Apply large language models and generative AI to a variety of use cases
Go to siteimport { axios } from "@pipedream/platform"
export default defineComponent({
props: {
azure_openai_service: {
type: "app",
app: "azure_openai_service",
}
},
async run({steps, $}) {
const data = {
"messages": [{ role: 'user', content: "Hello, world!" }],
}
return await axios($, {
method: "post",
url: `https://${this.azure_openai_service.$auth.resource_name}.openai.azure.com/openai/deployments/${this.azure_openai_service.$auth.deployment_name}/chat/completions?api-version=2023-05-15`,
headers: {
"Content-Type": `application/json`,
"api-key": `${this.azure_openai_service.$auth.api_key}`,
},
data,
})
},
})
Create completions for chat messages with the GPT-35-Turbo and GPT-4 models. See the documentation
Classify items into specific categories. See the documentation
Creates an image given a prompt, and returns a URL to the image. See the documentation
Summarizes a text message with the GPT-35-Turbo and GPT-4 models. See the documentation
Translate text from one language to another. See the documentation
Azure OpenAI Service uses API keys for authentication. When you connect your Azure OpenAI Service account, Pipedream securely stores the keys so you can easily authenticate to Azure OpenAI Service APIs in both code and no-code steps.
Before you start, you'll need to deploy a model in the Azure OpenAI Service.
Once that's done, enter the name of your Azure OpenAI resource, the deployment name you chose when you deployed the model, and your Azure OpenAI key below.