huangjia2019/rag-in-action
End-to-end RAG system design, evaluation, and optimization. 极客时间RAG训练营，RAG 10大组件全面拆解，4个实操项目吃透 RAG 全流程。RAG的落地，往往是面向业务做RAG，而不是反过来面向RAG做业务。这就是为什么我们需要针对不同场景、不同问题做针对性的调整、优化和定制化。魔鬼全在细节中，我们深入进去探究。
Implements 10 modular RAG components spanning data loading, chunking strategies, embedding with BGE/HuggingFace, vector storage (Milvus/Chroma), query expansion, re-ranking, and LLM integration with DeepSeek. Provides dual framework support for LangChain and LlamaIndex with GPU/CPU variants across Ubuntu, macOS, and Windows, including evaluation tools like RAGAS and TruLens for system performance benchmarking.
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