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This release is a pre-release and may not be stable for production use.

RAG Assistant

本地知识库问答智能体 — LLM 驱动的组合式语义检索与多库路由。 作者:wUwproject | 许可证:Apache 2.0

基于 local-rag-builder 技能构建的独立 RAG 智能体,支持 LM Studio / Ollama 双后端。


核心特性

特性 说明
组合式查询 LLM 自动对问题做实体/属性分词(entities + attrs),穷举组合后独立检索,SM3 去重合并,LLM 综合回答
多库路由 硬编码关键词 + 语义回退(FallbackRouter)双路由,自动匹配最相关的知识库
自修正决策 LLM 输出格式错误时自动反馈重试(最多 5 次),重试耗尽时清上下文重来
配置持久化 timeout / max_tokens / backend / model 全部保存到 config.json,刷新页面不丢
联网搜索 可选启用,扩展知识库覆盖范围

文件结构

agent/rag-assistant/
├── main.py                           # 入口,CLI + Web 双模式
├── rag_assistant/
│   ├── agent.py                      # Agent 决策循环
│   ├── web_ui.py                     # Web 界面(port 8765)
│   ├── llm_client.py                 # LLM 统一客户端
│   ├── rag_wrapper.py                # 技能封装层
│   ├── search.py                     # 联网搜索
│   └── memory.py                     # 记忆管理
├── scripts/                          # 技能核心模块
├── vendor/                           # 内嵌第三方库
└── data/                             # 运行时数据
    ├── config/rag_config.json        # LLM 与检索配置
    ├── models/                       # 嵌入/路由/rerank 模型
    ├── kb/                           # 知识库(Chroma 向量库)
    └── ...

快速开始

# 1. 安装依赖
pip install -r requirements.txt

# 2. 启动(需要 LM Studio 或 Ollama 运行中)
python main.py

# 3. 打开浏览器访问 http://localhost:8765

架构概览

用户输入
  → [LLM 决策层]
       ├─ 闲聊 → 直接回答
       └─ 知识库查询 → entities/attrs 分词
           → [组合展开器] 穷举 entities × attrs
           → [多切片检索] 每片独立走完整 RAG 流程
              1. 路由(嵌入模型 × KB签名/关键词)
              2. 检索(Chroma 相似度)
              3. (可选) 重排序(reranker)
              4. (可选) NLI 三向分类(entailment/neutral/contradiction)
           → [SM3 去重合并](保留 NLI 标签)
           → [LLM 综合回答](带 NLI 标签辅助判断)

依赖

  • LM Studio 或 Ollama(本地 LLM 推理服务)
  • Python 3.9+
  • 嵌入模型(推荐 BAAI/bge-small-zh-v1.5)
  • ChromaDB(向量存储,自动安装)

协议

Apache 2.0

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