RAG-GPT: Chat with any documents and summarize long PDF files with Langchain | Gradio App

RAG stands for Retrieval Augmented Generation and RAG-GPT is a powerful chatbot that supports three methods of usage: 1. *Chat with offline documents:* Engage with documents that you've pre-processed and vectorized. These documents will be integrated into your chat sessions. 2. *Chat with real-time uploads:* Easily upload documents during your chat sessions, allowing the chatbot to process and set up a RAG pipeline enabling the user to chat with the documents on the fly. 3. *Summarization Requests:* Request the chatbot to provide a comprehensive summary of an entire PDF or document in a single interaction, streamlining information retrieval. 00:01:30 Chatbot demo 00:07:04 GitHub repository explanation 00:08:15 RAG presentation (explaining different RAG techniques) 00:17:18 Project schema 00:26:50 Designing the data ingestion section 00:38:12 Designing the pipeline for connecting the GPT model to the vectorDB 00:46:45 Designing the chatbot interface 00:49:14 Connecting the backend to the chatbot interface 00:54:09 Testing the RAG side of the project 01:04:28 Designing and testing the document summarization section 01:19:26 Optimization strategies and deployment considerations 🚀 *GitHub Repository:* LLM-Zero-to-Hundred Project: https://github.com/Farzad-R/LLM-Zero-to-Hundred RAG-GPT project: https://github.com/Farzad-R/LLM-Zero-to-Hundred/tree/master/RAG-GPT 📚 *Main Libraries:* OpenAI: https://platform.openai.com/docs/models Gradio: https://www.gradio.app/docs/interface Langchain: https://python.langchain.com/docs/get_started/introduction Chroma: https://docs.trychroma.com/getting-started 📺 *Introduction to Text Embedding:* Watch the Video: https://www.youtube.com/watch?v=sxBr_afsvb0&t=454s #RAG #llm #ChatBot #GPT #Python #AI #OpenAI #Langchain #Gradio #chroma