Shaped API Docs

We just released Shaped's API docs at: docs.shaped.ai

Shaped API Docs
June 1, 2022
 • 
3
 min read
by 
Tullie Murrell

docs.shaped.ai

These docs provide an overview of how Shaped works, the design decisions behind the system, API references and several examples of how to get started. While building Shaped, we’ve strived to make the API as simple as possible, allowing any developer to surface relevant content to their users easily. We’ve been really excited to share these docs to the public so that you can see how easy it is to integrate ranking into your product.

Below we provide a sample from the docs that explains how to get started with Shaped.

Getting started with Shaped

Model API

The Model API allows you to manage your ranking models. It provides endpoints to create, list and delete your models. It can be hit without writing any explicit code — simply make a curl request from command line to get started.

For example, say you want to build a video recommendation model. You have three relevant tables stored in your BigQuery data warehouse:

  • user - contains your user rows and their demographic attributes
  • video - contains the video rows and their metadata attributes
  • interaction - contains the user click and impression events for each video

To create a model you need to map these tables to the schema of the create model endpoint like below:

Once this curl request is made your model will start training. Depending on how many interactions you provide, it’ll take up to a few hours to train before you can start retrieving ranked results from the Rank API.

Rank API

The Rank API is what you use to retrieve results from your ranking model. It is a real-time API designed to be integrated directly into your application.

Here’s an example curl request fetching several ranked videos for the user with id "Keanu". The response is a list of the most relevant video ids for Keanu.

How to get an API key?

Message us at hello@shaped.ai if you want to get early access to our API. We’re interested in supporting any of your ranking use-cases so don’t hesitate to get in touch!

Let us know if you have any feedback or questions about the docs. And if you want to help build the future of growth driving, machine-learning APIs, we’re hiring!

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