3.3 KiB

Исходник Постоянная ссылка Ответственный История

13 - Build a Spring AI microservice with Azure OpenAI

Prepare Azure OpenAI Service

First, define the following environment variables for use in creating the AI resources we'll use in this example:

export LOCATION="eastus"
export OPENAI_RESOURCE_NAME="<Azure-OpenAI-resource-name>"

Next, run the following command to create an Azure OpenAI resource in the the default resource group:

az cognitiveservices account create \
    -n ${OPENAI_RESOURCE_NAME} \
    -l ${LOCATION} \
    --kind OpenAI \
    --sku s0 \
    --custom-domain ${OPENAI_RESOURCE_NAME}

To create the model deployments for text-embedding-ada-002 and gpt-35-turbo-16k in your Azure OpenAI service, execute the following commands:

az cognitiveservices account deployment create \
    -n ${OPENAI_RESOURCE_NAME} \
    --deployment-name text-embedding-ada-002 \
    --model-name text-embedding-ada-002 \
    --model-version "2"  \
    --model-format OpenAI

az cognitiveservices account deployment create \
    -n ${OPENAI_RESOURCE_NAME} \
    --deployment-name gpt-35-turbo-16k \
    --model-name gpt-35-turbo-16k \
    --model-version "0613"  \
    --model-format OpenAI \
    --sku Standard \
    --capacity 120

You'll need to retrieve the endpoint and API key for your Azure OpenAI resource to proceed. To do so, follow the guidance in this doc.

Create a Spring AI microservice

The microservice that we create in this guide is available here.

To create our microservice, we will invoke the Spring Initalizr service from the command line:

NOTE: For now, please use the project link above.

Run locally

First, export the environment variables required by Spring AI:

export SPRING_AI_AZURE_OPENAI_API_KEY=<INSERT_KEY_HERE>
export SPRING_AI_AZURE_OPENAI_ENDPOINT=<INSERT_ENDPOINT_URL_HERE>

Next, start the service by running ./mvnw spring-boot:run.

Finally, open http://localhost:8080 in a browser to access the application.

Deploy to Azure Spring Apps

Use the following command to specify the app name on Azure Spring Apps and to allocate the required resources:

az spring app create \
    --name ai-weather-service \
    --cpu 2 \
    --memory 4Gi \
    --min-replicas 2 \
    --max-replicas 2 \
    --assign-endpoint true

Build the jar package using the command: ./mvnw clean package.

Use the following command to deploy the .jar file for the app:

az spring app deploy \
    --name ai-weather-service \
    --artifact-path target/demo-0.0.1-SNAPSHOT.jar \
    --env SPRING_AI_AZURE_OPENAI_API_KEY=${SPRING_AI_AZURE_OPENAI_API_KEY} SPRING_AI_AZURE_OPENAI_ENDPOINT=${SPRING_AI_AZURE_OPENAI_ENDPOINT} \
    --runtime-version Java_17

Test the project in the cloud

Go to "Apps" in your Azure Spring Apps instance.
- Verify that ai-weather-service has a Registration status which says 1/1. This shows that it is correctly registered in Spring Cloud Service Registry.
- Select ai-weather-service to see more information about the microservice.
Click the "URL" that is provided to open it in another browser tab, then type Basel into the Question prompt box. Click the Ask AI button to see the results!

3.3 KiB Исходник Постоянная ссылка Ответственный История