Developer builds advanced RAG for book series with multi-stage retrieval

A developer built a retrieval-augmented generation (RAG) system for the "A Song of Ice and Fire" book series, which includes both a full-text search and a RAG-powered chat interface. The RAG system employs a multi-stage retrieval pipeline, starting with dense and sparse retrieval methods, followed by fusion and reranking, before finally generating an answer using Llama 3.3 70B. The developer emphasizes the importance of full-text search for certain queries and highlights the effectiveness of instruction-tuned embeddings and a robust reranking process for improving RAG performance. AI

IMPACT Demonstrates advanced RAG techniques that could improve information retrieval in specialized domains.

RANK_REASON Developer's personal project detailing a technical implementation of a RAG system. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

• A Song of Ice and Fire
• BAAI/bge-m3
• BM25Okapi
• ChromaDB
• FTS5
• Groq
• Llama 3.3 70B
• Uma Busca de Gelo e Fogo

• product
• paper

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