docs: migrate use-case examples to v2 SDK methods

Replace legacy v1 code snippets in all use-case pages with actual v2 method names and parameters so examples match the current SDK behavior.

Made-with: Cursor

Author: VinciGit00

use-cases/ai-llm.mdx

@@ -33,13 +33,13 @@ class ArticleSchema(BaseModel):
     summary: Optional[str] = Field(description="Article summary or description")
 
 # Initialize the client
-client = Client()
+client = Client(api_key="your-api-key")
 
 try:
     # Scrape relevant content
-    response = client.smartscraper(
-        website_url="https://example.com/article",
-        user_prompt="Extract the main article content, title, author, and publication date",
+    response = client.extract(
+        url="https://example.com/article",
+        prompt="Extract the main article content, title, author, and publication date",
         output_schema=ArticleSchema
     )
 
@@ -72,15 +72,14 @@ class ResearchResults(BaseModel):
     articles: List[ResearchData]
 
 # Initialize the client
-client = Client()
+client = Client(api_key="your-api-key")
 
 try:
     # Search and scrape multiple sources
-    search_results = client.searchscraper(
-        user_prompt="What are the latest developments in artificial intelligence?",
+    search_results = client.search(
+        query="What are the latest developments in artificial intelligence?",
         output_schema=ResearchResults,
-        num_results=5,  # Number of websites to search (3-20)
-        extraction_mode=True  # Use AI extraction mode for structured data
+        num_results=5
     )
 
     # Process with your AI model

use-cases/content-aggregation.mdx

@@ -43,7 +43,7 @@ class NewsAggregationResult(BaseModel):
     sources: List[str] = Field(description="List of source URLs")
     timestamp: str = Field(description="Aggregation timestamp")
 
-client = Client()
+client = Client(api_key="your-api-key")
 
 # Define news sources to aggregate
 news_sources = [
@@ -56,9 +56,9 @@ news_sources = [
 aggregated_results = []
 
 for source in news_sources:
-    response = client.smartscraper(
-        website_url=source,
-        user_prompt="Extract all news articles from the homepage, including title, content, author, publication date, category, and tags. Also extract featured images if available.",
+    response = client.extract(
+        url=source,
+        prompt="Extract all news articles from the homepage, including title, content, author, publication date, category, and tags. Also extract featured images if available.",
         output_schema=NewsAggregationResult
     )
     aggregated_results.append(response)
@@ -110,50 +110,47 @@ class BlogMonitorResult(BaseModel):
     blog_url: str = Field(description="Blog homepage URL")
     last_updated: str = Field(description="Last monitoring timestamp")
 
-client = Client()
+client = Client(api_key="your-api-key")
 
 # Start the crawler job
-job = client.smartcrawler_initiate(
+job = client.crawl.start(
     url="https://example-blog.com",
-    user_prompt="Extract all blog posts from the last 7 days, including title, content, author, publication date, categories, and metadata. Calculate estimated reading time based on content length.",
-    extraction_mode="ai",
     depth=2,  # Crawl up to 2 levels deep
-    same_domain_only=True
+    include_patterns=["/blog/*"],
+    exclude_patterns=["/tag/*", "/author/*"]
 )
 
 # Wait for job completion and get results
-job_id = job.job_id
+job_id = job["id"]
 while True:
-    status = client.smartcrawler_get_status(job_id)
-    if status.state == "completed":
-        response = status.result
+    status = client.crawl.status(job_id)
+    if status.get("status") == "completed":
+        response = status.get("data", {})
         break
-    elif status.state in ["failed", "cancelled"]:
-        print(f"Job failed: {status.error}")
+    elif status.get("status") in ["failed", "cancelled", "error"]:
+        print(f"Job failed: {status.get('error')}")
         break
     time.sleep(5)  # Wait 5 seconds before checking again
 
 # Process the crawled content if successful
-if response:
-    print(f"Blog: {response.blog_url}")
-    print(f"Total Posts Found: {response.total_posts}")
-    print(f"Last Updated: {response.last_updated}\n")
+if response and response.get("pages"):
+    print(f"Total Pages Found: {len(response['pages'])}")
 
-    for post in response.posts:
+    for post in response["pages"]:
         # Check if post is recent
-        post_date = datetime.strptime(post.publication_date, "%Y-%m-%d")
+        post_date = datetime.strptime(post["publication_date"], "%Y-%m-%d")
         if post_date > datetime.now() - timedelta(days=7):
-            print(f"Title: {post.title}")
-            print(f"Author: {post.author}")
-            print(f"Published: {post.publication_date}")
-            print(f"Reading Time: {post.reading_time} minutes")
-            if post.categories:
-                print(f"Categories: {', '.join(post.categories)}")
-            if post.tags:
-                print(f"Tags: {', '.join(post.tags)}")
-            if post.excerpt:
-                print(f"\nExcerpt: {post.excerpt}")
-            print(f"URL: {post.url}\n")
+            print(f"Title: {post['title']}")
+            print(f"Author: {post.get('author', 'Unknown')}")
+            print(f"Published: {post['publication_date']}")
+            print(f"Reading Time: {post.get('reading_time', 'N/A')} minutes")
+            if post.get("categories"):
+                print(f"Categories: {', '.join(post['categories'])}")
+            if post.get("tags"):
+                print(f"Tags: {', '.join(post['tags'])}")
+            if post.get("excerpt"):
+                print(f"\nExcerpt: {post['excerpt']}")
+            print(f"URL: {post['url']}\n")
 ```
 
 ## Best Practices

use-cases/lead-generation.mdx

@@ -39,9 +39,9 @@ class CompanyContacts(BaseModel):
 client = Client(api_key="your-api-key")
 
 # Scrape company website
-response = client.smartscraper(
-    website_url="https://company.com/about",
-    user_prompt="Extract all contact information for decision makers and leadership team",
+response = client.extract(
+    url="https://company.com/about",
+    prompt="Extract all contact information for decision makers and leadership team",
     output_schema=CompanyContacts
 )
 
@@ -79,11 +79,10 @@ client = Client(api_key="your-api-key")
 
 try:
     # Search for businesses in a specific category
-    search_results = client.searchscraper(
-        user_prompt="Find software companies in San Francisco with their contact details",
+    search_results = client.search(
+        query="Find software companies in San Francisco with their contact details",
         output_schema=BusinessSearchResults,
-        num_results=10,  # Number of websites to search (3-20)
-        extraction_mode=True  # Use AI extraction mode for structured data
+        num_results=10
     )
 
     # Extract and validate leads
@@ -94,9 +93,9 @@ try:
 
         try:
             # Get more detailed information from company website
-            details = client.smartscraper(
-                website_url=business.website,
-                user_prompt="Extract detailed company information including team size, tech stack, and all contact methods",
+            details = client.extract(
+                url=business.website,
+                prompt="Extract detailed company information including team size, tech stack, and all contact methods",
                 output_schema=CompanyContacts  # Defined earlier in the file
             )
 

use-cases/market-intelligence.mdx

@@ -43,9 +43,9 @@ class PriceMonitorResult(BaseModel):
 client = Client()
 
 # Monitor competitor prices
-response = client.smartscraper(
-    website_url="https://competitor-store.com/category/products",
-    user_prompt="Extract pricing information for all products including name, current price, original price if available, and availability status",
+response = client.extract(
+    url="https://competitor-store.com/category/products",
+    prompt="Extract pricing information for all products including name, current price, original price if available, and availability status",
     output_schema=PriceMonitorResult
 )
 
@@ -86,8 +86,8 @@ class TrendAnalysisResult(BaseModel):
 client = Client()
 
 # Search and analyze market trends
-response = client.searchscraper(
-    user_prompt="Analyze market trends and sentiment in the electric vehicle industry. Focus on pricing trends, consumer preferences, and technological advancements.",
+response = client.search(
+    query="Analyze market trends and sentiment in the electric vehicle industry. Focus on pricing trends, consumer preferences, and technological advancements.",
     num_results=10,  # Number of sources to analyze
     output_schema=TrendAnalysisResult
 )

use-cases/research-analysis.mdx

@@ -47,8 +47,8 @@ class ResearchCollectionResult(BaseModel):
 client = Client()
 
 # Search and collect research papers
-response = client.searchscraper(
-    user_prompt="Find recent research papers on machine learning applications in healthcare, focusing on papers published in the last year. Extract complete paper details including abstract, citations, and DOI.",
+response = client.search(
+    query="Find recent research papers on machine learning applications in healthcare, focusing on papers published in the last year. Extract complete paper details including abstract, citations, and DOI.",
     num_results=15,  # Number of papers to collect
     output_schema=ResearchCollectionResult
 )
@@ -115,9 +115,9 @@ class IndustryAnalysis(BaseModel):
 client = Client()
 
 # Collect industry analysis data
-response = client.smartscraper(
-    website_url="https://industry-research-site.com/sector-analysis",
-    user_prompt="Extract comprehensive industry analysis including detailed market metrics, company profiles, trends, and regulatory factors. Focus on quantitative data where available.",
+response = client.extract(
+    url="https://industry-research-site.com/sector-analysis",
+    prompt="Extract comprehensive industry analysis including detailed market metrics, company profiles, trends, and regulatory factors. Focus on quantitative data where available.",
     output_schema=IndustryAnalysis
 )
 

use-cases/seo-analytics.mdx

@@ -63,9 +63,9 @@ target_keywords = [
 
 for keyword in target_keywords:
     # Analyze SERP data
-    response = client.smartscraper(
-        website_url=f"https://www.google.com/search?q={keyword}",
-        user_prompt="Extract detailed search results including positions, titles, descriptions, and all rich results. Also analyze ad presence and total result counts.",
+    response = client.extract(
+        url=f"https://www.google.com/search?q={keyword}",
+        prompt="Extract detailed search results including positions, titles, descriptions, and all rich results. Also analyze ad presence and total result counts.",
         output_schema=SERPAnalysis
     )
 
@@ -151,9 +151,9 @@ target_urls = [
 
 for url in target_urls:
     # Extract content metrics
-    response = client.smartscraper(
-        website_url=url,
-        user_prompt="Perform comprehensive content analysis including meta tags, headings structure, internal/external links, and structured data. Calculate content quality score based on best practices.",
+    response = client.extract(
+        url=url,
+        prompt="Perform comprehensive content analysis including meta tags, headings structure, internal/external links, and structured data. Calculate content quality score based on best practices.",
         output_schema=ContentMetrics
     )