rag-infrastructure
Build and operate Retrieval-Augmented Generation (RAG) infrastructure with vector stores, embedding pipelines, and hybrid search. Covers ingestion, chunking strategies, reranking, and production deployment patterns.
npx skills add majiayu000/claude-skill-registry --skill rag-infrastructure --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.
# RAG Infrastructure Production infrastructure for Retrieval-Augmented Generation: ingest documents, generate embeddings, store in vector databases, and serve grounded LLM responses. ## When to Use This Skill Use this skill when: - Building a knowledge base Q&A system over internal documents - Implementing semantic search over large document collections - Reducing LLM hallucinations with retrieved context - Setting up embedding pipelines and vector store infrastructure - Deploying hybrid search (dense + sparse/BM25) ## Prerequisites - Python 3.10+ with `pip` - A vector database (Qdrant, Weaviate, Pinecone, or pgvector) - An embedding model (OpenAI, Cohere, or local via `sentence-transformers`) - An LLM endpoint (OpenAI API or self-hosted vLLM) - Docker for local vector DB deployment ## Architecture Overview ``` Documents → Chunker → Embedder → Vector Store ↓ User Query → Embedder → Vector Store (search) → Reranker → LLM → Answer ``` ## Embedding Pipeline ```python from sentence_transformers import SentenceTransformer from qdrant_client import QdrantClient from qdrant_client.models import Distance, VectorParams, PointStruct import uuid # Local embedding model (no API cost) model = S
- When to Use This Skill
- Prerequisites
- Architecture Overview
- Embedding Pipeline
- Chunking Strategies
- Hybrid Search (Dense + Sparse)
- Reranking
- RAG Query Pipeline
- Docker Compose: Full RAG Stack
- Common Issues
- Best Practices
- Related Skills
What does the rag-infrastructure skill do?
Build and operate Retrieval-Augmented Generation (RAG) infrastructure with vector stores, embedding pipelines, and hybrid search. Covers ingestion, chunking strategies, reranking, and production deployment patterns.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill rag-infrastructure --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.