QAI-Server

QAI-Server is a Docker-based application designed to be a web interface for other projects inside of QRev.

Features

• Dockerized application for consistency and portability
• Campaign management
• Provides RESTful API endpoints for various tasks

🚀 Tech Stack

• ✅ Connection to the App Server Database: MongoDB.
• ✅ Vector Database: ChromaDB.
• ✅ SQL Relational Database: MySQL, MariaDB, SQLite3, anything that works with SQLAlchemy.

Prerequisites

• All of the above tech stack set up.

Code Requirements

• Python 3.11
• Docker
• Poetry

Getting Started

Clone the Repository

git clone https://github.com/qrev-ai/qrev.git

Go to the project folder

cd qrev/ai/projects/server

Setting up the .env file and config.toml file

For the .env file you can see the example placed in examples/example.env The config.toml configuration file is placed in your application directory under the name qai. Linux example ~/.config/qai/config.toml. An example config.toml

Run locally

From the server project folder. ai/projects/server

sh scripts/run_server.sh

Build and Run with Docker

Ensure that Docker is installed and running. Then, use the provided Makefile to build and run the project:

1. Build the Docker Image

   make build
   

2. Run the Application

   make up
   

   The application should be running on http://localhost:8081.

Run Unit Tests

Run the project's unit tests using the Makefile:

make test

Makefile Commands

• make test: Run all unit tests.
• make .build: Build the project using Poetry.
• make publish: Publish the project after building it.
• make heartbeat: Check if the server is alive.
• make loglevel: Set the server's log level.
• make list_collections: List all collections from the Chroma API.
• make campaign: Send a campaign query to the server.

AWS Commands

• make authenticate: Authenticate to the AWS ECR.
• make deploy_service: Deploy the service to an AWS ECS cluster.