building-rag-systems
Build production RAG systems with LangChain orchestration and Qdrant vector store. Covers 8 RAG architectures (Simple, HyDE, CRAG, Self-RAG, Agentic), document processing, semantic chunking, retrieval chains, and evaluation with LangSmith/RAGAS. Use when implementing RAG pipelines, semantic search, or AI knowledge systems. NOT for simple keyword search.
npx skills add majiayu000/claude-skill-registry --skill building-rag-systems --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.
# Building RAG Systems Production-grade RAG with LangChain, Qdrant, and advanced retrieval patterns. ## Quick Start (LangChain) ```bash # Dependencies pip install langchain langchain-qdrant langchain-openai langchain-text-splitters pip install qdrant-client fastembed # For hybrid search # Start Qdrant docker run -p 6333:6333 -p 6334:6334 qdrant/qdrant ``` ```python from langchain_openai import OpenAIEmbeddings from langchain_qdrant import QdrantVectorStore from langchain_text_splitters import RecursiveCharacterTextSplitter # Load and split documents text_splitter = RecursiveCharacterTextSplitter( chunk_size=1000, chunk_overlap=200, add_start_index=True, ) splits = text_splitter.split_documents(docs) # Create vector store embeddings = OpenAIEmbeddings(model="text-embedding-3-small") vector_store = QdrantVectorStore.from_documents( splits, embeddings, url="http://localhost:6333", collection_name="my_docs", ) # Search results = vector_store.similarity_search("query", k=4) ``` --- ## RAG Architectures (8 Patterns) | Pattern | When to Use | Key Feature | |---------|-------------|-------------| | **Simple RAG** | FAQ, known scope | Single retrieval → generate | | **+Memory** | Conversati
- Quick Start (LangChain)
- RAG Architectures (8 Patterns)
- Quick Start (Raw Qdrant)
- Ingestion Pipeline
- Architecture
- Semantic Chunking (NOT Fixed-Size)
- Change Detection (Incremental Updates)
- Batched Embeddings
- Qdrant Collection with Payload Indexes
- Retrieval Patterns
- Comprehensive Filter Builder
- Context Expansion (Walk Chunk Chain)
- Full Document Retrieval
- Payload Schema
Dependencies pip install langchain langchain-qdrant langchain-openai langchain-text-splitters pip install qdrant-client fastembed # For hybrid search Start Qdrant docker run -p 6333:6333 -p 6334:6334 qdrant/qdrant pip install qdrant-client openai pydantic python-frontmatter Core components
What does the building-rag-systems skill do?
Build production RAG systems with LangChain orchestration and Qdrant vector store. Covers 8 RAG architectures (Simple, HyDE, CRAG, Self-RAG, Agentic), document processing, semantic chunking, retrieval chains, and evaluation with LangSmith/RAGAS. Use when implementing RAG pipelines, semantic search, or AI knowledge systems. NOT for simple keyword search.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill building-rag-systems --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.