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-...
RAG-GPT project: https://github.com/Farzad-R/LLM-Zero-...

📚 Main Libraries:
OpenAI: https://platform.openai.com/docs/models
Gradio: https://www.gradio.app/docs/interface
Langchain: https://python.langchain.com/docs/get...
Chroma: https://docs.trychroma.com/getting-st...

📺 Introduction to Text Embedding:
Watch the Video: RAG explained: A Step-by-Step Guide t...

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