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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...

Streamlit apps rely on WebSockets, which can create challenges when embedding them directly in an iframe, especially in some browsers due to security restrictions. Instead, consider an alternative approach such as creating a simple JavaScript-based frontend that can interact with your Streamlit backend via an API, ensuring easy integration into client websites. Here is the demo Chat Bot application approaches: Backend Development 1. Model Setup:    - Use Ollama and Llama3 for natural language understanding and generation.    - Train your models with data specific to each business for better performance. 2. API Development:    - Create an API using a framework like FastAPI or Flask to handle requests and responses between the frontend and the backend models.    - Ensure the API supports multitenancy by handling different businesses' data separately. 3. Vector Store with FAISS:    - Use FAISS to create a vector store database for each busi...

  Photo by Andrea Piacquadio Recommender systems are a subclass of information filtering systems that seek to predict the "rating" or "preference" a user would give to an item. These systems are widely used in various domains, such as e-commerce, social media, and content streaming platforms, to provide personalized recommendations. The primary approaches to building recommender systems include collaborative filtering, content-based filtering, and hybrid methods. Types of Recommender Systems 1. Collaborative Filtering :    - User-Based : Recommends items by finding users similar to the target user and suggesting items that these similar users have liked.    - Item-Based : Recommends items by finding items similar to those the target user has liked.    - Matrix Factorization : Reduces the dimensionality of the user-item matrix to find latent factors that explain user preferences. 2. Content-Based Filtering :    - Recommends items based on the...

  Developing Microservices with Python, REST API, Nginx, and Kafka (End-to-End) Here's a step-by-step guide to developing microservices with the mentioned technologies: 1. Define Your Microservices: Break down Functionality: Identify distinct functionalities within your application that can be independent services. These services should have well-defined APIs for communication. Example: If you're building an e-commerce application, separate services could manage user accounts, products, orders, and payments. 2. Develop Python Microservices with RESTful APIs: Choose a Python framework: Popular options include Flask, FastAPI, and Django REST Framework. Develop each microservice as a separate Python application with clearly defined endpoints for API calls (GET, POST, PUT, DELETE). Use libraries like requests for making API calls between services if needed. Implement data persistence for each service using databases (e. g., PostgreSQL, MongoDB) or other ...