with Rasa and Filter?
Rasa is an open-source platform for building conversational AI applications, including chatbots and voice assistants. It offers robust API endpoints for training models, managing conversations, and interpreting user messages, thus enabling the development of sophisticated AI-driven communication tools. When used with Pipedream, Rasa can automate dialogue flow, extract insights from conversation data, or trigger actions in other apps based on conversational cues.
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
rasa: {
type: "app",
app: "rasa",
}
},
async run({steps, $}) {
const data = {
"key": `${this.rasa.$auth.key}`,
}
return await axios($, {
method: "post",
url: `https://api.rasa.io/v1/tokens`,
headers: {
"accept": `application/json`,
"Content-type": `application/json`,
},
auth: {
username: `${this.rasa.$auth.username}`,
password: `${this.rasa.$auth.password}`,
},
data,
})
},
})
The Filter API in Pipedream allows for real-time data processing within workflows. It's designed to evaluate data against predefined conditions, enabling workflows to branch or perform specific actions based on those conditions. This API is instrumental in creating efficient, targeted automations that respond dynamically to diverse datasets. Using the Filter API, you can refine streams of data, ensuring that subsequent steps in your Pipedream workflow only execute when the data meets your specified criteria. This cuts down on unnecessary processing and facilitates the creation of more intelligent, context-aware systems.
export default defineComponent({
async run({ steps, $ }) {
let condition = false
if (condition == false) {
$.flow.exit("Ending workflow early because the condition is false")
} else {
$.export("$summary", "Continuing workflow, since condition for ending was not met.")
}
},
})