{"id":50403563,"url":"https://github.com/developerslearnit/ai-document-processor-rag","last_synced_at":"2026-05-31T00:30:56.829Z","repository":{"id":353583927,"uuid":"1220044344","full_name":"developerslearnit/ai-document-processor-rag","owner":"developerslearnit","description":"The robust, enterprise-grade backend for the AI Document Processor, built with **.NET 10** using **Clean Architecture** principles. 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ASP.NET Core 10 Web API (Clean Architecture)\n└── frontend/     # Next.js 15 + Tailwind CSS 4 + Framer Motion\n```\n\n### 🔙 [Backend](./backend)\nBuilt with **.NET 10** following **Clean Architecture** principles:\n- **Domain**: Pure business logic and entities.\n- **Application**: Use cases, DTOs, and service orchestrators.\n- **Infrastructure**: Azure OpenAI, Blob Storage, and DB implementations.\n- **API**: RESTful controllers and Swagger documentation.\n\n### 🔜 [Frontend](./frontend)\nBuilt with **Next.js 15** for a premium user experience:\n- **Design**: Custom CSS + Tailwind CSS 4 with a \"World-Class\" aesthetic.\n- **Animations**: Framer Motion for high-fidelity transitions.\n- **State**: Zustand for lightweight global auth and document state.\n- **API**: Axios with centralized interceptors for secure communication.\n\n---\n\n## 🛠️ Technology Stack\n\n| Layer | Technology |\n| --- | --- |\n| **Framework** | .NET 10 \u0026 Next.js 15 |\n| **AI / LLM** | Azure OpenAI (GPT-4o \u0026 Text Embeddings) |\n| **Database** | PostgreSQL + pgvector |\n| **Storage** | Azure Blob Storage |\n| **Styling** | Tailwind CSS 4 \u0026 Vanilla CSS |\n| **Auth** | JWT (Custom implementation with BCrypt) |\n| **Jobs** | Quartz.NET |\n\n---\n\n## 🏃 Getting Started\n\n### 1. Prerequisites\n- [.NET 10 SDK](https://dotnet.microsoft.com/download/dotnet/10.0)\n- [Node.js 18+](https://nodejs.org/)\n- [PostgreSQL](https://www.postgresql.org/) (with pgvector extension)\n\n### 2. Backend Setup\n1. Navigate to `backend/AIDocument.Api`.\n2. Update `appsettings.json` with your Azure and Database credentials.\n3. Apply migrations: `dotnet ef database update`.\n4. Run: `dotnet run`.\n\n### 3. Frontend Setup\n1. Navigate to `frontend`.\n2. Install dependencies: `npm install`.\n3. Start the dev server: `npm run dev`.\n4. Access at `http://localhost:3000`.\n\n---\n\n## 📄 Documentation\nFor detailed setup instructions, architecture diagrams, and API documentation, please refer to the individual READMEs:\n- [Backend Documentation](./backend/README.md)\n- [Frontend Documentation](./frontend/README.md)\n\n---\n\n## 👤 Author\n**Adesina Mark Omoniyi**\n*Software Engineer \u0026 AI Solutions Architect*\n\n---\n\n## 🛡️ License\nThis project is licensed under the MIT License - see the LICENSE file for details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdeveloperslearnit%2Fai-document-processor-rag","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdeveloperslearnit%2Fai-document-processor-rag","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdeveloperslearnit%2Fai-document-processor-rag/lists"}