Extract Structured Data from Web Pages
Extract structured data from web pages using AgentQL queries or natural language. Integrates with LlamaIndex and Playwright for web scraping and browser
Why it matters
Automate the extraction of structured data from any web page, either via REST API or directly from a browser. This tool enables robust web scraping and data retrieval that remains resilient to website changes.
Outcomes
What it gets done
Extract structured data from a given URL using AgentQL queries or natural language prompts.
Extract structured data from the active browser page using AgentQL queries or natural language prompts.
Locate specific web elements within a browser page using natural language descriptions.
Integrate with Playwright for browser automation and interaction.
Install
Add it to your toolbox
Run in your project directory:
curl -fsSL https://spark.entire.vc/get/li-tool-tools-agentql | bash Capabilities
What this skill does
Pulls structured data fields from unstructured text.
Fetches and parses content from web pages.
Searches the web and retrieves relevant sources.
Controls a real browser to automate web workflows.
Overview
Llama Index Tools Agentql
What it does
This toolset provides AI assistants with the capability to interact with web pages and extract structured data. It offers functions to fetch data from a specified URL using a REST API or to extract information from the currently active browser tab. Additionally, it can locate specific web elements within a browser page using natural language descriptions.
How it connects
Utilize this tool when your AI assistant needs to gather specific information from websites, such as article content, product details, or user reviews, either through direct API calls or by simulating user interaction in a browser. It is designed for scenarios requiring robust and resilient web data extraction.
Source README
llama-index-tools-agentql
AgentQL provides web interaction and structured data extraction from any web page using an AgentQL query or a Natural Language prompt. AgentQL can be used across multiple languages and web pages without breaking over time and change.
Warning
Only supports async functions and playwright browser APIs, please refer to the following PR for more details: https://github.com/run-llama/llama_index/pull/17808
Installation
pip install llama-index-tools-agentql
You also need to configure the AGENTQL_API_KEY environment variable. You can acquire an API key from our Dev Portal.
Overview
AgentQL provides the following three function tools:
extract_web_data_with_rest_api: Extracts structured data as JSON from a web page given a URL using either an AgentQL query or a Natural Language description of the data.extract_web_data_from_browser: Extracts structured data as JSON from the active web page in a browser using either an AgentQL query or a Natural Language description. This tool must be used with a Playwright browser.get_web_element_from_browser: Finds a web element on the active web page in a browser using a Natural Language description and returns its CSS selector for further interaction. This tool must be used with a Playwright browser.
You can learn more about how to use AgentQL tools in this Jupyter notebook.
Extract data using REST API
from llama_index.tools.agentql import AgentQLRestAPIToolSpec
agentql_rest_api_tool = AgentQLRestAPIToolSpec()
await agentql_rest_api_tool.extract_web_data_with_rest_api(
url="https://www.agentql.com/blog",
query="{ posts[] { title url author date }}",
)
Work with data and web elements using browser
Setup
In order to use the extract_web_data_from_browser and get_web_element_from_browser, you need to have a Playwright browser instance. If you do not have an active instance, you can initiate one using the create_async_playwright_browser utility method from LlamaIndex's Playwright ToolSpec.
Note
AgentQL browser tools are best used along with LlamaIndex's Playwright tools.
from llama_index.tools.playwright.base import PlaywrightToolSpec
async_browser = await PlaywrightToolSpec.create_async_playwright_browser()
You can also use an existing browser instance via Chrome DevTools Protocol (CDP) connection URL:
p = await async_playwright().start()
async_browser = await p.chromium.connect_over_cdp("CDP_CONNECTION_URL")
Extract data from the active browser page
from llama_index.tools.agentql import AgentQLBrowserToolSpec
playwright_tool = PlaywrightToolSpec(async_browser=async_browser)
await playwright_tool.navigate_to("https://www.agentql.com/blog")
agentql_browser_tool = AgentQLBrowserToolSpec(async_browser=async_browser)
await agentql_browser_tool.extract_web_data_from_browser(
prompt="the blog posts with title and url",
)
Find a web element on the active browser page
next_page_button = await agentql_browser_tool.get_web_element_from_browser(
prompt="The next page navigation button",
)
await playwright_tool.click(next_page_button)
Agentic Usage
This tool has a more extensive example for agentic usage documented in this Jupyter notebook.
Run tests
In order to run integration tests, you need to configure L
Discussion
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