Optimize Vector Index Performance

Provides an index-type selection guide by data size - flat exact search under 10K vectors, HNSW from 10K to 1M, HNSW plus quantization from 1M to 100M, and IVF+PQ or DiskANN above 100M - the three tunable HNSW parameters (M controlling connections per node, where higher means better recall but more memory; efConstruction controlling build quality, where higher means a better index but slower build; and efSearch controlling search quality, where higher means better recall but slower search, each with its default value), and five quantization types by memory footprint: full precision FP32 at 4 bytes per dimension, half precision FP16 at 2 bytes, INT8 scalar at 1 byte, product quantization at roughly 32-64 bytes total regardless of dimension count, and binary quantization at dimensions divided by 8 bytes. Ships four templates: HNSW parameter tuning, quantization strategy selection, Qdrant-specific index configuration, and performance monitoring - a SearchMetrics dataclass capturing p50, p95, and p99 latency, recall, and QPS, a VectorSearchMonitor class that benchmarks a search function across repeated iterations and computes recall against a supplied ground-truth function by comparing predicted and actual result sets, and a profile_index_build function that times index construction across multiple batch sizes to report average batch time and vectors-per-second throughput.

Use it when choosing a vector index type for a given data size, tuning HNSW parameters, selecting a quantization strategy to trade memory for recall, configuring a Qdrant index, or benchmarking vector search latency and recall. Skip it for anything unrelated to vector index tuning or outside this scope.

Input is the vector dataset size, latency and memory constraints, and a target recall; output is the matching index type and HNSW parameter values, a quantization strategy with its memory tradeoff, a Qdrant configuration, and a SearchMetrics benchmark proving the chosen configuration hits its recall and latency targets.

Framework-agnostic index and parameter guidance applicable to HNSW-based vector stores generally, with one template specific to Qdrant's index configuration. The monitoring template uses NumPy for percentile latency calculations and works with any callable search function.

Engineers tuning a vector search index for production who need concrete parameter values and a way to measure the actual latency and recall tradeoff rather than guessing at HNSW settings. Also covers balancing the recall-versus-speed tradeoff directly, and scaling an index up toward billions of vectors.