moai-ml-rag
Retrieval-Augmented Generation systems, vector databases, embedding strategies, and production RAG architectures for enterprise LLM applications. Use when building RAG, semantic search, or knowledge-aware AI systems.
npx skills add majiayu000/claude-skill-registry --skill moai-ml-rag-jg-chalk-io-nora-livekit --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Retrieval-Augmented Generation (RAG) — Enterprise Production-grade RAG systems combining semantic search, vector databases, and LLM generation. ## Quick Start **5-Minute RAG with LangChain**: ```python from langchain.vectorstores import Pinecone from langchain.embeddings import OpenAIEmbeddings from langchain.llms import OpenAI from langchain.chains import RetrievalQA # 1. Create embeddings embeddings = OpenAIEmbeddings() # 2. Setup vector store vectorstore = Pinecone.from_documents( documents, embeddings, index_name="docs" ) # 3. Create RAG chain qa = RetrievalQA.from_chain_type( llm=OpenAI(), retriever=vectorstore.as_retriever(search_kwargs={"k": 3}) ) # 4. Ask questions answer = qa.run("What is the refund policy?") ``` **Auto-triggers**: RAG, retrieval, vector search, semantic search, embedding, knowledge base, Q&A --- ## Core Concepts ### RAG vs Fine-tuning | Aspect | RAG | Fine-tuning | | --------------------- | --------------------- | --------------------- | | **Knowledge Updates** | Instant (update docs) | Requires retraining | | **Cost** | Low ($50-500/mo) | High ($1000-10000) | | **Time to Deploy** | Minutes | Days/weeks | | **Knowledge Scope** | Unlimited | Fixed (train
- Quick Start
- Core Concepts
- RAG vs Fine-tuning
- The 4-Step RAG Pipeline
- Vector Databases
- Comparison
- Pinecone Example
- Embedding Models
- Model Comparison (2025)
- Usage Example
- Chunking Strategies
- Best Practices
- RAG Evaluation Metrics
- Key Metrics
What does the moai-ml-rag skill do?
Retrieval-Augmented Generation systems, vector databases, embedding strategies, and production RAG architectures for enterprise LLM applications. Use when building RAG, semantic search, or knowledge-aware AI systems.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill moai-ml-rag-jg-chalk-io-nora-livekit --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.