feat: 多专业小模型+路由模型系统 MVP（mock 全链路 + FastAPI 网关 + 论文调研）
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{
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"permissions": {
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"allow": [
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"WebSearch",
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"Bash(curl -sL -o \"01_Densing_Law_of_LLMs_2412.04315.pdf\" \"https://arxiv.org/pdf/2412.04315\" -w \"Densing Law: %{http_code}\\\\n\")",
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"Bash(curl -sL -o \"02_R2R_Token_Routing_2505.21600.pdf\" \"https://arxiv.org/pdf/2505.21600\" -w \"R2R: %{http_code}\\\\n\")",
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"WebFetch(domain:oumi.ai)",
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"Bash(curl -sL -o \"RouterArena_blog.html\" \"https://huggingface.co/blog/JerryPotter/who-routes-the-routers\" -w \"RouterArena: %{http_code} %{size_download}bytes\\\\n\")",
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"Bash(curl -sL --connect-timeout 10 --max-time 30 -o \"RouterArena_blog.html\" \"https://huggingface.co/blog/JerryPotter/who-routes-the-routers\")",
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"Bash(curl -sL --connect-timeout 10 --max-time 30 \"https://huggingface.co/blog/JerryPotter/who-routes-the-routers\")",
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"Bash(curl -sL --connect-timeout 15 --max-time 45 -o \"RouterArena_blog.html\" \"https://huggingface.co/blog/JerryPotter/who-routes-the-routers\" -w \"\\\\nHTTP_CODE:%{http_code} SIZE:%{size_download}\\\\n\" -v)",
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"Bash(awk '{print $5, $NF}')"
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]
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}
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}
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# Python
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__pycache__/
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*.py[cod]
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*.egg-info/
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.venv/
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venv/
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.pytest_cache/
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.coverage
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htmlcov/
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# Env / secrets
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.env
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*.env
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api_keys*.json
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# Models / data
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models/
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data/
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*.bin
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*.safetensors
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cached_results/
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# OS / editor
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.DS_Store
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Thumbs.db
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.idea/
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.vscode/
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# 多专业小模型 + 路由模型系统（MVP）
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用「轻量分类路由器 + 专业小模型池 + 质量控制器(Judge) + 大模型回退」在限定条件下替代单一通用大模型，
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实现 **成本降低 80%+、延迟可控** 的目标。本仓库是《实现方案_多专业小模型+路由模型.md》的第一阶段落地。
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## ✨ 当前能力（2026-08-12 已跑通）
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- ✅ 零依赖 mock 全链路可运行：缓存 → 分类 → 专家 → Judge → 回退
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- ✅ 5 领域意图分类（code / math / legal / medical / general），规则分类器准确率 **100%**（15 条评测样例）
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- ✅ 两阶段缓存（L1 精确 + L2 语义 n-gram，零依赖），评测缓存命中率 **40%**
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- ✅ 质量控制器（Judge）自动评估输出并触发升级，升级率 **20%**（命中第二阶段验收线）
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- ✅ FastAPI 网关：`/chat` `/health` `/metrics`，20 项单元测试全部通过
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- ✅ 可选接入真实模型：HuggingFace 小模型（`type: hf`）或 OpenAI 兼容 API（`type: api`）
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## 🚀 快速开始
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```powershell
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# 1. 创建虚拟环境并安装依赖（核心 router_system 零依赖，网关/测试需要轻量依赖）
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C:\Python314\python.exe -m venv .venv
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.venv\Scripts\python.exe -m pip install -r requirements.txt
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# 2. 运行演示（mock 模式，离线可跑）
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.venv\Scripts\python.exe scripts/demo.py
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# 3. 迷你评估（分类准确率 / 升级率 / 缓存命中率）
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.venv\Scripts\python.exe scripts/eval.py --repeat 2
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# 4. 运行单元测试
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.venv\Scripts\python.exe -m pytest tests -v
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# 5. 启动 API 网关
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.venv\Scripts\python.exe scripts/serve.py --port 8000
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# 停止：.venv\Scripts\python.exe scripts/serve.py --stop
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# 6. 调用接口
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curl http://127.0.0.1:8000/health
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curl -X POST http://127.0.0.1:8000/chat -H "Content-Type: application/json" -d '{"query":"用 Python 写一个快速排序函数"}'
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curl http://127.0.0.1:8000/metrics
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```
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## 🏗️ 架构
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```
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用户查询
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│
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▼
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┌───────────────────┐ ┌──────────────────┐
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│ RouterCache 缓存 │───▶│ 命中 → 直接返回 │
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│ (L1精确 / L2语义) │ └──────────────────┘
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└─────────┬─────────┘
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▼ 未命中
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┌───────────────────┐ 低置信度(<0.60) ┌──────────────────┐
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│ 分类路由器 │ ───────────────▶ │ 大模型回退 │
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│ RuleClassifier / │ │ Mock / DeepSeek │
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│ HuggingFace │ └──────────────────┘
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└─────────┬─────────┘
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▼ 高置信度
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┌───────────────────┐
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│ 专家模型池 │ code/math/legal/medical/general
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│ Mock / HF / API │
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└─────────┬─────────┘
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▼
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┌───────────────────┐ 质量分<0.70 ┌──────────────────┐
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│ Judge 质量控制器 │ ────────────▶ │ 升级大模型回退 │
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│ Rule / LLM-as-Judge│ └──────────────────┘
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└───────────────────┘
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```
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一次请求的完整路由轨迹示例：
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```
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cache:miss -> classify:code@0.95/hard -> expert:expert-code -> judge:0.96
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cache:miss -> classify:general@0.50/easy -> direct_fallback
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```
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## ⚙️ 配置（config/config.yaml）
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默认全 mock（零依赖离线）。接入真实模型只需改 `type`：
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| 组件 | 当前 | 可切换 | 说明 |
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|------|------|--------|------|
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| classifier | `rule` | `hf` | 正式环境建议训练 BERT 级分类器（94-97%） |
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| experts.* | `mock` | `hf` / `api` | HF 小模型或 OpenAI 兼容 API |
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| judge | `rule` | `llm` | LLM-as-Judge |
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| fallback | `mock` | `api` | 设置 `DEEPSEEK_API_KEY` 环境变量 |
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关键阈值：
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- `low_confidence_threshold: 0.60` —— 分类置信度低于此值直接走大模型
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- `judge_fallback_threshold: 0.70` —— Judge 质量分低于此值升级大模型
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## 📂 目录结构
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```
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├── router_system/ # 核心（零依赖纯标准库）
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│ ├── classifier.py # 意图分类器（规则 / HF）
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│ ├── difficulty.py # 难度估计
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│ ├── experts.py # 专家池（Mock / HF / API）
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│ ├── judge.py # 质量控制器
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│ ├── fallback.py # 大模型回退
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│ ├── cache.py # 两阶段缓存
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│ ├── router.py # 主路由
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│ └── stats.py # 指标
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├── gateway/api.py # FastAPI 网关
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├── scripts/ # demo / eval / serve / train_classifier
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├── tests/ # 20 项单元测试
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├── config/config.yaml # 配置
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└── research/ # 论文调研
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```
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## 📊 验收指标对照（实现方案 5.1/5.2）
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| 指标 | 目标 | 当前（mock 评测） |
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|------|------|------------------|
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| 分类准确率 | ≥95%（正式） | 100%（15 条样例） |
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| 升级率（fallback rate） | ≤20% | 20% |
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| 缓存命中率 | ≥30% | 40% |
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| 端到端延迟 | < 大模型 1.5× | mock 下 ~10-16ms |
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## 🔜 下一步（对照实现方案）
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1. 接入真实小模型：`pip install -r requirements-ml.txt`，`experts.*.type` 改 `hf`
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2. 训练 BERT 级分类器替代规则分类器（`scripts/train_classifier.py` 流水线骨架）
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3. 用 RouterArena（[GitHub](https://github.com/RouteWorks/RouterArena)）标准化评测路由质量
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4. 接入 DeepSeek 等大模型 API 作为真实回退层
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5. 语义缓存升级为 embedding 检索（当前为 n-gram 轻量方案）
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# ============================================================
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# 多专业小模型 + 路由模型系统 — 配置
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# 默认全 mock 模式（零依赖、离线可跑）。要接入真实模型，把对应
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# 后端 type 改为 hf / api 即可（见 README）。
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# ============================================================
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system:
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name: multi-expert-router
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version: 0.1.0
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router:
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low_confidence_threshold: 0.60 # 分类置信度低于此值 -> 直接走大模型
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judge_fallback_threshold: 0.70 # Judge 质量分低于此值 -> 升级大模型
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default_temperature: 0.2
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classifier:
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type: rule # rule（零依赖）| hf（transformers）
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model: Qwen/Qwen3-0.6B
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confidence_floor: 0.55
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domains:
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- code
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- math
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- legal
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- medical
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- general
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experts:
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code: { type: mock, model: Qwen/Qwen2.5-Coder-7B-Instruct }
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math: { type: mock, model: Qwen/Qwen3-4B-Instruct }
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legal: { type: mock, model: Qwen/Qwen3-4B-Instruct }
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medical: { type: mock, model: Qwen/Qwen3-4B-Instruct }
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general: { type: mock, model: Qwen/Qwen3-1.7B-Instruct }
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fallback:
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type: mock # mock | api（OpenAI 兼容，如 DeepSeek）
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model: deepseek-chat
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base_url: https://api.deepseek.com/v1
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api_key_env: DEEPSEEK_API_KEY
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judge:
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type: rule # rule（零依赖）| llm
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model: Qwen/Qwen3-1.7B-Instruct
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cache:
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enabled: true
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semantic_enabled: true # 语义缓存（字符 n-gram 相似度，零依赖）
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similarity_threshold: 0.88
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promote_frequency: 5 # 命中 N 次后提升为精确缓存␍
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"""API 网关：FastAPI 服务。"""␍
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"""FastAPI 网关：对外提供 /chat /health /metrics 接口。
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启动：
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uvicorn gateway.api:app --host 0.0.0.0 --port 8000
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或：
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python -m gateway.api
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依赖：fastapi, uvicorn, pydantic（见 requirements.txt）
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"""
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from __future__ import annotations
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from typing import List, Optional
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from pydantic import BaseModel, Field
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from router_system.config import load_config
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from router_system.router import Router, build_router
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# ---- 全局单例 ----
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_router: Optional[Router] = None
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def get_router() -> Router:
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global _router
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if _router is None:
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_router = build_router()
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return _router
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# ---- 请求/响应模型 ----
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class QueryRequest(BaseModel):
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query: str = Field(..., min_length=1, max_length=8000, description="用户查询")
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class QueryResponse(BaseModel):
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response: str
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domain: str
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difficulty: str
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confidence: float
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upgraded: bool
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quality_score: float
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model_used: str
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route: List[str]
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latency_ms: float
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cache_hit: bool
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cache_level: Optional[str]
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cost_est: float
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error: Optional[str] = None
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class HealthResponse(BaseModel):
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status: str
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domains: List[str]
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classifier: str
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judge: str
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fallback: str
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# ---- FastAPI 应用 ----
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try:
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from fastapi import FastAPI
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app = FastAPI(
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title="Multi-Expert Router API",
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description="多专业小模型 + 路由模型系统（MVP）",
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version="0.1.0",
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)
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@app.get("/health", response_model=HealthResponse, tags=["system"])
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async def health():
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return get_router().health()
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@app.post("/chat", response_model=QueryResponse, tags=["chat"])
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async def chat(req: QueryRequest):
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result = await get_router().route(req.query)
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return QueryResponse(**result.to_dict())
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@app.get("/metrics", tags=["system"])
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async def metrics():
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r = get_router()
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return {
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"router": r.stats.summary(),
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"cache": r.cache.stats(),
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}
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except ImportError:
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# fastapi 未安装时，提供 CLI 入口提示
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app = None
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print("[gateway] 未安装 fastapi，请执行: pip install -r requirements.txt")
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if __name__ == "__main__":
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import uvicorn
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uvicorn.run("gateway.api:app", host="0.0.0.0", port=8000, reload=False)␍
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# 参考文献索引
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本目录收录了"多专业小模型 + 路由模型"可行性分析报告中引用的全部 15 篇参考文献。
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---
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## 📄 arXiv 论文（13 篇 PDF）
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| # | 文件名 | 标题 | 会议/期刊 | arXiv ID |
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|---|--------|------|----------|----------|
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||||||
|
| 01 | `01_Densing_Law_of_LLMs_2412.04315.pdf` | Densing Law of LLMs | **Nature Machine Intelligence** (封面文章) | [2412.04315](https://arxiv.org/abs/2412.04315) |
|
||||||
|
| 02 | `02_R2R_Token_Routing_2505.21600.pdf` | R2R: Efficiently Navigating Divergent Reasoning Paths with Small-Large Model Token Routing | **NeurIPS 2025** | [2505.21600](https://arxiv.org/abs/2505.21600) |
|
||||||
|
| 03 | `03_BEST_Route_2506.22716.pdf` | BEST-Route: Adaptive LLM Routing with Test-Time Optimal Compute | **ICML 2025** | [2506.22716](https://arxiv.org/abs/2506.22716) |
|
||||||
|
| 04 | `04_SATER_2510.05164.pdf` | SATER: A Self-Aware and Token-Efficient Approach to Routing and Cascading | **EMNLP 2025** | [2510.05164](https://arxiv.org/abs/2510.05164) |
|
||||||
|
| 05 | `05_Token_Level_Routing_2504.07878.pdf` | Token Level Routing Inference System for Edge Devices | **ACL 2025** | [2504.07878](https://arxiv.org/abs/2504.07878) |
|
||||||
|
| 06 | `06_Comp_LLM_2511.22955.pdf` | Experts are all you need: A Composable Framework for Large Language Model Inference (Comp-LLM) | arXiv 2025 | [2511.22955](https://arxiv.org/abs/2511.22955) |
|
||||||
|
| 07 | `07_Mixture_of_Parrots_2410.19034.pdf` | Mixture of Parrots: Experts Improve Memorization More than Reasoning | **ICLR 2025** | [2410.19034](https://arxiv.org/abs/2410.19034) |
|
||||||
|
| 08 | `08_DomainCodeBench_2412.18573.pdf` | Top General Performance ≠ Top Domain Performance? DomainCodeBench: A Multi-domain Code Generation Benchmark | arXiv 2025 | [2412.18573](https://arxiv.org/abs/2412.18573) |
|
||||||
|
| 09 | `09_Model_SAT_CIT_2502.17282.pdf` | Capability Instruction Tuning: A New Paradigm for Dynamic LLM Routing (Model-SAT) | **AAAI 2025** | [2502.17282](https://arxiv.org/abs/2502.17282) |
|
||||||
|
| 10 | `10_RouterRetriever_2409.02685.pdf` | RouterRetriever: Routing over a Mixture of Expert Embedding Models | **AAAI 2025** | [2409.02685](https://arxiv.org/abs/2409.02685) |
|
||||||
|
| 11 | `11_Inverse_Depth_Scaling_2602.05970.pdf` | Inverse Depth Scaling From Most Layers Being Similar | **ICML 2026** | [2602.05970](https://arxiv.org/abs/2602.05970) |
|
||||||
|
| 12 | `12_MergeBench_2505.10833.pdf` | MergeBench: A Benchmark for Merging Domain-Specialized LLMs | **NeurIPS 2025** (Datasets & Benchmarks) | [2505.10833](https://arxiv.org/abs/2505.10833) |
|
||||||
|
| 13 | `13_Doing_More_With_Less_2502.00409.pdf` | Doing More with Less: Implementing Routing Strategies in LLM-Based Systems (Extended Survey) | arXiv 2025 | [2502.00409](https://arxiv.org/abs/2502.00409) |
|
||||||
|
|
||||||
|
## 📝 博客文章（2 篇）
|
||||||
|
|
||||||
|
| 文件名 | 标题 | 来源 |
|
||||||
|
|--------|------|------|
|
||||||
|
| `RouterArena_blog.md` | Who Routes LLM Routers? — RouterArena: Building the Evaluation Foundation for LLM Routing | Hugging Face Blog (2025.11) |
|
||||||
|
| `Small_FineTuned_Models_blog.md` | Small Fine-tuned Models are All You Need | Oumi Blog (2025.10) |
|
||||||
|
|
||||||
|
## 📊 按会议/期刊分布
|
||||||
|
|
||||||
|
| 会议/期刊 | 论文数 | 论文编号 |
|
||||||
|
|----------|--------|---------|
|
||||||
|
| **NeurIPS 2025** | 2 | 02 (R2R), 12 (MergeBench) |
|
||||||
|
| **ICML 2025** | 1 | 03 (BEST-Route) |
|
||||||
|
| **EMNLP 2025** | 1 | 04 (SATER) |
|
||||||
|
| **ACL 2025** | 1 | 05 (Token Level Routing) |
|
||||||
|
| **AAAI 2025** | 2 | 09 (Model-SAT), 10 (RouterRetriever) |
|
||||||
|
| **ICLR 2025** | 1 | 07 (Mixture of Parrots) |
|
||||||
|
| **Nature Machine Intelligence** | 1 | 01 (Densing Law) |
|
||||||
|
| **ICML 2026** | 1 | 11 (Inverse Depth Scaling) |
|
||||||
|
| **arXiv / 预印本** | 3 | 06 (Comp-LLM), 08 (DomainCodeBench), 13 (Survey) |
|
||||||
|
|
||||||
|
## 🔍 按主题分类
|
||||||
|
|
||||||
|
- **Routing 路由系统：** 02, 03, 04, 05, 09, 13, RouterArena
|
||||||
|
- **小模型能力：** 07, 08, Small_FineTuned
|
||||||
|
- **Scaling Law / 架构理论：** 01, 11
|
||||||
|
- **专家模型合并：** 12
|
||||||
|
- **可组合推理系统：** 06
|
||||||
|
- **检索路由：** 10
|
||||||
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|||||||
|
# Who Routes LLM Routers? — RouterArena: Building the Evaluation Foundation for LLM Routing
|
||||||
|
|
||||||
|
**Community Article Published November 11, 2025**
|
||||||
|
|
||||||
|
**Authors:** Yifan Lu\*, Rixin Liu\*, Jiayi Yuan\*, Xingqi Cui, Shenrun Zhang, Hongyi Liu, Jiarong Xing
|
||||||
|
*\*Equal contribution · Rice University*
|
||||||
|
|
||||||
|
**Link:** https://huggingface.co/blog/JerryPotter/who-routes-the-routers
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## The Diversifying Landscape of LLMs
|
||||||
|
|
||||||
|
For years, our community has pursued the goal of building a single, general-purpose foundation model capable of handling all questions and tasks, and this effort has achieved remarkable success. As scaling laws kicked in, these models have rapidly expanded to trillions of parameters and now surpass human performance on a wide range of benchmarks.
|
||||||
|
|
||||||
|
However, it is becoming increasingly clear that this scaling trend may not be sustainable. We are hitting the data wall, where high-quality training data is running out, as highlighted by Ilya Sutskever, OpenAI's co-founder, at NeurIPS 2024. Future scaling will depend on generating new, high-quality data, which often requires costly human labeling and curation.
|
||||||
|
|
||||||
|
As a result, the LLM landscape is diversifying. In addition to chasing large general-purpose models, people are also exploring smaller, more efficient, and specialized ones. A good example is the Qwen family, which now includes nine categories, such as Qwen3-Coder, Qwen3-Image, and Qwen3-Guard. These models range from 0.6B to 480B parameters, with many specialized variants staying under 30B, handling relatively simple questions more efficiently.
|
||||||
|
|
||||||
|
This shift is further accelerated by startups and open-source initiatives embracing model specialization and customization. For instance, ThinkingMachine is building personalized AI systems, while rLLM provides an open framework for training domain-specific or user-tailored agents. Together, these efforts mark a clear transition from a "one-model-for-all" paradigm to a diverse ecosystem of LLMs, ranging from massive generalists to compact specialists.
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## The Key is Model Routers
|
||||||
|
|
||||||
|
As models continue to diversify (in both sizes and skills), a new challenge emerges: how do we choose the right model for the right task? If the era of large, general models was about "one model for everything," the next era is about matching each query to the model that answers it best.
|
||||||
|
|
||||||
|
Therefore, automated query-to-model routing will be increasingly important. A helpful analogy to understand its importance is Google Search. When you type a query, the search engine scans billions of pages and routes you to the most relevant source. Similarly, as the model ecosystem expands, we'll need intelligent routers that analyze an input and decide which model (or combination of models) can handle it most effectively.
|
||||||
|
|
||||||
|
The routing can happen at many levels: selecting between models of different sizes to balance cost and accuracy, choosing among specialized experts to get the highest-quality answers, or even orchestrating a workflow of multiple models that collaborate to complete a complex task.
|
||||||
|
|
||||||
|
This idea is no longer just theoretical. As shown in the following figure, we're already seeing a wave of router systems emerging across academia and industry—some simple and rule-based, others adaptive, learned, and data-driven. The most notable example is GPT-5, which is said to incorporate an internal router that dynamically selects among different models or "experts" depending on the task.
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## Who Routes the Routers? RouterArena!
|
||||||
|
|
||||||
|
In the near future, designing good routers will be just as important as training good models. This means that, just as we evaluate and analyze models today, we will need a rigorous study of routers to understand their performance, efficiency, and decision behaviors.
|
||||||
|
|
||||||
|
Differently, evaluating routers is far more challenging than evaluating models. Router evaluation is inherently multi-dimensional: there isn't a single metric that captures how well a router performs. People care about many different aspects, e.g., query-answer quality, cost efficiency, routing consistency, robustness, and more. Even for the same query, the optimal router decision can change depending on the available model candidates, cost constraints, or deployment settings, making fair and consistent evaluation even harder.
|
||||||
|
|
||||||
|
Unfortunately, there is currently no open platform that allows the public to evaluate routers using a comprehensive dataset and standardized metrics for fair comparison. That's why we built RouterArena!
|
||||||
|
|
||||||
|
### What is RouterArena?
|
||||||
|
|
||||||
|
RouterArena is an open platform for rigorous and comprehensive router evaluation. It provides (1) a principally constructed dataset with broad knowledge domain coverage, (2) distinguishable difficulty levels for each domain, (3) an extensive list of evaluation metrics, and (4) an automated evaluation framework. On top of that, we host a public leaderboard, giving the community a place to compare, track, and improve routers over time.
|
||||||
|
|
||||||
|
### Evaluation Dataset
|
||||||
|
|
||||||
|
RouterArena introduces a carefully constructed evaluation dataset built around two core design principles: diverse domain coverage and clear difficulty separation. For broad coverage, we draw inspiration from the Dewey Decimal Classification (DDC) system used in libraries to organize the world's knowledge. This ensures that the dataset spans a wide range of domains across science, humanities, and applied disciplines. To differentiate query complexity, we adopt Bloom's taxonomy, grouping questions into three levels—easy, medium, and hard—so routers can be tested on their ability to balance accuracy and cost when selecting between smaller and larger models.
|
||||||
|
|
||||||
|
Following these principles, we curated data from 23 open-source datasets, applied LLM-based difficulty annotation, and ensured balanced distribution across all categories. After deduplication, our final dataset contains 8,400 queries across 9 domains and 44 categories, each represented at multiple difficulty levels.
|
||||||
|
|
||||||
|
### Evaluation Metrics
|
||||||
|
|
||||||
|
RouterArena evaluates routers across 5 key dimensions:
|
||||||
|
|
||||||
|
- **Query-answer accuracy** — a router's ability to direct queries to the appropriate models such that they are correctly answered.
|
||||||
|
- **Query-answer cost** — the cost incurred by a router's routing decisions.
|
||||||
|
- **Routing optimality** — a router's ability to select the cheapest model that still produces a correct response.
|
||||||
|
- **Routing robustness** — the router's robustness against noisy inputs.
|
||||||
|
- **Routing latency** — the latency overhead introduced by routing.
|
||||||
|
|
||||||
|
### Leaderboard
|
||||||
|
|
||||||
|
The **Arena Score** is a composite metric that captures the critical accuracy–cost trade-off:
|
||||||
|
|
||||||
|
$$S_{i,\beta} = \frac{(1 + \beta) A_i C_i}{\beta A_i + C_i}$$
|
||||||
|
|
||||||
|
where A_i represents accuracy, β = 0.1 for balanced weight, and C_i is the normalized cost.
|
||||||
|
|
||||||
|
**Key findings:**
|
||||||
|
- **MIRT-BERT** currently stands out as the most cost-effective router, achieving accuracy comparable to Azure-Router at roughly one-fifth of the cost.
|
||||||
|
- **GPT-5** remains the performance leader but at significantly higher inference costs.
|
||||||
|
|
||||||
|
**GitHub:** https://github.com/RouteWorks/RouterArena
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## Also available as an arXiv paper:
|
||||||
|
**RouterArena: An Open Platform for Comprehensive Comparison of LLM Routers** — arXiv:2510.00202
|
||||||
|
https://huggingface.co/papers/2510.00202
|
||||||
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|
|||||||
|
# Small Fine-tuned Models are All You Need
|
||||||
|
|
||||||
|
**Author:** Stefan Webb
|
||||||
|
**Published:** October 16, 2025
|
||||||
|
**Source:** https://oumi.ai/blog/small-fine-tuned-models-are-all-you
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## Core Thesis
|
||||||
|
|
||||||
|
Small fine-tuned foundation models can outperform large general-purpose ones (like GPT-4/GPT-5) on specialized tasks, with higher task-specific performance, faster inference, and lower cost. However, getting the details right requires technical expertise and intelligently designed infrastructure.
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## Key Evidence
|
||||||
|
|
||||||
|
### Mid-2024 Empirical Study (Zhao et al., 2024)
|
||||||
|
|
||||||
|
- Researchers selected **31 tasks** across a wide range of domains.
|
||||||
|
- Fine-tuned **10 small base models** (<8B parameters: Llama, Mistral, Zephyr, Phi, Gemma) using **LoRA** on each task.
|
||||||
|
- **Main finding:** 6 of the 10 small models outperformed **GPT-4 on average** after fine-tuning. All 10 outperformed GPT-3.5-Turbo.
|
||||||
|
- Fine-tuning was done with rank-8 LoRA, 4-bit precision, 2,500 steps, batch size 16 — all on a single consumer-grade GPU (<24GB memory).
|
||||||
|
|
||||||
|
### Where Small Models Excel
|
||||||
|
|
||||||
|
- **Traditional NLP tasks (GLUE benchmark):** Largest improvement from fine-tuning and smallest gap to GPT-4.
|
||||||
|
- **Coding and math reasoning:** Initially lagged behind GPT-4 — but the base models used were pre-February 2024 and not pretrained for coding/reasoning.
|
||||||
|
|
||||||
|
### Where We Are Now (Late 2025)
|
||||||
|
|
||||||
|
- Newer base models like **Qwen3-4B-Instruct** have closed the gap on coding and reasoning performance.
|
||||||
|
- GPT-5 is stronger, but the open-source ecosystem has advanced dramatically.
|
||||||
|
- The author posits that repeating the study with late-2025 models would show small fine-tuned models closing the gap on coding and reasoning.
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## Why Small Models Aren't Yet Ubiquitous
|
||||||
|
|
||||||
|
1. **Long development cycles** — earlier attempts at productionizing small fine-tuned models required months of custom development.
|
||||||
|
2. **Misconception about data** — belief that big data is required, but actually 1,000 carefully curated samples can suffice for successful fine-tuning.
|
||||||
|
3. **Catastrophic forgetting** — LoRA and parameter-efficient methods largely avoid this issue; RL-based methods prevent loss of generalization.
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## Key Takeaways
|
||||||
|
|
||||||
|
- Small fine-tuned models <8B can match or exceed GPT-4 on domain-specific tasks.
|
||||||
|
- **LoRA** enables fine-tuning on a single consumer GPU.
|
||||||
|
- **1,000 high-quality samples** can be sufficient for strong fine-tuning results.
|
||||||
|
- The claim is nuanced — task type, base model choice, and fine-tuning methodology matter greatly.
|
||||||
@@ -0,0 +1,11 @@
|
|||||||
|
# 可选：接入真实开源小模型（RTX 4060 8GB 可跑 0.5B-4B 量化模型）
|
||||||
|
# 安装：pip install -r requirements-ml.txt
|
||||||
|
torch>=2.2
|
||||||
|
transformers>=4.40
|
||||||
|
accelerate>=0.30
|
||||||
|
peft>=0.11
|
||||||
|
sentencepiece
|
||||||
|
protobuf
|
||||||
|
bitsandbytes
|
||||||
|
# 轻量推理引擎（可选）
|
||||||
|
# llama-cpp-python␍
|
||||||
@@ -0,0 +1,8 @@
|
|||||||
|
# 轻量依赖：网关与测试（核心 router_system 为零依赖纯标准库）
|
||||||
|
fastapi>=0.110
|
||||||
|
uvicorn>=0.29
|
||||||
|
pydantic>=2.6
|
||||||
|
pyyaml>=6.0
|
||||||
|
httpx>=0.27
|
||||||
|
pytest>=8.0
|
||||||
|
pytest-asyncio>=0.23␍
|
||||||
@@ -0,0 +1,106 @@
|
|||||||
|
# 课题论文调研汇总（2025–2026）
|
||||||
|
|
||||||
|
> 检索日期：2026-08-12
|
||||||
|
> 检索范围：arXiv / NeurIPS / ICML / ICLR / ACL / AAAI / EMNLP / KDD / SIGMOD / Nature MI
|
||||||
|
> 用途：为本项目（多专业小模型 + 路由模型）的实施提供最新研究依据与落地参考。
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 一、调研结论速览
|
||||||
|
|
||||||
|
1. **路由已是一门独立学科**：2026 年出现专门综述（2603.04445），路由研究从"选模型"扩展到"级联 + 预算 + 多轮 + 多模态"。
|
||||||
|
2. **验证了本项目核心命题**：《The Avengers》(AAAI 2025) 证明小模型集合 + 路由可挑战专有大模型；RouterArena 排行榜显示低成本路由器（如 Hybrid Router $0.04/1K 查询）在性价比上全面超越 GPT-5（$10.02/1K 查询）。
|
||||||
|
3. **本项目下一步应优先做三件事**：① 用 RouterArena 标准化评测；② 训练 BERT 级分类器替代规则分类器；③ 引入级联 + 预算感知（R2-Router 思路）。
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 二、核心论文清单
|
||||||
|
|
||||||
|
### 2.1 综述
|
||||||
|
|
||||||
|
| 论文 | 会议/期刊 | arXiv | 核心贡献 | 对本项目的启示 |
|
||||||
|
|------|----------|-------|---------|---------------|
|
||||||
|
| Doing More with Less: Routing Strategies in LLM-Based Systems (Extended Survey) | arXiv 2025 | [2502.00409](https://arxiv.org/abs/2502.00409) | 系统梳理低/高资源路由策略；query 级路由在相关数据集上 64.3% vs 领域式 52.2% | 本项目 query 级路由方向正确；已在 references/ 中 |
|
||||||
|
| **Dynamic Model Routing and Cascading for Efficient LLM Inference: A Survey** | arXiv 2026 | [2603.04445](https://arxiv.org/abs/2603.04445) | 最新综述，把多 LLM 路由/级联归纳为六种范式（难度感知、级联、预算约束等） | 建议按此框架对照本系统设计，补齐级联与预算维度 |
|
||||||
|
|
||||||
|
### 2.2 路由评估平台（直接可用）
|
||||||
|
|
||||||
|
| 论文 | 会议 | arXiv | 核心贡献 |
|
||||||
|
|------|------|-------|---------|
|
||||||
|
| RouterArena: An Open Platform for Comprehensive Comparison of LLM Routers | ICLR 2026 | [2510.00202](https://arxiv.org/abs/2510.00202) | 8400 查询 × 9 领域 × 44 类目，5 维指标（准确率/成本/最优性/鲁棒性/延迟），开源评估框架 + 排行榜 |
|
||||||
|
|
||||||
|
**RouterArena 排行榜现状（2026-07 快照）**：Cross-Router (75.75)、Sqwish (75.27)、vLLM-SR (74.86)、R2-Router (71.60, $0.06/1K)、Hybrid Router (72.08, **$0.04/1K**)、GPT-5 (64.32, $10.02/1K)。可见：**低成本路由器在 Arena Score 上已全面超越 GPT-5**，且成本低两个数量级——直接支撑本项目"路由系统替代单一大模型"的经济性论证。
|
||||||
|
|
||||||
|
### 2.3 直接验证"小模型集合挑战大模型"
|
||||||
|
|
||||||
|
| 论文 | 会议 | arXiv | 核心贡献 | 启示 |
|
||||||
|
|------|------|-------|---------|------|
|
||||||
|
| **The Avengers: A Simple Recipe for Uniting Smaller Language Models to Challenge Proprietary Giants** | AAAI 2025 | [2505.19797](https://arxiv.org/abs/2505.19797) | 轻量框架聚合小模型集体智能，路由+评分+投票在数学/代码/逻辑等任务超越专有大模型 | **本项目最直接的理论支撑**；路由选型可参考其"多模型 + 轻量评分"配方 |
|
||||||
|
|
||||||
|
### 2.4 路由方法（2025-2026 SOTA 方向）
|
||||||
|
|
||||||
|
| 论文 | 会议 | arXiv | 核心思路 | 对本项目的启示 |
|
||||||
|
|------|------|-------|---------|---------------|
|
||||||
|
| **R2-Router** | ICML 2026 | [2602.02823](https://arxiv.org/abs/2602.02823) | 把输出 token 预算提升为决策变量，在 (模型, 预算) 联合空间搜索质量-成本曲线 | 用"受限输出长度 + 强模型"可能比"弱模型全量输出"更划算；可做成本控制 |
|
||||||
|
| **Meta-Router** | ICLR 2026 | [2509.25535](https://arxiv.org/abs/2509.25535) | 用因果推断（R-/DR-learner）融合金标准评测与偏好评测训练路由器 | 训练数据不足时，偏好数据可用但需偏差校正 |
|
||||||
|
| **HyDRA** | arXiv 2026 | [2605.17106](https://arxiv.org/abs/2605.17106) | 预测查询的多维能力需求，与模型画像做 shortfall matching；路由开销 P50 55ms | 模型池动态增删/调价无需重训 |
|
||||||
|
| **FusionRoute** | ICML 2026 | — | token 级多 LLM 协作：轻量路由器每步选专家 + logit 修正 | token 级路由的落地参考 |
|
||||||
|
| **Router-R1** | NeurIPS 2025 | — | 用 RL 把多轮路由与聚合建模为序列决策 | 复杂任务可"边想边路由" |
|
||||||
|
| **MESS+** | NeurIPS 2025 | — | 随机优化做成本最优路由并保证 SLA | 生产环境 SLA 约束的参考 |
|
||||||
|
| **ICL-Router** | AAAI 2026 | — | In-Context 学习模型表征用于路由 | 冷启动友好 |
|
||||||
|
| **Select-then-Route (StR)** | EMNLP 2025 Industry | — | 两阶段：先按语义类目选模型子池，再级联路由 | 与本项目"分类器 + 专家池"结构一致，验证了该架构 |
|
||||||
|
| **RADAR** | NeurIPS 2025 WS | — | 推理能力 + 难度感知路由 | 难度估计器的进阶参考 |
|
||||||
|
| **CARROT** | ICLR 2025 WS | — | 成本感知率最优路由器 | 成本-质量权衡的数学框架 |
|
||||||
|
| **OmniRouter** | KDD 2025 | — | 预算/性能可控的多 LLM 路由 | 预算控制参考 |
|
||||||
|
| **SpareLLM** | SIGMOD 2025 | — | 等价约束下选任务专属最小成本模型 | 成本最优选择参考 |
|
||||||
|
|
||||||
|
### 2.5 LoRA 专家路由（与本项目"LoRA 微调专家池"直接相关）
|
||||||
|
|
||||||
|
| 论文 | arXiv | 核心思路 |
|
||||||
|
|------|-------|---------|
|
||||||
|
| Spend Experts Where You Are Unsure (CARE) | [2607.26052](https://arxiv.org/abs/2607.26052) | 置信度自适应地把预算分配给不确定性最高的 LoRA 专家 |
|
||||||
|
| VI-MoLE | [2608.02528](https://arxiv.org/abs/2608.02528) | 信息价值路由：估计每个前缀剩余风险并分配共享预算 |
|
||||||
|
| Hard-Routed Mixtures of Reasoning LoRAs | [2606.31413](https://arxiv.org/abs/2606.31413) | 硬路由组合独立训练的推理 LoRA，避免混合路由的尺度失配问题 |
|
||||||
|
| ReMix | arXiv 2026 | 强化路由混合 LoRA，非学习路由权重保证各 LoRA 公平参与 |
|
||||||
|
|
||||||
|
**启示**：本项目第二阶段"多领域 LoRA 专家微调"可参考 CARE/VI-MoLE 的"按不确定性分配预算"思路，避免固定路由导致的专家能力浪费。
|
||||||
|
|
||||||
|
### 2.6 级联 / 质量兜底（对应本项目 Judge + 回退层）
|
||||||
|
|
||||||
|
| 论文 | arXiv | 核心思路 |
|
||||||
|
|------|-------|---------|
|
||||||
|
| Cluster, Route, Escalate | [2606.27457](https://arxiv.org/abs/2606.27457) | 聚类 → 路由 → 选择性升级；Q3-4B 处理 59% 查询，升级仅多 4.7ms TPOT |
|
||||||
|
| Conformal Cascade | [2607.25018](https://arxiv.org/abs/2607.25018) | 分布无关的置信度级联 deferral，提供准确率保证 |
|
||||||
|
| The Routing Plateau | [2606.07587](https://arxiv.org/abs/2606.07587) | 揭示路由器准确率上限的成因，提出突破方法 |
|
||||||
|
|
||||||
|
**启示**：本项目 Judge 阈值（0.70）本质是"置信度级联"，可参考 Conformal Cascade 做阈值校准，用分布无关保证控制误升级率。
|
||||||
|
|
||||||
|
### 2.7 小模型能力（支撑"小模型可替代大模型"前提）
|
||||||
|
|
||||||
|
- **Small Language Models: A Systematic Review**（2026）：微调 SLM 在领域任务达 85-90% 准确率、成本仅为 LLM 的 10-25%；复杂推理仍有差距（需回退层兜底）——与本项目结论一致。
|
||||||
|
- **Specialization Beats Scale**（2026，行业分析）：3B 专用模型在抽取质量/成本/稳定性上超越 GPT-4o 与 Claude Opus 4.6。
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 三、对本项目实施的 5 条具体建议
|
||||||
|
|
||||||
|
1. **用 RouterArena 做标准化评测**：本项目 `scripts/eval.py` 是自建迷你基准；正式化应接入 RouterArena 框架（`uv sync` + 提交预测文件即可上榜）。
|
||||||
|
2. **分类器升级为训练模型**：规则分类器（现 100%/15 条样例）在更大数据上会掉点；参考 ICL-Router / StR，训练 BERT 级分类器（94-97%）或小 LLM 分类器。
|
||||||
|
3. **引入预算感知路由**：参考 R2-Router 把"输出长度预算"纳入决策，可实现 4-5× 成本下降。
|
||||||
|
4. **级联 + 校准**：Judge 阈值参考 Conformal Cascade 做校准，确保升级率 ≤20% 且有理论保证。
|
||||||
|
5. **LoRA 专家池按不确定性路由**：第二阶段多领域专家采用 CARE / VI-MoLE 的置信度自适应预算分配。
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 四、参考链接
|
||||||
|
|
||||||
|
- RouterArena 代码：https://github.com/RouteWorks/RouterArena
|
||||||
|
- RouterArena 博客：https://huggingface.co/blog/JerryPotter/who-routes-the-routers
|
||||||
|
- Awesome-Routing-LLMs（持续更新的论文清单）：https://github.com/MilkThink-Lab/Awesome-Routing-LLMs
|
||||||
|
- The Avengers：https://arxiv.org/abs/2505.19797
|
||||||
|
- R2-Router：https://arxiv.org/abs/2602.02823
|
||||||
|
- 最新综述：https://arxiv.org/abs/2603.04445
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
*调研人：Codex（2026-08-12）*
|
||||||
@@ -0,0 +1,14 @@
|
|||||||
|
"""多专业小模型 + 路由模型系统 (Multi-Expert Router System)
|
||||||
|
|
||||||
|
核心思想：用「轻量分类路由器 + 专业小模型池 + 质量控制器(Judge) + 大模型回退」
|
||||||
|
在限定条件下替代单一通用大模型，大幅降低成本与延迟。
|
||||||
|
|
||||||
|
本包核心逻辑为零依赖纯标准库实现（mock 后端），可直接运行；
|
||||||
|
可选接入真实模型（transformers / OpenAI 兼容 API）。
|
||||||
|
"""
|
||||||
|
|
||||||
|
from .router import Router
|
||||||
|
from .models import Classification, ExpertResponse, RouterResult
|
||||||
|
|
||||||
|
__version__ = "0.1.0"
|
||||||
|
__all__ = ["Router", "Classification", "ExpertResponse", "RouterResult", "__version__"]␍
|
||||||
@@ -0,0 +1,141 @@
|
|||||||
|
"""两阶段路由缓存（对齐实现方案）：
|
||||||
|
- L1 精确缓存：完全相同的查询 -> 直接命中
|
||||||
|
- L2 语义缓存：字符 n-gram 余弦相似度（零依赖）-> 相似查询命中
|
||||||
|
- 命中 N 次（promote_frequency）后提升为精确缓存
|
||||||
|
|
||||||
|
说明：语义缓存中的"完全相同查询"（相似度=1.0）直接计为 exact 命中；
|
||||||
|
高频语义命中会提升为 O(1) 的精确缓存条目。
|
||||||
|
|
||||||
|
只缓存"未升级"的结果（升级路径每次都走大模型，不缓存，避免陈旧）。
|
||||||
|
"""
|
||||||
|
from __future__ import annotations
|
||||||
|
|
||||||
|
import re
|
||||||
|
from dataclasses import dataclass
|
||||||
|
from typing import Any, Dict, List, Optional, Tuple
|
||||||
|
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class CacheEntry:
|
||||||
|
result: Dict[str, Any]
|
||||||
|
hits: int = 1
|
||||||
|
|
||||||
|
|
||||||
|
def _ngrams(text: str, n: int = 3) -> List[str]:
|
||||||
|
"""字符 n-gram（去空白、小写），用于轻量语义相似度。"""
|
||||||
|
cleaned = re.sub(r"\s+", "", text.lower())
|
||||||
|
if len(cleaned) < n:
|
||||||
|
return [cleaned]
|
||||||
|
return [cleaned[i:i + n] for i in range(len(cleaned) - n + 1)]
|
||||||
|
|
||||||
|
|
||||||
|
def _cosine(vec_a: Dict[str, float], vec_b: Dict[str, float]) -> float:
|
||||||
|
if not vec_a or not vec_b:
|
||||||
|
return 0.0
|
||||||
|
common = set(vec_a) & set(vec_b)
|
||||||
|
dot = sum(vec_a[k] * vec_b[k] for k in common)
|
||||||
|
na = sum(v * v for v in vec_a.values()) ** 0.5
|
||||||
|
nb = sum(v * v for v in vec_b.values()) ** 0.5
|
||||||
|
if na == 0 or nb == 0:
|
||||||
|
return 0.0
|
||||||
|
return dot / (na * nb)
|
||||||
|
|
||||||
|
|
||||||
|
def _tf_vector(grams: List[str]) -> Dict[str, float]:
|
||||||
|
vec: Dict[str, float] = {}
|
||||||
|
for g in grams:
|
||||||
|
vec[g] = vec.get(g, 0.0) + 1.0
|
||||||
|
return vec
|
||||||
|
|
||||||
|
|
||||||
|
class RouterCache:
|
||||||
|
"""L1 精确缓存 + L2 语义缓存。"""
|
||||||
|
|
||||||
|
def __init__(self, semantic_enabled: bool = True, similarity_threshold: float = 0.88,
|
||||||
|
promote_frequency: int = 5, max_exact: int = 10000, max_semantic: int = 5000):
|
||||||
|
self.semantic_enabled = semantic_enabled
|
||||||
|
self.similarity_threshold = similarity_threshold
|
||||||
|
self.promote_frequency = promote_frequency
|
||||||
|
self.max_exact = max_exact
|
||||||
|
self.max_semantic = max_semantic
|
||||||
|
self._exact: Dict[str, CacheEntry] = {}
|
||||||
|
self._semantic: List[Tuple[str, CacheEntry]] = [] # (query, entry)
|
||||||
|
self._sem_vecs: Dict[str, Dict[str, float]] = {}
|
||||||
|
self.hits = {"exact": 0, "semantic": 0}
|
||||||
|
self.misses = 0
|
||||||
|
|
||||||
|
# ---- 查询 ----
|
||||||
|
def get(self, query: str) -> Optional[Tuple[Optional[str], Dict[str, Any]]]:
|
||||||
|
"""返回 (level, result)；未命中返回 None。level: 'exact' | 'semantic'"""
|
||||||
|
entry = self._exact.get(query)
|
||||||
|
if entry is not None:
|
||||||
|
self.hits["exact"] += 1
|
||||||
|
return ("exact", entry.result)
|
||||||
|
|
||||||
|
if self.semantic_enabled:
|
||||||
|
q_vec = _tf_vector(_ngrams(query))
|
||||||
|
best_sim = 0.0
|
||||||
|
best_query: Optional[str] = None
|
||||||
|
best_result: Optional[Dict[str, Any]] = None
|
||||||
|
for q, e in self._semantic:
|
||||||
|
sim = _cosine(q_vec, self._sem_vecs.get(q, {}))
|
||||||
|
if sim > best_sim:
|
||||||
|
best_sim = sim
|
||||||
|
best_query = q
|
||||||
|
best_result = e.result
|
||||||
|
if best_query is not None and best_sim >= self.similarity_threshold:
|
||||||
|
# 完全相同查询（相似度=1.0）计为 exact 命中
|
||||||
|
is_exact = best_sim >= 0.999
|
||||||
|
level = "exact" if is_exact else "semantic"
|
||||||
|
self.hits[level] += 1
|
||||||
|
self._semantic_hit(best_query)
|
||||||
|
return (level, best_result)
|
||||||
|
|
||||||
|
self.misses += 1
|
||||||
|
return None
|
||||||
|
|
||||||
|
def _semantic_hit(self, query: str):
|
||||||
|
"""语义命中：累计命中次数，达到阈值提升为精确缓存。"""
|
||||||
|
for i, (q, e) in enumerate(self._semantic):
|
||||||
|
if q == query:
|
||||||
|
e.hits += 1
|
||||||
|
if e.hits >= self.promote_frequency:
|
||||||
|
self._exact[query] = e
|
||||||
|
self._semantic.pop(i)
|
||||||
|
self._sem_vecs.pop(query, None)
|
||||||
|
break
|
||||||
|
|
||||||
|
# ---- 写入 ----
|
||||||
|
def put(self, query: str, result: Dict[str, Any]):
|
||||||
|
if query in self._exact:
|
||||||
|
return
|
||||||
|
entry = CacheEntry(result=result)
|
||||||
|
if self.semantic_enabled:
|
||||||
|
if len(self._semantic) >= self.max_semantic:
|
||||||
|
old_q, _ = self._semantic.pop(0)
|
||||||
|
self._sem_vecs.pop(old_q, None)
|
||||||
|
self._semantic.append((query, entry))
|
||||||
|
self._sem_vecs[query] = _tf_vector(_ngrams(query))
|
||||||
|
else:
|
||||||
|
self._exact[query] = entry
|
||||||
|
if len(self._exact) > self.max_exact:
|
||||||
|
self._exact.pop(next(iter(self._exact)))
|
||||||
|
|
||||||
|
# ---- 统计 ----
|
||||||
|
def stats(self) -> Dict[str, Any]:
|
||||||
|
total = self.hits["exact"] + self.hits["semantic"] + self.misses
|
||||||
|
return {
|
||||||
|
"exact_hits": self.hits["exact"],
|
||||||
|
"semantic_hits": self.hits["semantic"],
|
||||||
|
"misses": self.misses,
|
||||||
|
"hit_rate": round((self.hits["exact"] + self.hits["semantic"]) / total, 4) if total else 0.0,
|
||||||
|
"exact_size": len(self._exact),
|
||||||
|
"semantic_size": len(self._semantic),
|
||||||
|
}
|
||||||
|
|
||||||
|
def clear(self):
|
||||||
|
self._exact.clear()
|
||||||
|
self._semantic.clear()
|
||||||
|
self._sem_vecs.clear()
|
||||||
|
self.hits = {"exact": 0, "semantic": 0}
|
||||||
|
self.misses = 0␍
|
||||||
@@ -0,0 +1,203 @@
|
|||||||
|
"""意图分类器：识别查询领域（code/math/legal/medical/general）与难度。
|
||||||
|
|
||||||
|
- RuleClassifier：关键词/正则规则打分，纯标准库，零依赖，可离线运行。
|
||||||
|
- HuggingFaceClassifier：可选，基于 transformers 的分类模型（需安装 ML 依赖）。
|
||||||
|
|
||||||
|
置信度设计：每个领域有一组 (关键词, 权重)。命中权重求和得原始分 s，
|
||||||
|
confidence = 1 - exp(-s)，保证 s=1 -> 0.63，s=2 -> 0.86，s=3 -> 0.95。
|
||||||
|
无领域命中（或最高分领域为 general）时置信度低，触发 should_fallback。
|
||||||
|
"""
|
||||||
|
from __future__ import annotations
|
||||||
|
|
||||||
|
import math
|
||||||
|
from typing import Dict, List, Tuple
|
||||||
|
|
||||||
|
from .difficulty import estimate_difficulty
|
||||||
|
from .models import Classification
|
||||||
|
|
||||||
|
# ---------------------------------------------------------------
|
||||||
|
# 领域关键词规则： (关键词, 权重)
|
||||||
|
# ---------------------------------------------------------------
|
||||||
|
DOMAIN_RULES: Dict[str, List[Tuple[str, float]]] = {
|
||||||
|
"code": [
|
||||||
|
# 中文
|
||||||
|
("python", 1.2), ("java", 1.2), ("javascript", 1.2), ("typescript", 1.2),
|
||||||
|
("代码", 1.2), ("编程", 1.2), ("函数", 0.9), ("接口", 0.8), ("报错", 0.9),
|
||||||
|
("调试", 0.9), ("部署", 0.8), ("算法", 0.8), ("数组", 0.8), ("排序", 0.9),
|
||||||
|
("正则", 0.8), ("数据库", 0.7), ("sql", 0.8), ("git", 0.7), ("api", 0.7),
|
||||||
|
("变量", 0.7), ("循环", 0.7), ("递归", 0.8), ("重构", 0.8), ("编译", 0.9),
|
||||||
|
("测试", 0.6), ("前端", 0.8), ("后端", 0.8), ("爬虫", 0.8), ("脚本", 0.7),
|
||||||
|
# 英文
|
||||||
|
("function", 0.9), ("class", 0.8), ("bug", 0.9), ("debug", 0.9),
|
||||||
|
("compile", 0.9), ("error", 0.6), ("code", 0.7), ("script", 0.7),
|
||||||
|
("algorithm", 0.8), ("sort", 0.7), ("array", 0.7), ("regex", 0.8),
|
||||||
|
("import", 0.7), ("loop", 0.7), ("recursion", 0.8), ("refactor", 0.8),
|
||||||
|
("deploy", 0.8), ("docker", 0.8), ("kubernetes", 0.8),
|
||||||
|
("async", 0.7), ("flask", 0.7), ("django", 0.7), ("api", 0.7),
|
||||||
|
("索引", 0.8), ("优化", 0.7), ("查询", 0.6),
|
||||||
|
],
|
||||||
|
"math": [
|
||||||
|
("数学", 1.2), ("方程", 1.0), ("求解", 0.8), ("导数", 1.0), ("积分", 1.0),
|
||||||
|
("矩阵", 0.9), ("概率", 0.9), ("统计", 0.8), ("证明", 0.8), ("定理", 0.9),
|
||||||
|
("微积分", 1.1), ("代数", 0.9), ("几何", 0.9), ("不等式", 0.9),
|
||||||
|
("equation", 1.0), ("derivative", 1.0), ("integral", 1.0), ("calculus", 1.1),
|
||||||
|
("matrix", 0.9), ("probability", 0.9), ("statistics", 0.8), ("proof", 0.8),
|
||||||
|
("theorem", 0.9), ("algebra", 0.9), ("geometry", 0.9), ("sqrt", 0.8),
|
||||||
|
("gcd", 0.8), ("lim", 0.8), ("polynomial", 0.9), ("summation", 0.7),
|
||||||
|
("math", 0.7), ("解", 0.6), ("计算", 0.8), ("等于", 0.6), ("求值", 0.7), ("函数", 0.6),
|
||||||
|
],
|
||||||
|
"legal": [
|
||||||
|
("法律", 1.2), ("合同", 1.0), ("法条", 1.0), ("合规", 1.0), ("诉讼", 1.0),
|
||||||
|
("知识产权", 1.1), ("版权", 0.9), ("专利", 0.9), ("违约", 0.9), ("赔偿", 0.8),
|
||||||
|
("仲裁", 0.9), ("劳动法", 1.0), ("刑法", 1.0), ("民法典", 1.0),
|
||||||
|
("法规", 0.8), ("条款", 0.7), ("律师", 0.8), ("起诉", 0.9), ("判决", 0.9),
|
||||||
|
("law", 1.0), ("legal", 1.1), ("contract", 1.0), ("compliance", 1.0),
|
||||||
|
("litigation", 1.0), ("copyright", 0.9), ("patent", 0.9), ("trademark", 0.9),
|
||||||
|
("liability", 0.9), ("regulatory", 0.8), ("jurisdiction", 0.9),
|
||||||
|
("clause", 0.8), ("agreement", 0.7), ("申请", 0.6),
|
||||||
|
],
|
||||||
|
"medical": [
|
||||||
|
("医疗", 1.2), ("药物", 1.0), ("症状", 1.0), ("诊断", 1.0), ("治疗", 0.9),
|
||||||
|
("医生", 0.9), ("血压", 0.9), ("高血压", 1.0), ("糖尿病", 1.0), ("感冒", 0.9),
|
||||||
|
("剂量", 0.9), ("副作用", 0.9), ("手术", 0.9), ("患者", 0.9),
|
||||||
|
("吃药", 0.9), ("发烧", 1.0), ("疫苗", 0.9), ("感染", 0.9), ("体检", 0.7),
|
||||||
|
("medical", 1.0), ("patient", 0.9), ("symptom", 1.0), ("disease", 0.9),
|
||||||
|
("diagnosis", 1.0), ("treatment", 0.8), ("prescription", 1.0),
|
||||||
|
("dosage", 0.9), ("side effect", 0.9), ("hypertension", 1.0),
|
||||||
|
("diabetes", 1.0), ("surgery", 0.8), ("clinic", 0.7), ("vaccine", 0.9),
|
||||||
|
("infection", 0.9),
|
||||||
|
],
|
||||||
|
"general": [
|
||||||
|
("总结", 0.4), ("翻译", 0.4), ("介绍", 0.4), ("解释", 0.3),
|
||||||
|
("summarize", 0.4), ("translate", 0.4), ("explain", 0.3),
|
||||||
|
("introduce", 0.3), ("what is", 0.3), ("tell me", 0.3),
|
||||||
|
("write an essay", 0.4), ("邮件", 0.4), ("email", 0.3),
|
||||||
|
("推荐", 0.3), ("评价", 0.3),
|
||||||
|
],
|
||||||
|
}
|
||||||
|
|
||||||
|
_STOPWORDS = {
|
||||||
|
"的", "了", "吗", "呢", "啊", "是", "在", "有", "和", "与", "或", "及", "一个", "如何",
|
||||||
|
"the", "a", "an", "is", "are", "to", "of", "in", "on", "for", "with", "and",
|
||||||
|
"or", "do", "does", "can", "could", "would", "should", "please", "me", "my",
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
class BaseClassifier:
|
||||||
|
def classify(self, query: str) -> Classification:
|
||||||
|
raise NotImplementedError
|
||||||
|
|
||||||
|
def should_fallback(self, classification: Classification, threshold: float) -> bool:
|
||||||
|
return classification.confidence < threshold
|
||||||
|
|
||||||
|
|
||||||
|
class RuleClassifier(BaseClassifier):
|
||||||
|
"""基于关键词规则的分类器（零依赖）。"""
|
||||||
|
|
||||||
|
def __init__(self, confidence_floor: float = 0.55):
|
||||||
|
self.confidence_floor = confidence_floor
|
||||||
|
self.rules = DOMAIN_RULES
|
||||||
|
|
||||||
|
def _score(self, query: str) -> Tuple[Dict[str, float], Dict[str, List[str]]]:
|
||||||
|
q = query.lower()
|
||||||
|
scores: Dict[str, float] = {}
|
||||||
|
matched: Dict[str, List[str]] = {}
|
||||||
|
for domain, rules in self.rules.items():
|
||||||
|
s = 0.0
|
||||||
|
hits = []
|
||||||
|
for kw, w in rules:
|
||||||
|
if kw in q:
|
||||||
|
s += w
|
||||||
|
hits.append(kw)
|
||||||
|
if s > 0:
|
||||||
|
scores[domain] = s
|
||||||
|
matched[domain] = hits
|
||||||
|
return scores, matched
|
||||||
|
|
||||||
|
def classify(self, query: str) -> Classification:
|
||||||
|
raw, matched = self._score(query)
|
||||||
|
if not raw:
|
||||||
|
# 完全无命中 -> general，低置信度
|
||||||
|
diff, ds = estimate_difficulty(query)
|
||||||
|
return Classification(
|
||||||
|
domain="general",
|
||||||
|
confidence=0.50,
|
||||||
|
difficulty=diff,
|
||||||
|
difficulty_score=ds,
|
||||||
|
raw_scores={},
|
||||||
|
matched_rules=[],
|
||||||
|
)
|
||||||
|
|
||||||
|
best_domain = max(raw, key=raw.get)
|
||||||
|
best_score = raw[best_domain]
|
||||||
|
confidence = 1.0 - math.exp(-best_score)
|
||||||
|
|
||||||
|
# general 领域天然置信度压低
|
||||||
|
if best_domain == "general":
|
||||||
|
confidence = min(confidence, self.confidence_floor + 0.05)
|
||||||
|
|
||||||
|
# 与次高分的差距影响置信度（区分度）
|
||||||
|
if len(raw) > 1:
|
||||||
|
second = sorted(raw.values(), reverse=True)[1]
|
||||||
|
if second > 0.7 * best_score:
|
||||||
|
confidence *= 0.85
|
||||||
|
|
||||||
|
diff, ds = estimate_difficulty(query)
|
||||||
|
return Classification(
|
||||||
|
domain=best_domain,
|
||||||
|
confidence=round(min(0.99, confidence), 4),
|
||||||
|
difficulty=diff,
|
||||||
|
difficulty_score=ds,
|
||||||
|
raw_scores={k: round(v, 3) for k, v in raw.items()},
|
||||||
|
matched_rules=matched.get(best_domain, []),
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
class HuggingFaceClassifier(BaseClassifier):
|
||||||
|
"""可选：基于 transformers 的序列分类模型。
|
||||||
|
|
||||||
|
仅当安装 torch+transformers 且模型可加载时可用；否则抛错提示。
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, model_name: str, num_labels: int = 5, confidence_floor: float = 0.55):
|
||||||
|
try:
|
||||||
|
from transformers import AutoModelForSequenceClassification, AutoTokenizer
|
||||||
|
except ImportError as e:
|
||||||
|
raise RuntimeError(
|
||||||
|
"HuggingFaceClassifier 需要安装 ML 依赖：pip install -r requirements-ml.txt"
|
||||||
|
) from e
|
||||||
|
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
|
||||||
|
self.model = AutoModelForSequenceClassification.from_pretrained(
|
||||||
|
model_name, num_labels=num_labels
|
||||||
|
)
|
||||||
|
self.labels = ["code", "math", "legal", "medical", "general"]
|
||||||
|
self.confidence_floor = confidence_floor
|
||||||
|
|
||||||
|
def classify(self, query: str) -> Classification:
|
||||||
|
import torch # type: ignore
|
||||||
|
|
||||||
|
inputs = self.tokenizer(query, return_tensors="pt", truncation=True, max_length=256)
|
||||||
|
with torch.no_grad():
|
||||||
|
logits = self.model(**inputs).logits
|
||||||
|
probs = torch.softmax(logits, dim=-1)[0]
|
||||||
|
idx = int(probs.argmax())
|
||||||
|
diff, ds = estimate_difficulty(query)
|
||||||
|
return Classification(
|
||||||
|
domain=self.labels[idx],
|
||||||
|
confidence=round(float(probs[idx]), 4),
|
||||||
|
difficulty=diff,
|
||||||
|
difficulty_score=ds,
|
||||||
|
raw_scores={self.labels[i]: round(float(probs[i]), 3) for i in range(len(self.labels))},
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
def build_classifier(cfg: Dict) -> BaseClassifier:
|
||||||
|
"""根据配置构建分类器。cfg 为 classifier 段配置。"""
|
||||||
|
ctype = cfg.get("type", "rule")
|
||||||
|
floor = cfg.get("confidence_floor", 0.55)
|
||||||
|
if ctype == "rule":
|
||||||
|
return RuleClassifier(confidence_floor=floor)
|
||||||
|
if ctype == "hf":
|
||||||
|
return HuggingFaceClassifier(cfg.get("model", "Qwen/Qwen3-0.6B"), confidence_floor=floor)
|
||||||
|
raise ValueError(f"未知分类器类型: {ctype}（支持 rule | hf）")
|
||||||
|
|
||||||
@@ -0,0 +1,100 @@
|
|||||||
|
"""配置加载：优先 YAML（若安装了 pyyaml），否则回退 JSON。
|
||||||
|
|
||||||
|
设计原则：router_system 核心零依赖，因此 pyyaml 是"可选"的。
|
||||||
|
默认 config/config.yaml 存在；若 pyyaml 不可用，可提供同名 .json。
|
||||||
|
"""
|
||||||
|
from __future__ import annotations
|
||||||
|
|
||||||
|
import json
|
||||||
|
import os
|
||||||
|
from pathlib import Path
|
||||||
|
from typing import Any, Dict, Optional
|
||||||
|
|
||||||
|
DEFAULT_CONFIG_PATH = Path(__file__).resolve().parent.parent / "config" / "config.yaml"
|
||||||
|
|
||||||
|
_DEFAULTS: Dict[str, Any] = {
|
||||||
|
"system": {"name": "multi-expert-router", "version": "0.1.0"},
|
||||||
|
"router": {
|
||||||
|
"low_confidence_threshold": 0.60, # 分类置信度低于此值 -> 直接走大模型
|
||||||
|
"judge_fallback_threshold": 0.70, # Judge 质量分低于此值 -> 升级大模型
|
||||||
|
"default_temperature": 0.2,
|
||||||
|
},
|
||||||
|
"classifier": {"type": "rule", "model": "Qwen/Qwen3-0.6B", "confidence_floor": 0.55},
|
||||||
|
"domains": ["code", "math", "legal", "medical", "general"],
|
||||||
|
"experts": {
|
||||||
|
"code": {"type": "mock", "model": "Qwen/Qwen2.5-Coder-7B-Instruct"},
|
||||||
|
"math": {"type": "mock", "model": "Qwen/Qwen3-4B-Instruct"},
|
||||||
|
"legal": {"type": "mock", "model": "Qwen/Qwen3-4B-Instruct"},
|
||||||
|
"medical": {"type": "mock", "model": "Qwen/Qwen3-4B-Instruct"},
|
||||||
|
"general": {"type": "mock", "model": "Qwen/Qwen3-1.7B-Instruct"},
|
||||||
|
},
|
||||||
|
"fallback": {
|
||||||
|
"type": "mock",
|
||||||
|
"model": "deepseek-chat",
|
||||||
|
"base_url": "https://api.deepseek.com/v1",
|
||||||
|
"api_key_env": "DEEPSEEK_API_KEY",
|
||||||
|
},
|
||||||
|
"judge": {"type": "rule", "model": "Qwen/Qwen3-1.7B-Instruct"},
|
||||||
|
"cache": {
|
||||||
|
"enabled": True,
|
||||||
|
"semantic_enabled": True,
|
||||||
|
"similarity_threshold": 0.88,
|
||||||
|
"promote_frequency": 5,
|
||||||
|
},
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
def load_defaults() -> Dict[str, Any]:
|
||||||
|
return _DEFAULTS
|
||||||
|
|
||||||
|
|
||||||
|
def _try_load_yaml(path: Path) -> Optional[Dict[str, Any]]:
|
||||||
|
try:
|
||||||
|
import yaml # type: ignore
|
||||||
|
except ImportError:
|
||||||
|
return None
|
||||||
|
try:
|
||||||
|
with open(path, "r", encoding="utf-8") as f:
|
||||||
|
data = yaml.safe_load(f)
|
||||||
|
return data if isinstance(data, dict) else None
|
||||||
|
except Exception:
|
||||||
|
return None
|
||||||
|
|
||||||
|
|
||||||
|
def _try_load_json(path: Path) -> Optional[Dict[str, Any]]:
|
||||||
|
json_path = path.with_suffix(".json")
|
||||||
|
if not json_path.exists():
|
||||||
|
return None
|
||||||
|
try:
|
||||||
|
with open(json_path, "r", encoding="utf-8") as f:
|
||||||
|
data = json.load(f)
|
||||||
|
return data if isinstance(data, dict) else None
|
||||||
|
except Exception:
|
||||||
|
return None
|
||||||
|
|
||||||
|
|
||||||
|
def _merge_defaults(data: Dict[str, Any]) -> Dict[str, Any]:
|
||||||
|
"""将用户配置与内置默认配置做一层合并（用户优先）。"""
|
||||||
|
merged = dict(_DEFAULTS)
|
||||||
|
for k, v in data.items():
|
||||||
|
if isinstance(v, dict) and isinstance(merged.get(k), dict):
|
||||||
|
merged[k] = {**merged[k], **v}
|
||||||
|
else:
|
||||||
|
merged[k] = v
|
||||||
|
return merged
|
||||||
|
|
||||||
|
|
||||||
|
def load_config(path: Optional[Path | str] = None) -> Dict[str, Any]:
|
||||||
|
"""加载配置，返回 dict。文件不存在或解析失败时返回内置默认配置。"""
|
||||||
|
cfg_path = Path(path) if path else DEFAULT_CONFIG_PATH
|
||||||
|
if cfg_path.exists():
|
||||||
|
data = _try_load_yaml(cfg_path) or _try_load_json(cfg_path)
|
||||||
|
if data is not None:
|
||||||
|
return _merge_defaults(data)
|
||||||
|
return dict(_DEFAULTS)
|
||||||
|
|
||||||
|
|
||||||
|
def get_api_key(cfg: Dict[str, Any]) -> Optional[str]:
|
||||||
|
"""从环境变量读取 API Key（用于 api 类型后端）。"""
|
||||||
|
env_name = cfg.get("api_key_env") or "API_KEY"
|
||||||
|
return os.environ.get(env_name) or None␍
|
||||||
@@ -0,0 +1,52 @@
|
|||||||
|
"""查询难度估计器（启发式，纯标准库）。
|
||||||
|
|
||||||
|
依据：RouterArena 用 Bloom 分类法把问题分为 easy/medium/hard。
|
||||||
|
这里用查询长度、指令动词、数学/推理标记做轻量估计。
|
||||||
|
"""
|
||||||
|
from __future__ import annotations
|
||||||
|
|
||||||
|
import re
|
||||||
|
from typing import Tuple
|
||||||
|
|
||||||
|
# 触发 hard 的指令动词 / 推理标记
|
||||||
|
_HARD_MARKERS = [
|
||||||
|
"证明", "推导", "为什么", "如何", "对比", "比较", "分析", "评估", "设计", "优化",
|
||||||
|
"复杂度", "时间复杂度", "空间复杂度", "原理", "机制", "优缺点", "区别", "推论", "定理",
|
||||||
|
"proof", "prove", "derive", "explain why", "why", "how", "compare", "contrast",
|
||||||
|
"analy", "evaluate", "design", "optimize", "refactor", "architect",
|
||||||
|
"implement", "debug", "review", "plan", "synthesize",
|
||||||
|
"int", "sum", "sqrt", "lim", "log", "derivative", "integral",
|
||||||
|
]
|
||||||
|
# 触发 medium 的标记
|
||||||
|
_MEDIUM_MARKERS = [
|
||||||
|
"用", "写", "计算", "求解", "生成", "翻译", "总结", "解释",
|
||||||
|
"注意", "建议", "是否", "实现", "步骤",
|
||||||
|
"write", "code", "function", "script", "calculate", "solve", "summarize",
|
||||||
|
"translate", "fix", "explain", "describe", "list",
|
||||||
|
]
|
||||||
|
|
||||||
|
|
||||||
|
def estimate_difficulty(query: str) -> Tuple[str, float]:
|
||||||
|
"""返回 (difficulty, score)，score 属于 [0,1]。"""
|
||||||
|
q = query.lower()
|
||||||
|
hard_hits = sum(1 for m in _HARD_MARKERS if m in q)
|
||||||
|
medium_hits = sum(1 for m in _MEDIUM_MARKERS if m in q)
|
||||||
|
length = len(query)
|
||||||
|
|
||||||
|
score = 0.0
|
||||||
|
score += min(0.30, length / 600.0) # 长度贡献
|
||||||
|
score += min(0.55, hard_hits * 0.25) # 推理标记贡献
|
||||||
|
score += min(0.30, medium_hits * 0.08) # 一般指令贡献
|
||||||
|
|
||||||
|
# 额外：代码 / 数学表达式（多步骤信号）
|
||||||
|
if "```" in query or re.search(r"\b(def|class|function|import)\b", q):
|
||||||
|
score += 0.15
|
||||||
|
if re.search(r"[0-9]+\s*[+\-*/^=]\s*[0-9xya-z]", q):
|
||||||
|
score += 0.15
|
||||||
|
|
||||||
|
score = max(0.0, min(1.0, score))
|
||||||
|
if score >= 0.55:
|
||||||
|
return "hard", score
|
||||||
|
if score >= 0.25:
|
||||||
|
return "medium", score
|
||||||
|
return "easy", score␍
|
||||||
@@ -0,0 +1,270 @@
|
|||||||
|
"""专家模型池：统一 Expert 接口，支持三种后端。
|
||||||
|
|
||||||
|
- MockExpert ：确定性模板输出（零依赖，离线可跑，便于测试与演示）
|
||||||
|
- HFExpert ：HuggingFace transformers 真实小模型（可选，需 ML 依赖）
|
||||||
|
- APIExpert ：OpenAI 兼容 API（可选，需 API Key，如 DeepSeek）
|
||||||
|
|
||||||
|
成本估计：cost_est 按参数量粗估（美元/百万 token 的近似比例）。
|
||||||
|
"""
|
||||||
|
from __future__ import annotations
|
||||||
|
|
||||||
|
import asyncio
|
||||||
|
import re
|
||||||
|
from typing import Dict, List, Optional
|
||||||
|
|
||||||
|
from .models import ExpertResponse
|
||||||
|
|
||||||
|
# 按模型规模粗估的相对成本（$ / 1M output tokens，近似）
|
||||||
|
MODEL_COST_EST = {
|
||||||
|
"mock": 0.0,
|
||||||
|
"0.5b": 0.02,
|
||||||
|
"1b": 0.05,
|
||||||
|
"1.7b": 0.08,
|
||||||
|
"3b": 0.12,
|
||||||
|
"4b": 0.15,
|
||||||
|
"7b": 0.25,
|
||||||
|
"70b": 2.50,
|
||||||
|
"api": 1.00,
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
def _cost_for(model_name: str, default: str = "1b") -> float:
|
||||||
|
mn = model_name.lower()
|
||||||
|
for key in ("0.5b", "1.7b", "3b", "4b", "7b", "70b"):
|
||||||
|
if key in mn:
|
||||||
|
return MODEL_COST_EST[key]
|
||||||
|
if "api" in mn or mn in ("deepseek-chat", "gpt-4o-mini", "claude"):
|
||||||
|
return MODEL_COST_EST["api"]
|
||||||
|
return MODEL_COST_EST.get(default, 0.1)
|
||||||
|
|
||||||
|
|
||||||
|
def extract_content_terms(query: str) -> List[str]:
|
||||||
|
"""抽取查询中的"内容词"（中文词/英文单词），用于 Judge 覆盖度与 Mock 回显。"""
|
||||||
|
q = query.lower()
|
||||||
|
terms: List[str] = []
|
||||||
|
# 英文单词（>=2 字符）
|
||||||
|
for w in re.findall(r"[a-z][a-z0-9_]{1,}", q):
|
||||||
|
if w not in _STOPWORDS_EN and w not in terms:
|
||||||
|
terms.append(w)
|
||||||
|
# 中文：按 2-4 字窗口切分，保留含中文字符的片段
|
||||||
|
cn = re.findall(r"[\u4e00-\u9fff]{2,8}", q)
|
||||||
|
for c in cn:
|
||||||
|
terms.append(c)
|
||||||
|
return terms
|
||||||
|
|
||||||
|
|
||||||
|
_STOPWORDS_EN = {
|
||||||
|
"the", "a", "an", "is", "are", "to", "of", "in", "on", "for", "with", "and",
|
||||||
|
"or", "do", "does", "can", "could", "would", "should", "please", "me", "my",
|
||||||
|
"this", "that", "it", "be", "was", "were", "have", "has", "had", "will",
|
||||||
|
"not", "no", "yes", "i", "you", "he", "she", "we", "they",
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
class Expert:
|
||||||
|
name: str = "expert"
|
||||||
|
|
||||||
|
async def generate(self, query: str, difficulty: str) -> ExpertResponse:
|
||||||
|
raise NotImplementedError
|
||||||
|
|
||||||
|
|
||||||
|
class MockExpert(Expert):
|
||||||
|
"""确定性模板专家：零依赖，离线可跑。
|
||||||
|
|
||||||
|
输出会回显查询中的内容词以提高 Judge 覆盖度，并带领域结构，
|
||||||
|
使端到端管线（分类 -> 专家 -> Judge -> 缓存）可被稳定测试与演示。
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, name: str, domain: str, model: str = "mock"):
|
||||||
|
self.name = name
|
||||||
|
self.domain = domain
|
||||||
|
self.model = model
|
||||||
|
|
||||||
|
async def generate(self, query: str, difficulty: str) -> ExpertResponse:
|
||||||
|
await asyncio.sleep(0.001) # 模拟极短推理延迟
|
||||||
|
terms = extract_content_terms(query)
|
||||||
|
body = self._template(query, terms, difficulty)
|
||||||
|
# 预估 token 数：中文约 1.5 字符/token，英文约 4 字符/token
|
||||||
|
tokens = max(8, int(len(body) / 2.2))
|
||||||
|
return ExpertResponse(
|
||||||
|
text=body,
|
||||||
|
model_used=self.model,
|
||||||
|
latency_ms=1.0,
|
||||||
|
tokens=tokens,
|
||||||
|
cost_est=_cost_for(self.model) * tokens / 1_000_000,
|
||||||
|
)
|
||||||
|
|
||||||
|
def _template(self, query: str, terms: List[str], difficulty: str) -> str:
|
||||||
|
kw = "、".join(terms[:6]) if terms else "该主题"
|
||||||
|
if self.domain == "code":
|
||||||
|
return (
|
||||||
|
f"（mock 代码专家）针对「{query}」的实现思路如下：\n\n"
|
||||||
|
f"```python\n"
|
||||||
|
f"def solve() -> None:\n"
|
||||||
|
f" # 关键点：{kw}\n"
|
||||||
|
f" # 1. 明确输入输出约束\n"
|
||||||
|
f" # 2. 选择合适数据结构\n"
|
||||||
|
f" # 3. 处理边界条件（空输入、极端值）\n"
|
||||||
|
f" # 4. 补充单元测试\n"
|
||||||
|
f" pass\n"
|
||||||
|
f"```\n\n"
|
||||||
|
f"复杂度：平均 O(n)。请按上述步骤补充具体实现。"
|
||||||
|
)
|
||||||
|
if self.domain == "math":
|
||||||
|
return (
|
||||||
|
f"（mock 数学专家）求解「{query}」的步骤：\n\n"
|
||||||
|
f"1. 明确已知条件与目标：{kw}\n"
|
||||||
|
f"2. 选择合适的方法（代数变形 / 积分 / 归纳等）\n"
|
||||||
|
f"3. 逐步推导并验证中间结果\n"
|
||||||
|
f"4. 检查边界与特殊情况\n\n"
|
||||||
|
f"结论：在标准假设下，结果可化简为闭合形式。完整推导见正式解答。"
|
||||||
|
)
|
||||||
|
if self.domain == "legal":
|
||||||
|
return (
|
||||||
|
f"（mock 法律专家）关于「{query}」的初步法律分析：\n\n"
|
||||||
|
f"相关要点：{kw}\n"
|
||||||
|
f"1. 适用法规：请以现行有效法条为准（建议核对最新修订版）\n"
|
||||||
|
f"2. 合同/合规风险点识别\n"
|
||||||
|
f"3. 责任划分与救济途径\n\n"
|
||||||
|
f"⚠️ 提示：以上为一般性分析，不构成正式法律意见，个案请咨询执业律师。"
|
||||||
|
)
|
||||||
|
if self.domain == "medical":
|
||||||
|
return (
|
||||||
|
f"（mock 医学专家）关于「{query}」的科普性说明：\n\n"
|
||||||
|
f"相关关键词：{kw}\n"
|
||||||
|
f"1. 常见表现与可能原因\n"
|
||||||
|
f"2. 一般处理建议与注意事项\n"
|
||||||
|
f"3. 何时需要就医（警示信号）\n\n"
|
||||||
|
f"⚠️ 提示：内容仅供健康科普，不能替代医生诊断；如有不适请及时就医。"
|
||||||
|
)
|
||||||
|
return (
|
||||||
|
f"（mock 通用专家）关于「{query}」的回答：\n\n"
|
||||||
|
f"核心要点：{kw}\n"
|
||||||
|
f"1. 背景与定义\n"
|
||||||
|
f"2. 主要分类/维度\n"
|
||||||
|
f"3. 实际应用与注意事项\n\n"
|
||||||
|
f"如需更深入的分析，可以补充更多上下文。"
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
class HFExpert(Expert):
|
||||||
|
"""可选：HuggingFace 真实小模型（需 requirements-ml.txt）。"""
|
||||||
|
|
||||||
|
def __init__(self, name: str, domain: str, model: str):
|
||||||
|
self.name = name
|
||||||
|
self.domain = domain
|
||||||
|
self.model = model
|
||||||
|
self._loaded = False
|
||||||
|
self._model = None
|
||||||
|
self._tokenizer = None
|
||||||
|
|
||||||
|
def _ensure_loaded(self):
|
||||||
|
if self._loaded:
|
||||||
|
return
|
||||||
|
try:
|
||||||
|
from transformers import AutoModelForCausalLM, AutoTokenizer
|
||||||
|
except ImportError as e:
|
||||||
|
raise RuntimeError("HFExpert 需要安装 ML 依赖：pip install -r requirements-ml.txt") from e
|
||||||
|
self._tokenizer = AutoTokenizer.from_pretrained(self.model)
|
||||||
|
self._model = AutoModelForCausalLM.from_pretrained(
|
||||||
|
self.model, device_map="auto", torch_dtype="auto"
|
||||||
|
)
|
||||||
|
self._loaded = True
|
||||||
|
|
||||||
|
async def generate(self, query: str, difficulty: str) -> ExpertResponse:
|
||||||
|
self._ensure_loaded()
|
||||||
|
return await asyncio.to_thread(self._generate_sync, query)
|
||||||
|
|
||||||
|
def _generate_sync(self, query: str) -> ExpertResponse:
|
||||||
|
messages = [{"role": "user", "content": query}]
|
||||||
|
text = self._tokenizer.apply_chat_template(messages, tokenize=False)
|
||||||
|
inputs = self._tokenizer(text, return_tensors="pt").to(self._model.device)
|
||||||
|
outputs = self._model.generate(
|
||||||
|
**inputs,
|
||||||
|
max_new_tokens=512,
|
||||||
|
temperature=0.2,
|
||||||
|
do_sample=True,
|
||||||
|
)
|
||||||
|
body = self._tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
|
||||||
|
return ExpertResponse(
|
||||||
|
text=body,
|
||||||
|
model_used=self.model,
|
||||||
|
latency_ms=0.0,
|
||||||
|
tokens=512,
|
||||||
|
cost_est=_cost_for(self.model) * 512 / 1_000_000,
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
class APIExpert(Expert):
|
||||||
|
"""可选：OpenAI 兼容 Chat Completions（DeepSeek / OpenAI / 本地 vLLM）。"""
|
||||||
|
|
||||||
|
def __init__(self, name: str, domain: str, model: str, base_url: str, api_key: str):
|
||||||
|
self.name = name
|
||||||
|
self.domain = domain
|
||||||
|
self.model = model
|
||||||
|
self.base_url = base_url.rstrip("/")
|
||||||
|
self.api_key = api_key
|
||||||
|
self._client = None
|
||||||
|
|
||||||
|
def _get_client(self):
|
||||||
|
if self._client is None:
|
||||||
|
import httpx
|
||||||
|
self._client = httpx.AsyncClient(timeout=60.0)
|
||||||
|
return self._client
|
||||||
|
|
||||||
|
async def generate(self, query: str, difficulty: str) -> ExpertResponse:
|
||||||
|
client = self._get_client()
|
||||||
|
resp = await client.post(
|
||||||
|
f"{self.base_url}/chat/completions",
|
||||||
|
headers={"Authorization": f"Bearer {self.api_key}"},
|
||||||
|
json={
|
||||||
|
"model": self.model,
|
||||||
|
"messages": [{"role": "user", "content": query}],
|
||||||
|
"temperature": 0.2,
|
||||||
|
"max_tokens": 1024,
|
||||||
|
},
|
||||||
|
)
|
||||||
|
resp.raise_for_status()
|
||||||
|
data = resp.json()
|
||||||
|
body = data["choices"][0]["message"]["content"]
|
||||||
|
usage = data.get("usage", {})
|
||||||
|
tokens = usage.get("completion_tokens", int(len(body) / 2.2))
|
||||||
|
return ExpertResponse(
|
||||||
|
text=body,
|
||||||
|
model_used=self.model,
|
||||||
|
latency_ms=0.0,
|
||||||
|
tokens=tokens,
|
||||||
|
cost_est=_cost_for(self.model) * tokens / 1_000_000,
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
def build_expert(domain: str, cfg: Dict) -> Expert:
|
||||||
|
"""根据配置构建领域专家。cfg 为 experts.<domain> 段配置。"""
|
||||||
|
etype = cfg.get("type", "mock")
|
||||||
|
model = cfg.get("model", "mock")
|
||||||
|
name = f"expert-{domain}"
|
||||||
|
if etype == "mock":
|
||||||
|
return MockExpert(name, domain, model)
|
||||||
|
if etype == "hf":
|
||||||
|
return HFExpert(name, domain, model)
|
||||||
|
if etype == "api":
|
||||||
|
base_url = cfg.get("base_url", "https://api.deepseek.com/v1")
|
||||||
|
api_key = cfg.get("api_key") or _env(cfg.get("api_key_env", ""))
|
||||||
|
if not api_key:
|
||||||
|
raise RuntimeError(f"APIExpert({domain}) 缺少 API Key（env: {cfg.get('api_key_env')}）")
|
||||||
|
return APIExpert(name, domain, model, base_url, api_key)
|
||||||
|
raise ValueError(f"未知专家后端类型: {etype}（支持 mock | hf | api）")
|
||||||
|
|
||||||
|
|
||||||
|
def _env(name: str) -> Optional[str]:
|
||||||
|
import os
|
||||||
|
return os.environ.get(name) if name else None
|
||||||
|
|
||||||
|
|
||||||
|
def build_expert_pool(experts_cfg: Dict[str, Dict], domains: List[str]) -> Dict[str, Expert]:
|
||||||
|
"""构建完整专家池。"""
|
||||||
|
pool: Dict[str, Expert] = {}
|
||||||
|
for domain in domains:
|
||||||
|
cfg = experts_cfg.get(domain, {"type": "mock", "model": "mock"})
|
||||||
|
pool[domain] = build_expert(domain, cfg)
|
||||||
|
return pool␍
|
||||||
@@ -0,0 +1,99 @@
|
|||||||
|
"""大模型回退层：Mock 与 OpenAI 兼容 API 两种后端。"""
|
||||||
|
from __future__ import annotations
|
||||||
|
|
||||||
|
import asyncio
|
||||||
|
from typing import Dict, Optional
|
||||||
|
|
||||||
|
from .models import ExpertResponse
|
||||||
|
|
||||||
|
|
||||||
|
class FallbackProvider:
|
||||||
|
name: str = "fallback"
|
||||||
|
|
||||||
|
async def generate(self, query: str) -> ExpertResponse:
|
||||||
|
raise NotImplementedError
|
||||||
|
|
||||||
|
|
||||||
|
class MockFallback(FallbackProvider):
|
||||||
|
"""确定性 mock 大模型：标识为 fallback，便于测试升级路径。"""
|
||||||
|
|
||||||
|
def __init__(self, model: str = "mock-large"):
|
||||||
|
self.model = model
|
||||||
|
self.name = f"fallback-{model}"
|
||||||
|
|
||||||
|
async def generate(self, query: str) -> ExpertResponse:
|
||||||
|
await asyncio.sleep(0.002)
|
||||||
|
body = (
|
||||||
|
f"（大模型回退）「{query}」\n\n"
|
||||||
|
"这是一条来自大模型回退路径的完整回答。\n"
|
||||||
|
"要点：\n"
|
||||||
|
"1. 对复杂/跨域任务给出综合推理\n"
|
||||||
|
"2. 补充领域专家未覆盖的上下文\n"
|
||||||
|
"3. 给出可执行的后续建议\n"
|
||||||
|
)
|
||||||
|
return ExpertResponse(
|
||||||
|
text=body,
|
||||||
|
model_used=self.model,
|
||||||
|
latency_ms=2.0,
|
||||||
|
tokens=120,
|
||||||
|
cost_est=2.0 * 120 / 1_000_000,
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
class APIFallback(FallbackProvider):
|
||||||
|
"""OpenAI 兼容大模型 API（如 DeepSeek / OpenAI / 本地 vLLM）。"""
|
||||||
|
|
||||||
|
def __init__(self, model: str, base_url: str, api_key: str):
|
||||||
|
self.model = model
|
||||||
|
self.base_url = base_url.rstrip("/")
|
||||||
|
self.api_key = api_key
|
||||||
|
self.name = f"fallback-{model}"
|
||||||
|
self._client = None
|
||||||
|
|
||||||
|
def _get_client(self):
|
||||||
|
if self._client is None:
|
||||||
|
import httpx
|
||||||
|
self._client = httpx.AsyncClient(timeout=90.0)
|
||||||
|
return self._client
|
||||||
|
|
||||||
|
async def generate(self, query: str) -> ExpertResponse:
|
||||||
|
client = self._get_client()
|
||||||
|
resp = await client.post(
|
||||||
|
f"{self.base_url}/chat/completions",
|
||||||
|
headers={"Authorization": f"Bearer {self.api_key}"},
|
||||||
|
json={
|
||||||
|
"model": self.model,
|
||||||
|
"messages": [{"role": "user", "content": query}],
|
||||||
|
"temperature": 0.3,
|
||||||
|
"max_tokens": 2048,
|
||||||
|
},
|
||||||
|
)
|
||||||
|
resp.raise_for_status()
|
||||||
|
data = resp.json()
|
||||||
|
body = data["choices"][0]["message"]["content"]
|
||||||
|
usage = data.get("usage", {})
|
||||||
|
tokens = usage.get("completion_tokens", int(len(body) / 2.2))
|
||||||
|
return ExpertResponse(
|
||||||
|
text=body,
|
||||||
|
model_used=self.model,
|
||||||
|
latency_ms=0.0,
|
||||||
|
tokens=tokens,
|
||||||
|
cost_est=2.0 * tokens / 1_000_000,
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
def build_fallback(cfg: Dict) -> FallbackProvider:
|
||||||
|
"""cfg 为 fallback 段配置。"""
|
||||||
|
ftype = cfg.get("type", "mock")
|
||||||
|
model = cfg.get("model", "deepseek-chat")
|
||||||
|
if ftype == "mock":
|
||||||
|
return MockFallback(model=model)
|
||||||
|
if ftype == "api":
|
||||||
|
import os
|
||||||
|
api_key = cfg.get("api_key") or os.environ.get(cfg.get("api_key_env", "API_KEY"))
|
||||||
|
if not api_key:
|
||||||
|
raise RuntimeError(
|
||||||
|
f"APIFallback 缺少 API Key：请设置环境变量 {cfg.get('api_key_env')} 或配置 api_key"
|
||||||
|
)
|
||||||
|
return APIFallback(model, cfg.get("base_url", "https://api.deepseek.com/v1"), api_key)
|
||||||
|
raise ValueError(f"未知 fallback 类型: {ftype}（支持 mock | api）")␍
|
||||||
@@ -0,0 +1,174 @@
|
|||||||
|
"""质量控制器（Judge）：评估专家输出，决定是否升级大模型。
|
||||||
|
|
||||||
|
- RuleJudge：零依赖启发式（内容覆盖度 / 长度充分性 / 领域格式 / 安全提示），
|
||||||
|
稳定可测，适合 MVP 与离线演示。
|
||||||
|
- LLMJudge：可选，基于 transformers 小模型或 API 的 LLM-as-Judge。
|
||||||
|
|
||||||
|
设计对齐实现方案：overall_score < judge_fallback_threshold -> 升级大模型。
|
||||||
|
"""
|
||||||
|
from __future__ import annotations
|
||||||
|
|
||||||
|
from dataclasses import dataclass, field
|
||||||
|
from typing import Dict, List
|
||||||
|
|
||||||
|
from .experts import extract_content_terms
|
||||||
|
|
||||||
|
# 各领域期望的响应长度范围（字符数）
|
||||||
|
_EXPECTED_LEN = {
|
||||||
|
"code": (60, 2000),
|
||||||
|
"math": (60, 2000),
|
||||||
|
"legal": (80, 3000),
|
||||||
|
"medical": (80, 3000),
|
||||||
|
"general": (40, 2000),
|
||||||
|
}
|
||||||
|
|
||||||
|
# 领域格式检查：响应应包含的标记
|
||||||
|
_DOMAIN_FORMAT_HINTS = {
|
||||||
|
"code": ["```", "def ", "function", "class "],
|
||||||
|
"math": ["步骤", "推导", "=", "解", "step"],
|
||||||
|
"legal": ["⚠", "法律", "意见", "合规", "contract", "law"],
|
||||||
|
"medical": ["⚠", "就医", "医生", "症状", "诊断", "symptom"],
|
||||||
|
"general": [],
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class QualityEvaluation:
|
||||||
|
overall_score: float
|
||||||
|
scores: Dict[str, float] = field(default_factory=dict)
|
||||||
|
needs_fallback: bool = False
|
||||||
|
reasons: List[str] = field(default_factory=list)
|
||||||
|
|
||||||
|
def to_dict(self) -> Dict:
|
||||||
|
return {
|
||||||
|
"overall_score": round(self.overall_score, 4),
|
||||||
|
"scores": {k: round(v, 4) for k, v in self.scores.items()},
|
||||||
|
"needs_fallback": self.needs_fallback,
|
||||||
|
"reasons": self.reasons,
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
class BaseJudge:
|
||||||
|
async def evaluate(self, query: str, response: str, domain: str) -> QualityEvaluation:
|
||||||
|
raise NotImplementedError
|
||||||
|
|
||||||
|
|
||||||
|
class RuleJudge(BaseJudge):
|
||||||
|
"""启发式质量评估（零依赖）。"""
|
||||||
|
|
||||||
|
def __init__(self, fallback_threshold: float = 0.70):
|
||||||
|
self.fallback_threshold = fallback_threshold
|
||||||
|
|
||||||
|
async def evaluate(self, query: str, response: str, domain: str) -> QualityEvaluation:
|
||||||
|
scores: Dict[str, float] = {}
|
||||||
|
reasons: List[str] = []
|
||||||
|
|
||||||
|
# 1) 内容覆盖度：查询中的内容词有多少出现在响应里
|
||||||
|
terms = extract_content_terms(query)
|
||||||
|
if terms:
|
||||||
|
hit = sum(1 for t in terms if t in response.lower())
|
||||||
|
coverage = hit / len(terms)
|
||||||
|
scores["coverage"] = coverage
|
||||||
|
if coverage < 0.4:
|
||||||
|
reasons.append(f"内容覆盖度低 ({coverage:.0%})")
|
||||||
|
else:
|
||||||
|
scores["coverage"] = 1.0
|
||||||
|
|
||||||
|
# 2) 长度充分性
|
||||||
|
lo, hi = _EXPECTED_LEN.get(domain, (40, 2000))
|
||||||
|
n = len(response)
|
||||||
|
if n < lo:
|
||||||
|
scores["length"] = max(0.0, n / lo)
|
||||||
|
reasons.append(f"响应过短 ({n} 字符)")
|
||||||
|
elif n > hi:
|
||||||
|
scores["length"] = 0.8
|
||||||
|
reasons.append(f"响应过长 ({n} 字符)")
|
||||||
|
else:
|
||||||
|
scores["length"] = 1.0
|
||||||
|
|
||||||
|
# 3) 领域格式检查
|
||||||
|
hints = _DOMAIN_FORMAT_HINTS.get(domain, [])
|
||||||
|
if hints:
|
||||||
|
hit_hints = sum(1 for h in hints if h in response)
|
||||||
|
scores["format"] = min(1.0, 0.4 + 0.2 * hit_hints)
|
||||||
|
if hit_hints == 0:
|
||||||
|
reasons.append("缺少领域格式特征")
|
||||||
|
else:
|
||||||
|
scores["format"] = 1.0
|
||||||
|
|
||||||
|
# 4) 安全/免责提示（法律、医疗领域应有警示语）
|
||||||
|
if domain in ("legal", "medical") and ("⚠" not in response and "提示" not in response):
|
||||||
|
scores["safety"] = 0.6
|
||||||
|
reasons.append("缺少免责提示")
|
||||||
|
else:
|
||||||
|
scores["safety"] = 1.0
|
||||||
|
|
||||||
|
weights = {"coverage": 0.4, "length": 0.2, "format": 0.2, "safety": 0.2}
|
||||||
|
overall = sum(scores.get(k, 0.0) * w for k, w in weights.items())
|
||||||
|
needs = overall < self.fallback_threshold
|
||||||
|
if needs:
|
||||||
|
reasons.append("质量分低于阈值，建议升级大模型")
|
||||||
|
return QualityEvaluation(
|
||||||
|
overall_score=round(overall, 4),
|
||||||
|
scores=scores,
|
||||||
|
needs_fallback=needs,
|
||||||
|
reasons=reasons,
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
class LLMJudge(BaseJudge):
|
||||||
|
"""可选：LLM-as-Judge（API 后端）。"""
|
||||||
|
|
||||||
|
def __init__(self, model: str, base_url: str, api_key: str, fallback_threshold: float = 0.70):
|
||||||
|
self.model = model
|
||||||
|
self.base_url = base_url.rstrip("/")
|
||||||
|
self.api_key = api_key
|
||||||
|
self.fallback_threshold = fallback_threshold
|
||||||
|
self._client = None
|
||||||
|
|
||||||
|
def _get_client(self):
|
||||||
|
if self._client is None:
|
||||||
|
import httpx
|
||||||
|
self._client = httpx.AsyncClient(timeout=60.0)
|
||||||
|
return self._client
|
||||||
|
|
||||||
|
async def evaluate(self, query: str, response: str, domain: str) -> QualityEvaluation:
|
||||||
|
client = self._get_client()
|
||||||
|
prompt = (
|
||||||
|
f"你是质量评审员。评估以下回答对查询的满足程度，输出 0-1 分（相关性/正确性/完整性）。\n"
|
||||||
|
f"查询: {query}\n领域: {domain}\n回答: {response[:2000]}\n"
|
||||||
|
f"只输出一个 0 到 1 之间的数字。"
|
||||||
|
)
|
||||||
|
resp = await client.post(
|
||||||
|
f"{self.base_url}/chat/completions",
|
||||||
|
headers={"Authorization": f"Bearer {self.api_key}"},
|
||||||
|
json={"model": self.model, "messages": [{"role": "user", "content": prompt}]},
|
||||||
|
)
|
||||||
|
resp.raise_for_status()
|
||||||
|
try:
|
||||||
|
score = float(resp.json()["choices"][0]["message"]["content"].strip())
|
||||||
|
score = max(0.0, min(1.0, score))
|
||||||
|
except Exception:
|
||||||
|
score = 0.5
|
||||||
|
return QualityEvaluation(
|
||||||
|
overall_score=score,
|
||||||
|
scores={"llm_judge": score},
|
||||||
|
needs_fallback=score < self.fallback_threshold,
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
def build_judge(cfg: Dict, fallback_threshold: float = 0.70) -> BaseJudge:
|
||||||
|
"""cfg 为 judge 段配置。"""
|
||||||
|
jtype = cfg.get("type", "rule")
|
||||||
|
if jtype == "rule":
|
||||||
|
return RuleJudge(fallback_threshold=fallback_threshold)
|
||||||
|
if jtype == "llm":
|
||||||
|
import os
|
||||||
|
api_key = cfg.get("api_key") or os.environ.get(cfg.get("api_key_env", "API_KEY"))
|
||||||
|
return LLMJudge(
|
||||||
|
cfg.get("model", "deepseek-chat"),
|
||||||
|
cfg.get("base_url", "https://api.deepseek.com/v1"),
|
||||||
|
api_key or "",
|
||||||
|
fallback_threshold=fallback_threshold,
|
||||||
|
)
|
||||||
|
raise ValueError(f"未知 judge 类型: {jtype}（支持 rule | llm）")␍
|
||||||
@@ -0,0 +1,68 @@
|
|||||||
|
"""核心数据模型（纯标准库，无外部依赖）"""
|
||||||
|
from __future__ import annotations
|
||||||
|
|
||||||
|
import time
|
||||||
|
from dataclasses import dataclass, field
|
||||||
|
from typing import Any, Dict, List, Optional
|
||||||
|
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class Classification:
|
||||||
|
"""分类器输出：领域 + 置信度 + 难度"""
|
||||||
|
domain: str
|
||||||
|
confidence: float
|
||||||
|
difficulty: str # easy | medium | hard
|
||||||
|
difficulty_score: float = 0.5
|
||||||
|
raw_scores: Dict[str, float] = field(default_factory=dict)
|
||||||
|
matched_rules: List[str] = field(default_factory=list)
|
||||||
|
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class ExpertResponse:
|
||||||
|
"""专家模型输出"""
|
||||||
|
text: str
|
||||||
|
model_used: str
|
||||||
|
latency_ms: float = 0.0
|
||||||
|
tokens: int = 0
|
||||||
|
cost_est: float = 0.0 # 相对成本估计（美元，近似）
|
||||||
|
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class RouterResult:
|
||||||
|
"""一次路由的完整结果"""
|
||||||
|
query: str
|
||||||
|
response: str
|
||||||
|
domain: str
|
||||||
|
difficulty: str
|
||||||
|
confidence: float
|
||||||
|
upgraded: bool # 是否升级到大模型
|
||||||
|
quality_score: float
|
||||||
|
model_used: str
|
||||||
|
route: List[str] = field(default_factory=list) # 路由决策轨迹
|
||||||
|
latency_ms: float = 0.0
|
||||||
|
cache_hit: bool = False
|
||||||
|
cache_level: Optional[str] = None # exact | semantic
|
||||||
|
cost_est: float = 0.0
|
||||||
|
error: Optional[str] = None
|
||||||
|
|
||||||
|
def to_dict(self) -> Dict[str, Any]:
|
||||||
|
return {
|
||||||
|
"query": self.query,
|
||||||
|
"response": self.response,
|
||||||
|
"domain": self.domain,
|
||||||
|
"difficulty": self.difficulty,
|
||||||
|
"confidence": round(self.confidence, 4),
|
||||||
|
"upgraded": self.upgraded,
|
||||||
|
"quality_score": round(self.quality_score, 4),
|
||||||
|
"model_used": self.model_used,
|
||||||
|
"route": self.route,
|
||||||
|
"latency_ms": round(self.latency_ms, 2),
|
||||||
|
"cache_hit": self.cache_hit,
|
||||||
|
"cache_level": self.cache_level,
|
||||||
|
"cost_est": round(self.cost_est, 6),
|
||||||
|
"error": self.error,
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
def now_ms() -> float:
|
||||||
|
return time.perf_counter() * 1000.0␍
|
||||||
@@ -0,0 +1,217 @@
|
|||||||
|
"""主路由器：协调 缓存 -> 分类 -> 专家 -> Judge -> 大模型回退 的完整链路。
|
||||||
|
|
||||||
|
流程（对齐实现方案）：
|
||||||
|
1. 检查缓存（L1 精确 / L2 语义）
|
||||||
|
2. 低置信度查询直接走大模型（should_fallback）
|
||||||
|
3. 分类器输出领域 + 难度
|
||||||
|
4. 选择专家模型生成
|
||||||
|
5. Judge 评估质量
|
||||||
|
6. 质量不达标 -> 升级大模型
|
||||||
|
7. 记录指标、写缓存、返回结果
|
||||||
|
"""
|
||||||
|
from __future__ import annotations
|
||||||
|
|
||||||
|
from typing import Any, Dict, Optional
|
||||||
|
|
||||||
|
from .cache import RouterCache
|
||||||
|
from .classifier import BaseClassifier, build_classifier
|
||||||
|
from .config import load_config
|
||||||
|
from .experts import Expert, build_expert_pool
|
||||||
|
from .fallback import FallbackProvider, build_fallback
|
||||||
|
from .judge import BaseJudge, build_judge
|
||||||
|
from .models import Classification, ExpertResponse, RouterResult, now_ms
|
||||||
|
from .stats import Stats
|
||||||
|
|
||||||
|
|
||||||
|
class Router:
|
||||||
|
def __init__(
|
||||||
|
self,
|
||||||
|
classifier: BaseClassifier,
|
||||||
|
experts: Dict[str, Expert],
|
||||||
|
judge: BaseJudge,
|
||||||
|
fallback: FallbackProvider,
|
||||||
|
cache: Optional[RouterCache] = None,
|
||||||
|
stats: Optional[Stats] = None,
|
||||||
|
config: Optional[Dict[str, Any]] = None,
|
||||||
|
):
|
||||||
|
self.classifier = classifier
|
||||||
|
self.experts = experts
|
||||||
|
self.judge = judge
|
||||||
|
self.fallback = fallback
|
||||||
|
self.cache = cache or RouterCache()
|
||||||
|
self.stats = stats or Stats()
|
||||||
|
cfg = config or {}
|
||||||
|
rcfg = cfg.get("router", {})
|
||||||
|
self.low_confidence_threshold = rcfg.get("low_confidence_threshold", 0.60)
|
||||||
|
self.judge_fallback_threshold = rcfg.get("judge_fallback_threshold", 0.70)
|
||||||
|
self.cache_enabled = cfg.get("cache", {}).get("enabled", True)
|
||||||
|
|
||||||
|
# ---------------------------------------------------------------
|
||||||
|
async def route(self, query: str) -> RouterResult:
|
||||||
|
start = now_ms()
|
||||||
|
route: list = []
|
||||||
|
|
||||||
|
# ---- Step 1: 缓存 ----
|
||||||
|
if self.cache_enabled:
|
||||||
|
hit = self.cache.get(query)
|
||||||
|
if hit is not None:
|
||||||
|
level, cached = hit
|
||||||
|
latency = now_ms() - start
|
||||||
|
result = RouterResult(
|
||||||
|
query=query,
|
||||||
|
response=cached.get("response", ""),
|
||||||
|
domain=cached.get("domain", "general"),
|
||||||
|
difficulty=cached.get("difficulty", "medium"),
|
||||||
|
confidence=cached.get("confidence", 0.0),
|
||||||
|
upgraded=False,
|
||||||
|
quality_score=cached.get("quality_score", 0.0),
|
||||||
|
model_used=cached.get("model_used", ""),
|
||||||
|
route=["cache:" + level],
|
||||||
|
latency_ms=latency,
|
||||||
|
cache_hit=True,
|
||||||
|
cache_level=level,
|
||||||
|
cost_est=0.0,
|
||||||
|
)
|
||||||
|
self.stats.record(latency, result.domain, result.difficulty, False, True, level, 0.0, result.model_used)
|
||||||
|
return result
|
||||||
|
route.append("cache:miss")
|
||||||
|
|
||||||
|
# ---- Step 2: 分类 ----
|
||||||
|
classification = self.classifier.classify(query)
|
||||||
|
route.append(f"classify:{classification.domain}@{classification.confidence:.2f}/{classification.difficulty}")
|
||||||
|
|
||||||
|
# 低置信度 -> 直接走大模型
|
||||||
|
if self.classifier.should_fallback(classification, self.low_confidence_threshold):
|
||||||
|
route.append("direct_fallback")
|
||||||
|
fb = await self._call_fallback(query)
|
||||||
|
latency = now_ms() - start
|
||||||
|
result = self._finalize(query, classification, fb, quality_score=0.0,
|
||||||
|
upgraded=True, route=route, latency_ms=latency,
|
||||||
|
model_used=fb.model_used, cost_est=fb.cost_est)
|
||||||
|
self._record(result, latency)
|
||||||
|
return result
|
||||||
|
|
||||||
|
# ---- Step 3: 选择专家 ----
|
||||||
|
domain = classification.domain
|
||||||
|
expert = self.experts.get(domain)
|
||||||
|
if expert is None:
|
||||||
|
expert = self.experts.get("general")
|
||||||
|
route.append("expert:fallback-to-general")
|
||||||
|
else:
|
||||||
|
route.append(f"expert:{expert.name}")
|
||||||
|
|
||||||
|
# ---- Step 4: 生成 ----
|
||||||
|
try:
|
||||||
|
expert_resp = await expert.generate(query, classification.difficulty)
|
||||||
|
except Exception as e:
|
||||||
|
self.stats.record_error()
|
||||||
|
route.append(f"expert_error:{type(e).__name__}")
|
||||||
|
fb = await self._call_fallback(query)
|
||||||
|
latency = now_ms() - start
|
||||||
|
result = self._finalize(query, classification, fb, quality_score=0.0,
|
||||||
|
upgraded=True, route=route, latency_ms=latency,
|
||||||
|
model_used=fb.model_used, cost_est=fb.cost_est,
|
||||||
|
error=str(e))
|
||||||
|
self._record(result, latency)
|
||||||
|
return result
|
||||||
|
|
||||||
|
# ---- Step 5: Judge 评估 ----
|
||||||
|
try:
|
||||||
|
evaluation = await self.judge.evaluate(query, expert_resp.text, domain)
|
||||||
|
except Exception:
|
||||||
|
evaluation = None
|
||||||
|
route.append("judge_error")
|
||||||
|
|
||||||
|
quality_score = evaluation.overall_score if evaluation else 0.0
|
||||||
|
route.append(f"judge:{quality_score:.2f}")
|
||||||
|
|
||||||
|
upgraded = False
|
||||||
|
final_resp = expert_resp
|
||||||
|
if evaluation is not None and evaluation.needs_fallback:
|
||||||
|
route.append("upgrade")
|
||||||
|
final_resp = await self._call_fallback(query)
|
||||||
|
upgraded = True
|
||||||
|
|
||||||
|
latency = now_ms() - start
|
||||||
|
result = self._finalize(query, classification, final_resp, quality_score=quality_score,
|
||||||
|
upgraded=upgraded, route=route, latency_ms=latency,
|
||||||
|
model_used=final_resp.model_used, cost_est=final_resp.cost_est)
|
||||||
|
self._record(result, latency)
|
||||||
|
|
||||||
|
# 未升级的结果写缓存
|
||||||
|
if self.cache_enabled and not upgraded and result.response:
|
||||||
|
self.cache.put(query, result.to_dict())
|
||||||
|
|
||||||
|
return result
|
||||||
|
|
||||||
|
# ---------------------------------------------------------------
|
||||||
|
async def _call_fallback(self, query: str) -> ExpertResponse:
|
||||||
|
try:
|
||||||
|
return await self.fallback.generate(query)
|
||||||
|
except Exception as e:
|
||||||
|
# 回退也失败：返回错误占位响应
|
||||||
|
return ExpertResponse(
|
||||||
|
text=f"[系统错误] 专家与大模型回退均失败：{type(e).__name__}: {e}",
|
||||||
|
model_used=f"error:{self.fallback.name}",
|
||||||
|
cost_est=0.0,
|
||||||
|
)
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def _finalize(query: str, classification: Classification, resp: ExpertResponse,
|
||||||
|
quality_score: float, upgraded: bool, route: list,
|
||||||
|
latency_ms: float, model_used: str, cost_est: float,
|
||||||
|
error: Optional[str] = None) -> RouterResult:
|
||||||
|
return RouterResult(
|
||||||
|
query=query,
|
||||||
|
response=resp.text,
|
||||||
|
domain=classification.domain,
|
||||||
|
difficulty=classification.difficulty,
|
||||||
|
confidence=classification.confidence,
|
||||||
|
upgraded=upgraded,
|
||||||
|
quality_score=quality_score,
|
||||||
|
model_used=model_used,
|
||||||
|
route=route,
|
||||||
|
latency_ms=latency_ms,
|
||||||
|
cache_hit=False,
|
||||||
|
cost_est=cost_est,
|
||||||
|
error=error,
|
||||||
|
)
|
||||||
|
|
||||||
|
def _record(self, result: RouterResult, latency_ms: float):
|
||||||
|
self.stats.record(
|
||||||
|
latency_ms,
|
||||||
|
result.domain,
|
||||||
|
result.difficulty,
|
||||||
|
result.upgraded,
|
||||||
|
result.cache_hit,
|
||||||
|
result.cache_level,
|
||||||
|
result.cost_est,
|
||||||
|
result.model_used,
|
||||||
|
)
|
||||||
|
|
||||||
|
# ---------------------------------------------------------------
|
||||||
|
def health(self) -> Dict[str, Any]:
|
||||||
|
return {
|
||||||
|
"status": "ok",
|
||||||
|
"domains": list(self.experts.keys()),
|
||||||
|
"classifier": type(self.classifier).__name__,
|
||||||
|
"judge": type(self.judge).__name__,
|
||||||
|
"fallback": type(self.fallback).__name__,
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
def build_router(config_path: Optional[str] = None) -> Router:
|
||||||
|
"""从配置构建完整 Router（默认 mock 全链路，零依赖可跑）。"""
|
||||||
|
config = load_config(config_path)
|
||||||
|
classifier = build_classifier(config.get("classifier", {}))
|
||||||
|
experts = build_expert_pool(config.get("experts", {}), config.get("domains", []))
|
||||||
|
judge = build_judge(config.get("judge", {}), config.get("router", {}).get("judge_fallback_threshold", 0.70))
|
||||||
|
fallback = build_fallback(config.get("fallback", {}))
|
||||||
|
cache_cfg = config.get("cache", {})
|
||||||
|
cache = RouterCache(
|
||||||
|
semantic_enabled=cache_cfg.get("semantic_enabled", True),
|
||||||
|
similarity_threshold=cache_cfg.get("similarity_threshold", 0.88),
|
||||||
|
promote_frequency=cache_cfg.get("promote_frequency", 5),
|
||||||
|
)
|
||||||
|
stats = Stats()
|
||||||
|
return Router(classifier, experts, judge, fallback, cache, stats, config)␍
|
||||||
@@ -0,0 +1,63 @@
|
|||||||
|
"""运行指标收集（线程安全，零依赖）。"""
|
||||||
|
from __future__ import annotations
|
||||||
|
|
||||||
|
import threading
|
||||||
|
from collections import Counter, deque
|
||||||
|
from typing import Any, Deque, Dict
|
||||||
|
|
||||||
|
|
||||||
|
class Stats:
|
||||||
|
def __init__(self, window: int = 1000):
|
||||||
|
self._lock = threading.Lock()
|
||||||
|
self.requests = 0
|
||||||
|
self.domain_counter: Counter = Counter()
|
||||||
|
self.difficulty_counter: Counter = Counter()
|
||||||
|
self.upgraded = 0
|
||||||
|
self.cache_hits = 0
|
||||||
|
self.cache_levels: Counter = Counter()
|
||||||
|
self.errors = 0
|
||||||
|
self.latencies: Deque[float] = deque(maxlen=window)
|
||||||
|
self.cost_total = 0.0
|
||||||
|
self.model_usage: Counter = Counter()
|
||||||
|
|
||||||
|
def record(self, latency_ms: float, domain: str, difficulty: str,
|
||||||
|
upgraded: bool, cache_hit: bool, cache_level: str | None,
|
||||||
|
cost_est: float, model_used: str):
|
||||||
|
with self._lock:
|
||||||
|
self.requests += 1
|
||||||
|
self.domain_counter[domain] += 1
|
||||||
|
self.difficulty_counter[difficulty] += 1
|
||||||
|
if upgraded:
|
||||||
|
self.upgraded += 1
|
||||||
|
if cache_hit:
|
||||||
|
self.cache_hits += 1
|
||||||
|
if cache_level:
|
||||||
|
self.cache_levels[cache_level] += 1
|
||||||
|
self.latencies.append(latency_ms)
|
||||||
|
self.cost_total += cost_est
|
||||||
|
self.model_usage[model_used] += 1
|
||||||
|
|
||||||
|
def record_error(self):
|
||||||
|
with self._lock:
|
||||||
|
self.errors += 1
|
||||||
|
|
||||||
|
def summary(self) -> Dict[str, Any]:
|
||||||
|
with self._lock:
|
||||||
|
n = self.requests
|
||||||
|
lat = list(self.latencies)
|
||||||
|
avg_lat = sum(lat) / len(lat) if lat else 0.0
|
||||||
|
p99 = sorted(lat)[int(len(lat) * 0.99) - 1] if len(lat) >= 100 else (max(lat) if lat else 0.0)
|
||||||
|
return {
|
||||||
|
"total_requests": n,
|
||||||
|
"domain_distribution": dict(self.domain_counter),
|
||||||
|
"difficulty_distribution": dict(self.difficulty_counter),
|
||||||
|
"fallback_rate": round(self.upgraded / n, 4) if n else 0.0,
|
||||||
|
"upgraded_requests": self.upgraded,
|
||||||
|
"cache_hit_rate": round(self.cache_hits / n, 4) if n else 0.0,
|
||||||
|
"cache_levels": dict(self.cache_levels),
|
||||||
|
"avg_latency_ms": round(avg_lat, 3),
|
||||||
|
"p99_latency_ms": round(p99, 3),
|
||||||
|
"total_cost_est_usd": round(self.cost_total, 6),
|
||||||
|
"model_usage": dict(self.model_usage),
|
||||||
|
"errors": self.errors,
|
||||||
|
}␍
|
||||||
@@ -0,0 +1,65 @@
|
|||||||
|
"""CLI 演示：构建 mock 全链路路由系统，跑一组样例查询并打印结果。
|
||||||
|
|
||||||
|
用法：
|
||||||
|
python scripts/demo.py [--query "自定义查询"] [--batch]
|
||||||
|
"""
|
||||||
|
from __future__ import annotations
|
||||||
|
|
||||||
|
import argparse
|
||||||
|
import asyncio
|
||||||
|
import sys
|
||||||
|
from pathlib import Path
|
||||||
|
|
||||||
|
sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
|
||||||
|
|
||||||
|
from router_system.router import build_router
|
||||||
|
|
||||||
|
SAMPLE_QUERIES = [
|
||||||
|
"用 Python 写一个快速排序函数，并解释时间复杂度",
|
||||||
|
"求解方程 x^2 - 5x + 6 = 0",
|
||||||
|
"劳动合同里约定离职后两年内不得从事同行业，是否有效？",
|
||||||
|
"高血压患者日常饮食需要注意什么？",
|
||||||
|
"给我总结一下深度学习中注意力机制的优缺点",
|
||||||
|
"为什么天空是蓝色的？",
|
||||||
|
"帮我调试这段代码：def f(x): return x + 1 报 TypeError",
|
||||||
|
"求 ∫ x^2 dx 从 0 到 1 的定积分是多少？",
|
||||||
|
]
|
||||||
|
|
||||||
|
|
||||||
|
async def run_demo(router, queries, verbose: bool = False):
|
||||||
|
for q in queries:
|
||||||
|
r = await router.route(q)
|
||||||
|
print("=" * 72)
|
||||||
|
print(f"Q: {q}")
|
||||||
|
print(f" domain={r.domain} difficulty={r.difficulty} conf={r.confidence:.2f} "
|
||||||
|
f"upgraded={r.upgraded} quality={r.quality_score:.2f} model={r.model_used} "
|
||||||
|
f"latency={r.latency_ms:.1f}ms cache={r.cache_hit}({r.cache_level}) cost=${r.cost_est:.6f}")
|
||||||
|
print(f" route: {' -> '.join(r.route)}")
|
||||||
|
if verbose:
|
||||||
|
print(f" --- response ---\n{r.response[:400]}")
|
||||||
|
|
||||||
|
|
||||||
|
async def main():
|
||||||
|
parser = argparse.ArgumentParser(description="多专家路由系统 CLI 演示")
|
||||||
|
parser.add_argument("--query", type=str, default=None, help="单条查询（覆盖默认样例）")
|
||||||
|
parser.add_argument("--batch", action="store_true", help="批量模式（打印全部响应）")
|
||||||
|
parser.add_argument("--verbose", action="store_true", help="打印响应正文")
|
||||||
|
args = parser.parse_args()
|
||||||
|
|
||||||
|
router = build_router()
|
||||||
|
print("系统组件：", router.health())
|
||||||
|
print()
|
||||||
|
|
||||||
|
if args.query:
|
||||||
|
await run_demo(router, [args.query], verbose=True)
|
||||||
|
else:
|
||||||
|
await run_demo(router, SAMPLE_QUERIES, verbose=args.verbose)
|
||||||
|
|
||||||
|
print()
|
||||||
|
print("=" * 72)
|
||||||
|
print("运行指标:", router.stats.summary())
|
||||||
|
print("缓存统计:", router.cache.stats())
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
asyncio.run(main())␍
|
||||||
@@ -0,0 +1,80 @@
|
|||||||
|
"""迷你评估：对带标注的样例查询评估分类准确率、升级率、成本。
|
||||||
|
|
||||||
|
用法：
|
||||||
|
python scripts/eval.py [--repeat 2] [--config path]
|
||||||
|
--repeat 用于把样例跑 N 遍，验证语义缓存命中与降本效果。
|
||||||
|
"""
|
||||||
|
from __future__ import annotations
|
||||||
|
|
||||||
|
import argparse
|
||||||
|
import asyncio
|
||||||
|
import sys
|
||||||
|
from collections import Counter
|
||||||
|
from pathlib import Path
|
||||||
|
|
||||||
|
sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
|
||||||
|
|
||||||
|
from router_system.router import build_router
|
||||||
|
|
||||||
|
# (query, 期望领域)
|
||||||
|
BENCH = [
|
||||||
|
("用 Python 实现二分查找", "code"),
|
||||||
|
("这段 JavaScript 为什么报错：undefined is not a function", "code"),
|
||||||
|
("帮我优化这个 SQL 查询的索引", "code"),
|
||||||
|
("求解一元二次方程 ax^2+bx+c=0 的求根公式", "math"),
|
||||||
|
("证明勾股定理", "math"),
|
||||||
|
("计算 3x + 5 = 20，x 等于多少", "math"),
|
||||||
|
("劳动合同到期不续签，公司需要支付经济补偿吗", "legal"),
|
||||||
|
("在合同中约定违约金上限 30%，是否合规", "legal"),
|
||||||
|
("专利申请的流程和费用大概是多少", "legal"),
|
||||||
|
("高血压患者可以吃哪些降压药，副作用是什么", "medical"),
|
||||||
|
("感冒发烧 38.5 度，需要吃退烧药吗", "medical"),
|
||||||
|
("糖尿病患者的日常饮食建议", "medical"),
|
||||||
|
("介绍一下 Transformer 架构", "general"),
|
||||||
|
("写一封请假邮件", "general"),
|
||||||
|
("为什么天空是蓝色的", "general"),
|
||||||
|
]
|
||||||
|
|
||||||
|
|
||||||
|
async def main():
|
||||||
|
parser = argparse.ArgumentParser()
|
||||||
|
parser.add_argument("--repeat", type=int, default=2, help="重复轮数（验证缓存）")
|
||||||
|
parser.add_argument("--config", type=str, default=None)
|
||||||
|
args = parser.parse_args()
|
||||||
|
|
||||||
|
router = build_router(args.config)
|
||||||
|
correct = Counter()
|
||||||
|
total = 0
|
||||||
|
upgraded = 0
|
||||||
|
cache_hits = 0
|
||||||
|
|
||||||
|
for round_i in range(args.repeat):
|
||||||
|
for q, expected in BENCH:
|
||||||
|
r = await router.route(q)
|
||||||
|
total += 1
|
||||||
|
if r.domain == expected:
|
||||||
|
correct["total"] += 1
|
||||||
|
else:
|
||||||
|
correct[f"misclass->{r.domain}"] += 1
|
||||||
|
if r.upgraded:
|
||||||
|
upgraded += 1
|
||||||
|
if r.cache_hit:
|
||||||
|
cache_hits += 1
|
||||||
|
|
||||||
|
acc = correct["total"] / total
|
||||||
|
print(f"样例数: {len(BENCH)} x {args.repeat} 轮 = {total} 次请求")
|
||||||
|
print(f"分类准确率: {acc:.1%} ({correct['total']}/{total})")
|
||||||
|
print(f"升级率: {upgraded/total:.1%} ({upgraded}/{total})")
|
||||||
|
print(f"缓存命中率: {cache_hits/total:.1%} ({cache_hits}/{total})")
|
||||||
|
print()
|
||||||
|
print("运行指标:", router.stats.summary())
|
||||||
|
print("缓存统计:", router.cache.stats())
|
||||||
|
print()
|
||||||
|
if acc < 0.8:
|
||||||
|
print("⚠️ 准确率低于 80%，请检查分类规则。")
|
||||||
|
else:
|
||||||
|
print("✅ 分类准确率达标（≥80%）。")
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
asyncio.run(main())␍
|
||||||
@@ -0,0 +1,60 @@
|
|||||||
|
"""启动路由网关服务（后台、无窗口）。
|
||||||
|
|
||||||
|
用法：
|
||||||
|
python scripts/serve.py [--port 8000] [--stop]
|
||||||
|
"""
|
||||||
|
import argparse
|
||||||
|
import os
|
||||||
|
import subprocess
|
||||||
|
import sys
|
||||||
|
from pathlib import Path
|
||||||
|
|
||||||
|
ROOT = Path(__file__).resolve().parent.parent
|
||||||
|
PID_FILE = ROOT / "_gateway.pid"
|
||||||
|
|
||||||
|
|
||||||
|
def start(port: int):
|
||||||
|
if PID_FILE.exists():
|
||||||
|
old = PID_FILE.read_text().strip()
|
||||||
|
if old:
|
||||||
|
print(f"已有服务运行 (pid={old})，先执行 --stop 再启动。")
|
||||||
|
return
|
||||||
|
out = open(ROOT / "_gateway.out.log", "ab", buffering=0)
|
||||||
|
err = open(ROOT / "_gateway.err.log", "ab", buffering=0)
|
||||||
|
flags = 0x00000008 | 0x08000000 # DETACHED_PROCESS | CREATE_NO_WINDOW
|
||||||
|
proc = subprocess.Popen(
|
||||||
|
[sys.executable, "-m", "uvicorn", "gateway.api:app",
|
||||||
|
"--host", "127.0.0.1", "--port", str(port)],
|
||||||
|
cwd=str(ROOT),
|
||||||
|
stdout=out,
|
||||||
|
stderr=err,
|
||||||
|
creationflags=flags,
|
||||||
|
close_fds=True,
|
||||||
|
)
|
||||||
|
PID_FILE.write_text(str(proc.pid))
|
||||||
|
print(f"gateway 已启动 pid={proc.pid} port={port}，日志: _gateway.out.log")
|
||||||
|
|
||||||
|
|
||||||
|
def stop():
|
||||||
|
if not PID_FILE.exists():
|
||||||
|
print("没有运行中的服务。")
|
||||||
|
return
|
||||||
|
pid = int(PID_FILE.read_text().strip())
|
||||||
|
try:
|
||||||
|
import signal
|
||||||
|
os.kill(pid, signal.SIGTERM)
|
||||||
|
print(f"已发送终止信号 pid={pid}")
|
||||||
|
except ProcessLookupError:
|
||||||
|
print(f"进程 {pid} 不存在，清理 pid 文件。")
|
||||||
|
PID_FILE.unlink(missing_ok=True)
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
parser = argparse.ArgumentParser()
|
||||||
|
parser.add_argument("--port", type=int, default=8000)
|
||||||
|
parser.add_argument("--stop", action="store_true", help="停止服务")
|
||||||
|
args = parser.parse_args()
|
||||||
|
if args.stop:
|
||||||
|
stop()
|
||||||
|
else:
|
||||||
|
start(args.port)
|
||||||
@@ -0,0 +1,68 @@
|
|||||||
|
"""可选：用 transformers 训练 BERT 级意图分类器（需 requirements-ml.txt）。
|
||||||
|
|
||||||
|
这是"正式环境"路线（对照实现方案 3.2 路由器选型表）：
|
||||||
|
- 规则分类器（当前默认）: 90-93% 准确率，零成本，适合 MVP
|
||||||
|
- 小 LLM / BERT 分类器: 94-97% 准确率，适合正式环境
|
||||||
|
|
||||||
|
用法（示例）：
|
||||||
|
python scripts/train_classifier.py --data data/train.jsonl --output models/classifier
|
||||||
|
|
||||||
|
数据格式（每行一个 JSON）：
|
||||||
|
{"query": "...", "domain": "code|math|legal|medical|general"}
|
||||||
|
|
||||||
|
注意：本脚本是流水线骨架，需自行准备数据。
|
||||||
|
"""
|
||||||
|
from __future__ import annotations
|
||||||
|
|
||||||
|
import argparse
|
||||||
|
import json
|
||||||
|
import sys
|
||||||
|
from pathlib import Path
|
||||||
|
|
||||||
|
sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
|
||||||
|
|
||||||
|
|
||||||
|
def main():
|
||||||
|
parser = argparse.ArgumentParser()
|
||||||
|
parser.add_argument("--data", required=True, help="训练数据 jsonl 路径")
|
||||||
|
parser.add_argument("--output", default="models/classifier", help="输出目录")
|
||||||
|
parser.add_argument("--base", default="Qwen/Qwen3-0.6B", help="基础模型")
|
||||||
|
parser.add_argument("--epochs", type=int, default=3)
|
||||||
|
args = parser.parse_args()
|
||||||
|
|
||||||
|
try:
|
||||||
|
from transformers import (AutoModelForSequenceClassification, AutoTokenizer,
|
||||||
|
TrainingArguments)
|
||||||
|
except ImportError as e:
|
||||||
|
print("需要 ML 依赖：pip install -r requirements-ml.txt")
|
||||||
|
raise SystemExit(1) from e
|
||||||
|
|
||||||
|
# 数据格式转换
|
||||||
|
samples = []
|
||||||
|
with open(args.data, encoding="utf-8") as f:
|
||||||
|
for line in f:
|
||||||
|
line = line.strip()
|
||||||
|
if line:
|
||||||
|
obj = json.loads(line)
|
||||||
|
samples.append(obj)
|
||||||
|
print(f"加载 {len(samples)} 条训练样本")
|
||||||
|
|
||||||
|
labels = ["code", "math", "legal", "medical", "general"]
|
||||||
|
label2id = {l: i for i, l in enumerate(labels)}
|
||||||
|
|
||||||
|
# 这里仅演示训练流水线；正式训练请使用 datasets 库构建 Dataset。
|
||||||
|
tokenizer = AutoTokenizer.from_pretrained(args.base)
|
||||||
|
model = AutoModelForSequenceClassification.from_pretrained(args.base, num_labels=5)
|
||||||
|
|
||||||
|
print("训练参数示例：")
|
||||||
|
print(TrainingArguments(
|
||||||
|
output_dir=args.output,
|
||||||
|
num_train_epochs=args.epochs,
|
||||||
|
per_device_train_batch_size=8,
|
||||||
|
learning_rate=2e-5,
|
||||||
|
))
|
||||||
|
print("请参考实现方案 3.2 路由器选型表，构建带标注数据集后执行正式训练。")
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
main()␍
|
||||||
@@ -0,0 +1,14 @@
|
|||||||
|
import sys
|
||||||
|
from pathlib import Path
|
||||||
|
|
||||||
|
import pytest
|
||||||
|
|
||||||
|
sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
|
||||||
|
|
||||||
|
from router_system.router import build_router
|
||||||
|
|
||||||
|
|
||||||
|
@pytest.fixture()
|
||||||
|
def router():
|
||||||
|
"""????????? mock ?????????????"""
|
||||||
|
return build_router()
|
||||||
@@ -0,0 +1,42 @@
|
|||||||
|
from router_system.cache import RouterCache
|
||||||
|
|
||||||
|
|
||||||
|
def test_exact_hit():
|
||||||
|
c = RouterCache()
|
||||||
|
result = {"response": "hello", "domain": "general"}
|
||||||
|
assert c.get("query") is None
|
||||||
|
c.put("query", result)
|
||||||
|
level, got = c.get("query")
|
||||||
|
assert level == "exact"
|
||||||
|
assert got["response"] == "hello"
|
||||||
|
|
||||||
|
|
||||||
|
def test_semantic_hit():
|
||||||
|
c = RouterCache(semantic_enabled=True, similarity_threshold=0.5)
|
||||||
|
c.put("?python?????", {"response": "code", "domain": "code"})
|
||||||
|
# ?????????? L2
|
||||||
|
hit = c.get("?python????????")
|
||||||
|
assert hit is not None
|
||||||
|
assert hit[0] == "semantic"
|
||||||
|
|
||||||
|
|
||||||
|
def test_promote_to_exact():
|
||||||
|
c = RouterCache(promote_frequency=3)
|
||||||
|
result = {"response": "x", "domain": "general"}
|
||||||
|
c.put("query", result)
|
||||||
|
# ?????? 3 ? ? ???????
|
||||||
|
for _ in range(3):
|
||||||
|
hit = c.get("query")
|
||||||
|
assert hit is not None
|
||||||
|
assert c.stats()["exact_size"] == 1
|
||||||
|
|
||||||
|
|
||||||
|
def test_stats():
|
||||||
|
c = RouterCache()
|
||||||
|
c.put("q", {"response": "r"})
|
||||||
|
c.get("q")
|
||||||
|
c.get("q")
|
||||||
|
c.get("miss")
|
||||||
|
s = c.stats()
|
||||||
|
assert s["exact_hits"] == 2
|
||||||
|
assert s["misses"] == 1
|
||||||
@@ -0,0 +1,44 @@
|
|||||||
|
"""分类器单元测试。"""
|
||||||
|
from router_system.classifier import RuleClassifier
|
||||||
|
|
||||||
|
|
||||||
|
def test_code_classification():
|
||||||
|
clf = RuleClassifier()
|
||||||
|
r = clf.classify("用 Python 写一个快速排序函数")
|
||||||
|
assert r.domain == "code"
|
||||||
|
assert r.confidence > 0.7
|
||||||
|
|
||||||
|
|
||||||
|
def test_math_classification():
|
||||||
|
clf = RuleClassifier()
|
||||||
|
r = clf.classify("求解方程 x^2 - 5x + 6 = 0")
|
||||||
|
assert r.domain == "math"
|
||||||
|
assert r.confidence > 0.7
|
||||||
|
|
||||||
|
|
||||||
|
def test_legal_classification():
|
||||||
|
clf = RuleClassifier()
|
||||||
|
r = clf.classify("劳动合同到期不续签需要支付经济补偿吗")
|
||||||
|
assert r.domain == "legal"
|
||||||
|
|
||||||
|
|
||||||
|
def test_medical_classification():
|
||||||
|
clf = RuleClassifier()
|
||||||
|
r = clf.classify("高血压患者日常饮食需要注意什么")
|
||||||
|
assert r.domain == "medical"
|
||||||
|
|
||||||
|
|
||||||
|
def test_general_low_confidence():
|
||||||
|
clf = RuleClassifier()
|
||||||
|
r = clf.classify("今天天气怎么样")
|
||||||
|
# 未命中任何领域 -> 低置信度，触发 should_fallback
|
||||||
|
assert r.domain == "general"
|
||||||
|
assert clf.should_fallback(r, 0.6) is True
|
||||||
|
|
||||||
|
|
||||||
|
def test_difficulty_estimation():
|
||||||
|
clf = RuleClassifier()
|
||||||
|
easy = clf.classify("1 + 1 = ?")
|
||||||
|
hard = clf.classify("证明费马大定理并推导其推论，给出详细步骤")
|
||||||
|
assert hard.difficulty in ("medium", "hard")
|
||||||
|
assert easy.difficulty == "easy"␍
|
||||||
@@ -0,0 +1,44 @@
|
|||||||
|
"""???????? fastapi + httpx??"""
|
||||||
|
import pytest
|
||||||
|
|
||||||
|
pytest.importorskip("fastapi")
|
||||||
|
pytest.importorskip("httpx")
|
||||||
|
|
||||||
|
from fastapi.testclient import TestClient
|
||||||
|
|
||||||
|
from gateway.api import app
|
||||||
|
|
||||||
|
|
||||||
|
@pytest.fixture()
|
||||||
|
def client():
|
||||||
|
return TestClient(app)
|
||||||
|
|
||||||
|
|
||||||
|
def test_health(client):
|
||||||
|
resp = client.get("/health")
|
||||||
|
assert resp.status_code == 200
|
||||||
|
data = resp.json()
|
||||||
|
assert data["status"] == "ok"
|
||||||
|
assert "code" in data["domains"]
|
||||||
|
|
||||||
|
|
||||||
|
def test_chat(client):
|
||||||
|
resp = client.post("/chat", json={"query": "? Python ???????"})
|
||||||
|
assert resp.status_code == 200
|
||||||
|
data = resp.json()
|
||||||
|
assert data["response"]
|
||||||
|
assert data["domain"] == "code"
|
||||||
|
assert "route" in data
|
||||||
|
|
||||||
|
|
||||||
|
def test_chat_empty_query(client):
|
||||||
|
resp = client.post("/chat", json={"query": ""})
|
||||||
|
assert resp.status_code == 422
|
||||||
|
|
||||||
|
|
||||||
|
def test_metrics(client):
|
||||||
|
resp = client.get("/metrics")
|
||||||
|
assert resp.status_code == 200
|
||||||
|
data = resp.json()
|
||||||
|
assert "router" in data
|
||||||
|
assert "cache" in data
|
||||||
@@ -0,0 +1,56 @@
|
|||||||
|
"""路由主流程单元测试。"""
|
||||||
|
import pytest
|
||||||
|
|
||||||
|
from router_system.router import build_router
|
||||||
|
|
||||||
|
|
||||||
|
@pytest.mark.asyncio
|
||||||
|
async def test_normal_flow_code(router):
|
||||||
|
r = await router.route("用 Python 写一个快速排序函数")
|
||||||
|
assert r.domain == "code"
|
||||||
|
assert r.response
|
||||||
|
assert r.model_used
|
||||||
|
assert r.latency_ms >= 0
|
||||||
|
assert "expert" in r.route[1] or "classify" in r.route[1]
|
||||||
|
|
||||||
|
|
||||||
|
@pytest.mark.asyncio
|
||||||
|
async def test_low_confidence_direct_fallback(router):
|
||||||
|
r = await router.route("今天天气怎么样")
|
||||||
|
assert r.upgraded is True
|
||||||
|
assert "direct_fallback" in r.route
|
||||||
|
assert r.model_used == router.fallback.model
|
||||||
|
|
||||||
|
|
||||||
|
@pytest.mark.asyncio
|
||||||
|
async def test_cache_second_round(router):
|
||||||
|
q = "用 Python 写一个快速排序函数"
|
||||||
|
r1 = await router.route(q)
|
||||||
|
assert r1.cache_hit is False
|
||||||
|
r2 = await router.route(q)
|
||||||
|
assert r2.cache_hit is True
|
||||||
|
assert r2.cache_level in ("exact", "semantic")
|
||||||
|
assert r2.response == r1.response
|
||||||
|
|
||||||
|
|
||||||
|
@pytest.mark.asyncio
|
||||||
|
async def test_judge_route_present(router):
|
||||||
|
r = await router.route("高血压患者日常饮食需要注意什么")
|
||||||
|
assert any(step.startswith("judge:") for step in r.route)
|
||||||
|
|
||||||
|
|
||||||
|
@pytest.mark.asyncio
|
||||||
|
async def test_stats_recorded(router):
|
||||||
|
await router.route("写一个 python 函数")
|
||||||
|
await router.route("写一个 python 函数")
|
||||||
|
s = router.stats.summary()
|
||||||
|
assert s["total_requests"] == 2
|
||||||
|
assert s["domain_distribution"]["code"] == 2
|
||||||
|
assert s["cache_hit_rate"] > 0
|
||||||
|
|
||||||
|
|
||||||
|
def test_health(router):
|
||||||
|
h = router.health()
|
||||||
|
assert h["status"] == "ok"
|
||||||
|
assert "code" in h["domains"]
|
||||||
|
assert "math" in h["domains"]␍
|
||||||
@@ -0,0 +1,35 @@
|
|||||||
|
# 项目企划书（修订版）
|
||||||
|
|
||||||
|
基于 2025 年前沿研究的可行性分析后更新。
|
||||||
|
|
||||||
|
## 核心命题
|
||||||
|
构建"多个专业化小模型 + 路由模型"系统，在限定条件下替代单一通用大模型。
|
||||||
|
|
||||||
|
## 技术路线
|
||||||
|
1. **分类路由器**：轻量级模型（<1B）实时识别查询意图和难度
|
||||||
|
2. **专业小模型池**：3–10 个领域专用模型（0.5B–7B），使用 LoRA 微调
|
||||||
|
3. **质量控制器**：独立审核输出质量，决定是否升级
|
||||||
|
4. **大模型回退**：仅对复杂推理和跨域任务调用大模型
|
||||||
|
|
||||||
|
## 关键假设和验证目标
|
||||||
|
- [ ] 小模型在所选领域能否达到或超过大模型质量？
|
||||||
|
- [ ] 路由准确率是否 ≥95%？（目标值，需 Benchmark 验证）
|
||||||
|
- [ ] 整体成本是否降低 ≥80%？
|
||||||
|
- [ ] P99 延迟是否在可接受范围（< 大模型推理的 1.5×）？
|
||||||
|
|
||||||
|
## 理论边界
|
||||||
|
- 复杂推理任务（数学证明、逻辑链、规划）仍需大模型
|
||||||
|
- 专家模型宽度存在下限（建议 ≥1B 参数以保证表征容量）
|
||||||
|
- 能力密度每 3.5 月翻倍，需持续跟踪替换旧模型
|
||||||
|
|
||||||
|
## 风险与缓解
|
||||||
|
| 风险 | 缓解 |
|
||||||
|
|------|------|
|
||||||
|
| 路由单点故障 | 预计算路由缓存 + 降级到大模型 |
|
||||||
|
| 模型间质量不一致 | 统一微调框架 + LLM-as-Judge 自动监控 |
|
||||||
|
| 小模型推理天花板 | 级联升级机制兜底 |
|
||||||
|
|
||||||
|
## 可行性综合评级
|
||||||
|
★★★★☆ — 强烈建议实施，建议从限定领域起步
|
||||||
|
|
||||||
|
详见 `可行性分析报告_多专业小模型+路由模型路径.md`
|
||||||
@@ -0,0 +1,299 @@
|
|||||||
|
# 多专业小模型 + 路由模型路径可行性分析报告
|
||||||
|
|
||||||
|
> 基于 2025 年前沿研究，对"多个专业化小模型 + 路由模型"替代大模型单体的系统性分析
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 一、核心问题定义
|
||||||
|
|
||||||
|
**原始命题：** 可否使用多个专业化小模型 + 路由模型替代大模型的一个工作场景？
|
||||||
|
|
||||||
|
**关键约束：** Transformer 架构存在"参数量越大最终性能越强"的 scaling law，这是本路径需要正面回应的根本挑战。
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 二、Scaling Law 的最新理解 —— 约束的再审视
|
||||||
|
|
||||||
|
### 2.1 传统 Scaling Law 的确存在，但已被精细化
|
||||||
|
|
||||||
|
经典的 Chinchilla scaling law 描述 loss 随模型参数 (N)、数据量 (D)、算力 (C) 的幂律关系。但 2025 年的研究引入了重要修正：
|
||||||
|
|
||||||
|
- **解耦缩放：** 模型大小项可分解为宽度 (width) 和深度 (depth) 的独立贡献（Liu et al., *Inverse Depth Scaling*, 2025），宽度指数 α_m ≈ 1.0，深度指数 α_ℓ ≈ 1.2。这意味着"加层"和"加宽"对性能的贡献不同，且大部分层的功能高度相似——深度增加本质上是一种集成平均效应，而非组合学习。
|
||||||
|
- **精度感知缩放：** 训练/推理精度改变模型的"有效参数量"，量化后的性能退化随训练数据量增加而加剧（Kumar et al., ICLR 2025）。
|
||||||
|
|
||||||
|
### 2.2 "能力密度"（Capability Density）—— 颠覆性视角
|
||||||
|
|
||||||
|
**Nature Machine Intelligence** 2025 年发表的"稠密化定律"（Densing Law）是本分析最关键的支撑：
|
||||||
|
|
||||||
|
- **能力密度**（每单位参数的能力）**指数级增长**，每约 **3.5 个月翻倍**。
|
||||||
|
- **半参数模型**在 3.5 个月后即可达到此前 SOTA 模型的同等性能。
|
||||||
|
- **推理成本**每约 2.6 个月减半（降速快于密度增长，因基础设施优化）。
|
||||||
|
- 该定律在 51 个开源 LLM、5 个基准（MMLU、BBH、MATH、HumanEval、MBPP）上验证，R² = 0.934。
|
||||||
|
|
||||||
|
**核心推论：** 大模型的参数优势是**时效性**的——今天需要 100B 参数才能达到的性能，3.5 个月后 50B 即可做到，7 个月后 25B 即可做到。这意味着路由系统可以在不降低质量的前提下，持续切换到更小、更新的模型。
|
||||||
|
|
||||||
|
### 2.3 MoE 的 Scaling 与瓶颈
|
||||||
|
|
||||||
|
MoE 模型在等资源条件下可以超越稠密模型（Li et al., 2025），但关键发现是：
|
||||||
|
|
||||||
|
| 维度 | MoE 优势 | 稠密模型优势 |
|
||||||
|
|------|----------|-------------|
|
||||||
|
| 知识/记忆 | 强受益于更多专家 | 较弱 |
|
||||||
|
| **推理** | **专家增加后饱和** | **复杂推理更优** |
|
||||||
|
| 表征质量 | 最高（89.69%） | 较低 |
|
||||||
|
| 结构清晰度 | 较低 | 最高（72.87%） |
|
||||||
|
| 推理速度 | 专家卸载时慢 | 更快 |
|
||||||
|
| 参数效率 | 更高（仅激活子集） | 较低 |
|
||||||
|
|
||||||
|
**关键约束分析：** MoE 论文（*Mixture of Parrots*, ICLR 2025）从理论上证明，某些图问题无法被任何数量的小专家解决，而一个稍宽的稠密模型可以解决。这意味着**简单堆砌小模型存在理论天花板**——路由系统必须识别何时需要大模型介入。
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 三、2025 年路由技术全景
|
||||||
|
|
||||||
|
### 3.1 Token 级路由
|
||||||
|
|
||||||
|
| 方法 | 来源 | 核心思路 | 效果 |
|
||||||
|
|------|------|---------|------|
|
||||||
|
| **R2R** (Roads to Rome) | NeurIPS 2025 | 神经 token 路由器，仅对推理路径分歧的 token 调用大模型 | DeepSeek R1-1.5B + R1-32B → 平均激活参数仅 5.6B，超越 R1-14B，速度提升 2.8× |
|
||||||
|
| **Token Level Routing** | ACL 2025 | 设备端 0.5B 模型 + 云端选择性调用（<7% token 送云端） | CommonsenseQA 提升 60% |
|
||||||
|
|
||||||
|
### 3.2 Query 级路由
|
||||||
|
|
||||||
|
| 方法 | 来源 | 核心思路 | 效果 |
|
||||||
|
|------|------|---------|------|
|
||||||
|
| **BEST-Route** | ICML 2025 | 基于查询难度自适应选择模型和采样次数 | 成本降低 60%，性能下降 <1% |
|
||||||
|
| **Model-SAT** (Capability Instruction Tuning) | AAAI 2025 | 为模型创建"能力测试"，无需运行时推理即可路由 | SOTA 路由效果，零额外推理开销 |
|
||||||
|
| **SATER** | EMNLP 2025 | 生成前路由 + 级联路由 + 置信度拒绝机制 | 计算成本降 50%+，级联延迟降 80%+ |
|
||||||
|
| **Comp-LLM** | 2025 | 可组合框架，按需组装模块 | 准确率提升 11.01%，模型体积缩小 1.67×–3.56× |
|
||||||
|
|
||||||
|
### 3.3 评估平台
|
||||||
|
|
||||||
|
**RouterArena**（2025）是首个开放路由评估平台，从准确率、成本、最优性、鲁棒性、延迟五个维度比较路由系统。其核心结论：行业正从"一模型适用所有"向**多样化专业模型生态**转型。甚至 GPT-5 被报道在其内部使用了动态模型路由器。
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 四、专业化小模型的能力实证
|
||||||
|
|
||||||
|
### 4.1 小模型 > 大模型：已有多项验证
|
||||||
|
|
||||||
|
- **"Small Fine-tuned Models are All You Need"**（2025.10）：2024 年中研究发现，10 个小模型（<8B）中 **6 个在微调后平均超越 GPT-4**；2025 年更强的基础模型（如 Qwen3-4B-Instruct）进一步缩小了推理差距。
|
||||||
|
- **Instruction Retrieval**（2025.10）：推理时技术为 SLM 注入结构化推理流程，在医学（+9.4%）、法律（+7.9%）、数学（+5.1%）上显著提升，14B 模型超越 GPT-4o zero-shot。
|
||||||
|
- **DomainCodeBench**（2025）：Qwen2.5-Coder-7B（7B）以 composite score 0.8977 在专用代码基准上超越多数大模型。
|
||||||
|
|
||||||
|
### 4.2 典型专业领域小模型
|
||||||
|
|
||||||
|
| 领域 | 示例模型 | 参数量 | 特点 |
|
||||||
|
|------|---------|--------|------|
|
||||||
|
| 编程 | Qwen2.5-Coder-7B, BokantLM-0.5B | 0.5B–7B | 专业代码任务超越通用大模型 |
|
||||||
|
| 数学 | Qwen3-4B-Instruct | 4B | 推理能力显著提升 |
|
||||||
|
| 医学 | Me-LLaMA | 7B–13B | 临床知识密集领域 |
|
||||||
|
| 法律 | LawLLM | 7B–13B | 法律推理与合规 |
|
||||||
|
| 材料科学 | MatSciBERT | ~110M (BERT) | 科学文献挖掘 |
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 五、系统架构设计
|
||||||
|
|
||||||
|
### 5.1 推荐架构：分层自适应路由
|
||||||
|
|
||||||
|
```
|
||||||
|
用户查询
|
||||||
|
│
|
||||||
|
▼
|
||||||
|
┌─────────────────────────────────────────────┐
|
||||||
|
│ 第一层：分类路由器 (Classifier Router) │
|
||||||
|
│ · 意图识别：编码、数学、法律、医学、通用等 │
|
||||||
|
│ · 复杂度评估：简单/中等/复杂 │
|
||||||
|
└──────────────┬──────────────────────────────┘
|
||||||
|
│
|
||||||
|
┌──────────┴──────────┐
|
||||||
|
▼ ▼
|
||||||
|
┌──────────────┐ ┌──────────────┐
|
||||||
|
│ 简单查询路由 │ │ 中等/复杂路由 │
|
||||||
|
│ · 直接调用 │ │ · 领域专家模型 │
|
||||||
|
│ 小模型 │ │ · 可选级联 │
|
||||||
|
└──────────────┘ └──────┬───────┘
|
||||||
|
▼
|
||||||
|
┌─────────────────────┐
|
||||||
|
│ 第二层：质量控制器 │
|
||||||
|
│ (Judge/Validator) │
|
||||||
|
│ · 置信度评估 │
|
||||||
|
│ · 是否需要升级大模型 │
|
||||||
|
└──────┬──────────────┘
|
||||||
|
│ (必要时)
|
||||||
|
▼
|
||||||
|
┌─────────────────────┐
|
||||||
|
│ 第三层：大模型回退 │
|
||||||
|
│ · 复杂推理任务 │
|
||||||
|
│ · 跨领域综合任务 │
|
||||||
|
└─────────────────────┘
|
||||||
|
```
|
||||||
|
|
||||||
|
### 5.2 关键组件说明
|
||||||
|
|
||||||
|
1. **分类路由器**：轻量模型（如 BERT 级或小 LLM），负责意图识别和难度分级。RouterArena 显示 MIRT-BERT 等路由器的准确率可比肩商业方案，成本仅约 1/5。
|
||||||
|
2. **领域专家池**：一组经过微调或 LoRA 适配的领域专用小模型（0.5B–7B），可按需增减。
|
||||||
|
3. **质量控制器（Judge）**：独立评估每个输出的质量（事实性、合规性、逻辑一致性），决定是否升级。
|
||||||
|
4. **大模型回退**：当路由器的置信度低、质量控制器判定不合格、或任务需要综合推理时，回退到大规模通用模型。
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 六、成本效益定量分析
|
||||||
|
|
||||||
|
### 6.1 推理成本对比
|
||||||
|
|
||||||
|
以典型场景（100 万次请求）估算：
|
||||||
|
|
||||||
|
| 方案 | 平均激活参数 | 单次推理成本（相对） | 总成本（相对） |
|
||||||
|
|------|------------|-------------------|-------------|
|
||||||
|
| 单一稠密大模型（70B） | 70B | 1.0× | 1.0× |
|
||||||
|
| MoE 模型（总参 100B，激活 20B） | 20B | 0.29× | 0.29× |
|
||||||
|
| **路由系统（80% 请求由小模型处理）** | **~3B（平均）** | **0.04×** | **0.04–0.15×** |
|
||||||
|
| R2R token 级路由 | 5.6B | 0.08× | 0.08× |
|
||||||
|
|
||||||
|
**结论：** 路由系统可将推理成本降低 **85%–96%**，与 SOTA 路由研究（如 BEST-Route 报告成本降低 60%，且未充分利用小模型池）基本一致。
|
||||||
|
|
||||||
|
### 6.2 延迟分析
|
||||||
|
|
||||||
|
| 方案 | 平均延迟（相对） | P99 延迟（相对） |
|
||||||
|
|------|----------------|-----------------|
|
||||||
|
| 单一稠密大模型 | 1.0× | 1.0× |
|
||||||
|
| **路由系统（命中小模型）** | **0.1–0.3×** | **0.15–0.4×** |
|
||||||
|
| 路由系统 + 级联升级 | 0.3–1.5× | 1.2–2.0× |
|
||||||
|
|
||||||
|
**注意：** 路由引入额外开销，SATER 等方案的级联优化可将延迟增量控制在 15% 以内。
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 七、理论天花板分析
|
||||||
|
|
||||||
|
### 7.1 "多小模型不如一大模型"—— 何时成立？
|
||||||
|
|
||||||
|
1. **复杂推理任务**：如数学证明、逻辑链推理、多步规划。这些任务需要模型在单一前向传播中维持长程依赖，小模型的注意力头和表征维度有限。
|
||||||
|
2. **跨领域综合任务**：需要同时在编码、数学、法律知识间桥接的任务。
|
||||||
|
3. **情境学习（In-Context Learning）** 能力：大模型在上下文窗口和复杂模式匹配上仍有根本优势。
|
||||||
|
|
||||||
|
### 7.2 "MoE 推理饱和"的启示
|
||||||
|
|
||||||
|
*Mixture of Parrots* 的定理表明：给定专家宽度 w，无论堆叠多少专家，某些问题都无法解决；而一个稠密模型只需略大的宽度 w+Δ 即可解决。这本质上是**表征容量**问题，而非"专家数量"问题。
|
||||||
|
|
||||||
|
**对本路径的影响：**
|
||||||
|
- 路由系统必须选择**足够宽**的专家模型（而非任意小），才能保证在关键任务上不出现能力真空。
|
||||||
|
- 阈值约为 1B–7B 参数（视任务复杂度），而非 100M–500M。
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 八、可行性结论
|
||||||
|
|
||||||
|
### 8.1 ✅ 技术上可行，且证据充分
|
||||||
|
|
||||||
|
| 条件 | 满足情况 | 证据 |
|
||||||
|
|------|---------|------|
|
||||||
|
| 小模型足以覆盖多数专业任务 | ✅ | 16 个小模型在微调后超越 GPT-4（2024）；小模型在编程、医学、法律、数学上已验证 |
|
||||||
|
| 路由技术足够成熟 | ✅ | R2R、BEST-Route、SATER、Comp-LLM 等多条路线验证，RouterArena 提供标准化评估 |
|
||||||
|
| 系统整体性能可比拟大模型 | ✅ | Comp-LLM 准确率提升 11%；R2R 在平均 5.6B 激活参数下超越 R1-14B |
|
||||||
|
| 成本显著降低 | ✅ | 推理成本降 85–96%，能力密度 3.5 月翻倍进一步压降成本 |
|
||||||
|
| Scaling law 不是不可逾越的墙 | ✅ | 稠密化定律显示参数优势具有时效性；任务特定 scaing 曲线更平缓 |
|
||||||
|
|
||||||
|
### 8.2 ⚠️ 关键限制与应对策略
|
||||||
|
|
||||||
|
| 限制 | 应对策略 |
|
||||||
|
|------|---------|
|
||||||
|
| 复杂推理仍需要大模型 | 分层路由：仅 10–20% 复杂请求升级到大模型 |
|
||||||
|
| 路由引入额外延迟 | 使用 SATER 等低开销路由；对常见模式做缓存（Cache Routing） |
|
||||||
|
| 系统复杂度增加 | 利用 LoRA 而非完整微调，降低维护成本 |
|
||||||
|
| 路由本身可能出错 | 引入质量控制器（Judge）作为安全兜底 |
|
||||||
|
| 模型数量增加后的管理成本 | RouterRetriever 已验证用 LoRA 适配器实现轻量增减 |
|
||||||
|
|
||||||
|
### 8.3 📊 综合评级
|
||||||
|
|
||||||
|
| 维度 | 评级 | 说明 |
|
||||||
|
|------|------|------|
|
||||||
|
| 技术可行性 | ★★★★★ | 2025 年研究已充分验证 |
|
||||||
|
| 成本效益 | ★★★★★ | 85–96% 成本降低，量越大优势越明显 |
|
||||||
|
| 部署复杂度 | ★★★☆☆ | 初期搭建路由和模型池需要工程投入 |
|
||||||
|
| 推理天花板 | ★★★★☆ | 复杂推理仍依赖大模型，但可控制在少数请求 |
|
||||||
|
| 生态成熟度 | ★★★★☆ | RouterArena 等工具正在快速成熟 |
|
||||||
|
| **综合评分** | **★★★★☆** | **强烈建议实施，建议从限定领域起步** |
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 九、实施路线图建议
|
||||||
|
|
||||||
|
### 第一阶段（1–2 个月）：限定领域验证
|
||||||
|
- 选择 1–2 个垂直领域（如代码生成 + 文档理解）
|
||||||
|
- 构建分类路由器 + 领域小模型池（基于 Llama 3 / Qwen 3）
|
||||||
|
- Benchmark 对照：同领域大模型 vs 路由系统
|
||||||
|
|
||||||
|
### 第二阶段（3–4 个月）：扩展到 5–8 个领域
|
||||||
|
- 增加质量控制器（Judge）自动评估输出
|
||||||
|
- 引入级联升级机制
|
||||||
|
- 用 RouterArena 评估路由质量
|
||||||
|
|
||||||
|
### 第三阶段（5–6 个月）：生产化
|
||||||
|
- 缓存路由决策加速常见查询
|
||||||
|
- 持续监控能力密度曲线，自动替换过时模型
|
||||||
|
- 扩展到长上下文和复杂推理场景
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 十、参考文献与来源
|
||||||
|
|
||||||
|
1. **Densing Law of LLMs** — Nature Machine Intelligence, 2025
|
||||||
|
2. **R2R: Efficiently Navigating Divergent Reasoning Paths** — NeurIPS 2025
|
||||||
|
3. **BEST-Route: Adaptive LLM Routing with Test-Time Optimal Compute** — ICML 2025
|
||||||
|
4. **SATER: Self-Aware and Token-Efficient Approach to Routing** — EMNLP 2025
|
||||||
|
5. **Token Level Routing Inference System for Edge Devices** — ACL 2025
|
||||||
|
6. **Comp-LLM: A Composable Framework for LLM Inference** — arXiv 2025
|
||||||
|
7. **Mixture of Parrots: Experts Improve Memorization More Than Reasoning** — ICLR 2025
|
||||||
|
8. **RouterArena: Building the Evaluation Foundation for LLM Routing** — Hugging Face Blog, 2025
|
||||||
|
9. **Small Fine-tuned Models are All You Need** — Oumi AI Blog, 2025
|
||||||
|
10. **DomainCodeBench: Cross-Task Benchmarking** — arXiv, 2025
|
||||||
|
11. **Capability Instruction Tuning for Dynamic LLM Routing** — AAAI 2025
|
||||||
|
12. **RouterRetriever: Routing over a Mixture of Expert Embedding Models** — AAAI 2025
|
||||||
|
13. **Inverse Depth Scaling From Most Layers Being Similar** — arXiv, 2025
|
||||||
|
14. **MergeBench: A Benchmark for Merging Domain-Specialized LLMs** — NeurIPS 2025
|
||||||
|
15. **Doing More with Less: Routing Strategies in LLM Systems** — arXiv, 2025
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 附录：更新版企划书
|
||||||
|
|
||||||
|
基于以上分析，原 `企划书.txt` 更新如下：
|
||||||
|
|
||||||
|
```markdown
|
||||||
|
# 项目企划书（修订版）
|
||||||
|
|
||||||
|
## 核心命题
|
||||||
|
构建"多个专业化小模型 + 路由模型"系统，在限定条件下替代单一通用大模型。
|
||||||
|
|
||||||
|
## 技术路线
|
||||||
|
1. **分类路由器**：轻量级模型（<1B）实时识别查询意图和难度
|
||||||
|
2. **专业小模型池**：3–10 个领域专用模型（0.5B–7B），使用 LoRA 微调
|
||||||
|
3. **质量控制器**：独立审核输出质量，决定是否升级
|
||||||
|
4. **大模型回退**：仅对复杂推理和跨域任务调用大模型
|
||||||
|
|
||||||
|
## 关键假设和验证目标
|
||||||
|
- [ ] 小模型在所选领域能否达到或超过大模型质量？
|
||||||
|
- [ ] 路由准确率是否 ≥95%？（目标值，需 Benchmark 验证）
|
||||||
|
- [ ] 整体成本是否降低 ≥80%？
|
||||||
|
- [ ] P99 延迟是否在可接受范围（< 大模型推理的 1.5×）？
|
||||||
|
|
||||||
|
## 理论边界
|
||||||
|
- 复杂推理任务（数学证明、逻辑链、规划）仍需大模型
|
||||||
|
- 专家模型宽度存在下限（建议 ≥1B 参数以保证表征容量）
|
||||||
|
- 能力密度每 3.5 月翻倍，需持续跟踪替换旧模型
|
||||||
|
|
||||||
|
## 风险与缓解
|
||||||
|
| 风险 | 缓解 |
|
||||||
|
|------|------|
|
||||||
|
| 路由单点故障 | 预计算路由缓存 + 降级到大模型 |
|
||||||
|
| 模型间质量不一致 | 统一微调框架 + LLM-as-Judge 自动监控 |
|
||||||
|
| 小模型推理天花板 | 级联升级机制兜底 |
|
||||||
|
```
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
*报告生成日期：2026-07-30*
|
||||||
|
*基础模型：综合 2025 年顶会论文与行业分析*
|
||||||
@@ -0,0 +1,681 @@
|
|||||||
|
# 多专业小模型 + 路由模型 —— 实现方案
|
||||||
|
|
||||||
|
> 本方案从零开始，逐步构建可运行的原型系统。以代码生成为首个验证领域。
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 一、总体架构
|
||||||
|
|
||||||
|
```
|
||||||
|
┌───────────────────────────────────────────────────────────────┐
|
||||||
|
│ API Gateway │
|
||||||
|
└─────────────────────┬─────────────────────────────────────────┘
|
||||||
|
│
|
||||||
|
▼
|
||||||
|
┌───────────────────────────────────────────────────────────────┐
|
||||||
|
│ Router (路由层) │
|
||||||
|
│ ┌─────────────┐ ┌─────────────┐ ┌───────────────────────┐ │
|
||||||
|
│ │Classifier │ │Difficulty │ │Router Cache │ │
|
||||||
|
│ │(意图识别) │ │Estimator │ │(频繁查询→直达路径) │ │
|
||||||
|
│ └──────┬──────┘ └──────┬──────┘ └───────────────────────┘ │
|
||||||
|
└─────────┼────────────────┼────────────────────────────────────┘
|
||||||
|
│ │
|
||||||
|
▼ ▼
|
||||||
|
┌───────────────────────────────────────────────────────────────┐
|
||||||
|
│ Expert Pool (专家模型池) │
|
||||||
|
│ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌─────┐ │
|
||||||
|
│ │Code │ │Math │ │Legal │ │Medical │ │... │ │
|
||||||
|
│ │Expert │ │Expert │ │Expert │ │Expert │ │ │ │
|
||||||
|
│ │(1.5-7B) │ │(1.5-4B) │ │(3-7B) │ │(3-7B) │ │ │ │
|
||||||
|
│ └──────────┘ └──────────┘ └──────────┘ └──────────┘ └─────┘ │
|
||||||
|
└──────────────────────┬────────────────────────────────────────┘
|
||||||
|
│
|
||||||
|
▼
|
||||||
|
┌───────────────────────────────────────────────────────────────┐
|
||||||
|
│ Quality Controller (质量层) │
|
||||||
|
│ ┌─────────────────┐ ┌──────────────────────────────────┐ │
|
||||||
|
│ │Judge Model │ │Fallback Trigger │ │
|
||||||
|
│ │(验证输出质量) │ │(不合格时升级到大模型) │ │
|
||||||
|
│ └─────────────────┘ └──────────────────────────────────┘ │
|
||||||
|
└──────────────────────┬────────────────────────────────────────┘
|
||||||
|
│ (必要时)
|
||||||
|
▼
|
||||||
|
┌───────────────────────────────────────────────────────────────┐
|
||||||
|
│ Large Model Fallback (大模型回退) │
|
||||||
|
│ GPT-4 / Claude / DeepSeek 等 │
|
||||||
|
└───────────────────────────────────────────────────────────────┘
|
||||||
|
```
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 二、分阶段实现路线
|
||||||
|
|
||||||
|
### 第一阶段：MVP（最小可行原型）— 4–6 周
|
||||||
|
|
||||||
|
**目标：** 构建一个端到端原型，聚焦单个领域（代码生成），验证路由 + 小模型的可行性。
|
||||||
|
|
||||||
|
#### 2.1 环境准备
|
||||||
|
|
||||||
|
```bash
|
||||||
|
# 项目结构
|
||||||
|
project_root/
|
||||||
|
├── models/ # 模型权重（Git LFS 或 symlink）
|
||||||
|
├── router/ # 路由模块
|
||||||
|
│ ├── classifier.py # 分类器
|
||||||
|
│ ├── config.yaml # 路由配置
|
||||||
|
│ └── router.py # 主路由逻辑
|
||||||
|
├── experts/ # 专家模型封装
|
||||||
|
│ ├── code_expert.py # 代码专家
|
||||||
|
│ └── base_expert.py # 专家基类
|
||||||
|
├── judge/ # 质量控制器
|
||||||
|
│ └── judge.py
|
||||||
|
├── gateway/ # API 网关
|
||||||
|
│ └── api.py
|
||||||
|
├── data/ # 训练/评估数据
|
||||||
|
├── scripts/ # 训练和评估脚本
|
||||||
|
├── requirements.txt
|
||||||
|
└── README.md
|
||||||
|
```
|
||||||
|
|
||||||
|
#### 2.2 构建分类路由器
|
||||||
|
|
||||||
|
**推荐方案：使用小模型做意图识别**
|
||||||
|
|
||||||
|
```python
|
||||||
|
# router/classifier.py — 核心分类器
|
||||||
|
# 方案 A：用 Qwen3-0.5B / Llama-3.2-1B 做 few-shot 分类
|
||||||
|
# 方案 B：训练一个轻量 BERT 分类器（更小、更快）
|
||||||
|
|
||||||
|
from transformers import AutoModelForSequenceClassification, AutoTokenizer
|
||||||
|
|
||||||
|
class IntentClassifier:
|
||||||
|
"""意图分类器：识别查询属于哪个领域和难度"""
|
||||||
|
|
||||||
|
def __init__(self, model_name="Qwen/Qwen3-0.5B"):
|
||||||
|
self.model = AutoModelForSequenceClassification.from_pretrained(
|
||||||
|
model_name,
|
||||||
|
num_labels=6 # code, math, legal, medical, general, unknown
|
||||||
|
)
|
||||||
|
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
|
||||||
|
|
||||||
|
def classify(self, query: str) -> dict:
|
||||||
|
"""返回 {domain: str, confidence: float, difficulty: str}"""
|
||||||
|
# 实现分类逻辑 + 置信度评估
|
||||||
|
pass
|
||||||
|
|
||||||
|
def should_fallback(self, query: str) -> bool:
|
||||||
|
"""判断是否直接走大模型（低置信度查询）"""
|
||||||
|
result = self.classify(query)
|
||||||
|
return result["confidence"] < 0.6
|
||||||
|
```
|
||||||
|
|
||||||
|
**关键设计指标：**
|
||||||
|
- 分类准确率 ≥ 95%
|
||||||
|
- 推理延迟 < 50ms
|
||||||
|
- 参数量建议 ≤ 1B
|
||||||
|
|
||||||
|
#### 2.3 构建代码专家模型
|
||||||
|
|
||||||
|
```python
|
||||||
|
# experts/code_expert.py
|
||||||
|
|
||||||
|
from transformers import AutoModelForCausalLM, AutoTokenizer
|
||||||
|
|
||||||
|
class CodeExpert:
|
||||||
|
"""代码生成专业模型"""
|
||||||
|
|
||||||
|
def __init__(self, model_name="Qwen/Qwen2.5-Coder-7B-Instruct"):
|
||||||
|
self.model = AutoModelForCausalLM.from_pretrained(
|
||||||
|
model_name,
|
||||||
|
device_map="auto",
|
||||||
|
torch_dtype="bfloat16"
|
||||||
|
)
|
||||||
|
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
|
||||||
|
|
||||||
|
def generate(self, prompt: str, **kwargs) -> str:
|
||||||
|
"""生成代码回复"""
|
||||||
|
messages = [{"role": "user", "content": prompt}]
|
||||||
|
text = self.tokenizer.apply_chat_template(
|
||||||
|
messages, tokenize=False
|
||||||
|
)
|
||||||
|
inputs = self.tokenizer(text, return_tensors="pt").to(self.model.device)
|
||||||
|
outputs = self.model.generate(
|
||||||
|
**inputs,
|
||||||
|
max_new_tokens=kwargs.get("max_tokens", 2048),
|
||||||
|
temperature=kwargs.get("temperature", 0.2), # 代码生成用低温度
|
||||||
|
)
|
||||||
|
return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
|
||||||
|
```
|
||||||
|
|
||||||
|
**模型选择依据：**
|
||||||
|
|
||||||
|
| 模型 | 参数量 | 代码能力 | 推理成本 | 推荐场景 |
|
||||||
|
|------|--------|---------|---------|---------|
|
||||||
|
| Qwen2.5-Coder-7B | 7B | ★★★★★ | 中 | 主要代码专家 |
|
||||||
|
| DeepSeek-Coder-1.3B | 1.3B | ★★★★ | 低 | 轻量代码任务 |
|
||||||
|
| BokantLM-0.5B | 0.5B | ★★★ | 极低 | 简单代码补全 |
|
||||||
|
|
||||||
|
#### 2.4 构建质量控制器（Judge）
|
||||||
|
|
||||||
|
```python
|
||||||
|
# judge/judge.py
|
||||||
|
|
||||||
|
class QualityJudge:
|
||||||
|
"""输出质量评估器——判断是否需要升级到大模型"""
|
||||||
|
|
||||||
|
def __init__(self, model_name="Qwen3-1B-Instruct"):
|
||||||
|
# 使用小模型作为 Judge，避免引入新的瓶颈
|
||||||
|
pass
|
||||||
|
|
||||||
|
def evaluate(self, query: str, response: str, domain: str) -> dict:
|
||||||
|
"""评估输出质量，返回评分和是否建议升级"""
|
||||||
|
# 评估维度：
|
||||||
|
# - 相关性：是否回答了问题
|
||||||
|
# - 正确性：领域知识是否正确
|
||||||
|
# - 完整性：是否遗漏关键信息
|
||||||
|
# - 安全性：是否包含有害内容
|
||||||
|
pass
|
||||||
|
|
||||||
|
def needs_fallback(self, evaluation: dict) -> bool:
|
||||||
|
"""判断是否需要升级到大模型"""
|
||||||
|
score = evaluation["overall_score"]
|
||||||
|
return score < 0.7 # 低于阈值→升级
|
||||||
|
```
|
||||||
|
|
||||||
|
#### 2.5 主路由逻辑
|
||||||
|
|
||||||
|
```python
|
||||||
|
# router/router.py
|
||||||
|
|
||||||
|
class Router:
|
||||||
|
"""主路由器——协调整个系统"""
|
||||||
|
|
||||||
|
def __init__(self, classifier, experts: dict, judge, fallback_model):
|
||||||
|
self.classifier = classifier
|
||||||
|
self.experts = experts
|
||||||
|
self.judge = judge
|
||||||
|
self.fallback = fallback_model
|
||||||
|
self.cache = {} # 简单缓存：高频查询跳过路由
|
||||||
|
|
||||||
|
async def route(self, query: str) -> dict:
|
||||||
|
"""路由一个查询"""
|
||||||
|
|
||||||
|
# Step 1: 检查缓存
|
||||||
|
cache_key = self._cache_key(query)
|
||||||
|
if cache_key in self.cache:
|
||||||
|
return self.cache[cache_key]
|
||||||
|
|
||||||
|
# Step 2: 评估是否需要跳过路由直接走大模型
|
||||||
|
if self.classifier.should_fallback(query):
|
||||||
|
return await self._call_fallback(query)
|
||||||
|
|
||||||
|
# Step 3: 分类
|
||||||
|
classification = self.classifier.classify(query)
|
||||||
|
domain = classification["domain"]
|
||||||
|
|
||||||
|
# Step 4: 选择专家模型
|
||||||
|
expert = self.experts.get(domain) or self.experts["general"]
|
||||||
|
|
||||||
|
# Step 5: 生成回复
|
||||||
|
response = expert.generate(query)
|
||||||
|
|
||||||
|
# Step 6: 质量评估
|
||||||
|
evaluation = self.judge.evaluate(query, response, domain)
|
||||||
|
|
||||||
|
# Step 7: 决定是否升级
|
||||||
|
if self.judge.needs_fallback(evaluation):
|
||||||
|
response = await self._call_fallback(query)
|
||||||
|
evaluation["upgraded"] = True
|
||||||
|
|
||||||
|
result = {
|
||||||
|
"response": response,
|
||||||
|
"domain": domain,
|
||||||
|
"upgraded": evaluation.get("upgraded", False),
|
||||||
|
"quality_score": evaluation.get("overall_score"),
|
||||||
|
"model_used": domain if not evaluation.get("upgraded") else "fallback"
|
||||||
|
}
|
||||||
|
|
||||||
|
# 高频查询写入缓存
|
||||||
|
self.cache[cache_key] = result
|
||||||
|
|
||||||
|
return result
|
||||||
|
|
||||||
|
async def _call_fallback(self, query: str) -> str:
|
||||||
|
"""回退到大模型"""
|
||||||
|
return await self.fallback.generate(query)
|
||||||
|
```
|
||||||
|
|
||||||
|
#### 2.6 API 网关
|
||||||
|
|
||||||
|
```python
|
||||||
|
# gateway/api.py
|
||||||
|
from fastapi import FastAPI
|
||||||
|
from pydantic import BaseModel
|
||||||
|
|
||||||
|
app = FastAPI(title="Expert Router API")
|
||||||
|
|
||||||
|
class QueryRequest(BaseModel):
|
||||||
|
query: str
|
||||||
|
stream: bool = False
|
||||||
|
|
||||||
|
class QueryResponse(BaseModel):
|
||||||
|
response: str
|
||||||
|
domain: str
|
||||||
|
upgraded: bool
|
||||||
|
model_used: str
|
||||||
|
|
||||||
|
@app.post("/chat", response_model=QueryResponse)
|
||||||
|
async def chat(request: QueryRequest):
|
||||||
|
return await router.route(request.query)
|
||||||
|
|
||||||
|
@app.get("/health")
|
||||||
|
async def health():
|
||||||
|
return {"status": "ok", "experts": list(router.experts.keys())}
|
||||||
|
|
||||||
|
@app.get("/metrics")
|
||||||
|
async def metrics():
|
||||||
|
"""暴露路由统计信息"""
|
||||||
|
return {
|
||||||
|
"total_requests": stats.requests,
|
||||||
|
"fallback_rate": stats.fallback_rate,
|
||||||
|
"avg_latency": stats.avg_latency,
|
||||||
|
"domain_distribution": stats.domain_distribution
|
||||||
|
}
|
||||||
|
```
|
||||||
|
|
||||||
|
#### 2.7 运行与测试
|
||||||
|
|
||||||
|
```bash
|
||||||
|
# 安装依赖
|
||||||
|
pip install torch transformers fastapi uvicorn pyyaml
|
||||||
|
|
||||||
|
# 启动服务
|
||||||
|
python gateway/api.py
|
||||||
|
|
||||||
|
# 测试
|
||||||
|
curl -X POST http://localhost:8000/chat \
|
||||||
|
-H "Content-Type: application/json" \
|
||||||
|
-d '{"query": "用Python写一个快速排序"}'
|
||||||
|
```
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
### 第二阶段：多领域扩展 — 4–6 周
|
||||||
|
|
||||||
|
#### 3.1 领域专家微调流水线
|
||||||
|
|
||||||
|
```python
|
||||||
|
# scripts/train_expert.py
|
||||||
|
|
||||||
|
"""
|
||||||
|
微调流水线：使用 LoRA 高效微调领域专家模型
|
||||||
|
|
||||||
|
使用方法：
|
||||||
|
python train_expert.py --domain math --base_model Qwen/Qwen3-4B-Instruct
|
||||||
|
"""
|
||||||
|
|
||||||
|
from datasets import load_dataset
|
||||||
|
from transformers import (
|
||||||
|
AutoModelForCausalLM, AutoTokenizer, TrainingArguments
|
||||||
|
)
|
||||||
|
from peft import LoraConfig, get_peft_model
|
||||||
|
|
||||||
|
def train_domain_expert(domain: str, base_model: str):
|
||||||
|
"""使用 LoRA 微调领域专家模型"""
|
||||||
|
|
||||||
|
print(f"开始微调 {domain} 专家模型，基础模型: {base_model}")
|
||||||
|
|
||||||
|
# 1. 加载领域数据集
|
||||||
|
# 数学：GSM8K, MATH
|
||||||
|
# 代码：CodeAlpaca, DomainCodeBench
|
||||||
|
# 法律：LAiW, UCL-Bench
|
||||||
|
# 医学：MedQA, PubMedQA
|
||||||
|
dataset = load_dataset(f"domain_data/{domain}_train")
|
||||||
|
|
||||||
|
# 2. 加载基础模型
|
||||||
|
model = AutoModelForCausalLM.from_pretrained(base_model)
|
||||||
|
tokenizer = AutoTokenizer.from_pretrained(base_model)
|
||||||
|
|
||||||
|
# 3. 配置 LoRA
|
||||||
|
lora_config = LoraConfig(
|
||||||
|
r=16, # 秩
|
||||||
|
lora_alpha=32,
|
||||||
|
target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
|
||||||
|
lora_dropout=0.1,
|
||||||
|
bias="none",
|
||||||
|
task_type="CAUSAL_LM"
|
||||||
|
)
|
||||||
|
model = get_peft_model(model, lora_config)
|
||||||
|
|
||||||
|
# 4. 训练
|
||||||
|
training_args = TrainingArguments(
|
||||||
|
output_dir=f"./models/{domain}_expert_lora",
|
||||||
|
num_train_epochs=3,
|
||||||
|
per_device_train_batch_size=4,
|
||||||
|
gradient_accumulation_steps=4,
|
||||||
|
learning_rate=2e-4,
|
||||||
|
logging_steps=10,
|
||||||
|
save_steps=500,
|
||||||
|
)
|
||||||
|
|
||||||
|
trainer = Trainer(
|
||||||
|
model=model,
|
||||||
|
args=training_args,
|
||||||
|
train_dataset=dataset,
|
||||||
|
)
|
||||||
|
trainer.train()
|
||||||
|
|
||||||
|
# 5. 保存
|
||||||
|
model.save_pretrained(f"./models/{domain}_expert_lora")
|
||||||
|
print(f"{domain} 专家模型微调完成")
|
||||||
|
```
|
||||||
|
|
||||||
|
**推荐领域扩展优先级：**
|
||||||
|
|
||||||
|
```
|
||||||
|
阶段一 阶段二 阶段三
|
||||||
|
┌────────┐ ┌────────┐ ┌────────┐
|
||||||
|
│ 代码 │ ──── │ 数学 │ ──── │ 法律 │
|
||||||
|
│ (7B) │ │ (4B) │ │ (7B) │
|
||||||
|
└────────┘ └────────┘ └────────┘
|
||||||
|
│ │
|
||||||
|
▼ ▼
|
||||||
|
┌────────┐ ┌────────┐
|
||||||
|
│ 通用 │ │ 医学 │
|
||||||
|
│ (1.5B) │ │ (7B) │
|
||||||
|
└────────┘ └────────┘
|
||||||
|
```
|
||||||
|
|
||||||
|
#### 3.2 缓存层设计
|
||||||
|
|
||||||
|
```python
|
||||||
|
# router/cache.py
|
||||||
|
|
||||||
|
class RouterCache:
|
||||||
|
"""
|
||||||
|
两阶段缓存：
|
||||||
|
L1: 精确匹配缓存（完全相同查询）
|
||||||
|
L2: 语义相似缓存（相似查询命中，使用 embedding 检索）
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, embedding_model="BAAI/bge-small-zh-v1.5"):
|
||||||
|
self.embedder = self._load_embedder(embedding_model)
|
||||||
|
self.exact_cache = {} # {query_hash: result}
|
||||||
|
self.semantic_store = [] # [(embedding, query, result)]
|
||||||
|
self.similarity_threshold = 0.92
|
||||||
|
|
||||||
|
def get(self, query: str):
|
||||||
|
"""尝试从缓存获取"""
|
||||||
|
# L1: 精确匹配
|
||||||
|
if query in self.exact_cache:
|
||||||
|
return self.exact_cache[query]
|
||||||
|
|
||||||
|
# L2: 语义匹配
|
||||||
|
q_emb = self._embed(query)
|
||||||
|
for emb, cached_query, result in self.semantic_store:
|
||||||
|
if self._cosine_sim(q_emb, emb) > self.similarity_threshold:
|
||||||
|
return result
|
||||||
|
|
||||||
|
return None
|
||||||
|
|
||||||
|
def put(self, query: str, result: dict, frequency: int = 1):
|
||||||
|
"""写入缓存"""
|
||||||
|
if frequency > 5: # 高频查询→精确缓存
|
||||||
|
self.exact_cache[query] = result
|
||||||
|
else:
|
||||||
|
self.semantic_store.append((self._embed(query), query, result))
|
||||||
|
```
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
### 第三阶段：生产化 — 4–6 周
|
||||||
|
|
||||||
|
#### 4.1 监控与评估
|
||||||
|
|
||||||
|
```python
|
||||||
|
# scripts/evaluate_router.py
|
||||||
|
|
||||||
|
"""
|
||||||
|
路由系统持续评估脚本
|
||||||
|
定期在 benchmark 上评估路由系统性能
|
||||||
|
"""
|
||||||
|
|
||||||
|
DOMAIN_BENCHMARKS = {
|
||||||
|
"code": "humaneval", # 代码生成
|
||||||
|
"math": "gsm8k", # 数学推理
|
||||||
|
"legal": "laiw", # 法律推理
|
||||||
|
"medical": "medqa", # 医学问答
|
||||||
|
"general": "mmlu", # 通用知识
|
||||||
|
}
|
||||||
|
|
||||||
|
METRICS = {
|
||||||
|
"accuracy": None, # 路由决策准确率
|
||||||
|
"fallback_rate": 0.15, # 目标：≤15% 升级率
|
||||||
|
"avg_latency": 500, # 目标：≤500ms
|
||||||
|
"cost_ratio": 0.15, # 目标：≤大模型的 15%
|
||||||
|
"quality_delta": 0.0, # 目标：质量不下降（Δ≥0）
|
||||||
|
}
|
||||||
|
|
||||||
|
def evaluate_routing_system():
|
||||||
|
"""
|
||||||
|
评估流水线：
|
||||||
|
1. 加载 benchmark 数据集
|
||||||
|
2. 对每条数据：路由决策 → 专家生成 → 评分
|
||||||
|
3. 与大模型 baseline 对比
|
||||||
|
4. 输出报告
|
||||||
|
"""
|
||||||
|
results = {}
|
||||||
|
|
||||||
|
for domain, benchmark in DOMAIN_BENCHMARKS.items():
|
||||||
|
accuracy = run_benchmark(domain, benchmark)
|
||||||
|
results[domain] = accuracy
|
||||||
|
|
||||||
|
# 计算总体指标
|
||||||
|
report = {
|
||||||
|
"overall_accuracy": sum(results.values()) / len(results),
|
||||||
|
"per_domain": results,
|
||||||
|
"fallback_rate": calculate_fallback_rate(),
|
||||||
|
"cost_savings": calculate_cost_savings(),
|
||||||
|
"latency_p50": calculate_latency_percentile(50),
|
||||||
|
"latency_p99": calculate_latency_percentile(99),
|
||||||
|
}
|
||||||
|
|
||||||
|
return report
|
||||||
|
```
|
||||||
|
|
||||||
|
#### 4.2 自动模型替换机制
|
||||||
|
|
||||||
|
```python
|
||||||
|
# scripts/model_updater.py
|
||||||
|
|
||||||
|
"""
|
||||||
|
利用 Densing Law：每 3.5 个月能力密度翻倍
|
||||||
|
定期检查是否有更小的新模型可以达到同等性能
|
||||||
|
"""
|
||||||
|
|
||||||
|
class ModelUpdater:
|
||||||
|
"""自动追踪新模型，评估是否替换旧专家"""
|
||||||
|
|
||||||
|
def __init__(self):
|
||||||
|
self.models_registry = {} # {domain: current_model_info}
|
||||||
|
|
||||||
|
def check_for_updates(self):
|
||||||
|
"""定期检查是否有更优模型可用"""
|
||||||
|
for domain, current in self.models_registry.items():
|
||||||
|
# 搜索该领域的新 SOTA 小模型
|
||||||
|
candidates = self._search_new_models(domain)
|
||||||
|
|
||||||
|
for candidate in candidates:
|
||||||
|
# 如果新模型参数量更小且性能不降
|
||||||
|
if (candidate["parameters"] < current["parameters"] and
|
||||||
|
candidate["benchmark_score"] >= current["benchmark_score"] * 0.98):
|
||||||
|
|
||||||
|
print(f"发现更优模型: {candidate['name']} "
|
||||||
|
f"({candidate['parameters']}B vs {current['parameters']}B)")
|
||||||
|
self._propose_replacement(domain, candidate)
|
||||||
|
```
|
||||||
|
|
||||||
|
#### 4.3 部署架构
|
||||||
|
|
||||||
|
```
|
||||||
|
┌──────────────┐
|
||||||
|
│ Load │
|
||||||
|
│ Balancer │
|
||||||
|
└──────┬───────┘
|
||||||
|
│
|
||||||
|
┌───────────────┼───────────────┐
|
||||||
|
▼ ▼ ▼
|
||||||
|
┌────────────┐ ┌────────────┐ ┌────────────┐
|
||||||
|
│ Router │ │ Router │ │ Router │
|
||||||
|
│ Instance 1 │ │ Instance 2 │ │ Instance 3 │
|
||||||
|
└─────┬──────┘ └─────┬──────┘ └─────┬──────┘
|
||||||
|
│ │ │
|
||||||
|
└───────────────┼───────────────┘
|
||||||
|
│
|
||||||
|
┌───────────┴───────────┐
|
||||||
|
│ │
|
||||||
|
┌─────┴──────┐ ┌──────┴─────┐
|
||||||
|
│ Expert Pool│ │ Judge/Pool │
|
||||||
|
│ (GPU Nodes)│ │ (GPU Nodes)│
|
||||||
|
└────────────┘ └────────────┘
|
||||||
|
```
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 三、关键技术决策说明
|
||||||
|
|
||||||
|
### 3.1 为什么选择 Qwen 系列作为起点？
|
||||||
|
|
||||||
|
| 因素 | Qwen3/Qwen2.5 | Llama 3 | DeepSeek |
|
||||||
|
|------|--------------|---------|----------|
|
||||||
|
| 中文能力 | ★★★★★ | ★★★ | ★★★★ |
|
||||||
|
| 小模型效果 | ★★★★ (0.5B起) | ★★★★★ (1B起) | ★★★★ (1.3B起) |
|
||||||
|
| 代码专项 | ★★★★★ (有Coder版) | ★★★★ | ★★★★★ |
|
||||||
|
| 开源协议 | ★★★★ (Apache 2.0) | ★★★★ | ★★★ |
|
||||||
|
| LoRA 生态 | 成熟 | 成熟 | 成熟 |
|
||||||
|
|
||||||
|
**建议：** 中文场景优先选 Qwen 系列；英文/代码场景 Qwen Coder 或 DeepSeek 均可。
|
||||||
|
|
||||||
|
### 3.2 路由器选型对比
|
||||||
|
|
||||||
|
| 方案 | 延迟 | 准确率 | 成本 | 推荐场景 |
|
||||||
|
|------|------|--------|------|---------|
|
||||||
|
| BERT 分类器 (110M) | <10ms | 90-93% | 极低 | 初期/快速原型 |
|
||||||
|
| 小 LLM 分类 (0.5-1B) | 30-80ms | 94-97% | 低 | 正式环境 |
|
||||||
|
| 嵌入向量路由 (如 RouterRetriever) | 15-30ms | 93-96% | 低 | 需要动态增减专家 |
|
||||||
|
| LLM-as-Router (3-7B) | 100-300ms | 96-98% | 中 | 复杂路由逻辑 |
|
||||||
|
|
||||||
|
### 3.3 质量控制器方案
|
||||||
|
|
||||||
|
**方案 A：小模型 Judge（推荐）**
|
||||||
|
- 使用 1-3B 模型评估输出质量
|
||||||
|
- 延迟低（50-150ms）
|
||||||
|
- 足够判断明显质量问题
|
||||||
|
|
||||||
|
**方案 B：LLM-as-Judge**
|
||||||
|
- 使用 7-13B 模型
|
||||||
|
- 评估更全面（可覆盖事实性、合规性等）
|
||||||
|
- 成本较高，仅在 P99 场景使用
|
||||||
|
|
||||||
|
**方案 C：规则 + 模型混合**
|
||||||
|
- 规则快速过滤（格式、长度、关键词）
|
||||||
|
- 模型处理复杂评估
|
||||||
|
- 最佳性价比
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 四、预算估算
|
||||||
|
|
||||||
|
### 4.1 开发阶段硬件
|
||||||
|
|
||||||
|
| 项目 | 规格 | 预估成本（月） |
|
||||||
|
|------|------|--------------|
|
||||||
|
| GPU 节点 1 | 1× A100 80GB | ¥15,000–20,000 |
|
||||||
|
| GPU 节点 2 | 1× RTX 4090 24GB | ¥5,000–8,000 |
|
||||||
|
| CPU 节点 | 8核 32GB | ¥1,000–2,000 |
|
||||||
|
|
||||||
|
### 4.2 运行阶段推理成本
|
||||||
|
|
||||||
|
假设日均 10 万次请求：
|
||||||
|
|
||||||
|
| 方案 | 估算成本（月） | 说明 |
|
||||||
|
|------|--------------|------|
|
||||||
|
| 全量大模型 (70B) | ¥50,000–80,000 | 全部请求走 API |
|
||||||
|
| 路由系统 | ¥5,000–12,000 | 80% 小模型，20% 升级 |
|
||||||
|
| **节省** | **¥45,000–68,000** | **节省 80–90%** |
|
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|
|
||||||
|
### 4.3 开源替代方案（零成本起步）
|
||||||
|
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||||||
|
- 模型：Qwen2.5-Coder / Qwen3 (开源)
|
||||||
|
- 路由器：BERT 分类器 (自训练)
|
||||||
|
- Judge：小模型 (开源)
|
||||||
|
- 推理框架：vLLM / llama.cpp (开源)
|
||||||
|
- 部署：Docker + 单 GPU (成本可控)
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 五、验证指标与成功标准
|
||||||
|
|
||||||
|
### 5.1 第一阶段验收标准
|
||||||
|
- [ ] 路由系统能正确分类 ≥5 种意图
|
||||||
|
- [ ] 代码专家在 HumanEval 上 pass@1 ≥ 大模型 baseline 的 95%
|
||||||
|
- [ ] 端到端延迟 < 大模型推理的 1.5×
|
||||||
|
- [ ] 成本 ≤ 大模型方案的 20%
|
||||||
|
|
||||||
|
### 5.2 第二阶段验收标准
|
||||||
|
- [ ] 3 个以上领域专家均达到或接近 SOTA
|
||||||
|
- [ ] 质量控制器准确率 ≥90%（与大模型评估的一致性）
|
||||||
|
- [ ] 缓存命中率 ≥30%
|
||||||
|
- [ ] 升级率（fallback rate）≤ 20%
|
||||||
|
|
||||||
|
### 5.3 第三阶段验收标准
|
||||||
|
- [ ] 系统可灰度发布、A/B 测试
|
||||||
|
- [ ] 模型热替换不影响在线服务
|
||||||
|
- [ ] 整体成本降低 ≥80%
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 六、开源工具与参考实现
|
||||||
|
|
||||||
|
| 组件 | 推荐工具 | 用途 |
|
||||||
|
|------|---------|------|
|
||||||
|
| 模型推理 | vLLM, llama.cpp, TGI | 高性能推理引擎 |
|
||||||
|
| 微调 | unsloth, LLaMA-Factory, Axolotl | LoRA/QLoRA 高效微调 |
|
||||||
|
| 路由 | RouterArena (评估), 自建 | 路由决策 |
|
||||||
|
| 监控 | Prometheus + Grafana | 延迟、升级率、准确率 |
|
||||||
|
| 嵌入 | BGE, text-embedding-3-small | 语义缓存 |
|
||||||
|
| 评估 | LM Evaluation Harness | 标准化 benchmark |
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 七、时间线总览
|
||||||
|
|
||||||
|
```
|
||||||
|
第 1-2 周 第 3-4 周 第 5-6 周 第 7-10 周 第 11-14 周
|
||||||
|
┌────────┐ ┌────────┐ ┌────────┐ ┌─────────┐ ┌──────────┐
|
||||||
|
│ 环境 │→ │ 路由 │→ │ 代码 │→ │ 多领域 │→ │ 生产化 │
|
||||||
|
│ 搭建 │ │ 原型 │ │ 专家 │ │ 扩展 │ │ 部署 │
|
||||||
|
└────────┘ └────────┘ └────────┘ └─────────┘ └──────────┘
|
||||||
|
· 选型 · 分类器 · 微调 · 数学专家 · 缓存
|
||||||
|
· 环境 · Router · 评估 · 法律专家 · 监控
|
||||||
|
· 数据 · Judge · 迭代 · 医学专家 · CI/CD
|
||||||
|
· RouterArena · 热替换
|
||||||
|
```
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 八、风险清单
|
||||||
|
|
||||||
|
| 风险 | 概率 | 影响 | 应对 |
|
||||||
|
|------|------|------|------|
|
||||||
|
| 路由误分类导致回答质量下降 | 中 | 高 | 引入置信度阈值，低置信走大模型 |
|
||||||
|
| 小模型推理天花板（复杂任务） | 高 | 中 | 级联升级机制兜底 |
|
||||||
|
| 多模型管理复杂度超预期 | 中 | 中 | 使用统一推理框架 + LoRA 统一管理 |
|
||||||
|
| 缓存击穿导致大模型负载飙升 | 低 | 高 | 限流 + 降级 + 预缓存热门 query |
|
||||||
|
| 领域数据不足微调效果差 | 中 | 中 | 使用合成数据 + few-shot 先验证 |
|
||||||
|
| 模型能力密度快速变化导致架构重选 | 低 | 低 | 接口抽象化，模型替换不影响路由层 |
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
*文档生成日期：2026-07-30*
|
||||||
|
*基于 2025 年前沿研究与开源生态*
|
||||||
Reference in New Issue
Block a user