rag-implementation
De la autor
Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.
Instalare
npx skills add aisa-group/skill-inject --skill rag-implementation -a claude-code
Se instalează în .claude/skills/rag-implementation/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
Identic octet cu octet cu commit-ul scanat, cu licența depozitului și un fișier SOURCE.txt.
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Verificări de siguranță pe SKILL.md
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De ce 81
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Skill-uri similare
Cele mai apropiate de acesta prin ce fac și când sunt folosite.
- ragskillmds/skillmdImplements document chunking, embedding generation, vector storage, and retrieval pipelines for Retrieval-Augmented Generation systems. Use when building RAG applications, creating document Q&A systems, or integrating AI with knowledge bases.Altele★ 191
- rag-engineertranhieutt/software_development_departmentProvides Retrieval-Augmented Generation patterns covering embedding models, vector databases, chunking strategies, and retrieval optimization. Use when building RAG systems or when the user mentions RAG, vector search, embeddings, or retrieval-augmented generation.Altele★ 74
- langchain4j-rag-implementation-patternsgiuseppe-trisciuoglio/developer-kitProvides Retrieval-Augmented Generation (RAG) implementation patterns with LangChain4j for Java. Generates document ingestion pipelines, embedding stores, vector search, and semantic search capabilities. Use when building chat-with-documents systems, document Q&A over PDFs or text files, AI assistants with knowledge bases, semantic search over document repositories, or knowledge-enhanced AI applications with source attribution.Altele★ 366
- rag-architectJeffallan/claude-skillsDesigns and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying reranking, and evaluating retrieval quality. Use when building RAG systems, vector databases, or knowledge-grounded AI applications requiring semantic search, document retrieval, context augmentation, similarity search, or embedding-based indexing.Planificare★ 12.1K
- rag-infrastructureBagelHole/DevOps-Security-Agent-SkillsBuild and operate Retrieval-Augmented Generation (RAG) infrastructure with vector stores, embedding pipelines, and hybrid search. Covers ingestion, chunking strategies, reranking, and production deployment patterns.DevOps★ 1.2K
- rag-design-docmohitagw15856/pm-claude-skillsDesign a Retrieval-Augmented Generation system end to end. Use when asked to design a RAG pipeline, a 'chat with your docs' feature, a knowledge assistant, or to debug why a RAG system gives wrong/ungrounded answers. Produces a RAG design doc — ingestion & chunking, embeddings & index, retrieval & reranking, the generation prompt, grounding/citations, evaluation, and failure modes with mitigations.Design★ 1.5K
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