In this tutorial, we demonstrate how to combine the power of Serpapi’s Google Search Ability with Gemin-1.5-Flash model of Google to create an advanced, end-to-research and analysis workflow within the Google Colab notebook. By defining an Advancedserpapi Python class, users gain access to increased search methods covering general web results, news articles and images, while taking advantage of Gemini to intensive analysis of those results. The code provides special utilities to target Marktekpost tutorials, collects materials in categories such as Langchen, Chatgate and MLOP, and then synthesizes the actionable insight using a prompt manufactured prompt.
!pip install google-search-results langchain-community langchain-core google-generativeai -q
import os
import json
from serpapi import GoogleSearch
import google.generativeai as genai
from datetime import datetimeWe install the required python package for the Gemini SDK of serpent discoveries, language utilities and Google. The latter imports bring the standard module (OS, JSON, Datetime) for environmental configuration, JSON handling, and timestamps, as well as to make API calls and Genai to interact with the Googleserach class of Serpapi to Serpapi’s Googleserach class.
SERPAPI_API_KEY = "Use Your API Key Here"
GEMINI_API_KEY = "Use Your API Key Here"
os.environ["SERPAPI_API_KEY"] = SERPAPI_API_KEY
genai.configure(api_key=GEMINI_API_KEY)
We assign the placeholder strings for your serpent and Gemini API keys, then set the snake of the serpent as an environmental variable (so the serpent automatically certifies) and configure the Gemini client with its API key so that you can invite the Gemini model.
class AdvancedSerpAPI:
def __init__(self, serpapi_key, gemini_key):
self.serpapi_key = serpapi_key
self.gemini_model = genai.GenerativeModel('gemini-1.5-flash')
def search_google(self, query, num_results=5, location="United States"):
"""Enhanced Google search with multiple parameters"""
params =
"engine": "google",
"q": query,
"api_key": self.serpapi_key,
"num": num_results,
"location": location,
"hl": "en",
"gl": "us"
search = GoogleSearch(params)
results = search.get_dict()
return self.extract_search_results(results)
def search_news(self, query, days_back=7):
"""Search for recent news articles"""
params =
"engine": "google_news",
"q": query,
"api_key": self.serpapi_key,
"gl": "us",
"hl": "en"
search = GoogleSearch(params)
results = search.get_dict()
return self.extract_news_results(results)
def search_images(self, query, num_images=10):
"""Search for images with metadata"""
params =
"engine": "google_images",
"q": query,
"api_key": self.serpapi_key,
"num": num_images
search = GoogleSearch(params)
results = search.get_dict()
return self.extract_image_results(results)
def extract_search_results(self, results):
"""Extract and clean search results"""
cleaned_results = []
if 'organic_results' in results:
for result in results['organic_results']:
cleaned_results.append(
'title': result.get('title', ''),
'link': result.get('link', ''),
'snippet': result.get('snippet', ''),
'position': result.get('position', 0)
)
return cleaned_results
def extract_news_results(self, results):
"""Extract news articles with timestamps"""
news_results = []
if 'news_results' in results:
for article in results['news_results']:
news_results.append(
'title': article.get('title', ''),
'link': article.get('link', ''),
'snippet': article.get('snippet', ''),
'date': article.get('date', ''),
'source': article.get('source', '')
)
return news_results
def extract_image_results(self, results):
"""Extract image results with metadata"""
image_results = []
if 'images_results' in results:
for img in results['images_results']:
image_results.append(
'title': img.get('title', ''),
'original': img.get('original', ''),
'thumbnail': img.get('thumbnail', ''),
'source': img.get('source', '')
)
return image_results
def analyze_with_gemini(self, search_results, analysis_prompt):
"""Use Gemini Flash to analyze search results"""
results_text = json.dumps(search_results, indent=2)
full_prompt = f"""
analysis_prompt
Search Results Data:
results_text
Please provide a comprehensive analysis based on the search results.
"""
try:
response = self.gemini_model.generate_content(full_prompt)
return response.text
except Exception as e:
return f"Gemini analysis failed: str(e)"
def search_marktechpost_tutorials(self, topic="", num_results=10):
"""Search specifically for trending tutorials from Marktechpost"""
queries = [
f"site:marktechpost.com topic tutorial guide how-to 2024 2025",
f"site:marktechpost.com trending topic tutorial",
f"site:marktechpost.com top topic books frameworks"
]
all_results = []
for query in queries:
params =
"engine": "google",
"q": query,
"api_key": self.serpapi_key,
"num": num_results // len(queries),
"hl": "en",
"gl": "us"
search = GoogleSearch(params)
results = search.get_dict()
extracted = self.extract_search_results(results)
all_results.extend(extracted)
unique_results = []
seen_links = set()
for result in all_results:
if result['link'] not in seen_links:
unique_results.append(result)
seen_links.add(result['link'])
return unique_results[:num_results]
def get_trending_marktechpost_content(self, categories=None):
"""Get trending content from Marktechpost across different categories"""
if categories is None:
categories = ["AI", "LLM", "Machine Learning", "Python", "Tutorial", "Framework"]
trending_content =
for category in categories:
print(f"🔍 Searching for trending category content...")
results = self.search_marktechpost_tutorials(category, num_results=5)
trending_contentAgentic AI = results
print(f"✅ Found len(results) category tutorials")
return trending_content
def smart_research(self, topic, research_depth="medium", focus_marktechpost=True):
"""Intelligent research combining multiple search types with Marktechpost focus"""
print(f"🔍 Starting smart research on: topic")
if focus_marktechpost:
marktechpost_results = self.search_marktechpost_tutorials(topic, num_results=8)
print(f"✅ Found len(marktechpost_results) Marktechpost tutorials")
web_results = self.search_google(f"topic tutorial guide", num_results=3)
print(f"✅ Found len(web_results) additional web results")
all_web_results = marktechpost_results + web_results
else:
all_web_results = self.search_google(f"topic overview facts", num_results=5)
print(f"✅ Found len(all_web_results) web results")
news_results = self.search_news(topic)
print(f"✅ Found len(news_results) news articles")
analysis_prompt = f"""
Analyze the search results about 'topic' with focus on Marktechpost content and provide:
1. Key tutorials and guides available
2. Trending topics and frameworks
3. Learning resources and books mentioned
4. Recent developments and updates
5. Practical implementation guides
6. Recommended learning path
Focus on actionable insights and learning resources.
"""
all_results =
"marktechpost_results": marktechpost_results if focus_marktechpost else [],
"web_results": all_web_results,
"news_results": news_results,
"search_topic": topic,
"timestamp": datetime.now().isoformat()
gemini_analysis = self.analyze_with_gemini(all_results, analysis_prompt)
return
"topic": topic,
"marktechpost_tutorials": marktechpost_results if focus_marktechpost else [],
"web_results": all_web_results,
"news_results": news_results,
"ai_analysis": gemini_analysis,
"total_sources": len(all_web_results) + len(news_results)
This class, Advancedserpapi, encounter Serpapi-based discovery methods (web, news and images) and supporting tasks to clean the resulting JSON data. It also integrates a Gemini-1.5-Flash model through anles_with_Gemini, to generate the AI-powered summary of any collected search data. Additional utilities include specially marktechpost tutorial looks, a “trending trending” routine and a combined “smart research” workflow in the categories that stitches tutorials, web results, news and Gemini analysis simultaneously.
def demo_marktechpost_tutorials():
"""Demo specifically focused on Marktechpost tutorials"""
searcher = AdvancedSerpAPI(SERPAPI_API_KEY, GEMINI_API_KEY)
print("🚀 Marktechpost Trending Tutorials Finder")
print("=" * 50)
print("\n📚 Demo 1: Trending Marktechpost Tutorials by Category")
trending_content = searcher.get_trending_marktechpost_content([
"LangChain", "ChatGPT", "Python", "AI", "MLOps"
])
for category, tutorials in trending_content.items():
print(f"\n🔥 Trending category Tutorials:")
for i, tutorial in enumerate(tutorials[:3], 1):
print(f" i. tutorial['title']")
print(f" 📎 tutorial['link']")
if tutorial['snippet']:
print(f" 📝 tutorial['snippet'][:100]...")
print("\n🎯 Demo 2: Deep Dive - LangChain Tutorials")
langchain_research = searcher.smart_research("LangChain", focus_marktechpost=True)
print(f"\n📊 Research Summary:")
print(f"Topic: langchain_research['topic']")
print(f"Marktechpost Tutorials Found: len(langchain_research['marktechpost_tutorials'])")
print(f"Total Sources: langchain_research['total_sources']")
print(f"\n🤖 AI Analysis Preview:")
print(langchain_research['ai_analysis'][:600] + "..." if len(langchain_research['ai_analysis']) > 600 else langchain_research['ai_analysis'])
print("\n📰 Demo 3: Latest AI Trends from Marktechpost")
ai_trends = searcher.search_marktechpost_tutorials("AI trends 2024 2025", num_results=5)
print("Recent AI trend articles:")
for i, article in enumerate(ai_trends, 1):
print(f"i. article['title']")
print(f" 🔗 article['link']")
def demo_advanced_serpapi():
"""Comprehensive demo of SerpAPI capabilities"""
searcher = AdvancedSerpAPI(SERPAPI_API_KEY, GEMINI_API_KEY)
print("🚀 Advanced SerpAPI Tutorial with Gemini Flash")
print("=" * 50)
print("\n📊 Demo 1: Smart Research on AI Technology")
research_results = searcher.smart_research("artificial intelligence 2024 trends")
print(f"\n🔍 Research Summary:")
print(f"Topic: research_results['topic']")
print(f"Total Sources: research_results['total_sources']")
print(f"\n🤖 AI Analysis Preview:")
print(research_results['ai_analysis'][:500] + "..." if len(research_results['ai_analysis']) > 500 else research_results['ai_analysis'])
print("\n📰 Demo 2: Recent News Search")
tech_news = searcher.search_news("technology breakthrough", days_back=7)
print(f"Found len(tech_news) recent tech news articles:")
for i, article in enumerate(tech_news[:3], 1):
print(f"i. article['title'][:80]...")
print(f" Source: article['source'] | Date: article['date']")
print("\n🖼️ Demo 3: Image Search")
space_images = searcher.search_images("space exploration 2024", num_images=5)
print(f"Found len(space_images) space-related images:")
for i, img in enumerate(space_images[:3], 1):
print(f"i. img['title'][:60]...")
print(f" Source: img['source']")
Demo_marktechpost_tutorials () Incenge the AdvancedSerpapi square and print trending tutorials from Marktekpost to trending tutorials (Langchain, Chatgpt, Python, AI, Mlops). It then conducts a “deep dive” smart research on “Langchen”, which reflects the preview of the tutorial count and the AI analysis of Gemini. Finally, it recurs and lists the articles of the top five “AI trends 2024–2025” of the Marktekpost.
In addition, DEMO_ADVANCED_SERPAI () creates an advancedserppi example, but focuses on a broad workflow: it runs smart research on “Artificial Intelligence 2024 Trends” and print the subject summary and AI analysis snipet. It then discovers a news for “technology success”, lists the first three articles with sources and dates, and concludes “Space Explore 2024” image results by bringing and displaying a handful of image results.
if __name__ == "__main__":
if SERPAPI_API_KEY == "your_serpapi_key_here" or GEMINI_API_KEY == "your_gemini_key_here":
print("⚠️ Please set your API keys before running the demo!")
print("1. Get SerpAPI key from: https://serpapi.com")
print("2. Get Gemini API key from: https://makersuite.google.com")
else:
print("🎯 Running Marktechpost-focused demo...")
demo_marktechpost_tutorials()
print("\n" + "="*50)
print("🌟 Running general demo...")
demo_advanced_serpapi()
def compare_search_engines(query, engines=['google', 'bing', 'duckduckgo']):
"""Compare results across different search engines"""
results =
for engine in engines:
params =
"engine": engine,
"q": query,
"api_key": SERPAPI_API_KEY
try:
search = GoogleSearch(params)
results[engine] = search.get_dict()
except Exception as e:
results[engine] = "error": str(e)
return results
def trending_searches(location="United States"):
"""Get trending searches"""
params =
"engine": "google_trends_trending_now",
"api_key": SERPAPI_API_KEY,
"geo": location
search = GoogleSearch(params)
return search.get_dict()
print("✅ Advanced SerpAPI Tutorial with Marktechpost Focus loaded successfully!")
print("🔑 Remember to set your API keys before running demos")
print("📚 New Functions: search_marktechpost_tutorials, get_trending_marktechpost_content")
print("🎯 Marktechpost-specific features: LangChain, ChatGPT, Python, AI, MLOps tutorials")
print("\n🚀 Quick Start Examples:")
print("searcher = AdvancedSerpAPI(SERPAPI_API_KEY, GEMINI_API_KEY)")
print("langchain_tutorials = searcher.search_marktechpost_tutorials('LangChain')")
print("trending_ai = searcher.get_trending_marktechpost_content(['AI', 'Python'])")
print("research = searcher.smart_research('ChatGPT', focus_marktechpost=True)")Finally, the section contains a pythan “main” guard that first verification your serpent and Gemini keys, motivates you to get them if they are still placeholders, and otherwise Marktekpost – focused and run general demo in sequence. It also defines two utility tasks: compare_Serch_N GINES, which questions several search engines (Google, Bing, DuckDuckgo) and returns to its raw JSON results or errors and trending_srace, which uses Google Trends & Point using today. After these definitions, the script prints a brief position message that confirms that the tutorial has been successfully loaded, reminds you of setting your API keys, and highlighting newly added methods to bring Marktekpost tutorial and trending materials.
In the conclusion, by following this tutorial, users will have a re-purpose, modular pythan class that streamlines web research and analysis, ranging keyword-powered discoveries to automatically conjecture conclusions using Gemini-Managed AI. The combination of Serpapi’s reliable discovery closing points and the natural language understanding of Gemini is ideal for a spontaneous “research-to-world” workflow, content creators, developers and technical teams, which need to be up-to-det with the latest tutorials and industry trends.
See notebook here, All credit for this research goes to the researchers of this project. Also, feel free to follow us Twitter And don’t forget to join us 95K+ ML Subredit More membership Our newspaper,
Asif razzaq is CEO of Marktechpost Media Inc .. As a visionary entrepreneur and engineer, ASIF is committed to using the ability of artificial intelligence for social good. His most recent effort is the launch of an Artificial Intelligence Media Platform, Marktekpost, which stands for his intensive coverage of machine learning and deep learning news, technically sound and easily understand by a comprehensive audience. The stage claims more than 2 million monthly ideas, reflecting its popularity among the audience.
