Quickstart

Quickly start deploying a production-ready core infrastructure stack for your LLM applications

This guide will walk you through setting up a production-ready core infrastructure stack for your LLM Application with minimal effort. In just a few steps, you’ll be able to setup Universal API, AI routing, AI Gateway and Observablity to track and analyze the performance and usage of your Large Language Model (LLM) applications.

1. Deploy AI studio

   Preparing docker environment

   docker-compose.yml

     version: '3.8'
   
     services:
       postgres:
         image: postgres:13
         environment:
           - POSTGRES_DB=aistudio
           - POSTGRES_USER=aistudio
           - POSTGRES_HOST_AUTH_METHOD=trust
         volumes:
           - postgres:/var/lib/postgresql/data
         ports:
           - 5432:5432
         healthcheck:
           test: [ "CMD-SHELL", "pg_isready -U aistudio" ]
           interval: 30s
           timeout: 30s
           retries: 3
         restart: always
   
       redis:
         image: redis:7.2-alpine
         ports:
           - 6379:6379
         command: redis-server --save 20 1 --loglevel warning
         volumes:
           - redis:/data
         healthcheck:
           test: ["CMD", "redis-cli", "ping"]
           interval: 1s
           timeout: 3s
           retries: 30
         restart: always
   
       clickhouse:
         image: clickhouse/clickhouse-server:24.1.5
         volumes:
           - clickhouse:/var/lib/clickhouse
         ports:
           - 9000:9000
           - 8123:8123
         restart: always
   
       migrate:
         image: missingstudio/ai:dev
         command: ["migrate"]
         environment:
           - GATEWAY_POSTGRES_URL=postgres://aistudio@postgres:5432/aistudio?sslmode=disable
         depends_on:
           - postgres
   
       aistudio:
         image: missingstudio/ai:dev
         environment:
           - GATEWAY_APP_HOST=0.0.0.0
           - GATEWAY_APP_PORT=8080
           - GATEWAY_REDIS_HOST=redis
           - GATEWAY_REDIS_PORT=6379
           - GATEWAY_POSTGRES_URL=postgres://aistudio@postgres:5432/aistudio?sslmode=disable
         command: ["start"]
         ports:
           - 8080:8080
         depends_on:
           - redis
           - postgres
           - clickhouse
           - migrate
   
     volumes:
       redis: {}
       postgres: {}
       clickhouse: {}
   

   Launch AI studio gateway server

     docker-compose up -d
   
2. Generate an API Key

   With AI studio running, the next step is to generate an API key for resource access from AI studio

   To generate your first API key, you can use the following command:

   shell

   curl --request POST \
     --url http://127.0.0.1:8080/api/v1/keys \
     --header 'Content-Type: application/json' \
     --data '{
       "name": "Base"
     }'
   

   Save the API Key Remember to include this API key in the X-Ms-Api-Key header for all future API interactions

You’re geared up and ready to go! 🚀

Following these steps should have you AI studio up and running to power up LLMOps for your LLM appplications. If you have any questions or need support, reach out to our Discord Community.

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Providers

Integrate your LLM Provider with AI studio

Connections

Connect to your existing Observablity Platform