Installation Instructions ------------------------- .. raw:: html

Follow these steps to install and run the RAG - Retrieval-Augmented Generation application locally:

Clone the repository:

git clone https://github.com/imadmaalouf02/RAG.git

Install the dependencies:

Make sure you have Python installed. You can install the required dependencies using the following command:

pip install -r requirements.txt

Alternative Conda installation:

If you are using Conda as your environment manager, you can install Streamlit with:

conda install -c conda-forge streamlit

Run the application:

Once all dependencies are installed, run the Streamlit app using:

streamlit run app.py

Access the app in your browser:

After running the command, Streamlit will open the application in your default browser. If not, you can access it by navigating to:

http://localhost:8501

Step-by-Step Guide for Using the Application -------------------------------------------- .. raw:: html

Upload your document:

On the main page of the application, you will see an option to upload a PDF file. Use the file upload widget to select your document.

Select the language model:

Choose one of the available language models from the dropdown menu. The models include:

Llama 2: Ideal for general text generation and comprehension.

Mistral: Fast and efficient for text generation.

CodeLlama: Specialized for generating and understanding code.

Llama 3.1: Advanced model for more complex language tasks.

Choose the task:

Select the task you want to perform from the available options:

Summarization: Get a concise summary of the document.

Translation: Translate the document content to another language.

Question-Answering: Ask questions about the document and receive detailed responses.

View the results:

Once the task is complete, the output will be displayed on the same page. Depending on the task, you'll either see a summary, translation, or answers to your questions.