ai-agent-book 精选快照（<2MB 代码与文档，来自 github.com/bojieli/ai-agent-book）

chapter4/multimodal-agent/quickstart.py

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+"""
+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 = """<?xml version="1.0" encoding="UTF-8"?>
+    <svg width="200" height="200" xmlns="http://www.w3.org/2000/svg">
+        <rect x="10" y="10" width="180" height="180" fill="lightblue" stroke="black" stroke-width="2"/>
+        <circle cx="100" cy="100" r="50" fill="yellow" stroke="orange" stroke-width="3"/>
+        <text x="100" y="105" text-anchor="middle" font-size="20" fill="black">Hello AI!</text>
+    </svg>
+    """
+    
+    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 = "✅ Configured" if has_key else "❌ Not configured"
+        print(f"   {provider.capitalize()}: {status}")
+    
+    if not any(api_keys.values()):
+        print("\n⚠️  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("🚀 Multimodal Agent Quickstart")
+    print("=" * 60)
+    
+    # Run basic tests
+    await test_basic_functionality()
+    
+    # Run conversation test
+    await test_conversation_mode()
+    
+    print("\n" + "="*60)
+    print("✅ 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 <path> --query <question>")
+    print("3. Start interactive mode: python main.py --interactive")
+    print("4. Run comparisons: python demo.py <file> <query>")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())