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print(f"Artifact Data (first 10 bytes): {image_artifact.inline_data.data[:10]}...")
@@ -174,7 +174,6 @@ Understanding artifacts involves grasping a few key components: the service that
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*`data` (`bytes`): The raw binary content of the artifact.
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*`mime_type` (`str`): A standard MIME type string (e.g., `'application/pdf'`, `'image/png'`, `'audio/mpeg'`) describing the nature of the binary data. **This is crucial for correct interpretation when loading the artifact.**
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=== "Python"
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```python
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print(f"Created Python artifact with MIME type: {pdf_artifact_py.inline_data.mime_type}")
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```
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=== "Java"
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```java
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***Cleanup Strategy:** For persistent storage like `GcsArtifactService`, artifacts remain until explicitly deleted. If artifacts represent temporary data or have a limited lifespan, implement a strategy for cleanup. This might involve:
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* Using GCS lifecycle policies on the bucket.
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* Building specific tools or administrative functions that utilize the `artifact_service.delete_artifact` method (note: delete is *not* exposed via context objects for safety).
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* Carefully managing filenames to allow pattern-based deletion if needed.
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* Carefully managing filenames to allow pattern-based deletion if needed.
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