dendrite-python-sdk 1.0.2

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Dendrite

Dendrite is an SDK that makes it easy to interact with and scrape websites using prompts.

# Extracting data from a page is as simple as doing page.scrape(prompt)
startup_urls = await page.scrape("Extract a list of valid urls for each listed startup's YC page")

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Installation:

pip install dendrite-python-sdk && playwright install

Quick start:

Here is a very simple example of how to use Dendrite.

Install dendrite and asyncio like so: pip install asyncio dendrite-python-sdk && playwright install and create a file called main.py. Use the code below and run it with python main.py from the terminal.

main.py

from dendrite_python_sdk import DendriteBrowser
import asyncio

async def main():
    # Launch browser locally
    dendrite_browser = DendriteBrowser(openai_api_key=...) # Use your OpenAI key here

    # Go to google.com
    page = await dendrite_browser.goto("https://google.com")

    # Get the reject cookies button
    close_cookies = await page.get_interactable_element("reject cookies popup button")

    # Click the button, returns IncorrectOutcomeException if the popup wasn't closed
    await close_cookies.click(expected_outcome="That the cookies popup closed.")

    # Get the search bar
    search_bar = await page.get_interactable_element("Return the search bar")

    # Enter "hello world" into it.
    await search_bar.fill(
        "hello world", expected_outcome="The words 'hello world' to have been entered"
    )

    # Use the Playwright page to press enter.
    await page.get_playwright_page().keyboard.press("Enter")

    await asyncio.sleep(2)
    await dendrite_browser.close()


asyncio.run(main())

More Advanced Example:

With Dendrite we can do more than just finding and using elements.

We can scrape data from pages and ensure that the returned data followed a certain structure by prompting, using a JSON schema or passing in a pydantic model.

Below is an example of how he can, see more examples in dendrite_python_sdk/examples.

import os
import asyncio

from dendrite_python_sdk import DendriteBrowser
from dotenv import load_dotenv, find_dotenv
import pandas as pd

load_dotenv(find_dotenv())


async def main():
    dendrite_browser = DendriteBrowser(
        openai_api_key=os.environ.get("OPENAI_API_KEY", ""),
        dendrite_api_key=os.environ.get("DENDRITE_API_KEY", "") # Generate a dendrite API at https://dendrite.se to get better rate limits and latency.
    )

    page = await dendrite_browser.goto(
        "https://ycombinator.com/companies", scroll_through_entire_page=False
    )

    # Get elements with prompts instead of using brittle selectors
    search_bar = await page.get_interactable_element(
        "The search bar used to find startups"
    )
    await search_bar.fill("AI agent")

    # Scraping data is as easy as writing a good prompt
    await page.scroll_through_entire_page()
    startup_urls = await page.scrape(
        "Extract a list of valid urls for each listed startup's YC page"
    )

    # Let's go to every AI agent startup's page and fetch information about the founders
    startup_info = []
    for url in startup_urls:
        page = await dendrite_browser.goto(url)
        startup_details = await page.scrape(
            "Extract a dict like this: {name: str, slogan: str, founders_social_media_urls: a string where each url is separated by a linebreak}"
        )
        print("startup_details: ", startup_details)
        startup_info.append(startup_details)

    df = pd.DataFrame(startup_info)
    df.to_excel("ai_agent_founders.xlsx", index=False)


asyncio.run(main())

Class Documentation

DendriteBrowser

DendriteBrowser is an abstraction on top of playwright's browser. You can do everything you would normally do with Playwright but with a few new handy AI functions. Access

Methods

__init__(self, openai_api_key: str, id=None, playwright_options: Any = {"headless": False})

Initializes the DendriteBrowser class.

Params:

• openai_api_key (str): Your OpenAI API Key.
• id (Optional): The unique ID for the browser session.
• playwright_options (dict): Options to configure Playwright browser.

get_active_page(self) -> DendritePage

Returns the active page.

goto(self, url: str, scroll_through_entire_page: Optional[bool] = True) -> DendritePage

Navigates to the specified URL.

Params:

• url (str): URL of the webpage.
• scroll_through_entire_page (Optional[bool]): Whether to scroll through the entire page.

google_search(self, query: str, filter_results_prompt: Optional[str] = None, load_all_results: Optional[bool] = True) -> List[SearchResult]

Performs a Google search and returns search results.

Params:

• query (str): The search query.
• filter_results_prompt (Optional[str]): Prompt to filter results.
• load_all_results (Optional[bool]): Whether to load all results.

new_page(self) -> DendritePage

Opens a new page in the browser.

close(self)

Closes the browser context.

DendritePage

Abstraction layer on top of Playwright's Page class. Use get_playwright_locator to get the playwright page.

Methods

get_playwright_page(self) -> Page

Returns the Playwright page instance.

scrape(self, prompt: str, expected_return_data: Optional[str] = None, return_data_json_schema: Optional[Any] = None, pydantic_return_model: Optional[Type[BaseModel]] = None) -> Any

Scrapes data from the page based on a prompt.

Params:

• prompt (str): The prompt to gather data.
• expected_return_data (Optional[str]): Expected return data.
• return_data_json_schema (Optional[Any]): JSON schema for return data.
• pydantic_return_model (Optional[Type[BaseModel]]): Pydantic model for return data.

scroll_through_entire_page(self) -> None

Scrolls through the entire page.

get_interactable_element(self, prompt: str) -> DendriteLocator

Returns an interactable element based on a prompt.

DendriteLocator

Abstraction layer on top of Playwright's Locator class. Use get_playwright_locator to get the playwright locator.

Methods

get_playwright_locator(self) -> Locator

Returns the Playwright locator.

wait_for_page_changes(self, old_url: str, timeout: float = 2)

Waits for changes in the page. Params:

• old_url (str): The URL before the interaction.
• timeout (float): The time to wait for changes.

click(self, expected_outcome="", *args, **kwargs) -> InteractionResponse

Clicks the element and handles the interaction.

Params:

• expected_outcome (str): Expected outcome after clicking.

fill(self, value: str, expected_outcome="", *args, **kwargs)

Fills the element with the provided value.

Params:

• value (str): The value to fill.
• expected_outcome (str): Expected outcome after filling.