graph LR
Client_Applications["Client Applications"]
API_Gateway_Orchestration["API Gateway & Orchestration"]
Core_AI_SDLC_Services["Core AI SDLC Services"]
AI_Model_Integration_Layer["AI Model Integration Layer"]
Data_Storage_Management["Data Storage & Management"]
Automation_Execution_Engine["Automation & Execution Engine"]
OmniParser_Utility["OmniParser Utility"]
Unclassified["Unclassified"]
Client_Applications -- "initiates requests to" --> API_Gateway_Orchestration
API_Gateway_Orchestration -- "routes requests for SDLC tasks to" --> Core_AI_SDLC_Services
API_Gateway_Orchestration -- "routes automation requests to" --> Automation_Execution_Engine
Core_AI_SDLC_Services -- "sends prompts and data for AI processing to" --> AI_Model_Integration_Layer
Core_AI_SDLC_Services -- "stores and retrieves SDLC artifacts from" --> Data_Storage_Management
Core_AI_SDLC_Services -- "utilizes parsing capabilities from" --> OmniParser_Utility
Automation_Execution_Engine -- "stores and retrieves execution logs and artifacts from" --> Data_Storage_Management
Automation_Execution_Engine -- "retrieves generated code and triggers tests within" --> Core_AI_SDLC_Services
click API_Gateway_Orchestration href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/hai-build/API_Gateway_Orchestration.md" "Details"
click Core_AI_SDLC_Services href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/hai-build/Core_AI_SDLC_Services.md" "Details"
click AI_Model_Integration_Layer href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/hai-build/AI_Model_Integration_Layer.md" "Details"
click Data_Storage_Management href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/hai-build/Data_Storage_Management.md" "Details"
click Automation_Execution_Engine href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/hai-build/Automation_Execution_Engine.md" "Details"
The hai-build project is architected as a cohesive system designed to streamline the AI-driven Software Development Life Cycle. User interactions originate from Client Applications, which communicate through a central API Gateway & Orchestration layer. This gateway intelligently directs requests to either the Core AI SDLC Services for intelligent task execution, such as requirements engineering and code generation, or to the Automation & Execution Engine for managing and running automated workflows. The Core AI SDLC Services leverages an AI Model Integration Layer to interact with various AI providers and an OmniParser Utility for efficient data extraction. All persistent data, including SDLC artifacts, configurations, and execution logs, are managed by the Data Storage & Management component. This logical separation of concerns ensures a scalable and maintainable architecture, providing a clear flow of data and control across the SDLC process.
Provides the interactive front-end for users, enabling them to initiate SDLC tasks, view results, and configure the suite. This component encompasses various client types (web, desktop, IDE plugins).
Related Classes/Methods:
client_applications.main
API Gateway & Orchestration [Expand]
Acts as the central entry point for all client requests, routing them to appropriate backend services. It handles authentication, authorization, and orchestrates complex workflows involving multiple services.
Related Classes/Methods:
api_gateway.router
Core AI SDLC Services [Expand]
The central intelligence hub, encompassing AI-powered services for requirements engineering, code generation, and quality assurance. It processes SDLC-specific tasks using AI models.
Related Classes/Methods:
core_sdlc_services.processor
AI Model Integration Layer [Expand]
Manages the integration with diverse AI models (Azure OpenAI, OpenAI Native, AWS Bedrock, Google Gemini), handling dynamic model selection, prompt engineering, and secure communication.
Related Classes/Methods:
ai_integration.manager
Data Storage & Management [Expand]
Provides persistent storage and management for all project-related data, including requirements, generated code, test results, AI model configurations, and user data.
Related Classes/Methods:
data_storage.database
Automation & Execution Engine [Expand]
Orchestrates and executes various automation tasks across the SDLC, such as build pipelines, deployment scripts, and running automated tests, integrating with CI/CD tools.
Related Classes/Methods:
automation_engine.executor
A specialized utility service designed for parsing and extracting structured information from diverse unstructured or semi-structured formats (e.g., code files, documentation).
Related Classes/Methods:
omni_parser.parser
Component for all unclassified files and utility functions (Utility functions/External Libraries/Dependencies)
Related Classes/Methods: None