Building Multimodal RAG Application #5: Multimodal Retrieval from Vector Stores

Author(s): Youssef Hosni

Originally published on Towards AI.

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Multimodal RAG combines textual and visual data to enrich the retrieval process, enhancing large language models’ ability to generate more contextually accurate and detailed responses by accessing multiple data types.

This article, the fifth in an ongoing series on building Multimodal Retrieval-Augmented Generation (RAG) applications, dives into the essentials of setting up multimodal retrieval using vector stores.

Starting with environment setup, this guide covers installing and configuring the LanceDB vector database, a robust solution for managing and querying multimodal data. Next, it demonstrates how to ingest both text and image data into LanceDB using LangChain, a popular framework for managing LLM workflows.

The article concludes with a practical walkthrough of performing multimodal retrieval, enabling efficient searches across both text and image data, which can significantly enhance RAG applications by leveraging rich, diverse information sources.

This article is the Fifth in the ongoing series of Building Multimodal RAG Application:

Introduction to Multimodal RAG Applications (Published)Multimodal Embeddings (Published)Multimodal RAG Application Architecture (Published)Processing Videos for Multimodal RAG (Published)Multimodal Retrieval from Vector Stores (You are here!)Large Vision Language Models (LVLMs) (Coming soon!)Multimodal RAG with Multimodal LangChain (Coming soon!)Putting it All Together! Building Multimodal RAG Application (Coming soon!)

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Published via Towards AI