LLM-Based RAG System

Overview

This project is designed to create a Retrieval-Augmented Generation (RAG) system using a Large Language Model (LLM). The system integrates with an API to scrape content from the internet and uses an API to serve the LLM-generated answers. A simple front-end interface is provided to interact with the system. Note: ONLY use the packages provided in the requirements.txt file (similar/alternative packages are ok only if they perform similar task/function).

Process Overview

1. User Input via Streamlit Interface:

   • The user interacts with a Streamlit-based front-end where they can input their query.

2. Query Sent to Flask Backend:

   • The query entered by the user is sent from the Streamlit interface to a Flask backend via an API call.

3. Internet Search and Article Scraping:

   • The Flask backend searches the internet for the query using a designated API. It retrieves the top relevant articles and scrapes their content, extracting only the useful text (headings and paragraphs).

4. Content Processing:

   • The scraped content is processed to create a coherent input, which is then passed to the LLM for generating a response.

5. LLM Response Generation:

   • The processed content and the user's query are used to generate a contextual answer using the LLM. The LLM is accessed via an API, and the generated response is returned to the Flask backend.

6. Response Sent Back to Streamlit Interface:

   • The Flask backend sends the generated answer back to the Streamlit interface, where it is displayed to the user.

Setup Instructions

Step 1: Clone or download the Repository (if emailed)

git clone https://github.com/RaghuRam2005/llm_based_search.git
cd project_name

Or download it

Step 2: Set Up a Virtual Environment

You can use venv or conda to create an isolated environment for this project.

Using venv

python -m venv env
source env/bin/activate  # On Windows, use `env\Scripts\activate`

Using conda

conda create --name project_env python=3.8
conda activate project_env

Step 3: Install Requirements

pip install -r requirements.txt

Step 4: Set Up Environment Variables

Create a .env file in the root directory and add your API keys in a way it can be accessed in the app.

Step 5: Run the Flask Backend

Navigate to the flask_app directory and start the Flask server:

cd flask_app
python app.py

Step 6: Run the Streamlit Frontend

In a new terminal, run the Streamlit app:

cd streamlit_app
streamlit run app.py

Step 7: Open the Application

Open your web browser and go to http://localhost:8501. You can now interact with the system by entering your query.

Project Structure

• flask_app/: Contains the backend Flask API and utility functions.
• streamlit_app/: Contains the Streamlit front-end code.
• .env: Stores API keys (make sure this file is not included in version control).
• requirements.txt: Lists the project dependencies.