Qdrant Vector Search
技能 已验证 活跃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.
To provide a robust and performant vector similarity search engine for building production-ready RAG systems and semantic search applications.
功能
- High-performance vector search
- Support for RAG and semantic search
- Hybrid search (vectors + metadata filtering)
- Scalable vector storage
- Rust-powered performance
使用场景
- Building production RAG systems
- Implementing fast nearest neighbor search
- Developing scalable vector storage solutions
- Enabling real-time recommendation systems
非目标
- Simpler embedded use cases (recommend Chroma)
- Maximum raw speed for batch processing (recommend FAISS)
- Fully managed, zero-ops solutions (recommend Pinecone)
- Preference for GraphQL interfaces (recommend Weaviate)
Trust
- info:Issues AttentionWith 17 open and 4 closed issues in the last 90 days, the closure rate is below the optimal threshold, suggesting slower response times.
安装
npx skills add davila7/claude-code-templates通过 npx 运行 Vercel skills CLI(skills.sh)— 需要本地安装 Node.js,以及至少一个兼容 skills 的智能体(Claude Code、Cursor、Codex 等)。前提是仓库遵循 agentskills.io 格式。
质量评分
已验证类似扩展
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.
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.
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.
Mongodb Search And Ai
100指导 MongoDB 用户实现和优化 Atlas Search（全文搜索）、Vector Search（语义搜索）和 Hybrid Search 解决方案。当用户需要为文本查询（自动完成、模糊匹配、分面搜索）、语义相似性（嵌入、RAG 应用）或组合方法构建搜索功能时，请使用此技能。当用户需要文本包含、子字符串匹配（“包含”、“包括”、“出现在”）、不区分大小写或多字段文本搜索，或跨多个字段进行具有可变组合的过滤时，也请使用此技能。提供有关选择正确的搜索类型、创建索引、构建查询和使用 MongoDB MCP 服务器优化性能的工作流。
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.
V3 Memory Unification
99Unify 6+ memory systems into AgentDB with HNSW indexing for 150x-12,500x search improvements. Implements ADR-006 (Unified Memory Service) and ADR-009 (Hybrid Memory Backend).