crawl4ai/docs/examples/llm_extraction_openai_pricing.py

import os
import time
from crawl4ai.web_crawler import WebCrawler
from crawl4ai.chunking_strategy import *
from crawl4ai.extraction_strategy import *
from crawl4ai.crawler_strategy import *

url = r'https://openai.com/api/pricing/'

crawler = WebCrawler()
crawler.warmup()

from pydantic import BaseModel, Field

class 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 for the OpenAI model.")

result = crawler.run(
    url=url,
    word_count_threshold=1,
    extraction_strategy= LLMExtractionStrategy(
        # provider= "openai/gpt-4o", api_token = os.getenv('OPENAI_API_KEY'), 
        provider= "groq/llama-3.1-70b-versatile", api_token = os.getenv('GROQ_API_KEY'), 
        schema=OpenAIModelFee.model_json_schema(),
        extraction_type="schema",
        instruction="From the crawled content, extract all mentioned model names along with their "\
            "fees for input and output tokens. Make sure not to miss anything 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,
)

model_fees = json.loads(result.extracted_content)

print(len(model_fees))

with open(".data/data.json", "w", encoding="utf-8") as f:
    f.write(result.extracted_content)