google-gemini
[New Tutorial] End-to-End Multimodal Chatbot with Gemini API: Combined Vision and Text Processing
Description of the feature request:
Proposed Multimodal Tutorial Contribution
I'd like to contribute a comprehensive tutorial notebook that demonstrates how to build a multimodal chatbot using Google's Gemini API. This implementation includes several essential features missing from existing examples:
Tutorial Overview
The notebook guides developers through building a chatbot that can:
- Process natural language text inputs
- Analyze and understand images (computer vision)
- Handle combined text and image inputs simultaneously (true multimodal prompts)
- Maintain conversation history with both textual and visual context
- Format and present responses with markdown support
- Stream responses in real-time for better user experience
- Parameter Customization (Detailed examples of optimizing Gemini model parameters for different use cases)
- Basic Error Handling and Rate Limiting
What problem are you trying to solve with this feature?
Addressing Current Documentation Gaps
While the Gemini Cookbook contains various examples, there's currently a gap in comprehensive tutorials specifically demonstrating:
-
True multimodal integration: Most examples treat text and vision capabilities separately, rather than showing how to integrate them seamlessly in a single application.
-
Conversation management with visual context: Developers need guidance on how to maintain conversation history that includes both text and images.
-
Parameter optimization techniques for different types of responses (creative, analytical, code generation)
Any other information you'd like to share?
Technical Requirements
- Google API key for Gemini
- Python 3.9+
- Libraries: google-generativeai, pillow, IPython, pandas, matplotlib
I'm happy to make any adjustments based on feedback to ensure this contribution meets the cookbook's standards.
