Jeason
7013e9db70
- 新增 ConversationHistoryManager.get_tenant_summary() 按租户聚合会话统计 - get_sessions_paginated() 和 get_conversation_analytics() 增加 tenant_id 过滤 - 新增 GET /api/conversations/tenants 租户汇总端点 - sessions 和 analytics API 端点支持 tenant_id 查询参数 - 前端实现租户卡片列表视图和租户详情会话表格视图 - 实现面包屑导航、搜索范围限定、统计面板上下文切换 - 会话删除后自动检测空租户并返回列表视图 - dashboard.html 添加租户视图 DOM 容器 - 交互模式与知识库租户分组视图保持一致
153 lines
5.3 KiB
Plaintext
153 lines
5.3 KiB
Plaintext
# .env.example
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# This file contains all the environment variables needed to run the application.
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# Copy this file to .env and fill in the values for your environment.
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# ============================================================================
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# SERVER CONFIGURATION
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# ============================================================================
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# The host the web server will bind to.
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SERVER_HOST=0.0.0.0
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# The port for the main Flask web server.
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SERVER_PORT=5001
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# The port for the WebSocket server for real-time chat.
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WEBSOCKET_PORT=8765
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# Set to "True" for development to enable debug mode and auto-reloading.
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# Set to "False" for production.
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DEBUG_MODE=False
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# Logging level for the application. Options: DEBUG, INFO, WARNING, ERROR, CRITICAL
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LOG_LEVEL=INFO
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# 租户标识 — 多项目共用同一套代码时，用不同的 TENANT_ID 隔离数据
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TENANT_ID=default
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# ============================================================================
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# DATABASE CONFIGURATION
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# ============================================================================
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# The connection string for the primary database.
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# Format for MySQL: mysql+pymysql://<user>:<password>@<host>:<port>/<dbname>?charset=utf8mb4
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# Format for SQLite: sqlite:///./local_test.db
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# 使用本地 SQLite（推荐用于开发和测试）
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DATABASE_URL=sqlite:///./data/tsp_assistant.db
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# 远程 MySQL（生产环境使用，需要时取消注释）
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# DATABASE_URL=mysql+pymysql://tsp_assistant:123456@jeason.online/tsp_assistant?charset=utf8mb4
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# ============================================================================
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# LARGE LANGUAGE MODEL (LLM) CONFIGURATION
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# ============================================================================
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# The provider of the LLM. Supported: "qwen", "openai", "anthropic"
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LLM_PROVIDER=qwen
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# The API key for your chosen LLM provider.
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LLM_API_KEY=sk-c0dbefa1718d46eaa897199135066f00
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# The base URL for the LLM API. This is often needed for OpenAI-compatible endpoints.
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LLM_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
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# The specific model to use, e.g., "qwen-plus-latest", "gpt-3.5-turbo", "claude-3-sonnet-20240229"
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LLM_MODEL=qwen-plus-latest
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# The temperature for the model's responses (0.0 to 2.0).
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LLM_TEMPERATURE=0.7
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# The maximum number of tokens to generate in a response.
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LLM_MAX_TOKENS=2000
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# The timeout in seconds for API calls to the LLM.
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LLM_TIMEOUT=30
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# ============================================================================
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# FEISHU (LARK) INTEGRATION CONFIGURATION
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# ============================================================================
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# The App ID of your Feishu enterprise application.
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FEISHU_APP_ID=cli_a8b50ec0eed1500d
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# The App Secret of your Feishu enterprise application.
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FEISHU_APP_SECRET=ccxkE7ZCFQZcwkkM1rLy0ccZRXYsT2xK
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# The Verification Token for validating event callbacks (if configured).
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FEISHU_VERIFICATION_TOKEN=
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# The Encrypt Key for decrypting event data (if configured).
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FEISHU_ENCRYPT_KEY=
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# The App Token of the Feishu multi-dimensional table document.
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FEISHU_APP_TOKEN=XXnEbiCmEaMblSs6FDJcFCqsnIg
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# The ID of the Feishu multi-dimensional table for data synchronization.
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FEISHU_TABLE_ID=tblnl3vJPpgMTSiP
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# ============================================================================
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# AI ACCURACY CONFIGURATION
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# ============================================================================
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# The similarity threshold (0.0 to 1.0) for auto-approving an AI suggestion.
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AI_AUTO_APPROVE_THRESHOLD=0.95
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# The similarity threshold below which the human-provided resolution is preferred.
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AI_USE_HUMAN_RESOLUTION_THRESHOLD=0.90
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# The similarity threshold for flagging a suggestion for manual review.
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AI_MANUAL_REVIEW_THRESHOLD=0.80
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# The default confidence score for an AI suggestion.
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AI_SUGGESTION_CONFIDENCE=0.95
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# The confidence score assigned when a human resolution is used.
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AI_HUMAN_RESOLUTION_CONFIDENCE=0.90
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# ============================================================================
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# REDIS CACHE CONFIGURATION
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# ============================================================================
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# Redis server host (use localhost for local development)
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REDIS_HOST=localhost
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# Redis server port
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REDIS_PORT=6379
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# Redis database number (0-15)
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REDIS_DB=0
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# Redis password (leave empty if no password)
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REDIS_PASSWORD=
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# Redis connection pool size
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REDIS_POOL_SIZE=10
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# Redis cache default TTL in seconds (3600 = 1 hour)
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REDIS_DEFAULT_TTL=3600
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# Enable Redis cache (set to False to disable caching)
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REDIS_ENABLED=True
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# ============================================================================
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# EMBEDDING CONFIGURATION (知识库向量检索 - 本地模型)
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# ============================================================================
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# 启用 Embedding 语义检索（禁用则降级为关键词匹配）
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EMBEDDING_ENABLED=True
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# 本地 embedding 模型名称（首次运行自动从 HuggingFace 下载）
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# 推荐模型:
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# BAAI/bge-small-zh-v1.5 (~95MB, 512维, 中文效果好, 内存占用~150MB)
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# BAAI/bge-base-zh-v1.5 (~400MB, 768维, 中文效果更好)
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# shibing624/text2vec-base-chinese (~400MB, 768维, 中文专优)
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EMBEDDING_MODEL=BAAI/bge-small-zh-v1.5
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# 向量维度（需与模型匹配）
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EMBEDDING_DIMENSION=512
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# 语义搜索相似度阈值（0.0-1.0，越高越严格）
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EMBEDDING_SIMILARITY_THRESHOLD=0.5
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# Embedding 缓存过期时间（秒），默认 1 天
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EMBEDDING_CACHE_TTL=86400
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