Intelligent Document Search and Query System

This project is an advanced application using microservice architecture to provide an AI-powered document search and query platform. Users can upload documents to the system and utilize text mining and artificial intelligence technologies for advanced search and question-answering capabilities.

🌟 Features 2

• Document Upload and Processing: Supports PDF, TXT, DOCX, and other document formats
• Automatic OCR: Automatically converts scanned documents and images to text
• Semantic Search: Quickly find relevant documents with meaning-based search
• AI Queries: Ask complex questions based on the content of your documents
• Multi-language Support: Support for multiple languages including Turkish and English
• Secure Access Control: Role-based authorization system

🏗️ Architecture 2

This system has a modular structure that includes the following microservices:

• API Gateway: Receives all client requests and routes them to relevant services
• Document Processing: Document processing, text extraction, and OCR
• Search Service: Full-text search and indexing
• Storage Service: Document storage and metadata management
• Vector Service: Document vector embeddings and semantic search
• Query Engine: Natural language queries and AI responses

🚀 Technology Stack 2

• Backend: Node.js, Fastify, gRPC
• Frontend: SvelteKit
• Database: PostgreSQL, Drizzle ORM
• Search Engines: Typesense (text search), Qdrant (vector search)
• Messaging: Apache Kafka
• Storage: MinIO (S3-compatible object storage)
• AI/ML: OpenAI (embeddings, question-answering)

🛠️ Installation 2

Requirements 2

• Node.js 18+
• PostgreSQL
• MinIO (optional, for file storage)
• Apache Kafka (optional, for asynchronous communication)
• Typesense (optional, for full-text search)
• Qdrant (optional, for vector search)
• OpenAI API key (for AI functionality)

Getting Started

1. Clone the project:

   git clone https://github.com/username/intelligent-document-search.git
   cd intelligent-document-search

2. Install dependencies:

   npm install

3. Configure the .env file (copy .env.example for reference)

4. Initialize the database:

   npm run db:push

5. Start the application:

   npm run dev

The application will be running at http://localhost:5000 by default.

📊 Environmental Variables

Variable	Description	Default
DATABASE_URL	PostgreSQL database connection URL	postgresql://user:password@localhost:5432/docusearch
OPENAI_API_KEY	OpenAI API key	-
FRONTEND_PORT	Frontend service port	5000
SERVER_PORT	API Gateway port	8000
JWT_SECRET	JWT token encryption key	your-secret-key

📝 API Documentation

Core API endpoints:

• POST /auth/register - New user registration
• POST /auth/login - User login
• GET /documents - Document list
• POST /documents/upload - Upload new document
• GET /documents/:id - Document details
• POST /search - Search within documents
• POST /query - Ask questions in natural language about documents

👨‍💻 Development

When adding new features or modifying existing code, please follow these steps:

1. Create a new branch
2. Make and test your changes
3. Run test suites: npm test
4. Commit your changes
5. Create a pull request

🤝 Contributing

Contributions are welcome! Please read our contribution guidelines before contributing.

📜 License

This project is licensed under the MIT License.

🙏 Acknowledgements

• OpenAI - For AI models
• Svelte - Reactive UI library
• Fastify - Fast web framework
• PostgreSQL - Reliable relational database

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© 2025 Intelligent Document Search and Query System