Learn how to build sophisticated AI agents that leverage LanceDB’s multimodal capabilities for intelligent data retrieval and reasoning.
| Agent Type | Description |
|---|---|
| Multimodal Recipe Agent View Tutorial |
Build an AI agent that understands both text and images to help users find recipes. Uses PydanticAI with LanceDB for multimodal storage and retrieval, featuring semantic search, image similarity, and conversational interfaces. |
What You’ll Learn
- Agent Architecture: How to structure AI agents with tools and memory
- Multimodal Data: Storing and retrieving both text and image embeddings
- Tool Integration: Connecting agents to LanceDB for data access
- Conversational Interfaces: Building chat-based user experiences
- Production Deployment: Making agents ready for real-world use
Key Features
- Semantic Search: Find relevant content using natural language queries
- Image Understanding: Process and search through visual content
- Tool-based Reasoning: Agents that can use multiple tools to solve complex problems
- Conversation Memory: Maintain context across multiple interactions
- Streamlit Integration: Build interactive web interfaces for your agents
These tutorials build on LanceDB’s core capabilities to create intelligent, multimodal AI agents that can understand and reason about complex data.