RagRank

RagRank utilizes real-time data ingestion and high-performance processing to modeling and anomaly detection on a scalable engine platform.

RagRank is aimed at developers who need a straightforward, dependable solution.

Highlights

• RagRank utilizes real-time data ingestion
• and high-performance processing to modeling
• and anomaly detection on a

• RagRank utilizes real-time data ingestion
• and high-performance processing to modeling
• and anomaly detection on a

• python
• python framework (Flask/Django/FastAPI or equivalent)
• Pytest for testing

1. Clone the repository: git clone https://github.com/fuad403273/RagRank.git
2. Install dependencies: pip install -r requirements.txt
3. Run the test suite: pytest

RagRank is configured through environment variables (see .env.example). Key options:

• APP_ENV: development or production.
• PORT: Port the server listens on.
• LOG_LEVEL: debug, info, or error.

Contributions are welcome and appreciated. Please submit pull requests and issues through the GitHub interface.

Released under the MIT License — see the LICENSE file.