The Google Cloud Platform, including BigQuery
Deletes one or more vectors by ID, from a single namespace. See the documentation.
Looks up and returns vectors by ID, from a single namespace.. See the documentation.
Searches a namespace, using a query vector. It retrieves the ids of the most similar items in a namespace, along with their similarity scores. See the documentation.
Inserts rows into a BigQuery table. See the docs and for an example here.
Updates vector in a namespace. If a value is included, it will overwrite the previous value. See the documentation.
The Google Cloud API opens a world of possibilities for enhancing cloud operations and automating tasks. It empowers you to manage, scale, and fine-tune various services within the Google Cloud Platform (GCP) programmatically. With Pipedream, you can harness this power to create intricate workflows, trigger cloud functions based on events from other apps, manage resources, and analyze data, all in a serverless environment. The ability to interconnect GCP services with numerous other apps enriches automation, making it easier to synchronize data, streamline development workflows, and deploy applications efficiently.
module.exports = defineComponent({
props: {
google_cloud: {
type: "app",
app: "google_cloud",
}
},
async run({steps, $}) {
// Required workaround to get the @google-cloud/storage package
// working correctly on Pipedream
require("@dylburger/umask")()
const { Storage } = require('@google-cloud/storage')
const key = JSON.parse(this.google_cloud.$auth.key_json)
// Creates a client from a Google service account key.
// See https://cloud.google.com/nodejs/docs/reference/storage/1.6.x/global#ClientConfig
const storage = new Storage({
projectId: key.project_id,
credentials: {
client_email: key.client_email,
private_key: key.private_key,
}
})
// Uncomment this section and rename for your bucket before running this code
// const bucketName = 'pipedream-test-bucket';
await storage.createBucket(bucketName)
console.log(`Bucket ${bucketName} created.`)
},
})
The Pinecone API enables you to work with vector databases, which are essential for building and scaling applications with AI features like recommendation systems, image recognition, and natural language processing. On Pipedream, you can create serverless workflows integrating Pinecone with other apps, automate data ingestion, query vector databases in response to events, and orchestrate complex data processing pipelines that leverage Pinecone's similarity search.
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
pinecone: {
type: "app",
app: "pinecone",
}
},
async run({steps, $}) {
return await axios($, {
url: `https://api.pinecone.io/collections`,
headers: {
"Api-Key": `${this.pinecone.$auth.api_key}`,
},
})
},
})