Quick overview
RAG Architect covers the full lifecycle of retrieval-augmented generation pipelines, from document chunking and embedding selection to vector database setup, retrieval strategy tuning, and production deployment. It provides concrete guidance on tradeoffs between chunking methods, embedding models, and retrieval approaches including hybrid search and reranking.
Covers the entire RAG stack in one place, including tradeoffs that are typically scattered across separate documentation for chunking libraries, vector databases, and evaluation frameworks.
Common tasks
- Building a Q&A chatbot over internal company documentation
- Choosing a vector database for a production deployment
- Debugging low retrieval precision in an existing RAG system
- Setting up RAGAS evaluation for a knowledge base search feature
- Reducing embedding API costs for a large document corpus
Install paths
Primary command
openclaw install alirezarezvani/rag-architect
ClawHub installer
npx clawhub@latest install alirezarezvani/rag-architect
OpenClaw CLI
openclaw skills install alirezarezvani/rag-architect
Direct OpenClaw install
openclaw install alirezarezvani/rag-architect
Skill metadata
- Category: Coding Agents & IDEs
- Language: Markdown
- Version: 2.1.1
- Security status: Benign
Review upstream source
The full public SKILL.md body is not directly fetchable for this entry right now, so this page is using the best available catalog metadata. Review the upstream source page for the latest files, version history, and security scan details: https://clawhub.ai/alirezarezvani/rag-architect
