Hybrid RRF retrieval fixes RAG failures on Kannada literature

A developer detailed the challenges of building a Retrieval-Augmented Generation (RAG) system for a scanned Kannada novel, highlighting that retrieval, not the language model, was the primary bottleneck. The initial approach using standard multilingual embeddings in ChromaDB failed due to Kannada's agglutinative nature and the scarcity of literary text, leading to inaccurate and hallucinated responses. The solution involved a hybrid retrieval system combining BM25 and dense embeddings with Reciprocal Rank Fusion, alongside a regex router for exact page queries, significantly improving faithfulness and context recall. AI

IMPACT Demonstrates how advanced retrieval techniques can overcome LLM limitations in specialized language contexts.

RANK_REASON Developer shares a technical solution to a specific problem in building a RAG system.

Read on dev.to — LLM tag →

• BM25
• chromadb
• Gemini
• Heli Hogu Kaarana
• Himavanta creatures
• Kannada
• MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers
• RAGAS
• Ravi Belagere
• reciprocal rank fusion

• product
• other

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