How to Optimize DB‑GPT RAG Pipelines for Better Retrieval and Knowledge Processing

This article explains how to use the DB‑GPT application framework to improve Retrieval‑Augmented Generation (RAG) by detailing knowledge loading, chunking, embedding, graph extraction, storage options, retrieval strategies, workflow customization, evaluation metrics, and real‑world case studies.

AIDB-GPTRAG

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How to Optimize DB‑GPT RAG Pipelines for Better Retrieval and Knowledge Processing