Initial commit: 炼妖壶 (Lianyaohu) - 稷下学宫AI辩论系统
- 🏛️ 稷下学宫八仙论道AI辩论系统 - 🌍 天下体系资本生态分析 - 🔒 安全配置管理 (Doppler集成) - 📊 RapidAPI永动机数据引擎 - 🎨 Streamlit现代化界面 - ✅ 清理所有敏感信息泄露
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internal/analysis/Mistral_KAG_Resource_Configuration_Guide.md
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internal/analysis/Mistral_KAG_Resource_Configuration_Guide.md
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# Mistral + KAG 资源配置完整指南
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## 🎯 资源配置策略概览
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### 配置原则
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```
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资源配置策略:
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├── 成本优化 (免费资源优先)
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├── 性能平衡 (避免瓶颈)
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├── 扩展性 (支持业务增长)
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└── 可靠性 (生产级稳定)
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```
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## 💰 免费资源配置方案
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### 1. Mistral模型资源
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#### OpenRouter免费额度
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```yaml
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# OpenRouter Mistral配置
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mistral_config:
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provider: "openrouter"
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models:
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free_tier:
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- model: "mistralai/mistral-7b-instruct:free"
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limit: "200 requests/day"
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context: "32k tokens"
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cost: "$0"
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- model: "mistralai/mixtral-8x7b-instruct:free"
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limit: "20 requests/day"
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context: "32k tokens"
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cost: "$0"
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api_config:
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base_url: "https://openrouter.ai/api/v1"
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api_key: "${OPENROUTER_API_KEY}"
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headers:
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HTTP-Referer: "https://your-domain.com"
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X-Title: "太公心易系统"
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```
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#### 官方Mistral免费层
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```yaml
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# Mistral官方免费配置
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mistral_official:
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provider: "mistral"
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free_tier:
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model: "mistral-small-latest"
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limit: "1M tokens/month"
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context: "32k tokens"
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cost: "$0"
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api_config:
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base_url: "https://api.mistral.ai/v1"
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api_key: "${MISTRAL_API_KEY}"
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```
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### 2. KAG部署资源
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#### 轻量级部署配置
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```yaml
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# KAG轻量级配置
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kag_config:
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deployment_mode: "lightweight"
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# 计算资源
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compute:
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cpu: "4 cores"
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memory: "8GB RAM"
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storage: "50GB SSD"
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gpu: "optional (CPU推理)"
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# 组件配置
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components:
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knowledge_extractor:
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model: "BAAI/bge-large-zh-v1.5" # 免费开源
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device: "cpu"
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batch_size: 16
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graph_builder:
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backend: "networkx" # 轻量级图库
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storage: "sqlite" # 本地存储
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reasoning_engine:
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type: "hybrid"
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symbolic_engine: "owlready2" # 开源
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neural_engine: "mistral" # 通过API
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```
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## 🏗️ 资源架构设计
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### 分层资源配置
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```
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资源分层架构:
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┌─────────────────────────────────────┐
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│ 应用层资源 │
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│ - N8N: 1GB RAM │
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│ - 太公心易UI: 512MB RAM │
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├─────────────────────────────────────┤
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│ 智能体层资源 │
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│ - AutoGen: 2GB RAM │
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│ - 11仙智能体: 共享Mistral API │
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├─────────────────────────────────────┤
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│ 认知中间件层资源 │
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│ - KAG服务: 4GB RAM, 4 CPU │
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│ - 知识图谱: 2GB存储 │
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├─────────────────────────────────────┤
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│ 模型层资源 │
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│ - Mistral API: 免费额度 │
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│ - BGE嵌入: 本地CPU推理 │
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├─────────────────────────────────────┤
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│ 数据层资源 │
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│ - Milvus: 4GB RAM, 20GB存储 │
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│ - MongoDB: 2GB RAM, 10GB存储 │
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└─────────────────────────────────────┘
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总计: 16GB RAM, 8 CPU, 80GB存储
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```
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## 🐳 Docker Compose配置
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### 完整的容器化部署
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```yaml
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# docker-compose.yml
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version: '3.8'
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services:
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# KAG知识中间件
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kag-service:
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image: kag:latest
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container_name: taigong-kag
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ports:
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- "8080:8080"
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environment:
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- MISTRAL_API_KEY=${MISTRAL_API_KEY}
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- OPENROUTER_API_KEY=${OPENROUTER_API_KEY}
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- KAG_MODE=lightweight
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volumes:
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- ./kag_data:/app/data
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- ./kag_config:/app/config
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mem_limit: 4g
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cpus: 2.0
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restart: unless-stopped
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depends_on:
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- milvus
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- mongodb
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# Milvus向量数据库
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milvus:
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image: milvusdb/milvus:latest
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container_name: taigong-milvus
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ports:
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- "19530:19530"
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environment:
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- ETCD_ENDPOINTS=etcd:2379
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- MINIO_ADDRESS=minio:9000
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volumes:
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- ./milvus_data:/var/lib/milvus
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mem_limit: 4g
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cpus: 2.0
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restart: unless-stopped
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# MongoDB文档数据库
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mongodb:
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image: mongo:latest
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container_name: taigong-mongodb
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ports:
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- "27017:27017"
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environment:
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- MONGO_INITDB_ROOT_USERNAME=admin
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- MONGO_INITDB_ROOT_PASSWORD=${MONGO_PASSWORD}
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volumes:
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- ./mongo_data:/data/db
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mem_limit: 2g
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cpus: 1.0
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restart: unless-stopped
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# N8N工作流
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n8n:
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image: n8nio/n8n:latest
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container_name: taigong-n8n
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ports:
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- "5678:5678"
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environment:
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- N8N_BASIC_AUTH_ACTIVE=true
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- N8N_BASIC_AUTH_USER=${N8N_USER}
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- N8N_BASIC_AUTH_PASSWORD=${N8N_PASSWORD}
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- WEBHOOK_URL=https://your-domain.com
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volumes:
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- ./n8n_data:/home/node/.n8n
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mem_limit: 1g
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cpus: 1.0
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restart: unless-stopped
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# 太公心易应用
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taigong-app:
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build: ./app
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container_name: taigong-xinyi
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ports:
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- "8501:8501"
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environment:
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- KAG_API_URL=http://kag-service:8080
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- MISTRAL_API_KEY=${MISTRAL_API_KEY}
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volumes:
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- ./app_data:/app/data
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mem_limit: 1g
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cpus: 1.0
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restart: unless-stopped
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depends_on:
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- kag-service
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# Redis缓存
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redis:
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image: redis:alpine
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container_name: taigong-redis
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ports:
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- "6379:6379"
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volumes:
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- ./redis_data:/data
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mem_limit: 512m
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cpus: 0.5
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restart: unless-stopped
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# 网络配置
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networks:
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default:
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name: taigong-network
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driver: bridge
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# 数据卷
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volumes:
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kag_data:
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milvus_data:
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mongo_data:
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n8n_data:
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app_data:
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redis_data:
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```
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## ⚙️ 环境变量配置
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### .env文件
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```bash
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# .env
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# API密钥
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MISTRAL_API_KEY=your_mistral_api_key
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OPENROUTER_API_KEY=your_openrouter_key
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COHERE_API_KEY=your_cohere_key
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# 数据库配置
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MONGO_PASSWORD=your_mongo_password
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REDIS_PASSWORD=your_redis_password
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# N8N配置
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N8N_USER=admin
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N8N_PASSWORD=your_n8n_password
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# KAG配置
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KAG_MODE=lightweight
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KAG_LOG_LEVEL=INFO
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# Milvus配置
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MILVUS_HOST=milvus
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MILVUS_PORT=19530
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# 应用配置
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APP_ENV=production
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APP_DEBUG=false
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```
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## 📊 资源监控配置
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### Prometheus + Grafana监控
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```yaml
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# monitoring/docker-compose.monitoring.yml
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version: '3.8'
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services:
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prometheus:
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image: prom/prometheus:latest
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container_name: taigong-prometheus
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ports:
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- "9090:9090"
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volumes:
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- ./prometheus.yml:/etc/prometheus/prometheus.yml
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- prometheus_data:/prometheus
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command:
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- '--config.file=/etc/prometheus/prometheus.yml'
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- '--storage.tsdb.path=/prometheus'
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mem_limit: 1g
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cpus: 0.5
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grafana:
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image: grafana/grafana:latest
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container_name: taigong-grafana
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ports:
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- "3000:3000"
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environment:
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- GF_SECURITY_ADMIN_PASSWORD=${GRAFANA_PASSWORD}
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volumes:
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- grafana_data:/var/lib/grafana
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- ./grafana/dashboards:/etc/grafana/provisioning/dashboards
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mem_limit: 512m
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cpus: 0.5
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volumes:
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prometheus_data:
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grafana_data:
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```
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## 💡 成本优化策略
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### 免费资源最大化利用
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```python
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# 智能API路由配置
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class APIResourceManager:
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def __init__(self):
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self.providers = {
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"openrouter_free": {
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"daily_limit": 200,
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"current_usage": 0,
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"models": ["mistral-7b-instruct:free"]
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},
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"mistral_free": {
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"monthly_limit": 1000000, # tokens
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"current_usage": 0,
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"models": ["mistral-small-latest"]
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},
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"local_models": {
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"unlimited": True,
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"models": ["bge-large-zh-v1.5"]
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}
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}
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def get_best_provider(self, task_type, complexity):
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"""智能选择最佳提供商"""
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if task_type == "embedding":
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return "local_models"
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if complexity == "simple" and self.providers["openrouter_free"]["current_usage"] < 180:
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return "openrouter_free"
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if self.providers["mistral_free"]["current_usage"] < 900000:
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return "mistral_free"
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# 降级到本地模型
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return "local_models"
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```
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## 🚀 部署脚本
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### 一键部署脚本
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```bash
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#!/bin/bash
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# deploy.sh
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echo "🚀 开始部署太公心易 + KAG + Mistral系统..."
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# 1. 检查依赖
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echo "📋 检查系统依赖..."
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command -v docker >/dev/null 2>&1 || { echo "请先安装Docker"; exit 1; }
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command -v docker-compose >/dev/null 2>&1 || { echo "请先安装Docker Compose"; exit 1; }
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# 2. 创建目录结构
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echo "📁 创建目录结构..."
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mkdir -p {kag_data,milvus_data,mongo_data,n8n_data,app_data,redis_data}
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mkdir -p {kag_config,monitoring}
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# 3. 检查环境变量
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echo "🔑 检查环境变量..."
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if [ ! -f .env ]; then
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echo "请先配置.env文件"
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exit 1
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fi
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# 4. 启动服务
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echo "🐳 启动Docker服务..."
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docker-compose up -d
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# 5. 等待服务就绪
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echo "⏳ 等待服务启动..."
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sleep 30
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# 6. 健康检查
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echo "🏥 执行健康检查..."
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curl -f http://localhost:8080/health || echo "KAG服务未就绪"
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curl -f http://localhost:19530/health || echo "Milvus服务未就绪"
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curl -f http://localhost:5678/healthz || echo "N8N服务未就绪"
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echo "✅ 部署完成！"
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echo "🌐 访问地址："
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echo " - 太公心易应用: http://localhost:8501"
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echo " - N8N工作流: http://localhost:5678"
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echo " - KAG API: http://localhost:8080"
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echo " - 监控面板: http://localhost:3000"
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```
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## 📈 扩展配置
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### 生产环境扩展
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```yaml
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# 生产环境资源配置
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production_config:
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compute:
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cpu: "16 cores"
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memory: "64GB RAM"
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storage: "500GB SSD"
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gpu: "NVIDIA T4 (可选)"
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high_availability:
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replicas: 3
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load_balancer: "nginx"
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failover: "automatic"
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monitoring:
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metrics: "prometheus"
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logging: "elasticsearch"
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alerting: "alertmanager"
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```
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## 🎯 总结
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**推荐的资源配置策略：**
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1. **开发/测试**: 使用免费API + 轻量级部署
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2. **小规模生产**: 混合免费+付费API + 中等资源
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3. **大规模生产**: 私有化部署 + 充足资源
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**关键配置要点：**
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- ✅ 充分利用免费API额度
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- ✅ 智能路由避免超限
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- ✅ 容器化部署便于扩展
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- ✅ 监控资源使用情况
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想要我帮你根据你的具体需求调整这个配置方案吗？🤔
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