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:*

📚 *Main Libraries:*

📺 *Introduction to Text Embedding:*

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

[mikew]

Hi there. Quick question. I know the documentation states to open and modify the "cfg.py" but noticed it was replaced with app_config.yml and the other configuration are located in "load_config.py". I am receiving an error with the values I supplied so I was wondering what is the format and values the following configurations are looking for value-wise? I am using OpenAI for instance and not Azure. Thanks!

openai.api_type = os.getenv("OPENAI_API_TYPE")
openai.api_base = os.getenv("OPENAI_API_BASE")
openai.api_version =

[navanshukhare]

This is one video if you want to learn and make an advanced RAG Project. Other videos are also equally great and I love your approach to how organized you are in your videos; your code quality is just WOW.

[alikhalili]

This is an incredibly informative and well-structured video! The detailed breakdown of the RAG-GPT chatbot, along with the time-stamped sections, makes it easy to navigate and understand. The inclusion of real-time document uploads and summarization requests showcases the versatility of this chatbot. The GitHub links and references to the main libraries used are very helpful for those who want to delve deeper. Keep up the great work! Looking forward to more content like this. 👏👏👏

[RZOLTANM]

Really good and perfectly articulated presentation on RAG. Thank you!

[deborahjamesmathew]

Thank you so much for the informative video

[SaddamBinSyed]

A very well done @Farzad. Great explanation. This is exactly the concept I was looking to understand and implement. You are simply 100x amazing. I am highly excited to listen to your other videos as well. Thanks for keeping this channel so informative. One suggestion from my side: next time, please use local LLMs like Ollama Llama 3.1 so those who cannot afford it will benefit.

[LorenzoPozzi-gh]

Very good content, thanks for the video!!

[thaukhoorz]

Thank you so much for this! Excellent instructions, excellent documentation.

[yes]

Thanks for this project! Very useful! Will watch every1 of ur videos from now on! ☺

[musumo]

Great work! Would love to see this with LiteLLM as an option and some sort of basic user login system…along the lines of open webui

[zlatomirradev]

Nice project, thank you very much for the great content sir👏👏👏

[JJaitley]

What are your suggestions on cleaning the company docs before chunking? Some of the challenges faced are how to handle the index pages in multiple pdfs also the headers and footers. You should definitely make some video related to cleaning a pdf before chunking much needed.

[AlonAvramson]

Thank you! great video and explanations!

[rajasengupta]

This is the most comprehensive RAG tutorial video I have seen on YouTube. What a great effort and command over the subject sir!

I am from a low-code business analyst background so I heavily depend upon co-pilot to guide me on python script functionality

Still I was able to set up the system as explained by you on my local PC, however I am getting the error on executing python src\raggpt_app.py

"
import pwd
ModuleNotFoundError: No module named 'pwd'
"

Can you guide me on what I am missing
Many thanks

[RetiredVet]

You did a great job, but the videos are so small, I have to constantly expand them to read it. It would be nice if you could read the text without going full screen all the time.

[doctorbill]

I just discovered your video today in my feed. This is an excellent project with great attention to detail. Very well done.

I cloned it and saw a project in your bullet list called "Open Source LLMs" along with the note that it is coming soon. Do you have any idea when that might be? This is important for those of us wanting to run LLMs with RAG locally on our machines. Very much looking forward to seeing this. Thanks for your work,

[PrinceBrosnan]

is there a similar ready-made solution on the site "poe"? I am a beginner and want such a model, but not to make it, but to work with it

[revanthreddy]

hello sir is the open ai api key paid ? do we have to pay for it in order to access it and use it ?

[OBRosewell]

dude!!!! INSANE!! such a good tutorial. you rock.

my one question is: credits. Will the vector function save you credits? e..g i want to build a legal document reader & Q&A. Some docs are 100 pages long. wont each doc cost hundreds in API credits? OR is that what vectorisation & DBs are for?

[kunalsatpute]

Very detailed explanation and thank you for making it open source.

Is there any plan to advancement to this application? like
1 advance rag pipeline which can extract text and table data or image based on user question
2 create vector db based on text image and table data?
3 providing login and admin panel to track the information like no of token used by different users etc
4 using react node for better app experience?
5 Complete deployment process ?