rag-implementation
Implement advanced RAG patterns with vector databases, embeddings, and LangGraph for grounded LLM applications.
Install
npx skills add https://github.com/wshobson/agents --skill rag-implementationStats
| Total installs | 4,924 |
| Weekly installs | 4.9K |
| GitHub stars | 32.4K |
| First seen | Jan 20, 2026 |
| Source | @wshobson/agents |
Summary
- Supports six vector database options (Pinecone, Weaviate, Milvus, Chroma, Qdrant, pgvector) and six embedding models optimized for different use cases and providers
- Covers five advanced retrieval patterns: hybrid search combining dense and sparse retrieval, multi-query generation, contextual compression, parent document retrieval, and HyDE (hypothetical document embeddings)
- Includes four document chunking strategies (recursive character, token-based, semantic, markdown header) and metadata filtering, MMR diversity balancing, and cross-encoder reranking for optimization
- Provides complete LangGraph implementation examples with async retrieval and generation nodes, plus evaluation metrics for measuring retrieval precision, recall, answer relevance, and faithfulness
Tags
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FAQ
- How many installs does rag-implementation have?
- rag-implementation has 4,924 total installs and 4.9K installs this week.
- Where is rag-implementation hosted?
- rag-implementation is published by @wshobson/agents at https://github.com/wshobson/agents.
- How many GitHub stars does rag-implementation have?
- rag-implementation has 32.4K GitHub stars.
- When was rag-implementation first indexed?
- OrangeBot.AI first indexed rag-implementation on Jan 20, 2026.
- How do I install rag-implementation?
- Run: npx skills add https://github.com/wshobson/agents --skill rag-implementation