1. generate_reply: 改为优先关键词匹配,不匹配则调用 AI 2. _ai_generate_reply: 改进 prompt,加入对话上下文、微信风格要求 3. 要求回复简洁(50字以内),符合聊天风格
82 lines
2.3 KiB
YAML
82 lines
2.3 KiB
YAML
# WeChat Agent 配置文件示例
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# ============================================
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# VLM 视觉模型配置(阿里云百炼)
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# ============================================
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# 阿里云百炼平台: https://bailian.console.aliyun.com/
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# API Key 获取: https://bailian.console.aliyun.com/cn-beijing#/APIKey
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#
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# 支持的模型:
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# - qwen-vl-latest (推荐,VL 理解)
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# - qwen-vl2-7b
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# - qwen-vl2-72b
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# - qwen2-vl-72b-instruct
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# - qwen2.5-vl-72b-instruct
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# - qwen-omni-series (全模态)
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# ============================================
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vlm:
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model_type: bailian # bailian / qwen-vl / gpt-4v
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# 阿里云百炼 API (OpenAI 兼容格式)
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api_base: https://dashscope.aliyuncs.com/compatible-mode/v1
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# API Key: 设置环境变量 ALIBABA_CLOUD_API_KEY 或 DASHSCOPE_API_KEY
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# 或直接在这里填写:
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# api_key: your-api-key-here
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api_key: ""
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model_name: qwen-vl-plus # qwen-vl-plus / qwen-vl-max
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max_tokens: 2048
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temperature: 0.7
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llm:
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api_base: https://dashscope.aliyuncs.com/compatible-mode/v1
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# LLM API Key (同上,可以使用相同的 API Key)
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api_key: ""
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model_name: qwen-plus # 或 qwen-max, qwen-turbo 等
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max_tokens: 2048
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temperature: 0.7
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wechat:
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client_version: "3.8.x" # 推荐微信版本
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poll_interval: 2.0 # 轮询间隔(秒)
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screenshot_interval: 1.0 # 截图间隔(秒)
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window_title: "微信" # 微信窗口标题
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# 回复规则
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rules:
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# 关键词回复示例(优先匹配)
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- keywords:
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- 你好
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- hi
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- hello
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reply_type: keyword
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reply_content: "您好,有什么可以帮您的?"
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enabled: true
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# AI 回复模式(无匹配关键词时,使用 LLM 生成回复)
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# 这是默认模式,会根据对话上下文生成自然回复
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- keywords: []
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reply_type: AI
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reply_content: ""
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enabled: true
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# 知识库(可选,后续接入 OpenViking)
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knowledge_base:
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url: http://192.168.5.5:1933
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# 日志级别
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log_level: INFO
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# ============================================
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# 环境变量
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# ============================================
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# 推荐将敏感信息放在环境变量中:
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#
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# Linux/macOS:
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# export ALIBABA_CLOUD_API_KEY=your-api-key
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# export DASHSCOPE_API_KEY=your-api-key
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#
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# Windows:
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# set ALIBABA_CLOUD_API_KEY=your-api-key
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#
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# 或使用 .env 文件 (需要 python-dotenv):
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# ALIBABA_CLOUD_API_KEY=your-api-key
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