Microsoft Teams has communities, events, chats, channels, meetings, storage, tasks, and calendars in one place.
Emit new event when a new message is posted in a channel
Emit new event when a new message is received in a chat
Emit new event when a new team is joined by the authenticated user
Create a new channel in Microsoft Teams. See the docs here
Generate embeddings using Google PaLM. See the docs here
Get the list of shift instances for a team. See the documentation
The Microsoft Teams API on Pipedream allows you to automate tasks, streamline communication, and integrate with other services to enhance the functionality of Teams as a collaboration hub. With this API, you can send messages to channels, orchestrate complex workflows based on Teams events, and manage Teams' settings programmatically.
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
microsoft_teams: {
type: "app",
app: "microsoft_teams",
}
},
async run({steps, $}) {
return await axios($, {
url: `https://graph.microsoft.com/v1.0/me`,
headers: {
Authorization: `Bearer ${this.microsoft_teams.$auth.oauth_access_token}`,
},
})
},
})
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,
},
})
},
})