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contributing/samples/multimodal/static_non_text_content/README.md

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+# Static Non-Text Content Sample Agent
+
+This sample demonstrates ADK's static instruction feature with non-text content (images and files).
+
+## Features Demonstrated
+
+- **Static instructions with mixed content**: Text, images, and file references in a single static instruction
+- **Reference ID generation**: Non-text parts are automatically given reference IDs (`inline_data_0`, `file_data_1`, etc.)
+- **Gemini Files API integration**: Demonstrates uploading documents and using file_data
+- **Mixed content types**: inline_data for images, file_data for documents
+- **API variant detection**: Different behavior for Gemini API vs Vertex AI
+- **GCS file references**: Support for both GCS URI and HTTPS URL access methods in Vertex AI
+
+## Static Instruction Content
+
+The agent includes:
+
+1. **Text instructions**: Guide the agent on how to behave
+1. **Sample image**: A 1x1 yellow pixel PNG (`sample_chart.png`) as inline binary data
+
+**Gemini Developer API:**
+3\. **Contributing guide**: A sample document uploaded to Gemini Files API and referenced via file_data
+
+**Vertex AI:**
+3\. **Research paper**: Gemma research paper from Google Cloud Storage via GCS file reference
+4\. **AI research paper**: Same research paper accessed via HTTPS URL for comparison
+
+## Content Used
+
+**All API variants:**
+
+- **Image**: Base64-encoded 1x1 yellow pixel PNG (embedded in code as `inline_data`)
+
+**Gemini Developer API:**
+
+- **Document**: Sample contributing guide text (uploaded to Gemini Files API as `file_data`)
+  - Contains sample guidelines and best practices for development
+  - Demonstrates Files API upload and file_data reference functionality
+  - Files are automatically cleaned up after 48 hours by the Gemini API
+
+**Vertex AI:**
+
+- **Gemma Research Paper**: Research paper accessed via GCS URI (as `file_data`)
+  - GCS URI: `gs://cloud-samples-data/generative-ai/pdf/2403.05530.pdf`
+  - Demonstrates native GCS file access in Vertex AI
+  - PDF format with technical AI research content about Gemini 1.5
+- **AI Research Paper**: Same research paper accessed via HTTPS URL (as `file_data`)
+  - HTTPS URL: `https://storage.googleapis.com/cloud-samples-data/generative-ai/pdf/2403.05530.pdf`
+  - Demonstrates HTTPS file access in Vertex AI
+  - Agent can discover these are the same document and compare access methods
+
+## Setup
+
+### Setup API Credentials
+
+Create a `.env` file in the project root with your API credentials:
+
+```bash
+# Choose Model Backend: 0 -> ML Dev, 1 -> Vertex
+GOOGLE_GENAI_USE_ENTERPRISE=1
+
+# ML Dev backend config
+GOOGLE_API_KEY=your_google_api_key_here
+
+# Vertex backend config
+GOOGLE_CLOUD_PROJECT=your_project_id
+GOOGLE_CLOUD_LOCATION=us-central1
+```
+
+The agent will automatically load environment variables on startup.
+
+## Usage
+
+### Default Test Prompts (Recommended)
+
+```bash
+cd contributing/samples
+python -m static_non_text_content.main
+```
+
+This runs test prompts that demonstrate the static content features:
+
+- **Gemini Developer API**: 4 prompts testing inline_data + Files API upload
+- **Vertex AI**: 5 prompts testing inline_data + GCS/HTTPS file access comparison
+
+### Interactive Mode
+
+```bash
+cd contributing/samples
+adk run static_non_text_content
+```
+
+Use ADK's built-in interactive mode for free-form conversation.
+
+### Single Prompt
+
+```bash
+cd contributing/samples
+python -m static_non_text_content.main --prompt "What reference materials do you have access to?"
+```
+
+### With Debug Logging
+
+```bash
+cd contributing/samples
+python -m static_non_text_content.main --debug --prompt "What is the Gemma research paper about?"
+```
+
+## Default Test Prompts
+
+The sample automatically runs test prompts when no `--prompt` is specified:
+
+**All API variants:**
+
+1. "What reference materials do you have access to?"
+1. "Can you describe the sample chart that was provided to you?"
+1. "How do the inline image and file references in your instructions help you answer questions?"
+
+**Gemini Developer API only:**
+4\. "What does the contributing guide document say about best practices?"
+
+**Vertex AI only (additional prompts):**
+5\. "What is the Gemma research paper about and what are its key contributions?"
+6\. "Can you compare the research papers you have access to? Are they related or different?"
+
+**Gemini Developer API** tests: `inline_data` (image) + Files API `file_data` (uploaded document)
+**Vertex AI** tests: `inline_data` (image) + GCS URI `file_data` + HTTPS URL `file_data` (same document via different access methods)
+
+## How It Works
+
+1. **Static Instruction Processing**: The `static_instruction` content is processed during agent initialization
+1. **Reference Generation**: Non-text parts get references like `[Reference to inline binary data: inline_data_0 ('sample_chart.png', type: image/png)]` in the system instruction
+1. **User Content Creation**: The actual binary data/file references are moved to user contents with proper role attribution
+1. **Model Understanding**: The model receives both the descriptive references and the actual content for analysis
+
+## Code Structure
+
+- `agent.py`: Defines the agent with static instruction containing mixed content
+- `main.py`: Runnable script with interactive and single-prompt modes
+- `__init__.py`: Package initialization following ADK conventions
+
+This sample serves as a test case for the static instruction with non-text parts feature using both `inline_data` and `file_data`.