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inquiries, automate responses, and continuously improve the knowledge base for more efficient customer service.\n\n![Sequence Diagrm of main workflow](./doc/ticketing-diagram.png)\n\n## Features\n\n- AI-powered chatbot for initial customer interaction\n- Automated ticket creation for unresolved queries\n- Real-time notifications for agents and customers\n- Knowledge base integration for quick answers\n- Sentiment analysis for prioritizing urgent issues\n- Scalable architecture using microservices\n\n## System Architecture\n\nThe CustoMater AI Ticketing System is built on a microservices architecture, utilizing the following components:\n\n- Frontend: React-based user interface\n- Backend: NestJS API services\n- Database: PostgreSQL for ticket and user data storage\n- Message Queue: RabbitMQ for event-driven communication\n- Search Engine: Elasticsearch for knowledge base\n\n## Getting Started\n\n### Prerequisites\n\n- Node.js (v18 or later)\n- Docker and Docker Compose\n\n## Installation\n\n```bash\n$ npm install\n```\n\n## Running the app\n\n```bash\n# development\n$ npm run start\n\n# watch mode\n$ npm run start:dev\n\n# production mode\n$ npm run start:prod\n```\n\n## Test\n\n```bash\n# unit tests\n$ npm run test\n\n# e2e tests\n$ npm run test:e2e\n\n# test coverage\n$ npm run test:cov\n```\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE.md](https://en.wikipedia.org/wiki/MIT_License) page for details.\n\n## Acknowledgments\n\n- OpenAI for providing the GPT model used in our chatbot\n- Elasticsearch for powering our knowledge base search\n- The open-source community for the various libraries and tools used in this 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