114 lines
4.1 KiB
YAML
114 lines
4.1 KiB
YAML
# ============================================================
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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 -> 直接走最后处理者
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judge_fallback_threshold: 0.70 # Judge 质量分低于此值 -> 升级最后处理者
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default_temperature: 0.2
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# 专家系统内核执行配置（L0 默认零参数）
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execution:
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mode: rule # rule（默认，L0 零参数）| hybrid
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planner: rule # rule（规则拆解）| hf（可选小模型拆解）
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expert_backend: rule # rule（规则执行器）| hf | api（本地小模型按需加载）
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model_level: L0 # L0 纯规则 | L1 分类/Planner增强 | L2 领域生成
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max_plan_depth: 3 # 任务拆解深度上限
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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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- finance
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- life
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- education
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- general
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# 两级路由：大领域分组（用户接口指定 group → 组内路由模型 → 组内专业小模型）
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# 组内路由模型只识别本组领域，体积约为统一路由模型的 1/4
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domain_groups:
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tech: [code, math]
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professional: [legal, medical, finance]
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lifestyle: [life, education]
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general: [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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finance: { type: mock, model: Qwen/Qwen3-4B-Instruct }
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life: { type: mock, model: Qwen/Qwen3-1.7B-Instruct }
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education: { type: mock, model: Qwen/Qwen3-1.7B-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 # none（降级模板）| mock | local（本地≤8B 按需加载）| api
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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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# ============================================================
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# v2：端云协同 LLM 协作系统（《实现方案_v2》）配置段
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# v1 段（上方）保留，供 legacy 路由（POST /chat/legacy）使用。
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# ============================================================
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runtime:
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llama_server:
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binary: bin/llama-server.exe # 捆绑上游 release，不改源码（D1）
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model: models/qwen3.5-4b-q4_k_m.gguf
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port: 8901
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hw_profile: auto # auto | gpu12 | gpu8 | cpu
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extra_args: ["-fa", "-ctk", "q8_0", "-ctv", "q8_0", "--cache-reuse", "256"]
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tiers: # 三档硬件模板（保守默认，可手动覆盖）
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gpu12: {ngl: 99, ctx: 32768}
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gpu8: {ngl: 14, ctx: 16384}
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cpu: {ngl: 0, ctx: 8192}
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architect: # 大模型（API）
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model: deepseek-v4-flash
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base_url: https://api.deepseek.com
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api_key_env: DEEPSEEK_API_KEY
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temperature: 0.2
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timeout_s: 60
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worker: # 小模型（本地）
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backend: llama_server
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temperature: 0.3
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max_fix_attempts: 2
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per_step_timeout_s: 300
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pipeline:
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fast_path: true
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rounds_cap: 6
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api_token_cap: 8000
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breach_policy: architect_do # architect_do | local_only
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review:
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queue_db: data/review.sqlite3
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sample_rate: 0.10 # 随机抽样送审
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force_tags: [safety] # brief.tags 命中即强制送审
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