🙈 Vision utilities for web interaction agents 🙈

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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.

tarsier.mp4

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.

Installation

pip install tarsier

Usage

Visit our cookbook for agent examples using Tarsier:

• An autonomous LangChain web agent 🦜⛓️
• An autonomous LlamaIndex web agent 🦙

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

• Google Cloud Vision
• Amazon Textract (Coming Soon)
• Microsoft Azure Computer Vision (Coming Soon)

Roadmap

• Add documentation and examples
• Clean up interfaces and add unit tests
• Launch
• Improve OCR text performance
• Add options to customize tagging styling
• Add support for other browsers drivers as necessary
• Add support for other OCR services as necessary

Citations

bibtex
@misc{reworkd2023tarsier,
  title        = {Tarsier},
  author       = {Rohan Pandey and Adam Watkins and Asim Shrestha and Srijan Subedi},
  year         = {2023},
  howpublished = {GitHub},
  url          = {https://github.com/reworkd/tarsier}
}