with Mistral AI and Google Vertex AI?
Emit new event when a new batch job is completed. See the Documentation
Emit new event when a new batch job fails. See the Documentation
Emit new event when a new AI model is registered or becomes available. See the Documentation
Create a new batch job, it will be queued for processing. See the Documentation
Examines an image or video following given instructions. Results will contain the analysis findings. See the documentation
Analyzes a specified text for its underlying sentiment. See the documentation
Download a batch job results file to the /tmp directory. See the Documentation
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
mistral_ai: {
type: "app",
app: "mistral_ai",
}
},
async run({steps, $}) {
return await axios($, {
url: `https://api.mistral.ai/v1/models`,
headers: {
Authorization: `Bearer ${this.mistral_ai.$auth.api_key}`,
"content-type": `application/json`,
},
})
},
})
With the Google Vertex AI API, you can tap into a robust suite of AI tools offered by Google Cloud to build, deploy, and scale machine learning models. Whether you're processing data, training custom models, or using pre-trained ones, Vertex AI provides a unified platform for AI development. In Pipedream, you can create serverless workflows that interact with Vertex AI, allowing you to automate tasks like model training, prediction, and resource management without provisioning your own infrastructure.
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
google_vertex_ai: {
type: "app",
app: "google_vertex_ai",
}
},
async run({steps, $}) {
return await axios($, {
url: `https://www.googleapis.com/oauth2/v1/userinfo`,
headers: {
Authorization: `Bearer ${this.google_vertex_ai.$auth.oauth_access_token}`,
},
})
},
})