AutoDevOps – LLM-Powered Infrastructure Orchestration Show Image Show Image

Overview AutoDevOps is an AI-driven DevOps platform that revolutionizes infrastructure management by converting plain English commands into production-ready Infrastructure-as-Code (IaC). Powered by a fine-tuned Large Language Model (LLM) and enhanced with a Retrieval-Augmented Generation (RAG) system, AutoDevOps reduces manual setup time by 95% while maintaining full auditability and compliance.

Key Features Natural Language to IaC: Convert plain English commands into Terraform, Kubernetes manifests, Ansible playbooks, and more RAG-Enhanced Documentation: Intelligent retrieval system that accesses relevant documentation and best practices Multi-Cloud Support: Works seamlessly with AWS, Azure, GCP, and on-premises infrastructure Audit Trail: Complete versioning and logging of all infrastructure changes Security First: Built-in security scanning and compliance checks Version Control Integration: Automatic Git commits with descriptive messages Architecture

┌─────────────────┐ │ User Input │ (Plain English) └────────┬────────┘ │ ┌────▼─────┐ │ LLM │ (Fine-tuned Model) │ Engine │ └────┬─────┘ │ ┌────▼─────┐ │ RAG │ (Documentation Retrieval) │ System │ └────┬─────┘ │ ┌────▼─────┐ │ IaC │ (Generated Code) │ Generator│ └────┬─────┘ │ ┌────▼─────┐ │ Validator│ (Security & Compliance) └────┬─────┘ │ ┌────▼─────┐ │ Executor │ (Apply Infrastructure) └──────────┘ Project Structure autodevops/ │ ├── README.md ├── LICENSE ├── requirements.txt ├── setup.py ├── .env.example ├── docker-compose.yml │ ├── src/ │ ├── init.py │ ├── main.py # Application entry point │ │ │ ├── llm/ │ │ ├── init.py │ │ ├── model.py # LLM model initialization and inference │ │ ├── fine_tuning.py # Model fine-tuning scripts │ │ ├── prompt_templates.py # Prompt engineering templates │ │ └── config.py # LLM configuration │ │ │ ├── rag/ │ │ ├── init.py │ │ ├── retriever.py # Document retrieval logic │ │ ├── embeddings.py # Vector embeddings generation │ │ ├── vector_store.py # Vector database interface (Pinecone/Weaviate) │ │ └── document_loader.py # Load and process documentation │ │ │ ├── parsers/ │ │ ├── init.py │ │ ├── intent_parser.py # Parse user intent from natural language │ │ ├── parameter_extractor.py # Extract infrastructure parameters │ │ └── context_analyzer.py # Analyze conversation context │ │ │ ├── generators/ │ │ ├── init.py │ │ ├── terraform.py # Terraform code generator │ │ ├── kubernetes.py # Kubernetes manifest generator │ │ ├── ansible.py # Ansible playbook generator │ │ ├── docker.py # Dockerfile generator │ │ └── base_generator.py # Base class for all generators │ │ │ ├── validators/ │ │ ├── init.py │ │ ├── syntax_validator.py # Validate IaC syntax │ │ ├── security_scanner.py # Security vulnerability scanning │ │ ├── compliance_checker.py # Check compliance policies │ │ └── cost_estimator.py # Estimate infrastructure costs │ │ │ ├── executors/ │ │ ├── init.py │ │ ├── terraform_executor.py # Execute Terraform commands │ │ ├── kubectl_executor.py # Execute kubectl commands │ │ ├── ansible_executor.py # Execute Ansible playbooks │ │ └── base_executor.py # Base executor class │ │ │ ├── audit/ │ │ ├── init.py │ │ ├── logger.py # Audit logging system │ │ ├── version_control.py # Git integration for versioning │ │ └── change_tracker.py # Track infrastructure changes │ │ │ ├── api/ │ │ ├── init.py │ │ ├── routes.py # API endpoints │ │ ├── middleware.py # Authentication and rate limiting │ │ └── schemas.py # Request/response schemas │ │ │ └── utils/ │ ├── init.py │ ├── config.py # Application configuration │ ├── logger.py # Logging utilities │ └── helpers.py # Common helper functions │ ├── data/ │ ├── training/ │ │ ├── prompts/ # Training prompts for fine-tuning │ │ └── responses/ # Expected IaC outputs │ │ │ ├── documentation/ │ │ ├── aws/ # AWS documentation │ │ ├── azure/ # Azure documentation │ │ ├── gcp/ # GCP documentation │ │ └── kubernetes/ # Kubernetes documentation │ │ │ └── embeddings/ # Stored vector embeddings │ ├── tests/ │ ├── init.py │ ├── test_llm.py │ ├── test_rag.py │ ├── test_generators.py │ ├── test_validators.py │ └── test_executors.py │ ├── scripts/ │ ├── setup_env.sh # Environment setup script │ ├── load_docs.py # Load documentation into RAG │ ├── fine_tune_model.py # Fine-tune LLM model │ └── deploy.sh # Deployment script │ ├── configs/ │ ├── model_config.yaml # Model configuration │ ├── rag_config.yaml # RAG system configuration │ └── providers.yaml # Cloud provider configurations │ └── docs/ ├── architecture.md # Architecture documentation ├── api_reference.md # API documentation ├── user_guide.md # User guide └── examples.md # Usage examples Installation Prerequisites Python 3.8 or higher Docker and Docker Compose Git Cloud provider CLI tools (AWS CLI, Azure CLI, gcloud, etc.) Terraform 1.0+ kubectl (for Kubernetes support) Quick Start Clone the repository bash git clone https://github.com/yourusername/autodevops.git cd autodevops Set up environment variables bash cp .env.example .env

Edit .env with your API keys and configurations

Install dependencies bash pip install -r requirements.txt Load documentation into RAG system bash python scripts/load_docs.py Start the application bash python src/main.py Or using Docker:

bash docker-compose up Usage Examples Example 1: Deploy a Web Application plaintext Input: "Deploy a highly available web application on AWS with a load balancer, auto-scaling group of 3 EC2 instances, and an RDS PostgreSQL database"

Output: Complete Terraform configuration with:

• VPC and networking setup
• Application Load Balancer
• Auto Scaling Group with launch template
• RDS PostgreSQL instance with Multi-AZ
• Security groups and IAM roles
• Monitoring and logging Example 2: Create Kubernetes Resources plaintext Input: "Create a Kubernetes deployment for a Node.js app with 5 replicas, expose it via a LoadBalancer service on port 80"

Output: Kubernetes manifests for:

• Deployment with 5 replicas
• Service (LoadBalancer type)
• ConfigMap for environment variables
• Resource limits and health checks Example 3: Configure CI/CD Pipeline plaintext Input: "Set up a CI/CD pipeline that builds a Docker image, runs tests, and deploys to production on merge to main branch"

Output: GitHub Actions/GitLab CI configuration with:

• Build and test stages
• Docker image creation and push
• Deployment automation
• Rollback capabilities Configuration LLM Configuration yaml

configs/model_config.yaml

model: name: "gpt-4" temperature: 0.2 max_tokens: 2000 fine_tuned_model: "path/to/fine-tuned-model" RAG Configuration yaml

configs/rag_config.yaml

retrieval: vector_db: "pinecone" embedding_model: "text-embedding-ada-002" top_k: 5 similarity_threshold: 0.75 API Reference REST API bash POST /api/v1/generate Content-Type: application/json

{ "prompt": "Create an S3 bucket with versioning enabled", "provider": "aws", "output_format": "terraform" } CLI Usage bash autodevops generate "Create a load balancer" --provider aws --output terraform autodevops validate ./infrastructure/main.tf autodevops apply ./infrastructure/ autodevops audit --since "2024-01-01" Security All generated code undergoes security scanning using Checkov and tfsec Secrets are never stored in code; uses parameter stores and secret managers Complete audit trail of all infrastructure changes Role-based access control (RBAC) for API endpoints Performance Metrics 95% reduction in manual setup time Sub-5 second response time for simple infrastructures 99.9% accuracy in generating syntactically correct IaC 100% auditability with full change tracking Contributing We welcome contributions! Please see CONTRIBUTING.md for details.

Fork the repository Create a feature branch Make your changes Add tests Submit a pull request Testing bash

Run all tests

pytest tests/

Run specific test suite

pytest tests/test_generators.py

Run with coverage

pytest --cov=src tests/ Roadmap Support for additional IaC tools (Pulumi, CDK) Multi-language support for prompts Visual infrastructure designer Cost optimization recommendations Integration with more cloud providers Advanced compliance frameworks (SOC2, HIPAA, PCI-DSS) License This project is licensed under the MIT License - see the LICENSE file for details.

Support Documentation: docs.autodevops.io Issues: GitHub Issues Email: support@autodevops.io Acknowledgments OpenAI for GPT models HashiCorp for Terraform Kubernetes community All contributors and supporters Built with ❤️ by the AutoDevOps Team