Build high-performance vector search systems
qdrant-vector-searchskillsetup L3★9,423
Orchestra-Research/AI-Research-SKILLs ↗What it does
Search vectors with rich payload filtering, multi-vector storage, and distributed scaling
Best for
Production RAG requiring on-premise control, Rust performance, or complex payload filtering during search.
Inputs
- · vectors (any dimension)
- · payload (arbitrary JSON)
- · sparse vectors optional
Outputs
- · scored results
- · with payloads
Requires
- · qdrant-client>=1.12.0
- · Qdrant server (Docker or cloud)
Preconditions
Qdrant instance running (localhost:6333 or remote); collections pre-created
Failure modes
Filter syntax error returns empty results; distance metric mismatch invalidates scores; replication lag on distributed setup
Trust signals
- · Rust-powered memory safety
- · Rich filtering support
- · Raft consensus for distributed setup