Sqora — Multimodal Research & Deployment Repository

Professional, production-oriented repository hosting a suite of services, tooling, and experiments for text-to-speech, model training, inference orchestration, and retrieval-augmented workflows. This repository is organized to support development, local testing, containerized deployment, and Kubernetes-based production rollout.

Key Features

• Modular components for TTS, model training, and inference orchestration.
• Docker Compose and Kubernetes manifests for reproducible deployments.
• Example pipelines for data preparation, model fine-tuning, and vector DB storage.
• Utilities and scripts to run experiments, tests, and local development.

Repository Layout

• app.py — Primary Python entrypoint for lightweight services.
• docker-compose.yml — Root Docker Compose configuration for local stacks.
• sqora/ — Backend service and related frontend assets.
• HeadTTS/ — HeadTTS integration and frontend components.
• manim-trainer/ — Training tooling, datasets, and model training scripts.
• k8s/ — Kubernetes manifests and deployment helper scripts.
• Vector_DB/ — Vector database storage and orchestration artifacts.

For more detail on components, inspect the directories at the repository root.

Requirements

• Linux or macOS (Linux recommended for parity with CI/production images)
• Docker Engine and Docker Compose (for local containerized execution)
• Python 3.10+ and a virtual environment for local Python development
• kubectl and Helm for Kubernetes deployments

Quickstart — Local (Docker Compose)

1. Start Docker and ensure you have sufficient memory and CPU available.
2. From the repository root, run:

docker-compose up --build

This will launch the services defined in docker-compose.yml for local integration and testing.

Quickstart — Python Virtualenv (developer)

1. Create and activate a virtual environment:

python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

2. Run a simple service locally:

python app.py

Adjust commands according to the service or module you are iterating on.

Kubernetes Deployment

Production-grade manifests and kustomizations are provided in the k8s/ directory to deploy services with proper configmaps, ingress, and HPA. Common steps:

# Apply namespace and base resources
kubectl apply -k k8s/

# Or use provided Helm values for monitoring and observability
# See k8s/helm for example values

Refer to k8s/README.md (if present) for project-specific instructions.

Development Workflow

• Use feature branches and open pull requests for changes.
• Keep commits focused and well-documented.
• Add or update tests in the relevant tests/ directory and run them locally before pushing.

Common tasks:

# Run unit tests (example)
# pytest -q

# Lint and format
black .
flake8 .

Configuration

Configuration and secrets are environment-driven. For local dev, use an .env file or the Docker Compose environment sections. For production, provision secrets via your orchestrator (Kubernetes Secrets, HashiCorp Vault, or cloud provider secret managers).

Observability & Monitoring

The k8s/ folder contains optional manifests for Prometheus and Loki integration. Logs and metrics are expected to be exported by each service and aggregated by the platform of choice.

Contributing

Contributions are welcome. Please follow these guidelines:

• Open an issue to discuss larger changes first.
• Use small, focused pull requests and include tests where applicable.
• Follow the repository's code style and testing practices.

Security

Report security issues privately via the repository's configured security contact. Do not disclose vulnerabilities in public issues.

License

This repository may contain multiple components with separate licenses. Check individual directories for license files (for example HeadTTS/LICENSE). If a single license is required, include a top-level LICENSE file.

Contact & Support

For questions about deployment, architecture, or contribution practices, open an issue or contact the maintainers listed in the project metadata.

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This README is intended as a professional, high-level overview and developer onboarding document. If you would like a tailored README per subproject (for example manim-trainer/, HeadTTS/, or sqora/), I can generate individual, focused READMEs that include step-by-step examples and known runtime configurations.