Advanced representations reduce storage or first-stage search work by discarding or reorganizing information. halfvec lowers precision, binary quantization converts dense values to bits, sparsevec stores non-zero coordinates, and subvector indexes search a coordinate slice. Use them for candidate generation, then rerank with original vectors when needed and measure quality by critical slice.
Query expressions must match the index. For binary search, retrieve a larger candidate set by Hamming distance and rerank those rows using full-vector cosine or the model's metric.
An expression index over subvector(embedding, start, count) can accelerate a coarse first stage. Retrieve candidates with the same expression and rerank by the full vector. Coordinate slices are not guaranteed to preserve semantic importance uniformly, so evaluate rather than assuming.
Use it for models or engineered features with many true zeros and a supported metric. It stores index/value pairs and total dimensions. Do not sparsify a dense model arbitrarily without a validated transformation and quality experiment.
For each representation, record table/index size, build time, write latency, candidate latency, full rerank latency, exact recall, task metrics, and slice regressions. Confirm plans use expression indexes. Test model migrations because expression dimensions are encoded in DDL.
Design a controlled comparison of full vector, halfvec, binary quantization, sparsevec, and subvector candidate search for [workload]. Generate matching DDL/query expressions, reranking SQL, candidate-depth sweep, size/build/write measures, and slice-level quality gates.
Verification contract: Keep the original exact vectors and baseline until the alternate representation passes held-out quality and rollback rehearsal.