GenAI-Ops-Suite 🛠️🧠🤖

A comprehensive suite of tools, scripts, frameworks, and best practices for operationalizing Generative AI models (LLMs, image generators, etc.) effectively, reliably, and responsibly in production.

🌟 Vision

Generative AI is transforming industries, but operationalizing these powerful models presents unique challenges beyond traditional MLOps. This suite aims to provide practical resources to streamline the development, deployment, management, and monitoring of production-grade GenAI applications.

🎯 Key Goals & Areas of Focus

This suite will cover critical aspects of the GenAI lifecycle:

• Prompt Engineering & Management:
  • Versioning and tracking prompts.
  • Frameworks for A/B testing and evaluating prompt performance.
  • Tools for prompt templating and dynamic construction.
• Fine-Tuning Operations:
  • Reproducible pipelines for fine-tuning foundation models (e.g., using LoRA, QLoRA).
  • Data preparation, versioning, and quality checks for fine-tuning datasets.
  • Experiment tracking for fine-tuning runs.
• Evaluation & Monitoring (GenAI Specific):
  • Techniques for detecting hallucinations, factual inaccuracies, and bias.
  • Monitoring for safety, toxicity, and responsible AI principles.
  • Tracking token usage, API costs, latency, and throughput.
  • Assessing output quality (coherence, relevance, style).
• Deployment & Serving:
  • Optimized serving solutions for large models (e.g., vLLM, TGI).
  • API wrapper templates for GenAI model endpoints.
  • Strategies for A/B testing and canary deployments of GenAI models.
• Security & Safety for GenAI:
  • Mitigation techniques for prompt injection and jailbreaking.
  • Content moderation and filtering integrations.
  • Data privacy considerations for GenAI inputs/outputs.
• Cost Optimization & Management:
  • Tools for estimating and tracking GenAI API and infrastructure costs.
  • Strategies for model cascading and efficient resource utilization.
• Integration & Orchestration (e.g., RAG):
  • Frameworks for Retrieval Augmented Generation (RAG) pipelines.
  • Connecting GenAI models with vector databases, knowledge bases, and external tools.
• Human-in-the-Loop (HITL):
  • Frameworks for incorporating human review and feedback.

🛠️ Technology Stack (Anticipated)

• Python 3.x
• Kubernetes (for scalable deployment and orchestration)
• Frameworks: LangChain, LlamaIndex, Haystack
• LLM APIs: OpenAI, Hugging Face, Cohere, Anthropic, etc.
• Vector Databases: Pinecone, Weaviate, ChromaDB, FAISS
• MLOps Tools: MLflow, Kubeflow, DVC (for data/model versioning)
• Observability: Prometheus, Grafana, OpenTelemetry
• Containerization: Docker

🏁 Getting Started

(This section will evolve as components are added)

1. Clone the repository:

   git clone https://github.com/raghu-007/GenAI-Ops-Suite.git
   cd GenAI-Ops-Suite

2. Install dependencies (once requirements.txt is populated):

   pip install -r requirements.txt

3. Explore the directories for specific tools, scripts, and examples.

📂 Project Structure (Planned)

GenAI-Ops-Suite/ ├── 01-prompt-engineering/ │ ├── prompt-versioning/ │ └── prompt-evaluators/ ├── 02-fine-tuning-operations/ │ └── fine-tuning-pipelines/ ├── 03-evaluation-monitoring/ │ ├── hallucination-detection/ │ └── cost-trackers/ ├── 04-deployment-serving/ │ └── api-wrappers/ ├── 05-security-safety/ │ └── prompt-injection-defense/ ├── 06-rag-frameworks/ │ └── vector-db-integration/ ├── 07-example-applications/ │ └── chatbot-with-monitoring/ ├── docs/ # Detailed documentation, best practices ├── scripts/ # General utility scripts ├── .github/ # GitHub specific files (workflows, templates) ├── .gitignore ├── LICENSE ├── README.md └── CONTRIBUTING.md

🤝 Contributing

Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

Please see CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us.

📜 License

This project is licensed under the Apache 2.0 License.