🍳 Multimodal Recipe Agent with LanceDB and PydanticAI

In this tutorial, you'll build an intelligent AI agent that can understand both text and images to help users discover recipes. The agent uses LanceDB for multimodal data storage and PydanticAI for intelligent reasoning.

What You'll Learn

• How to build AI agents with multimodal capabilities
• Using LanceDB for efficient vector storage and retrieval
• Creating custom tools for PydanticAI agents
• Building conversational interfaces with Streamlit
• Handling both text and image inputs in a single agent

Prerequisites

This tutorial assumes you have:

• Python 3.8+ installed
• Basic understanding of vector databases
• Familiarity with AI/ML concepts (helpful but not required)

Let's get started!

1. Setup and Installation

First, let's install the required dependencies:

2. Data Preparation

For this tutorial, we'll use a recipe dataset with both text and images. Let's start by setting up our data directory and downloading a sample dataset:

3. Setting Up LanceDB

Now let's set up LanceDB to store our recipe data with both text and image embeddings:

4. Building the AI Agent

Now let's create our PydanticAI agent with custom tools for recipe search:

5. Testing the Agent

Let's test our agent with some sample queries:

6. Summary and Next Steps

Congratulations! You've built a complete multimodal recipe agent with the following features:

What You've Accomplished

1. Multimodal Data Storage: Used LanceDB to store both text and image embeddings
2. AI Agent Development: Created a PydanticAI agent with custom tools
3. Semantic Search: Implemented text-based recipe search using vector similarity
4. Production Features: Added proper error handling and data conversion

Key Technologies Used

• 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

Next Steps

1. Add Image Search: Implement the image search functionality
2. Scale Up: Use a larger recipe dataset
3. Deploy: Deploy your agent to a cloud platform
4. Enhance UI: Add more interactive features
5. Add More Tools: Extend the agent with additional capabilities

Running Your Agent

To run your complete recipe agent, you can create a simple script:

	# Simple test script
result = agent.run_sync("Find me some dessert recipes")
print(result.data)

Your agent is now ready to help users discover recipes through natural language conversations!