🚀 Building a Multimodal RAG: LlamaIndex + LanceDB + Gemini 2.0 Flash

Ready to build the next generation of AI applications? This video is your complete guide to creating a powerful Multimodal Retrieval-Augmented Generation (RAG) system that goes beyond just text!

We'll be leveraging a cutting-edge stack to unify and query your complex data:

🧠 Gemini 2.0 Flash: The fast and powerful multimodal large language model from Google to handle diverse inputs and generate high-quality, context-aware responses.

🦙 LlamaIndex: The premier data framework for connecting your custom data sources (text, images, PDFs, etc.) to Large Language Models (LLMs) via an optimized RAG pipeline.

💾 LanceDB: The AI-native vector database built on the Lance columnar format, offering a unified, performant solution for storing, indexing, and executing ultra-fast hybrid search over multimodal data at scale.

What You Will Learn:
Understand the Multimodal RAG architecture and how it enables "any-to-any" search (e.g., querying an image with a text prompt).
Set up your development environment and configure Gemini 2.0 Flash for both embedding and generation.
Use LlamaIndex to ingest, process, and chunk documents containing various modalities (text, images, and more).
Integrate LanceDB for efficient and scalable storage of your multimodal embeddings and metadata.
Implement a full RAG pipeline to retrieve relevant multimodal context and generate grounded, accurate answers.

See a practical demo of the Multimodal RAG system in action!

Whether you're a data scientist, a machine learning engineer, or an AI developer, this tutorial will equip you with the skills to move from traditional text-only RAG to a truly multimodal application.

🔗 Resources & Code:
GitHub Repository (Code): https://github.com/Mayurji/Explore-Li...
LlamaIndex Documentation: https://developers.llamaindex.ai/pyth...
LanceDB Documentation: https://lancedb.com/docs/

Required Libraries: llama-index, lancedb, google-genai (and related LlamaIndex packages)

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