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API ref docs redirect #119

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2 changes: 2 additions & 0 deletions docs/.gitignore
Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,8 @@
.docusaurus
.cache-loader
.package-lock.json
# api-reference-docs are generated by sphinx and placed here.
/static/api-reference-docs/

# Misc
.DS_Store
Expand Down
8 changes: 6 additions & 2 deletions docs/docs/api-reference/api-reference.md
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@@ -1,3 +1,7 @@
# API Reference
---
id: redirect
title: API Reference
hide_title: true
---

Click [here](pathname:///python-sdk/api-reference-docs/) to access a detailed documentation of the SDK with all available methods.
<meta http-equiv="refresh" content="0; url=/python-sdk/api-reference-docs/" />
22 changes: 17 additions & 5 deletions docs/docs/building-applications/edge.md
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@@ -1,6 +1,21 @@
# Using Groundlight on the edge

Starting your model evaluations at the edge reduces latency, cost, network bandwidth, and energy. Once you have downloaded and installed your Groundlight edge models, you can configure the Groundlight SDK to use your edge environment by configuring the 'endpoint' which the SDK connects to. You can do this either directly in code as such:
If your account has access to edge models, you can download and install them to your edge devices.
This allows you to run your model evaluations on the edge, reducing latency, cost, network bandwidth, and energy.

## How the Edge Endpoint works

The Edge Endpoint runs as a set of docker containers on an "edge device". This edge device can be an NVIDIA Jetson device, rack-mounted server, or even a Raspberry Pi. The Edge Endpoint is responsible for downloading and running the models,
and for communicating with the Groundlight cloud service.

To use the edge endpoint, simply configure the Groundlight SDK to use the edge endpoint's URL instead of the cloud endpoint.
All application logic will work seamlessly and unchanged with the Groundlight Edge Endpoint, except some ML answers will
return much faster locally. The only visible difference is that image queries answered at the edge endpoint will have the prefix `iqe_` instead of `iq_` for image queries answered in the cloud. `iqe_` stands for "image query edge". Edge-originated
image queries will not appear in the cloud dashboard.

## Configuring the Edge Endpoint

To configure the Groundlight SDK to use the edge endpoint, you can either pass the endpoint URL to the Groundlight constructor like:

```python
from groundlight import Groundlight
Expand All @@ -12,7 +27,4 @@ or by setting the `GROUNDLIGHT_ENDPOINT` environment variable like:
```bash
export GROUNDLIGHT_ENDPOINT=http://localhost:6717
python your_app.py
```

(Edge model download is not yet generally available. Work with your Solutions Engineer to set up edge inference.)

```
4 changes: 4 additions & 0 deletions docs/src/css/custom.css
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Expand Up @@ -34,3 +34,7 @@ See: https://docusaurus.io/docs/styling-layout#styling-your-site-with-infima
--ifm-color-primary-lightest: #c8dbf5;
--docusaurus-highlighted-code-line-bg: rgba(68, 105, 165, 0.3);
}

.page-content[data-id='redirect'] {
display: none;
}
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