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Merge pull request #5 from ScrapeGraphAI/pre/beta
Searchscraper Tool
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‎CHANGELOG.md‎

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## [1.3.0-beta.1](https://github.com/ScrapeGraphAI/langchain-scrapegraph/compare/v1.2.1-beta.1...v1.3.0-beta.1) (2025-02-22)
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### Features
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* searchscraper ([6a96801](https://github.com/ScrapeGraphAI/langchain-scrapegraph/commit/6a968015d9c8f4ce798111850b0f000c3317c467))
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* updated tests searchscraper ([a771564](https://github.com/ScrapeGraphAI/langchain-scrapegraph/commit/a771564838b637f6aef0277e5ca3d723208d6701))
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## [1.2.1-beta.1](https://github.com/ScrapeGraphAI/langchain-scrapegraph/compare/v1.2.0...v1.2.1-beta.1) (2025-01-02)
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### Bug Fixes
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* updated docs url ([f7b640c](https://github.com/ScrapeGraphAI/langchain-scrapegraph/commit/f7b640c29d9780a30212acb19b09247b765a41ff))
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## [1.2.0](https://github.com/ScrapeGraphAI/langchain-scrapegraph/compare/v1.1.0...v1.2.0) (2024-12-18)
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‎README.md‎

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[![License](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)
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[![Python Support](https://img.shields.io/pypi/pyversions/langchain-scrapegraph.svg)](https://pypi.org/project/langchain-scrapegraph/)
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[![Documentation](https://img.shields.io/badge/Documentation-Latest-green)](https://scrapegraphai.com/documentation)
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[![Documentation](https://img.shields.io/badge/Documentation-Latest-green)](https://docs.scrapegraphai.com/integrations/langchain)
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Supercharge your LangChain agents with AI-powered web scraping capabilities. LangChain-ScrapeGraph provides a seamless integration between [LangChain](https://github.com/langchain-ai/langchain) and [ScrapeGraph AI](https://scrapegraphai.com), enabling your agents to extract structured data from websites using natural language.
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print(result)
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```
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<details>
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<summary>🔍 Using Output Schemas with SmartscraperTool</summary>
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You can define the structure of the output using Pydantic models:
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### 🌐 SearchscraperTool
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Search and extract structured information from the web using natural language prompts.
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```python
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from typing import List
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from pydantic import BaseModel, Field
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from langchain_scrapegraph.tools import SmartScraperTool
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from langchain_scrapegraph.tools import SearchScraperTool
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class WebsiteInfo(BaseModel):
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title: str = Field(description="The main title of the webpage")
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description: str = Field(description="The main description or first paragraph")
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urls: List[str] = Field(description="The URLs inside the webpage")
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# Initialize with schema
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tool = SmartScraperTool(llm_output_schema=WebsiteInfo)
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# Initialize the tool (uses SGAI_API_KEY from environment)
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tool = SearchScraperTool()
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# The output will conform to the WebsiteInfo schema
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# Search and extract information using natural language
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result = tool.invoke({
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"website_url": "https://www.example.com",
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"user_prompt": "Extract the website information"
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"user_prompt": "What are the key features and pricing of ChatGPT Plus?"
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})
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print(result)
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# {
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# "title": "Example Domain",
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# "description": "This domain is for use in illustrative examples...",
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# "urls": ["https://www.iana.org/domains/example"]
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# "product": {
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# "name": "ChatGPT Plus",
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# "description": "Premium version of ChatGPT..."
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# },
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# "features": [...],
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# "pricing": {...},
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# "reference_urls": [
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# "https://openai.com/chatgpt",
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# ...
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# ]
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# }
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```
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</details>
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### 💻 LocalscraperTool
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Extract information from HTML content using AI.
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```python
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from langchain_scrapegraph.tools import LocalScraperTool
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tool = LocalScraperTool()
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result = tool.invoke({
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"user_prompt": "Extract all contact information",
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"website_html": "<html>...</html>"
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})
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print(result)
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```
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<details>
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<summary>🔍 Using Output Schemas with LocalscraperTool</summary>
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<summary>🔍 Using Output Schemas with SearchscraperTool</summary>
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You can define the structure of the output using Pydantic models:
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```python
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from typing import Optional
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from typing import List, Dict
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from pydantic import BaseModel, Field
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from langchain_scrapegraph.tools import LocalScraperTool
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from langchain_scrapegraph.tools import SearchScraperTool
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class CompanyInfo(BaseModel):
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name: str = Field(description="The company name")
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description: str = Field(description="The company description")
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email: Optional[str] = Field(description="Contact email if available")
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phone: Optional[str] = Field(description="Contact phone if available")
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class ProductInfo(BaseModel):
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name: str = Field(description="Product name")
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features: List[str] = Field(description="List of product features")
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pricing: Dict[str, Any] = Field(description="Pricing information")
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reference_urls: List[str] = Field(description="Source URLs for the information")
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# Initialize with schema
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tool = LocalScraperTool(llm_output_schema=CompanyInfo)
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html_content = """
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<html>
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<body>
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<h1>TechCorp Solutions</h1>
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<p>We are a leading AI technology company.</p>
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<div class="contact">
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<p>Email: [email protected]</p>
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<p>Phone: (555) 123-4567</p>
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</div>
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</body>
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</html>
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"""
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# The output will conform to the CompanyInfo schema
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tool = SearchScraperTool(llm_output_schema=ProductInfo)
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# The output will conform to the ProductInfo schema
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result = tool.invoke({
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"website_html": html_content,
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"user_prompt": "Extract the company information"
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"user_prompt": "What are the key features and pricing of ChatGPT Plus?"
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})
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print(result)
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# {
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# "name": "TechCorp Solutions",
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# "description": "We are a leading AI technology company.",
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# "email": "[email protected]",
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# "phone": "(555) 123-4567"
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# "name": "ChatGPT Plus",
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# "features": [
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# "GPT-4 access",
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# "Faster response speed",
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# ...
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# ],
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# "pricing": {
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# "amount": 20,
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# "currency": "USD",
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# "period": "monthly"
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# },
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# "reference_urls": [
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# "https://openai.com/chatgpt",
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# ...
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# ]
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# }
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```
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</details>

‎examples/agent_example.py‎

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from langchain_scrapegraph.tools import (
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GetCreditsTool,
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LocalScraperTool,
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SearchScraperTool,
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SmartScraperTool,
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)
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# Initialize the tools
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tools = [
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SmartScraperTool(),
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LocalScraperTool(),
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GetCreditsTool(),
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SearchScraperTool(),
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]
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# Create the prompt template

‎examples/localscraper_tool.py‎

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‎examples/localscraper_tool_schema.py‎

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‎examples/searchscraper_tool.py‎

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from scrapegraph_py.logger import sgai_logger
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from langchain_scrapegraph.tools import SearchScraperTool
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sgai_logger.set_logging(level="INFO")
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# Will automatically get SGAI_API_KEY from environment
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tool = SearchScraperTool()
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# Example prompt
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user_prompt = "What are the key features and pricing of ChatGPT Plus?"
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# Use the tool
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result = tool.invoke({"user_prompt": user_prompt})
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print("\nResult:", result)

‎examples/searchscraper_tool_schema.py‎

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from typing import Dict, List
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from pydantic import BaseModel, Field
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from scrapegraph_py.logger import sgai_logger
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from langchain_scrapegraph.tools import SearchScraperTool
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class Feature(BaseModel):
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name: str = Field(description="Name of the feature")
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description: str = Field(description="Description of the feature")
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class PricingPlan(BaseModel):
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name: str = Field(description="Name of the pricing plan")
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price: Dict[str, str] = Field(
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description="Price details including amount, currency, and period"
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)
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features: List[str] = Field(description="List of features included in the plan")
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class ProductInfo(BaseModel):
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name: str = Field(description="Name of the product")
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description: str = Field(description="Description of the product")
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features: List[Feature] = Field(description="List of product features")
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pricing: Dict[str, List[PricingPlan]] = Field(description="Pricing information")
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reference_urls: List[str] = Field(description="Source URLs for the information")
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sgai_logger.set_logging(level="INFO")
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# Initialize with Pydantic model class
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tool = SearchScraperTool(llm_output_schema=ProductInfo)
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# Example prompt
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user_prompt = "What are the key features and pricing of ChatGPT Plus?"
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# Use the tool - output will conform to ProductInfo schema
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result = tool.invoke({"user_prompt": user_prompt})
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print("\nResult:", result)

‎examples/smartscraper_tool_schema.py‎

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# Initialize with Pydantic model class
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tool = SmartScraperTool(llm_output_schema=WebsiteInfo)
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# Example website and prompt
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# Example 1: Using website URL
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website_url = "https://www.example.com"
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user_prompt = "Extract info about the website"
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# Use the tool - output will conform to WebsiteInfo schema
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result = tool.invoke({"website_url": website_url, "user_prompt": user_prompt})
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print(result)
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# Use the tool with URL
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result_url = tool.invoke({"website_url": website_url, "user_prompt": user_prompt})
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print("\nResult from URL:", result_url)
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# Example 2: Using HTML content directly
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html_content = """
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<html>
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<body>
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<h1>Example Domain</h1>
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<p>This domain is for use in illustrative examples.</p>
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<a href="https://www.iana.org/domains/example">More information...</a>
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</body>
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</html>
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"""
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# Use the tool with HTML content
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result_html = tool.invoke(
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{
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"website_url": website_url, # Still required but will be overridden
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"website_html": html_content,
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"user_prompt": user_prompt,
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}
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)
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print("\nResult from HTML:", result_html)

‎langchain_scrapegraph/tools/__init__.py‎

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from .credits import GetCreditsTool
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from .localscraper import LocalScraperTool
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from .markdownify import MarkdownifyTool
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from .searchscraper import SearchScraperTool
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from .smartscraper import SmartScraperTool
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__all__ = ["SmartScraperTool", "GetCreditsTool", "MarkdownifyTool", "LocalScraperTool"]
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__all__ = ["SmartScraperTool", "GetCreditsTool", "MarkdownifyTool", "SearchScraperTool"]

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