DocSense

DocSense - Smart PDF Comparison and Analysis

This project provides a web application for comparing PDF documents using different cosine similarity scans and a chatbot interface to interact with the processed PDFs. The application is built using Streamlit, FastAPI, and LangChain.

Features

• PDF Comparison: Compare two PDFs using different levels of cosine similarity scans.
  • Document-Level Cosine Similarity Scan
  • Sentence-Level Cosine Similarity Scan
  • Preprocessed Sentence-Level Cosine Similarity Scan
• ChatBot: Interact with the content of the PDFs using a chatbot powered by custom LLM models (LLAMA 3 and Google Gemini).

Installation

1. Clone the repository:

   git clone https://github.com/lakshya324/DocSense.git
   cd DocSense

2. Install the required packages:

   pip install -r requirements.txt

3. Set up environment variables: Create a .env file in the root directory and add your environment variables:

   GOOGLE_API_KEY=your-google-api-key
   DEPLOYED=False
   

4. Set up LLAMA 3 and OLLAMA: Install LLAMA 3 (8B) and OLLAMA by running the following command:

   ollama run llama3

Usage

1. Run the Streamlit app:

   streamlit run app.py

2. Navigate to the Home Page:

   • Upload two PDFs to compare.
   • Select the type of scan (Document-Level, Sentence-Level, Preprocessed Sentence-Level).
   • Choose the embedding type (Count Vectorizer, TF-IDF Vectorizer, all-MiniLM-L6-v2).
   • Click "Submit" to compare the PDFs and get the similarity score.

3. Navigate to the ChatBot Page:

   • Load the PDFs into the vector database.
   • Interact with the PDFs using the chatbot by selecting an LLM model and entering your query.

Project Structure

├── app.py                     # Main Streamlit application
├── compare.py                 # PDF comparison logic
├── pdf_extractor.py           # PDF text extraction logic
├── text_preprocessing.py      # Pre-processing of PDF text
├── LLM
│   ├── gemini.py              # Google Gemini LLM integration
│   ├── llama3.py              # LLAMA 3 LLM integration (OLLAMA)
│   ├── prompt.py              # Prompt generation logic
├── embeddings
│   ├── CountVectorizer.py     # Count Vectorization logic
│   ├── TfidfVectorizer.py     # TF-IDF Vectorization logic
│   ├── all_MiniLM_L6_v2.py    # All-MiniLM-L6-v2 Vectorization logic
├── result.py                  # Chatbot result processing
├── requirements.txt           # Required Python packages
└── .env                       # Environment variables (not included in the repository)

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgements

• Streamlit
• Meta Llama 3
• OLLAMA
• Chroma Vector DB
• LangChain