Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
Data Science
Towards Data Science·Aug 12·5 min read
Building Multimodal Workflows with a Local LLM
TL;DR
Gemma 4 and Ollama enable the creation of multimodal workflows that integrate image inputs and structured outputs, enhancing local LLM capabilities. This development allows for more versatile applications in AI-driven tasks.
✦ Why It Matters
Start building multimodal applications today using Gemma 4 and Ollama to enhance your AI projects.
Key Takeaways
- Use Ollama to run Gemma 4 locally for multimodal tasks.
- Implement a three-stage workflow for photo analysis.
- Define structured output schemas for consistent data handling.
- Create applications that utilize LLMs for enhanced user experiences.
How It Works
The workflow processes images by first preparing them and extracting metadata, then analyzing each photo with Gemma 4 to generate structured records. Finally, it synthesizes these records into a cohesive trip memory, allowing for a comprehensive understanding of the travel experience.