# Cursor Chat: ai-agent-book

## Metadata
- **Project**: ai-agent-book
- **Path**: `/Users/boj`
- **Date**: 2025-09-18 17:09:08
- **Session ID**: `4d22c3f8-9ca7-49b7-ac35-d0af5220dfc4`

## Conversation

### 👤 You

In projects/week3/multimodal-agent, create an educational agent to compare multiple multimodal content extraction techniques:

1. model native multimodality:
- Gemini 2.5 Pro only, implement the document understanding API @https://ai.google.dev/gemini-api/docs/document-processing  and image understanding API @https://ai.google.dev/gemini-api/docs/image-understanding  and audio understanding API @https://ai.google.dev/gemini-api/docs/audio 
- GPT-5 native multimodal API (follows openai multimodal format)
- Doubao 1.6 native multimodal API (follows openai multimodal format)

2. extract to text:
- PDF: (1) OCR @https://mistral.ai/news/mistral-ocr   (2) image-based OCR: OpenAI GPT-5
- Image: let (1) GPT-5 or (2) doubao 1.6 model summarize the image with contextual information
- Audio: (1) Whisper API in OpenAI to generate transcript, (2) use Gemini 2.5 Pro audio understanding API (above) to generate text

3. Multimodal analysis as tools: use the extract to text method to add a contextual text summary in context, but additionally add tools for the agent to read the original source again and answer questions.
- Image: a tool to input an image URL or local file path, and text query, output an answer. Use GPT-5 / doubao 1.6.
- Audio: a tool to input an audio URL or local file path, and text query, output an answer. Use Gemini 2.5 Pro audio understanding API 
- PDF: a tool to input a PDF and output an answer. Use Gemini 2.5 Pro document understanding API

The agent framework should support all the models above, allowing users to switch models and modes. model native multimodality is exclusive with extract to text. Multimodal analysis as tools is an additive function in addition to "extract to text". it is especially useful when the user asks subsequent questions, where pure extract to text loses the context.

The agent should follow a standard OpenAI tool call and conversation history format (see week1/context project), and subsequent questions from the user should be appended as user message to the unified conversation history. The model's internal reasoning and response should be streamed to the console. When "Multimodal analysis as tools" is enabled, support the multimodal understanding tools.

---

### 🤖 Cursor Assistant

I'll create a comprehensive multimodal agent that compares different content extraction techniques. Let me start by setting up the project structure and implementing the agent with support for all three modes.

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### 🤖 Cursor Assistant

Let me create the project directory structure and implement the multimodal agent.

---

### 🤖 Cursor Assistant

## Summary

I've successfully created a comprehensive multimodal agent in `projects/week3/multimodal-agent` that compares multiple content extraction techniques across different AI providers. Here's what was implemented:

### Core Features

1. **Three Extraction Modes:**
   - **Native Multimodality**: Direct processing using models' built-in capabilities
   - **Extract to Text**: Converts multimodal content to text first, then analyzes
   - **Multimodal Analysis Tools**: Additive functionality for detailed follow-up questions

2. **Multi-Provider Support:**
   - **Gemini 2.5 Pro**: Full support for PDFs, images, and audio
   - **GPT-5/GPT-4o**: Native image support, text extraction for other formats
   - **Doubao 1.6**: Native image support following OpenAI format

3. **Advanced Features:**
   - Streaming responses for better user experience
   - OpenAI-compatible conversation history and tool calling format
   - Multimodal tools for specialized analysis of images, audio, and PDFs
   - Mode switching during runtime
   - Persistent conversation context

### Project Structure

```
projects/week3/multimodal-agent/
├── agent.py           # Core agent implementation with all modes
├── config.py          # Configuration and model settings
├── main.py            # Main entry point with CLI interface
├── demo.py            # Comparison demos across modes and models
├── quickstart.py      # Quick testing with sample files
├── test_multimodal.py # Unit tests for all components
├── requirements.txt   # Python dependencies
├── env.example        # API key configuration template
└── README.md          # Comprehensive documentation
```

### Key Implementation Details

The agent framework:
- Follows the standard OpenAI tool call and conversation history format from the week1/context project
- Supports streaming responses with proper chunking
- Implements three distinct processing paths based on the extraction mode
- Handles multiple file types (PDF, images, audio) with appropriate processing
- Provides tools for subsequent analysis when "Multimodal analysis as tools" is enabled

### Usage Examples

1. **Interactive Mode:**
   ```bash
   python main.py --interactive
   ```

2. **Process Files:**
   ```bash
   python main.py --file document.pdf --query "Summarize this"
   python main.py --mode extract_to_text --tools --file image.jpg
   ```

3. **Compare Modes:**
   ```bash
   python demo.py document.pdf "What are the key points?"
   ```

The implementation allows users to:
- Switch between models and modes dynamically
- Process PDFs, images, and audio files
- Use native multimodal capabilities or extract to text first
- Enable specialized tools for detailed follow-up questions
- Maintain conversation context across multiple interactions

The system is designed to be educational, showing the trade-offs between different approaches to multimodal AI processing.

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*Exported from [Cursor View](https://github.com/saharmor/cursor-view)*