Huawei Cloud Functiongraph Function Create

FunctionGraph Function Creation Skill

Overview

This skill creates FunctionGraph functions on Huawei Cloud based on user-provided parameters including function name, runtime, and code content.

Architecture: Uses Huawei Cloud FunctionGraph Python SDK to interact with FunctionGraph service.

Applicable Scenarios:

• Quickly create cloud functions without manual console login
• Batch creation of multiple functions
• Foundation component for workflow deployment
• CI/CD integration for function deployment

Typical Use Cases:

1. "Create a Python function named my_handler"
2. "Deploy this code to FunctionGraph"
3. "Batch create 10 functions from template"

Prerequisites

1. Python Environment

• Python 3.9+
• Verify: python --version

2. SDK Installation

pip install huaweicloudsdkfunctiongraph

3. Authentication Configuration

• Valid Huawei Cloud credentials (AK/SK mode)
• Configure via environment variables:

export HUAWEI_AK="your-access-key"
export HUAWEI_SK="your-secret-key"
export HUAWEI_REGION="cn-north-4"
export HUAWEI_PROJECT_ID="your-project-id"

Security Rules:

• NEVER expose AK/SK values in code or logs
• NEVER let users input AK/SK directly in conversation
• ALWAYS use environment variables for credentials
• Recommend using IAM user instead of main account

4. IAM Permission Requirements

• functiongraph:function:create - Create function
• functiongraph:function:get - Query function details
• functiongraph:function:list - List functions

See IAM Policies for detailed permission configuration.

Usage

Command

cd scripts
python create_function.py   --name <function_name>   --runtime <runtime>   --han
dler <handler>   --code <code_content>   --memory <memory_size>   --timeout <timeout>

Parameter Confirmation

Runtime Options

Verification Method

See Verification Method for detailed procedures.

Quick Verification

## Verify function exists using SDK

from huaweicloudsdkfunctiongraph.v2.functiongraph_client import FunctionGraphClient
from huaweicloudsdkfunctiongraph.v2.model import ListFunctionsRequest

# List functions to verify creation
client = FunctionGraphClient.new_builder().build()
request = ListFunctionsRequest()
response = client.list_functions(request)
print(f"Total functions: {len(response.functions)}")

Output Format

The skill returns a structured JSON object with the following fields:

• function_urn: Unique resource identifier of the created function
• function_name: Name of the created function
• runtime: Runtime environment
• memory_size: Allocated memory in MB
• timeout: Function timeout in seconds
• handler: Function entry point
• description: Function description (if provided)

Example output:

{
  "function_urn": "urn:fss:cn-north-4:project_id:function:default:my_function",
  "function_name": "my_function",
  "runtime": "Python3.9",
  "memory_size": 128,
  "timeout": 3,
  "handler": "index.handler",
  "description": "Test function created by skill"
}

Best Practices

1. Test before production: Always test in a development environment first
2. Monitor resources: Set up monitoring for function invocations and errors
3. Use environment variables: Store sensitive data in environment variables
4. Implement error handling: Add proper error handling in function code
5. Set appropriate timeouts: Configure timeout based on function logic

Error Handling

References

• FunctionGraph Documentation
• Python SDK Documentation
• IAM Policies Guide
• Verification Method