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README.md

Custom Code Executor Agent Sample

This directory contains a sample agent that demonstrates how to customize a CodeExecutor to perform environment setup before executing code. The specific example shows how to add support for Japanese fonts in matplotlib plots by subclassing VertexAiCodeExecutor.

Overview

This sample showcases a powerful pattern for customizing code execution environments. By extending a base CodeExecutor, you can inject setup code to prepare the environment before a user's code is run. This enables advanced use cases that require specific configurations, in this case, adding custom fonts.

Key Concept: CodeExecutor Customization

The Agent Development Kit (ADK) allows for powerful customization of code execution by extending existing CodeExecutor classes. By subclassing an executor (e.g., VertexAiCodeExecutor) and overriding its execute_code method, you can inject custom logic to:

  • Modify the code before it's executed.
  • Add files to the execution environment.
  • Process the results after execution.

Example: Adding Japanese Font Support

The CustomCodeExecutor in this sample solves a common issue where non-Latin characters do not render correctly in plots generated by matplotlib due to missing fonts in the execution environment.

It achieves this by: 1. Subclassing VertexAiCodeExecutor: It inherits all the functionality of the standard Vertex AI code executor. 2. Overriding execute_code: Before calling the parent's execute_code method, it performs the following steps: a. Downloads a Japanese font file (NotoSerifJP). b. Adds the font file to the list of files to be uploaded to the execution environment. c. Prepends a Python code snippet to the user's code. This snippet uses matplotlib.font_manager to register the newly available font file, making it available for plotting. 3. Executing the modified code: The combined code (setup snippet + original code) is then executed in the Vertex AI environment, which now has the Japanese font available for matplotlib.

This ensures that any plots generated during the agent's session can correctly display Japanese characters.

How to use

Prerequisites

Ensure you have configured your environment for using Google Cloud Vertex AI. You will need to have a Google Cloud Project with the Vertex AI API enabled.

Running the agent

You can run this agent using the ADK CLI.

To interact with the agent through the command line:

adk run contributing/samples/custom_code_execution "Plot a bar chart with these categories and values: {'リンゴ': 10, 'バナナ': 15, 'オレンジ': 8}. Title the chart '果物の在庫' (Fruit Stock)."

To use the web interface:

adk web contributing/samples/

Then select custom_code_execution from the list of agents and interact with it.