import osfrom crawl4ai import AsyncWebCrawlerfrom crawl4ai.extraction_strategy import LLMExtractionStrategyfrom pydantic import BaseModel, Fieldclass OpenAIModelFee(BaseModel): model_name: str = Field(..., description="Name of the OpenAI model.") input_fee: str = Field(..., description="Fee for input token for the OpenAI model.") output_fee: str = Field(..., description="Fee for output token ßfor the OpenAI model.")url = 'https://openai.com/api/pricing/'crawler = AsyncWebCrawler()crawler.warmup()result = crawler.run( url=url, word_count_threshold=1, extraction_strategy= LLMExtractionStrategy( provider= "openai/gpt-4o", api_token = os.getenv('OPENAI_API_KEY'), schema=OpenAIModelFee.schema(), extraction_type="schema", instruction="""From the crawled content, extract all mentioned model names along with their fees for input and output tokens. Do not miss any models in the entire content. One extracted model JSON format should look like this: {"model_name": "GPT-4", "input_fee": "US$10.00 / 1M tokens", "output_fee": "US$30.00 / 1M tokens"}.""" ), bypass_cache=True, )print(result.extracted_content)
framework: crewaitopic: extract model pricing from websitesroles: web_scraper: backstory: An expert in web scraping with a deep understanding of extracting structured data from online sources. https://openai.com/api/pricing/ https://www.anthropic.com/pricing https://cohere.com/pricing goal: Gather model pricing data from various websites role: Web Scraper tasks: scrape_model_pricing: description: Scrape model pricing information from the provided list of websites. expected_output: Raw HTML or JSON containing model pricing data. tools: - 'ModelFeeTool'dependencies: []