pgvector hnsw.ef_search = 40
pgvector index: CREATE INDEX vs_bench_index ON public.vs_bench USING hnsw (embedding vector_cosine_ops)
elasticsearch embedding mapping: "embedding":{"type":"dense_vector","dims":384,"index":true,"similarity":"cosine","index_options":{"type":"bbq_hnsw","m":16,"ef_construction":100,"rescore_vector":{"oversample":3.0}}}
qdrant points_count=30000 indexed_vectors_count=30000
qdrant hnsw_config: "hnsw_config":{"m":16,"ef_construct":100,"full_scan_threshold":10000,"max_indexing_threads":0,"on_disk":false}
qdrant indexing_threshold (KB): 10000
redis num_docs=30000 percent_indexed=1