with Mistral AI and Databricks?
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
Retrieve the output and metadata of a single task run. 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`,
},
})
},
})
The Databricks API allows you to interact programmatically with Databricks services, enabling you to manage clusters, jobs, notebooks, and other resources within Databricks environments. Through Pipedream, you can leverage these APIs to create powerful automations and integrate with other apps for enhanced data processing, transformation, and analytics workflows. This unlocks possibilities like automating cluster management, dynamically running jobs based on external triggers, and orchestrating complex data pipelines with ease.
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
databricks: {
type: "app",
app: "databricks",
}
},
async run({steps, $}) {
return await axios($, {
url: `https://${this.databricks.$auth.domain}.cloud.databricks.com/api/2.0/preview/scim/v2/Me`,
headers: {
Authorization: `Bearer ${this.databricks.$auth.access_token}`,
},
})
},
})