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Streamlit + Readme update: copy to cURL (#22)

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Suchintan
2024-03-04 12:41:38 -05:00
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@@ -27,8 +27,35 @@
<img src="images/geico_shu_recording_cropped.gif"/>
</p>
Want to see more examples of Skyvern in action? Click [here](#real-world-examples-of-skyvern)!
Traditional approaches to browser automations required writing custom scripts for websites, often relying on DOM parsing and XPath-based interactions which would break whenever the website layouts changed.
Instead of only relying on code-defined XPath interactions, Skyvern adds computer vision and LLMs to the mix to parse items in the viewport in real-time, create a plan for interaction and interact with them.
This approach gives us a few advantages:
1. Skyvern can operate on websites it’s never seen before, as it’s able to map visual elements to actions necessary to complete a workflow, without any customized code
1. Skyvern is resistant to website layout changes, as there are no pre-determined XPaths or other selectors our system is looking for while trying to navigate
1. Skyvern leverages LLMs to reason through interactions to ensure we can cover complex situations. Examples include:
1. If you wanted to get an auto insurance quote from Geico, the answer to a common question “Were you eligible to drive at 18?” could be inferred from the driver receiving their license at age 16
1. If you were doing competitor analysis, it’s understanding that an Arnold Palmer 22 oz can at 7/11 is almost definitely the same product as a 23 oz can at Gopuff (even though the sizes are slightly different, which could be a rounding error!)
Want to see examples of Skyvern in action? Jump to [#real-world-examples-of-skyvern](#real-world-examples-of-skyvern)
# How it works
Skyvern was inspired by the Task-Driven autonomous agent design popularized by [BabyAGI](https://github.com/yoheinakajima/babyagi) and [AutoGPT](https://github.com/Significant-Gravitas/AutoGPT) -- with one major bonus: we give Skyvern the ability to interact with websites using browser automation libraries like [Playwright](https://playwright.dev/).
<picture>
<source media="(prefers-color-scheme: dark)" srcset="images/skyvern-system-diagram-dark.png" />
<img src="images/skyvern-system-diagram-light.png" />
</picture>
<!-- TODO (suchintan):
Expand the diagram above to go deeper into how:
1. We draw bounding boxes
2. We parse the HTML + extract the image to generate an interactable element map
-->
# Quickstart
This quickstart guide will walk you through getting Skyvern up and running on your local machine.
@@ -72,20 +99,26 @@ pre-commit install
## Running your first automation
### Executing tasks (UI)
Once you have the UI running, you can start an automation by filling out the fields shown in the UI and clicking "Execute"
# How it works
Skyvern was inspired by the Task-Driven autonomous agent design popularized by [BabyAGI](https://github.com/yoheinakajima/babyagi) and [AutoGPT](https://github.com/Significant-Gravitas/AutoGPT) -- with one major difference: we give Skyvern the ability to interact with websites using browser automation libraries like [Playwright](https://playwright.dev/).
<p align="center">
<img src="images/skyvern_visualizer_run_task.png"/>
</p>
<picture>
<source media="(prefers-color-scheme: dark)" srcset="images/skyvern-system-diagram-dark.png"/>
<img src="images/skyvern-system-diagram-light.png"/>
</picture>
### Executing tasks (cURL)
```
curl -X POST -H 'Content-Type: application/json' -H 'x-api-key: {Your local API key}' -d '{
"url": "https://www.geico.com",
"webhook_callback_url": "",
"navigation_goal": "Navigate through the website until you generate an auto insurance quote. Do not generate a home insurance quote. If this page contains an auto insurance quote, consider the goal achieved",
"data_extraction_goal": "Extract all quote information in JSON format including the premium amount, the timeframe for the quote.",
"navigation_payload": "{Your data here}",
"proxy_location": "NONE"
}' http://0.0.0.0:8000/api/v1/tasks
```
<!-- > TODO (suchintan):
Expand the diagram above to go deeper into how:
1. We draw bounding boxes
2. We parse the HTML + extract the image to generate an interactable element map
-->
# Real-world examples of Skyvern
<!-- > TODO (suchintan):
@@ -123,18 +156,6 @@ More extensive documentation can be found on our [documentation website](https:/
Our focus is bringing stability to browser-based workflows. We leverage LLMs to create an AI Agent capable of interacting with websites like you or I would — all via a simple API call.
Traditional approaches required writing custom scripts for websites, often relying on DOM parsing and XPath-based interactions which would break whenever the website layouts changed.
Skyvern operates like a human — increasing reliability by not relying on fragile scripts, instead relying on computer vision to parse items in the viewport and interact with them the way a human would.
This approach gives us a few advantages:
1. Skyvern can operate on websites it’s never seen before, as it’s able to map visual elements to actions necessary to complete a workflow, without any customized code
1. Skyvern is resistant to website layout changes, as there are no pre-determined XPaths or other selectors our system is looking for while trying to navigate
1. Skyvern is able to circumvent or navigate through many bot detection methods as many of them rely on allowing people to access the websites
1. Skyvern leverages LLMs to reason through interactions to ensure we can cover complex situations. Examples include:
1. If you wanted to get an auto insurance quote from Geico, the answer to a common question “Were you eligible to drive at 18?” could be inferred from the driver receiving their license at age 16
1. If you were doing competitor analysis, it’s understanding that an Arnold Palmer 22 oz can at 7/11 is almost definitely the same product as a 23 oz can at Gopuff (even though the sizes are slightly different, which could be a rounding error!)
# Feature Roadmap