Vision utilities for web interaction agents

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Tarsier

If you've tried using an LLM to automate web interactions, you've probably run into questions like:

• How should you feed the webpage to an LLM? (e.g. HTML, Accessibility Tree, Screenshot)
• How do you map LLM responses back to web elements?
• How can you inform a text-only LLM about the page's visual structure?

At Reworkd, we iterated on all these problems across tens of thousands of real web tasks to build a powerful perception system for web agents... Tarsier! In the video below, we use Tarsier to provide webpage perception for a minimalistic GPT-4 LangChain web agent.

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

How does it work?

Tarsier visually tags interactable elements on a page via brackets + an ID e.g. [23]. In doing this, we provide a mapping between elements and IDs for an LLM to take actions upon (e.g. CLICK [23]). We define interactable elements as buttons, links, or input fields that are visible on the page; Tarsier can also tag all textual elements if you pass tag_text_elements=True.

Furthermore, we've developed an OCR algorithm to convert a page screenshot into a whitespace-structured string (almost like ASCII art) that an LLM even without vision can understand. Since current vision-language models still lack fine-grained representations needed for web interaction tasks, this is critical. On our internal benchmarks, unimodal GPT-4 + Tarsier-Text beats GPT-4V + Tarsier-Screenshot by 10-20%!

| Tagged Screenshot | Tagged Text Representation | | :-------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------: | | | |

Installation

shell pip install tarsier

Usage

Visit our cookbook for agent examples using Tarsier:

• An autonomous LangChain web agent
• An autonomous LlamaIndex web agent

We currently support 2 OCR engines: Google Vision and Microsoft Azure. To create service account credentials for Google, follow the instructions on this SO answer https://stackoverflow.com/a/46290808/1780891

The credentials for Microsoft Azure are stored as a simple JSON consisting of an API key and an endpoint

json { "key": "<enter_your_api_key>", "endpoint": "<enter_your_api_endpoint>" }

These values can be found in the keys and endpoint section of the computer vision resource. See the instructions at https://learn.microsoft.com/en-us/answers/questions/854952/dont-find-your-key-and-your-endpoint

Otherwise, basic Tarsier usage might look like the following:

```python import asyncio

from playwright.asyncapi import asyncplaywright from tarsier import Tarsier, GoogleVisionOCRService, MicrosoftAzureOCRService import json

def loadocrcredentials(jsonfilepath): with open(jsonfilepath) as f: credentials = json.load(f) return credentials

async def main(): # To create the service account key, follow the instructions on this SO answer https://stackoverflow.com/a/46290808/1780891

google_cloud_credentials = load_ocr_credentials('./google_service_acc_key.json')
#microsoft_azure_credentials = load_ocr_credentials('./microsoft_azure_credentials.json')

ocr_service = GoogleVisionOCRService(google_cloud_credentials)
#ocr_service = MicrosoftAzureOCRService(microsoft_azure_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()) ```

Keep in mind that Tarsier tags different types of elements differently to help your LLM identify what actions are performable on each element. Specifically:

• [#ID]: text-insertable fields (e.g. textarea, input with textual type)
• [@ID]: hyperlinks (<a> tags)
• [$ID]: other interactable elements (e.g. button, select)
• [ID]: plain text (if you pass tag_text_elements=True)

Local Development

Setup

We have provided a handy setup script to get you up and running with Tarsier development.

shell ./script/setup.sh

If you modify any TypeScript files used by Tarsier, you'll need to execute the following command. This compiles the TypeScript into JavaScript, which can then be utilized in the Python package.

shell npm run build

Testing

We use pytest for testing. To run the tests, simply run:

shell poetry run pytest .

Linting

Prior to submitting a potential PR, please run the following to format your code:

shell ./script/format.sh

Supported OCR Services

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

Roadmap

• [x] Add documentation and examples
• [x] Clean up interfaces and add unit tests
• [x] Launch
• [x] Improve OCR text performance
• [ ] Add options to customize tagging styling
• [ ] Add support for other browsers drivers 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} }