kb-agent-platform
KB-Agent-Platform —— 企业知识库问答平台(RAG + Multi-Agent + MCP)
一句话:基于 RAG 与 LangGraph Supervisor 的企业知识库 Agent, SSE 流式问答带引用溯源,MCP 双入口,docker compose 一键部署。
架构
前端 Vue3 ──SSE──> FastAPI ──> Supervisor(LLM 路由) ──┬─> KB Agent ──> Qdrant(4096维)
└─> Chat Agent
MCP Host(Cursor/Claude) ──stdio──> MCP Server ──> 同一个 Qdrant

Related MCP server: tero-mcp-lite
快速开始
https://github.com/embracingTheFuture/kb-agent-platform.git && cd kb-agent-platform
cp .env.example .env # 填入你的 key
docker compose up --build -d
docker compose run --rm api python task2_pipeline.py # 入库
效果截图

质量:评估集回归
6 条用例覆盖路由正确性 + 引用完整性,容器内可复现。
编号 | 前15个字 | 预期 | 实际 | 是否通过测试 |
编号:1 | 前15个字:阶段二的时间安排和主项目定位。 | 预期:KB | 实际:KB | PASS |
编号:2 | 前15个字:路线图里 Day16 要干什么 | 预期:KB | 实际:KB | PASS |
编号:3 | 前15个字:用一句话介绍一下你自己。 | 预期:CHAT | 实际:CHAT | PASS |
编号:4 | 前15个字:帮我写一句关于秋天的诗。 | 预期:CHAT | 实际:CHAT | PASS |
编号:5 | 前15个字:2024年奥运会中国队拿了多少 | 预期:CHAT | 实际:CHAT | PASS |
编号:6 | 前15个字: | 预期:CHAT | 实际:CHAT | PASS |
6 条用例,6 通过,0 失败 |
技术要点
手写 ReAct 内核(工具注册表 + while 循环,不用框架)
自研切分-向量化-检索管线,Qdrant 余弦相似度
引用溯源:编号上下文 + [i] 标注 + citations 结构
Supervisor 多智能体路由 + 会话记忆压缩(滚动摘要)
MCP Server 双入口 / SSE 流式 / 评估集回归
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