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: [ Ссылка ]
RAG-GPT project: [ Ссылка ]

📚 *Main Libraries:*
OpenAI: [ Ссылка ]
Gradio: [ Ссылка ]
Langchain: [ Ссылка ]
Chroma: [ Ссылка ]

📺 *Introduction to Text Embedding:*
Watch the Video: [ Ссылка ]

#RAG #llm #ChatBot #GPT #Python #AI #OpenAI #Langchain #Gradio #chroma

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