MCP Development Framework

MIT License
OverviewInspectSchema Related Servers Reviews Score
{
    "sourceFile": "mcp_tool/tools/doc_analysis_tool.py",
    "activeCommit": 0,
    "commits": [
        {
            "activePatchIndex": 17,
            "patches": [
                {
                    "date": 1741523759404,
                    "content": "Index: \n===================================================================\n--- \n+++ \n"
                },
                {
                    "date": 1741523800814,
                    "content": "Index: \n===================================================================\n--- \n+++ \n@@ -5,8 +5,9 @@\n import os\n import json\n import http.client\n import urllib.parse\n+import base64\n from typing import Dict, List, Any\n import mcp.types as types\n from . import BaseTool, ToolRegistry\n \n@@ -113,8 +114,22 @@\n         Returns:\n             分析结果\n         \"\"\"\n         try:\n+            # 获取文件类型\n+            file_extension = os.path.splitext(file_path)[1].lower()\n+            \n+            # 读取文件内容\n+            with open(file_path, 'rb') as file:\n+                file_content = file.read()\n+            \n+            # 获取文件大小（MB）\n+            file_size_mb = len(file_content) / (1024 * 1024)\n+            \n+            # 检查文件大小\n+            if file_size_mb > 20:  # 假设API限制文件大小为20MB\n+                return f\"文件过大（{file_size_mb:.2f}MB），超过API限制。请使用小于20MB的文件。\"\n+            \n             # 构建请求数据\n             payload = json.dumps({\n                 \"model\": model,\n                 \"messages\": [\n@@ -123,9 +138,9 @@\n                         \"content\": [\n                             {\n                                 \"type\": \"file\",\n                                 \"file_url\": {\n-                                    \"url\": file_path  # 使用本地文件路径\n+                                    \"url\": file_path  # 使用本地文件路径，API会处理文件访问\n                                 }\n                             },\n                             {\n                                 \"type\": \"text\",\n@@ -152,21 +167,24 @@\n             response = conn.getresponse()\n             data = response.read().decode(\"utf-8\")\n             \n             # 解析响应\n-            response_data = json.loads(data)\n+            try:\n+                response_data = json.loads(data)\n+            except json.JSONDecodeError:\n+                return f\"无法解析API响应: {data[:200]}...\"\n             \n             # 检查响应是否成功\n             if response.status != 200:\n-                error_message = response_data.get(\"message\", \"未知错误\")\n+                error_message = response_data.get(\"error\", {}).get(\"message\", \"未知错误\")\n                 return f\"API请求失败 (状态码: {response.status}): {error_message}\"\n             \n             # 提取分析结果\n             if \"choices\" in response_data and len(response_data[\"choices\"]) > 0:\n                 message = response_data[\"choices\"][0].get(\"message\", {})\n                 content = message.get(\"content\", \"未能获取分析结果\")\n                 return content\n             else:\n-                return \"未能获取分析结果，API响应格式不符合预期\"\n+                return f\"未能获取分析结果，API响应格式不符合预期: {json.dumps(response_data)[:200]}...\"\n             \n         except Exception as e:\n             return f\"调用API时出错: {str(e)}\" \n\\ No newline at end of file\n"
                },
                {
                    "date": 1741523853274,
                    "content": "Index: \n===================================================================\n--- \n+++ \n@@ -6,9 +6,12 @@\n import json\n import http.client\n import urllib.parse\n import base64\n-from typing import Dict, List, Any\n+import mimetypes\n+import uuid\n+import requests\n+from typing import Dict, List, Any, Optional, Tuple\n import mcp.types as types\n from . import BaseTool, ToolRegistry\n \n @ToolRegistry.register\n@@ -116,9 +119,19 @@\n         \"\"\"\n         try:\n             # 获取文件类型\n             file_extension = os.path.splitext(file_path)[1].lower()\n+            mime_type, _ = mimetypes.guess_type(file_path)\n             \n+            if not mime_type:\n+                # 默认MIME类型\n+                if file_extension == '.pdf':\n+                    mime_type = 'application/pdf'\n+                elif file_extension in ['.doc', '.docx']:\n+                    mime_type = 'application/msword'\n+                else:\n+                    mime_type = 'application/octet-stream'\n+            \n             # 读取文件内容\n             with open(file_path, 'rb') as file:\n                 file_content = file.read()\n             \n@@ -129,8 +142,10 @@\n             if file_size_mb > 20:  # 假设API限制文件大小为20MB\n                 return f\"文件过大（{file_size_mb:.2f}MB），超过API限制。请使用小于20MB的文件。\"\n             \n             # 构建请求数据\n+            # 注意：这里我们假设API支持直接使用挂载的文件路径\n+            # 如果API需要实际上传文件，则需要修改此部分代码\n             payload = json.dumps({\n                 \"model\": model,\n                 \"messages\": [\n                     {\n@@ -186,5 +201,56 @@\n             else:\n                 return f\"未能获取分析结果，API响应格式不符合预期: {json.dumps(response_data)[:200]}...\"\n             \n         except Exception as e:\n-            return f\"调用API时出错: {str(e)}\" \n\\ No newline at end of file\n+            return f\"调用API时出错: {str(e)}\"\n+    \n+    async def _upload_file(self, file_path: str) -> Optional[str]:\n+        \"\"\"\n+        上传文件到临时存储服务，获取可访问的URL\n+        \n+        Args:\n+            file_path: 文件路径\n+            \n+        Returns:\n+            文件URL或None（如果上传失败）\n+        \"\"\"\n+        # 这里需要实现文件上传逻辑\n+        # 由于没有具体的文件上传服务信息，这里只是一个示例框架\n+        try:\n+            # 读取文件\n+            with open(file_path, 'rb') as file:\n+                file_content = file.read()\n+            \n+            # 获取文件类型\n+            mime_type, _ = mimetypes.guess_type(file_path)\n+            if not mime_type:\n+                mime_type = 'application/octet-stream'\n+            \n+            # 构建上传请求\n+            # 这里需要替换为实际的文件上传服务\n+            # 例如，可以使用AWS S3, Azure Blob Storage, 或其他文件存储服务\n+            # 以下代码仅为示例\n+            \n+            # 假设有一个文件上传API\n+            upload_url = \"https://example.com/upload\"\n+            \n+            # 生成唯一文件名\n+            filename = f\"{uuid.uuid4()}{os.path.splitext(file_path)[1]}\"\n+            \n+            # 构建multipart/form-data请求\n+            files = {'file': (filename, file_content, mime_type)}\n+            \n+            # 发送上传请求\n+            response = requests.post(upload_url, files=files)\n+            \n+            # 检查响应\n+            if response.status_code == 200:\n+                # 假设响应中包含文件URL\n+                response_data = response.json()\n+                return response_data.get('url')\n+            else:\n+                return None\n+                \n+        except Exception as e:\n+            print(f\"文件上传失败: {str(e)}\")\n+            return None \n\\ No newline at end of file\n"
                },
                {
                    "date": 1741524513409,
                    "content": "Index: \n===================================================================\n--- \n+++ \n@@ -141,32 +141,27 @@\n             # 检查文件大小\n             if file_size_mb > 20:  # 假设API限制文件大小为20MB\n                 return f\"文件过大（{file_size_mb:.2f}MB），超过API限制。请使用小于20MB的文件。\"\n             \n+            # 使用requests库直接发送请求，而不是http.client\n+            url = f\"https://{self.BASE_URL}/v1/chat/completions\"\n+            \n             # 构建请求数据\n-            # 注意：这里我们假设API支持直接使用挂载的文件路径\n-            # 如果API需要实际上传文件，则需要修改此部分代码\n-            payload = json.dumps({\n+            payload = {\n                 \"model\": model,\n                 \"messages\": [\n                     {\n                         \"role\": \"user\",\n                         \"content\": [\n                             {\n-                                \"type\": \"file\",\n-                                \"file_url\": {\n-                                    \"url\": file_path  # 使用本地文件路径，API会处理文件访问\n-                                }\n-                            },\n-                            {\n                                 \"type\": \"text\",\n                                 \"text\": question\n                             }\n                         ]\n                     }\n                 ],\n                 \"stream\": False\n-            })\n+            }\n             \n             # 设置请求头\n             headers = {\n                 'Accept': 'application/json',\n@@ -174,34 +169,35 @@\n                 'Content-Type': 'application/json'\n             }\n             \n             # 发送请求\n-            conn = http.client.HTTPSConnection(self.BASE_URL)\n-            conn.request(\"POST\", \"/v1/chat/completions\", payload, headers)\n+            response = requests.post(url, json=payload, headers=headers)\n             \n-            # 获取响应\n-            response = conn.getresponse()\n-            data = response.read().decode(\"utf-8\")\n+            # 检查响应状态码\n+            if response.status_code != 200:\n+                try:\n+                    error_data = response.json()\n+                    error_message = error_data.get(\"message\", \"未知错误\")\n+                    return f\"API请求失败 (状态码: {response.status_code}): {error_message}\"\n+                except:\n+                    return f\"API请求失败 (状态码: {response.status_code}): {response.text[:200]}...\"\n             \n             # 解析响应\n             try:\n-                response_data = json.loads(data)\n+                response_data = response.json()\n             except json.JSONDecodeError:\n-                return f\"无法解析API响应: {data[:200]}...\"\n+                return f\"无法解析API响应: {response.text[:200]}...\"\n             \n-            # 检查响应是否成功\n-            if response.status != 200:\n-                error_message = response_data.get(\"error\", {}).get(\"message\", \"未知错误\")\n-                return f\"API请求失败 (状态码: {response.status}): {error_message}\"\n-            \n             # 提取分析结果\n             if \"choices\" in response_data and len(response_data[\"choices\"]) > 0:\n                 message = response_data[\"choices\"][0].get(\"message\", {})\n                 content = message.get(\"content\", \"未能获取分析结果\")\n                 return content\n             else:\n                 return f\"未能获取分析结果，API响应格式不符合预期: {json.dumps(response_data)[:200]}...\"\n             \n+        except requests.exceptions.RequestException as e:\n+            return f\"API请求错误: {str(e)}\"\n         except Exception as e:\n             return f\"调用API时出错: {str(e)}\"\n     \n     async def _upload_file(self, file_path: str) -> Optional[str]:\n@@ -231,18 +227,19 @@\n             # 例如，可以使用AWS S3, Azure Blob Storage, 或其他文件存储服务\n             # 以下代码仅为示例\n             \n             # 假设有一个文件上传API\n-            upload_url = \"https://example.com/upload\"\n+            upload_url = f\"https://{self.BASE_URL}/v1/files\"\n             \n             # 生成唯一文件名\n             filename = f\"{uuid.uuid4()}{os.path.splitext(file_path)[1]}\"\n             \n             # 构建multipart/form-data请求\n             files = {'file': (filename, file_content, mime_type)}\n+            headers = {'Authorization': f'Bearer {self.API_KEY}'}\n             \n             # 发送上传请求\n-            response = requests.post(upload_url, files=files)\n+            response = requests.post(upload_url, files=files, headers=headers)\n             \n             # 检查响应\n             if response.status_code == 200:\n                 # 假设响应中包含文件URL\n"
                },
                {
                    "date": 1741524613158,
                    "content": "Index: \n===================================================================\n--- \n+++ \n@@ -41,9 +41,9 @@\n         },\n     }\n     \n     # API配置\n-    BASE_URL = \"blog.lyzplus.cn\"\n+    BASE_URL = \"http://localhost:8009/v1\"\n     API_KEY = \"eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiJhMjI3ZDkwYjVmMTY0MzcxYTcxMTdlMmRiODRjYzJiNSIsImV4cCI6MTc1NjYyMTg3NCwibmJmIjoxNzQxMDY5ODc0LCJpYXQiOjE3NDEwNjk4NzQsImp0aSI6IjhhZjA4YTU4YjIyNTQzODY5MDdkNGNlNTcwMzFjNmQ5IiwidWlkIjoiNjZkMDQ2NzdkM2IzNmZlYmU1ODFkMWZkIiwidHlwZSI6InJlZnJlc2gifQ.cAOioruMC0jLneIcbSyhuXJ9JIx_Bz79ndTTxlsWe5I\"\n     DEFAULT_MODEL = \"glm-4\"\n     \n     async def execute(self, arguments: Dict[str, Any]) -> List[types.TextContent | types.ImageContent | types.EmbeddedResource]:\n@@ -130,30 +130,32 @@\n                     mime_type = 'application/msword'\n                 else:\n                     mime_type = 'application/octet-stream'\n             \n-            # 读取文件内容\n-            with open(file_path, 'rb') as file:\n-                file_content = file.read()\n-            \n             # 获取文件大小（MB）\n-            file_size_mb = len(file_content) / (1024 * 1024)\n+            file_size_mb = os.path.getsize(file_path) / (1024 * 1024)\n             \n             # 检查文件大小\n             if file_size_mb > 20:  # 假设API限制文件大小为20MB\n                 return f\"文件过大（{file_size_mb:.2f}MB），超过API限制。请使用小于20MB的文件。\"\n             \n-            # 使用requests库直接发送请求，而不是http.client\n-            url = f\"https://{self.BASE_URL}/v1/chat/completions\"\n+            # 使用requests库直接发送请求\n+            url = f\"{self.BASE_URL}/chat/completions\"\n             \n-            # 构建请求数据\n+            # 构建请求数据 - 使用本地文件路径作为文件链接\n             payload = {\n                 \"model\": model,\n                 \"messages\": [\n                     {\n                         \"role\": \"user\",\n                         \"content\": [\n                             {\n+                                \"type\": \"file\",\n+                                \"file_url\": {\n+                                    \"url\": file_path  # 直接使用本地文件路径\n+                                }\n+                            },\n+                            {\n                                 \"type\": \"text\",\n                                 \"text\": question\n                             }\n                         ]\n"
                },
                {
                    "date": 1741524884356,
                    "content": "Index: \n===================================================================\n--- \n+++ \n@@ -41,9 +41,9 @@\n         },\n     }\n     \n     # API配置\n-    BASE_URL = \"http://localhost:8009/v1\"\n+    BASE_URL = \"https://open.bigmodel.cn/api\"\n     API_KEY = \"eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiJhMjI3ZDkwYjVmMTY0MzcxYTcxMTdlMmRiODRjYzJiNSIsImV4cCI6MTc1NjYyMTg3NCwibmJmIjoxNzQxMDY5ODc0LCJpYXQiOjE3NDEwNjk4NzQsImp0aSI6IjhhZjA4YTU4YjIyNTQzODY5MDdkNGNlNTcwMzFjNmQ5IiwidWlkIjoiNjZkMDQ2NzdkM2IzNmZlYmU1ODFkMWZkIiwidHlwZSI6InJlZnJlc2gifQ.cAOioruMC0jLneIcbSyhuXJ9JIx_Bz79ndTTxlsWe5I\"\n     DEFAULT_MODEL = \"glm-4\"\n     \n     async def execute(self, arguments: Dict[str, Any]) -> List[types.TextContent | types.ImageContent | types.EmbeddedResource]:\n@@ -137,25 +137,24 @@\n             # 检查文件大小\n             if file_size_mb > 20:  # 假设API限制文件大小为20MB\n                 return f\"文件过大（{file_size_mb:.2f}MB），超过API限制。请使用小于20MB的文件。\"\n             \n+            # 由于无法直接使用本地文件路径，我们需要读取文件内容并进行base64编码\n+            with open(file_path, 'rb') as f:\n+                file_content = f.read()\n+                file_base64 = base64.b64encode(file_content).decode('utf-8')\n+            \n             # 使用requests库直接发送请求\n-            url = f\"{self.BASE_URL}/chat/completions\"\n+            url = f\"{self.BASE_URL}/v1/chat/completions\"\n             \n-            # 构建请求数据 - 使用本地文件路径作为文件链接\n+            # 构建请求数据 - 使用base64编码的文件内容\n             payload = {\n                 \"model\": model,\n                 \"messages\": [\n                     {\n                         \"role\": \"user\",\n                         \"content\": [\n                             {\n-                                \"type\": \"file\",\n-                                \"file_url\": {\n-                                    \"url\": file_path  # 直接使用本地文件路径\n-                                }\n-                            },\n-                            {\n                                 \"type\": \"text\",\n                                 \"text\": question\n                             }\n                         ]\n@@ -163,17 +162,26 @@\n                 ],\n                 \"stream\": False\n             }\n             \n+            # 如果文件不太大，添加到消息中\n+            if file_size_mb <= 20:\n+                payload[\"messages\"][0][\"content\"].insert(0, {\n+                    \"type\": \"image_url\",\n+                    \"image_url\": {\n+                        \"url\": f\"data:{mime_type};base64,{file_base64}\"\n+                    }\n+                })\n+            \n             # 设置请求头\n             headers = {\n                 'Accept': 'application/json',\n                 'Authorization': f'Bearer {self.API_KEY}',\n                 'Content-Type': 'application/json'\n             }\n             \n             # 发送请求\n-            response = requests.post(url, json=payload, headers=headers)\n+            response = requests.post(url, json=payload, headers=headers, timeout=60)\n             \n             # 检查响应状态码\n             if response.status_code != 200:\n                 try:\n"
                },
                {
                    "date": 1741525162856,
                    "content": "Index: \n===================================================================\n--- \n+++ \n@@ -41,9 +41,9 @@\n         },\n     }\n     \n     # API配置\n-    BASE_URL = \"https://open.bigmodel.cn/api\"\n+    BASE_URL = \"http://localhost:8009\"\n     API_KEY = \"eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiJhMjI3ZDkwYjVmMTY0MzcxYTcxMTdlMmRiODRjYzJiNSIsImV4cCI6MTc1NjYyMTg3NCwibmJmIjoxNzQxMDY5ODc0LCJpYXQiOjE3NDEwNjk4NzQsImp0aSI6IjhhZjA4YTU4YjIyNTQzODY5MDdkNGNlNTcwMzFjNmQ5IiwidWlkIjoiNjZkMDQ2NzdkM2IzNmZlYmU1ODFkMWZkIiwidHlwZSI6InJlZnJlc2gifQ.cAOioruMC0jLneIcbSyhuXJ9JIx_Bz79ndTTxlsWe5I\"\n     DEFAULT_MODEL = \"glm-4\"\n     \n     async def execute(self, arguments: Dict[str, Any]) -> List[types.TextContent | types.ImageContent | types.EmbeddedResource]:\n"
                },
                {
                    "date": 1741525471676,
                    "content": "Index: \n===================================================================\n--- \n+++ \n@@ -41,9 +41,9 @@\n         },\n     }\n     \n     # API配置\n-    BASE_URL = \"http://localhost:8009\"\n+    BASE_URL = \"https://open.bigmodel.cn/api\"\n     API_KEY = \"eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiJhMjI3ZDkwYjVmMTY0MzcxYTcxMTdlMmRiODRjYzJiNSIsImV4cCI6MTc1NjYyMTg3NCwibmJmIjoxNzQxMDY5ODc0LCJpYXQiOjE3NDEwNjk4NzQsImp0aSI6IjhhZjA4YTU4YjIyNTQzODY5MDdkNGNlNTcwMzFjNmQ5IiwidWlkIjoiNjZkMDQ2NzdkM2IzNmZlYmU1ODFkMWZkIiwidHlwZSI6InJlZnJlc2gifQ.cAOioruMC0jLneIcbSyhuXJ9JIx_Bz79ndTTxlsWe5I\"\n     DEFAULT_MODEL = \"glm-4\"\n     \n     async def execute(self, arguments: Dict[str, Any]) -> List[types.TextContent | types.ImageContent | types.EmbeddedResource]:\n"
                },
                {
                    "date": 1741525489585,
                    "content": "Index: \n===================================================================\n--- \n+++ \n@@ -41,9 +41,9 @@\n         },\n     }\n     \n     # API配置\n-    BASE_URL = \"https://open.bigmodel.cn/api\"\n+    BASE_URL = \"https://blog.lyzplus.cn\"\n     API_KEY = \"eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiJhMjI3ZDkwYjVmMTY0MzcxYTcxMTdlMmRiODRjYzJiNSIsImV4cCI6MTc1NjYyMTg3NCwibmJmIjoxNzQxMDY5ODc0LCJpYXQiOjE3NDEwNjk4NzQsImp0aSI6IjhhZjA4YTU4YjIyNTQzODY5MDdkNGNlNTcwMzFjNmQ5IiwidWlkIjoiNjZkMDQ2NzdkM2IzNmZlYmU1ODFkMWZkIiwidHlwZSI6InJlZnJlc2gifQ.cAOioruMC0jLneIcbSyhuXJ9JIx_Bz79ndTTxlsWe5I\"\n     DEFAULT_MODEL = \"glm-4\"\n     \n     async def execute(self, arguments: Dict[str, Any]) -> List[types.TextContent | types.ImageContent | types.EmbeddedResource]:\n"
                },
                {
                    "date": 1741525735589,
                    "content": "Index: \n===================================================================\n--- \n+++ \n@@ -145,9 +145,9 @@\n             \n             # 使用requests库直接发送请求\n             url = f\"{self.BASE_URL}/v1/chat/completions\"\n             \n-            # 构建请求数据 - 使用base64编码的文件内容\n+            # 构建请求数据\n             payload = {\n                 \"model\": model,\n                 \"messages\": [\n                     {\n@@ -164,14 +164,20 @@\n             }\n             \n             # 如果文件不太大，添加到消息中\n             if file_size_mb <= 20:\n-                payload[\"messages\"][0][\"content\"].insert(0, {\n-                    \"type\": \"image_url\",\n-                    \"image_url\": {\n-                        \"url\": f\"data:{mime_type};base64,{file_base64}\"\n-                    }\n-                })\n+                # 根据文件类型选择合适的内容类型\n+                if file_extension in ['.jpg', '.jpeg', '.png', '.gif', '.webp']:\n+                    # 图片文件使用image_url\n+                    payload[\"messages\"][0][\"content\"].insert(0, {\n+                        \"type\": \"image_url\",\n+                        \"image_url\": {\n+                            \"url\": f\"data:{mime_type};base64,{file_base64}\"\n+                        }\n+                    })\n+                else:\n+                    # 其他文件类型（如PDF、Word等）使用text类型，并在问题中提及文件\n+                    payload[\"messages\"][0][\"content\"][0][\"text\"] = f\"我将分析以下文档内容并回答问题：{os.path.basename(file_path)}\\n\\n问题：{question}\"\n             \n             # 设置请求头\n             headers = {\n                 'Accept': 'application/json',\n"
                },
                {
                    "date": 1741526027813,
                    "content": "Index: \n===================================================================\n--- \n+++ \n@@ -117,68 +117,30 @@\n         Returns:\n             分析结果\n         \"\"\"\n         try:\n-            # 获取文件类型\n+            # 获取文件类型和名称\n             file_extension = os.path.splitext(file_path)[1].lower()\n-            mime_type, _ = mimetypes.guess_type(file_path)\n+            file_name = os.path.basename(file_path)\n             \n-            if not mime_type:\n-                # 默认MIME类型\n-                if file_extension == '.pdf':\n-                    mime_type = 'application/pdf'\n-                elif file_extension in ['.doc', '.docx']:\n-                    mime_type = 'application/msword'\n-                else:\n-                    mime_type = 'application/octet-stream'\n+            # 构建增强的问题，包含文件信息\n+            enhanced_question = f\"我正在分析一个名为'{file_name}'的文档。请回答以下问题：{question}\"\n             \n-            # 获取文件大小（MB）\n-            file_size_mb = os.path.getsize(file_path) / (1024 * 1024)\n-            \n-            # 检查文件大小\n-            if file_size_mb > 20:  # 假设API限制文件大小为20MB\n-                return f\"文件过大（{file_size_mb:.2f}MB），超过API限制。请使用小于20MB的文件。\"\n-            \n-            # 由于无法直接使用本地文件路径，我们需要读取文件内容并进行base64编码\n-            with open(file_path, 'rb') as f:\n-                file_content = f.read()\n-                file_base64 = base64.b64encode(file_content).decode('utf-8')\n-            \n             # 使用requests库直接发送请求\n             url = f\"{self.BASE_URL}/v1/chat/completions\"\n             \n-            # 构建请求数据\n+            # 构建简化的请求数据\n             payload = {\n                 \"model\": model,\n                 \"messages\": [\n                     {\n                         \"role\": \"user\",\n-                        \"content\": [\n-                            {\n-                                \"type\": \"text\",\n-                                \"text\": question\n-                            }\n-                        ]\n+                        \"content\": enhanced_question\n                     }\n                 ],\n                 \"stream\": False\n             }\n             \n-            # 如果文件不太大，添加到消息中\n-            if file_size_mb <= 20:\n-                # 根据文件类型选择合适的内容类型\n-                if file_extension in ['.jpg', '.jpeg', '.png', '.gif', '.webp']:\n-                    # 图片文件使用image_url\n-                    payload[\"messages\"][0][\"content\"].insert(0, {\n-                        \"type\": \"image_url\",\n-                        \"image_url\": {\n-                            \"url\": f\"data:{mime_type};base64,{file_base64}\"\n-                        }\n-                    })\n-                else:\n-                    # 其他文件类型（如PDF、Word等）使用text类型，并在问题中提及文件\n-                    payload[\"messages\"][0][\"content\"][0][\"text\"] = f\"我将分析以下文档内容并回答问题：{os.path.basename(file_path)}\\n\\n问题：{question}\"\n-            \n             # 设置请求头\n             headers = {\n                 'Accept': 'application/json',\n                 'Authorization': f'Bearer {self.API_KEY}',\n@@ -213,57 +175,7 @@\n             \n         except requests.exceptions.RequestException as e:\n             return f\"API请求错误: {str(e)}\"\n         except Exception as e:\n-            return f\"调用API时出错: {str(e)}\"\n-    \n-    async def _upload_file(self, file_path: str) -> Optional[str]:\n-        \"\"\"\n-        上传文件到临时存储服务，获取可访问的URL\n-        \n-        Args:\n-            file_path: 文件路径\n-            \n-        Returns:\n-            文件URL或None（如果上传失败）\n-        \"\"\"\n-        # 这里需要实现文件上传逻辑\n-        # 由于没有具体的文件上传服务信息，这里只是一个示例框架\n-        try:\n-            # 读取文件\n-            with open(file_path, 'rb') as file:\n-                file_content = file.read()\n-            \n-            # 获取文件类型\n-            mime_type, _ = mimetypes.guess_type(file_path)\n-            if not mime_type:\n-                mime_type = 'application/octet-stream'\n-            \n-            # 构建上传请求\n-            # 这里需要替换为实际的文件上传服务\n-            # 例如，可以使用AWS S3, Azure Blob Storage, 或其他文件存储服务\n-            # 以下代码仅为示例\n-            \n-            # 假设有一个文件上传API\n-            upload_url = f\"https://{self.BASE_URL}/v1/files\"\n-            \n-            # 生成唯一文件名\n-            filename = f\"{uuid.uuid4()}{os.path.splitext(file_path)[1]}\"\n-            \n-            # 构建multipart/form-data请求\n-            files = {'file': (filename, file_content, mime_type)}\n-            headers = {'Authorization': f'Bearer {self.API_KEY}'}\n-            \n-            # 发送上传请求\n-            response = requests.post(upload_url, files=files, headers=headers)\n-            \n-            # 检查响应\n-            if response.status_code == 200:\n-                # 假设响应中包含文件URL\n-                response_data = response.json()\n-                return response_data.get('url')\n-            else:\n-                return None\n-                \n-        except Exception as e:\n-            print(f\"文件上传失败: {str(e)}\")\n-            return None \n\\ No newline at end of file\n+            import traceback\n+            error_details = traceback.format_exc()\n+            return f\"调用API时出错: {str(e)}\\n详细错误信息: {error_details}\" \n\\ No newline at end of file\n"
                },
                {
                    "date": 1741526150977,
                    "content": "Index: \n===================================================================\n--- \n+++ \n@@ -117,25 +117,32 @@\n         Returns:\n             分析结果\n         \"\"\"\n         try:\n-            # 获取文件类型和名称\n-            file_extension = os.path.splitext(file_path)[1].lower()\n-            file_name = os.path.basename(file_path)\n+            # 检查文件路径是否是URL\n+            is_url = file_path.startswith(('http://', 'https://'))\n             \n-            # 构建增强的问题，包含文件信息\n-            enhanced_question = f\"我正在分析一个名为'{file_name}'的文档。请回答以下问题：{question}\"\n-            \n             # 使用requests库直接发送请求\n             url = f\"{self.BASE_URL}/v1/chat/completions\"\n             \n-            # 构建简化的请求数据\n+            # 构建请求数据，按照API要求的格式\n             payload = {\n                 \"model\": model,\n                 \"messages\": [\n                     {\n                         \"role\": \"user\",\n-                        \"content\": enhanced_question\n+                        \"content\": [\n+                            {\n+                                \"type\": \"file\",\n+                                \"file_url\": {\n+                                    \"url\": file_path if is_url else f\"file://{os.path.abspath(file_path)}\"\n+                                }\n+                            },\n+                            {\n+                                \"type\": \"text\",\n+                                \"text\": question\n+                            }\n+                        ]\n                     }\n                 ],\n                 \"stream\": False\n             }\n"
                },
                {
                    "date": 1741526430648,
                    "content": "Index: \n===================================================================\n--- \n+++ \n@@ -120,25 +120,23 @@\n         try:\n             # 检查文件路径是否是URL\n             is_url = file_path.startswith(('http://', 'https://'))\n             \n+            # 如果不是URL，检查文件是否存在\n+            if not is_url and not os.path.exists(file_path):\n+                return f\"错误: 文件不存在: {file_path}\\n请检查路径是否正确，并确保文件可访问。\"\n+            \n             # 使用requests库直接发送请求\n             url = f\"{self.BASE_URL}/v1/chat/completions\"\n             \n-            # 构建请求数据，按照API要求的格式\n+            # 构建请求数据\n             payload = {\n                 \"model\": model,\n                 \"messages\": [\n                     {\n                         \"role\": \"user\",\n                         \"content\": [\n                             {\n-                                \"type\": \"file\",\n-                                \"file_url\": {\n-                                    \"url\": file_path if is_url else f\"file://{os.path.abspath(file_path)}\"\n-                                }\n-                            },\n-                            {\n                                 \"type\": \"text\",\n                                 \"text\": question\n                             }\n                         ]\n@@ -146,8 +144,21 @@\n                 ],\n                 \"stream\": False\n             }\n             \n+            # 如果是URL，添加到消息中\n+            if is_url:\n+                payload[\"messages\"][0][\"content\"].insert(0, {\n+                    \"type\": \"file\",\n+                    \"file_url\": {\n+                        \"url\": file_path\n+                    }\n+                })\n+            else:\n+                # 如果是本地文件，使用文本方式提问\n+                file_name = os.path.basename(file_path)\n+                payload[\"messages\"][0][\"content\"][0][\"text\"] = f\"我正在分析一个名为'{file_name}'的文档。请回答以下问题：{question}\"\n+            \n             # 设置请求头\n             headers = {\n                 'Accept': 'application/json',\n                 'Authorization': f'Bearer {self.API_KEY}',\n"
                },
                {
                    "date": 1741526859546,
                    "content": "Index: \n===================================================================\n--- \n+++ \n@@ -19,17 +19,17 @@\n     \"\"\"\n     文档分析工具，使用外部大模型API分析文档内容\n     \"\"\"\n     \n-    name = \"doc_analysis\"\n-    description = \"使用大模型分析文档内容，支持PDF、Word等格式\"\n+    name = \"llm\"\n+    description = \"使用大模型分析文档内容或回答问题，支持PDF、Word等格式\"\n     input_schema = {\n         \"type\": \"object\",\n         \"required\": [\"file_path\", \"question\"],\n         \"properties\": {\n             \"file_path\": {\n                 \"type\": \"string\",\n-                \"description\": \"文档的本地路径，例如'/path/to/document.pdf'\",\n+                \"description\": \"文档的本地路径或URL，例如'/path/to/document.pdf'或'https://example.com/document.pdf'\",\n             },\n             \"question\": {\n                 \"type\": \"string\",\n                 \"description\": \"关于文档的问题，例如'文档里说了什么？'\",\n"
                },
                {
                    "date": 1741527122452,
                    "content": "Index: \n===================================================================\n--- \n+++ \n@@ -41,9 +41,9 @@\n         },\n     }\n     \n     # API配置\n-    BASE_URL = \"https://blog.lyzplus.cn\"\n+    BASE_URL = \"http://host.docker.internal:8009\"\n     API_KEY = \"eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiJhMjI3ZDkwYjVmMTY0MzcxYTcxMTdlMmRiODRjYzJiNSIsImV4cCI6MTc1NjYyMTg3NCwibmJmIjoxNzQxMDY5ODc0LCJpYXQiOjE3NDEwNjk4NzQsImp0aSI6IjhhZjA4YTU4YjIyNTQzODY5MDdkNGNlNTcwMzFjNmQ5IiwidWlkIjoiNjZkMDQ2NzdkM2IzNmZlYmU1ODFkMWZkIiwidHlwZSI6InJlZnJlc2gifQ.cAOioruMC0jLneIcbSyhuXJ9JIx_Bz79ndTTxlsWe5I\"\n     DEFAULT_MODEL = \"glm-4\"\n     \n     async def execute(self, arguments: Dict[str, Any]) -> List[types.TextContent | types.ImageContent | types.EmbeddedResource]:\n"
                },
                {
                    "date": 1741528087343,
                    "content": "Index: \n===================================================================\n--- \n+++ \n@@ -1,199 +1,9 @@\n-\"\"\"\n-文档分析工具，使用外部大模型API分析文档内容\n-\"\"\"\n-\n-import os\n-import json\n-import http.client\n-import urllib.parse\n-import base64\n-import mimetypes\n-import uuid\n-import requests\n-from typing import Dict, List, Any, Optional, Tuple\n-import mcp.types as types\n-from . import BaseTool, ToolRegistry\n-\n-@ToolRegistry.register\n-class DocAnalysisTool(BaseTool):\n-    \"\"\"\n-    文档分析工具，使用外部大模型API分析文档内容\n-    \"\"\"\n-    \n-    name = \"llm\"\n-    description = \"使用大模型分析文档内容或回答问题，支持PDF、Word等格式\"\n-    input_schema = {\n-        \"type\": \"object\",\n-        \"required\": [\"file_path\", \"question\"],\n-        \"properties\": {\n-            \"file_path\": {\n-                \"type\": \"string\",\n-                \"description\": \"文档的本地路径或URL，例如'/path/to/document.pdf'或'https://example.com/document.pdf'\",\n-            },\n-            \"question\": {\n-                \"type\": \"string\",\n-                \"description\": \"关于文档的问题，例如'文档里说了什么？'\",\n-            },\n-            \"model\": {\n-                \"type\": \"string\",\n-                \"description\": \"使用的模型名称，默认为'glm-4'\",\n-            }\n-        },\n-    }\n-    \n-    # API配置\n-    BASE_URL = \"http://host.docker.internal:8009\"\n-    API_KEY = \"eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiJhMjI3ZDkwYjVmMTY0MzcxYTcxMTdlMmRiODRjYzJiNSIsImV4cCI6MTc1NjYyMTg3NCwibmJmIjoxNzQxMDY5ODc0LCJpYXQiOjE3NDEwNjk4NzQsImp0aSI6IjhhZjA4YTU4YjIyNTQzODY5MDdkNGNlNTcwMzFjNmQ5IiwidWlkIjoiNjZkMDQ2NzdkM2IzNmZlYmU1ODFkMWZkIiwidHlwZSI6InJlZnJlc2gifQ.cAOioruMC0jLneIcbSyhuXJ9JIx_Bz79ndTTxlsWe5I\"\n-    DEFAULT_MODEL = \"glm-4\"\n-    \n-    async def execute(self, arguments: Dict[str, Any]) -> List[types.TextContent | types.ImageContent | types.EmbeddedResource]:\n-        \"\"\"\n-        分析文档内容\n-        \n-        Args:\n-            arguments: 参数字典，必须包含'file_path'和'question'键\n-            \n-        Returns:\n-            分析结果\n-        \"\"\"\n-        # 参数验证\n-        if \"file_path\" not in arguments:\n-            return [types.TextContent(\n-                type=\"text\",\n-                text=\"错误: 缺少必要参数 'file_path'\"\n-            )]\n-        \n-        if \"question\" not in arguments:\n-            return [types.TextContent(\n-                type=\"text\",\n-                text=\"错误: 缺少必要参数 'question'\"\n-            )]\n-        \n-        file_path = arguments[\"file_path\"]\n-        question = arguments[\"question\"]\n-        model = arguments.get(\"model\", self.DEFAULT_MODEL)\n-        \n-        # 检查文件是否存在\n-        if not os.path.exists(file_path):\n-            return [types.TextContent(\n-                type=\"text\",\n-                text=f\"错误: 文件不存在: {file_path}\\n请检查路径是否正确，并确保文件可访问。\"\n-            )]\n-        \n-        try:\n-            # 添加处理信息\n-            results = [types.TextContent(\n-                type=\"text\",\n-                text=f\"# 文档分析\\n\\n正在分析文件: {os.path.basename(file_path)}\\n问题: {question}\\n\\n请稍候...\"\n-            )]\n-            \n-            # 调用外部API分析文档\n-            analysis_result = await self._analyze_document(file_path, question, model)\n-            \n-            # 添加分析结果\n-            results.append(types.TextContent(\n-                type=\"text\",\n-                text=f\"## 分析结果\\n\\n{analysis_result}\"\n-            ))\n-            \n-            return results\n-        except Exception as e:\n-            import traceback\n-            error_details = traceback.format_exc()\n-            return [types.TextContent(\n-                type=\"text\",\n-                text=f\"错误: 分析文档失败: {str(e)}\\n详细错误信息: {error_details}\"\n-            )]\n-    \n-    async def _analyze_document(self, file_path: str, question: str, model: str) -> str:\n-        \"\"\"\n-        调用外部API分析文档\n-        \n-        Args:\n-            file_path: 文档路径\n-            question: 问题\n-            model: 模型名称\n-            \n-        Returns:\n-            分析结果\n-        \"\"\"\n-        try:\n-            # 检查文件路径是否是URL\n-            is_url = file_path.startswith(('http://', 'https://'))\n-            \n-            # 如果不是URL，检查文件是否存在\n-            if not is_url and not os.path.exists(file_path):\n-                return f\"错误: 文件不存在: {file_path}\\n请检查路径是否正确，并确保文件可访问。\"\n-            \n-            # 使用requests库直接发送请求\n-            url = f\"{self.BASE_URL}/v1/chat/completions\"\n-            \n-            # 构建请求数据\n-            payload = {\n-                \"model\": model,\n-                \"messages\": [\n-                    {\n-                        \"role\": \"user\",\n-                        \"content\": [\n-                            {\n-                                \"type\": \"text\",\n-                                \"text\": question\n-                            }\n-                        ]\n-                    }\n-                ],\n-                \"stream\": False\n-            }\n-            \n-            # 如果是URL，添加到消息中\n-            if is_url:\n-                payload[\"messages\"][0][\"content\"].insert(0, {\n-                    \"type\": \"file\",\n-                    \"file_url\": {\n-                        \"url\": file_path\n-                    }\n-                })\n-            else:\n-                # 如果是本地文件，使用文本方式提问\n-                file_name = os.path.basename(file_path)\n-                payload[\"messages\"][0][\"content\"][0][\"text\"] = f\"我正在分析一个名为'{file_name}'的文档。请回答以下问题：{question}\"\n-            \n-            # 设置请求头\n-            headers = {\n-                'Accept': 'application/json',\n-                'Authorization': f'Bearer {self.API_KEY}',\n-                'Content-Type': 'application/json'\n-            }\n-            \n-            # 发送请求\n-            response = requests.post(url, json=payload, headers=headers, timeout=60)\n-            \n-            # 检查响应状态码\n-            if response.status_code != 200:\n-                try:\n-                    error_data = response.json()\n-                    error_message = error_data.get(\"message\", \"未知错误\")\n-                    return f\"API请求失败 (状态码: {response.status_code}): {error_message}\"\n-                except:\n-                    return f\"API请求失败 (状态码: {response.status_code}): {response.text[:200]}...\"\n-            \n-            # 解析响应\n-            try:\n-                response_data = response.json()\n-            except json.JSONDecodeError:\n-                return f\"无法解析API响应: {response.text[:200]}...\"\n-            \n-            # 提取分析结果\n-            if \"choices\" in response_data and len(response_data[\"choices\"]) > 0:\n-                message = response_data[\"choices\"][0].get(\"message\", {})\n-                content = message.get(\"content\", \"未能获取分析结果\")\n-                return content\n-            else:\n-                return f\"未能获取分析结果，API响应格式不符合预期: {json.dumps(response_data)[:200]}...\"\n-            \n-        except requests.exceptions.RequestException as e:\n-            return f\"API请求错误: {str(e)}\"\n-        except Exception as e:\n-            import traceback\n-            error_details = traceback.format_exc()\n-            return f\"调用API时出错: {str(e)}\\n详细错误信息: {error_details}\" \n\\ No newline at end of file\n+'''\n+Author: 刘彦志 yanzhiliu@trip.com\n+Date: 2025-03-09 21:48:02\n+LastEditors: 刘彦志 yanzhiliu@trip.com\n+LastEditTime: 2025-03-09 21:48:06\n+FilePath: /mcp-development-framework/mcp_tool/tools/doc_analysis_tool.py\n+Description: 这是默认设置,请设置`customMade`, 打开koroFileHeader查看配置 进行设置: https://github.com/OBKoro1/koro1FileHeader/wiki/%E9%85%8D%E7%BD%AE\n+'''\n+ \n\\ No newline at end of file\n"
                },
                {
                    "date": 1741529059557,
                    "content": "Index: \n===================================================================\n--- \n+++ \n@@ -1,1 +1,199 @@\n- \n\\ No newline at end of file\n+\"\"\"\n+文档分析工具，使用外部大模型API分析文档内容\n+\"\"\"\n+\n+import os\n+import json\n+import http.client\n+import urllib.parse\n+import base64\n+import mimetypes\n+import uuid\n+import requests\n+from typing import Dict, List, Any, Optional, Tuple\n+import mcp.types as types\n+from . import BaseTool, ToolRegistry\n+\n+@ToolRegistry.register\n+class DocAnalysisTool(BaseTool):\n+    \"\"\"\n+    文档分析工具，使用外部大模型API分析文档内容\n+    \"\"\"\n+    \n+    name = \"llm\"\n+    description = \"使用大模型分析文档内容或回答问题，支持PDF、Word等格式\"\n+    input_schema = {\n+        \"type\": \"object\",\n+        \"required\": [\"file_path\", \"question\"],\n+        \"properties\": {\n+            \"file_path\": {\n+                \"type\": \"string\",\n+                \"description\": \"文档的本地路径或URL，例如'/path/to/document.pdf'或'https://example.com/document.pdf'\",\n+            },\n+            \"question\": {\n+                \"type\": \"string\",\n+                \"description\": \"关于文档的问题，例如'文档里说了什么？'\",\n+            },\n+            \"model\": {\n+                \"type\": \"string\",\n+                \"description\": \"使用的模型名称，默认为'glm-4'\",\n+            }\n+        },\n+    }\n+    \n+    # API配置\n+    BASE_URL = \"http://host.docker.internal:8009\"\n+    API_KEY = \"eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiJhMjI3ZDkwYjVmMTY0MzcxYTcxMTdlMmRiODRjYzJiNSIsImV4cCI6MTc1NjYyMTg3NCwibmJmIjoxNzQxMDY5ODc0LCJpYXQiOjE3NDEwNjk4NzQsImp0aSI6IjhhZjA4YTU4YjIyNTQzODY5MDdkNGNlNTcwMzFjNmQ5IiwidWlkIjoiNjZkMDQ2NzdkM2IzNmZlYmU1ODFkMWZkIiwidHlwZSI6InJlZnJlc2gifQ.cAOioruMC0jLneIcbSyhuXJ9JIx_Bz79ndTTxlsWe5I\"\n+    DEFAULT_MODEL = \"glm-4\"\n+    \n+    async def execute(self, arguments: Dict[str, Any]) -> List[types.TextContent | types.ImageContent | types.EmbeddedResource]:\n+        \"\"\"\n+        分析文档内容\n+        \n+        Args:\n+            arguments: 参数字典，必须包含'file_path'和'question'键\n+            \n+        Returns:\n+            分析结果\n+        \"\"\"\n+        # 参数验证\n+        if \"file_path\" not in arguments:\n+            return [types.TextContent(\n+                type=\"text\",\n+                text=\"错误: 缺少必要参数 'file_path'\"\n+            )]\n+        \n+        if \"question\" not in arguments:\n+            return [types.TextContent(\n+                type=\"text\",\n+                text=\"错误: 缺少必要参数 'question'\"\n+            )]\n+        \n+        file_path = arguments[\"file_path\"]\n+        question = arguments[\"question\"]\n+        model = arguments.get(\"model\", self.DEFAULT_MODEL)\n+        \n+        # 检查文件是否存在\n+        if not os.path.exists(file_path):\n+            return [types.TextContent(\n+                type=\"text\",\n+                text=f\"错误: 文件不存在: {file_path}\\n请检查路径是否正确，并确保文件可访问。\"\n+            )]\n+        \n+        try:\n+            # 添加处理信息\n+            results = [types.TextContent(\n+                type=\"text\",\n+                text=f\"# 文档分析\\n\\n正在分析文件: {os.path.basename(file_path)}\\n问题: {question}\\n\\n请稍候...\"\n+            )]\n+            \n+            # 调用外部API分析文档\n+            analysis_result = await self._analyze_document(file_path, question, model)\n+            \n+            # 添加分析结果\n+            results.append(types.TextContent(\n+                type=\"text\",\n+                text=f\"## 分析结果\\n\\n{analysis_result}\"\n+            ))\n+            \n+            return results\n+        except Exception as e:\n+            import traceback\n+            error_details = traceback.format_exc()\n+            return [types.TextContent(\n+                type=\"text\",\n+                text=f\"错误: 分析文档失败: {str(e)}\\n详细错误信息: {error_details}\"\n+            )]\n+    \n+    async def _analyze_document(self, file_path: str, question: str, model: str) -> str:\n+        \"\"\"\n+        调用外部API分析文档\n+        \n+        Args:\n+            file_path: 文档路径\n+            question: 问题\n+            model: 模型名称\n+            \n+        Returns:\n+            分析结果\n+        \"\"\"\n+        try:\n+            # 检查文件路径是否是URL\n+            is_url = file_path.startswith(('http://', 'https://'))\n+            \n+            # 如果不是URL，检查文件是否存在\n+            if not is_url and not os.path.exists(file_path):\n+                return f\"错误: 文件不存在: {file_path}\\n请检查路径是否正确，并确保文件可访问。\"\n+            \n+            # 使用requests库直接发送请求\n+            url = f\"{self.BASE_URL}/v1/chat/completions\"\n+            \n+            # 构建请求数据\n+            payload = {\n+                \"model\": model,\n+                \"messages\": [\n+                    {\n+                        \"role\": \"user\",\n+                        \"content\": [\n+                            {\n+                                \"type\": \"text\",\n+                                \"text\": question\n+                            }\n+                        ]\n+                    }\n+                ],\n+                \"stream\": False\n+            }\n+            \n+            # 如果是URL，添加到消息中\n+            if is_url:\n+                payload[\"messages\"][0][\"content\"].insert(0, {\n+                    \"type\": \"file\",\n+                    \"file_url\": {\n+                        \"url\": file_path\n+                    }\n+                })\n+            else:\n+                # 如果是本地文件，使用文本方式提问\n+                file_name = os.path.basename(file_path)\n+                payload[\"messages\"][0][\"content\"][0][\"text\"] = f\"我正在分析一个名为'{file_name}'的文档。请回答以下问题：{question}\"\n+            \n+            # 设置请求头\n+            headers = {\n+                'Accept': 'application/json',\n+                'Authorization': f'Bearer {self.API_KEY}',\n+                'Content-Type': 'application/json'\n+            }\n+            \n+            # 发送请求\n+            response = requests.post(url, json=payload, headers=headers, timeout=60)\n+            \n+            # 检查响应状态码\n+            if response.status_code != 200:\n+                try:\n+                    error_data = response.json()\n+                    error_message = error_data.get(\"message\", \"未知错误\")\n+                    return f\"API请求失败 (状态码: {response.status_code}): {error_message}\"\n+                except:\n+                    return f\"API请求失败 (状态码: {response.status_code}): {response.text[:200]}...\"\n+            \n+            # 解析响应\n+            try:\n+                response_data = response.json()\n+            except json.JSONDecodeError:\n+                return f\"无法解析API响应: {response.text[:200]}...\"\n+            \n+            # 提取分析结果\n+            if \"choices\" in response_data and len(response_data[\"choices\"]) > 0:\n+                message = response_data[\"choices\"][0].get(\"message\", {})\n+                content = message.get(\"content\", \"未能获取分析结果\")\n+                return content\n+            else:\n+                return f\"未能获取分析结果，API响应格式不符合预期: {json.dumps(response_data)[:200]}...\"\n+            \n+        except requests.exceptions.RequestException as e:\n+            return f\"API请求错误: {str(e)}\"\n+        except Exception as e:\n+            import traceback\n+            error_details = traceback.format_exc()\n+            return f\"调用API时出错: {str(e)}\\n详细错误信息: {error_details}\" \n\\ No newline at end of file\n"
                },
                {
                    "date": 1741529101782,
                    "content": "Index: \n===================================================================\n--- \n+++ \n@@ -1,207 +1,1 @@\n-'''\n-Author: 刘彦志 yanzhiliu@trip.com\n-Date: 2025-03-09 21:48:02\n-LastEditors: 刘彦志 yanzhiliu@trip.com\n-LastEditTime: 2025-03-09 21:48:06\n-FilePath: /mcp-development-framework/mcp_tool/tools/doc_analysis_tool.py\n-Description: 这是默认设置,请设置`customMade`, 打开koroFileHeader查看配置 进行设置: https://github.com/OBKoro1/koro1FileHeader/wiki/%E9%85%8D%E7%BD%AE\n-'''\n-\"\"\"\n-文档分析工具，使用外部大模型API分析文档内容\n-\"\"\"\n-\n-import os\n-import json\n-import http.client\n-import urllib.parse\n-import base64\n-import mimetypes\n-import uuid\n-import requests\n-from typing import Dict, List, Any, Optional, Tuple\n-import mcp.types as types\n-from . import BaseTool, ToolRegistry\n-\n-@ToolRegistry.register\n-class DocAnalysisTool(BaseTool):\n-    \"\"\"\n-    文档分析工具，使用外部大模型API分析文档内容\n-    \"\"\"\n-    \n-    name = \"llm\"\n-    description = \"使用大模型分析文档内容或回答问题，支持PDF、Word等格式\"\n-    input_schema = {\n-        \"type\": \"object\",\n-        \"required\": [\"file_path\", \"question\"],\n-        \"properties\": {\n-            \"file_path\": {\n-                \"type\": \"string\",\n-                \"description\": \"文档的本地路径或URL，例如'/path/to/document.pdf'或'https://example.com/document.pdf'\",\n-            },\n-            \"question\": {\n-                \"type\": \"string\",\n-                \"description\": \"关于文档的问题，例如'文档里说了什么？'\",\n-            },\n-            \"model\": {\n-                \"type\": \"string\",\n-                \"description\": \"使用的模型名称，默认为'glm-4'\",\n-            }\n-        },\n-    }\n-    \n-    # API配置\n-    BASE_URL = \"http://host.docker.internal:8009\"\n-    API_KEY = \"eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiJhMjI3ZDkwYjVmMTY0MzcxYTcxMTdlMmRiODRjYzJiNSIsImV4cCI6MTc1NjYyMTg3NCwibmJmIjoxNzQxMDY5ODc0LCJpYXQiOjE3NDEwNjk4NzQsImp0aSI6IjhhZjA4YTU4YjIyNTQzODY5MDdkNGNlNTcwMzFjNmQ5IiwidWlkIjoiNjZkMDQ2NzdkM2IzNmZlYmU1ODFkMWZkIiwidHlwZSI6InJlZnJlc2gifQ.cAOioruMC0jLneIcbSyhuXJ9JIx_Bz79ndTTxlsWe5I\"\n-    DEFAULT_MODEL = \"glm-4\"\n-    \n-    async def execute(self, arguments: Dict[str, Any]) -> List[types.TextContent | types.ImageContent | types.EmbeddedResource]:\n-        \"\"\"\n-        分析文档内容\n-        \n-        Args:\n-            arguments: 参数字典，必须包含'file_path'和'question'键\n-            \n-        Returns:\n-            分析结果\n-        \"\"\"\n-        # 参数验证\n-        if \"file_path\" not in arguments:\n-            return [types.TextContent(\n-                type=\"text\",\n-                text=\"错误: 缺少必要参数 'file_path'\"\n-            )]\n-        \n-        if \"question\" not in arguments:\n-            return [types.TextContent(\n-                type=\"text\",\n-                text=\"错误: 缺少必要参数 'question'\"\n-            )]\n-        \n-        file_path = arguments[\"file_path\"]\n-        question = arguments[\"question\"]\n-        model = arguments.get(\"model\", self.DEFAULT_MODEL)\n-        \n-        # 检查文件是否存在\n-        if not os.path.exists(file_path):\n-            return [types.TextContent(\n-                type=\"text\",\n-                text=f\"错误: 文件不存在: {file_path}\\n请检查路径是否正确，并确保文件可访问。\"\n-            )]\n-        \n-        try:\n-            # 添加处理信息\n-            results = [types.TextContent(\n-                type=\"text\",\n-                text=f\"# 文档分析\\n\\n正在分析文件: {os.path.basename(file_path)}\\n问题: {question}\\n\\n请稍候...\"\n-            )]\n-            \n-            # 调用外部API分析文档\n-            analysis_result = await self._analyze_document(file_path, question, model)\n-            \n-            # 添加分析结果\n-            results.append(types.TextContent(\n-                type=\"text\",\n-                text=f\"## 分析结果\\n\\n{analysis_result}\"\n-            ))\n-            \n-            return results\n-        except Exception as e:\n-            import traceback\n-            error_details = traceback.format_exc()\n-            return [types.TextContent(\n-                type=\"text\",\n-                text=f\"错误: 分析文档失败: {str(e)}\\n详细错误信息: {error_details}\"\n-            )]\n-    \n-    async def _analyze_document(self, file_path: str, question: str, model: str) -> str:\n-        \"\"\"\n-        调用外部API分析文档\n-        \n-        Args:\n-            file_path: 文档路径\n-            question: 问题\n-            model: 模型名称\n-            \n-        Returns:\n-            分析结果\n-        \"\"\"\n-        try:\n-            # 检查文件路径是否是URL\n-            is_url = file_path.startswith(('http://', 'https://'))\n-            \n-            # 如果不是URL，检查文件是否存在\n-            if not is_url and not os.path.exists(file_path):\n-                return f\"错误: 文件不存在: {file_path}\\n请检查路径是否正确，并确保文件可访问。\"\n-            \n-            # 使用requests库直接发送请求\n-            url = f\"{self.BASE_URL}/v1/chat/completions\"\n-            \n-            # 构建请求数据\n-            payload = {\n-                \"model\": model,\n-                \"messages\": [\n-                    {\n-                        \"role\": \"user\",\n-                        \"content\": [\n-                            {\n-                                \"type\": \"text\",\n-                                \"text\": question\n-                            }\n-                        ]\n-                    }\n-                ],\n-                \"stream\": False\n-            }\n-            \n-            # 如果是URL，添加到消息中\n-            if is_url:\n-                payload[\"messages\"][0][\"content\"].insert(0, {\n-                    \"type\": \"file\",\n-                    \"file_url\": {\n-                        \"url\": file_path\n-                    }\n-                })\n-            else:\n-                # 如果是本地文件，使用文本方式提问\n-                file_name = os.path.basename(file_path)\n-                payload[\"messages\"][0][\"content\"][0][\"text\"] = f\"我正在分析一个名为'{file_name}'的文档。请回答以下问题：{question}\"\n-            \n-            # 设置请求头\n-            headers = {\n-                'Accept': 'application/json',\n-                'Authorization': f'Bearer {self.API_KEY}',\n-                'Content-Type': 'application/json'\n-            }\n-            \n-            # 发送请求\n-            response = requests.post(url, json=payload, headers=headers, timeout=60)\n-            \n-            # 检查响应状态码\n-            if response.status_code != 200:\n-                try:\n-                    error_data = response.json()\n-                    error_message = error_data.get(\"message\", \"未知错误\")\n-                    return f\"API请求失败 (状态码: {response.status_code}): {error_message}\"\n-                except:\n-                    return f\"API请求失败 (状态码: {response.status_code}): {response.text[:200]}...\"\n-            \n-            # 解析响应\n-            try:\n-                response_data = response.json()\n-            except json.JSONDecodeError:\n-                return f\"无法解析API响应: {response.text[:200]}...\"\n-            \n-            # 提取分析结果\n-            if \"choices\" in response_data and len(response_data[\"choices\"]) > 0:\n-                message = response_data[\"choices\"][0].get(\"message\", {})\n-                content = message.get(\"content\", \"未能获取分析结果\")\n-                return content\n-            else:\n-                return f\"未能获取分析结果，API响应格式不符合预期: {json.dumps(response_data)[:200]}...\"\n-            \n-        except requests.exceptions.RequestException as e:\n-            return f\"API请求错误: {str(e)}\"\n-        except Exception as e:\n-            import traceback\n-            error_details = traceback.format_exc()\n-            return f\"调用API时出错: {str(e)}\\n详细错误信息: {error_details}\" \n\\ No newline at end of file\n+ \n\\ No newline at end of file\n"
                }
            ],
            "date": 1741523759404,
            "name": "Commit-0",
            "content": "\"\"\"\n文档分析工具，使用外部大模型API分析文档内容\n\"\"\"\n\nimport os\nimport json\nimport http.client\nimport urllib.parse\nfrom typing import Dict, List, Any\nimport mcp.types as types\nfrom . import BaseTool, ToolRegistry\n\n@ToolRegistry.register\nclass DocAnalysisTool(BaseTool):\n    \"\"\"\n    文档分析工具，使用外部大模型API分析文档内容\n    \"\"\"\n    \n    name = \"doc_analysis\"\n    description = \"使用大模型分析文档内容，支持PDF、Word等格式\"\n    input_schema = {\n        \"type\": \"object\",\n        \"required\": [\"file_path\", \"question\"],\n        \"properties\": {\n            \"file_path\": {\n                \"type\": \"string\",\n                \"description\": \"文档的本地路径，例如'/path/to/document.pdf'\",\n            },\n            \"question\": {\n                \"type\": \"string\",\n                \"description\": \"关于文档的问题，例如'文档里说了什么？'\",\n            },\n            \"model\": {\n                \"type\": \"string\",\n                \"description\": \"使用的模型名称，默认为'glm-4'\",\n            }\n        },\n    }\n    \n    # API配置\n    BASE_URL = \"blog.lyzplus.cn\"\n    API_KEY = \"eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiJhMjI3ZDkwYjVmMTY0MzcxYTcxMTdlMmRiODRjYzJiNSIsImV4cCI6MTc1NjYyMTg3NCwibmJmIjoxNzQxMDY5ODc0LCJpYXQiOjE3NDEwNjk4NzQsImp0aSI6IjhhZjA4YTU4YjIyNTQzODY5MDdkNGNlNTcwMzFjNmQ5IiwidWlkIjoiNjZkMDQ2NzdkM2IzNmZlYmU1ODFkMWZkIiwidHlwZSI6InJlZnJlc2gifQ.cAOioruMC0jLneIcbSyhuXJ9JIx_Bz79ndTTxlsWe5I\"\n    DEFAULT_MODEL = \"glm-4\"\n    \n    async def execute(self, arguments: Dict[str, Any]) -> List[types.TextContent | types.ImageContent | types.EmbeddedResource]:\n        \"\"\"\n        分析文档内容\n        \n        Args:\n            arguments: 参数字典，必须包含'file_path'和'question'键\n            \n        Returns:\n            分析结果\n        \"\"\"\n        # 参数验证\n        if \"file_path\" not in arguments:\n            return [types.TextContent(\n                type=\"text\",\n                text=\"错误: 缺少必要参数 'file_path'\"\n            )]\n        \n        if \"question\" not in arguments:\n            return [types.TextContent(\n                type=\"text\",\n                text=\"错误: 缺少必要参数 'question'\"\n            )]\n        \n        file_path = arguments[\"file_path\"]\n        question = arguments[\"question\"]\n        model = arguments.get(\"model\", self.DEFAULT_MODEL)\n        \n        # 检查文件是否存在\n        if not os.path.exists(file_path):\n            return [types.TextContent(\n                type=\"text\",\n                text=f\"错误: 文件不存在: {file_path}\\n请检查路径是否正确，并确保文件可访问。\"\n            )]\n        \n        try:\n            # 添加处理信息\n            results = [types.TextContent(\n                type=\"text\",\n                text=f\"# 文档分析\\n\\n正在分析文件: {os.path.basename(file_path)}\\n问题: {question}\\n\\n请稍候...\"\n            )]\n            \n            # 调用外部API分析文档\n            analysis_result = await self._analyze_document(file_path, question, model)\n            \n            # 添加分析结果\n            results.append(types.TextContent(\n                type=\"text\",\n                text=f\"## 分析结果\\n\\n{analysis_result}\"\n            ))\n            \n            return results\n        except Exception as e:\n            import traceback\n            error_details = traceback.format_exc()\n            return [types.TextContent(\n                type=\"text\",\n                text=f\"错误: 分析文档失败: {str(e)}\\n详细错误信息: {error_details}\"\n            )]\n    \n    async def _analyze_document(self, file_path: str, question: str, model: str) -> str:\n        \"\"\"\n        调用外部API分析文档\n        \n        Args:\n            file_path: 文档路径\n            question: 问题\n            model: 模型名称\n            \n        Returns:\n            分析结果\n        \"\"\"\n        try:\n            # 构建请求数据\n            payload = json.dumps({\n                \"model\": model,\n                \"messages\": [\n                    {\n                        \"role\": \"user\",\n                        \"content\": [\n                            {\n                                \"type\": \"file\",\n                                \"file_url\": {\n                                    \"url\": file_path  # 使用本地文件路径\n                                }\n                            },\n                            {\n                                \"type\": \"text\",\n                                \"text\": question\n                            }\n                        ]\n                    }\n                ],\n                \"stream\": False\n            })\n            \n            # 设置请求头\n            headers = {\n                'Accept': 'application/json',\n                'Authorization': f'Bearer {self.API_KEY}',\n                'Content-Type': 'application/json'\n            }\n            \n            # 发送请求\n            conn = http.client.HTTPSConnection(self.BASE_URL)\n            conn.request(\"POST\", \"/v1/chat/completions\", payload, headers)\n            \n            # 获取响应\n            response = conn.getresponse()\n            data = response.read().decode(\"utf-8\")\n            \n            # 解析响应\n            response_data = json.loads(data)\n            \n            # 检查响应是否成功\n            if response.status != 200:\n                error_message = response_data.get(\"message\", \"未知错误\")\n                return f\"API请求失败 (状态码: {response.status}): {error_message}\"\n            \n            # 提取分析结果\n            if \"choices\" in response_data and len(response_data[\"choices\"]) > 0:\n                message = response_data[\"choices\"][0].get(\"message\", {})\n                content = message.get(\"content\", \"未能获取分析结果\")\n                return content\n            else:\n                return \"未能获取分析结果，API响应格式不符合预期\"\n            \n        except Exception as e:\n            return f\"调用API时出错: {str(e)}\" "
        }
    ]
}