ScrapeGraph

本笔记本提供了快速入门ScrapeGraph 工具的概述。有关所有ScrapeGraph功能和配置的详细文档，请前往API参考。

有关ScrapeGraph AI的更多信息：

概述

集成详情

类	包	可序列化	JS支持
SmartScraperTool	langchain-scrapegraph	✅	❌
MarkdownifyTool	langchain-scrapegraph	✅	❌
LocalScraperTool	langchain-scrapegraph	✅	❌
GetCreditsTool	langchain-scrapegraph	✅	❌

工具特性

工具	用途	输入	输出
SmartScraperTool	从网站提取结构化数据	URL + prompt	JSON
MarkdownifyTool	将网页转换为markdown	URL	Markdown文本
LocalScraperTool	从HTML内容中提取数据	HTML + prompt	JSON
GetCreditsTool	检查API积分	无	积分信息

设置

集成需要以下包：

%pip install --quiet -U langchain-scrapegraph

Note: you may need to restart the kernel to use updated packages.

凭证

你需要一个ScrapeGraph AI API密钥来使用这些工具。请在scrapegraphai.com获取。

import getpass
import os

if not os.environ.get("SGAI_API_KEY"):
    os.environ["SGAI_API_KEY"] = getpass.getpass("ScrapeGraph AI API key:\n")

设置LangSmith以获得一流的可观察性也是有益的（但不是必需的）：

os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_API_KEY"] = getpass.getpass()

实例化

这里我们展示如何实例化ScrapeGraph工具的实例：

from langchain_scrapegraph.tools import (
    GetCreditsTool,
    LocalScraperTool,
    MarkdownifyTool,
    SmartScraperTool,
)

smartscraper = SmartScraperTool()
markdownify = MarkdownifyTool()
localscraper = LocalScraperTool()
credits = GetCreditsTool()

调用

直接使用参数调用

让我们分别尝试每个工具：

# SmartScraper
result = smartscraper.invoke(
    {
        "user_prompt": "Extract the company name and description",
        "website_url": "https://scrapegraphai.com",
    }
)
print("SmartScraper Result:", result)

# Markdownify
markdown = markdownify.invoke({"website_url": "https://scrapegraphai.com"})
print("\nMarkdownify Result (first 200 chars):", markdown[:200])

local_html = """
<html>
    <body>
        <h1>Company Name</h1>
        <p>We are a technology company focused on AI solutions.</p>
        <div class="contact">
            <p>Email: contact@example.com</p>
            <p>Phone: (555) 123-4567</p>
        </div>
    </body>
</html>
"""

# LocalScraper
result_local = localscraper.invoke(
    {
        "user_prompt": "Make a summary of the webpage and extract the email and phone number",
        "website_html": local_html,
    }
)
print("LocalScraper Result:", result_local)

# Check credits
credits_info = credits.invoke({})
print("\nCredits Info:", credits_info)

SmartScraper Result: {'company_name': 'ScrapeGraphAI', 'description': "ScrapeGraphAI is a powerful AI web scraping tool that turns entire websites into clean, structured data through a simple API. It's designed to help developers and AI companies extract valuable data from websites efficiently and transform it into formats that are ready for use in LLM applications and data analysis."}

Markdownify Result (first 200 chars): [![ScrapeGraphAI Logo](https://scrapegraphai.com/images/scrapegraphai_logo.svg)ScrapeGraphAI](https://scrapegraphai.com/)

PartnersPricingFAQ[Blog](https://scrapegraphai.com/blog)DocsLog inSign up

Op
LocalScraper Result: {'company_name': 'Company Name', 'description': 'We are a technology company focused on AI solutions.', 'contact': {'email': 'contact@example.com', 'phone': '(555) 123-4567'}}

Credits Info: {'remaining_credits': 49679, 'total_credits_used': 914}

使用ToolCall调用

我们也可以使用模型生成的ToolCall来调用工具：

model_generated_tool_call = {
    "args": {
        "user_prompt": "Extract the main heading and description",
        "website_url": "https://scrapegraphai.com",
    },
    "id": "1",
    "name": smartscraper.name,
    "type": "tool_call",
}
smartscraper.invoke(model_generated_tool_call)

ToolMessage(content='{"main_heading": "Get the data you need from any website", "description": "Easily extract and gather information with just a few lines of code with a simple api. Turn websites into clean and usable structured data."}', name='SmartScraper', tool_call_id='1')

链式调用

让我们使用我们的工具与LLM来分析一个网站：

Select chat model:

pip install -qU langchain-openai

import getpass
import os

if not os.environ.get("OPENAI_API_KEY"):
  os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter API key for OpenAI: ")

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4o-mini")

from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnableConfig, chain

prompt = ChatPromptTemplate(
    [
        (
            "system",
            "You are a helpful assistant that can use tools to extract structured information from websites.",
        ),
        ("human", "{user_input}"),
        ("placeholder", "{messages}"),
    ]
)

llm_with_tools = llm.bind_tools([smartscraper], tool_choice=smartscraper.name)
llm_chain = prompt | llm_with_tools


@chain
def tool_chain(user_input: str, config: RunnableConfig):
    input_ = {"user_input": user_input}
    ai_msg = llm_chain.invoke(input_, config=config)
    tool_msgs = smartscraper.batch(ai_msg.tool_calls, config=config)
    return llm_chain.invoke({**input_, "messages": [ai_msg, *tool_msgs]}, config=config)


tool_chain.invoke(
    "What does ScrapeGraph AI do? Extract this information from their website https://scrapegraphai.com"
)

API Reference:ChatPromptTemplate | RunnableConfig | chain

AIMessage(content='ScrapeGraph AI is an AI-powered web scraping tool that efficiently extracts and converts website data into structured formats via a simple API. It caters to developers, data scientists, and AI researchers, offering features like easy integration, support for dynamic content, and scalability for large projects. It supports various website types, including business, e-commerce, and educational sites. Contact: contact@scrapegraphai.com.', additional_kwargs={'tool_calls': [{'id': 'call_shkRPyjyAtfjH9ffG5rSy9xj', 'function': {'arguments': '{"user_prompt":"Extract details about the products, services, and key features offered by ScrapeGraph AI, as well as any unique selling points or innovations mentioned on the website.","website_url":"https://scrapegraphai.com"}', 'name': 'SmartScraper'}, 'type': 'function'}], 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 47, 'prompt_tokens': 480, 'total_tokens': 527, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-2024-08-06', 'system_fingerprint': 'fp_c7ca0ebaca', 'finish_reason': 'stop', 'logprobs': None}, id='run-45a12c86-d499-4273-8c59-0db926799bc7-0', tool_calls=[{'name': 'SmartScraper', 'args': {'user_prompt': 'Extract details about the products, services, and key features offered by ScrapeGraph AI, as well as any unique selling points or innovations mentioned on the website.', 'website_url': 'https://scrapegraphai.com'}, 'id': 'call_shkRPyjyAtfjH9ffG5rSy9xj', 'type': 'tool_call'}], usage_metadata={'input_tokens': 480, 'output_tokens': 47, 'total_tokens': 527, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}})

API参考

有关所有ScrapeGraph功能和配置的详细文档，请访问Langchain API参考：https://python.langchain.com/docs/integrations/tools/scrapegraph

或者到官方SDK仓库：https://github.com/ScrapeGraphAI/langchain-scrapegraph

工具概念指南
工具操作指南

概述​

集成详情​

工具特性​

设置​

凭证​

实例化​

调用​

直接使用参数调用​

使用ToolCall调用​

链式调用​

API参考​

相关​

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概述