vector-index-tuning

Tune HNSW parameters by adjusting M, efConstruction, and efSearch to balance recall and latency in vector search. Increasing M and efConstruction improves graph connectivity and recall during index building, while efSearch controls query-time accuracy and speed.

Reduce memory usage in large-scale similarity search by applying quantization strategies like INT8, Product Quantization (PQ), or Binary quantization. These techniques compress vector embeddings, significantly lowering the memory footprint while maintaining acceptable search accuracy.

Yes, you can optimize vector search for 1 million vectors by tuning HNSW parameters and applying quantization to achieve low-latency results with acceptable recall. The approach estimates memory requirements and recommends parameters for large-scale recommendation systems.

Product Quantization works for vector index performance tuning by compressing high-dimensional vectors into smaller subvectors, reducing memory footprint. It divides the vector space into subspaces and quantizes each independently, enabling efficient similarity search with minimal recall degradation.

INT8 quantization for similarity search compresses vectors to 8-bit integers, which reduces memory but may cause slight recall degradation compared to full precision. It is effective for large datasets where memory savings outweigh minor accuracy losses, but requires careful tuning.

Yes, you need hnswlib, qdrant-client, scikit-learn, and numpy to implement vector index tuning. These libraries provide the foundational tools for building HNSW indexes, connecting to vector databases, computing metrics, and handling numerical operations required for optimization.