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https://github.com/cyse7125-su24-team10/llm-cve
A Retrieval-Augmented Generation (RAG) model built using LLaMA 3.1 and LangChain.
https://github.com/cyse7125-su24-team10/llm-cve
langchain llama3 ollama pinecone python streamlit
Last synced: 3 days ago
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A Retrieval-Augmented Generation (RAG) model built using LLaMA 3.1 and LangChain.
- Host: GitHub
- URL: https://github.com/cyse7125-su24-team10/llm-cve
- Owner: cyse7125-su24-team10
- License: mit
- Created: 2024-08-12T16:04:45.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2024-09-09T20:20:48.000Z (18 days ago)
- Last Synced: 2024-09-13T07:01:02.526Z (15 days ago)
- Topics: langchain, llama3, ollama, pinecone, python, streamlit
- Language: Python
- Homepage:
- Size: 407 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
Awesome Lists containing this project
README
# llm-cve
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).
## Prerequisites
- 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.
## Project Structure
- `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
## Setup and Installation
### Local Setup
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
```
### Docker Setup
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
```
## Usage
### Generating Embeddings
1. Place your JSON files containing CVE data in the `data` directory.
2. Run the embedding generation script:
```
python pinecone_db.py
```
### Running the Query Interface
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.
## Customization
- 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.
## Contributing
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.
## License
This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details.