Showing posts with label microservices. Show all posts
Showing posts with label microservices. Show all posts

Friday

Microservices Application with Flutter Flask MongoDB RabbitMQ

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:5672/

    networks:

      - microservice-network


  mongodb:

    image: mongo:latest

    container_name: mongodb

    ports:

      - "27017:27017"

    networks:

      - microservice-network


  rabbitmq:

    image: rabbitmq:3-management

    container_name: rabbitmq

    ports:

      - "5672:5672"

      - "15672:15672"

    networks:

      - microservice-network


  frontend:

    build: ./frontend

    container_name: frontend

    ports:

      - "8080:8080"

    depends_on:

      - backend

    networks:

      - microservice-network


networks:

  microservice-network:

    driver: bridge

```


2. Backend Service


2.1 `backend/Dockerfile`

```dockerfile

FROM python:3.9-slim


WORKDIR /app


COPY requirements.txt requirements.txt

RUN pip install -r requirements.txt


COPY . .


CMD ["python", "main.py"]

```


2.2 `backend/requirements.txt`

```txt

fastapi

pymongo

pika

uvicorn

```


2.3 `backend/config.py`

```python

import os


MONGO_URI = os.getenv('MONGO_URI')

RABBITMQ_URI = os.getenv('RABBITMQ_URI')

```


2.4 `backend/main.py`

```python

from fastapi import FastAPI

from pymongo import MongoClient

import pika

import config


app = FastAPI()


client = MongoClient(config.MONGO_URI)

db = client.flutterdb


# RabbitMQ Connection

params = pika.URLParameters(config.RABBITMQ_URI)

connection = pika.BlockingConnection(params)

channel = connection.channel()


@app.get("/")

async def read_root():

    return {"message": "Backend service running"}


@app.post("/data")

async def create_data(data: dict):

    db.collection.insert_one(data)

    channel.basic_publish(exchange='', routing_key='flutter_queue', body=str(data))

    return {"message": "Data inserted and sent to RabbitMQ"}

```


3. Frontend Service


3.1 `frontend/Dockerfile`

```dockerfile

FROM cirrusci/flutter:stable


WORKDIR /app


COPY . .


RUN flutter build web


CMD ["flutter", "run", "-d", "chrome"]

```


3.2 `frontend/pubspec.yaml`

```yaml

name: flutter_app

description: A new Flutter project.


version: 1.0.0+1


environment:

  sdk: ">=2.7.0 <3.0.0"


dependencies:

  flutter:

    sdk: flutter

  http: ^0.13.3


dev_dependencies:

  flutter_test:

    sdk: flutter

```


#### 3.3 `frontend/lib/main.dart`

```dart

import 'package:flutter/material.dart';

import 'package:http/http.dart' as http;


void main() {

  runApp(MyApp());

}


class MyApp extends StatelessWidget {

  @override

  Widget build(BuildContext context) {

    return MaterialApp(

      title: 'Flutter Demo',

      theme: ThemeData(

        primarySwatch: Colors.blue,

      ),

      home: MyHomePage(),

    );

  }

}


class MyHomePage extends StatefulWidget {

  @override

  _MyHomePageState createState() => _MyHomePageState();

}


class _MyHomePageState extends State<MyHomePage> {

  Future<void> sendData() async {

    final response = await http.post(

      Uri.parse('http://backend:8000/data'),

      body: {'key': 'value'},

    );

    print('Response status: ${response.statusCode}');

    print('Response body: ${response.body}');

  }


  @override

  Widget build(BuildContext context) {

    return Scaffold(

      appBar: AppBar(

        title: Text('Flutter Microservice App'),

      ),

      body: Center(

        child: ElevatedButton(

          onPressed: sendData,

          child: Text('Send Data to Backend'),

        ),

      ),

    );

  }

}

```


4. `README.md`

```markdown

# Microservice Application


## Overview


This is a microservice application setup consisting of a Flutter app (frontend), a FastAPI service (backend), MongoDB, and RabbitMQ. All services are orchestrated using Docker Compose.


## How to Run


1. Clone the repository:

   ```bash

   git clone https://github.com/your-repo/microservice-app.git

   cd microservice-app

   ```


2. Build and run the containers:

   ```bash

   docker-compose up --build

   ```


3. Access the services:

   - Frontend: `http://localhost:8080`

   - Backend: `http://localhost:8000`

   - RabbitMQ Management: `http://localhost:15672`

   - MongoDB: `mongodb://localhost:27017`

```


### Instructions to Run the Application

1. Ensure Docker and Docker Compose are installed on your machine.

2. Place the folder structure and files as described above.

3. Navigate to the root of the `microservice-app` folder.

4. Run `docker-compose up --build` to build and start the application.

5. Access the frontend on `http://localhost:8080`, backend on `http://localhost:8000`, and RabbitMQ Management UI on `http://localhost:15672`.


This setup provides a working microservice application with a Flutter frontend, FastAPI backend, MongoDB for storage, and RabbitMQ for messaging.

Saturday

Introducing the Local Copilot Chatbot Application: Your Ultimate Document-Based Query Assistant



                                        
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 queries. Imagine having a copilot that understands your documents, provides precise answers, and adapts to your needs. That's exactly what our application does. It transforms your document uploads into a dynamic knowledge base that you can query using natural language.


Key Features


- Interactive Chatbot Interface: Engage with a responsive chatbot that provides accurate answers based on your document content.

- Document Upload and Processing: Upload your documents, and our system processes them into a searchable knowledge base.

- Vector Knowledge Base with RAG System: Utilize a sophisticated Retrieval-Augmented Generation (RAG) system that combines vector embeddings and document retrieval to deliver precise responses.

- Microservices Architecture: Our application uses a microservices approach, keeping the front-end and back-end isolated for greater flexibility and scalability.

- Session Management: Each user's interaction is managed through unique sessions, allowing for individualized queries and responses.

- Redis Cache with KNN: Used KNN algorithm with Redis cache to find similar questions already asked in session to get a faster response back.


Technologies Used


1. Flask: The back-end of our application is powered by Flask, a lightweight web framework that facilitates smooth interaction between the front-end and the chatbot service.

2. Ollama and Phi3 Models: These models form the core of our chatbot’s capabilities, enabling sophisticated language understanding and generation.

3. Chroma and Sentence Transformers: Chroma handles the vector database for document retrieval, while Sentence Transformers provide embeddings to compare and find relevant documents.

4. Redis: Used for caching responses to improve performance and reduce query times.

5. Docker: The entire application, including all its components, runs within Docker containers. This approach ensures consistent development and deployment environments, making it easy to manage dependencies and run the application locally.

6. Asynchronous Processing: Handles multiple user requests simultaneously, ensuring a smooth and efficient user experience.


How It Works


1. Document Upload: Start by uploading your documents through the front-end application. These documents are processed and stored in a vector knowledge base.

2. Knowledge Base Creation: Our system converts the document content into vector embeddings, making it searchable through the Chroma database.

3. Query Handling: When you pose a question, the chatbot uses the RAG system to retrieve relevant documents and generate a precise response.

4. Caching and Performance Optimization: Responses are cached in Redis to speed up future queries and enhance the overall performance of the system.

5. Session Management: Each session is tracked independently, ensuring personalized interactions and allowing multiple users to operate concurrently without interference.


What Can You Expect?


- Accurate Responses: The combination of advanced models and efficient retrieval systems ensures that you receive relevant and accurate answers.

- Flexible Integration: The microservices architecture allows for easy integration with various front-end frameworks and other back-end services.

- Enhanced Productivity: Quickly find and retrieve information from large volumes of documents, saving time and improving decision-making.

- Local Development: With all components running in Docker containers, you can easily set up and run the application on your local system.


Get Started


To explore the Local Copilot Chatbot Application, follow the setup instructions provided in our GitHub repository. Experience the power of a well-integrated chatbot system that understands your documents and delivers insightful answers at your fingertips.


System Used:

Medium power low RAM. However, if you can use 32GB RAM with Nvidia GPU and i7 CPU would be great and run after the first compilation.



GitHub Repo

https://github.com/dhirajpatra/ollama-langchain-streamlit

High Scale Architecture

 

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: Relational databases (PostgreSQL/MySQL) for storing user information.

- Transaction Data: NoSQL databases (MongoDB/Cassandra) for handling high-velocity transaction data.

- Cache Layer: Redis or Memcached for caching frequent queries and session data.


4. Middleware Layer:

- Message Queue: Kafka or RabbitMQ for handling asynchronous communication between microservices.

- Service Mesh: Istio for managing microservices communication, security, and monitoring.


5. Integration Layer:

- Third-Party Services: Integration with banking APIs, payment gateways, and other financial services.

- Security Services: Integration with identity and access management (IAM) services for user authentication and authorization (OAuth 2.0, OpenID Connect).


6. Security Layer:

- Data Encryption: SSL/TLS for data in transit, and AES for data at rest.

- Threat Detection: Tools like AWS GuardDuty, Azure Security Center.

- Compliance: Ensure compliance with banking regulations (PCI-DSS, GDPR).


7. Deployment and DevOps:

- Containerization: Docker for containerizing applications.

- Orchestration: Kubernetes for managing containerized applications.

- CI/CD Pipeline: Jenkins/GitHub Actions for continuous integration and deployment.

- Monitoring & Logging: Prometheus, Grafana for monitoring; ELK Stack for logging.


8. Scalability & Reliability:

- Auto-scaling: AWS Auto Scaling, Azure Scale Sets.

- Load Balancing: AWS Elastic Load Balancer, NGINX.

- Disaster Recovery: Multi-region deployment, regular backups.


Diagram Overview:


```

User Interface (Web/Mobile Apps)

        |

     API Gateway

        |

    Chatbot Engine (NLP, Chatbot Logic)

        |

  Business Logic Layer (Microservices)

        |

       DB Layer (SQL, NoSQL, Cache)

        |

   Middleware (Message Queue, Service Mesh)

        |

Integration Layer (Third-Party APIs, Security Services)

        |

  Security Layer (Encryption, Threat Detection, Compliance)

        |

Deployment & DevOps (CI/CD, Containerization, Orchestration, Monitoring)

        |

Scalability & Reliability (Auto-scaling, Load Balancing, Disaster Recovery)

```


This architecture ensures that the banking chatbot application is scalable, secure, and efficient, capable of handling a large user base with high availability.

Thursday

Rollback in Microservices

 



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

  1. Compensating Transactions: 


    • Each microservice implements a compensating transaction that reverses its actions if the overall transaction fails.
    • Example (Order Placement):
      • Order service: Create an order record (compensate: delete order).
      • Inventory service: Reduce stock (compensate: increase stock).
      • Payment service: Capture payment (compensate: refund payment).
    • Pros: Flexible, independent service development.
    • Cons: Requires careful design and implementation for all microservices.
  2. Event Sourcing and CQRS (Command Query Responsibility Segregation): 


    • Events represent state changes in the system.
    • CQRS separates read (queries) and write (commands) operations.
    • Rollback involves replaying events from a persistent store (e.g., event database) up to the failure point, potentially with compensating actions.
    • Pros: Strong consistency, audit trails, scalability for reads.
    • Cons: Increased complexity, potential performance overhead.
  3. Messaging with Idempotency: 


    • Use asynchronous messaging queues for communication between microservices.
    • Design messages to be idempotent (producing the same effect even if processed multiple times).
    • In case of failures, replay messages to retry operations.
    • Pros: Loose coupling, fault tolerance, potential for message deduplication.
    • Cons: Requires additional infrastructure and message design considerations.
  4. Circuit Breakers and Timeouts: 


    • Implement circuit breakers to automatically stop sending requests to a failing microservice.
    • Set timeouts for microservice calls to prevent hanging requests.
    • When a failure occurs, the client initiates rollback or retries as appropriate.
    • Pros: Fault isolation, prevent cascading failures.
    • Cons: Requires configuration and tuning for effective behavior.

Choosing the Right Technique

The optimal technique depends on your specific e-commerce application's requirements and complexity. Consider:

  • Transaction patterns
  • Data consistency needs
  • Microservice development complexity
  • Performance requirements

Additional Considerations

  • Rollback Coordination: Designate a central coordinator (e.g., saga pattern) or distributed consensus mechanism to orchestrate rollback across services if necessary.
  • Rollback Testing: Thoroughly test rollback scenarios to ensure data consistency and proper recovery.
  • Monitoring and Alerting: Monitor application and infrastructure health to detect failures and initiate rollbacks proactively.

Example Code (Illustrative - Replace with Language-Specific Code)

Compensating Transaction (Order Service):

Python
def create_order(self, order_data):
    try:
        # Create order record
        # ...
        return order_id
    except Exception as e:
        self.compensate_order(order_id)
        raise e  # Re-raise to propagate the error

def compensate_order(self, order_id):
    # Delete order record
    # ...

Event Sourcing (Order Placement Example):

Python
def place_order(self, order_data):
    # Create order event
    event = OrderPlacedEvent(order_data)
    # Store event in persistent store
    self.event_store.save(event)

Remember to tailor the code to your specific programming language and framework.

By effectively implementing rollback strategies, you can ensure the resilience and reliability of your e-commerce microservices architecture, even in the face of failures.