Tune Qdrant collection HNSW graph parameters and enable on-disk payload indexing for large collections

Steps

1. At collection creation set hnsw_config.m (number of bidirectional links per node, default 16) and hnsw_config.ef_construct (candidate list size during build, default 100) in the vectors config
2. Higher m (e.g. 32–64) improves recall at the cost of more memory and slower indexing; higher ef_construct improves index quality at build time but does not affect query speed
3. Set hnsw_config.on_disk=true to store the HNSW graph on disk rather than RAM for collections larger than available memory — expect higher query latency
4. Enable on-disk payload storage by setting payload_storage_type='on_disk' at collection creation to avoid RAM exhaustion for large metadata payloads
5. Create payload indexes for filtered searches: POST /collections/{name}/index with field_name and field_schema (e.g. 'integer', 'keyword') so Qdrant can prune candidates before vector comparison
6. Tune ef (search-time candidate list size) per query via params.hnsw_ef in the search request — higher values improve recall at the cost of latency