Real Simple Syndication
Determine the sentiment of the given text (positive, negative, or neutral). See the documentation.
Retrieve multiple RSS feeds and return a merged array of items sorted by date See documentation
Identify and extract significant keywords from the given text. See the documentation.
Generate a blog post based on the given prompt. See the documentation.
Generate a short summary for news headlines. See the documentation.
The RSS app allows users to automatically fetch and parse updates from web feeds. This functionality is pivotal for staying abreast of content changes or updates from websites, blogs, and news outlets that offer RSS feeds. With Pipedream, you can harness the RSS API to trigger workflows that enable a broad range of automations, like content aggregation, monitoring for specific keywords, notifications, and data synchronization across platforms.
module.exports = defineComponent({
props: {
rss: {
type: "app",
app: "rss",
}
},
async run({steps, $}) {
// Retrieve items from a sample feed
const Parser = require('rss-parser');
const parser = new Parser();
const stories = []
// Replace with your feed URL
const url = "https://pipedream.com/community/latest.rss"
const feed = await parser.parseURL(url);
const { title, items } = feed
this.title = title
if (!items.length) {
$end("No new stories")
}
this.items = items
},
})
The Metatext.AI Pre-built AI Models API offers various artificial intelligence capabilities such as natural language processing, image recognition, and sentiment analysis. This API enables users to add AI features to their applications without the need for extensive machine learning expertise. Utilizing this API in Pipedream workflows allows for automation and integration with other services, making it possible to process and analyze text and images within a serverless environment efficiently.
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
metatext_ai_pre_build_ai_models_api: {
type: "app",
app: "metatext_ai_pre_build_ai_models_api",
}
},
async run({steps, $}) {
const data = {
"text": `{your_text}`,
}
return await axios($, {
method: "post",
url: `https://api.metatext.ai/hub-inference/sentiment-analysis`,
headers: {
"Content-Type": `application/json`,
"x-api-key": `${this.metatext_ai_pre_build_ai_models_api.$auth.api_key}`,
},
data,
})
},
})