Qdrant Vector Search
Skill Verified ActiveHigh-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.
To enable users to build production-ready RAG systems by providing a high-performance, scalable vector similarity search engine.
Features
- High-performance vector similarity search
- Integration for RAG and semantic search
- Scalable vector storage with Rust-powered performance
- Hybrid search with metadata filtering
- Examples for common RAG frameworks (LangChain, LlamaIndex)
Use Cases
- Building production RAG systems requiring low latency
- Implementing hybrid search (vectors + metadata filtering)
- Deploying scalable vector storage with full data control
- Developing real-time recommendation systems
Non-Goals
- Simpler setup for embedded use cases (use Chroma instead)
- Maximum raw speed for research/batch processing (use FAISS instead)
- Fully managed zero-ops solutions (use Pinecone instead)
Workflow
- Connect to Qdrant instance
- Create a collection with vector parameters
- Upsert points (vectors + payload)
- Perform search or filtered search operations
- Integrate results into RAG pipeline
Prerequisites
- Qdrant client library
- Qdrant server instance (local, Docker, or cloud)
Installation
First, add the marketplace
/plugin marketplace add Orchestra-Research/AI-Research-SKILLs/plugin install AI-Research-SKILLs@ai-research-skillsQuality Score
VerifiedTrust Signals
Similar Extensions
Embedding Strategies
100Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.
AgentDB Vector Search
99Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
Qdrant Vector Search
95High-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.
Rag Architect
100Use when the user asks to design RAG pipelines, optimize retrieval strategies, choose embedding models, implement vector search, or build knowledge retrieval systems.
Embeddings
99Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration. Use when: semantic search, pattern matching, similarity queries, knowledge retrieval. Skip when: exact text matching, simple lookups, no semantic understanding needed.
Vector Search Workflows
99Vector search indexing and querying workflows using MCP Vector Search, including setup, reindexing, auto-index strategies, and MCP integration.