| from app.config import config |
| from app.tool.python_execute import PythonExecute |
|
|
|
|
| class NormalPythonExecute(PythonExecute): |
| """A tool for executing Python code with timeout and safety restrictions.""" |
|
|
| name: str = "python_execute" |
| description: str = """Execute Python code for in-depth data analysis / data report(task conclusion) / other normal task without direct visualization.""" |
| parameters: dict = { |
| "type": "object", |
| "properties": { |
| "code_type": { |
| "description": "code type, data process / data report / others", |
| "type": "string", |
| "default": "process", |
| "enum": ["process", "report", "others"], |
| }, |
| "code": { |
| "type": "string", |
| "description": """Python code to execute. |
| # Note |
| 1. The code should generate a comprehensive text-based report containing dataset overview, column details, basic statistics, derived metrics, timeseries comparisons, outliers, and key insights. |
| 2. Use print() for all outputs so the analysis (including sections like 'Dataset Overview' or 'Preprocessing Results') is clearly visible and save it also |
| 3. Save any report / processed files / each analysis result in worksapce directory: {directory} |
| 4. Data reports need to be content-rich, including your overall analysis process and corresponding data visualization. |
| 5. You can invode this tool step-by-step to do data analysis from summary to in-depth with data report saved also""".format( |
| directory=config.workspace_root |
| ), |
| }, |
| }, |
| "required": ["code"], |
| } |
|
|
| async def execute(self, code: str, code_type: str | None = None, timeout=5): |
| return await super().execute(code, timeout) |
|
|
