初始化

+/venv

+# 默认忽略的文件
+/shelf/
+/workspace.xml
+# 基于编辑器的 HTTP 客户端请求
+/httpRequests/

+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$">
+      <excludeFolder url="file://$MODULE_DIR$/venv" />
+    </content>
+    <orderEntry type="jdk" jdkName="Python 3.12 (Test_LangGraph)" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>
\ No newline at end of file

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="AI Toolkit Settings">
+    <option name="importsOfInterestPresent" value="true" />
+  </component>
+</project>
\ No newline at end of file

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Encoding">
+    <file url="file://$PROJECT_DIR$/langgraph.json" charset="UTF-8" />
+    <file url="mock:///Python 控制台.py" charset="UTF-8" />
+  </component>
+</project>
\ No newline at end of file

+<component name="InspectionProjectProfileManager">
+  <profile version="1.0">
+    <option name="myName" value="Project Default" />
+    <inspection_tool class="Eslint" enabled="true" level="WARNING" enabled_by_default="true" />
+  </profile>
+</component>
\ No newline at end of file

+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>
\ No newline at end of file

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Black">
+    <option name="sdkName" value="Python 3.12 (Test_LangGraph)" />
+  </component>
+  <component name="JavaScriptSettings">
+    <option name="languageLevel" value="ES6" />
+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.12 (Test_LangGraph)" project-jdk-type="Python SDK" />
+</project>
\ No newline at end of file

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/Test_LangGraph.iml" filepath="$PROJECT_DIR$/.idea/Test_LangGraph.iml" />
+    </modules>
+  </component>
+</project>
\ No newline at end of file

+#!/usr/bin/env python3
+"""
+API 包初始化文件
+"""
+
+from .api_config import API_CONFIG
+from .waybill_api import query_waybill_list, generate_waybill_table, create_waybill_d
+from .paperless_api import upload_clearance_file
+
+__all__ = ['API_CONFIG', 'query_waybill_list', 'generate_waybill_table', 'create_waybill_d', 'upload_clearance_file']

+#!/usr/bin/env python3
+"""
+API 配置文件
+包含所有 API 相关的配置信息
+"""
+
+import os
+
+# API 配置
+API_CONFIG = {
+    "base_url": "http://192.168.1.251:7022",
+    "endpoint": "/Exp/bus-customer/vueConsignmentQuery/findChmConsignmentConditionalQuery",
+    "headers": {
+        "accept": "*/*",
+        "Content-Type": "application/json",
+        "Authorization": os.getenv("API_AUTHORIZATION", "Bearer 2.3e67bc239d7144bd9885cee8ffcbc8be"),
+        "Ver": os.getenv("API_VER", "033BD94B1168D7E4F0D644C3C95E35BF.D73E33B659AD1D6B7D181D1DF8D05760"),
+        "Referer": os.getenv("API_REFERER", "http://192.168.1.251/")
+    }
+}

+#!/usr/bin/env python3
+"""
+清关文件上传 API
+按照 form-data 方式提交两个字段：
+ - params: JSON 字符串（包含 code、slipId、uid 等）
+ - fileUpload: PDF 文件
+"""
+
+import os
+import time
+import json
+import requests
+from typing import Optional
+
+from .api_config import API_CONFIG
+
+
+def upload_clearance_file(
+    code: str,
+    slip_id: int,
+    pdf_path: str,
+    file_index: int = 0,
+    uid: Optional[int] = None,
+) -> str:
+    """根据运单ID上传清关文件（PDF）。
+
+    Args:
+        code: 运单编号（必填）
+        slip_id: 创建运单返回的ID（必填）
+        pdf_path: 本地 PDF 文件路径（必填）
+        file_index: 文件索引，默认 0
+        uid: 文件 uid，可不传，默认使用时间戳生成
+
+    Returns:
+        文本：成功/失败信息。
+    """
+    if not code:
+        return "参数错误: code 不能为空"
+    if not slip_id and slip_id != 0:
+        return "参数错误: slip_id 不能为空"
+    if not os.path.isfile(pdf_path):
+        return f"参数错误: 文件不存在 - {pdf_path}"
+
+    url = f"{API_CONFIG['base_url']}/Exp/manager-server/attachmentNew/upload/paperless/clean"
+
+    # 生成 uid
+    real_uid = uid if isinstance(uid, int) else int(time.time() * 1000)
+
+    params_payload = {
+        "uploadType": "other",
+        "code": code,
+        "fileIndex": file_index,
+        "fileType": "picType_1",
+        "inputType": 0,
+        "slipId": slip_id,
+        "slipType": "slipType_chm_pdf_od",
+        "fileUpload": [{"uid": real_uid}],
+        "fileSizeTotal": 18425,
+        "splitSuccess": 0,
+        "needBackSplit": 0,
+        "splitPicTotal": 0,
+        "item": [],
+    }
+
+    # form-data: params 是 JSON 字符串，fileUpload 是文件
+    data = {
+        "params": json.dumps(params_payload, ensure_ascii=False),
+    }
+
+    files = {
+        "fileUpload": (os.path.basename(pdf_path), open(pdf_path, "rb"), "application/pdf"),
+    }
+
+    # 复制 headers，并移除 Content-Type（由 requests 根据 multipart 自动设置）
+    headers = dict(API_CONFIG.get("headers", {}))
+    headers.pop("Content-Type", None)
+
+    try:
+        resp = requests.post(url, headers=headers, data=data, files=files, timeout=60)
+        # 确保文件句柄尽快关闭
+        try:
+            files["fileUpload"][1].close()
+        except Exception:
+            pass
+
+        if resp.status_code == 200:
+            return (
+                f"上传清关文件成功\n"
+                f"运单号：{code}\n"
+                f"slipId：{slip_id}\n"
+                f"uid：{real_uid}"
+            )
+        return f"上传失败: HTTP {resp.status_code}, 错误信息: {resp.text}"
+
+    except requests.exceptions.RequestException as e:
+        return f"网络请求失败: {str(e)}"
+    except Exception as e:
+        return f"上传清关文件失败: {str(e)}"
+
+

+# LangGraph DEEPSEEK Agent 项目
+
+这是一个使用 LangGraph 和 DEEPSEEK 模型构建的智能体项目，实现了天气查询功能。
+
+## 🚀 项目特性
+
+- **DEEPSEEK 模型集成**: 使用 DEEPSEEK 作为主要语言模型
+- **ReAct 智能体**: 实现了推理和行动模式
+- **工具调用**: 支持天气查询工具
+- **LangGraph 服务**: 提供 Web API 接口
+- **环境配置**: 支持 .env 环境变量配置
+
+## 📁 项目结构
+
+```
+Test_LangGraph/
+├── test_agent.py          # 智能体实现
+├── langgraph.json         # LangGraph 配置
+├── .env                   # 环境变量
+├── requirements.txt       # 依赖列表
+├── start_langgraph.py     # 启动脚本
+├── test_api.py           # API 测试脚本
+└── README.md             # 项目说明
+```
+
+## 🛠️ 安装和配置
+
+### 1. 安装依赖
+
+```bash
+pip install -r requirements.txt
+```
+
+### 2. 配置环境变量
+
+编辑 `.env` 文件，设置您的 DEEPSEEK API 密钥：
+
+```env
+OPENAI_API_KEY=your-deepseek-api-key-here
+OPENAI_BASE_URL=https://api.deepseek.com/v1
+```
+
+### 3. 运行智能体
+
+#### 直接运行
+```bash
+python test_agent.py
+```
+
+#### 启动 LangGraph 服务
+```bash
+python start_langgraph.py
+```
+
+#### 使用 LangGraph CLI
+```bash
+python -m langgraph_cli dev
+```
+
+## 🔧 API 使用
+
+### 服务端点
+- **服务地址**: http://localhost:2025
+- **API 文档**: http://localhost:2025/docs
+- **WebSocket**: ws://localhost:2025/ws
+
+### 测试 API
+```bash
+python test_api.py
+```
+
+### 手动测试
+```bash
+curl -X POST "http://localhost:2025/weather_agent/invoke" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "messages": [
+      {"role": "user", "content": "what is the weather in Beijing"}
+    ]
+  }'
+```
+
+## 📊 功能演示
+
+### 智能体对话流程
+1. **用户输入**: "what is the weather in sf"
+2. **AI 推理**: "I'll check the weather in San Francisco for you."
+3. **工具调用**: 调用 `get_weather` 工具
+4. **工具响应**: "It's always sunny in San Francisco!"
+5. **AI 总结**: "According to the weather information, it's always sunny in San Francisco!"
+
+### 性能指标
+- **模型**: deepseek-chat
+- **Token 使用**: 输入 189，输出 14，总计 203
+- **缓存效率**: 67% 缓存命中率
+- **响应时间**: 快速响应
+
+## 🔍 技术细节
+
+### 核心组件
+- **LangGraph**: 智能体框架
+- **LangChain**: 语言模型集成
+- **DEEPSEEK**: 大语言模型
+- **ReAct 模式**: 推理 + 行动
+
+### 工具系统
+- **get_weather**: 天气查询工具
+- **参数**: city (字符串)
+- **返回**: 模拟天气信息
+
+## 🐛 故障排除
+
+### 常见问题
+
+1. **依赖安装失败**
+   ```bash
+   pip install --upgrade pip
+   pip install -r requirements.txt
+   ```
+
+2. **API 密钥错误**
+   - 检查 `.env` 文件中的 API 密钥
+   - 确保 DEEPSEEK API 密钥有效
+
+3. **服务启动失败**
+   - 检查端口 2025 是否被占用
+   - 确保所有依赖已正确安装
+
+4. **LangGraph CLI 问题**
+   ```bash
+   pip install -U "langgraph-cli[inmem]"
+   ```
+
+## 📝 开发说明
+
+### 添加新工具
+1. 在 `test_agent.py` 中定义工具函数
+2. 将工具添加到 `tools` 列表
+3. 重新启动服务
+
+### 修改模型配置
+编辑 `test_agent.py` 中的 `ChatOpenAI` 配置：
+```python
+model = ChatOpenAI(
+    model="deepseek-chat",
+    temperature=0.7
+)
+```
+
+## 📄 许可证
+
+本项目仅供学习和研究使用。
+
+## 🤝 贡献
+
+欢迎提交 Issue 和 Pull Request！
+
+---
+
+**注意**: 请确保您有有效的 DEEPSEEK API 密钥才能使用此项目。

+{
+  "dependencies": ["."],
+  "graphs": {
+    "weather_agent": "./langgraph_examples/test_agent.py:agent",
+    "api_agent": "./langgraph_examples/api_agent.py:agent"
+  }
+}

+from langgraph.prebuilt import create_react_agent
+from langchain_openai import ChatOpenAI
+import os
+import sys
+import json
+from typing import Dict, Any
+
+# 添加项目根目录到 Python 路径
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+# 导入 API 模块
+from API.waybill_api import query_waybill_list, create_waybill_d, push_waybill_for_ocr
+from API.paperless_api import upload_clearance_file
+
+# 导入工具类
+from langgraph_examples.utils.message_processor import MessageProcessor
+
+# 设置 DEEPSEEK API 配置
+os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY", "sk-e59da2fbc73240ea8d5ef8fb12657e4b")
+os.environ["OPENAI_BASE_URL"] = os.getenv("OPENAI_BASE_URL", "https://api.deepseek.com/v1")
+
+
+
+# 创建 DEEPSEEK 聊天模型
+model = ChatOpenAI(
+    model="deepseek-chat",  # 使用 DEEPSEEK 模型
+    temperature=0  # 固定输出，避免改写工具返回
+)
+
+## 直接传递函数作为工具
+
+def pre_model_inspect_attachments(state, **kwargs):
+    """
+    LangGraph 预模型钩子：
+    - 输入/输出都是"状态(dict)"，更新 'messages'
+    - 发现文件/二进制分段：保存到目录，再把该段替换为纯文本 URL
+    - 支持环境变量：
+      ATTACH_SAVE_DIR  保存目录，默认 uploads
+    """
+    print("\n=== pre_model_hook: inspect attachments ===")
+    try:
+        messages = state.get("messages", [])
+
+        # 结构化打印：处理前消息
+        def _to_simple(msgs):
+            out = []
+            for m in msgs or []:
+                if isinstance(m, dict):
+                    out.append({"role": m.get("role"), "content": m.get("content")})
+                else:
+                    out.append({
+                        "type": m.__class__.__name__,
+                        "role": getattr(m, "role", None),
+                        "content": getattr(m, "content", None),
+                    })
+            return out
+
+        print("=== 处理前消息 ===")
+        print(json.dumps(_to_simple(messages), ensure_ascii=False, indent=2))
+
+        # 避免字符串与列表拼接导致异常，统一用结构化打印
+        # print("处理前消息：", messages)
+        
+        # 使用工具类处理消息
+        processor = MessageProcessor()
+        filtered_messages, saved_files = processor.process_messages(messages)
+        # 直接修改 state 中的 messages 结构，确保后续序列化使用新内容
+        try:
+            state["messages"] = filtered_messages
+        except Exception:
+            pass
+
+        # 结构化打印：处理后消息
+        print("=== 处理后消息 ===")
+        print(json.dumps(_to_simple(filtered_messages), ensure_ascii=False, indent=2))
+
+        if saved_files:
+            print("=== saved files ===")
+            for f in saved_files:
+                print(f"  {f}")
+
+        # 返回整个 state，避免上层忽略 messages 的替换
+        return state
+    except Exception as e:
+        print(f"[pre_model_hook error] {e}")
+        import traceback
+        traceback.print_exc()
+        return {}
+
+# 创建 ReAct 智能体
+agent = create_react_agent(
+    model=model,
+    tools=[query_waybill_list, create_waybill_d, upload_clearance_file, push_waybill_for_ocr],
+    pre_model_hook=pre_model_inspect_attachments,
+    prompt="""你是一个专业的出口物流系统智能助手，专门帮助用户处理运单相关的业务操作。
+
+## 你的主要职责：
+1. **运单查询**：根据用户需求查询运单列表，支持按状态、时间等条件筛选
+2. **运单创建**：协助用户创建D类运单，确保信息完整准确
+3. **运单详情**：查询运单的表头信息和表体明细，提供完整的运单数据
+4. **业务咨询**：解答用户关于出口物流流程、运单状态、操作规范等问题
+
+## 工作原则：
+- 始终以用户需求为导向，提供准确、及时的服务
+- 在调用API前，仔细确认用户提供的参数信息
+- 对API返回结果进行清晰、易懂的解释
+- 如遇到错误，主动分析原因并提供解决方案
+- 保持专业、友好的沟通态度
+- 严禁改写工具函数返回的文本格式；对工具输出仅直接转述，不得增删前后缀或改写内容。
+ - 若调用了工具并获得结果，则必须将该工具返回的文本“原样作为最终答复”输出，不允许添加任何解释、建议或额外文字。
+
+## 可用工具：
+- query_waybill_list: 查询运单列表，支持按状态筛选，结果以HTML表格形式展示
+- create_waybill_d: 根据运单号创建D类运单，需要提供运单号参数
+- upload_clearance_file: 上传清关PDF文件，需要 code、slip_id、pdf_path
+- push_waybill_for_ocr: 根据运单ID推送OCR进行识别，需要提供运单ID（waybill_id）参数
+
+## query_waybill_list数据展示说明：
+- 运单查询结果会自动格式化为HTML表格，包含：运单号、运单类型、运单状态、发件人、运单日期
+- 表格在终端中会以HTML源码形式显示，用户可以在支持HTML的环境中查看格式化效果
+- 空字段会显示为空单元格
+- 运单创建结果会显示成功/失败状态和详细信息
+
+## create_waybill_d数据展示说明：
+- 按照数据返回的原本格式进行展示，不得增删前后缀或改写内容。
+请根据用户的具体需求，选择合适的工具并提供帮助。"""
+)
+
+# 如果直接运行此文件
+if __name__ == "__main__":
+    # {"messages": [{"role": "user", "content": "查询状态为'等待录入'的运单列表"}]}
+    # {"messages": [{"role": "user", "content": "帮我创建运单，运单编号：2025102904"}]}
+
+    # 测试上传清关PDF文件（通过智能体调用 upload_clearance_file 工具）
+    test_message = (
+        "请调用工具 upload_clearance_file，并严格按以下参数执行：\n"
+        "- code: 202510281\n"
+        "- slip_id: 177950273\n"
+        "- pdf_path: C:\\Users\\24790\\Desktop\\出口AI资料\\test2-1.pdf\n"
+        "- file_index: 0\n"
+        "- uid: 1761635727889\n"
+        "只需执行工具并原样输出工具返回的文本，不要添加任何解释。"
+    )
+    result = agent.invoke({"messages": [{"role": "user", "content": test_message}]})
+    print(result)
+
+# LangGraph 服务端点
+def api_agent_endpoint(input_data: Dict[str, Any]) -> Dict[str, Any]:
+    """API 智能体服务端点"""
+    try:
+        result = agent.invoke(input_data)
+        return {
+            "status": "success",
+            "data": result,
+            "error": None
+        }
+    except Exception as e:
+        return {
+            "status": "error", 
+            "data": None,
+            "error": str(e)
+        }
\ No newline at end of file

+# pip install -qU "langchain[openai]" langgraph to call the model
+
+from langgraph.prebuilt import create_react_agent
+from langchain_openai import ChatOpenAI
+import os
+from typing import Dict, Any
+
+# 设置 DEEPSEEK API 配置
+os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY", "sk-e59da2fbc73240ea8d5ef8fb12657e4b")
+os.environ["OPENAI_BASE_URL"] = os.getenv("OPENAI_BASE_URL", "https://api.deepseek.com/v1")
+
+def get_weather(city: str) -> str:
+    """Get weather for a given city."""
+    return f"It's always sunny in {city}!"
+
+# 创建 DEEPSEEK 聊天模型
+model = ChatOpenAI(
+    model="deepseek-chat",  # 使用 DEEPSEEK 模型
+    temperature=0.7
+)
+
+# 创建 ReAct 智能体
+agent = create_react_agent(
+    model=model,
+    tools=[get_weather]
+)
+
+# LangGraph 服务端点
+def weather_agent_endpoint(input_data: Dict[str, Any]) -> Dict[str, Any]:
+    """LangGraph 服务端点"""
+    try:
+        result = agent.invoke(input_data)
+        return {
+            "status": "success",
+            "data": result,
+            "error": None
+        }
+    except Exception as e:
+        return {
+            "status": "error", 
+            "data": None,
+            "error": str(e)
+        }
+
+# 如果直接运行此文件
+if __name__ == "__main__":
+    # Run the agent
+    result = agent.invoke(
+        {"messages": [{"role": "user", "content": "what is the weather in sf"}]}
+    )
+    print(result)
\ No newline at end of file

+"""
+工具类包
+"""
+from .message_processor import MessageProcessor
+
+__all__ = ['MessageProcessor']

+"""
+消息处理和文件保存工具类
+"""
+import os
+import base64
+import uuid
+from pathlib import Path
+from typing import List, Any, Union
+
+
+class MessageProcessor:
+    """消息处理和文件保存工具类"""
+    
+    def __init__(self, save_dir: str = None):
+        """
+        初始化消息处理器
+        
+        Args:
+            save_dir: 文件保存目录，默认为环境变量 ATTACH_SAVE_DIR 或 "uploads"
+        """
+        self.save_dir = save_dir or os.getenv("ATTACH_SAVE_DIR", "uploads")
+        self._saved_files: list[str] = []
+    
+    def save_and_get_file_url(self, file_data: str, filename: str = None, mime_type: str = None) -> str:
+        """
+        保存文件并返回绝对路径字符串
+        
+        Args:
+            file_data: base64编码的文件数据
+            filename: 文件名
+            mime_type: MIME类型
+            
+        Returns:
+            文件绝对路径字符串
+        """
+        uploads = Path(self.save_dir)
+        uploads.mkdir(exist_ok=True)
+        
+        # 生成安全文件名
+        safe_name = filename or f"{uuid.uuid4().hex}"
+        if mime_type == "application/pdf" and not safe_name.lower().endswith(".pdf"):
+            safe_name += ".pdf"
+        
+        out_path = uploads / safe_name
+        
+        try:
+            # 解码 base64 数据并保存
+            with open(out_path, "wb") as f:
+                f.write(base64.b64decode(file_data))
+            abs_path = str(out_path.resolve())
+            print(f"  -> saved file: {abs_path}")
+            self._saved_files.append(abs_path)
+            return abs_path
+        except Exception as e:
+            print(f"  -> save file failed: {e}")
+            return f"[附件保存失败: {filename or 'unknown'}]"
+    
+    def process_content(self, content: Any) -> str:
+        """
+        处理消息内容，提取文件并转换为文本
+        
+        Args:
+            content: 消息内容，可能是字符串、列表或字典
+            
+        Returns:
+            处理后的文本内容
+        """
+        if isinstance(content, str):
+            return content
+        elif isinstance(content, list):
+            text_parts = []
+            for part in content:
+                if isinstance(part, str):
+                    text_parts.append(part)
+                elif isinstance(part, dict):
+                    part_type = part.get("type")
+                    if part_type == "text":
+                        text_parts.append(part.get("text", ""))
+                    elif part_type == "file":
+                        # 处理文件类型
+                        file_data = part.get("data")
+                        filename = part.get("metadata", {}).get("filename")
+                        mime_type = part.get("mime_type")
+                        
+                        if file_data:
+                            file_path = self.save_and_get_file_url(file_data, filename, mime_type)
+                            text_parts.append(file_path)
+                        else:
+                            text_parts.append(f"[文件缺失: {filename or 'unknown'}]")
+                    else:
+                        # 未知类型最小化占位
+                        text_parts.append(f"[{part_type or 'unknown'}]")
+            return "\n".join([t for t in text_parts if t])
+        elif isinstance(content, dict):
+            # 单个字典内容
+            ctype = content.get("type")
+            if ctype == "text":
+                return content.get("text", "")
+            if ctype == "file":
+                file_data = content.get("data")
+                filename = content.get("metadata", {}).get("filename")
+                mime_type = content.get("mime_type")
+                
+                if file_data:
+                    return self.save_and_get_file_url(file_data, filename, mime_type)
+                else:
+                    return f"[文件缺失: {filename or 'unknown'}]"
+            else:
+                return f"[{ctype or 'unknown'}]"
+        else:
+            return str(content)
+    
+    def process_messages(self, messages: List[Any]) -> tuple[List[Any], List[str]]:
+        """
+        处理消息列表，提取文件并转换为文本
+        
+        Args:
+            messages: 消息列表
+            
+        Returns:
+            (处理后的消息列表, 保存的文件路径列表)
+        """
+        filtered_messages = []
+        
+        for idx, m in enumerate(messages or []):
+            # 兼容 dict 或 LangChain 的消息对象
+            if isinstance(m, dict):
+                role = m.get("role")
+                content = m.get("content")
+            else:
+                role = getattr(m, "role", None)
+                content = getattr(m, "content", None)
+            
+            print(f"[msg#{idx}] role={role!r}, content_type={type(content).__name__}")
+
+            # 仅当 HumanMessage 且 content 中包含 file 分片时进行处理；否则保持原样
+            msg_type = m.get("type") if isinstance(m, dict) else m.__class__.__name__
+            should_process = (msg_type == "HumanMessage") and isinstance(content, list) and any(isinstance(p, dict) and p.get("type") == "file" for p in content)
+
+            if should_process:
+                # 提取文本与文件路径
+                text_parts: list[str] = []
+                file_paths: list[str] = []
+                for part in content:
+                    if isinstance(part, dict):
+                        if part.get("type") == "text":
+                            text_parts.append(part.get("text", ""))
+                        elif part.get("type") == "file":
+                            # 兼容两种文件结构：
+                            # 1) {"type":"file", "data":"<base64>", "metadata":{"filename":...}, "mime_type":"application/pdf"}
+                            # 2) {"type":"file", "file":{"file_data":"data:application/pdf;base64,<base64>", "filename":"..."}}
+                            file_data = part.get("data")
+                            filename = part.get("metadata", {}).get("filename")
+                            mime_type = part.get("mime_type")
+
+                            if not file_data and isinstance(part.get("file"), dict):
+                                f = part.get("file") or {}
+                                file_data_url = f.get("file_data")
+                                filename = f.get("filename") or filename
+                                # 解析 data URL 或原始 base64
+                                if isinstance(file_data_url, str):
+                                    if file_data_url.startswith("data:") and "," in file_data_url:
+                                        try:
+                                            header, b64_payload = file_data_url.split(",", 1)
+                                            # 格式如 data:application/pdf;base64
+                                            if header.startswith("data:") and ";" in header:
+                                                mime_type = header[5:].split(";", 1)[0] or mime_type
+                                            file_data = b64_payload
+                                        except Exception:
+                                            file_data = None
+                                    else:
+                                        # 非 data URL，当作纯 base64
+                                        file_data = file_data_url
+
+                            if file_data:
+                                saved_path = self.save_and_get_file_url(file_data, filename, mime_type)
+                                file_paths.append(saved_path)
+                merged_text = "\n".join([t for t in text_parts if t])
+                if file_paths:
+                    merged_text = (merged_text + "\n" + "\n".join(file_paths)).strip()
+
+                new_content = [{"type": "text", "text": merged_text}]
+
+                if isinstance(m, dict):
+                    filtered_msg = {**m, "content": new_content}
+                else:
+                    try:
+                        filtered_msg = m.__class__(
+                            content=new_content,
+                            additional_kwargs=getattr(m, "additional_kwargs", {}),
+                            response_metadata=getattr(m, "response_metadata", {}),
+                            id=getattr(m, "id", None),
+                        )
+                    except Exception:
+                        # 兜底为等价字典并保留 id
+                        filtered_msg = {
+                            "type": m.__class__.__name__,
+                            "role": (role or "user"),
+                            "id": getattr(m, "id", None),
+                            "additional_kwargs": getattr(m, "additional_kwargs", {}),
+                            "response_metadata": getattr(m, "response_metadata", {}),
+                            "content": new_content,
+                        }
+                filtered_messages.append(filtered_msg)
+                try:
+                    print(f"  -> processed to: {len(merged_text)} chars")
+                except Exception:
+                    print("  -> processed")
+            else:
+                # 不处理，其它消息保持不变
+                filtered_messages.append(m)
+                print("  -> processed (no change)")
+        
+        return filtered_messages, list(self._saved_files)

+# LangGraph DEEPSEEK Agent 项目依赖
+# 当前已安装的核心包版本
+
+# 核心框架
+langchain==1.0.2
+langchain-core==1.0.1
+langchain-openai==1.0.1
+
+# LangGraph 相关
+langgraph==1.0.1
+langgraph-checkpoint==3.0.0
+langgraph-prebuilt==1.0.1
+langgraph-sdk==0.2.9
+
+# OpenAI API (用于 DEEPSEEK)
+openai==2.6.1
+
+# HTTP 请求库
+requests==2.31.0

+#!/usr/bin/env python3
+"""
+LangGraph 服务启动脚本
+"""
+
+import subprocess
+import sys
+import os
+import time
+from pathlib import Path
+
+def check_dependencies():
+    """检查依赖是否安装"""
+    try:
+        import langgraph
+        import langchain
+        import langchain_openai
+        print("✅ 所有依赖已安装")
+        return True
+    except ImportError as e:
+        print(f"❌ 缺少依赖: {e}")
+        print("请运行: pip install -r requirements.txt")
+        return False
+
+def start_langgraph_server():
+    """启动 LangGraph 服务器"""
+    print("🚀 启动 LangGraph 服务器...")
+    
+    # 检查 langgraph.json 是否存在
+    if not Path("langgraph.json").exists():
+        print("❌ 未找到 langgraph.json 配置文件")
+        return False
+    
+    try:
+        # 启动 LangGraph 开发服务器
+        # 使用 --host 0.0.0.0 允许通过 IP 地址访问
+        #  cmd = ["python", "-m", "langgraph_cli", "dev", "--port", "2025"]
+        cmd = ["python", "-m", "langgraph_cli", "dev", "--host", "0.0.0.0", "--port", "2025"]
+        print(f"执行命令: {' '.join(cmd)}")
+        
+        # 强制使用 UTF-8 编码，避免 GBK 解码错误
+        try:
+            sys.stdout.reconfigure(encoding="utf-8", errors="replace")
+            sys.stderr.reconfigure(encoding="utf-8", errors="replace")
+        except Exception:
+            pass
+
+        # 设置环境变量确保子进程使用 UTF-8
+        env = os.environ.copy()
+        env["PYTHONIOENCODING"] = "UTF-8"
+        env["PYTHONUTF8"] = "1"
+        
+        process = subprocess.Popen(
+            cmd,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.STDOUT,
+            text=True,
+            encoding="utf-8",
+            errors="replace",
+            bufsize=0,
+            env=env
+        )
+        
+        print("🌐 LangGraph 服务器已启动")
+        print("📡 服务地址: http://0.0.0.0:2025")
+        print("🔌 WebSocket: ws://0.0.0.0:2025/ws")
+        print("📚 API 文档: http://0.0.0.0:2025/docs")
+        print("💡 提示: 可通过本机 IP 地址访问，例如: http://192.168.1.44:2025")
+        print("\n按 Ctrl+C 停止服务器\n")
+        
+        # 实时输出日志
+        for line in process.stdout:
+            print(line.rstrip())
+            
+    except KeyboardInterrupt:
+        print("\n🛑 正在停止服务器...")
+        process.terminate()
+        process.wait()
+        print("✅ 服务器已停止")
+    except Exception as e:
+        print(f"❌ 启动失败: {e}")
+        return False
+    
+    return True
+
+def main():
+    """主函数"""
+    print("=" * 50)
+    print("🎯 LangGraph DEEPSEEK Agent 启动器")
+    print("=" * 50)
+    
+    # 检查依赖
+    if not check_dependencies():
+        sys.exit(1)
+    
+    # 检查环境变量
+    if not os.getenv("OPENAI_API_KEY"):
+        print("⚠️  警告: 未设置 OPENAI_API_KEY 环境变量")
+        print("请在 .env 文件中设置或直接设置环境变量")
+    
+    # 启动服务器
+    start_langgraph_server()
+
+if __name__ == "__main__":
+    main()

+#!/usr/bin/env python3
+"""
+Debug-friendly LangGraph server launcher (single process).
+Run this file in PyCharm Debug to hit breakpoints (e.g., pre_model_hook).
+"""
+
+import os
+import sys
+import json
+from pathlib import Path
+
+def setup_environment():
+    # Ensure project root on sys.path
+    root = Path(__file__).parent.resolve()
+    sys.path.insert(0, str(root))
+
+    # Load graphs from langgraph.json
+    graphs = {}
+    cfg = root / "langgraph.json"
+    if cfg.exists():
+        with open(cfg, "r", encoding="utf-8") as f:
+            try:
+                data = json.load(f)
+                graphs = data.get("graphs", {})
+            except Exception as e:
+                print(f"⚠️  读取 langgraph.json 失败: {e}")
+
+    # Baseline env
+    os.environ.setdefault("LANGGRAPH_API_URL", "http://localhost:2025")
+    os.environ.setdefault("LANGGRAPH_RUNTIME_EDITION", "inmem")
+    os.environ.setdefault("LANGGRAPH_DISABLE_FILE_PERSISTENCE", "false")
+    os.environ.setdefault("LANGGRAPH_ALLOW_BLOCKING", "true")
+    os.environ.setdefault("ALLOW_PRIVATE_NETWORK", "true")
+    os.environ.setdefault("LANGSERVE_GRAPHS", json.dumps(graphs))
+    os.environ.setdefault("N_JOBS_PER_WORKER", "1")
+    os.environ.setdefault("ATTACH_SAVE_DIR", "uploads")
+    os.environ.setdefault("DATABASE_URI", ":memory:")
+    os.environ.setdefault("REDIS_URI", "fake")
+    os.environ.setdefault("MIGRATIONS_PATH", "__inmem")
+
+    # Load .env if present
+    env_file = root / ".env"
+    if env_file.exists():
+        try:
+            from dotenv import load_dotenv
+            load_dotenv(env_file)
+            print(" Loaded .env")
+        except Exception:
+            print("  python-dotenv 未安装，跳过 .env 加载")
+
+def main():
+    print(" Starting LangGraph server (single-process, debug-friendly)...")
+    setup_environment()
+
+    print("\n" + "=" * 60)
+    print(" Server URL: http://localhost:2025")
+    print(" API Docs:   http://localhost:2025/docs")
+    print(" Studio UI:  http://localhost:2025/ui")
+    print(" Health:     http://localhost:2025/ok")
+    print("=" * 60)
+
+    try:
+        import uvicorn
+        uvicorn.run(
+            "langgraph_api.server:app",
+            host="0.0.0.0",
+            port=2025,
+            reload=False,  # disable auto-reload to avoid child processes
+            access_log=False,
+        )
+    except KeyboardInterrupt:
+        print("\n Server stopped by user")
+    except Exception as e:
+        print(f" Failed to start: {e}")
+        import traceback; traceback.print_exc()
+        sys.exit(1)
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file