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A complete microservice application setup with a Flutter app, MongoDB, and RabbitMQ, along with all the necessary files and folder structure. The setup uses Docker Compose to orchestrate the services. Folder Structure ``` microservice-app/ │ ├── backend/ │   ├── Dockerfile │   ├── requirements.txt │   ├── main.py │   └── config.py │ ├── frontend/ │   ├── Dockerfile │   ├── pubspec.yaml │   └── lib/ │       └── main.dart │ ├── docker-compose.yml └── README.md ``` 1. `docker-compose.yml` ```yaml version: '3.8' services:   backend:     build: ./backend     container_name: backend     ports:       - "8000:8000"     depends_on:       - mongodb       - rabbitmq     environment:       - MONGO_URI=mongodb://mongodb:27017/flutterdb       - RABBITMQ_URI=amqp://guest:guest@rabbitmq...

                                                  actual screenshot taken of the knowledge bot Introducing the Local Copilot Chatbot Application: Your Ultimate Document-Based Query Assistant In today's fast-paced world, finding precise information quickly can make a significant difference. Our Local Copilot Chatbot Application offers a cutting-edge solution for accessing and querying document-based knowledge with remarkable efficiency. This Flask-based application utilizes the powerful Ollama and Phi3 models to deliver an interactive, intuitive chatbot experience. Here's a deep dive into what our application offers and how it leverages modern technologies to enhance your productivity. What is the Local Copilot Chatbot Application? The Local Copilot Chatbot Application is designed to serve as your personal assistant for document-based queri...

  For a banking chatbot application designed to serve 10 million users, the architecture must ensure scalability, reliability, and security. Here's a potential architecture: 1. Front-End Layer: - User Interface: Web and mobile applications (React.js for web, React Native for mobile) connected with CDN. - API Gateway:  Manages all the API requests from the client-side. 2. Back-End Layer: - Chatbot Engine:   - Natural Language Processing (NLP): Utilizes services like Google Dialogflow, Microsoft Bot Framework, or custom NLP models deployed on cloud platforms.   - Chatbot Logic: Python/Node.js microservices to handle user queries, integrated with NLP. - Business Logic Layer:   - Microservices Architecture: Separate microservices for different functionalities like user authentication, transaction processing, account management, etc. (Node.js/Spring Boot).   - API Management:  Tools like Kong or AWS API Gateway. 3. Database Layer: - User Data: Relation...

  I'd be glad to explain microservice application rollback for e-commerce, incorporating best practices and addressing potential challenges: Understanding Rollback Requirements in E-commerce Transactional Consistency: When a failure occurs during an update spanning multiple microservices (e.g., order placement involving product inventory, user account, and payment), consistent rollback across all affected services is crucial. Partial Success Scenarios: If some microservices succeed but others fail (e.g., payment goes through but inventory update fails), a mechanism to undo completed operations and handle partial rollbacks is essential. Data Integrity: Rollback strategies should maintain data integrity by preventing data inconsistencies or data loss. Rollback Techniques for E-commerce Microservices Compensating Transactions:  Each microservice implements a compensating transaction that reverses its actions if the overall transaction fails. Example (Order Placement):...