"""
Quickstart script for testing multimodal agent
Creates sample files and demonstrates capabilities
"""
import asyncio
import base64
from pathlib import Path
import os
from agent import MultimodalAgent, MultimodalContent
from config import ExtractionMode, Config
def create_sample_files():
"""Create sample files for testing"""
# Create test_files directory
test_dir = Path("test_files")
test_dir.mkdir(exist_ok=True)
# Create a simple text-based "image" (SVG)
svg_content = """
"""
svg_path = test_dir / "sample.svg"
svg_path.write_text(svg_content, encoding="utf-8")
print(f"Created: {svg_path}")
# Create a simple text file that we'll treat as a "document"
doc_content = """
# Sample Document for Multimodal Agent Testing
## Introduction
This is a test document created for demonstrating the multimodal agent's capabilities.
The agent can process this document in different modes:
1. **Native Mode**: Direct processing using the model's built-in capabilities
2. **Extract to Text**: Convert to text first, then analyze
3. **With Tools**: Use specialized tools for detailed analysis
## Key Features
- Support for multiple file formats (PDF, images, audio)
- Multiple AI model providers (Gemini, OpenAI, Doubao)
- Streaming responses for better user experience
- Tool calling for advanced analysis
## Technical Details
The system uses a unified message format compatible with OpenAI's API structure,
making it easy to switch between different providers while maintaining consistency.
## Conclusion
This multimodal agent demonstrates state-of-the-art AI capabilities for
content understanding and analysis across different modalities.
"""
doc_path = test_dir / "sample_document.txt"
doc_path.write_text(doc_content, encoding="utf-8")
print(f"Created: {doc_path}")
return test_dir
async def test_basic_functionality():
"""Test basic agent functionality"""
print("\n" + "="*60)
print("QUICKSTART: Testing Multimodal Agent")
print("="*60)
# Check API keys
config = Config()
api_keys = config.validate_api_keys()
print("\n1. API Key Status:")
print("-" * 40)
for provider, has_key in api_keys.items():
status = "ā Configured" if has_key else "ā Not configured"
print(f" {provider.capitalize()}: {status}")
if not any(api_keys.values()):
print("\nā ļø Warning: No API keys configured!")
print("Please copy env.example to .env and add your API keys.")
return
# Create sample files
print("\n2. Creating Sample Files:")
print("-" * 40)
test_dir = create_sample_files()
# Test with available model
if api_keys["gemini"]:
model = "gemini-3.5-flash"
print(f"\n3. Testing with {model}:")
print("-" * 40)
agent = MultimodalAgent(
model=model,
mode=ExtractionMode.EXTRACT_TO_TEXT,
enable_tools=False
)
# Process the text document
doc_path = test_dir / "sample_document.txt"
content = MultimodalContent(
type="text",
path=str(doc_path),
data=doc_path.read_bytes()
)
print("Processing sample document...")
try:
# Simulate as if it's a PDF for demonstration
content.type = "pdf"
result = await agent._extract_pdf_to_text(content)
print("Extracted content preview:")
print(result[:300] + "..." if len(result) > 300 else result)
# Answer a question
print("\nAsking a question about the document...")
answer = await agent._answer_with_context(
result,
"What are the three modes mentioned in the document?"
)
print("Answer:", answer)
except Exception as e:
print(f"Error: {e}")
elif api_keys["openai"]:
model = "gpt-5.6-luna"
print(f"\n3. Testing with {model}:")
print("-" * 40)
agent = MultimodalAgent(
model=model,
mode=ExtractionMode.EXTRACT_TO_TEXT,
enable_tools=False
)
print("Note: OpenAI models work best with images.")
print("For document processing, Gemini is recommended.")
else:
print("\n3. Skipping tests - no API keys configured")
async def test_conversation_mode():
"""Test conversation mode with streaming"""
config = Config()
if not config.gemini_api_key and not config.openai_api_key:
print("\nSkipping conversation test - no API keys configured")
return
print("\n" + "="*60)
print("4. Testing Conversation Mode")
print("="*60)
# Use available model
if config.gemini_api_key:
model = "gemini-3.5-flash"
else:
model = "gpt-5.6-luna"
agent = MultimodalAgent(
model=model,
mode=ExtractionMode.EXTRACT_TO_TEXT,
enable_tools=True
)
print(f"Using model: {model}")
print("Tools: Enabled")
print("\nStarting conversation...")
print("-" * 40)
# Simulate a conversation
messages = [
"Hello! I'm testing the multimodal agent. Can you explain what you can do?",
"What types of files can you process?",
"How do the different extraction modes work?"
]
for message in messages:
print(f"\nUser: {message}")
print("Assistant: ", end="", flush=True)
try:
response_text = ""
async for chunk in agent.chat(message, stream=True):
print(chunk, end="", flush=True)
response_text += chunk
print()
# Small delay for readability
await asyncio.sleep(0.5)
except Exception as e:
print(f"\nError: {e}")
break
async def main():
"""Run all quickstart tests"""
print("š Multimodal Agent Quickstart")
print("=" * 60)
# Run basic tests
await test_basic_functionality()
# Run conversation test
await test_conversation_mode()
print("\n" + "="*60)
print("ā Quickstart Complete!")
print("="*60)
print("\nNext steps:")
print("1. Add your API keys to .env file")
print("2. Try with your own files: python main.py --file --query ")
print("3. Start interactive mode: python main.py --interactive")
print("4. Run comparisons: python demo.py ")
if __name__ == "__main__":
asyncio.run(main())