RAG

RAG is a Streamlit-based application that allows users to interact with document databases using a conversational interface. The application supports uploading documents, extracting embeddings for efficient querying, and generating responses in real-time.

Features

• Document Upload: Supports PDF, DOCX, and TXT file uploads.
• Embedding Storage: Automatically processes uploaded documents and stores embeddings for efficient querying.
• Real-Time Conversational Interface: Allows users to query the document database and receive responses in a streaming, conversational manner.
• Session Management: Maintains chat history and handles file uploads within the session.
• SQL Integration: Integrates SQLite to allow dynamic SQL querying and natural language querying of the database.

Installation

Prerequisites

• Python 3.8 or higher
• Virtual environment tool (optional but recommended)

Setup

1. Clone the Repository:

   git clone https://github.com/TejasGupta-27/RAG.git
   cd RAG

2. Create and Activate a Virtual Environment:

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

3. Install Dependencies:

   pip install -r requirements.txt

4. Run the Application:

   streamlit run ui.py

   Access the Interface: Open your web browser and go to http://localhost:8501.

Usage

Uploading Documents

• Navigate to the Sidebar:

  • Click the "Browse files" button under the "Upload a File" section.
  • Select your PDF, DOCX, or TXT file.

• Automatic Processing:

  • The document will be automatically processed, and embeddings will be stored.

• Querying the Database:

  • Enter your query in the text input field in the sidebar.
  • Click "Send" to submit your query.
  • The bot will respond in the main chat area with relevant information extracted from the uploaded documents.

Viewing Chat History

• The chat history will be displayed in the main conversation area, showing both user queries and bot responses.

Project Structure

├── data/                          # Directory for temporary files
│   └── temp/                      # Temporary storage for uploaded files
├── preprocessing.py               # Script for document text extraction and chunking
├── rag_pipeline.py                # Script for generating responses and storing embeddings
├── ui.py                          # Main Streamlit application
├── database_handler.py            # Script for handling database queries and interactions
├── logo.png                       # Logo image displayed in the sidebar
├── requirements.txt               # Python dependencies
└── README.md                      # Project documentation

Future Enhancements

• Enhanced Query Expansion: Implement more advanced query expansion techniques to improve document retrieval accuracy.
• Multi-Language Support: Add support for processing and querying documents in multiple languages.
• User Authentication: Introduce user authentication to manage document access and interaction history.