with Elastic Email and Google PaLM?
Emit new event when a new contact is added to a mailing list. See the documentation
Emit new event when a recipient clicks a link in an email. See the documentation
Emit new event when a recipient opens an email. See the documentation
Adds a new contact to a mailing list. See the documentation
Generate embeddings using Google PaLM. See the docs here
Sends an email to one or more recipients. See the documentation
The Elastic Email API allows you to integrate a robust email sending platform into your Pipedream workflows. Use it to send emails, manage contacts, and track campaign statistics. With this API, you can automate email notifications, synchronize subscriber lists, or trigger marketing campaigns based on user actions.
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
elastic_email: {
type: "app",
app: "elastic_email",
}
},
async run({steps, $}) {
return await axios($, {
url: `https://api.elasticemail.com/v2/account/profileoverview`,
headers: {
"X-ElasticEmail-ApiKey": `${this.elastic_email.$auth.api_key}`,
},
})
},
})
The Google PaLM API is a cutting-edge language model that allows developers to integrate advanced natural language understanding into their applications. On Pipedream, you can harness this power to create serverless workflows that react to various triggers and perform actions based on the insights and outputs from PaLM. Whether it's generating content, summarizing text, or understanding user intent, PaLM's capabilities can be integrated into Pipedream workflows to automate complex tasks involving language.
import { v1beta2 } from "@google-ai/generativelanguage";
import { GoogleAuth } from "google-auth-library";
export default defineComponent({
props: {
google_palm_api: {
type: "app",
app: "google_palm_api",
}
},
async run({ steps, $ }) {
const client = new v1beta2.TextServiceClient({
authClient: new GoogleAuth().fromAPIKey(this.google_palm_api.$auth.palm_api_key),
});
const text = "Repeat after me: one, two,";
const model = "models/text-bison-001";
return await client
.generateText({
model,
prompt: {
text,
},
})
},
})