233 lines
28 KiB
JSON
233 lines
28 KiB
JSON
{
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"id": "conv_20251026_192421_735",
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"title": "我叫什么",
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"created_at": "2025-10-26T19:24:21.735986",
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"updated_at": "2025-10-26T19:28:25.298031",
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"messages": [
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{
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"role": "user",
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"content": "我叫什么",
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"timestamp": "2025-10-26T19:24:26.576447"
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},
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{
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"role": "assistant",
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"content": "根据我的记忆记录，您的姓名是**寇子赋**。",
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"timestamp": "2025-10-26T19:24:27.667097"
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},
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{
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"role": "user",
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"content": "请查看文件",
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"timestamp": "2025-10-26T19:26:40.850784"
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},
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{
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"role": "assistant",
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"content": "我来查看您上传的文件。根据项目结构，文件位于 `user_upload/xlsx`。",
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"timestamp": "2025-10-26T19:26:42.182029"
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},
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{
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"role": "assistant",
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"content": "",
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"timestamp": "2025-10-26T19:26:42.184410",
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"tool_calls": [
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{
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"id": "read_file:0",
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"index": 0,
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"type": "function",
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"function": {
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"name": "read_file",
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"arguments": "{\"path\": \"user_upload/xlsx\"}"
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}
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}
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]
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},
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{
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"role": "tool",
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"content": "{\"success\": false, \"requires_confirmation\": true, \"message\": \"\\u9605\\u8bfb\\u5de5\\u5177\\u53ea\\u80fd\\u7528\\u4e8e\\u9605\\u8bfb\\u5c0f\\u6587\\u4ef6\\u3001\\u4e34\\u65f6\\u6587\\u4ef6\\u3001\\u4e0d\\u91cd\\u8981\\u7684\\u6587\\u4ef6\\u3002\\u5982\\u679c\\u8981\\u67e5\\u770b\\u6838\\u5fc3\\u6587\\u4ef6\\u3001\\u9700\\u8981\\u591a\\u6b21\\u4fee\\u6539\\u7684\\u6587\\u4ef6\\u3001\\u91cd\\u8981\\u7684\\u6587\\u4ef6\\uff0c\\u8bf7\\u4f7f\\u7528\\u805a\\u7126\\u529f\\u80fd\\u3002\\u8bf7\\u786e\\u8ba4\\u4f7f\\u7528\\u9605\\u8bfb\\u8fd8\\u662f\\u805a\\u7126\\uff1f\", \"instruction\": \"\\u8bf7\\u4f7f\\u7528 confirm_read_or_focus \\u5de5\\u5177\\u6765\\u9009\\u62e9\\u64cd\\u4f5c\\u65b9\\u5f0f\\uff0c\\u6587\\u4ef6\\u8def\\u5f84: user_upload/xlsx\", \"file_path\": \"user_upload/xlsx\"}",
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"timestamp": "2025-10-26T19:26:43.992204",
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"tool_call_id": "read_file:0",
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"name": "read_file"
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},
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{
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"role": "assistant",
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"content": "我需要确认使用哪种方式来查看这个文件。由于这是一个Excel文件（.xlsx格式），我需要选择合适的方式：",
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"timestamp": "2025-10-26T19:26:48.079680"
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},
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{
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"role": "assistant",
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"content": "",
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"timestamp": "2025-10-26T19:26:48.088845",
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"tool_calls": [
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{
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"id": "confirm_read_or_focus:1",
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"index": 0,
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"type": "function",
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"function": {
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"name": "confirm_read_or_focus",
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"arguments": "{\"file_path\": \"user_upload/xlsx\", \"choice\": \"read\", \"reason\": \"这是一个Excel文件，我需要查看其内容，但看起来文件较大（43.9KB），可能包含大量数据。先尝试读取查看基本结构和内容。\"}"
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}
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}
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]
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},
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{
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"role": "tool",
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"content": "{\"success\": false, \"error\": \"\\u8bfb\\u53d6\\u6587\\u4ef6\\u5931\\u8d25: 'utf-8' codec can't decode byte 0x87 in position 10: invalid start byte\"}",
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"timestamp": "2025-10-26T19:26:49.905183",
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"tool_call_id": "confirm_read_or_focus:1",
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"name": "confirm_read_or_focus"
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},
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{
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"role": "assistant",
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"content": "这个文件是二进制格式的Excel文件，无法直接用文本方式读取。让我用其他方法来查看文件内容：",
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"timestamp": "2025-10-26T19:26:51.680106"
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},
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{
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"role": "assistant",
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"content": "",
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"timestamp": "2025-10-26T19:26:51.682839",
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"tool_calls": [
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{
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"id": "run_command:2",
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"index": 0,
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"type": "function",
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"function": {
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"name": "run_command",
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"arguments": "{\"command\": \"file user_upload/xlsx\"}"
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}
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}
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]
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},
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{
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"role": "tool",
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"content": "{\"success\": true, \"output\": \"user_upload/xlsx: Microsoft Excel 2007+\\n\", \"stdout\": \"user_upload/xlsx: Microsoft Excel 2007+\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"file user_upload/xlsx\"}",
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"timestamp": "2025-10-26T19:26:53.491365",
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"tool_call_id": "run_command:2",
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"name": "run_command"
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},
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{
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"role": "assistant",
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"content": "",
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"timestamp": "2025-10-26T19:26:54.688210",
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"tool_calls": [
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{
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"id": "run_command:3",
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"index": 0,
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"type": "function",
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"function": {
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"name": "run_command",
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"arguments": "{\"command\": \"ls -la user_upload/\"}"
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}
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}
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]
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},
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{
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"role": "tool",
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"content": "{\"success\": true, \"output\": \"total 52\\ndrwxr-xr-x 2 root root 4096 Oct 26 19:26 .\\ndrwxr-xr-x 3 root root 4096 Oct 26 19:13 ..\\n-rw-r--r-- 1 root root 44997 Oct 26 19:26 xlsx\\n\", \"stdout\": \"total 52\\ndrwxr-xr-x 2 root root 4096 Oct 26 19:26 .\\ndrwxr-xr-x 3 root root 4096 Oct 26 19:13 ..\\n-rw-r--r-- 1 root root 44997 Oct 26 19:26 xlsx\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"ls -la user_upload/\"}",
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"timestamp": "2025-10-26T19:26:56.494685",
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"tool_call_id": "run_command:3",
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"name": "run_command"
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},
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{
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"role": "assistant",
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"content": "我确认这是一个Microsoft Excel 2007+格式的文件（.xlsx），大小约45KB。由于这是二进制文件，我无法直接读取其内容。让我尝试用Python来读取和分析这个Excel文件：",
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"timestamp": "2025-10-26T19:27:10.642965"
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},
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{
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"role": "assistant",
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"content": "",
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"timestamp": "2025-10-26T19:27:10.645772",
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"tool_calls": [
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{
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"id": "run_python:4",
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"index": 0,
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"type": "function",
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"function": {
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"name": "run_python",
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"arguments": "{\"code\": \"import pandas as pd\\nimport openpyxl\\n\\n# 尝试读取Excel文件\\nfile_path = \\\"user_upload/xlsx\\\"\\n\\ntry:\\n # 首先尝试用pandas读取\\n df = pd.read_excel(file_path)\\n print(\\\"=== 文件基本信息 ===\\\")\\n print(f\\\"文件名: {file_path}\\\")\\n print(f\\\"工作表形状: {df.shape} (行数, 列数)\\\")\\n print(f\\\"列名: {list(df.columns)}\\\")\\n print(f\\\"数据类型:\\\\n{df.dtypes}\\\")\\n print(\\\"\\\\n=== 前5行数据预览 ===\\\")\\n print(df.head())\\n \\nexcept Exception as e:\\n print(f\\\"用pandas读取失败: {e}\\\")\\n \\n # 尝试用openpyxl读取工作簿信息\\n try:\\n wb = openpyxl.load_workbook(file_path)\\n print(\\\"=== 工作簿信息 ===\\\")\\n print(f\\\"工作表名称: {wb.sheetnames}\\\")\\n \\n # 读取第一个工作表\\n ws = wb.active\\n print(f\\\"活动工作表: {ws.title}\\\")\\n print(f\\\"工作表范围: {ws.max_row} 行 x {ws.max_column} 列\\\")\\n \\n # 显示前5行和前5列的数据\\n print(\\\"\\\\n=== 前5行x前5列数据预览 ===\\\")\\n for row in range(1, min(6, ws.max_row + 1)):\\n row_data = []\\n for col in range(1, min(6, ws.max_column + 1)):\\n cell_value = ws.cell(row=row, column=col).value\\n row_data.append(str(cell_value) if cell_value is not None else \\\"None\\\")\\n print(f\\\"行{row}: {row_data}\\\")\\n \\n except Exception as e2:\\n print(f\\\"用openpyxl读取也失败: {e2}\\\")\"}"
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}
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}
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]
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},
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{
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"role": "tool",
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"content": "{\"success\": false, \"output\": \"\\n[错误输出]\\nTraceback (most recent call last):\\n File \\\"/opt/agent/agents/agents/users/kzf/project/.temp_code.py\\\", line 1, in <module>\\n import pandas as pd\\nModuleNotFoundError: No module named 'pandas'\\n\", \"stdout\": \"\", \"stderr\": \"Traceback (most recent call last):\\n File \\\"/opt/agent/agents/agents/users/kzf/project/.temp_code.py\\\", line 1, in <module>\\n import pandas as pd\\nModuleNotFoundError: No module named 'pandas'\\n\", \"return_code\": 1, \"command\": \"python3 \\\"/opt/agent/agents/agents/users/kzf/project/.temp_code.py\\\"\", \"code\": \"import pandas as pd\\nimport openpyxl\\n\\n# 尝试读取Excel文件\\nfile_path = \\\"user_upload/xlsx\\\"\\n\\ntry:\\n # 首先尝试用pandas读取\\n df = pd.read_excel(file_path)\\n print(\\\"=== 文件基本信息 ===\\\")\\n print(f\\\"文件名: {file_path}\\\")\\n print(f\\\"工作表形状: {df.shape} (行数, 列数)\\\")\\n print(f\\\"列名: {list(df.columns)}\\\")\\n print(f\\\"数据类型:\\\\n{df.dtypes}\\\")\\n print(\\\"\\\\n=== 前5行数据预览 ===\\\")\\n print(df.head())\\n \\nexcept Exception as e:\\n print(f\\\"用pandas读取失败: {e}\\\")\\n \\n # 尝试用openpyxl读取工作簿信息\\n try:\\n wb = openpyxl.load_workbook(file_path)\\n print(\\\"=== 工作簿信息 ===\\\")\\n print(f\\\"工作表名称: {wb.sheetnames}\\\")\\n \\n # 读取第一个工作表\\n ws = wb.active\\n print(f\\\"活动工作表: {ws.title}\\\")\\n print(f\\\"工作表范围: {ws.max_row} 行 x {ws.max_column} 列\\\")\\n \\n # 显示前5行和前5列的数据\\n print(\\\"\\\\n=== 前5行x前5列数据预览 ===\\\")\\n for row in range(1, min(6, ws.max_row + 1)):\\n row_data = []\\n for col in range(1, min(6, ws.max_column + 1)):\\n cell_value = ws.cell(row=row, column=col).value\\n row_data.append(str(cell_value) if cell_value is not None else \\\"None\\\")\\n print(f\\\"行{row}: {row_data}\\\")\\n \\n except Exception as e2:\\n print(f\\\"用openpyxl读取也失败: {e2}\\\")\"}",
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"timestamp": "2025-10-26T19:27:12.454145",
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"tool_call_id": "run_python:4",
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"name": "run_python"
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},
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{
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"role": "assistant",
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"content": "看起来需要安装相关的Python包。让我先安装pandas和openpyxl：",
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"timestamp": "2025-10-26T19:27:14.351205"
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},
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{
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"role": "assistant",
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"content": "",
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"timestamp": "2025-10-26T19:27:14.355333",
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"tool_calls": [
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{
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"id": "run_command:5",
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"index": 0,
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"type": "function",
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"function": {
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"name": "run_command",
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"arguments": "{\"command\": \"pip install pandas openpyxl\"}"
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}
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}
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]
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},
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{
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"role": "tool",
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"content": "{\"success\": true, \"output\": \"Looking in indexes: http://mirrors.cloud.aliyuncs.com/pypi/simple/\\nCollecting pandas\\n Downloading http://mirrors.cloud.aliyuncs.com/pypi/packages/e5/63/cd7d615331b328e287d8233ba9fdf191a9c2d11b6af0c7a59cfcec23de68/pandas-2.3.3-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl (12.4 MB)\\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 12.4/12.4 MB 47.3 MB/s eta 0:00:00\\nCollecting openpyxl\\n Downloading http://mirrors.cloud.aliyuncs.com/pypi/packages/c0/da/977ded879c29cbd04de313843e76868e6e13408a94ed6b987245dc7c8506/openpyxl-3.1.5-py2.py3-none-any.whl (250 kB)\\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 250.9/250.9 kB 24.9 MB/s eta 0:00:00\\nCollecting numpy>=1.26.0 (from pandas)\\n Downloading http://mirrors.cloud.aliyuncs.com/pypi/packages/f9/58/30a85127bfee6f108282107caf8e06a1f0cc997cb6b52cdee699276fcce4/numpy-2.3.4-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (16.6 MB)\\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 16.6/16.6 MB 57.1 MB/s eta 0:00:00\\nCollecting python-dateutil>=2.8.2 (from pandas)\\n Downloading http://mirrors.cloud.aliyuncs.com/pypi/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl (229 kB)\\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 229.9/229.9 kB 21.0 MB/s eta 0:00:00\\nCollecting pytz>=2020.1 (from pandas)\\n Downloading http://mirrors.cloud.aliyuncs.com/pypi/packages/81/c4/34e93fe5f5429d7570ec1fa436f1986fb1f00c3e0f43a589fe2bbcd22c3f/pytz-2025.2-py2.py3-none-any.whl (509 kB)\\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 509.2/509.2 kB 38.0 MB/s eta 0:00:00\\nCollecting tzdata>=2022.7 (from pandas)\\n Downloading http://mirrors.cloud.aliyuncs.com/pypi/packages/5c/23/c7abc0ca0a1526a0774eca151daeb8de62ec457e77262b66b359c3c7679e/tzdata-2025.2-py2.py3-none-any.whl (347 kB)\\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 347.8/347.8 kB 33.6 MB/s eta 0:00:00\\nCollecting et-xmlfile (from openpyxl)\\n Downloading http://mirrors.cloud.aliyuncs.com/pypi/packages/c1/8b/5fe2cc11fee489817272089c4203e679c63b570a5aaeb18d852ae3cbba6a/et_xmlfile-2.0.0-py3-none-any.whl (18 kB)\\nCollecting six>=1.5 (from python-dateutil>=2.8.2->pandas)\\n Downloading http://mirrors.cloud.aliyuncs.com/pypi/packages/b7/ce/149a00dd41f10bc29e5921b496af8b574d8413afcd5e30dfa0ed46c2cc5e/six-1.17.0-py2.py3-none-any.whl (11 kB)\\nInstalling collected packages: pytz, tzdata, six, numpy, et-xmlfile, python-dateutil, openpyxl, pandas\\nSuccessfully installed et-xmlfile-2.0.0 numpy-2.3.4 openpyxl-3.1.5 pandas-2.3.3 python-dateutil-2.9.0.post0 pytz-2025.2 six-1.17.0 tzdata-2025.2\\n\", \"stdout\": \"Looking in indexes: http://mirrors.cloud.aliyuncs.com/pypi/simple/\\nCollecting pandas\\n Downloading http://mirrors.cloud.aliyuncs.com/pypi/packages/e5/63/cd7d615331b328e287d8233ba9fdf191a9c2d11b6af0c7a59cfcec23de68/pandas-2.3.3-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl (12.4 MB)\\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 12.4/12.4 MB 47.3 MB/s eta 0:00:00\\nCollecting openpyxl\\n Downloading http://mirrors.cloud.aliyuncs.com/pypi/packages/c0/da/977ded879c29cbd04de313843e76868e6e13408a94ed6b987245dc7c8506/openpyxl-3.1.5-py2.py3-none-any.whl (250 kB)\\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 250.9/250.9 kB 24.9 MB/s eta 0:00:00\\nCollecting numpy>=1.26.0 (from pandas)\\n Downloading http://mirrors.cloud.aliyuncs.com/pypi/packages/f9/58/30a85127bfee6f108282107caf8e06a1f0cc997cb6b52cdee699276fcce4/numpy-2.3.4-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (16.6 MB)\\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 16.6/16.6 MB 57.1 MB/s eta 0:00:00\\nCollecting python-dateutil>=2.8.2 (from pandas)\\n Downloading http://mirrors.cloud.aliyuncs.com/pypi/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl (229 kB)\\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 229.9/229.9 kB 21.0 MB/s eta 0:00:00\\nCollecting pytz>=2020.1 (from pandas)\\n Downloading http://mirrors.cloud.aliyuncs.com/pypi/packages/81/c4/34e93fe5f5429d7570ec1fa436f1986fb1f00c3e0f43a589fe2bbcd22c3f/pytz-2025.2-py2.py3-none-any.whl (509 kB)\\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 509.2/509.2 kB 38.0 MB/s eta 0:00:00\\nCollecting tzdata>=2022.7 (from pandas)\\n Downloading http://mirrors.cloud.aliyuncs.com/pypi/packages/5c/23/c7abc0ca0a1526a0774eca151daeb8de62ec457e77262b66b359c3c7679e/tzdata-2025.2-py2.py3-none-any.whl (347 kB)\\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 347.8/347.8 kB 33.6 MB/s eta 0:00:00\\nCollecting et-xmlfile (from openpyxl)\\n Downloading http://mirrors.cloud.aliyuncs.com/pypi/packages/c1/8b/5fe2cc11fee489817272089c4203e679c63b570a5aaeb18d852ae3cbba6a/et_xmlfile-2.0.0-py3-none-any.whl (18 kB)\\nCollecting six>=1.5 (from python-dateutil>=2.8.2->pandas)\\n Downloading http://mirrors.cloud.aliyuncs.com/pypi/packages/b7/ce/149a00dd41f10bc29e5921b496af8b574d8413afcd5e30dfa0ed46c2cc5e/six-1.17.0-py2.py3-none-any.whl (11 kB)\\nInstalling collected packages: pytz, tzdata, six, numpy, et-xmlfile, python-dateutil, openpyxl, pandas\\nSuccessfully installed et-xmlfile-2.0.0 numpy-2.3.4 openpyxl-3.1.5 pandas-2.3.3 python-dateutil-2.9.0.post0 pytz-2025.2 six-1.17.0 tzdata-2025.2\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"pip install pandas openpyxl\"}",
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"timestamp": "2025-10-26T19:27:33.791606",
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"tool_call_id": "run_command:5",
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"name": "run_command"
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},
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{
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"role": "assistant",
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"content": "很好！现在让我用Python来读取和分析这个Excel文件：",
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"timestamp": "2025-10-26T19:27:45.657153"
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},
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{
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"role": "assistant",
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"content": "",
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"timestamp": "2025-10-26T19:27:45.661599",
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"tool_calls": [
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{
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"id": "run_python:6",
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"index": 0,
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"type": "function",
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"function": {
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"name": "run_python",
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"arguments": "{\"code\": \"import pandas as pd\\nimport openpyxl\\n\\n# 读取Excel文件\\nfile_path = \\\"user_upload/xlsx\\\"\\n\\ntry:\\n # 首先尝试用pandas读取\\n df = pd.read_excel(file_path)\\n print(\\\"=== 文件基本信息 ===\\\")\\n print(f\\\"文件名: {file_path}\\\")\\n print(f\\\"工作表形状: {df.shape} (行数, 列数)\\\")\\n print(f\\\"列名: {list(df.columns)}\\\")\\n print(f\\\"数据类型:\\\\n{df.dtypes}\\\")\\n print(\\\"\\\\n=== 前5行数据预览 ===\\\")\\n print(df.head())\\n \\n print(\\\"\\\\n=== 数据统计信息 ===\\\")\\n print(df.describe())\\n \\nexcept Exception as e:\\n print(f\\\"用pandas读取失败: {e}\\\")\\n \\n # 尝试用openpyxl读取工作簿信息\\n try:\\n wb = openpyxl.load_workbook(file_path)\\n print(\\\"=== 工作簿信息 ===\\\")\\n print(f\\\"工作表名称: {wb.sheetnames}\\\")\\n \\n # 读取第一个工作表\\n ws = wb.active\\n print(f\\\"活动工作表: {ws.title}\\\")\\n print(f\\\"工作表范围: {ws.max_row} 行 x {ws.max_column} 列\\\")\\n \\n # 显示前5行和前5列的数据\\n print(\\\"\\\\n=== 前5行x前5列数据预览 ===\\\")\\n for row in range(1, min(6, ws.max_row + 1)):\\n row_data = []\\n for col in range(1, min(6, ws.max_column + 1)):\\n cell_value = ws.cell(row=row, column=col).value\\n row_data.append(str(cell_value) if cell_value is not None else \\\"None\\\")\\n print(f\\\"行{row}: {row_data}\\\")\\n \\n except Exception as e2:\\n print(f\\\"用openpyxl读取也失败: {e2}\\\")\"}"
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}
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}
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{
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"role": "tool",
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"content": "{\"success\": true, \"output\": \"=== 文件基本信息 ===\\n文件名: user_upload/xlsx\\n工作表形状: (108, 16) (行数, 列数)\\n列名: ['招考单位', '单位代码', '招考职位', '职位代码', '职位简介', '职位类型', '录用人数', '学历', '学位', '研究生专业\\\\n名称及代码', '本科专业\\\\n名称及代码', '大专专业\\\\n名称及代码', '是否要求2年以上基层工作经历', '是否限应届毕业生报考', '其他要求', '考区']\\n数据类型:\\n招考单位 object\\n单位代码 int64\\n招考职位 object\\n职位代码 int64\\n职位简介 object\\n职位类型 object\\n录用人数 int64\\n学历 object\\n学位 object\\n研究生专业\\\\n名称及代码 object\\n本科专业\\\\n名称及代码 object\\n大专专业\\\\n名称及代码 float64\\n是否要求2年以上基层工作经历 object\\n是否限应届毕业生报考 object\\n其他要求 object\\n考区 object\\ndtype: object\\n\\n=== 前5行数据预览 ===\\n 招考单位 单位代码 招考职位 ... 是否限应届毕业生报考 其他要求 考区\\n0 广东省生态环境厅 1990054 广东省惠州生态环境监测站一级科员 ... 否 NaN 惠州\\n1 广州市越秀区商务局 1010425 对外经济贸易科四级主任科员以下 ... 应届毕业生 NaN 广州\\n2 广州市海珠区城市管理和综合执法局 1010534 环境卫生管理科一级行政执法员 ... 否 NaN 广州\\n3 广州市黄埔区教育局 1010915 综合业务科一级科员 ... 2026届高校毕业生 NaN 广州\\n4 深圳市规划和自然资源局 1020006 南山管理局二级主任科员以下 ... 应届毕业生 NaN 深圳\\n\\n[5 rows x 16 columns]\\n\\n=== 数据统计信息 ===\\n 单位代码 职位代码 录用人数 大专专业\\\\n名称及代码\\ncount 1.080000e+02 1.080000e+02 108.000000 0.0\\nmean 1.110235e+06 1.110236e+13 1.092593 NaN\\nstd 1.070277e+05 1.070277e+12 0.321708 NaN\\nmin 1.010425e+06 1.010425e+13 1.000000 NaN\\n25% 1.050401e+06 1.050402e+13 1.000000 NaN\\n50% 1.090150e+06 1.090151e+13 1.000000 NaN\\n75% 1.170177e+06 1.170177e+13 1.000000 NaN\\nmax 1.990054e+06 1.990054e+13 3.000000 NaN\\n\", \"stdout\": \"=== 文件基本信息 ===\\n文件名: user_upload/xlsx\\n工作表形状: (108, 16) (行数, 列数)\\n列名: ['招考单位', '单位代码', '招考职位', '职位代码', '职位简介', '职位类型', '录用人数', '学历', '学位', '研究生专业\\\\n名称及代码', '本科专业\\\\n名称及代码', '大专专业\\\\n名称及代码', '是否要求2年以上基层工作经历', '是否限应届毕业生报考', '其他要求', '考区']\\n数据类型:\\n招考单位 object\\n单位代码 int64\\n招考职位 object\\n职位代码 int64\\n职位简介 object\\n职位类型 object\\n录用人数 int64\\n学历 object\\n学位 object\\n研究生专业\\\\n名称及代码 object\\n本科专业\\\\n名称及代码 object\\n大专专业\\\\n名称及代码 float64\\n是否要求2年以上基层工作经历 object\\n是否限应届毕业生报考 object\\n其他要求 object\\n考区 object\\ndtype: object\\n\\n=== 前5行数据预览 ===\\n 招考单位 单位代码 招考职位 ... 是否限应届毕业生报考 其他要求 考区\\n0 广东省生态环境厅 1990054 广东省惠州生态环境监测站一级科员 ... 否 NaN 惠州\\n1 广州市越秀区商务局 1010425 对外经济贸易科四级主任科员以下 ... 应届毕业生 NaN 广州\\n2 广州市海珠区城市管理和综合执法局 1010534 环境卫生管理科一级行政执法员 ... 否 NaN 广州\\n3 广州市黄埔区教育局 1010915 综合业务科一级科员 ... 2026届高校毕业生 NaN 广州\\n4 深圳市规划和自然资源局 1020006 南山管理局二级主任科员以下 ... 应届毕业生 NaN 深圳\\n\\n[5 rows x 16 columns]\\n\\n=== 数据统计信息 ===\\n 单位代码 职位代码 录用人数 大专专业\\\\n名称及代码\\ncount 1.080000e+02 1.080000e+02 108.000000 0.0\\nmean 1.110235e+06 1.110236e+13 1.092593 NaN\\nstd 1.070277e+05 1.070277e+12 0.321708 NaN\\nmin 1.010425e+06 1.010425e+13 1.000000 NaN\\n25% 1.050401e+06 1.050402e+13 1.000000 NaN\\n50% 1.090150e+06 1.090151e+13 1.000000 NaN\\n75% 1.170177e+06 1.170177e+13 1.000000 NaN\\nmax 1.990054e+06 1.990054e+13 3.000000 NaN\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"python3 \\\"/opt/agent/agents/agents/users/kzf/project/.temp_code.py\\\"\", \"code\": \"import pandas as pd\\nimport openpyxl\\n\\n# 读取Excel文件\\nfile_path = \\\"user_upload/xlsx\\\"\\n\\ntry:\\n # 首先尝试用pandas读取\\n df = pd.read_excel(file_path)\\n print(\\\"=== 文件基本信息 ===\\\")\\n print(f\\\"文件名: {file_path}\\\")\\n print(f\\\"工作表形状: {df.shape} (行数, 列数)\\\")\\n print(f\\\"列名: {list(df.columns)}\\\")\\n print(f\\\"数据类型:\\\\n{df.dtypes}\\\")\\n print(\\\"\\\\n=== 前5行数据预览 ===\\\")\\n print(df.head())\\n \\n print(\\\"\\\\n=== 数据统计信息 ===\\\")\\n print(df.describe())\\n \\nexcept Exception as e:\\n print(f\\\"用pandas读取失败: {e}\\\")\\n \\n # 尝试用openpyxl读取工作簿信息\\n try:\\n wb = openpyxl.load_workbook(file_path)\\n print(\\\"=== 工作簿信息 ===\\\")\\n print(f\\\"工作表名称: {wb.sheetnames}\\\")\\n \\n # 读取第一个工作表\\n ws = wb.active\\n print(f\\\"活动工作表: {ws.title}\\\")\\n print(f\\\"工作表范围: {ws.max_row} 行 x {ws.max_column} 列\\\")\\n \\n # 显示前5行和前5列的数据\\n print(\\\"\\\\n=== 前5行x前5列数据预览 ===\\\")\\n for row in range(1, min(6, ws.max_row + 1)):\\n row_data = []\\n for col in range(1, min(6, ws.max_column + 1)):\\n cell_value = ws.cell(row=row, column=col).value\\n row_data.append(str(cell_value) if cell_value is not None else \\\"None\\\")\\n print(f\\\"行{row}: {row_data}\\\")\\n \\n except Exception as e2:\\n print(f\\\"用openpyxl读取也失败: {e2}\\\")\"}",
|
||
"timestamp": "2025-10-26T19:27:47.474692",
|
||
"tool_call_id": "run_python:6",
|
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"name": "run_python"
|
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},
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{
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"role": "assistant",
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"content": "太好了！我成功读取了您的Excel文件。这是一个包含**公务员招考信息**的数据表，让我为您总结一下文件内容：\n\n## 📊 文件概览\n\n**文件类型**: Microsoft Excel 2007+ 格式 \n**数据规模**: 108行 × 16列 \n**内容类型**: 公务员招考职位信息\n\n## 📋 数据字段说明\n\n该Excel文件包含以下16个字段：\n\n1. **招考单位** - 招聘单位名称\n2. **单位代码** - 单位唯一标识码\n3. **招考职位** - 具体职位名称\n4. **职位代码** - 职位唯一标识码\n5. **职位简介** - 职位描述\n6. **职位类型** - 职位分类\n7. **录用人数** - 招聘人数\n8. **学历** - 学历要求\n9. **学位** - 学位要求\n10. **研究生专业名称及代码** - 研究生专业要求\n11. **本科专业名称及代码** - 本科专业要求\n12. **大专专业名称及代码** - 大专专业要求\n13. **是否要求2年以上基层工作经历** - 基层经验要求\n14. **是否限应届毕业生报考** - 应届限制\n15. **其他要求** - 额外要求\n16. **考区** - 考试地区\n\n## 📈 数据统计\n\n- **总职位数**: 108个\n- **平均录用人数**: 约1.09人/职位\n- **主要考区**: 包括广州、深圳、惠州等地\n\n## 🔍 示例数据\n\n从预览数据可以看到，这些职位涵盖：\n- 广东省生态环境厅\n- 广州市各区政府部门\n- 深圳市规划和自然资源局\n- 等多个政府机关和事业单位\n\n这个文件看起来是广东省公务员考试的职位表，包含了详细的招聘要求和条件。您希望我帮您进行什么具体的分析或处理吗？",
|
||
"timestamp": "2025-10-26T19:27:57.460040"
|
||
}
|
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],
|
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"todo_list": null,
|
||
"metadata": {
|
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"project_path": "/opt/agent/agents/agents/users/kzf/project",
|
||
"project_relative_path": "users/kzf/project",
|
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"thinking_mode": true,
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"updated_at": "2025-10-26T19:28:25.298403"
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