HybridRAG: An Innovative RAG Framework for Chatbots

Retrieval-Augmented Generation (RAG) is an effective technique for grounding LLM chatbot responses on external knowledge. However, many RAG studies assume well-structured textual sources, limiting their practical application.

HybridRAG addresses this limitation by ingesting unstructured PDF documents via Optical Character Recognition (OCR) and layout analysis. The system converts the documents into hierarchical text chunks and pre-generates a question-answer (QA) knowledge base using an LLM.

During querying, HybridRAG searches for matches in the pre-generated QA knowledge base. If it finds a suitable answer, it provides it immediately. Otherwise, it falls back to generating a response at query time. Tests on OHRBench demonstrate that HybridRAG offers superior answer quality and lower latency compared to a standard RAG system.

For those evaluating on-premise deployments, there are trade-offs to consider carefully. AI-RADAR offers analytical frameworks on /llm-onpremise to evaluate these trade-offs.