Kubernetes Issue Management using RAG

This project implements a Retrieval-Augmented Generation (RAG) system for managing and analyzing Kubernetes GitHub issues. It uses FAISS for efficient vector similarity search and integrates with LangChain and OpenAI GPT-4 for intelligent issue analysis and response generation.

Features

• Fetch Kubernetes issues from GitHub using GraphQL API
• Preprocess and clean issue data
• Generate embeddings using sentence transformers
• Efficient similarity search using FAISS
• RAG-powered issue analysis and response generation
• FastAPI-based REST API for easy integration
• Interactive CLI for direct vector database queries

Project Structure

rag-devops/
├── scripts/                  # Python scripts for each phase
│   ├── fetch_github_issues.py  # GitHub issue fetching
│   ├── preprocess_issues.py    # Data preprocessing
│   ├── embed_issues.py         # Embedding generation
│   ├── query_vector_db.py      # Interactive vector similarity search
│   ├── evaluate_rag.py         # RAG system evaluation
│   ├── validate_rag.py         # Validation of RAG responses
│   └── langchain_rag.py        # Core RAG implementation
├── embeddings/              # Directory containing FAISS index
│   ├── index.faiss         # FAISS vector index (created by embed_issues.py)
│   └── index.pkl           # Document metadata (created by embed_issues.py)
├── data/                   # Data directory
│   ├── k8s_issues.json     # Raw GitHub issues
│   └── k8s_issues_preprocessed.json  # Preprocessed issues
├── .env                    # Environment variables (not tracked)
├── .gitignore             # Git ignore rules
├── requirements.txt       # Python dependencies
└── README.md             # This file

Setup

1. Clone the repository:

   git clone https://github.com/your-username/rag-devops.git
   cd rag-devops

2. Create and activate a virtual environment:

   python3 -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Create a .env file with your API keys:

   GITHUB_TOKEN=your_github_token
   OPENAI_API_KEY=your_openai_api_key
   

Usage

1. Fetch Kubernetes issues:

   python scripts/fetch_github_issues.py

2. Preprocess the issues:

   python scripts/preprocess_issues.py

3. Generate embeddings and create FAISS index:

   python scripts/embed_issues.py

   This will create two files in the embeddings/ directory:

   • index.faiss: The FAISS vector index
   • index.pkl: Document metadata and mappings

4. Query the vector database:

   python scripts/query_vector_db.py

   This starts an interactive CLI where you can:

   • Enter questions about Kubernetes issues
   • Get top-k most relevant results with similarity scores
   • View issue content previews
   • Type 'exit' to quit

5. Evaluate the RAG system:

   python scripts/evaluate_rag.py

6. Validate RAG responses:

   python scripts/validate_rag.py

Development

• The project uses Python 3.8+
• Dependencies are managed through requirements.txt
• Code style follows PEP 8 guidelines
• Each script is modular and well-documented
• Logging is implemented for better debugging
• Error handling is implemented throughout the codebase

Troubleshooting

If you encounter issues:

1. Make sure you've run the scripts in order:

   • fetch_github_issues.py
   • preprocess_issues.py
   • embed_issues.py before running query_vector_db.py

2. Check that the embeddings/ directory contains:

   • index.faiss
   • index.pkl

3. Verify your environment variables in .env

4. Check the logs for detailed error messages

License

MIT License - see LICENSE file for details

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request