ai-tech-crawler 0.0.2

Autonomous Scraping Agent: Scrape URLs with prompts and schema

SmartScrapingAgent

SmartScrapingAgent is a Python package designed to simplify web scraping using state-of-the-art LLMs (Large Language Models) and customizable schemas. With this package, you can extract structured data efficiently from large, complex web pages.

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Installation

Step 1: Install Playwright

Install Playwright, which is required for handling headless browsing:

playwright install

Step 2: Install SmartScrapingAgent

Install the Smart Scraping Agent package:

pip install ai-tech-crawler

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Usage

Here is a step-by-step guide to using the Smart Scraping Agent package:

N.B.: This import is required only for jupyter notebooks, since they have their own eventloop

pip install nest-asyncio

import nest_asyncio

nest_asyncio.apply()

Step 1: Import Required Modules

Import necessary modules and classes:

import os, json
from dotenv import load_dotenv


load_dotenv()

Step 2: Define the Configuration

Set up the configuration for the scraping pipeline:

agent_config = {
    "llm": {
        "api_key": os.getenv('OPENAI_API_KEY'),
        "model": "openai/gpt-4o-mini",
        # Uncomment for other models
        # "model": "ollama/nemotron-mini",
        # "device": "cuda",
        # "model_kwargs": {'response_format': {"type": "json_object"}}
    },
    "verbose": True,
    "headless": True,
    "max_retries": 3
}

Step 3: Write Your Prompt

Define a simple prompt to guide the scraping process:

simple_prompt = """
Extract all the trending topics, their search volumes, when it started trending and the trend breakdown from the website's content.
"""

Step 4: Load the Schema

Use the schema to define the structure of the extracted data: Schema can be:

1. format instruction string with examples
2. dict
3. json
4. pydantic or BaseModel

schema_ = {
    'trends': [
        {
            'topic': 'Trending topic',
            'search_volume': 'Search Volume of a topic',
            'started': 'Time when it started trending',
            'trend_breakdown': 'A trend may consist of multiple queries that are variants of the same search or considered to be related. Trend breakdown details these queries.'
         }
    ],
    'other_links':[
        'list of any other reference URLs'
    ]
}

N.B.: For better results use a valid pydantic schema which is a subclass of BaseModel.

Step 5: Instantiate the SmartScraperAgent

Create an instance of the SmartScraperAgent with the necessary parameters:

from ai_tech_crawler import SmartScraperAgent

url = "https://trends.google.com/trending"

smart_scraper_agent = SmartScraperAgent(
    prompt=simple_prompt,
    source=url,
    config=agent_config,
    schema=schema_
)

Step 6: Run the Scraper

Execute the scraping pipeline and process the results:

result = smart_scraper_agent.run()
print(json.dumps(result, indent=4))

Load a Webpage content as Markdown

markdown_content = smart_scraper_agent.get_markdown()
print(markdown_content)

Load recursive webpages and split it into Documents

documents = smart_scraper_agent.load_indepth_and_split(depth=2)
print(documents[0].page_content)
print(documents[0].nmetadata)

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Key Features

• LLM-Powered: Leverage advanced models like GPT for smart data extraction.
• Schema-Driven: Flexible schema design to control output format.
• Headless Browsing: Playwright integration for efficient, non-visual browsing.
• Customizable: Fine-tune the pipeline using configurations and custom merge methods.

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Contributing

Contributions are welcome! Feel free to submit issues or pull requests on the GitHub repository.

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License

This project is licensed under the MIT License. See the LICENSE file for details.