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Artem Oroberts
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This change introduces a powerful pattern for customizing code execution environments by extending a base `CodeExecutor`. It showcases how to inject setup code to prepare the environment before a user's code is run, enabling advanced use cases that require specific configurations. As a practical example, this change implements `CustomCodeExecutor`, a subclass of `VertexAiCodeExecutor`, to solve the problem of rendering non-standard characters in `matplotlib` plots (Issue #2993). The custom executor programmatically adds a Japanese font to the `matplotlib` font manager at runtime. This is achieved by overriding the `execute_code` method to add font files to execution input and prepend the necessary font-loading logic. This approach is not limited to fonts and can be adapted for other setup tasks. Fixes: #2993 PiperOrigin-RevId: 825240143
72 lines
2.9 KiB
Markdown
72 lines
2.9 KiB
Markdown
# Custom Code Executor Agent Sample
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This directory contains a sample agent that demonstrates how to customize a
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`CodeExecutor` to perform environment setup before executing code. The specific
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example shows how to add support for Japanese fonts in `matplotlib` plots by
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subclassing `VertexAiCodeExecutor`.
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## Overview
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This sample showcases a powerful pattern for customizing code execution
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environments. By extending a base `CodeExecutor`, you can inject setup code to
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prepare the environment before a user's code is run. This enables advanced use
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cases that require specific configurations, in this case, adding custom fonts.
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## Key Concept: `CodeExecutor` Customization
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The Agent Development Kit (ADK) allows for powerful customization of code
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execution by extending existing `CodeExecutor` classes. By subclassing an
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executor (e.g., `VertexAiCodeExecutor`) and overriding its `execute_code`
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method, you can inject custom logic to:
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- Modify the code before it's executed.
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- Add files to the execution environment.
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- Process the results after execution.
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## Example: Adding Japanese Font Support
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The `CustomCodeExecutor` in this sample solves a common issue where non-Latin
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characters do not render correctly in plots generated by `matplotlib` due to
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missing fonts in the execution environment.
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It achieves this by: 1. **Subclassing `VertexAiCodeExecutor`**: It inherits all
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the functionality of the standard Vertex AI code executor. 2. **Overriding
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`execute_code`**: Before calling the parent's `execute_code` method, it performs
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the following steps: a. Downloads a Japanese font file (`NotoSerifJP`). b. Adds
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the font file to the list of files to be uploaded to the execution environment.
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c. Prepends a Python code snippet to the user's code. This snippet uses
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`matplotlib.font_manager` to register the newly available font file, making it
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available for plotting. 3. **Executing the modified code**: The combined code
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(setup snippet + original code) is then executed in the Vertex AI environment,
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which now has the Japanese font available for `matplotlib`.
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This ensures that any plots generated during the agent's session can correctly
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display Japanese characters.
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## How to use
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### Prerequisites
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Ensure you have configured your environment for using
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[Google Cloud Vertex AI](https://google.github.io/adk-docs/get-started/quickstart/#gemini---google-cloud-vertex-ai).
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You will need to have a Google Cloud Project with the Vertex AI API enabled.
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### Running the agent
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You can run this agent using the ADK CLI.
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To interact with the agent through the command line:
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```bash
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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)."
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```
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To use the web interface:
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```bash
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adk web contributing/samples/
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```
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Then select `custom_code_execution` from the list of agents and interact with
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it.
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