and search latency differently.

Yes, vector index tuning works with hnswlib to optimize performance. The skill uses hnswlib to implement HNSW parameter tuning, allowing you to adjust efConstruction and efSearch to meet specific recall and latency targets.