This commit is contained in:
48
.env
48
.env
@@ -1,33 +1,55 @@
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# =============================================================================
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# 本地开发环境配置
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# 用于 python app/backend.py 和 streamlit run frontend/frontend.py
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# Agent1 环境配置文件
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# 用法: cp .env.example .env 然后修改配置值
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# =============================================================================
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# -----------------------------------------------------------------------------
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# AI 模型 API 密钥
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# AI 模型 API 密钥（必需 - 请填入真实值）
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# -----------------------------------------------------------------------------
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ZHIPUAI_API_KEY=4d568a4367f1442bbc226cc0daf84566.44SsKVWkVIM2Mkeg
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DEEPSEEK_API_KEY=sk-e74b13ac778f4b7eb29afa418a14421e
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VLLM_LOCAL_KEY=token-abc123
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LLAMACPP_API_KEY=token-abc123
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# -----------------------------------------------------------------------------
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# vLLM 服务配置
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# llama.cpp 服务配置
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# -----------------------------------------------------------------------------
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# 本地开发时，vLLM 通常在 localhost 运行
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VLLM_BASE_URL=http://localhost:8000/v1
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# 主 LLM 服务 (Gemma-4-E2B GGUF) - 端口 8081
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VLLM_BASE_URL=http://localhost:8081/v1
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# Embedding 服务 (embeddinggemma-300M GGUF) - 端口 8082
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VLLM_EMBEDDING_URL=http://localhost:8082/v1
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# -----------------------------------------------------------------------------
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# Mem0 记忆层配置
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# -----------------------------------------------------------------------------
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# ⭐ 注意：Mem0 现在直接复用主 LLM 实例，无需单独配置
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# Qdrant 向量数据库地址（远程服务器）
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QDRANT_URL=http://115.190.121.151:6333
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QDRANT_COLLECTION_NAME=mem0_user_memories
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# -----------------------------------------------------------------------------
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# 数据库配置
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# -----------------------------------------------------------------------------
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# 本地开发时，数据库在 localhost 运行
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DB_URI=postgresql://postgres:mysecretpassword@localhost:5432/langgraph_db?sslmode=disable
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# PostgreSQL 连接字符串（远程服务器）
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DB_URI=postgresql://postgres:mysecretpassword@115.190.121.151:5432/langgraph_db?sslmode=disable
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# -----------------------------------------------------------------------------
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# 前端配置
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# -----------------------------------------------------------------------------
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# 本地开发时，后端也在 localhost 运行
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API_URL=http://localhost:8001/chat
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# 后端 API 地址（本地开发使用 8003 端口，避免与 vLLM 冲突）
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API_URL=http://localhost:8003/chat
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# 本地开发 - 显示所有调试信息
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# -----------------------------------------------------------------------------
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# 应用行为配置
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# -----------------------------------------------------------------------------
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# 记忆提取间隔：每 N 轮对话执行一次记忆提取
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MEMORY_SUMMARIZE_INTERVAL=10
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# 是否启用 Graph 执行追踪（调试用）
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ENABLE_GRAPH_TRACE=true
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# -----------------------------------------------------------------------------
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# 日志配置
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# -----------------------------------------------------------------------------
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LOG_LEVEL=DEBUG
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DEBUG=true
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DEBUG=true
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45
.env.docker
45
.env.docker
@@ -4,32 +4,49 @@
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# =============================================================================
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# -----------------------------------------------------------------------------
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# AI 模型 API 密钥（必需 - 请修改为真实值）
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# AI 模型 API 密钥（必需 - 请填入真实值）
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# -----------------------------------------------------------------------------
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ZHIPUAI_API_KEY=your_zhipuai_api_key_here
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ZHIPUAI_API_KEY=your_api_key_here
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DEEPSEEK_API_KEY=your_deepseek_api_key_here
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VLLM_LOCAL_KEY=token-abc123
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LLAMACPP_API_KEY=token-abc123
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# -----------------------------------------------------------------------------
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# vLLM 服务配置
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# llama.cpp 服务配置
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# -----------------------------------------------------------------------------
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# Docker 部署时，如果 vLLM 在宿主机运行，使用 FRP 穿透地址或宿主机 IP
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# 如果 vLLM 也在 Docker 中，使用 Docker 服务名或容器 IP
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VLLM_BASE_URL=http://115.190.121.151:18000/v1
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# 主 LLM 服务 (Gemma-4-E2B GGUF) - 端口 8081
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VLLM_BASE_URL=http://localhost:8081/v1
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# Embedding 服务 (embeddinggemma-300M GGUF) - 端口 8082
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VLLM_EMBEDDING_URL=http://localhost:8082/v1
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# -----------------------------------------------------------------------------
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# Mem0 记忆层配置
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# -----------------------------------------------------------------------------
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# ⭐ 注意：Mem0 现在直接复用主 LLM 实例，无需单独配置
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# Qdrant 向量数据库（远程服务器上的独立容器）
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QDRANT_URL=http://115.190.121.151:6333
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QDRANT_COLLECTION_NAME=mem0_user_memories
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# -----------------------------------------------------------------------------
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# 数据库配置
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# -----------------------------------------------------------------------------
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# Docker Compose 内部网络，使用服务名 'postgres'
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DB_URI=postgresql://postgres:mysecretpassword@postgres:5432/langgraph_db?sslmode=disable
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# PostgreSQL 连接字符串（远程服务器上的独立容器）
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DB_URI=postgresql://postgres:mysecretpassword@115.190.121.151:5432/langgraph_db?sslmode=disable
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# -----------------------------------------------------------------------------
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# 前端配置（通过 docker-compose.yml 注入，此处仅作文档说明）
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# 前端配置
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# -----------------------------------------------------------------------------
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# 注意：API_URL 在 docker-compose.yml 中已配置为 http://backend:8001/chat
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# 本地无需设置，Docker 容器启动时会自动注入
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# API_URL=http://backend:8001/chat
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# Docker Compose 内部网络，使用服务名 'backend'
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API_URL=http://backend:8001/chat
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# 生产环境 - 仅显示关键信息
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# -----------------------------------------------------------------------------
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# 应用行为配置
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# -----------------------------------------------------------------------------
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MEMORY_SUMMARIZE_INTERVAL=10
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ENABLE_GRAPH_TRACE=false
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# -----------------------------------------------------------------------------
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# 日志配置
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# -----------------------------------------------------------------------------
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LOG_LEVEL=WARNING
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DEBUG=false
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69
.env.example
Normal file
69
.env.example
Normal file
@@ -0,0 +1,69 @@
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# =============================================================================
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# Agent1 环境配置模板
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# 用法: cp .env.example .env 然后修改配置值
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# =============================================================================
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# -----------------------------------------------------------------------------
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# AI 模型 API 密钥（必需 - 请填入真实值）
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# -----------------------------------------------------------------------------
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ZHIPUAI_API_KEY=your_api_key_here
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DEEPSEEK_API_KEY=your_deepseek_api_key_here
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LLAMACPP_API_KEY=token-abc123
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# -----------------------------------------------------------------------------
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# llama.cpp 服务配置
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# -----------------------------------------------------------------------------
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# 主 LLM 服务 (Gemma-4-E2B GGUF)
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# 本地开发: http://localhost:8081/v1
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# Docker 部署: 根据实际部署调整
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VLLM_BASE_URL=http://localhost:8081/v1
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# Embedding 服务 (embeddinggemma-300M GGUF)
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# 本地开发: http://localhost:8082/v1
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VLLM_EMBEDDING_URL=http://localhost:8082/v1
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# -----------------------------------------------------------------------------
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# Mem0 记忆层配置
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# -----------------------------------------------------------------------------
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# ⭐ 注意：Mem0 现在直接复用主 LLM 实例，无需单独配置
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# Qdrant 向量数据库地址
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# 本地开发: http://localhost:6333
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# 远程服务器: http://115.190.121.151:6333
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# Docker Compose: http://qdrant:6333
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QDRANT_URL=http://115.190.121.151:6333
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QDRANT_COLLECTION_NAME=mem0_user_memories
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# -----------------------------------------------------------------------------
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# 数据库配置
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# -----------------------------------------------------------------------------
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# PostgreSQL 连接字符串
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# 本地开发: postgresql://postgres:mysecretpassword@localhost:5432/langgraph_db?sslmode=disable
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# 远程服务器: postgresql://postgres:mysecretpassword@115.190.121.151:5432/langgraph_db?sslmode=disable
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# Docker Compose: postgresql://postgres:mysecretpassword@postgres:5432/langgraph_db?sslmode=disable
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DB_URI=postgresql://postgres:mysecretpassword@115.190.121.151:5432/langgraph_db?sslmode=disable
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# -----------------------------------------------------------------------------
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# 前端配置
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# -----------------------------------------------------------------------------
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# 后端 API 地址
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# 本地开发: http://localhost:8001/chat
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# Docker Compose: http://backend:8001/chat
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API_URL=http://localhost:8001/chat
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# -----------------------------------------------------------------------------
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# 应用行为配置
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# -----------------------------------------------------------------------------
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# 记忆提取间隔：每 N 轮对话执行一次记忆提取（默认 10）
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MEMORY_SUMMARIZE_INTERVAL=10
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# 是否启用 Graph 执行追踪（调试用，默认 true）
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ENABLE_GRAPH_TRACE=true
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# -----------------------------------------------------------------------------
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# 日志配置
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# -----------------------------------------------------------------------------
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# 日志级别: DEBUG, INFO, WARNING, ERROR, CRITICAL
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LOG_LEVEL=INFO
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# 是否启用调试模式（默认 false）
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DEBUG=false
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186
QUICKSTART.md
186
QUICKSTART.md
@@ -22,11 +22,13 @@ vim .env # 或使用你喜欢的编辑器
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**必需配置项**：
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- `ZHIPUAI_API_KEY` - 智谱 AI API 密钥（从 [智谱开放平台](https://open.bigmodel.cn/) 获取）
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- `VLLM_LOCAL_KEY` - 本地 vLLM 服务认证 Token（与 vLLM 容器的 `--api-key` 参数一致）
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- `LLAMACPP_API_KEY` - llama.cpp 服务认证 Token（与容器启动参数一致，默认 `token-abc123`）
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**可选配置项**：
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- `VLLM_BASE_URL` - vLLM 服务地址（默认已配置为 FRP 穿透地址）
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- `DB_URI` - PostgreSQL 连接字符串（默认已配置，使用 Docker 服务名 `postgres`）
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- `VLLM_BASE_URL` - LLM 服务地址（默认已配置为 `http://localhost:8081/v1`）
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- `VLLM_EMBEDDING_URL` - Embedding 服务地址（默认已配置为 `http://localhost:8082/v1`）
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- `DB_URI` - PostgreSQL 连接字符串（默认已配置，使用远程服务器地址）
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- `QDRANT_URL` - Qdrant 向量数据库地址（默认已配置，使用远程服务器地址）
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|
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**注意**：Docker Compose 部署时，`API_URL` 由 `docker-compose.yml` 自动注入，无需在 `.env` 中配置。
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@@ -70,60 +72,51 @@ docker compose down
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#### 前置要求
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- Python 3.10+
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- Docker（用于 PostgreSQL）
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#### 1. 启动 PostgreSQL
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```bash
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docker run -d \
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--name postgres-langgraph \
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-e POSTGRES_PASSWORD=mysecretpassword \
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-e POSTGRES_DB=langgraph_db \
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-p 5432:5432 \
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-v ~/docker_volumes/postgres_data:/var/lib/postgresql/data \
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postgres:16
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```
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#### 2. 安装依赖
|
||||
#### 1. 安装依赖
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|
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```bash
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pip install -r requirement.txt
|
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```
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|
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#### 3. 配置环境变量
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#### 2. 配置环境变量
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||||
|
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复制并编辑 `.env` 文件：
|
||||
|
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```
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# 基于 Docker 模板创建，然后修改为本地配置
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# 基于 Docker 模板创建
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cp .env.docker .env
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vim .env
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```
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|
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**本地开发需要修改以下配置**：
|
||||
|
||||
```env
|
||||
``env
|
||||
ZHIPUAI_API_KEY=your_api_key_here
|
||||
VLLM_LOCAL_KEY=token-abc123
|
||||
LLAMACPP_API_KEY=token-abc123
|
||||
|
||||
# 本地开发时，vLLM 和数据库都在 localhost
|
||||
VLLM_BASE_URL=http://localhost:8000/v1
|
||||
DB_URI=postgresql://postgres:mysecretpassword@localhost:5432/langgraph_db?sslmode=disable
|
||||
# 本地开发时，llama.cpp 服务在 localhost
|
||||
VLLM_BASE_URL=http://localhost:8081/v1
|
||||
VLLM_EMBEDDING_URL=http://localhost:8082/v1
|
||||
|
||||
# 数据库和向量存储使用远程服务器
|
||||
DB_URI=postgresql://postgres:mysecretpassword@115.190.121.151:5432/langgraph_db?sslmode=disable
|
||||
QDRANT_URL=http://115.190.121.151:6333
|
||||
|
||||
# 本地开发时，后端也在 localhost
|
||||
API_URL=http://localhost:8001/chat
|
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API_URL=http://localhost:8003/chat
|
||||
```
|
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|
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#### 4. 启动服务
|
||||
#### 3. 启动服务
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||||
|
||||
**终端 1 - 后端：**
|
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```bash
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python backend.py
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python app/backend.py
|
||||
```
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|
||||
**终端 2 - 前端：**
|
||||
```bash
|
||||
streamlit run frontend.py
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cd frontend && streamlit run app.py
|
||||
```
|
||||
|
||||
浏览器自动打开前端页面（如果配置了 Nginx，访问 `http://your-domain.com`；否则访问 http://localhost:8501）
|
||||
@@ -136,7 +129,7 @@ streamlit run frontend.py
|
||||
|
||||
| 文件 | 用途 |
|
||||
|------|------|
|
||||
| `docker-compose.yml` | 服务编排配置 |
|
||||
| `docker-compose.yml` | 服务编排配置（仅包含 backend 和 frontend） |
|
||||
| `Dockerfile.backend` | 后端镜像构建 |
|
||||
| `Dockerfile.frontend` | 前端镜像构建 |
|
||||
| `.gitea/workflows/deploy.yml` | CI/CD 自动化部署 |
|
||||
@@ -145,49 +138,32 @@ streamlit run frontend.py
|
||||
|
||||
```yaml
|
||||
services:
|
||||
postgres: # PostgreSQL 数据库
|
||||
backend: # FastAPI 后端服务
|
||||
backend: # FastAPI 后端服务（连接远程 PostgreSQL 和 Qdrant）
|
||||
frontend: # Streamlit 前端界面
|
||||
```
|
||||
|
||||
**特性：**
|
||||
- ✅ PostgreSQL 健康检查，确保数据库就绪后才启动后端
|
||||
- ✅ 数据持久化到 Docker volume
|
||||
- ✅ 通过环境变量连接远程 PostgreSQL 和 Qdrant
|
||||
- ✅ 自动重启策略（`restart: unless-stopped`）
|
||||
- ✅ 内部网络隔离，外部无法直接访问数据库
|
||||
- ✅ 内部网络隔离
|
||||
|
||||
### 只更新特定服务
|
||||
|
||||
```bash
|
||||
# 只重新构建后端（不影响数据库）
|
||||
# 只重新构建后端
|
||||
docker compose up -d --build backend
|
||||
|
||||
# 只重新启动前端
|
||||
docker compose up -d frontend
|
||||
```
|
||||
|
||||
### 数据持久化
|
||||
|
||||
PostgreSQL 数据存储在命名 volume `pg_data` 中：
|
||||
|
||||
```bash
|
||||
# 查看 volume
|
||||
docker volume ls | grep pg_data
|
||||
|
||||
# 备份数据
|
||||
docker run --rm -v pg_data:/data -v $(pwd):/backup alpine tar czf /backup/pg_backup.tar.gz /data
|
||||
|
||||
# 恢复数据
|
||||
docker run --rm -v pg_data:/data -v $(pwd):/backup alpine tar xzf /backup/pg_backup.tar.gz -C /
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 🔧 开发指南
|
||||
|
||||
### 添加新工具
|
||||
|
||||
在 `tools.py` 中添加：
|
||||
在 `app/tools.py` 中添加：
|
||||
|
||||
```python
|
||||
@tool
|
||||
@@ -209,7 +185,7 @@ def my_new_tool(param: str) -> str:
|
||||
|
||||
### 添加新模型
|
||||
|
||||
在 `agent.py` 中：
|
||||
在 `app/agent.py` 中：
|
||||
|
||||
```python
|
||||
def _create_new_model_llm(self):
|
||||
@@ -227,7 +203,7 @@ model_configs = {
|
||||
}
|
||||
```
|
||||
|
||||
在前端 `frontend.py` 中添加选项：
|
||||
在前端 `frontend/app.py` 中添加选项：
|
||||
|
||||
```python
|
||||
MODEL_OPTIONS = {
|
||||
@@ -246,11 +222,8 @@ docker compose exec backend bash
|
||||
# 查看实时日志
|
||||
docker compose logs -f backend
|
||||
|
||||
# 检查数据库连接
|
||||
docker compose exec postgres psql -U postgres -d langgraph_db -c "\dt"
|
||||
|
||||
# 测试后端 API
|
||||
curl http://localhost:8001/
|
||||
curl http://localhost:8001/health
|
||||
```
|
||||
|
||||
---
|
||||
@@ -267,11 +240,10 @@ curl http://localhost:8001/
|
||||
|
||||
**部署流程：**
|
||||
1. 检出代码
|
||||
2. 安装 Python 依赖（验证用）
|
||||
3. 准备环境变量
|
||||
4. 重新构建并启动前后端（不影响数据库）
|
||||
5. 健康检查（等待后端就绪）
|
||||
6. 清理无用 Docker 资源
|
||||
2. 准备环境变量
|
||||
3. 重新构建并启动前后端（不影响远程数据库）
|
||||
4. 健康检查（等待后端就绪）
|
||||
5. 清理无用 Docker 资源
|
||||
|
||||
**配置 Secrets：**
|
||||
|
||||
@@ -285,23 +257,20 @@ curl http://localhost:8001/
|
||||
|
||||
### 常见问题
|
||||
|
||||
#### 1. PostgreSQL 连接失败
|
||||
#### 1. 无法连接远程数据库
|
||||
|
||||
```bash
|
||||
# 检查容器状态
|
||||
docker compose ps postgres
|
||||
# 测试 PostgreSQL 连接
|
||||
psql -h 115.190.121.151 -U postgres -d langgraph_db -c "SELECT version();"
|
||||
|
||||
# 查看日志
|
||||
docker compose logs postgres
|
||||
|
||||
# 测试连接
|
||||
docker compose exec postgres pg_isready -U postgres
|
||||
# 测试 Qdrant 连接
|
||||
curl http://115.190.121.151:6333/collections
|
||||
```
|
||||
|
||||
**解决方案：**
|
||||
- 确认容器正在运行
|
||||
- 检查密码是否正确
|
||||
- 等待健康检查通过（约 10-30 秒）
|
||||
- 确认远程服务器防火墙开放了 5432 和 6333 端口
|
||||
- 检查网络连接是否正常
|
||||
- 验证用户名和密码是否正确
|
||||
|
||||
#### 2. 后端启动失败
|
||||
|
||||
@@ -318,71 +287,32 @@ lsof -i :8001
|
||||
- 端口 8001 被占用
|
||||
- 依赖包缺失
|
||||
|
||||
#### 3. 前端无法连接后端（NameResolutionError）
|
||||
#### 3. 前端无法连接后端
|
||||
|
||||
**错误信息：**
|
||||
```
|
||||
HTTPConnectionPool(host='backend', port=8001): Max retries exceeded with url: /chat
|
||||
(Caused by NameResolutionError("HTTPConnection(host='backend', port=8001): Failed to resolve 'backend'"))
|
||||
HTTPConnectionPool(host='backend', port=8001): Max retries exceeded
|
||||
```
|
||||
|
||||
**原因分析：**
|
||||
- 前端容器和后端容器不在同一个 Docker 网络中
|
||||
- docker-compose.yml 中的服务名配置错误
|
||||
- 环境变量 `API_URL` 配置不正确
|
||||
|
||||
**解决方案：**
|
||||
|
||||
1. **检查容器是否在同一网络中：**
|
||||
```bash
|
||||
# 查看所有 Docker 网络
|
||||
docker network ls
|
||||
|
||||
# 检查 ai-network 网络中的容器
|
||||
docker network inspect docker_ai-network
|
||||
```
|
||||
|
||||
2. **确认服务名正确：**
|
||||
2. **验证环境变量配置：**
|
||||
```bash
|
||||
# 查看运行中的容器
|
||||
docker compose ps
|
||||
|
||||
# 应该看到：ai-backend, ai-frontend, ai-postgres
|
||||
```
|
||||
|
||||
3. **验证环境变量配置：**
|
||||
```bash
|
||||
# 进入前端容器检查环境变量
|
||||
docker compose exec frontend env | grep API_URL
|
||||
|
||||
# 应该输出：API_URL=http://backend:8001/chat
|
||||
```
|
||||
|
||||
4. **重启服务：**
|
||||
3. **重启服务：**
|
||||
```bash
|
||||
# 完全停止并重新启动所有服务
|
||||
docker compose down
|
||||
docker compose up -d --build
|
||||
|
||||
# 查看启动日志
|
||||
docker compose logs -f
|
||||
```
|
||||
|
||||
5. **测试网络连通性：**
|
||||
```bash
|
||||
# 从前端容器 ping 后端服务
|
||||
docker compose exec frontend ping backend
|
||||
|
||||
# 从前端容器访问后端 API
|
||||
docker compose exec frontend curl http://backend:8001/health
|
||||
```
|
||||
|
||||
**重要提示：**
|
||||
- Docker Compose 会自动创建名为 `<项目目录>_ai-network` 的网络
|
||||
- 容器间通过**服务名**（而非容器名）进行通信
|
||||
- 在 `docker-compose.yml` 中，服务名是 `backend`、`frontend`、`postgres`
|
||||
- 确保所有服务都连接到同一个自定义网络（`ai-network`）
|
||||
|
||||
#### 4. 模型初始化失败
|
||||
|
||||
```bash
|
||||
@@ -405,36 +335,21 @@ docker compose logs backend | grep -i "model\|error"
|
||||
|
||||
1. **检查 .env 文件格式：**
|
||||
```bash
|
||||
# 确保文件末尾没有多余字符（如 EOF）
|
||||
cat -A .env
|
||||
|
||||
# 正确格式应该是每行一个变量，无多余空格或特殊字符
|
||||
```
|
||||
|
||||
2. **验证环境变量已加载：**
|
||||
```bash
|
||||
# 检查后端容器的环境变量
|
||||
docker compose exec backend env | grep ZHIPUAI_API_KEY
|
||||
|
||||
# 检查前端容器的环境变量
|
||||
docker compose exec frontend env | grep API_URL
|
||||
```
|
||||
|
||||
3. **重新构建容器：**
|
||||
```bash
|
||||
# 修改 .env 后需要重新创建容器
|
||||
docker compose down
|
||||
docker compose up -d --build
|
||||
```
|
||||
|
||||
4. **确认 .env 文件位置：**
|
||||
```bash
|
||||
# .env 文件应该在项目根目录（与 docker-compose.yml 的父目录同级）
|
||||
ls -la .env
|
||||
|
||||
# docker-compose.yml 中使用了 context: .. ，所以 .env 应该在上一级目录
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 📊 监控和维护
|
||||
@@ -468,11 +383,11 @@ docker compose logs -f backend frontend
|
||||
### 备份和恢复
|
||||
|
||||
```bash
|
||||
# 备份数据库
|
||||
docker compose exec postgres pg_dump -U postgres langgraph_db > backup.sql
|
||||
# 备份远程数据库
|
||||
pg_dump -h 115.190.121.151 -U postgres langgraph_db > backup.sql
|
||||
|
||||
# 恢复数据库
|
||||
cat backup.sql | docker compose exec -T postgres psql -U postgres langgraph_db
|
||||
cat backup.sql | psql -h 115.190.121.151 -U postgres langgraph_db
|
||||
```
|
||||
|
||||
---
|
||||
@@ -491,14 +406,13 @@ cat backup.sql | docker compose exec -T postgres psql -U postgres langgraph_db
|
||||
- 启用 HTTPS
|
||||
- 配置日志轮转
|
||||
- 设置资源限制（CPU、内存）
|
||||
- 定期备份数据库
|
||||
- 定期备份远程数据库
|
||||
|
||||
---
|
||||
|
||||
## 📞 获取帮助
|
||||
|
||||
- **完整文档**: [README.md](README.md)
|
||||
- **RAG 示例**: `rag_example.py`
|
||||
- **报告问题**: 提交 Issue 并附上日志
|
||||
|
||||
---
|
||||
|
||||
157
README.md
157
README.md
@@ -21,6 +21,7 @@
|
||||
- ✅ **高可用架构**：模型自动降级，确保服务稳定
|
||||
- ✅ **前后端分离**：FastAPI 后端 + Streamlit 前端
|
||||
- ✅ **Docker 部署**：一键启动所有服务
|
||||
- ✅ **远程服务架构**：PostgreSQL 和 Qdrant 部署在远程服务器
|
||||
|
||||
---
|
||||
|
||||
@@ -30,13 +31,13 @@
|
||||
|
||||
| 层级 | 技术选型 | 说明 |
|
||||
|------|---------|------|
|
||||
| **LLM 服务** | 智谱 AI API / vLLM (Gemma-4) | 云端 API 或本地推理 |
|
||||
| **Embedding** | 智谱 Embedding API | 向量嵌入（无需 PyTorch） |
|
||||
| **LLM 服务** | 智谱 AI API / llama.cpp (Gemma-4 GGUF) | 云端 API 或本地推理 |
|
||||
| **Embedding** | llama.cpp (embeddinggemma-300M GGUF) | 本地向量嵌入服务 |
|
||||
| **Agent 框架** | LangGraph + LangChain | 工作流编排 |
|
||||
| **向量数据库** | ChromaDB / pgvector | RAG 知识检索 |
|
||||
| **向量数据库** | Qdrant | RAG 知识检索（远程服务器） |
|
||||
| **后端框架** | FastAPI + Uvicorn | RESTful API + WebSocket |
|
||||
| **前端框架** | Streamlit | 交互式 Web 界面 |
|
||||
| **数据库** | PostgreSQL 16 | 对话记忆持久化 |
|
||||
| **数据库** | PostgreSQL 16 | 对话记忆持久化（远程服务器） |
|
||||
| **容器化** | Docker + Docker Compose | 服务编排 |
|
||||
|
||||
### 架构图
|
||||
@@ -63,34 +64,52 @@
|
||||
│ │ - Weather │ │
|
||||
│ │ - File IO │ │
|
||||
│ │ - Web Scrap│ │
|
||||
│ │ - RAG │ │
|
||||
│ │ - Memory │ │
|
||||
│ └────────────┘ │
|
||||
└────────┬─────────┘
|
||||
│
|
||||
┌────┴────┐
|
||||
↓ ↓
|
||||
┌────────┐ ┌──────────┐
|
||||
│PostgreSQL│ │ChromaDB │
|
||||
│(记忆存储)│ │(向量检索)│
|
||||
└────────┘ └──────────┘
|
||||
┌────┴────────────────────┐
|
||||
↓ ↓
|
||||
┌──────────────┐ ┌──────────────┐
|
||||
│ PostgreSQL │ │ Qdrant │
|
||||
│ (远程服务器) │ │ (远程服务器) │
|
||||
│ 115.190... │ │ 115.190... │
|
||||
└──────────────┘ └──────────────┘
|
||||
```
|
||||
|
||||
### 项目结构
|
||||
|
||||
```
|
||||
Agent1/
|
||||
├── agent.py # Agent 服务核心（多模型管理）
|
||||
├── graph_builder.py # LangGraph 状态图构建器
|
||||
├── tools.py # 工具函数定义（@tool 装饰器）
|
||||
├── backend.py # FastAPI 后端应用
|
||||
├── frontend.py # Streamlit 前端界面
|
||||
├── rag_example.py # RAG 实现示例（无 PyTorch）
|
||||
├── docker-compose.yml # Docker 服务编排
|
||||
├── Dockerfile.backend # 后端镜像构建
|
||||
├── Dockerfile.frontend # 前端镜像构建
|
||||
├── requirement.txt # Python 依赖
|
||||
├── .env # 环境变量配置
|
||||
└── user_docs/ # 用户文档目录
|
||||
├── app/
|
||||
│ ├── __init__.py
|
||||
│ ├── config.py # 配置管理
|
||||
│ ├── state.py # 状态定义
|
||||
│ ├── prompts.py # 提示模板
|
||||
│ ├── logger.py # 日志工具
|
||||
│ ├── tools.py # 工具函数定义
|
||||
│ ├── memory/
|
||||
│ │ ├── __init__.py
|
||||
│ │ └── mem0_client.py # Mem0 客户端封装
|
||||
│ ├── nodes/
|
||||
│ │ ├── __init__.py
|
||||
│ │ ├── router.py # 路由决策
|
||||
│ │ ├── llm_call.py # LLM 调用节点
|
||||
│ │ ├── tool_call.py # 工具执行节点
|
||||
│ │ ├── retrieve_memory.py # 记忆检索节点
|
||||
│ │ └── summarize.py # 记忆存储节点
|
||||
│ ├── graph_builder.py # LangGraph 图构建器
|
||||
│ ├── agent.py # Agent 服务核心
|
||||
│ └── backend.py # FastAPI 后端应用
|
||||
├── frontend/
|
||||
│ └── app.py # Streamlit 前端界面
|
||||
├── docker/
|
||||
│ ├── docker-compose.yml # Docker 服务编排
|
||||
│ ├── Dockerfile.backend # 后端镜像构建
|
||||
│ └── Dockerfile.frontend # 前端镜像构建
|
||||
├── requirement.txt # Python 依赖
|
||||
├── .env # 环境变量配置
|
||||
└── user_docs/ # 用户文档目录
|
||||
├── a.txt
|
||||
├── b.pdf
|
||||
└── c.xlsx
|
||||
@@ -104,9 +123,9 @@ Agent1/
|
||||
|
||||
### 方式一：Docker Compose（推荐）
|
||||
|
||||
```
|
||||
```bash
|
||||
# 1. 配置环境变量
|
||||
cp .env.example .env
|
||||
cp .env.docker .env
|
||||
# 编辑 .env 文件，填入真实的 API Key
|
||||
|
||||
# 2. 启动所有服务
|
||||
@@ -121,21 +140,19 @@ docker compose -f docker/docker-compose.yml up -d --build
|
||||
|
||||
### 方式二：本地开发模式
|
||||
|
||||
```
|
||||
# 1. 启动 PostgreSQL
|
||||
docker run -d --name postgres-langgraph \
|
||||
-e POSTGRES_PASSWORD=mysecretpassword \
|
||||
-e POSTGRES_DB=langgraph_db \
|
||||
-p 5432:5432 postgres:16
|
||||
|
||||
# 2. 安装依赖
|
||||
```bash
|
||||
# 1. 安装依赖
|
||||
pip install -r requirement.txt
|
||||
|
||||
# 2. 配置环境变量
|
||||
cp .env.docker .env
|
||||
# 编辑 .env，根据本地/远程环境调整配置
|
||||
|
||||
# 3. 启动后端
|
||||
python backend.py
|
||||
python app/backend.py
|
||||
|
||||
# 4. 启动前端（新终端）
|
||||
streamlit run frontend.py
|
||||
cd frontend && streamlit run app.py
|
||||
```
|
||||
|
||||
---
|
||||
@@ -161,7 +178,7 @@ streamlit run frontend.py
|
||||
| 📑 解析 PDF | "总结 b.pdf 的主要内容" |
|
||||
| 📊 Excel 数据 | "显示 c.xlsx 的数据" |
|
||||
| 🌐 网页抓取 | "抓取 https://example.com 的内容" |
|
||||
| 🔍 知识库检索 | "根据知识库回答：XXX" |
|
||||
| 🔍 长期记忆 | "记住我喜欢吃川菜" → "我有什么饮食偏好？" |
|
||||
|
||||
### 多模型切换
|
||||
|
||||
@@ -179,9 +196,9 @@ streamlit run frontend.py
|
||||
|
||||
### 添加新工具
|
||||
|
||||
在 `tools.py` 中添加新的 `@tool` 装饰函数：
|
||||
在 `app/tools.py` 中添加新的 `@tool` 装饰函数：
|
||||
|
||||
```
|
||||
```python
|
||||
@tool
|
||||
def my_new_tool(param: str) -> str:
|
||||
"""
|
||||
@@ -201,9 +218,9 @@ def my_new_tool(param: str) -> str:
|
||||
|
||||
### 添加新模型
|
||||
|
||||
在 `agent.py` 的 `initialize()` 方法中添加模型配置：
|
||||
在 `app/agent.py` 的 `initialize()` 方法中添加模型配置：
|
||||
|
||||
```
|
||||
```python
|
||||
model_configs = {
|
||||
"zhipu": self._create_zhipu_llm,
|
||||
"local": self._create_local_llm,
|
||||
@@ -215,7 +232,7 @@ model_configs = {
|
||||
|
||||
项目包含完整的 Docker 配置：
|
||||
|
||||
- **docker-compose.yml**：服务编排（PostgreSQL + Backend + Frontend）
|
||||
- **docker-compose.yml**：服务编排（Backend + Frontend，连接远程数据库）
|
||||
- **Dockerfile.backend**：后端镜像构建
|
||||
- **Dockerfile.frontend**：前端镜像构建
|
||||
- **.gitea/workflows/deploy.yml**：CI/CD 自动化部署
|
||||
@@ -228,54 +245,62 @@ model_configs = {
|
||||
|
||||
### 配置文件说明
|
||||
|
||||
项目使用两个环境配置文件：
|
||||
项目采用三层环境配置文件体系：
|
||||
|
||||
| 文件 | 用途 | 是否提交 Git |
|
||||
|------|------|------------|
|
||||
| `.env.example` | 配置模板 | ✅ 是 |
|
||||
| `.env` | 实际使用的配置 | ❌ 否（已忽略） |
|
||||
| `.env.docker` | Docker 部署模板 | ✅ 是 |
|
||||
|
||||
**使用方法：**
|
||||
|
||||
- **本地开发**：手动创建 `.env`，配置 `localhost` 相关地址
|
||||
- **Docker 部署**：`cp .env.docker .env`，然后修改 API Key
|
||||
- **本地开发**：`cp .env.example .env`，修改为 localhost 相关地址
|
||||
- **Docker 部署**：`cp .env.docker .env`，使用远程服务器地址
|
||||
|
||||
### 必需的环境变量
|
||||
|
||||
代码中所有使用 `os.getenv()` 的地方都必须在 `.env` 文件中定义：
|
||||
|
||||
| 变量名 | 说明 | 本地开发示例 | Docker 部署示例 |
|
||||
|--------|------|------------|----------------|
|
||||
| `ZHIPUAI_API_KEY` | 智谱 AI API 密钥 | `your_key_here` | `your_key_here` |
|
||||
| `VLLM_LOCAL_KEY` | vLLM 认证 Token | `token-abc123` | `token-abc123` |
|
||||
| `VLLM_BASE_URL` | vLLM 服务地址 | `http://localhost:8000/v1` | `http://115.190.121.151:18000/v1` |
|
||||
| `DB_URI` | PostgreSQL 连接字符串 | `postgresql://...@localhost:5432/...` | `postgresql://...@postgres:5432/...` |
|
||||
| `API_URL` | 后端 API 地址 | `http://localhost:8001/chat` | （由 docker-compose.yml 注入） |
|
||||
| `DEEPSEEK_API_KEY` | DeepSeek API 密钥 | `your_key_here` | `your_key_here` |
|
||||
| `LLAMACPP_API_KEY` | llama.cpp 认证 Token | `token-abc123` | `token-abc123` |
|
||||
| `VLLM_BASE_URL` | LLM 服务地址 | `http://localhost:8081/v1` | `http://localhost:8081/v1` |
|
||||
| `VLLM_EMBEDDING_URL` | Embedding 服务地址 | `http://localhost:8082/v1` | `http://localhost:8082/v1` |
|
||||
| `QDRANT_URL` | Qdrant 地址 | `http://115.190.121.151:6333` | `http://115.190.121.151:6333` |
|
||||
| `DB_URI` | PostgreSQL 连接字符串 | `postgresql://...@115.190.121.151:5432/...` | `postgresql://...@115.190.121.151:5432/...` |
|
||||
| `API_URL` | 后端 API 地址 | `http://localhost:8003/chat` | （由 docker-compose.yml 注入） |
|
||||
|
||||
### 配置示例
|
||||
|
||||
#### 本地开发 (.env)
|
||||
```bash
|
||||
```
|
||||
ZHIPUAI_API_KEY=your_api_key_here
|
||||
VLLM_LOCAL_KEY=token-abc123
|
||||
VLLM_BASE_URL=http://localhost:8000/v1
|
||||
DB_URI=postgresql://postgres:mysecretpassword@localhost:5432/langgraph_db?sslmode=disable
|
||||
API_URL=http://localhost:8001/chat
|
||||
DEEPSEEK_API_KEY=your_deepseek_api_key_here
|
||||
LLAMACPP_API_KEY=token-abc123
|
||||
VLLM_BASE_URL=http://localhost:8081/v1
|
||||
VLLM_EMBEDDING_URL=http://localhost:8082/v1
|
||||
QDRANT_URL=http://115.190.121.151:6333
|
||||
DB_URI=postgresql://postgres:mysecretpassword@115.190.121.151:5432/langgraph_db?sslmode=disable
|
||||
API_URL=http://localhost:8003/chat
|
||||
```
|
||||
|
||||
#### Docker 部署 (.env.docker)
|
||||
```bash
|
||||
```
|
||||
ZHIPUAI_API_KEY=your_api_key_here
|
||||
VLLM_LOCAL_KEY=token-abc123
|
||||
VLLM_BASE_URL=http://115.190.121.151:18000/v1
|
||||
DB_URI=postgresql://postgres:mysecretpassword@postgres:5432/langgraph_db?sslmode=disable
|
||||
# API_URL 在 docker-compose.yml 中配置为 http://backend:8001/chat
|
||||
DEEPSEEK_API_KEY=your_deepseek_api_key_here
|
||||
LLAMACPP_API_KEY=token-abc123
|
||||
VLLM_BASE_URL=http://localhost:8081/v1
|
||||
VLLM_EMBEDDING_URL=http://localhost:8082/v1
|
||||
QDRANT_URL=http://115.190.121.151:6333
|
||||
DB_URI=postgresql://postgres:mysecretpassword@115.190.121.151:5432/langgraph_db?sslmode=disable
|
||||
# API_URL 在 docker-compose.yml 中配置为 http://backend:8003/chat
|
||||
```
|
||||
|
||||
### 注意事项
|
||||
|
||||
- ⚠️ **不要硬编码敏感信息**：所有 API Key 必须通过环境变量配置
|
||||
- ⚠️ **Docker 网络差异**：容器内使用服务名（如 `postgres`、`backend`），本地使用 `localhost`
|
||||
- ⚠️ **远程服务依赖**：确保可以访问远程 PostgreSQL (115.190.121.151:5432) 和 Qdrant (115.190.121.151:6333)
|
||||
- ⚠️ **修改后重启**：修改 `.env` 后，Docker 部署需要执行 `docker compose down && docker compose up -d --build`
|
||||
|
||||
---
|
||||
@@ -284,13 +309,13 @@ DB_URI=postgresql://postgres:mysecretpassword@postgres:5432/langgraph_db?sslmode
|
||||
|
||||
### 常见问题
|
||||
|
||||
**Q: 无法连接 PostgreSQL？**
|
||||
**Q: 无法连接远程数据库？**
|
||||
```bash
|
||||
# 检查容器状态
|
||||
docker ps | grep postgres
|
||||
# 测试 PostgreSQL
|
||||
psql -h 115.190.121.151 -U postgres -d langgraph_db -c "SELECT version();"
|
||||
|
||||
# 查看日志
|
||||
docker logs postgres-langgraph
|
||||
# 测试 Qdrant
|
||||
curl http://115.190.121.151:6333/collections
|
||||
```
|
||||
|
||||
**Q: 后端启动失败？**
|
||||
|
||||
38
app/agent.py
38
app/agent.py
@@ -6,16 +6,15 @@ AI Agent 服务类 - 支持多模型动态切换
|
||||
import os
|
||||
from dotenv import load_dotenv
|
||||
from langchain_community.chat_models import ChatZhipuAI
|
||||
from langchain_core.messages import HumanMessage
|
||||
from langchain_openai import ChatOpenAI
|
||||
from pydantic import SecretStr
|
||||
from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
|
||||
|
||||
# 本地模块
|
||||
from app.graph_builder import GraphBuilder, GraphContext
|
||||
from app.tools import AVAILABLE_TOOLS, TOOLS_BY_NAME
|
||||
from app.logger import debug, info, warning, error
|
||||
from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
|
||||
from langgraph.store.postgres.aio import AsyncPostgresStore
|
||||
|
||||
|
||||
load_dotenv()
|
||||
|
||||
@@ -23,15 +22,13 @@ load_dotenv()
|
||||
class AIAgentService:
|
||||
"""异步 AI Agent 服务，支持多模型动态切换，使用外部传入的 checkpointer"""
|
||||
|
||||
def __init__(self, checkpointer: AsyncPostgresSaver, store: AsyncPostgresStore):
|
||||
def __init__(self, checkpointer: AsyncPostgresSaver):
|
||||
"""
|
||||
初始化服务
|
||||
Args:
|
||||
checkpointer: 已经初始化的 AsyncPostgresSaver 实例
|
||||
store: 已经初始化的 AsyncPostgresStore 实例
|
||||
"""
|
||||
self.checkpointer = checkpointer
|
||||
self.store = store
|
||||
self.graphs = {} # 存储不同模型对应的 graph 实例
|
||||
|
||||
def _create_zhipu_llm(self):
|
||||
@@ -68,19 +65,19 @@ class AIAgentService:
|
||||
# vLLM 服务地址：优先从环境变量读取，适配 Docker、FRP 穿透和本地开发
|
||||
vllm_base_url = os.getenv(
|
||||
"VLLM_BASE_URL",
|
||||
"http://115.190.121.151:18000/v1"
|
||||
"http://localhost:8081/v1"
|
||||
)
|
||||
|
||||
return ChatOpenAI(
|
||||
base_url=vllm_base_url,
|
||||
api_key=SecretStr(os.getenv("VLLM_LOCAL_KEY", "")),
|
||||
api_key=SecretStr(os.getenv("LLAMACPP_API_KEY", "token-abc123")),
|
||||
model="gemma-4-E2B-it",
|
||||
timeout=60.0, # 请求超时时间（秒）
|
||||
max_retries=2, # 失败后自动重试次数
|
||||
)
|
||||
|
||||
async def initialize(self):
|
||||
"""预编译所有模型的 graph（使用传入的 checkpointer 和 store）"""
|
||||
"""预编译所有模型的 graph（使用传入的 checkpointer）"""
|
||||
model_configs = {
|
||||
"zhipu": self._create_zhipu_llm,
|
||||
"deepseek": self._create_deepseek_llm,
|
||||
@@ -92,7 +89,7 @@ class AIAgentService:
|
||||
info(f"🔄 正在初始化模型 '{model_name}'...")
|
||||
llm = llm_creator()
|
||||
builder = GraphBuilder(llm, AVAILABLE_TOOLS, TOOLS_BY_NAME).build()
|
||||
graph = builder.compile(checkpointer=self.checkpointer, store=self.store)
|
||||
graph = builder.compile(checkpointer=self.checkpointer)
|
||||
self.graphs[model_name] = graph
|
||||
info(f"✅ 模型 '{model_name}' 初始化成功")
|
||||
except Exception as e:
|
||||
@@ -121,14 +118,27 @@ class AIAgentService:
|
||||
"elapsed_time": float # 调用耗时（秒）
|
||||
}
|
||||
"""
|
||||
# 尝试使用指定模型，如果不可用则循环尝试其他模型
|
||||
if model not in self.graphs:
|
||||
fallback_model = next(iter(self.graphs.keys()))
|
||||
warning(f"警告: 模型 '{model}' 不可用，已切换到 '{fallback_model}'")
|
||||
model = fallback_model
|
||||
warning(f"警告: 模型 '{model}' 不可用，尝试切换到其他可用模型")
|
||||
found = False
|
||||
for available_model in self.graphs.keys():
|
||||
try:
|
||||
# 这里可以添加额外的模型可用性检查逻辑
|
||||
model = available_model
|
||||
found = True
|
||||
info(f"已切换到可用模型: '{model}'")
|
||||
break
|
||||
except Exception as e:
|
||||
warning(f"模型 '{available_model}' 也不可用: {str(e)}")
|
||||
continue
|
||||
|
||||
if not found:
|
||||
raise RuntimeError(f"错误: 没有任何可用的模型。当前注册的模型: {list(self.graphs.keys())}")
|
||||
|
||||
graph = self.graphs[model]
|
||||
config = {"configurable": {"thread_id": thread_id}}
|
||||
input_state = {"messages": [HumanMessage(content=message)]}
|
||||
input_state = {"messages": [{"role": "user", "content": message}]}
|
||||
context = GraphContext(user_id=user_id)
|
||||
|
||||
result = await graph.ainvoke(input_state, config=config, context=context)
|
||||
|
||||
@@ -12,7 +12,6 @@ from fastapi import FastAPI, HTTPException, WebSocket, WebSocketDisconnect, Depe
|
||||
from fastapi.middleware.cors import CORSMiddleware
|
||||
from pydantic import BaseModel
|
||||
from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
|
||||
from langgraph.store.postgres.aio import AsyncPostgresStore
|
||||
from app.agent import AIAgentService
|
||||
from app.logger import debug, info, warning, error
|
||||
|
||||
@@ -23,23 +22,19 @@ load_dotenv()
|
||||
# 优先级：环境变量 DB_URI > Docker 内部服务名 > 本地开发地址
|
||||
DB_URI = os.getenv(
|
||||
"DB_URI",
|
||||
"postgresql://postgres:mysecretpassword@localhost:5432/langgraph_db?sslmode=disable"
|
||||
"postgresql://postgres:mysecretpassword@ai-postgres:5432/langgraph_db?sslmode=disable"
|
||||
)
|
||||
|
||||
|
||||
@asynccontextmanager
|
||||
async def lifespan(app: FastAPI):
|
||||
"""应用生命周期管理：创建并注入全局服务"""
|
||||
# 1. 创建数据库连接池并初始化表
|
||||
async with (
|
||||
AsyncPostgresSaver.from_conn_string(DB_URI) as checkpointer,
|
||||
AsyncPostgresStore.from_conn_string(DB_URI) as store
|
||||
):
|
||||
# 1. 创建数据库连接池并初始化表（仅 checkpointer）
|
||||
async with AsyncPostgresSaver.from_conn_string(DB_URI) as checkpointer:
|
||||
await checkpointer.setup()
|
||||
await store.setup()
|
||||
|
||||
# 2. 构建 AI Agent 服务
|
||||
agent_service = AIAgentService(checkpointer,store)
|
||||
agent_service = AIAgentService(checkpointer)
|
||||
await agent_service.initialize()
|
||||
|
||||
# 3. 将服务实例存入 app.state
|
||||
@@ -155,4 +150,6 @@ async def websocket_endpoint(
|
||||
|
||||
if __name__ == "__main__":
|
||||
import uvicorn
|
||||
uvicorn.run(app, host="0.0.0.0", port=8001)
|
||||
# 使用环境变量或默认端口 8003（避免与 vLLM 的 8001 端口冲突）
|
||||
port = int(os.getenv("BACKEND_PORT", "8003"))
|
||||
uvicorn.run(app, host="0.0.0.0", port=port)
|
||||
|
||||
23
app/config.py
Normal file
23
app/config.py
Normal file
@@ -0,0 +1,23 @@
|
||||
"""
|
||||
环境变量集中管理模块
|
||||
所有配置项统一定义，避免散落在各个文件中
|
||||
"""
|
||||
|
||||
import os
|
||||
|
||||
|
||||
# ========== Graph 执行追踪配置 ==========
|
||||
# 是否启用 Graph 流转追踪（通过环境变量控制）
|
||||
ENABLE_GRAPH_TRACE = os.getenv("ENABLE_GRAPH_TRACE", "true").lower() == "true"
|
||||
|
||||
# ========== 记忆提取配置 ==========
|
||||
# 记忆提取间隔：每 N 轮对话生成一次摘要
|
||||
MEMORY_SUMMARIZE_INTERVAL = int(os.getenv("MEMORY_SUMMARIZE_INTERVAL", "10"))
|
||||
|
||||
# ========== Mem0 记忆层配置 ==========
|
||||
# Qdrant 向量数据库地址
|
||||
QDRANT_URL = os.getenv("QDRANT_URL", "http://localhost:6333")
|
||||
QDRANT_COLLECTION_NAME = os.getenv("QDRANT_COLLECTION_NAME", "mem0_user_memories")
|
||||
|
||||
# vLLM Embedding 服务地址 (用于 Mem0 的向量化)
|
||||
VLLM_EMBEDDING_URL = os.getenv("VLLM_EMBEDDING_URL", "http://localhost:8082/v1")
|
||||
@@ -1,95 +1,27 @@
|
||||
"""
|
||||
LangGraph 状态图构建模块 - 完全面向对象风格，无嵌套函数
|
||||
LangGraph 状态图构建模块 - 精简版，仅负责组装图
|
||||
所有节点逻辑已拆分到独立模块
|
||||
"""
|
||||
|
||||
import operator
|
||||
import asyncio
|
||||
import time
|
||||
import os
|
||||
from typing import Literal, Annotated, Any
|
||||
from langchain_core.language_models import BaseLLM
|
||||
from langchain_core.messages import AnyMessage, AIMessage, ToolMessage, SystemMessage
|
||||
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
|
||||
from langchain_core.runnables import RunnableLambda
|
||||
from langgraph.graph import StateGraph, START, END
|
||||
from typing_extensions import TypedDict
|
||||
from langgraph.store.postgres.aio import AsyncPostgresStore
|
||||
from langgraph.runtime import Runtime
|
||||
from dataclasses import dataclass
|
||||
import uuid
|
||||
from langchain_core.prompt_values import ChatPromptValue
|
||||
|
||||
# 本地模块
|
||||
from app.logger import debug, info, warning, error
|
||||
from app.state import MessagesState, GraphContext
|
||||
from app.nodes import (
|
||||
create_llm_call_node,
|
||||
create_tool_call_node,
|
||||
create_retrieve_memory_node,
|
||||
create_summarize_node,
|
||||
should_continue
|
||||
)
|
||||
from app.memory import Mem0Client
|
||||
|
||||
|
||||
# 是否启用 Graph 流转追踪（通过环境变量控制）
|
||||
ENABLE_GRAPH_TRACE = os.getenv("ENABLE_GRAPH_TRACE", "true").lower() == "true"
|
||||
|
||||
|
||||
class MessagesState(TypedDict):
|
||||
"""对话状态类型定义"""
|
||||
messages: Annotated[list[AnyMessage], operator.add]
|
||||
llm_calls: int
|
||||
memory_context:str
|
||||
last_token_usage: dict # 本次调用的 token 使用详情
|
||||
last_elapsed_time: float # 本次调用耗时（秒）
|
||||
|
||||
@dataclass
|
||||
class GraphContext:
|
||||
user_id: str
|
||||
# 可扩展更多上下文信息
|
||||
|
||||
def _log_state_change(node_name: str, state: MessagesState, prefix: str = "进入"):
|
||||
"""
|
||||
通用辅助函数：打印节点状态变化
|
||||
|
||||
Args:
|
||||
node_name: 节点名称
|
||||
state: 当前状态
|
||||
prefix: 前缀（"进入" 或 "离开"）
|
||||
"""
|
||||
if not ENABLE_GRAPH_TRACE:
|
||||
return
|
||||
|
||||
messages = state.get("messages", [])
|
||||
msg_count = len(messages)
|
||||
last_msg = messages[-1] if messages else None
|
||||
last_info = ""
|
||||
if last_msg:
|
||||
content_preview = str(last_msg.content)[:100].replace("\n", " ")
|
||||
last_info = f"{last_msg.type.upper()}: {content_preview}"
|
||||
info(f"🔄 [{node_name}] {prefix} | 消息数:{msg_count} | 最后一条:{last_info}")
|
||||
|
||||
def _print_llm_input(prompt_value: ChatPromptValue) -> ChatPromptValue:
|
||||
"""
|
||||
RunnableLambda 回调函数：打印格式化后发送给 LLM 的完整消息
|
||||
|
||||
Args:
|
||||
prompt_value: ChatPromptValue 对象，包含格式化后的消息列表
|
||||
|
||||
Returns:
|
||||
原样返回 prompt_value，不影响链式调用
|
||||
"""
|
||||
if not ENABLE_GRAPH_TRACE:
|
||||
return prompt_value
|
||||
|
||||
messages = prompt_value.messages # ChatPromptValue 提供 .messages 属性
|
||||
|
||||
debug("\n" + "=" * 80)
|
||||
debug("📤 [LLM输入] 格式化后发送给大模型的完整消息:")
|
||||
debug(f" 总消息数: {len(messages)}")
|
||||
debug("-" * 80)
|
||||
for i, msg in enumerate(messages):
|
||||
content_preview = str(msg.content) # 完整输出
|
||||
debug(f" [{i}] {msg.type.upper():10s}: {content_preview}")
|
||||
debug( "\n"+"=" * 80 + "\n")
|
||||
|
||||
return prompt_value
|
||||
|
||||
class GraphBuilder:
|
||||
"""LangGraph 状态图构建器 - 所有节点均为类方法"""
|
||||
"""LangGraph 状态图构建器 - 仅负责组装图"""
|
||||
|
||||
def __init__(self, llm: BaseLLM, tools: list, tools_by_name: dict[str, Any]):
|
||||
def __init__(self, llm: BaseLLM, tools: list, tools_by_name: dict):
|
||||
"""
|
||||
初始化构建器
|
||||
|
||||
@@ -101,304 +33,44 @@ class GraphBuilder:
|
||||
self.llm = llm
|
||||
self.tools = tools
|
||||
self.tools_by_name = tools_by_name
|
||||
self._llm_with_tools = llm.bind_tools(tools)
|
||||
self._prompt = self._create_prompt()
|
||||
self._chain = (
|
||||
self._prompt
|
||||
| RunnableLambda(_print_llm_input)
|
||||
| self._llm_with_tools
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def _create_prompt() -> ChatPromptTemplate:
|
||||
"""创建系统提示模板（静态方法，无需访问实例）"""
|
||||
system_template = (
|
||||
"你是一个个人生活助手和数据分析助手，请使用中文交流。\n\n"
|
||||
"【用户背景信息】\n"
|
||||
"以下是对当前用户的已知信息和长期记忆，你必须优先采纳并在回答中体现：\n"
|
||||
"{memory_context}\n"
|
||||
"若包含姓名、偏好等个人信息，请自然融入回应（例如称呼名字、提及偏好）。\n\n"
|
||||
"【可用工具与使用规则】\n"
|
||||
"- 获取温度/天气：`get_current_temperature`\n"
|
||||
"- 读取文本文件：`read_local_file`（限定目录 `./user_docs`）\n"
|
||||
"- 读取PDF摘要：`read_pdf_summary`（限定目录 `./user_docs`）\n"
|
||||
"- 读取Excel表格：`read_excel_as_markdown`（限定目录 `./user_docs`）\n"
|
||||
"- 抓取网页内容：`fetch_webpage_content`\n"
|
||||
"工具调用时请直接返回所需参数，无需额外说明。\n\n"
|
||||
"【回答要求（必须遵守）】\n"
|
||||
"1. 回答必须简洁、直接，禁止描述任何思考过程或内心活动。\n"
|
||||
"2. 优先利用已知用户信息进行个性化回复。\n"
|
||||
"3. 若无信息可依，礼貌询问或提供通用帮助。"
|
||||
)
|
||||
return ChatPromptTemplate.from_messages([
|
||||
("system", system_template),
|
||||
MessagesPlaceholder(variable_name="messages")
|
||||
])
|
||||
|
||||
async def call_llm(self, state: MessagesState, runtime: Runtime[GraphContext]) -> dict:
|
||||
"""
|
||||
LLM 调用节点（异步方法）
|
||||
注意：因为 self._chain.invoke 是同步方法，使用 run_in_executor 避免阻塞事件循环
|
||||
"""
|
||||
_log_state_change("llm_call", state, "进入")
|
||||
|
||||
memory_context = state.get("memory_context", "暂无用户信息")
|
||||
loop = asyncio.get_event_loop()
|
||||
start_time = time.time()
|
||||
|
||||
try:
|
||||
response = await loop.run_in_executor(
|
||||
None,
|
||||
lambda: self._chain.invoke({
|
||||
"messages": state["messages"],
|
||||
"memory_context": memory_context
|
||||
})
|
||||
)
|
||||
|
||||
elapsed_time = time.time() - start_time
|
||||
|
||||
# 提取 token 用量（兼容不同 LLM 提供商的元数据格式）
|
||||
token_usage = {}
|
||||
input_tokens = 0
|
||||
output_tokens = 0
|
||||
|
||||
# 尝试从 response_metadata 中提取
|
||||
if hasattr(response, 'response_metadata') and response.response_metadata:
|
||||
meta = response.response_metadata
|
||||
if 'token_usage' in meta:
|
||||
token_usage = meta['token_usage']
|
||||
elif 'usage' in meta:
|
||||
token_usage = meta['usage']
|
||||
|
||||
# 尝试从 additional_kwargs 中提取
|
||||
if not token_usage and hasattr(response, 'additional_kwargs'):
|
||||
add_kwargs = response.additional_kwargs
|
||||
if 'llm_output' in add_kwargs and 'token_usage' in add_kwargs['llm_output']:
|
||||
token_usage = add_kwargs['llm_output']['token_usage']
|
||||
|
||||
# 提取具体的 token 数值
|
||||
if token_usage:
|
||||
input_tokens = token_usage.get('prompt_tokens', token_usage.get('input_tokens', 0))
|
||||
output_tokens = token_usage.get('completion_tokens', token_usage.get('output_tokens', 0))
|
||||
|
||||
# 打印响应统计信息
|
||||
info(f"⏱️ [LLM统计] 调用耗时: {elapsed_time:.2f}秒")
|
||||
info(f"📊 [LLM统计] Token用量: 输入={input_tokens}, 输出={output_tokens}, 总计={input_tokens + output_tokens}")
|
||||
if token_usage:
|
||||
debug(f"📋 [LLM统计] 详细用量: {token_usage}")
|
||||
|
||||
# 打印 LLM 的完整输出
|
||||
debug("\n" + "="*80)
|
||||
debug("📥 [LLM输出] 大模型返回的完整响应:")
|
||||
debug(f" 消息类型: {response.type.upper()}")
|
||||
debug(f" 内容长度: {len(str(response.content))} 字符")
|
||||
debug("-"*80)
|
||||
debug(f"{response.content}")
|
||||
debug("="*80 + "\n")
|
||||
|
||||
result = {
|
||||
"messages": [response],
|
||||
"llm_calls": state.get('llm_calls', 0) + 1,
|
||||
"last_token_usage": token_usage,
|
||||
"last_elapsed_time": elapsed_time
|
||||
}
|
||||
|
||||
_log_state_change("llm_call", {**state, **result}, "离开")
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
elapsed_time = time.time() - start_time
|
||||
error(f"\n❌ [LLM错误] 调用失败 (耗时: {elapsed_time:.2f}秒)")
|
||||
error(f" 错误类型: {type(e).__name__}")
|
||||
error(f" 错误信息: {str(e)}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
debug("="*80 + "\n")
|
||||
|
||||
# 返回一个友好的错误消息
|
||||
error_response = AIMessage(
|
||||
content="抱歉，模型暂时无法响应，可能是网络超时或服务繁忙，请稍后再试。"
|
||||
)
|
||||
error_result = {
|
||||
"messages": [error_response],
|
||||
"llm_calls": state.get('llm_calls', 0),
|
||||
"last_token_usage": {},
|
||||
"last_elapsed_time": elapsed_time
|
||||
}
|
||||
|
||||
_log_state_change("llm_call", state, "离开（异常）")
|
||||
return error_result
|
||||
|
||||
async def call_tools(self, state: MessagesState, runtime: Runtime[GraphContext]) -> dict:
|
||||
"""
|
||||
工具执行节点（异步方法）
|
||||
对于每个工具调用，在线程池中执行同步工具函数
|
||||
"""
|
||||
_log_state_change("tool_node", state, "进入")
|
||||
|
||||
last_message = state['messages'][-1]
|
||||
if not isinstance(last_message, AIMessage) or not last_message.tool_calls:
|
||||
_log_state_change("tool_node", state, "离开（无工具调用）")
|
||||
return {"messages": []}
|
||||
|
||||
results = []
|
||||
loop = asyncio.get_event_loop()
|
||||
|
||||
info(f"🛠️ [工具调用] 准备执行 {len(last_message.tool_calls)} 个工具")
|
||||
|
||||
for tool_call in last_message.tool_calls:
|
||||
tool_name = tool_call["name"]
|
||||
tool_args = tool_call["args"]
|
||||
tool_id = tool_call["id"]
|
||||
tool_func = self.tools_by_name.get(tool_name)
|
||||
|
||||
debug(f" ├─ 调用工具: {tool_name} 参数: {tool_args}")
|
||||
|
||||
if tool_func is None:
|
||||
err_msg = f"Tool {tool_name} not found"
|
||||
debug(f" └─ ❌ {err_msg}")
|
||||
results.append(ToolMessage(content=err_msg, tool_call_id=tool_id))
|
||||
continue
|
||||
|
||||
try:
|
||||
# 修复闭包问题：将变量作为默认参数传入 lambda
|
||||
# 如果工具支持异步 (ainvoke)，优先使用异步调用
|
||||
if hasattr(tool_func, 'ainvoke'):
|
||||
observation = await tool_func.ainvoke(tool_args)
|
||||
else:
|
||||
observation = await loop.run_in_executor(
|
||||
None,
|
||||
lambda args=tool_args: tool_func.invoke(args) # 默认参数捕获当前值
|
||||
)
|
||||
|
||||
# 字符打印
|
||||
result_preview = str(observation).replace("\n", " ")
|
||||
debug(f" └─ ✅ 结果: {result_preview}")
|
||||
results.append(ToolMessage(content=str(observation), tool_call_id=tool_id))
|
||||
except Exception as e:
|
||||
debug(f" └─ ❌ 异常: {e}")
|
||||
results.append(ToolMessage(content=f"Error: {e}", tool_call_id=tool_id))
|
||||
|
||||
info(f"🛠️ [工具调用] 执行完成，返回 {len(results)} 条 ToolMessage")
|
||||
|
||||
result = {"messages": results}
|
||||
_log_state_change("tool_node", {**state, **result}, "离开")
|
||||
return result
|
||||
|
||||
@staticmethod
|
||||
def should_continue(state: MessagesState) -> Literal['tool_node', 'save_memory', 'END']:
|
||||
"""决定下一步：工具调用、保存记忆还是结束"""
|
||||
last_message = state["messages"][-1]
|
||||
|
||||
# 1. 如果需要调用工具，优先进入工具节点
|
||||
if isinstance(last_message, AIMessage) and last_message.tool_calls:
|
||||
if ENABLE_GRAPH_TRACE:
|
||||
info(f"🔀 [路由决策] 检测到 {len(last_message.tool_calls)} 个工具调用 → 转向 'tool_node'")
|
||||
return 'tool_node'
|
||||
|
||||
# 2. 如果是 AI 的最终回复，可以考虑进入记忆保存节点（可增加判断逻辑）
|
||||
# 这里简单处理：只要没有工具调用，且是 AI 消息，就尝试保存记忆。
|
||||
if isinstance(last_message, AIMessage):
|
||||
if ENABLE_GRAPH_TRACE:
|
||||
info(f"🔀 [路由决策] 收到 AI 最终回复（无工具调用） → 转向 'save_memory'")
|
||||
return 'save_memory'
|
||||
|
||||
# 3. 其他情况（如只有用户消息）直接结束
|
||||
if ENABLE_GRAPH_TRACE:
|
||||
info(f"🔀 [路由决策] 非 AI 消息（如纯用户消息） → 结束流程")
|
||||
return 'END'
|
||||
|
||||
async def retrieve_memory(self, state: MessagesState, runtime: Runtime[GraphContext]) -> dict:
|
||||
"""搜索并返回长期记忆"""
|
||||
_log_state_change("retrieve_memory", state, "进入")
|
||||
|
||||
user_id = runtime.context.user_id
|
||||
namespace = ("memories", user_id)
|
||||
query = str(state["messages"][-1].content)
|
||||
|
||||
debug(f"\n{'='*60}")
|
||||
debug(f"🔎 [记忆检索] 开始检索")
|
||||
debug(f" ├─ 用户ID: {user_id}")
|
||||
debug(f" ├─ 命名空间: {namespace}")
|
||||
debug(f" ├─ 查询内容: '{query}'")
|
||||
debug(f" └─ 消息总数: {len(state['messages'])}")
|
||||
|
||||
try:
|
||||
memories = await runtime.store.asearch(namespace, query=query)
|
||||
debug(f"✅ [记忆检索] 检索完成，找到 {len(memories)} 条相关记忆")
|
||||
|
||||
if memories:
|
||||
memory_text = "\n".join([m.value["data"] for m in memories])
|
||||
debug(f"📚 [记忆内容]")
|
||||
for i, memory in enumerate(memories, 1):
|
||||
debug(f" [{i}] {memory.value['data']}")
|
||||
debug(f"{'='*60}\n")
|
||||
result = {"memory_context": memory_text}
|
||||
_log_state_change("retrieve_memory", {**state, **result}, "离开")
|
||||
return result
|
||||
else:
|
||||
debug(f"⚠️ [记忆检索] 未找到相关记忆")
|
||||
debug(f"{'='*60}\n")
|
||||
result = {"memory_context": ""}
|
||||
_log_state_change("retrieve_memory", {**state, **result}, "离开")
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
error(f"❌ [记忆检索] 检索失败: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
debug(f"{'='*60}\n")
|
||||
result = {"memory_context": ""}
|
||||
_log_state_change("retrieve_memory", {**state, **result}, "离开（异常）")
|
||||
return result
|
||||
|
||||
async def save_memory(self, state: MessagesState, runtime: Runtime[GraphContext]) -> dict:
|
||||
"""尝试从对话中提取并保存长期记忆"""
|
||||
_log_state_change("save_memory", state, "进入")
|
||||
|
||||
# 获取最后一条用户消息（通常是要记住的内容的来源）
|
||||
user_messages = [msg for msg in state["messages"] if msg.type == "human"]
|
||||
if not user_messages:
|
||||
_log_state_change("save_memory", state, "离开（无用户消息）")
|
||||
return {}
|
||||
|
||||
last_user_msg = user_messages[-1].content.lower()
|
||||
|
||||
# 简单触发逻辑：包含"记住"或"保存"等关键词
|
||||
if any(keyword in last_user_msg for keyword in ["记住", "保存", "别忘了"]):
|
||||
# 提取记忆内容（这里仅作示例，实际可用 LLM 提取）
|
||||
memory_content = f"用户说过：{last_user_msg}"
|
||||
user_id = runtime.context.user_id
|
||||
namespace = ("memories", user_id)
|
||||
await runtime.store.aput(namespace, str(uuid.uuid4()), {"data": memory_content})
|
||||
info(f"✅ 长期记忆已保存：{memory_content}")
|
||||
|
||||
_log_state_change("save_memory", state, "离开")
|
||||
return {}
|
||||
# ⭐ 创建 Mem0 客户端（懒加载，首次使用时初始化）
|
||||
self.mem0_client = Mem0Client(llm)
|
||||
|
||||
def build(self) -> StateGraph:
|
||||
"""
|
||||
构建未编译的状态图（返回 StateGraph 实例）
|
||||
图中节点直接使用实例方法 call_llm, call_tools
|
||||
构建未编译的状态图
|
||||
|
||||
Returns:
|
||||
StateGraph 实例
|
||||
"""
|
||||
builder = StateGraph(MessagesState,context_schema=GraphContext)
|
||||
builder.add_node("retrieve_memory", self.retrieve_memory)
|
||||
builder.add_node("llm_call", self.call_llm)
|
||||
builder.add_node("tool_node", self.call_tools)
|
||||
builder.add_node("save_memory", self.save_memory)
|
||||
builder = StateGraph(MessagesState, context_schema=GraphContext)
|
||||
|
||||
# ⭐ 通过工厂函数创建节点（依赖注入）
|
||||
retrieve_memory_node = create_retrieve_memory_node(self.mem0_client)
|
||||
llm_call_node = create_llm_call_node(self.llm, self.tools)
|
||||
tool_call_node = create_tool_call_node(self.tools_by_name)
|
||||
summarize_node = create_summarize_node(self.mem0_client)
|
||||
|
||||
# 添加节点
|
||||
builder.add_node("retrieve_memory", retrieve_memory_node)
|
||||
builder.add_node("llm_call", llm_call_node)
|
||||
builder.add_node("tool_node", tool_call_node)
|
||||
builder.add_node("summarize", summarize_node)
|
||||
|
||||
# 添加边
|
||||
builder.add_edge(START, "retrieve_memory")
|
||||
builder.add_edge("retrieve_memory", "llm_call")
|
||||
builder.add_conditional_edges(
|
||||
"llm_call",
|
||||
self.should_continue,
|
||||
should_continue,
|
||||
{
|
||||
"tool_node": "tool_node",
|
||||
"save_memory": "save_memory",
|
||||
"summarize": "summarize",
|
||||
'END': END
|
||||
}
|
||||
)
|
||||
builder.add_edge("tool_node", "llm_call")
|
||||
builder.add_edge("save_memory", END)
|
||||
builder.add_edge("summarize", END)
|
||||
|
||||
return builder
|
||||
return builder
|
||||
|
||||
7
app/memory/__init__.py
Normal file
7
app/memory/__init__.py
Normal file
@@ -0,0 +1,7 @@
|
||||
"""
|
||||
Mem0 记忆层模块
|
||||
"""
|
||||
|
||||
from app.memory.mem0_client import Mem0Client
|
||||
|
||||
__all__ = ["Mem0Client"]
|
||||
144
app/memory/mem0_client.py
Normal file
144
app/memory/mem0_client.py
Normal file
@@ -0,0 +1,144 @@
|
||||
"""
|
||||
Mem0 记忆层客户端封装模块
|
||||
负责 Mem0 的初始化、检索和存储
|
||||
"""
|
||||
|
||||
import os
|
||||
from typing import Optional, List, Dict, Any
|
||||
from mem0 import AsyncMemory
|
||||
|
||||
# 本地模块
|
||||
from app.config import QDRANT_URL, QDRANT_COLLECTION_NAME, VLLM_EMBEDDING_URL
|
||||
from app.logger import info, warning, error
|
||||
|
||||
|
||||
class Mem0Client:
|
||||
"""Mem0 异步客户端封装类"""
|
||||
|
||||
def __init__(self, llm_instance):
|
||||
"""
|
||||
初始化 Mem0 客户端
|
||||
|
||||
Args:
|
||||
llm_instance: LangChain LLM 实例（用于事实提取）
|
||||
"""
|
||||
self.llm = llm_instance
|
||||
self.mem0: Optional[AsyncMemory] = None
|
||||
self._initialized = False
|
||||
|
||||
async def initialize(self):
|
||||
"""异步初始化 Mem0 客户端"""
|
||||
if self._initialized:
|
||||
return
|
||||
|
||||
try:
|
||||
# 检查 Qdrant 是否可达 (可选)
|
||||
import requests
|
||||
try:
|
||||
resp = requests.get(f"{QDRANT_URL}/collections", timeout=2)
|
||||
if resp.status_code == 200:
|
||||
info(f"✅ Qdrant 服务正常: {QDRANT_URL}")
|
||||
except Exception:
|
||||
warning(f"⚠️ 无法连接到 Qdrant: {QDRANT_URL}，Mem0 将尝试自动连接")
|
||||
|
||||
config = {
|
||||
# 向量存储：复用 Qdrant 实例
|
||||
"vector_store": {
|
||||
"provider": "qdrant",
|
||||
"config": {
|
||||
"collection_name": QDRANT_COLLECTION_NAME,
|
||||
"host": QDRANT_URL.split("://")[1].split(":")[0] if "://" in QDRANT_URL else "localhost",
|
||||
"port": int(QDRANT_URL.split(":")[-1]) if ":" in QDRANT_URL.split("://")[-1] else 6333,
|
||||
"embedding_model_dims": 768, # embeddinggemma-300m 输出 768 维
|
||||
}
|
||||
},
|
||||
# 事实提取 LLM：直接复用传入的 LangChain 实例
|
||||
"llm": {
|
||||
"provider": "langchain",
|
||||
"config": {
|
||||
"model": self.llm # 直接传入 LangChain 模型实例
|
||||
}
|
||||
},
|
||||
# Embedding：指向 vLLM 服务
|
||||
"embedder": {
|
||||
"provider": "openai",
|
||||
"embedding_dims": 768, # 关键：将维度参数提升到顶层
|
||||
"config": {
|
||||
"model": "google/embeddinggemma-300m",
|
||||
"api_key": "EMPTY",
|
||||
"api_base": VLLM_EMBEDDING_URL,
|
||||
# 注意：不要在此处传递 dimensions 参数，避免与 vLLM v0.7.2 不兼容
|
||||
}
|
||||
},
|
||||
"version": "v1.1"
|
||||
}
|
||||
|
||||
self.mem0 = AsyncMemory.from_config(config)
|
||||
self._initialized = True
|
||||
info(f"✅ Mem0 初始化成功 (Embedding: vLLM@8002, Vector: Qdrant, LLM: 复用现有实例)")
|
||||
|
||||
except Exception as e:
|
||||
error(f"❌ Mem0 初始化失败: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
self.mem0 = None
|
||||
|
||||
async def search_memories(self, query: str, user_id: str, limit: int = 5) -> List[str]:
|
||||
"""
|
||||
检索相关记忆
|
||||
|
||||
Args:
|
||||
query: 查询文本
|
||||
user_id: 用户 ID
|
||||
limit: 返回结果数量限制
|
||||
|
||||
Returns:
|
||||
List[str]: 记忆事实列表
|
||||
"""
|
||||
if not self.mem0:
|
||||
warning("⚠️ Mem0 未初始化，跳过记忆检索")
|
||||
return []
|
||||
|
||||
try:
|
||||
memories = await self.mem0.search(query, user_id=user_id, limit=limit)
|
||||
|
||||
if memories and "results" in memories:
|
||||
facts = [m["memory"] for m in memories["results"] if m.get("memory")]
|
||||
if facts:
|
||||
info(f"🔍 [记忆检索] Mem0 返回 {len(facts)} 条记忆")
|
||||
return facts
|
||||
|
||||
info("🔍 [记忆检索] 未找到相关记忆")
|
||||
return []
|
||||
|
||||
except Exception as e:
|
||||
warning(f"⚠️ Mem0 检索失败: {e}")
|
||||
return []
|
||||
|
||||
async def add_memories(self, messages: List[Dict[str, str]], user_id: str) -> bool:
|
||||
"""
|
||||
添加记忆（自动提取事实并存储）
|
||||
|
||||
Args:
|
||||
messages: 消息列表，格式为 [{"role": "user/assistant/system", "content": "..."}]
|
||||
user_id: 用户 ID
|
||||
|
||||
Returns:
|
||||
bool: 是否成功
|
||||
"""
|
||||
if not self.mem0:
|
||||
warning("⚠️ Mem0 未初始化，跳过记忆添加")
|
||||
return False
|
||||
|
||||
try:
|
||||
result = await self.mem0.add(
|
||||
messages,
|
||||
user_id=user_id,
|
||||
metadata={"type": "conversation"}
|
||||
)
|
||||
info(f"📝 [记忆添加] 已提交给 Mem0 进行事实提取")
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
error(f"❌ Mem0 记忆添加失败: {e}")
|
||||
return False
|
||||
17
app/nodes/__init__.py
Normal file
17
app/nodes/__init__.py
Normal file
@@ -0,0 +1,17 @@
|
||||
"""
|
||||
节点模块 - 导出所有 LangGraph 节点函数
|
||||
"""
|
||||
|
||||
from app.nodes.router import should_continue
|
||||
from app.nodes.llm_call import create_llm_call_node
|
||||
from app.nodes.tool_call import create_tool_call_node
|
||||
from app.nodes.retrieve_memory import create_retrieve_memory_node
|
||||
from app.nodes.summarize import create_summarize_node
|
||||
|
||||
__all__ = [
|
||||
"should_continue",
|
||||
"create_llm_call_node",
|
||||
"create_tool_call_node",
|
||||
"create_retrieve_memory_node",
|
||||
"create_summarize_node",
|
||||
]
|
||||
139
app/nodes/llm_call.py
Normal file
139
app/nodes/llm_call.py
Normal file
@@ -0,0 +1,139 @@
|
||||
"""
|
||||
LLM 调用节点模块
|
||||
负责调用大语言模型并处理响应
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import time
|
||||
from typing import Any, Dict
|
||||
from langchain_core.language_models import BaseLLM
|
||||
from langchain_core.messages import AIMessage
|
||||
from langchain_core.runnables import RunnableLambda
|
||||
from langgraph.runtime import Runtime
|
||||
|
||||
# 本地模块
|
||||
from app.state import MessagesState, GraphContext
|
||||
from app.prompts import create_system_prompt
|
||||
from app.utils.logging import log_state_change, print_llm_input
|
||||
from app.logger import debug, info, error
|
||||
|
||||
|
||||
def create_llm_call_node(llm: BaseLLM, tools: list):
|
||||
"""
|
||||
工厂函数：创建 LLM 调用节点
|
||||
|
||||
Args:
|
||||
llm: LangChain LLM 实例
|
||||
tools: 工具列表
|
||||
|
||||
Returns:
|
||||
异步节点函数
|
||||
"""
|
||||
# 构建调用链
|
||||
prompt = create_system_prompt()
|
||||
llm_with_tools = llm.bind_tools(tools)
|
||||
chain = prompt | RunnableLambda(print_llm_input) | llm_with_tools
|
||||
|
||||
async def call_llm(state: MessagesState, runtime: Runtime[GraphContext]) -> Dict[str, Any]:
|
||||
"""
|
||||
LLM 调用节点（异步方法）
|
||||
|
||||
Args:
|
||||
state: 当前对话状态
|
||||
runtime: LangGraph 运行时上下文
|
||||
|
||||
Returns:
|
||||
更新后的状态字典
|
||||
"""
|
||||
log_state_change("llm_call", state, "进入")
|
||||
|
||||
memory_context = state.get("memory_context", "暂无用户信息")
|
||||
loop = asyncio.get_event_loop()
|
||||
start_time = time.time()
|
||||
|
||||
try:
|
||||
response = await loop.run_in_executor(
|
||||
None,
|
||||
lambda: chain.invoke({
|
||||
"messages": state["messages"],
|
||||
"memory_context": memory_context
|
||||
})
|
||||
)
|
||||
|
||||
elapsed_time = time.time() - start_time
|
||||
|
||||
# 提取 token 用量（兼容不同 LLM 提供商的元数据格式）
|
||||
token_usage = {}
|
||||
input_tokens = 0
|
||||
output_tokens = 0
|
||||
|
||||
# 尝试从 response_metadata 中提取
|
||||
if hasattr(response, 'response_metadata') and response.response_metadata:
|
||||
meta = response.response_metadata
|
||||
if 'token_usage' in meta:
|
||||
token_usage = meta['token_usage']
|
||||
elif 'usage' in meta:
|
||||
token_usage = meta['usage']
|
||||
|
||||
# 尝试从 additional_kwargs 中提取
|
||||
if not token_usage and hasattr(response, 'additional_kwargs'):
|
||||
add_kwargs = response.additional_kwargs
|
||||
if 'llm_output' in add_kwargs and 'token_usage' in add_kwargs['llm_output']:
|
||||
token_usage = add_kwargs['llm_output']['token_usage']
|
||||
|
||||
# 提取具体的 token 数值
|
||||
if token_usage:
|
||||
input_tokens = token_usage.get('prompt_tokens', token_usage.get('input_tokens', 0))
|
||||
output_tokens = token_usage.get('completion_tokens', token_usage.get('output_tokens', 0))
|
||||
|
||||
# 打印响应统计信息
|
||||
info(f"⏱️ [LLM统计] 调用耗时: {elapsed_time:.2f}秒")
|
||||
info(f"📊 [LLM统计] Token用量: 输入={input_tokens}, 输出={output_tokens}, 总计={input_tokens + output_tokens}")
|
||||
if token_usage:
|
||||
debug(f"📋 [LLM统计] 详细用量: {token_usage}")
|
||||
|
||||
# 打印 LLM 的完整输出
|
||||
debug("\n" + "="*80)
|
||||
debug("📥 [LLM输出] 大模型返回的完整响应:")
|
||||
debug(f" 消息类型: {response.type.upper()}")
|
||||
debug(f" 内容长度: {len(str(response.content))} 字符")
|
||||
debug("-"*80)
|
||||
debug(f"{response.content}")
|
||||
debug("="*80 + "\n")
|
||||
|
||||
result = {
|
||||
"messages": [response],
|
||||
"llm_calls": state.get('llm_calls', 0) + 1,
|
||||
"last_token_usage": token_usage,
|
||||
"last_elapsed_time": elapsed_time,
|
||||
"turns_since_last_summary": state.get('turns_since_last_summary', 0) + 1 # 递增计数器
|
||||
}
|
||||
|
||||
log_state_change("llm_call", {**state, **result}, "离开")
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
elapsed_time = time.time() - start_time
|
||||
error(f"\n❌ [LLM错误] 调用失败 (耗时: {elapsed_time:.2f}秒)")
|
||||
error(f" 错误类型: {type(e).__name__}")
|
||||
error(f" 错误信息: {str(e)}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
debug("="*80 + "\n")
|
||||
|
||||
# 返回一个友好的错误消息
|
||||
error_response = AIMessage(
|
||||
content="抱歉，模型暂时无法响应，可能是网络超时或服务繁忙，请稍后再试。"
|
||||
)
|
||||
error_result = {
|
||||
"messages": [error_response],
|
||||
"llm_calls": state.get('llm_calls', 0),
|
||||
"last_token_usage": {},
|
||||
"last_elapsed_time": elapsed_time,
|
||||
"turns_since_last_summary": state.get('turns_since_last_summary', 0) + 1 # 即使出错也递增计数器
|
||||
}
|
||||
|
||||
log_state_change("llm_call", state, "离开（异常）")
|
||||
return error_result
|
||||
|
||||
return call_llm
|
||||
75
app/nodes/retrieve_memory.py
Normal file
75
app/nodes/retrieve_memory.py
Normal file
@@ -0,0 +1,75 @@
|
||||
"""
|
||||
记忆检索节点模块
|
||||
负责从 Mem0 检索相关长期记忆
|
||||
"""
|
||||
|
||||
from typing import Any, Dict
|
||||
from langgraph.runtime import Runtime
|
||||
|
||||
# 本地模块
|
||||
from app.state import MessagesState, GraphContext
|
||||
from app.memory.mem0_client import Mem0Client
|
||||
from app.utils.logging import log_state_change
|
||||
from app.logger import debug
|
||||
|
||||
|
||||
def create_retrieve_memory_node(mem0_client: Mem0Client):
|
||||
"""
|
||||
工厂函数：创建记忆检索节点
|
||||
|
||||
Args:
|
||||
mem0_client: Mem0 客户端实例
|
||||
|
||||
Returns:
|
||||
异步节点函数
|
||||
"""
|
||||
|
||||
async def retrieve_memory(state: MessagesState, runtime: Runtime[GraphContext]) -> Dict[str, Any]:
|
||||
"""
|
||||
记忆检索节点 - 使用 Mem0
|
||||
|
||||
Args:
|
||||
state: 当前对话状态
|
||||
runtime: LangGraph 运行时上下文
|
||||
|
||||
Returns:
|
||||
包含 memory_context 的状态更新
|
||||
"""
|
||||
log_state_change("retrieve_memory", state, "进入")
|
||||
|
||||
user_id = runtime.context.user_id
|
||||
|
||||
# 兼容 dict 和对象两种消息格式
|
||||
last_msg = state["messages"][-1]
|
||||
if isinstance(last_msg, dict):
|
||||
query = str(last_msg.get("content", ""))
|
||||
else:
|
||||
query = str(last_msg.content)
|
||||
memory_text_parts = []
|
||||
|
||||
# 确保 Mem0 已初始化（懒加载）
|
||||
if not mem0_client._initialized:
|
||||
await mem0_client.initialize()
|
||||
|
||||
if mem0_client.mem0:
|
||||
try:
|
||||
# 异步调用 Mem0 语义检索
|
||||
facts = await mem0_client.search_memories(query, user_id=user_id, limit=5)
|
||||
|
||||
if facts:
|
||||
memory_text_parts.append(f"【相关长期记忆】\n" + "\n".join(f"- {f}" for f in facts))
|
||||
else:
|
||||
debug("🔍 [记忆检索] 未找到相关记忆")
|
||||
except Exception as e:
|
||||
from app.logger import warning
|
||||
warning(f"⚠️ Mem0 检索失败: {e}")
|
||||
else:
|
||||
from app.logger import warning
|
||||
warning("⚠️ Mem0 未初始化，跳过记忆检索")
|
||||
|
||||
memory_context = "\n\n".join(memory_text_parts) if memory_text_parts else "暂无用户信息"
|
||||
result = {"memory_context": memory_context}
|
||||
log_state_change("retrieve_memory", {**state, **result}, "离开")
|
||||
return result
|
||||
|
||||
return retrieve_memory
|
||||
48
app/nodes/router.py
Normal file
48
app/nodes/router.py
Normal file
@@ -0,0 +1,48 @@
|
||||
"""
|
||||
路由决策节点
|
||||
根据当前状态决定下一步走向
|
||||
"""
|
||||
|
||||
from typing import Literal
|
||||
from langchain_core.messages import AIMessage
|
||||
|
||||
# 本地模块
|
||||
from app.config import ENABLE_GRAPH_TRACE, MEMORY_SUMMARIZE_INTERVAL
|
||||
from app.state import MessagesState
|
||||
from app.logger import info
|
||||
|
||||
|
||||
def should_continue(state: MessagesState) -> Literal['tool_node', 'summarize', 'END']:
|
||||
"""
|
||||
决定下一步：工具调用、生成摘要还是结束
|
||||
|
||||
Args:
|
||||
state: 当前对话状态
|
||||
|
||||
Returns:
|
||||
下一个节点名称或 END
|
||||
"""
|
||||
last_message = state["messages"][-1]
|
||||
|
||||
# 1. 如果需要调用工具，优先进入工具节点
|
||||
if isinstance(last_message, AIMessage) and last_message.tool_calls:
|
||||
if ENABLE_GRAPH_TRACE:
|
||||
info(f"🔀 [路由决策] 检测到 {len(last_message.tool_calls)} 个工具调用 → 转向 'tool_node'")
|
||||
return 'tool_node'
|
||||
|
||||
# 2. 如果是 AI 的最终回复，判断是否达到摘要生成阈值
|
||||
if isinstance(last_message, AIMessage):
|
||||
turns = state.get("turns_since_last_summary", 0)
|
||||
if turns >= MEMORY_SUMMARIZE_INTERVAL:
|
||||
if ENABLE_GRAPH_TRACE:
|
||||
info(f"🔀 [路由决策] 收到 AI 最终回复，已达摘要阈值({turns}/{MEMORY_SUMMARIZE_INTERVAL}) → 转向 'summarize'")
|
||||
return 'summarize'
|
||||
else:
|
||||
if ENABLE_GRAPH_TRACE:
|
||||
info(f"🔀 [路由决策] 收到 AI 最终回复，未达摘要阈值({turns}/{MEMORY_SUMMARIZE_INTERVAL}) → 结束流程")
|
||||
return 'END'
|
||||
|
||||
# 3. 其他情况（如只有用户消息）直接结束
|
||||
if ENABLE_GRAPH_TRACE:
|
||||
info(f"🔀 [路由决策] 非 AI 消息（如纯用户消息） → 结束流程")
|
||||
return 'END'
|
||||
86
app/nodes/summarize.py
Normal file
86
app/nodes/summarize.py
Normal file
@@ -0,0 +1,86 @@
|
||||
"""
|
||||
记忆存储节点模块
|
||||
负责将对话历史提交给 Mem0 进行事实提取和存储
|
||||
"""
|
||||
|
||||
from typing import Any, Dict
|
||||
from langgraph.runtime import Runtime
|
||||
|
||||
# 本地模块
|
||||
from app.state import MessagesState, GraphContext
|
||||
from app.memory.mem0_client import Mem0Client
|
||||
from app.utils.logging import log_state_change
|
||||
from app.logger import debug, info, error, warning
|
||||
|
||||
|
||||
def create_summarize_node(mem0_client: Mem0Client):
|
||||
"""
|
||||
工厂函数：创建记忆存储节点
|
||||
|
||||
Args:
|
||||
mem0_client: Mem0 客户端实例
|
||||
|
||||
Returns:
|
||||
异步节点函数
|
||||
"""
|
||||
|
||||
async def summarize_conversation(state: MessagesState, runtime: Runtime[GraphContext]) -> Dict[str, Any]:
|
||||
"""
|
||||
记忆存储节点 - 使用 Mem0
|
||||
|
||||
Args:
|
||||
state: 当前对话状态
|
||||
runtime: LangGraph 运行时上下文
|
||||
|
||||
Returns:
|
||||
重置计数器的状态更新
|
||||
"""
|
||||
log_state_change("summarize", state, "进入")
|
||||
|
||||
messages = state["messages"]
|
||||
if len(messages) < 4:
|
||||
debug("📝 [记忆添加] 对话过短，跳过")
|
||||
return {"turns_since_last_summary": 0}
|
||||
|
||||
user_id = runtime.context.user_id
|
||||
|
||||
# 确保 Mem0 已初始化（懒加载）
|
||||
if not mem0_client._initialized:
|
||||
await mem0_client.initialize()
|
||||
|
||||
# 将整个对话历史转换为 Mem0 需要的消息格式
|
||||
mem0_messages = []
|
||||
for msg in messages:
|
||||
# 兼容 dict 和对象两种格式
|
||||
if isinstance(msg, dict):
|
||||
msg_type = msg.get("type", "")
|
||||
msg_content = msg.get("content", "")
|
||||
else:
|
||||
msg_type = getattr(msg, 'type', '')
|
||||
msg_content = getattr(msg, 'content', '')
|
||||
|
||||
if msg_type == "human":
|
||||
mem0_messages.append({"role": "user", "content": msg_content})
|
||||
elif msg_type == "ai":
|
||||
mem0_messages.append({"role": "assistant", "content": msg_content})
|
||||
elif msg_type == "tool":
|
||||
mem0_messages.append({"role": "system", "content": f"[工具返回] {msg_content}"})
|
||||
|
||||
if mem0_client.mem0:
|
||||
try:
|
||||
# 异步调用 Mem0 自动提取并存储事实
|
||||
success = await mem0_client.add_memories(
|
||||
mem0_messages,
|
||||
user_id=user_id
|
||||
)
|
||||
if success:
|
||||
info(f"📝 [记忆添加] 已提交给 Mem0 进行事实提取")
|
||||
except Exception as e:
|
||||
error(f"❌ Mem0 记忆添加失败: {e}")
|
||||
else:
|
||||
warning("⚠️ Mem0 未初始化，跳过记忆添加")
|
||||
|
||||
log_state_change("summarize", state, "离开")
|
||||
return {"turns_since_last_summary": 0}
|
||||
|
||||
return summarize_conversation
|
||||
90
app/nodes/tool_call.py
Normal file
90
app/nodes/tool_call.py
Normal file
@@ -0,0 +1,90 @@
|
||||
"""
|
||||
工具执行节点模块
|
||||
负责执行 AI 调用的工具函数
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
from typing import Any, Dict
|
||||
from langchain_core.messages import AIMessage, ToolMessage
|
||||
from langgraph.runtime import Runtime
|
||||
|
||||
# 本地模块
|
||||
from app.state import MessagesState, GraphContext
|
||||
from app.utils.logging import log_state_change
|
||||
from app.logger import debug, info
|
||||
|
||||
|
||||
def create_tool_call_node(tools_by_name: Dict[str, Any]):
|
||||
"""
|
||||
工厂函数：创建工具执行节点
|
||||
|
||||
Args:
|
||||
tools_by_name: 名称到工具函数的映射字典
|
||||
|
||||
Returns:
|
||||
异步节点函数
|
||||
"""
|
||||
|
||||
async def call_tools(state: MessagesState, runtime: Runtime[GraphContext]) -> Dict[str, Any]:
|
||||
"""
|
||||
工具执行节点（异步方法）
|
||||
|
||||
Args:
|
||||
state: 当前对话状态
|
||||
runtime: LangGraph 运行时上下文
|
||||
|
||||
Returns:
|
||||
包含 ToolMessage 的状态更新
|
||||
"""
|
||||
log_state_change("tool_node", state, "进入")
|
||||
|
||||
last_message = state['messages'][-1]
|
||||
if not isinstance(last_message, AIMessage) or not last_message.tool_calls:
|
||||
log_state_change("tool_node", state, "离开（无工具调用）")
|
||||
return {"messages": []}
|
||||
|
||||
results = []
|
||||
loop = asyncio.get_event_loop()
|
||||
|
||||
info(f"🛠️ [工具调用] 准备执行 {len(last_message.tool_calls)} 个工具")
|
||||
|
||||
for tool_call in last_message.tool_calls:
|
||||
tool_name = tool_call["name"]
|
||||
tool_args = tool_call["args"]
|
||||
tool_id = tool_call["id"]
|
||||
tool_func = tools_by_name.get(tool_name)
|
||||
|
||||
debug(f" ├─ 调用工具: {tool_name} 参数: {tool_args}")
|
||||
|
||||
if tool_func is None:
|
||||
err_msg = f"Tool {tool_name} not found"
|
||||
debug(f" └─ ❌ {err_msg}")
|
||||
results.append(ToolMessage(content=err_msg, tool_call_id=tool_id))
|
||||
continue
|
||||
|
||||
try:
|
||||
# 修复闭包问题：将变量作为默认参数传入 lambda
|
||||
# 如果工具支持异步 (ainvoke)，优先使用异步调用
|
||||
if hasattr(tool_func, 'ainvoke'):
|
||||
observation = await tool_func.ainvoke(tool_args)
|
||||
else:
|
||||
observation = await loop.run_in_executor(
|
||||
None,
|
||||
lambda args=tool_args: tool_func.invoke(args) # 默认参数捕获当前值
|
||||
)
|
||||
|
||||
# 字符打印
|
||||
result_preview = str(observation).replace("\n", " ")
|
||||
debug(f" └─ ✅ 结果: {result_preview}")
|
||||
results.append(ToolMessage(content=str(observation), tool_call_id=tool_id))
|
||||
except Exception as e:
|
||||
debug(f" └─ ❌ 异常: {e}")
|
||||
results.append(ToolMessage(content=f"Error: {e}", tool_call_id=tool_id))
|
||||
|
||||
info(f"🛠️ [工具调用] 执行完成，返回 {len(results)} 条 ToolMessage")
|
||||
|
||||
result = {"messages": results}
|
||||
log_state_change("tool_node", {**state, **result}, "离开")
|
||||
return result
|
||||
|
||||
return call_tools
|
||||
38
app/prompts.py
Normal file
38
app/prompts.py
Normal file
@@ -0,0 +1,38 @@
|
||||
"""
|
||||
提示模板管理模块
|
||||
所有系统提示和对话模板统一定义
|
||||
"""
|
||||
|
||||
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
|
||||
|
||||
|
||||
def create_system_prompt() -> ChatPromptTemplate:
|
||||
"""
|
||||
创建系统提示模板
|
||||
|
||||
Returns:
|
||||
ChatPromptTemplate: 包含系统指令和消息占位符的提示模板
|
||||
"""
|
||||
system_template = (
|
||||
"你是一个个人生活助手和数据分析助手，请使用中文交流。\n\n"
|
||||
"【用户背景信息】\n"
|
||||
"以下是对当前用户的已知信息和长期记忆，你必须优先采纳并在回答中体现：\n"
|
||||
"{memory_context}\n"
|
||||
"若包含姓名、偏好等个人信息，请自然融入回应（例如称呼名字、提及偏好）。\n\n"
|
||||
"【可用工具与使用规则】\n"
|
||||
"- 获取温度/天气：`get_current_temperature`\n"
|
||||
"- 读取文本文件：`read_local_file`（限定目录 `./user_docs`）\n"
|
||||
"- 读取PDF摘要：`read_pdf_summary`（限定目录 `./user_docs`）\n"
|
||||
"- 读取Excel表格：`read_excel_as_markdown`（限定目录 `./user_docs`）\n"
|
||||
"- 抓取网页内容：`fetch_webpage_content`\n"
|
||||
"工具调用时请直接返回所需参数，无需额外说明。\n\n"
|
||||
"【回答要求（必须遵守）】\n"
|
||||
"1. 回答必须简洁、直接，禁止描述任何思考过程或内心活动。\n"
|
||||
"2. 优先利用已知用户信息进行个性化回复。\n"
|
||||
"3. 若无信息可依，礼貌询问或提供通用帮助。"
|
||||
)
|
||||
|
||||
return ChatPromptTemplate.from_messages([
|
||||
("system", system_template),
|
||||
MessagesPlaceholder(variable_name="messages")
|
||||
])
|
||||
27
app/state.py
Normal file
27
app/state.py
Normal file
@@ -0,0 +1,27 @@
|
||||
"""
|
||||
LangGraph 状态定义模块
|
||||
包含 MessagesState 和 GraphContext
|
||||
"""
|
||||
|
||||
import operator
|
||||
from typing import Annotated, Any
|
||||
from typing_extensions import TypedDict
|
||||
from dataclasses import dataclass
|
||||
from langchain_core.messages import AnyMessage
|
||||
|
||||
|
||||
class MessagesState(TypedDict):
|
||||
"""对话状态类型定义"""
|
||||
messages: Annotated[list[AnyMessage], operator.add]
|
||||
llm_calls: int
|
||||
memory_context: str
|
||||
last_token_usage: dict # 本次调用的 token 使用详情
|
||||
last_elapsed_time: float # 本次调用耗时（秒）
|
||||
turns_since_last_summary: int # 距离上次生成摘要的轮数
|
||||
|
||||
|
||||
@dataclass
|
||||
class GraphContext:
|
||||
"""图执行上下文"""
|
||||
user_id: str
|
||||
# 可扩展更多上下文信息
|
||||
7
app/utils/__init__.py
Normal file
7
app/utils/__init__.py
Normal file
@@ -0,0 +1,7 @@
|
||||
"""
|
||||
工具模块
|
||||
"""
|
||||
|
||||
from app.utils.logging import log_state_change, print_llm_input
|
||||
|
||||
__all__ = ["log_state_change", "print_llm_input"]
|
||||
61
app/utils/logging.py
Normal file
61
app/utils/logging.py
Normal file
@@ -0,0 +1,61 @@
|
||||
"""
|
||||
LangGraph 节点日志工具模块
|
||||
提供状态流转追踪和 LLM 输入输出打印功能
|
||||
"""
|
||||
|
||||
from app.config import ENABLE_GRAPH_TRACE
|
||||
from app.logger import debug, info
|
||||
|
||||
|
||||
def log_state_change(node_name: str, state: dict, prefix: str = "进入"):
|
||||
"""
|
||||
记录状态变化日志
|
||||
|
||||
Args:
|
||||
node_name: 节点名称
|
||||
state: 当前状态
|
||||
prefix: 日志前缀（"进入" 或 "离开"）
|
||||
"""
|
||||
from app.logger import info
|
||||
|
||||
messages = state.get("messages", [])
|
||||
msg_count = len(messages)
|
||||
last_msg = messages[-1] if messages else None
|
||||
last_info = ""
|
||||
if last_msg:
|
||||
# 兼容 dict 和对象两种格式
|
||||
if isinstance(last_msg, dict):
|
||||
content_preview = str(last_msg.get("content", ""))[:100].replace("\n", " ")
|
||||
msg_type = last_msg.get("type", "unknown")
|
||||
else:
|
||||
content_preview = str(last_msg.content)[:100].replace("\n", " ")
|
||||
msg_type = getattr(last_msg, 'type', 'unknown')
|
||||
last_info = f"{msg_type.upper()}: {content_preview}"
|
||||
info(f"🔄 [{node_name}] {prefix} | 消息数:{msg_count} | 最后一条:{last_info}")
|
||||
|
||||
|
||||
def print_llm_input(prompt_value):
|
||||
"""
|
||||
RunnableLambda 回调函数：打印格式化后发送给 LLM 的完整消息
|
||||
|
||||
Args:
|
||||
prompt_value: ChatPromptValue 对象，包含格式化后的消息列表
|
||||
|
||||
Returns:
|
||||
原样返回 prompt_value，不影响链式调用
|
||||
"""
|
||||
if not ENABLE_GRAPH_TRACE:
|
||||
return prompt_value
|
||||
|
||||
messages = prompt_value.messages # ChatPromptValue 提供 .messages 属性
|
||||
|
||||
debug("\n" + "=" * 80)
|
||||
debug("📤 [LLM输入] 格式化后发送给大模型的完整消息:")
|
||||
debug(f" 总消息数: {len(messages)}")
|
||||
debug("-" * 80)
|
||||
for i, msg in enumerate(messages):
|
||||
content_preview = str(msg.content) # 完整输出
|
||||
debug(f" [{i}] {msg.type.upper():10s}: {content_preview}")
|
||||
debug("\n" + "=" * 80 + "\n")
|
||||
|
||||
return prompt_value
|
||||
@@ -1,23 +1,6 @@
|
||||
services:
|
||||
postgres:
|
||||
image: postgres:16
|
||||
container_name: ai-postgres
|
||||
environment:
|
||||
POSTGRES_PASSWORD: mysecretpassword # 请替换为强密码
|
||||
POSTGRES_DB: langgraph_db
|
||||
volumes:
|
||||
- pg_data:/var/lib/postgresql/data
|
||||
networks:
|
||||
- ai-network
|
||||
healthcheck:
|
||||
test: [ "CMD-SHELL", "pg_isready -U postgres" ]
|
||||
interval: 10s
|
||||
timeout: 5s
|
||||
retries: 5
|
||||
restart: unless-stopped
|
||||
# 如需外部访问数据库，取消下面注释
|
||||
# ports:
|
||||
# - "5432:5432"
|
||||
# ⭐ PostgreSQL 和 Qdrant 已迁移到远程服务器 (115.190.121.151)
|
||||
# 不再需要在本地 Docker Compose 中运行这些服务
|
||||
|
||||
backend:
|
||||
build:
|
||||
@@ -27,15 +10,18 @@ services:
|
||||
environment:
|
||||
- ZHIPUAI_API_KEY=${ZHIPUAI_API_KEY}
|
||||
- VLLM_LOCAL_KEY=${VLLM_LOCAL_KEY}
|
||||
- DB_URI=postgresql://postgres:mysecretpassword@postgres:5432/langgraph_db?sslmode=disable
|
||||
# ⭐ 使用远程服务器地址
|
||||
- DB_URI=postgresql://postgres:mysecretpassword@115.190.121.151:5432/langgraph_db?sslmode=disable
|
||||
- QDRANT_URL=http://115.190.121.151:6333
|
||||
- QDRANT_COLLECTION_NAME=user_memories
|
||||
- EMBEDDING_MODEL=text-embedding-3-small
|
||||
- MEMORY_SUMMARIZE_INTERVAL=${MEMORY_SUMMARIZE_INTERVAL:-10}
|
||||
volumes:
|
||||
- ../data/user_docs:/app/data/user_docs # 挂载文档目录
|
||||
- ../logs:/app/logs
|
||||
networks:
|
||||
- ai-network
|
||||
depends_on:
|
||||
postgres:
|
||||
condition: service_healthy
|
||||
# ⭐ 移除对 postgres 和 qdrant 的依赖
|
||||
restart: unless-stopped
|
||||
ports:
|
||||
- "8001:8001"
|
||||
@@ -60,5 +46,7 @@ networks:
|
||||
ai-network:
|
||||
driver: bridge
|
||||
|
||||
volumes:
|
||||
pg_data:
|
||||
# ⭐ PostgreSQL 和 Qdrant 已迁移到远程服务器，不再需要本地卷
|
||||
# volumes:
|
||||
# pg_data:
|
||||
# qdrant_storage:
|
||||
|
||||
@@ -1,5 +1,4 @@
|
||||
# Core
|
||||
transformers>=4.35.0 # 仅用于分词器，不需要模型推理
|
||||
pypdf>=3.0.0
|
||||
pandas>=2.0.0
|
||||
requests>=2.31.0
|
||||
@@ -12,9 +11,13 @@ langchain-community>=0.0.10
|
||||
langchain-core>=0.1.0
|
||||
langchain-openai>=0.0.5
|
||||
langchain-text-splitters>=0.1.0
|
||||
langchain-qdrant>=0.1.0 # Qdrant 向量存储集成
|
||||
|
||||
# Vector Database
|
||||
chromadb>=0.4.0 # 轻量级向量数据库，可选 torch 但不强制
|
||||
qdrant-client>=1.7.0 # Qdrant 客户端
|
||||
|
||||
# Mem0 (Memory Layer)
|
||||
mem0ai>=0.1.0
|
||||
|
||||
# LangGraph
|
||||
langgraph>=0.0.30
|
||||
|
||||
179
scripts/start.sh
179
scripts/start.sh
@@ -88,11 +88,22 @@ check_config() {
|
||||
check_warn "VLLM_LOCAL_KEY 未配置（如不使用本地模型可忽略）"
|
||||
fi
|
||||
|
||||
# 检查 DB_URI
|
||||
if grep -q "^DB_URI=" "$PROJECT_DIR/.env" 2>/dev/null; then
|
||||
check_pass "DB_URI 已配置"
|
||||
# 检查 DB_URI (远程服务器)
|
||||
if grep -q "^DB_URI=.*115.190.121.151" "$PROJECT_DIR/.env" 2>/dev/null; then
|
||||
check_pass "DB_URI 已配置（远程服务器）"
|
||||
elif grep -q "^DB_URI=" "$PROJECT_DIR/.env" 2>/dev/null; then
|
||||
check_warn "DB_URI 已配置（非远程服务器地址）"
|
||||
else
|
||||
check_warn "DB_URI 未配置（将使用默认值）"
|
||||
check_fail "DB_URI 未配置"
|
||||
fi
|
||||
|
||||
# 检查 QDRANT_URL (远程服务器)
|
||||
if grep -q "^QDRANT_URL=.*115.190.121.151" "$PROJECT_DIR/.env" 2>/dev/null; then
|
||||
check_pass "QDRANT_URL 已配置（远程服务器）"
|
||||
elif grep -q "^QDRANT_URL=" "$PROJECT_DIR/.env" 2>/dev/null; then
|
||||
check_warn "QDRANT_URL 已配置（非远程服务器地址）"
|
||||
else
|
||||
check_fail "QDRANT_URL 未配置"
|
||||
fi
|
||||
|
||||
# 3. 检查 Docker 环境
|
||||
@@ -130,6 +141,31 @@ check_config() {
|
||||
fi
|
||||
done
|
||||
|
||||
# 5. 检查远程服务连接
|
||||
echo ""
|
||||
echo "🌐 检查远程服务连接..."
|
||||
|
||||
# 测试 PostgreSQL 连接
|
||||
if command -v psql &> /dev/null; then
|
||||
# 注意：这里假设密码为 mysecretpassword，如果不同需调整或从 env 读取
|
||||
if PGPASSWORD=mysecretpassword psql -h 115.190.121.151 -U postgres -d langgraph_db -c "SELECT 1;" &> /dev/null; then
|
||||
check_pass "PostgreSQL 远程连接正常 (115.190.121.151:5432)"
|
||||
else
|
||||
check_fail "PostgreSQL 远程连接失败"
|
||||
echo " 提示: 检查网络连接和防火墙设置"
|
||||
fi
|
||||
else
|
||||
check_warn "psql 客户端未安装，跳过 PostgreSQL 连接测试"
|
||||
fi
|
||||
|
||||
# 测试 Qdrant 连接
|
||||
if curl -s http://115.190.121.151:6333/collections &> /dev/null; then
|
||||
check_pass "Qdrant 远程连接正常 (115.190.121.151:6333)"
|
||||
else
|
||||
check_fail "Qdrant 远程连接失败"
|
||||
echo " 提示: 检查网络连接和防火墙设置"
|
||||
fi
|
||||
|
||||
# 总结
|
||||
echo ""
|
||||
echo "=========================================="
|
||||
@@ -150,90 +186,101 @@ check_config() {
|
||||
}
|
||||
|
||||
# =============================================================================
|
||||
# Docker 容器检查函数
|
||||
# Docker 容器检查函数（仅检查 llama.cpp 服务）
|
||||
# =============================================================================
|
||||
check_vllm() {
|
||||
echo -e "${BLUE}🔍 检查 vLLM 容器...${NC}"
|
||||
if ! docker ps --format '{{.Names}}' | grep -q "^gemma4-server$"; then
|
||||
echo -e "${YELLOW}⚠️ vLLM 容器未运行${NC}"
|
||||
check_llamacpp() {
|
||||
echo -e "${BLUE}🔍 检查 llama.cpp LLM 容器...${NC}"
|
||||
if ! docker ps --format '{{.Names}}' | grep -q "^gemma4-llamacpp-server$"; then
|
||||
echo -e "${YELLOW}⚠️ llama.cpp LLM 容器未运行${NC}"
|
||||
return 1
|
||||
else
|
||||
echo -e "${GREEN}✓ vLLM 容器正在运行 (gemma4-server)${NC}"
|
||||
echo -e "${GREEN}✓ llama.cpp LLM 容器正在运行 (gemma4-llamacpp-server)${NC}"
|
||||
return 0
|
||||
fi
|
||||
}
|
||||
|
||||
check_postgres() {
|
||||
echo -e "${BLUE}🔍 检查 PostgreSQL 容器...${NC}"
|
||||
if ! docker ps --format '{{.Names}}' | grep -q "^postgres-langgraph$"; then
|
||||
echo -e "${YELLOW}⚠️ PostgreSQL 容器未运行${NC}"
|
||||
check_embedding() {
|
||||
echo -e "${BLUE}🔍 检查 llama.cpp Embedding 容器...${NC}"
|
||||
if ! docker ps --format '{{.Names}}' | grep -q "^embedding-server$"; then
|
||||
echo -e "${YELLOW}⚠️ llama.cpp Embedding 容器未运行${NC}"
|
||||
return 1
|
||||
else
|
||||
echo -e "${GREEN}✓ PostgreSQL 容器正在运行 (postgres-langgraph)${NC}"
|
||||
echo -e "${GREEN}✓ llama.cpp Embedding 容器正在运行 (embedding-server)${NC}"
|
||||
return 0
|
||||
fi
|
||||
}
|
||||
|
||||
# =============================================================================
|
||||
# 启动 Docker 依赖服务
|
||||
# 启动 Docker 依赖服务（llama.cpp）
|
||||
# =============================================================================
|
||||
start_vllm() {
|
||||
echo -e "${BLUE}🚀 启动 vLLM 容器...${NC}"
|
||||
start_llamacpp() {
|
||||
echo -e "${BLUE}🚀 启动 llama.cpp LLM 容器...${NC}"
|
||||
|
||||
# 检查模型文件
|
||||
if [ ! -d "/home/huang/Study/AIModel/gemma-4-E2B-it" ]; then
|
||||
echo -e "${RED}✗ 错误：模型目录不存在: /home/huang/Study/AIModel/gemma-4-E2B-it${NC}"
|
||||
if [ ! -f "/home/huang/Study/AIModel/GGUF/Gemma-4-E2B-Uncensored-HauhauCS-Aggressive-Q6_K_P.gguf" ]; then
|
||||
echo -e "${RED}✗ 错误：LLM 模型文件不存在${NC}"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
if [ ! -f "/home/huang/Study/AIModel/GGUF/mmproj-Gemma-4-E2B-Uncensored-HauhauCS-Aggressive-f16.gguf" ]; then
|
||||
echo -e "${RED}✗ 错误：多模态投影文件不存在${NC}"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
docker run -d \
|
||||
--name gemma4-server \
|
||||
--name gemma4-llamacpp-server \
|
||||
--restart=unless-stopped \
|
||||
--group-add=video \
|
||||
--cap-add=SYS_PTRACE \
|
||||
--security-opt seccomp=unconfined \
|
||||
--device=/dev/kfd \
|
||||
--device=/dev/dri \
|
||||
-v /home/huang/Study/AIModel/gemma-4-E2B-it:/models/gemma-4-E2B-it \
|
||||
-e VLLM_ROCM_USE_AITER=0 \
|
||||
-e HF_TOKEN="${HF_TOKEN:-}" \
|
||||
-p 8000:8000 \
|
||||
--ipc=host \
|
||||
--entrypoint vllm \
|
||||
my-vllm-gemma4:working \
|
||||
serve /models/gemma-4-E2B-it \
|
||||
--served-model-name gemma-4-E2B-it \
|
||||
--dtype auto \
|
||||
--api-key token-abc123 \
|
||||
--trust-remote-code \
|
||||
--port 8000 \
|
||||
--gpu-memory-utilization 0.85 \
|
||||
--max-model-len 8192
|
||||
-v /home/huang/Study/AIModel/GGUF:/models \
|
||||
-p 8081:8080 \
|
||||
ghcr.io/ggml-org/llama.cpp:server-rocm \
|
||||
-m /models/Gemma-4-E2B-Uncensored-HauhauCS-Aggressive-Q6_K_P.gguf \
|
||||
--mmproj /models/mmproj-Gemma-4-E2B-Uncensored-HauhauCS-Aggressive-f16.gguf \
|
||||
--host 0.0.0.0 \
|
||||
--port 8080 \
|
||||
-ngl 99
|
||||
|
||||
echo -e "${GREEN}✓ vLLM 容器已启动${NC}"
|
||||
echo -e "${GREEN}✓ llama.cpp LLM 容器已启动 (端口 8081)${NC}"
|
||||
echo -e "${YELLOW}⏳ 等待模型加载（可能需要几分钟）...${NC}"
|
||||
sleep 10
|
||||
sleep 15
|
||||
}
|
||||
|
||||
start_postgres() {
|
||||
echo -e "${BLUE}🚀 启动 PostgreSQL 容器...${NC}"
|
||||
start_embedding() {
|
||||
echo -e "${BLUE}🚀 启动 llama.cpp Embedding 容器...${NC}"
|
||||
|
||||
# 检查模型文件
|
||||
if [ ! -f "/home/huang/Study/AIModel/GGUF/embeddinggemma-300M-Q8_0.gguf" ]; then
|
||||
echo -e "${RED}✗ 错误：Embedding 模型文件不存在${NC}"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
docker run -d \
|
||||
--name postgres-langgraph \
|
||||
-e POSTGRES_PASSWORD=mysecretpassword \
|
||||
-e POSTGRES_DB=langgraph_db \
|
||||
-p 5432:5432 \
|
||||
-v ~/docker_volumes/postgres_data:/var/lib/postgresql/data \
|
||||
postgres:16
|
||||
--name embedding-server \
|
||||
--restart=unless-stopped \
|
||||
--group-add=video \
|
||||
--device=/dev/kfd \
|
||||
--device=/dev/dri \
|
||||
-v /home/huang/Study/AIModel/GGUF:/models \
|
||||
-p 8082:8080 \
|
||||
ghcr.io/ggml-org/llama.cpp:server-rocm \
|
||||
-m /models/embeddinggemma-300M-Q8_0.gguf \
|
||||
--host 0.0.0.0 \
|
||||
--port 8080 \
|
||||
-ngl 99 \
|
||||
--embeddings \
|
||||
-c 512
|
||||
|
||||
echo -e "${GREEN}✓ PostgreSQL 容器已启动${NC}"
|
||||
sleep 3
|
||||
echo -e "${GREEN}✓ llama.cpp Embedding 容器已启动 (端口 8082)${NC}"
|
||||
sleep 5
|
||||
}
|
||||
|
||||
# =============================================================================
|
||||
# 启动 Python 服务
|
||||
# =============================================================================
|
||||
start_backend() {
|
||||
echo -e "\n${BLUE}🚀 启动后端服务 (端口 8001)...${NC}"
|
||||
echo -e "\n${BLUE}🚀 启动后端服务 (端口 8003)...${NC}"
|
||||
cd "$PROJECT_DIR"
|
||||
|
||||
# 加载 .env 文件中的环境变量
|
||||
@@ -242,6 +289,7 @@ start_backend() {
|
||||
set +a
|
||||
|
||||
export PYTHONPATH="$PROJECT_DIR"
|
||||
export BACKEND_PORT=8003
|
||||
python app/backend.py &
|
||||
BACKEND_PID=$!
|
||||
echo -e "${GREEN}✓ 后端服务已启动 (PID: $BACKEND_PID)${NC}"
|
||||
@@ -263,7 +311,6 @@ start_frontend() {
|
||||
echo -e "${GREEN}✓ 前端服务已启动 (PID: $FRONTEND_PID)${NC}"
|
||||
echo -e "${GREEN}✓ 访问地址:${NC}"
|
||||
echo -e " 本地开发: http://localhost:8501"
|
||||
echo -e " Nginx代理: http://your-domain.com"
|
||||
}
|
||||
|
||||
# =============================================================================
|
||||
@@ -271,30 +318,28 @@ start_frontend() {
|
||||
# =============================================================================
|
||||
docker_up() {
|
||||
echo -e "${BLUE}🐳 使用 Docker Compose 启动所有服务...${NC}"
|
||||
cd "$PROJECT_DIR"
|
||||
cd "$PROJECT_DIR/docker"
|
||||
|
||||
# 检查 .env 文件
|
||||
if [ ! -f ".env" ]; then
|
||||
if [ ! -f "../.env" ]; then
|
||||
echo -e "${RED}✗ 错误：.env 文件不存在${NC}"
|
||||
echo " 请先复制配置文件:"
|
||||
echo " cp .env.docker .env # 服务器部署"
|
||||
echo " 或"
|
||||
echo " cp .env.local .env # 本地开发"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
docker compose -f docker/docker-compose.yml up -d --build
|
||||
docker compose up -d --build
|
||||
|
||||
echo -e "\n${GREEN}✓ Docker Compose 服务已启动${NC}"
|
||||
echo -e "${BLUE}📊 查看服务状态:${NC} docker compose -f docker/docker-compose.yml ps"
|
||||
echo -e "${BLUE}📝 查看日志:${NC} docker compose -f docker/docker-compose.yml logs -f"
|
||||
echo -e "${BLUE}📊 查看服务状态:${NC} docker compose ps"
|
||||
echo -e "${BLUE}📝 查看日志:${NC} docker compose logs -f"
|
||||
echo -e "${BLUE}🌐 访问应用:${NC} http://localhost:8501"
|
||||
}
|
||||
|
||||
docker_down() {
|
||||
echo -e "${BLUE}🛑 停止 Docker Compose 服务...${NC}"
|
||||
cd "$PROJECT_DIR"
|
||||
docker compose -f docker/docker-compose.yml down
|
||||
cd "$PROJECT_DIR/docker"
|
||||
docker compose down
|
||||
echo -e "${GREEN}✓ 服务已停止${NC}"
|
||||
}
|
||||
|
||||
@@ -312,8 +357,8 @@ cleanup() {
|
||||
echo -e "${GREEN}✓ 前端服务已停止${NC}"
|
||||
fi
|
||||
echo -e "${YELLOW}💡 提示：Docker 容器需要手动停止${NC}"
|
||||
echo -e " 停止 vLLM: docker stop gemma4-server"
|
||||
echo -e " 停止 PostgreSQL: docker stop postgres-langgraph"
|
||||
echo -e " 停止 llama.cpp LLM: docker stop gemma4-llamacpp-server"
|
||||
echo -e " 停止 llama.cpp Embedding: docker stop embedding-server"
|
||||
echo -e " 或使用: $0 docker-down"
|
||||
exit 0
|
||||
}
|
||||
@@ -331,8 +376,8 @@ case "${1:-help}" in
|
||||
|
||||
backend)
|
||||
check_config || exit 1
|
||||
check_vllm || start_vllm
|
||||
check_postgres || start_postgres
|
||||
check_llamacpp || start_llamacpp
|
||||
check_embedding || start_embedding
|
||||
start_backend
|
||||
echo -e "\n${GREEN}后端服务正在运行，按 Ctrl+C 停止${NC}"
|
||||
wait $BACKEND_PID
|
||||
@@ -347,8 +392,8 @@ case "${1:-help}" in
|
||||
|
||||
both)
|
||||
check_config || exit 1
|
||||
check_vllm || start_vllm
|
||||
check_postgres || start_postgres
|
||||
check_llamacpp || start_llamacpp
|
||||
check_embedding || start_embedding
|
||||
start_backend
|
||||
start_frontend
|
||||
echo -e "\n${GREEN}所有服务正在运行，按 Ctrl+C 停止 Python 服务${NC}"
|
||||
|
||||
Reference in New Issue
Block a user