Not Just Text: RAG That Sees Images and Reads Tables 🧠🔍
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Retrieval-Augmented Generation (RAG) enhances the capabilities of Large Language Models (LLMs) by providing additional context for more accurate responses. This guide demonstrates building a multimodal RAG system that processes not only text but also tables and images from documents. Using the Unstructured library and GPT-4.1, it outlines parsing PDFs, summarizing content, creating embeddings, and storing vectorized data in ChromaDB. The approach aims to improve document understanding by integrating various content types, addressing accuracy, stability, and other potential risks.
Table of contents
A practical guide to creating a smarter, multimodal retrieval-augmented generation pipeline using GPT4.1 and Unstructured Library.Introduction:Solution Approach:Parsing PDF:Summarizing Images and Tables:Processing the Chunks and Summary:Create Embedding and Vector Database:Building Response Model Dynamic Prompt:Generating Response:Conclusion:What’s Next ?Sort: