LLMs have stunned the world with their capacity to create realistic images, code, and dialogue. Undoubtedly, ChatGPT has taken the world by storm. Millions are using it. But while it's great for general-purpose knowledge, it only knows information it was trained on, which is pre-2021 generally available internet data. It lacks awareness of your private data and remains uninformed about recent data sources. Thus, to improve them in that regard, we can provide them with information that we retrieved from a search step. This makes them more factual and gives a better ability to provide the model with up-to-date information, without the need to retrain these massive models. This is precisely what a retrieval-augmented LLM or Retrieval-Augmented Generation (RAG) system is. Indeed, this repository will precisely outline the creation of an RAG system and elucidate the optimization steps involved.

• RAG
• Tech Stack
• Installation
• Useful Links
• Contact

• LangChain
• LlamaIndex
• Azure OpenAI
• Gradio

1. Clone the Github repository

   git clone https://github.com/zekaouinoureddine/Adding-Private-Data-to-LLMs.git

2. Requirements Cd to the project directory and ensure that you have Python 3 installed, along with the necessary dependencies.

   cd Adding-Private-Data-to-LLMs
   pip install -r requirements.txt

3. Run the Gradio app

   python rag.py

Visit http://127.0.0.1:7860 on your machine to test the app. You should see something like the following:

Blog	Plateform	Language	Notebook
Ask Your Own Data	Hiberus Blog	ES
Ask Your Own Data	Medium	EN
Ask Your Web Pages	Hiberus Blog	ES
Ask Your Web Pages	Medium	EN

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• LinkedIn: Nour Eddine ZEKAOUI
• Twitter: @NZekaoui

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