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SOLUTION BLUEPRINT
Retrieval-Augmented Generation (RAG) Architecture
Production RAG goes beyond simple semantic search — requiring document parsing, semantic chunking, hybrid retrieval re-ranking, and context window compression. Discover verified RAG orchestration libraries.
📐 Architectural Requirements
- Semantic document parsing and recursive chunking strategies
- Hybrid BM25 + Vector retrieval with cross-encoder re-ranking
- Context window compression and conversation history summarization
- Evaluation metrics: Context Relevance, Faithfulness, and Answer Correctness
🎯 Decision & Evaluation Criteria
- Support for diverse document types (PDF, Markdown, HTML, Code)
- Integration with state-of-the-art re-ranking models (Cohere, BGE)
- Built-in RAG evaluation and hallucination detection tools
Featured Vetted Repositories
Top open-source packages indexed for this solution blueprint.
LlamaIndex
Data framework for connecting custom data sources to LLMs.
LangChain
Comprehensive toolsuite for building RAG pipelines and vector retrievers.
ChromaDB
Embedded vector database optimized for quick RAG prototyping.