> ## Documentation Index
> Fetch the complete documentation index at: https://docs.lancedb.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Multimodal Agent
> Build an AI agent that understands both text and images to help users find recipes using LanceDB and PydanticAI
[](https://colab.research.google.com/drive/1pxavAGoXa-KSh_4HxNpvP2AjHPcIRpbq?usp=sharing)
Ever wanted to combine the power of text and images in a single AI agent? In this tutorial,
you'll build an agent that can understand both text and images to help users discover recipes that are relevant to them. The approach shown combines LanceDB's multimodal capabilities with [Pydantic AI](https://ai.pydantic.dev/) for the agentic workflow.
## Key Technologies
* **LanceDB**: Embedded retrieval library and multimodal lakehouse 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
## Tutorial Overview
### Option 1: Notebook
The notebook shows how to work through the steps and prepare a small sample recipe dataset, generate both text and image
embeddings, store everything efficiently in LanceDB, and then build a PydanticAI agent with custom tools to
query it. You'll finish by testing the agent against a few example questions to see the full multimodal flow
end to end.
This simple tutorial provides a step-by-step workflow with a small demo dataset of 4 examples.
No local setup required - just click and start learning about multimodal agents.
[](https://colab.research.google.com/drive/1pxavAGoXa-KSh_4HxNpvP2AjHPcIRpbq?usp=sharing)
### Option 2: Demo Application (Local Setup)
The demo application is the full codebase: you'll download and process a real recipe dataset with thousands
of items, run a Streamlit chat interface that supports image upload, and follow a structure that includes
production-minded touches like error handling, logging, and monitoring. Everything you need to deploy is
included.
Download the files for the full demo application here.
### Dataset Information
* **Source**: [Kaggle Recipe Dataset](https://www.kaggle.com/datasets/pes12017000148/food-ingredients-and-recipe-dataset-with-images)
* **Size**: Thousands of recipes with images
* **Format**: CSV file with recipe data and image references
### Setup
```bash bash icon="code" theme={"theme":{"light":"vitesse-light","dark":"catppuccin-mocha"}}
# 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
```