Multi-modal RAG with Captioning
This code implements one of the multiple ways of multi-model RAG. It extracts and processes text and images from PDFs, utilizing a multi-modal Retrieval-Augmented Generation (RAG) system for summarizing and retrieving content for question answering.
Efficiently summarize complex documents to facilitate easy retrieval and concise responses for multi-modal data.
What you'll learn
- 1PyMuPDF: For extracting text and images from PDFs.
- 2Gemini 1.5-flash model: To summarize images and tables.
- 3Cohere Embeddings: For embedding document splits.
- 4Chroma Vectorstore: To store and retrieve document embeddings.
- 5LangChain: To orchestrate the retrieval and generation pipeline.
About this tutorial
This hands-on Jupyter notebook is part of RAG Techniques, a free open-source repository by Nir Diamant covering rag techniques with runnable code examples and detailed explanations.
RAG Made Simple
The book that extends this repo: 22 RAG techniques with the intuition behind each, side-by-side comparisons of when each wins (and quietly fails), and original illustrations.
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