A Practical Guide to Reducing LLM Hallucinations with Sandboxed Code Interpreter

docker run --privileged -p 2000:2000 -v d:\piston:'/piston' --name piston_api ghcr.io/engineer-man/piston

git clone https://github.com/engineer-man/piston

node cli/index.js ppman install python

import piston from "piston-client";

const codeInterpreter = piston({ server: "http://localhost:2000" });

const result = await codeInterpreter.execute('python', 'print("Hello World!")');

console.log(result);

Your task is to transform natural language text into Python code that extracts datetime-related scheduling parameters from user input.  

## Instructions:  
- You are allowed to use only the "datetime" and "calendar" libraries.  
- You can define additional private helper methods to improve code readability and modularize validation logic.  
- Do not include any import statements in the output.  
- Assume all input timestamps are provided in the GMT+8 timezone. Adjust calculations accordingly.  
- The output should be a single method definition with the following characteristics:  
  - Method name: \`getCustomerSchedulingParameters\`  
  - Arguments: None  
  - Return: A JSON object with the keys:  
    - \`appointment_date\`: The day of the month (integer or \`None\`).  
    - \`appointment_month\`: The month of the year (integer or \`None\`).  
    - \`appointment_year\`: The year (integer or \`None\`).  
    - \`appointment_time_hour\`: The hour of the day in 24-hour format (integer or \`None\`).  
    - \`appointment_time_minute\`: The minute of the hour (integer or \`None\`).  
    - \`duration_hours\`: The duration of the appointment in hours (float or \`None\`).  
    - \`frequency\`: The recurrence of the appointment. Can be \`"Adhoc"\`, \`"Daily"\`, \`"Weekly"\`, or \`"Monthly"\` (string or \`None\`).  

- If a specific value is not found in the text, return \`None\` for that field.  
- Focus only on extracting values explicitly mentioned in the input text; do not make assumptions.  
- Do not include print statements or logging in the output.  

## Example:  

### Input:  
"I want to book an appointment for next Monday at 2pm for 2.5 hours."  

### Output:  
def getCustomerSchedulingParameters():  
    """Extracts and returns scheduling parameters from user input in GMT+8 timezone.  

    Returns:  
        A JSON object with the required scheduling parameters.  
    """  
    def _get_next_monday():  
        """Helper function to calculate the date of the next Monday."""  
        current_time = datetime.utcnow() + timedelta(hours=8)  # Adjust to GMT+8  
        today = current_time.date()  
        days_until_monday = (7 - today.weekday() + 0) % 7  # Monday is 0  
        return today + timedelta(days=days_until_monday)  

    next_monday = _get_next_monday()  
    return {  
        "appointment_date": next_monday.day,  
        "appointment_month": next_monday.month,  
        "appointment_year": next_monday.year,  
        "appointment_time_hour": 14,  
        "appointment_time_minute": 0,  
        "duration_hours": 2.5,  
        "frequency": "Adhoc"  
    }

### Notes:
Ensure the output is plain Python code without any formatting or additional explanations.

const pythonCodeToInvoke = `
import sys
import datetime
import calendar
import json

${state.pythonValidationMethod}

${state.pythonParametersExtractionMethod}

parameters = getCustomerSchedulingParameters()

valiation_errors = validateCustomerSchedulingParameters(parameters["appointment_year"], parameters["appointment_month"], parameters["appointment_date"], parameters["appointment_time_hour"], parameters["appointment_time_minute"], parameters["duration_hours"], parameters["frequency"])

print(json.dumps({"validation_errors": valiation_errors}))`;

    const traceableCodeInterpreterFunction = await traceable((pythonCodeToInvoke: string) => codeInterpreter.execute('python', pythonCodeToInvoke, { args: [] }));
    const result = await traceableCodeInterpreterFunction(pythonCodeToInvoke);