This project generates embeddings for CVE (Common Vulnerabilities and Exposures) descriptions, stores them in a Pinecone vector database, and provides a Streamlit interface for querying the data using a language model(llama3).

• Docker
• Python 3.9+ (if running locally)
• Pinecone API key
• Access to the BAAI/bge-small-en-v1.5 model
• Ollama installed with llama3.1.

• pinecone_db.py: Script for generating embeddings and updating the Pinecone index
• app.py: Streamlit application for querying the CVE data
• Dockerfile: Instructions for building the Docker image
• data/: Directory containing JSON files with sample CVE data
• .env: Environment file for storing API keys and configuration
• requirements.txt: List of Python dependencies

1. Clone this repository:

   git clone /~https://github.com/vk-NEU7/llm-cve.git
   cd llm-cve
   

2. Create a virtual environment and activate it:

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

3. Install the required packages:

   pip install -r requirements.txt
   

4. Create a .env file in the project root and add your API keys:

   PINECONE_API_KEY=your_pinecone_api_key_here
   PINECONE_CLOUD=aws
   PINECONE_REGION=us-east-1
   LLM_HOST=http://localhost:11434
   

1. Build the Docker image:

   docker build -t cve-query-app .
   

2. Run the Docker container:

   docker run -p 8501:8501 --env-file .env cve-query-app
   

1. Place your JSON files containing CVE data in the data directory.

2. Run the embedding generation script:

   python pinecone_db.py
   

1. Start the Streamlit app:

   streamlit run app.py
   

   Or if using Docker, the app will start automatically when you run the container.

2. Open a web browser and navigate to http://localhost:8501.

3. Enter your question in the text input field and press Enter to get a response.

• To use a different embedding model, update the model_name in the HuggingFaceBgeEmbeddings initialization in both pinecone_db.py and app.py.
• Adjust the chunk_size and chunk_overlap in the RecursiveCharacterTextSplitter in pinecone_db.py to change how documents are split.
• Modify the Streamlit interface in app.py to add more features or change the layout.

We welcome contributions to improve the llm-cve project! If you'd like to contribute, please follow these steps:

1. Fork the Repository: Click the "Fork" button on the top right of the project page.
2. Create a Branch: Create a new branch for your changes.
3. Make Your Changes: Implement your changes and test thoroughly.
4. Submit a Pull Request: Open a Pull Request with a clear description of your changes.

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