RAG System Using LLaMA 3.2 with knowledge base of Threatmon-feeds-IOC

Introduction

This project implements a Retrieval-Augmented Generation (RAG) based system where users ask question related to the ThreatMon-Reports-IOC and it will response to the query. This project is completed using Web Sockets Fast API. Testing

Setting Up

Follow these steps to set up and run the project:

1. Clone the repository:

   git clone https://github.com/cyber-evangelists/threat-mon-rag
   

2. Navigate to the project root directory:

   threat-mon-rag
   

3. Make sure that the docker is installed on your system:

   docker --version
   

   If docker is not installed, run the following command:

   sudo apt install docker
   

4. In the same directory, create a file name .env and add following API key

   GROQ_API_KEY=your_api_key
   LANGCHAIN_API_KEY=your_langchain_api_key
   LANGCHAIN_PROJECT=project_name
   LANGCHAIN_ENDPOINT="https://api.smith.langchain.com"
   LANGCHAIN_TRACING_V2=true
   

5. Build the docker environment::

   docker compose up --build
   

6. Access the graio app by pasting this URL:

   http://localhost:7860/
   

7. There is button Ingest data, click on this button to first ingest data into qdrant vector database. Then Enter Query and click on Search button, and the response will be shown below.