chore: import upstream snapshot with attribution

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`.

contributing/samples/multimodal/static_non_text_content/__init__.py

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+# Copyright 2026 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Static non-text content sample agent package."""
+
+from . import agent

contributing/samples/multimodal/static_non_text_content/agent.py

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+# Copyright 2026 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Static non-text content sample agent demonstrating static instructions with non-text parts."""
+
+import base64
+
+from dotenv import load_dotenv
+from google.adk.agents.llm_agent import Agent
+from google.genai import types
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Sample image data (a simple 1x1 yellow pixel PNG)
+SAMPLE_IMAGE_DATA = base64.b64decode(
+    "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mP8/5+hHgAHggJ/PchI7wAAAABJRU5ErkJggg=="
+)
+
+# Sample document content (simplified contributing guide)
+SAMPLE_DOCUMENT = """# Contributing Guide
+
+## Best Practices
+
+1. **Code Quality**: Always write clean, well-documented code
+2. **Testing**: Include comprehensive tests for new features
+3. **Documentation**: Update documentation when adding new functionality
+4. **Review Process**: Submit pull requests for code review
+5. **Conventions**: Follow established coding conventions and style guides
+
+## Guidelines
+
+- Use meaningful variable and function names
+- Write descriptive commit messages
+- Keep functions small and focused
+- Handle errors gracefully
+- Consider performance implications
+- Maintain backward compatibility when possible
+
+This guide helps ensure consistent, high-quality contributions to the project.
+"""
+
+
+def create_static_instruction_with_file_upload():
+  """Create static instruction content with both inline_data and file_data.
+
+  This function creates a static instruction that demonstrates both inline_data
+  (for images) and file_data (for documents). Always includes Files API upload,
+  and adds additional GCS file reference when using Vertex AI.
+  """
+  import os
+  import tempfile
+
+  from google.adk.utils.variant_utils import get_google_llm_variant
+  from google.adk.utils.variant_utils import GoogleLLMVariant
+
+  from google import genai
+
+  # Determine API variant
+  api_variant = get_google_llm_variant()
+  print(f"Using API variant: {api_variant}")
+
+  # Prepare file data parts based on API variant
+  file_data_parts = []
+
+  if api_variant == GoogleLLMVariant.VERTEX_AI:
+    print("Using Vertex AI - adding GCS URI and HTTPS URL references")
+
+    # Add GCS file reference
+    file_data_parts.append(
+        types.Part(
+            file_data=types.FileData(
+                file_uri=(
+                    "gs://cloud-samples-data/generative-ai/pdf/2403.05530.pdf"
+                ),
+                mime_type="application/pdf",
+                display_name="Gemma Research Paper",
+            )
+        )
+    )
+
+    # Add the same document via HTTPS URL to demonstrate both access methods
+    file_data_parts.append(
+        types.Part(
+            file_data=types.FileData(
+                file_uri="https://storage.googleapis.com/cloud-samples-data/generative-ai/pdf/2403.05530.pdf",
+                mime_type="application/pdf",
+                display_name="AI Research Paper (HTTPS)",
+            )
+        )
+    )
+
+    additional_text = (
+        " You also have access to a Gemma research paper from GCS"
+        " and an AI research paper from HTTPS URL."
+    )
+
+  else:
+    print("Using Gemini Developer API - uploading to Files API")
+    client = genai.Client()
+
+    # Check if file already exists
+    display_name = "Contributing Guide"
+    uploaded_file = None
+
+    # List existing files to see if we already uploaded this document
+    existing_files = client.files.list()
+    for file in existing_files:
+      if file.display_name == display_name:
+        uploaded_file = file
+        print(f"Reusing existing file: {file.name} ({file.display_name})")
+        break
+
+    # If file doesn't exist, upload it
+    if uploaded_file is None:
+      # Create a temporary file with the sample document
+      with tempfile.NamedTemporaryFile(
+          mode="w", suffix=".md", delete=False
+      ) as f:
+        f.write(SAMPLE_DOCUMENT)
+        temp_file_path = f.name
+
+      try:
+        # Upload the file to Gemini Files API
+        uploaded_file = client.files.upload(file=temp_file_path)
+        print(
+            "Uploaded new file:"
+            f" {uploaded_file.name} ({uploaded_file.display_name})"
+        )
+      finally:
+        # Clean up temporary file
+        if os.path.exists(temp_file_path):
+          os.unlink(temp_file_path)
+
+    # Add Files API file data part
+    file_data_parts.append(
+        types.Part(
+            file_data=types.FileData(
+                file_uri=uploaded_file.uri,
+                mime_type="text/markdown",
+                display_name="Contributing Guide",
+            )
+        )
+    )
+
+    additional_text = (
+        " You also have access to the contributing guide document."
+    )
+
+  # Create static instruction with mixed content
+  parts = [
+      types.Part.from_text(
+          text=(
+              "You are an AI assistant that analyzes images and documents."
+              " You have access to the following reference materials:"
+          )
+      ),
+      # Add a sample image as inline_data
+      types.Part(
+          inline_data=types.Blob(
+              data=SAMPLE_IMAGE_DATA,
+              mime_type="image/png",
+              display_name="sample_chart.png",
+          )
+      ),
+      types.Part.from_text(
+          text=f"This is a sample chart showing color data.{additional_text}"
+      ),
+  ]
+
+  # Add all file_data parts
+  parts.extend(file_data_parts)
+
+  # Add instruction text
+  if api_variant == GoogleLLMVariant.VERTEX_AI:
+    instruction_text = """
+When users ask questions, you should:
+1. Use the reference chart above to provide context when discussing visual data or charts
+2. Reference the Gemma research paper (from GCS) when discussing AI research, model architectures, or technical details
+3. Reference the AI research paper (from HTTPS) when discussing research topics
+4. Be helpful and informative in your responses
+5. Explain how the provided reference materials relate to their questions"""
+  else:
+    instruction_text = """
+When users ask questions, you should:
+1. Use the reference chart above to provide context when discussing visual data or charts
+2. Reference the contributing guide document when explaining best practices and guidelines
+3. Be helpful and informative in your responses
+4. Explain how the provided reference materials relate to their questions"""
+
+  instruction_text += """
+
+Remember: The reference materials above are available to help you provide better answers."""
+
+  parts.append(types.Part.from_text(text=instruction_text))
+
+  static_instruction_content = types.Content(parts=parts)
+
+  return static_instruction_content
+
+
+# Create the root agent with Files API integration
+root_agent = Agent(
+    name="static_non_text_content_demo_agent",
+    description=(
+        "Demonstrates static instructions with non-text content (inline_data"
+        " and file_data features)"
+    ),
+    static_instruction=create_static_instruction_with_file_upload(),
+    instruction=(
+        "Please analyze the user's question and provide helpful insights."
+        " Reference the materials provided in your static instructions when"
+        " relevant."
+    ),
+)

contributing/samples/multimodal/static_non_text_content/main.py

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+"""Static non-text content sample agent main script."""
+
+# Copyright 2026 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import asyncio
+import logging
+import sys
+import time
+
+from google.adk.cli.utils import logs
+from google.adk.runners import InMemoryRunner
+
+from . import agent
+
+APP_NAME = "static_non_text_content_demo"
+USER_ID = "demo_user"
+
+logs.setup_adk_logger(level=logging.INFO)
+
+
+async def call_agent_async(
+    runner, user_id: str, session_id: str, prompt: str
+) -> str:
+  """Helper function to call agent and return final response."""
+  from google.adk.agents.run_config import RunConfig
+  from google.genai import types
+
+  content = types.Content(
+      role="user", parts=[types.Part.from_text(text=prompt)]
+  )
+
+  final_response_text = ""
+  async for event in runner.run_async(
+      user_id=user_id,
+      session_id=session_id,
+      new_message=content,
+      run_config=RunConfig(save_input_blobs_as_artifacts=False),
+  ):
+    if event.content and event.content.parts:
+      if text := "".join(part.text or "" for part in event.content.parts):
+        if event.author != "user":
+          final_response_text += text
+
+  return final_response_text or "No response received"
+
+
+def process_arguments():
+  """Parses command-line arguments."""
+  parser = argparse.ArgumentParser(
+      description=(
+          "A demo script that tests static instructions with non-text content."
+      ),
+      epilog=(
+          "Example usage: \n\tpython -m static_non_text_content.main --prompt"
+          " 'What can you see in the reference chart?'\n\tpython -m"
+          " static_non_text_content.main --prompt 'What is the Gemma research"
+          " paper about?'\n\tpython -m static_non_text_content.main  # Runs"
+          " default test prompts\n\tadk run"
+          " contributing/samples/static_non_text_content  # Interactive mode\n"
+      ),
+      formatter_class=argparse.RawTextHelpFormatter,
+  )
+
+  parser.add_argument(
+      "--prompt",
+      type=str,
+      help=(
+          "Single prompt to send to the agent. If not provided, runs"
+          " default test prompts."
+      ),
+  )
+
+  parser.add_argument(
+      "--debug",
+      action="store_true",
+      help="Enable debug logging to see internal processing details.",
+  )
+
+  return parser.parse_args()
+
+
+async def run_default_test_prompts(runner):
+  """Run default test prompts to demonstrate static content features."""
+  from google.adk.utils.variant_utils import get_google_llm_variant
+  from google.adk.utils.variant_utils import GoogleLLMVariant
+
+  api_variant = get_google_llm_variant()
+
+  print("=== Static Non-Text Content Demo Agent - Default Test Prompts ===")
+  print(
+      "Running test prompts to demonstrate inline_data and file_data"
+      " features..."
+  )
+  print(f"API Variant: {api_variant}")
+  print(
+      "Use 'adk run contributing/samples/static_non_text_content' for"
+      " interactive mode.\n"
+  )
+
+  # Create session
+  session = await runner.session_service.create_session(
+      app_name=APP_NAME, user_id=USER_ID
+  )
+
+  # Common test prompts for all API variants
+  test_prompts = [
+      "What reference materials do you have access to?",
+      "Can you describe the sample chart that was provided to you?",
+      (
+          "How do the inline image and file references in your instructions "
+          "help you answer questions?"
+      ),
+  ]
+
+  # Add API-specific prompts
+  if api_variant == GoogleLLMVariant.VERTEX_AI:
+    # Vertex AI has research papers instead of contributing guide
+    test_prompts.extend([
+        (
+            "What is the Gemma research paper about and what are its key "
+            "contributions?"
+        ),
+        (
+            "Can you compare the research papers you have access to? Are they "
+            "related or different?"
+        ),
+    ])
+  else:
+    # Gemini Developer API has contributing guide document
+    test_prompts.append(
+        "What does the contributing guide document say about best practices?"
+    )
+
+  for i, prompt in enumerate(test_prompts, 1):
+    print(f"Test {i}/{len(test_prompts)}: {prompt}")
+    print("-" * 60)
+
+    try:
+      response = await call_agent_async(runner, USER_ID, session.id, prompt)
+      print(f"Response: {response}")
+    except (ConnectionError, TimeoutError, ValueError) as e:
+      print(f"Error: {e}")
+
+    print(f"\n{'=' * 60}\n")
+
+
+async def single_prompt_mode(runner, prompt: str):
+  """Run the agent with a single prompt."""
+  print("=== Static Non-Text Content Demo Agent - Single Prompt Mode ===")
+  print(f"Prompt: {prompt}")
+  print("-" * 50)
+
+  # Create session
+  session = await runner.session_service.create_session(
+      app_name=APP_NAME, user_id=USER_ID
+  )
+
+  response = await call_agent_async(runner, USER_ID, session.id, prompt)
+  print(f"Agent Response:\n{response}")
+
+
+async def main():
+  args = process_arguments()
+
+  if args.debug:
+    logs.setup_adk_logger(level=logging.DEBUG)
+    print("Debug logging enabled. You'll see internal processing details.\n")
+
+  print("Initializing Static Non-Text Content Demo Agent...")
+  print(f"Agent: {agent.root_agent.name}")
+  print(f"Model: {agent.root_agent.model}")
+  print(f"Description: {agent.root_agent.description}")
+
+  # Show information about static instruction content
+  if agent.root_agent.static_instruction:
+    static_parts = agent.root_agent.static_instruction.parts
+    text_parts = sum(1 for part in static_parts if part.text)
+    image_parts = sum(1 for part in static_parts if part.inline_data)
+    file_parts = sum(1 for part in static_parts if part.file_data)
+
+    print("Static instruction contains:")
+    print(f"  - {text_parts} text parts")
+    print(f"  - {image_parts} inline image(s)")
+    print(f"  - {file_parts} file reference(s)")
+
+  print("-" * 50)
+
+  runner = InMemoryRunner(
+      agent=agent.root_agent,
+      app_name=APP_NAME,
+  )
+
+  if args.prompt:
+    await single_prompt_mode(runner, args.prompt)
+  else:
+    await run_default_test_prompts(runner)
+
+
+if __name__ == "__main__":
+  start_time = time.time()
+  try:
+    asyncio.run(main())
+  except KeyboardInterrupt:
+    print("\nExiting...")
+  except Exception as e:
+    print(f"Unexpected error: {e}", file=sys.stderr)
+    sys.exit(1)
+  finally:
+    end_time = time.time()
+    print(f"\nExecution time: {end_time - start_time:.2f} seconds")