ai-rag
FeaturedDesigns retrieval-augmented generation and search systems. Use when choosing retrieval, chunking, hybrid search, grounding, or RAG evaluation patterns.
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Quality Score: 89/100
Skill Content
Details
- Author
- vasilyu1983
- Repository
- vasilyu1983/AI-Agents-public
- Created
- 9 months ago
- Last Updated
- 1 weeks ago
- Language
- Python
- License
- MIT
Integrates with
Similar Skills
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rag-and-agents
Production reference for RAG (Retrieval-Augmented Generation) and AI agent development covering document parsing, chunking strategies (parent-child, contextual retrieval), embedding models, vector databases, hybrid search with reranking, GraphRAG, RAGAS evaluation, agent frameworks (LangGraph, CrewAI, Microsoft Agent Framework, Foundry Agent Service), MCP, multi-agent patterns, computer use, and Azure-native RAG (Azure AI Search, Foundry IQ). Use when designing or debugging RAG pipelines, choosing vector databases, building agent systems, evaluating retrieval quality, or architecting Azure AI Search solutions.
rag
Use when building retrieval-augmented generation. Covers chunking, embedding and hybrid search, reranking, grounding and citation, and diagnosing whether a bad answer is a retrieval failure or a generation failure.
rag-architect
Use when building RAG systems, vector databases, or knowledge-grounded AI applications requiring semantic search, document retrieval, or context augmentation.