This web application implements a Retrieval-Augmented Generation (RAG) backend system to analyze and compare two provided market research reports.

Watch the Demo

Click on the image below to watch the demo

Key Features

Core Capabilities

• Document analysis using RAG and LangChain
• Semantic search with FAISS vector store
• Real-time topic modeling and sentiment analysis
• Interactive data visualizations
• Cold start prevention system

Technical Features

• Responsive UI with Streamlit
• FastAPI backend with async support
• OpenAI integration for advanced NLP
• Comprehensive test coverage
• Performance optimized with caching

Tech Stack

• Frontend: Streamlit (for responsive web interface)
• Backend: FastAPI
• Models used: LangChain, OpenAI API
• Vector Store: FAISS
• PDF Processing: LangChain PyPDFLoader

Development

Backend Development

The backend is built with FastAPI and includes:

• Async request handling
• RAG implementation with LangChain
• FAISS vector store for document retrieval
• Automatic cold start prevention

Frontend Development

The frontend uses Streamlit and features:

• Interactive visualizations with Plotly
• Real-time analytics dashboard
• Responsive design
• Session state management

Performance Optimization

The platform includes several performance optimizations:

• Cold start prevention using keepalive system
• Efficient vector store management
• Response caching
• Optimized state management
• Lazy loading of components

Error Handling

The application implements comprehensive error handling:

• Backend validation with Pydantic
• Frontend error feedback
• Graceful degradation
• Detailed error logging

Installation

To set up and run the RAG Chatbot, follow these steps:

Step 1: Clone the repository

Clone the repository to your local machine:

git clone https://github.com/Sal68190/BI-coding-challenge.git
cd BI-coding-challenge

Step 2: Set up a virtual environment

Create and activate a virtual environment to manage dependencies:

For Linux/MacOS:

python3 -m venv venv
source venv/bin/activate

For Windows:

python -m venv venv
venv\Scripts\activate

Step 3: Install dependencies

Install the required dependencies by running:

pip install -r backend/requirements.txt

Step 4: Set up the environment variables

Ensure you have all the necessary environment variables set up (if applicable), such as API keys or file paths, as described in the documentation or project configuration files.

Step 5: Running the application

After setting up the environment, start the backend and frontend servers:

Backend Server: Start the FastAPI server:

uvicorn api.main:app --reload

This will run the server at http://localhost:8000.

Frontend Interface: Start the Streamlit frontend:

streamlit run frontend/app.py

This will open the chat interface in your browser at http://localhost:8501.

Now, the RAG Chatbot should be fully functional on your local machine.