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One FastAPI application however multiple developers can work simultaneously on different services. This approach uses separate service classes, routers, and a main application file. Folder Structure fastapi_app/ ├── app/ │ ├── __init__.py │ ├── main.py │ ├── core/ │ │ ├── __init__.py │ │ └── config.py │ ├── services/ │ │ ├── __init__.py │ │ ├── service1.py │ │ ├── service2.py │ │ ├── service3.py │ │ └── service4.py │ ├── routers/ │ │ ├── __init__.py │ │ ├── router1.py │ │ ├── router2.py │ │ ├── router3.py │ │ └── router4.py │ └── models/ │ ├── __init__.py │ └── models.py └── requirements.txt Example Code app/main.py from fastapi import FastAPI from app.routers import router1, router2, router3, router4 app = FastAPI() app.include_router(router1.router) app.include_router(router2.router) app.include_router(router3.router) app.include_router(router4.router) if __name__ == "__main__": import uvicorn ...

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...

Architecture Details for Code Generation Engine (Low-code) 1. Backend Framework: - Python Framework:   - FastAPI: A modern, fast (high-performance) web framework for building APIs with Python 3.6+ based on standard Python type hints.   - SQLAlchemy: SQL toolkit and Object-Relational Mapping (ORM) library for database management.   - Jinja2: A templating engine for rendering dynamic content.   - Pydantic: Data validation and settings management using Python type annotations. 2. Application Structure: - Project Root:   - `app/`     - `main.py` (Entry point of the application)     - `models/`       - `models.py` (Database models)     - `schemas/`       - `schemas.py` (Data validation schemas)     - `api/`       - `endpoints/`         - `code_generation.py` (Endpoints related to code generation)     - `core/`       - `config.py` (Configu...

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...

  intel OpenVino framework OpenVINO can help accelerate the processing of your local LLM (Large Language Model) application generation in several ways. OpenVINO can significantly aid in developing LLM and Generative AI applications on a local system like a laptop by providing optimized performance and efficient resource usage. Here are some key benefits: 1. Optimized Performance : OpenVINO optimizes models for Intel hardware, improving inference speed and efficiency, which is crucial for running complex LLM and Generative AI models on a laptop. 2. Hardware Acceleration : It leverages CPU, GPU, and other accelerators available on Intel platforms, making the most out of your laptop's hardware capabilities. 3. Ease of Integration : OpenVINO supports popular deep learning frameworks like TensorFlow, PyTorch, and ONNX, allowing seamless integration and conversion of pre-trained models into the OpenVINO format. 4. Edge Deployment : It is designed for edge deployment, making it suitable ...

  Photo by SevenStorm JUHASZIMRUS by pexel Differences Between CPU-based Multi-threading and Multi-processing CPU-based Multi-threading : - Concept: Uses multiple threads within a single process. - Shared Memory: Threads share the same memory space. - I/O Bound Tasks: Effective for tasks that spend a lot of time waiting for I/O operations. - Global Interpreter Lock (GIL): In Python, the GIL can be a limiting factor for CPU-bound tasks since it allows only one thread to execute Python bytecode at a time. CPU-based Multi-processing : - Concept: Uses multiple processes, each with its own memory space. - Separate Memory: Processes do not share memory, leading to more isolation. - CPU Bound Tasks: Effective for tasks that require significant CPU computation since each process can run on a different CPU core. - No GIL: Each process has its own Python interpreter and memory space, so the GIL is not an issue. CUDA with PyTorch : - Concept: Utilizes the GPU for parallel computation. - Massi...