What strategies can be used to compress or quantize not just the vectors but also the index metadata (such as storing pointers or graph links more compactly) to save space?

To compress vectors and index metadata efficiently, developers can apply quantization, encoding optimizations, and structural adjustments. For vectors, techniques like scalar quantization reduce precision (e.g., 32-bit floats to 8-bit integers), while product quantization divides vectors into subvectors and compresses each separately. Binary quantization simplifies vectors to compact bit representations. These methods reduce storage but require balancing accuracy. For metadata, graph-based indexes like HNSW can use delta encoding and variable-length codes (e.g., Elias-Fano) to store neighbor lists more efficiently. Tree-based indexes like Annoy can use bitmasks or smaller integer types for pointers, reducing overhead without losing essential information.

Metadata compression often involves rethinking data structures. In graph indexes, adjacency lists can be optimized by grouping nodes with similar connections and storing shared patterns once. For example, HNSW could encode the difference between consecutive neighbor IDs (delta encoding) and apply a variable-byte scheme to represent small deltas in fewer bytes. Tree structures can use hierarchical bitmasks to represent paths compactly—for instance, a 16-bit mask could encode a path depth of 16 in a binary tree. Additionally, using fixed-size arrays instead of dynamic lists for small neighbor sets (e.g., limiting links to 16 neighbors) avoids pointer overhead.

Hybrid approaches combine vector and metadata compression. For instance, a product-quantized HNSW index might store compressed vectors alongside delta-encoded neighbor lists. Developers should also consider trade-offs: aggressive compression can increase query latency due to decoding costs. For example, binary quantization speeds up distance calculations via bitwise operations but sacrifices precision. Testing is critical—comparing metrics like recall rate and latency helps identify the right balance. Open-source libraries (FAISS, Annoy) offer built-in compression options, allowing developers to experiment with configurations like PQ-16 (16-byte product quantization) or adjusting graph link limits without rebuilding from scratch.