Tarsier Versions Save

Vision utilities for web interaction agents 👀

v0.5.0

5 months ago

What's Changed

New Contributors

Full Changelog: https://github.com/reworkd/tarsier/compare/v0.4.0...v0.5.0

v0.4.0

6 months ago

🎉 What's Changed

👀 New Contributors

Full Changelog: https://github.com/reworkd/tarsier/compare/v0.3.1...v0.4.0

v0.3.1

6 months ago

Tarsier Monkey

🙈 Vision utilities for web interaction agents 🙈

Python

🔗 Main site   •   🐦 Twitter   •   📢 Discord

Announcing Tarsier

If you've tried using GPT-4(V) to automate web interactions, you've probably run into questions like:

  • How do you map LLM responses back into web elements?
  • How can you mark up a page for an LLM better understand its action space?
  • How do you feed a "screenshot" to a text-only LLM?

At Reworkd, we found ourselves reusing the same utility libraries to solve these problems across multiple projects. Because of this we're now open-sourcing this simple utility library for multimodal web agents... Tarsier! The video below demonstrates Tarsier usage by feeding a page snapshot into a langchain agent and letting it take actions.

https://github.com/reworkd/tarsier/assets/50181239/af12beda-89b5-4add-b888-d780b353304b

How does it work?

Tarsier works by visually "tagging" interactable elements on a page via brackets + an id such as [1]. In doing this, we provide a mapping between elements and ids for GPT-4(V) to take actions upon. We define interactable elements as buttons, links, or input fields that are visible on the page.

Can provide a textual representation of the page. This means that Tarsier enables deeper interaction for even non multi-modal LLMs. This is important to note given performance issues with existing vision language models. Tarsier also provides OCR utils to convert a page screenshot into a whitespace-structured string that an LLM without vision can understand.

Usage

Visit our cookbook for agent examples using Tarsier:

Otherwise, basic Tarsier usage might look like the following:

import asyncio

from playwright.async_api import async_playwright
from tarsier import Tarsier, GoogleVisionOCRService

async def main():
    google_cloud_credentials = {}

    ocr_service = GoogleVisionOCRService(google_cloud_credentials)
    tarsier = Tarsier(ocr_service)

    async with async_playwright() as p:
        browser = await p.chromium.launch(headless=False)
        page = await browser.new_page()
        await page.goto("https://news.ycombinator.com")

        page_text, tag_to_xpath = await tarsier.page_to_text(page)

        print(tag_to_xpath)  # Mapping of tags to x_paths
        print(page_text)  # My Text representation of the page


if __name__ == '__main__':
    asyncio.run(main())

Supported OCR Services

Special shoutout to @KhoomeiK for making this happen! ❤️