Multimodal Agent

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What You’ll Build

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Colab Tutorial (Sample Data)

• Interactive Learning: Step-by-step notebook with sample recipes
• Core Concepts: Learn multimodal agent development
• No Setup Required: Run directly in your browser

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Full Demo Application (Real Dataset)

• Complete Streamlit App: Full chat interface with image upload
• Real Recipe Dataset: Thousands of actual recipes with images
• Production Features: Error handling, logging, and deployment considerations
• Multimodal Search: Both text and image-based recipe discovery

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Tutorial Overview

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Colab Tutorial (Quick Start)

1. Data Preparation: Work with sample recipe data
2. Embedding Generation: Create text and image embeddings
3. LanceDB Setup: Store multimodal data efficiently
4. Agent Development: Build a PydanticAI agent with custom tools
5. Testing: Try the agent with sample queries

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Full Demo (Complete Application)

1. Real Dataset: Download and process thousands of recipes
2. Streamlit Interface: Full chat application with image upload
3. Production Features: Error handling, logging, and monitoring
4. Deployment Ready: Complete with all necessary files

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Prerequisites

• Python 3.8+
• Basic understanding of vector databases
• Familiarity with AI agents (helpful but not required)

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Quick Start

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Option 1: Interactive Tutorial (Google Colab)

This Colab notebook provides a step-by-step tutorial with sample data. No setup required - just click and start learning about multimodal agents.

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Option 2: Full Demo Application (Local Setup)

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Download the Complete Tutorial

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Setup Instructions

# 1. Extract the downloaded files to a folder
# 2. Navigate to the folder in terminal
cd multimodal-recipe-agent

# 3. Install dependencies with uv
uv sync

# 4. Download the Kaggle dataset
# Visit: https://www.kaggle.com/datasets/pes12017000148/food-ingredients-and-recipe-dataset-with-images
# Extract recipes.csv to the data/ folder

# 5. Import the dataset
uv run python import.py

# 6. Run the complete Streamlit chat application
uv run streamlit run app.py

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Dataset Information

• Source: Kaggle Recipe Dataset
• Size: Thousands of recipes with images
• Format: CSV file with recipe data and image references

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Code Files

• multimodal-recipe-agent.ipynb - Interactive Jupyter notebook tutorial
• agent.py - Complete PydanticAI agent implementation
• app.py - Streamlit chat interface
• import.py - Data import and processing script
• pyproject.toml - Modern Python project configuration
• uv.lock - Locked dependency versions for reproducible builds
• README.md - Complete project documentation

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Folder Structure

multimodal-recipe-agent/
├── multimodal-recipe-agent.ipynb  # Interactive tutorial
├── agent.py                       # Core agent implementation
├── app.py                         # Streamlit chat interface
├── import.py                      # Data processing script
├── pyproject.toml                 # Project configuration
├── uv.lock                        # Dependency lock file
├── README.md                      # Project documentation
└── data/                          # Generated data (created after import)
    ├── recipes.csv               # Recipe dataset
    ├── images/                   # Recipe images
    └── recipes.lance             # LanceDB database

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Key Technologies

• LanceDB: Multimodal vector database for efficient storage and retrieval
• PydanticAI: Modern AI agent framework with type safety
• Sentence Transformers: Text embeddings for semantic search
• CLIP: Vision-language model for image understanding
• Streamlit: Interactive web application framework