https://github.com/vinitparekh17/gpcs
A modern backend API in cloud driven approach.
https://github.com/vinitparekh17/gpcs
aws aws-examples backend-api cloud express gcp-vertex-ai grafana grafana-loki nodejs payment-integration prometheus typescript
Last synced: 6 months ago
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A modern backend API in cloud driven approach.
- Host: GitHub
- URL: https://github.com/vinitparekh17/gpcs
- Owner: vinitparekh17
- License: apache-2.0
- Created: 2024-06-21T05:38:12.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-11-27T09:08:48.000Z (over 1 year ago)
- Last Synced: 2024-11-27T09:32:09.792Z (over 1 year ago)
- Topics: aws, aws-examples, backend-api, cloud, express, gcp-vertex-ai, grafana, grafana-loki, nodejs, payment-integration, prometheus, typescript
- Language: TypeScript
- Homepage:
- Size: 639 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# GPCS Backend
( Image generated by Gemini-pro )
# Description
GPCS (General Purpose Chat System) API is an AI powered chat system that allows users to interact with a chatbot. The chatbot is capable of answering questions, providing information, and making recommendations. The chatbot is powered by Google's Generative AI model ( Gemini-pro )
This API is built with cloud driven approach in mind, it uses AWS S3 for storing files, AWS Polly for text-to-speech, GCP Vertex AI ( Multimodal text, image, audio, video, PDF, code, and chat ) for chatbot, GCP Speech API for speech-to-text, Stripe and Razorpay for payment processing and websockets for real-time chat communication ( Between users and chatbot only )
# Features
- User authentication
- AI powered chatbot
- Voice interaction with the chatbot
- Hybrid payment system (Stripe and Razorpay)
- Real-time chat through websockets
# Requirements
- [NodeJS](https://nodejs.org/en/download) ( v20.6.x or above )
- [AWS S3](https://aws.amazon.com/s3/)
- [AWS RDS](https://aws.amazon.com/rds/)
- [AWS Polly](https://aws.amazon.com/polly/)
- [AWS Keyspaces](https://aws.amazon.com/keyspaces/)
- [AWS Secrets Manager](https://aws.amazon.com/secrets-manager/)
- [GCP Vertax API](https://cloud.google.com/vertex-ai?hl=en)
- [GCP Speech API](https://cloud.google.com/speech-to-text)
- [Stripe API](https://docs.stripe.com/api)
- [Razorpay API](https://razorpay.com/docs/api/)
- [Docker](https://www.docker.com/) ( Optional but recommended )
# Deployment Diagram
This diagram illustrates the deployment architecture of the GPCS API. The API deployment architecture is designed to be scalable, fault-tolerant and most importantly secure infrastructure.
To achieve this, I preferred to use AWS Elastic Container Service (ECS) with Fargate launch type. This allows me to run the API in a containerized environment without worrying about the underlying infrastructure.
The reason of choosing ECS over EC2 is because ECS is a fully managed container orchestration service that allows me to run, stop, and manage Docker containers on a cluster. It also provides features like auto-scaling, load balancing, and monitoring.
All this advantages comes with relatively less efforts to manage and worry about infra and security which helps me to be more productive.
# Installation
1. Clone the repository
```bash
git clone https://github.com/vinitparekh17/gpcs
```
2. Install dependencies
```bash
yarn install
```
3. Create a `.env` file in the root directory as per the `.env.example` file
```bash
cp .env.example .env
```
4. Start the server
```bash
yarn server-dev # For development
yarn build && yarn start # For production
```
# Run with Docker
1. Build the Docker image
```bash
docker build -t gpcs -f ./docker .
```
2. Run the Docker container
```bash
docker run -p 8080:8080 gpcs -d --name gpcs
```
# Logging and Monitoring
## Overview
## Components
### Grafana
- Web-based analytics and interactive visualization platform
- Supports multiple data sources
- Provides customizable dashboards for real-time monitoring
### Prometheus
- Open-source systems monitoring and alerting toolkit
- Collects and stores metrics as time-series data
- Supports powerful query language (PromQL)
### Loki
- Lightweight log aggregation system
- Designed for cloud-native environments
- Optimized for storing and querying container logs
## Setup Requirements
- Docker
- Docker Compose
- Minimum system resources:
- 4 GB RAM
- 2 CPU cores
## Configuration
1. Install Docker
2. Configure Prometheus targets
3. Set up Loki log collection
4. Configure Grafana data sources
5. Create monitoring dashboards
## Useful Links
- [Grafana Official Site](https://grafana.com/)
- [Prometheus Documentation](https://prometheus.io/docs/)
- [Loki GitHub Repository](https://github.com/grafana/loki)
> [!IMPORTANT]
> Make sure to have MongoDB running on your local machine or provide the connection string in the `.env` file
> Also, make sure to have the required AWS, GCP, Stripe and Razorpay credentials in the `.env` file
> which are required for the API to work properly.