Hugging Face
Build, train and deploy state of the art models powered by the reference open source in machine learning.
Integrate the Hugging Face API with the Snowflake API
Setup the Hugging Face API trigger to run a workflow which integrates with the Snowflake API. Pipedream's integration platform allows you to integrate Hugging Face and Snowflake remarkably fast. Free for developers.
Popular Ways to Connect Hugging Face with Snowflake#
Popular Hugging Face and Snowflake Triggers#
Popular Hugging Face and Snowflake Actions#
Want to have a nice know-it-all bot that can answer any question?. This action allows you to ask a question and get an answer from a trained model. See the docs.
This task reads some image input and outputs the likelihood of classes. This action allows you to classify images into categories. See the docs.
This task is well known to translate text from one language to another. See the docs.
This task reads some image input and outputs the likelihood of classes and bounding boxes of detected objects. See the docs.
Overview of Hugging Face#
The Hugging Face API provides access to a vast range of machine learning models, primarily for natural language processing (NLP) tasks like text classification, translation, summarization, and question answering. It lets you leverage pre-trained models and fine-tune them on your data. Using the API within Pipedream, you can automate workflows that involve language processing, integrate AI insights into your apps, or respond to events with AI-generated content.
Connect Hugging Face#
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
hugging_face: {
type: "app",
app: "hugging_face",
}
},
async run({steps, $}) {
return await axios($, {
url: `https://huggingface.co/api/whoami-v2`,
headers: {
Authorization: `Bearer ${this.hugging_face.$auth.access_token}`,
},
})
},
})
Overview of Snowflake#
Snowflake offers a cloud database and related tools to help developers create robust, secure, and scalable data warehouses. See Snowflake's Key Concepts & Architecture.
Getting Started
1. Create a user, role and warehouse in Snowflake
Snowflake recommends you create a new user, role, and warehouse when you integrate a third-party tool like Pipedream. This way, you can control permissions via the user / role, and separate Pipedream compute and costs with the warehouse. You can do this directly in the Snowflake UI.
We recommend you create a read-only account if you only need to query Snowflake. If you need to insert data into Snowflake, add permissions on the appropriate objects after you create your user.
2. Enter those details in Pipedream
Visit https://pipedream.com/accounts. Click the button to Connect an App. Enter the required Snowflake account data.
You'll only need to connect your account once in Pipedream. You can connect this account to multiple workflows to run queries against Snowflake, insert data, and more.
3. Build your first workflow
Visit https://pipedream.com/new to build your first workflow. Pipedream workflows let you connect Snowflake with 1,000+ other apps. You can trigger workflows on Snowflake queries, sending results to Slack, Google Sheets, or any app that exposes an API. Or you can accept data from another app, transform it with Python, Node.js, Go or Bash code, and insert it into Snowflake.
Learn more at Pipedream University.
Connect Snowflake#
import { promisify } from 'util'
import snowflake from 'snowflake-sdk'
export default defineComponent({
props: {
snowflake: {
type: "app",
app: "snowflake",
}
},
async run({steps, $}) {
const connection = snowflake.createConnection({
...this.snowflake.$auth,
application: "PIPEDREAM_PIPEDREAM",
})
const connectAsync = promisify(connection.connect)
await connectAsync()
async function connExecuteAsync(options) {
return new Promise((resolve, reject) => {
connection.execute({
...options,
complete: function(err, stmt, rows) {
if (err) {
reject(err)
} else {
resolve({stmt, rows})
}
}
})
})
}
// See https://docs.snowflake.com/en/user-guide/nodejs-driver-use.html#executing-statements
const { rows } = await connExecuteAsync({
sqlText: `SELECT CURRENT_TIMESTAMP()`,
})
return rows
},
})