# 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)
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