# 定义大模型from langchain_openai import ChatOpenAIllm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613")
# 定义提取方法def extract(content: str, schema: dict):    from langchain.chains import create_extraction_chain    return create_extraction_chain(schema=schema, llm=llm).invoke(content)
import pprintfrom langchain_text_splitters import RecursiveCharacterTextSplitterdef scrape_with_playwright(urls, schema):    # 加载数据    loader = AsyncChromiumLoader(urls)    docs = loader.load()    # 数据转换    bs_transformer = BeautifulSoupTransformer()    # 提取其中的span标签    docs_transformed = bs_transformer.transform_documents(        docs, tags_to_extract=["span"]    )    # 数据切分    splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(    chunk_size=1000, chunk_overlap=0)    splits = splitter.split_documents(docs_transformed)    # 因为数据量太大，输入第一片数据使用，传入使用的架构    extracted_content = extract(schema=schema, content=splits[0].page_content)    pprint.pprint(extracted_content)    return extracted_content
urls = ["https://ceshiren.com/"]schema = {    "properties": {        "title": {"type": "string"},        "url": {"type": "string"},    },    "required": ["title", "url"],}extracted_content = scrape_with_playwright(urls, schema=schema)