llm-rag
Build and optimize Retrieval-Augmented Generation (RAG) pipelines. Covers document loading and parsing, chunking strategies (fixed-size, semantic, recursive, sentence-based), embedding models (OpenAI, Sentence Transformers, Cohere), vector stores (Chroma, Pinecone, Weaviate, Qdrant, pgvector, FAISS), hybrid search (dense + sparse, BM25 + embeddings), reranking (Cohere, cross-encoders), query transformation, multi-step retrieval, agentic RAG, evaluation (RAGAS), context window optimization, metadata filtering, multi-modal RAG, and production RAG architecture. Use when building RAG systems, improving retrieval quality, or deploying knowledge-augmented LLM applications.
- Version
- 1.0
- License
- Apache-2.0
Pinned to revision 45cf0fa3c5e7, so it is the text this page describes rather than whatever the author pushed since.
Files
- skills/llm-rag/SKILL.md
- skills/llm-rag/references/REFERENCE.md
- skills/llm-rag/scripts/build_rag.py
- skills/llm-rag/scripts/evaluate_rag.py
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