with Dialpad and Pinecone?
Emit new call event subscription. See Event doc and webhook doc
Emit new contact event subscription. See Event doc and webhook doc
Emit new SMS event subscription. See Event doc and webhook doc
Emit update contact event subscription. See Event doc and webhook doc
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
Updates vector in a namespace. If a value is included, it will overwrite the previous value. See the documentation
Writes vectors into a namespace. If a new value is upserted for an existing vector ID, it will overwrite the previous value. See the documentation
The Dialpad API taps into the core of Dialpad's communication platform, allowing for the automation of voice and messaging workflows. By leveraging this API through Pipedream, you can interact with call data, manage users, and automate sending of SMS messages, among other tasks. This enables the creation of intricate, automated processes that can enhance business communication efficiency, customer support, and team collaboration within your organization.
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
dialpad: {
type: "app",
app: "dialpad",
}
},
async run({steps, $}) {
return await axios($, {
url: `https://dialpad.com/api/v2/users/me`,
headers: {
Authorization: `Bearer ${this.dialpad.$auth.oauth_access_token}`,
},
})
},
})
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}`,
},
})
},
})