qdrant-vector-search
High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.
npx skills add Orchestra-Research/AI-Research-SKILLs --skill qdrant --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Qdrant - Vector Similarity Search Engine High-performance vector database written in Rust for production RAG and semantic search. ## When to use Qdrant **Use Qdrant when:** - Building production RAG systems requiring low latency - Need hybrid search (vectors + metadata filtering) - Require horizontal scaling with sharding/replication - Want on-premise deployment with full data control - Need multi-vector storage per record (dense + sparse) - Building real-time recommendation systems **Key features:** - **Rust-powered**: Memory-safe, high performance - **Rich filtering**: Filter by any payload field during search - **Multiple vectors**: Dense, sparse, multi-dense per point - **Quantization**: Scalar, product, binary for memory efficiency - **Distributed**: Raft consensus, sharding, replication - **REST + gRPC**: Both APIs with full feature parity **Use alternatives instead:** - **Chroma**: Simpler setup, embedded use cases - **FAISS**: Maximum raw speed, research/batch processing - **Pinecone**: Fully managed, zero ops preferred - **Weaviate**: GraphQL preference, built-in vectorizers ## Quick start ### Installation ```bash # Python client pip install qdrant-client # Docker (recom
- When to use Qdrant
- Quick start
- Installation
- Basic usage
- Core concepts
- Points - Basic data unit
- Collections - Vector containers
- Distance metrics
- Search operations
- Basic search
- Filtered search
- Batch search
- RAG integration
- With sentence-transformers
Python client pip install qdrant-client Docker (recommended for development) docker run -p 6333:6333 -p 6334:6334 qdrant/qdrant Docker with persistent storage docker run -p 6333:6333 -p 6334:6334 \ qdrant/qdrant
What does the qdrant-vector-search skill do?
High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.
How do I install it?
Run `npx skills add Orchestra-Research/AI-Research-SKILLs --skill qdrant --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From Orchestra-Research/AI-Research-SKILLs, a repository with 11,391 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.