MySQL is an open-source relational database management system.
Emit new event when you add or modify a new row in a table. See the docs here
Emit new event when new rows are returned from a custom query. See the docs here
Emit new event when a new table is added to a database. See the docs here
Determine the sentiment of the given text (positive, negative, or neutral). See the documentation.
Identify and extract significant keywords from the given text. See the documentation.
The MySQL application on Pipedream enables direct interaction with your MySQL databases, allowing you to perform CRUD operations—create, read, update, delete—on your data with ease. You can leverage these capabilities to automate data synchronization, report generation, and event-based triggers that kick off workflows in other apps. With Pipedream's serverless platform, you can connect MySQL to hundreds of other services without managing infrastructure, crafting complex code, or handling authentication.
import mysql from '@pipedream/mysql';
export default defineComponent({
props: {
mysql,
},
async run({steps, $}) {
// Component source code:
// https://github.com/PipedreamHQ/pipedream/tree/master/components/mysql
const queryObj = {
sql: "SELECT NOW()",
values: [], // Ignored since query does not contain placeholders
};
return await this.mysql.executeQuery(queryObj);
},
});
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,
})
},
})