Multimodal Recipe Agent

Learn how to build a sophisticated AI agent that can understand both text and images to help users discover recipes. This tutorial demonstrates how to combine LanceDB’s multimodal capabilities with PydanticAI to create an intelligent recipe assistant.

What You’ll Build

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

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

Tutorial Overview

Colab Tutorial (Quick Start)

Interactive notebook covering:

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

Full Demo (Complete Application)

Complete codebase including:

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

Prerequisites

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

Quick Start

Option 1: Interactive Tutorial (Google Colab)

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

Option 2: Full Demo Application (Local Setup)

Download the Complete Tutorial

Download Tutorial Files

Setup Instructions

code

# 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

Complete experience! This gives you the full Streamlit chat interface with a real recipe dataset. Requires downloading the dataset from Kaggle but provides the complete production-ready application.

Dataset Information

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

Code Files

This tutorial includes complete, runnable code:

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

Folder Structure

When you download the tutorial, organize your files like this:

code

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

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

Ready to build your first multimodal AI agent? Let’s get started!

View Tutorial Notebook