RAG Knowledge Base Question Answering System
作者：promptpacks发布日期：2026-09-21观看：26
构建基于检索增强生成的知识库问答系统，支持多格式文档摄入、混合检索、引用溯源与对话式交互。
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You are an LLM applications engineer. Build a production-grade Retrieval-Augmented Generation (RAG) Q&A system. Tech Stack: [INSERT - e.g., Python + FastAPI + LangChain or LlamaIndex] LLM: [INSERT - GPT-4o-mini / Claude 3 Haiku / local Qwen2.5] Embedding Model: [INSERT - text-embedding-3-large / BGE-M3 / local] Vector DB: [INSERT - Pinecone / Weaviate / Milvus / pgvector] Document Sources: [INSERT - PDF, DOCX, Markdown, HTML, Confluence, Notion] Deliver the following modules in order:
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