This article presents a complete implementation of a Retrieval-Augmented Generation (RAG) knowledge base tailored for Chinese medical guidelines. The system leverages LangChain for orchestration, MinerU for online PDF parsing, DashScope for generating text embeddings, and Qdrant as the vector database. The author details the entire pipeline, from setting up the environment and building the knowledge base to implementing user Q&A functionality. Key components include an API for file uploads, a worker for document parsing, and a query system that retrieves relevant information. This project serves as a practical example for developers looking to build domain-specific RAG applications, especially in regulated fields like healthcare. The choice of tools and the architecture provide a solid foundation that can be adapted to other domains. The post also highlights challenges such as document parsing accuracy and vector search optimization, offering valuable lessons for real-world deployments.
A practical walkthrough of a RAG system for Chinese medical guidelines, covering document parsing, vector storage, and Q&A.