[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-co11ap5e-enterprise-rag":3},{"id":4,"slug":5,"name":6,"summary":7,"stack":8,"background":13,"responsibility":14,"workflow":15,"architecture":21,"highlights":22,"retrospective":27,"repositoryUrl":28,"liveUrl":29,"featured":30},8,"co11ap5e-enterprise-rag","co11ap5e_enterprise_rag（企业智能知识库平台）","基于 RAG 架构的企业知识库问答系统，支持多格式文档解析、向量检索、知识库管理与 LLM 生成带依据的回答，提供 REST API 接入。",[9,10,11,12],"FastAPI","Qdrant","DashScope","RAG","构建一个可落地的 RAG 知识库平台：文档上传解析 → 切片 → Embedding 向量化 → 语义检索 → 交给 LLM 生成带依据的回答，覆盖企业知识问答的完整链路。","独立完成后端架构与核心模块。\n\n- 支持 PDF \u002F Word \u002F PPT \u002F Excel 多格式文档解析\n- 基于 Embedding 向量化与 Qdrant 语义检索\n- 实现知识库生命周期管理与检索增强问答\n- 基于 FastAPI 提供 chat \u002F documents \u002F knowledge_base REST 接口",[16,17,18,19,20],"用户上传文档","解析文档并切片","Embedding 向量化存入 Qdrant","用户提问后检索相关片段","LLM 结合片段生成带依据的回答","FastAPI + Uvicorn 提供接口，Qdrant 做向量检索，DashScope 提供 LLM 与 Embedding，SQLAlchemy 管理元数据，PyMuPDF \u002F python-docx \u002F python-pptx \u002F openpyxl 负责多格式解析。",[23,24,25,26],"多格式办公文档解析","Qdrant 语义向量检索","知识库全生命周期管理","REST API 便于前端 \u002F 客户端接入","RAG 的价值在「检索的质量决定回答的质量」。把文档解析、切片、向量化、检索与生成串成清晰链路后，扩展新文档类型与新检索策略都变得可控。","https:\u002F\u002Fgithub.com\u002FS1rryNut\u002Fco11ap5e_enterprise_rag",null,true]