Hierarchical Reranking Unleashed: A Step-by-Step Implementation of Enhanced RAG In Retrieval-Augmented Generation (RAG), The most toughest part is answer relevance and hallucinations. Conventional …

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This technical guide demonstrates implementing hierarchical reranking in Retrieval-Augmented Generation systems to improve answer accuracy and reduce hallucinations. The architecture combines internal knowledge retrieval from Qdrant vector database with external web search, using LlamaIndex agents to orchestrate a two-stage reranking process. First, retrieved nodes are reranked against the user query, then further refined using external web context. The implementation uses Gemini embeddings and LLMs, with complete code examples showing agent creation, vector store indexing, and evaluation metrics. Results show perfect correctness scores on sample queries from a pulmonology knowledge base.