Run Custom Python Code in FastGPT Sandbox V2

Custom Python Code Execution in Sandbox V2

The FastGPT Sandbox V2 node allows executing custom Python logic within your workflow, with pre-built, reusable code snippets for common data processing tasks. All provided examples follow a standardized structure: a top-level main function that accepts input arguments and returns a JSON-serializable dictionary of results. The sandbox environment restricts external dependencies to Python’s standard library, with a dedicated SystemHelper utility available for HTTP requests.

Pre-Built Code Examples

Five ready-to-use code snippets cover common workflow needs:

1. Data Statistics: Calculates descriptive statistics for a list of numeric values.
2. Date Processing: Parses and manipulates calendar dates in YYYY-MM-DD format.
3. HTTP Request - API Call: Makes authenticated GET requests to external APIs.
4. JSON Data Processing: Parses JSON strings and extracts targeted fields.
5. Regular Expression Matching: Extracts valid email addresses from input text.

Each example’s full code is available in collapsible sections below: <details> <summary>Data Statistics</summary>

import math

def main(numbers):
    if not numbers:
        return {"error": "no data"}

    mean = sum(numbers) / len(numbers)
    variance = sum((x - mean)**2 for x in numbers) / len(numbers)

    return {
        "mean": mean,
        "max": max(numbers),
        "min": min(numbers),
        "std": math.sqrt(variance)
    }

</details>

<details> <summary>Date Processing</summary>

from datetime import datetime, timedelta

def main(date_str):
    dt = datetime.strptime(date_str, "%Y-%m-%d")
    next_week = dt + timedelta(days=7)

    return {
        "input": date_str,
        "next_week": next_week.strftime("%Y-%m-%d"),
        "weekday": dt.strftime("%A")
    }

</details>

<details> <summary>HTTP Request - API Call</summary>

def main(api_url, api_key):
    res = SystemHelper.httpRequest(
        api_url,
        method="GET",
        headers={"Authorization": f"Bearer {api_key}"},
        timeout=10
    )

    return {
        "status": res["status"],
        "data": res["data"]
    }

</details>

<details> <summary>JSON Data Processing</summary>

import json

def main(json_str):
    data = json.loads(json_str)

    # Extract specific fields
    result = {
        "names": [item["name"] for item in data if "name" in item],
        "count": len(data)
    }

    return result

</details>

<details> <summary>Regular Expression Matching</summary>

import re

def main(text):
    # Extract all email addresses
    emails = re.findall(r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b', text)

    return {
        "emails": emails,
        "count": len(emails)
    }

</details>

Input and Output Specifications

A standardized parameter table outlines the required inputs and returned outputs for each example:

Source: FastGPT official source

Applicability and version scope

Use this page for the documented Workflow nodes scenario. Confirm the FastGPT, dependency, API, and deployment versions in the official source before applying a change.

Safety guardrails

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Rollback guidance

Restore the prior technical-content authority snapshot. Restore saved configuration and data snapshots, then repeat the smallest verification scenario.