In this tutorial, we show how to combine the power of Google Google search capabilities with Google’s Gemini-1.5-flash model to create advanced, end-to-end research and analysis workflow in Google Colab notebook. By defining Advancer Python class, users get successful search methods covering general web results, news articles and images. The code provides special utilities to target marktecopost tutorials, synthesize activist insights by using langchain, chatGPT and MLOP in categories, and then carefully constructed prompts.
!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 Python packages required for Serpapy Search, Langchain Utilities and Google’s Gemini SDK. Subsequent imports bring standard modules (OS, JSOn, DatTime) for environmental configuration, JSON handling and timestamps, as well as bring the API Calls to Calls and Geni to communicate with the Gemini model.
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 placement strings to your serpent and Gemini API keys, then set the serp ap pee as an environment variable (so Serp ap PA automatically certified Calls) and adjust the Gemini client with its API key so you can request 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_content(category) = 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 contains Serpapi -based search methods (web, news and images) and auxiliary functions to clear the resulting JSOn data. It also integrates Gemini-1.5-flash model, through analysis_with_gamini, to create an AI-powered summary of any collected search data. Additional utilities include specialized marktecopost tutorial lookups, “gate trending” routine and combined “smart research” workflows in the category that are stitches together with tutorials, web results, news and gemini analysis.
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_marketpost_tutorials () starts the AdvanceerPapy class and prints trending tutorials from MarketechPost for the list of categories (Langchain, Chatgupt, Python, AI, MLOP). He then does smart research “Deep Vanda Dive” on “Langchen”, which has a preview of tutorial calculations and AI analysis of Genini. Finally, it lists and lists the top five recent “AI Trends 2024–2025” articles of MarketecPost.
Also, Demo_wadons_Sarpy () creates an advanced example but it focuses on widespread workflow: it runs smart research on “artificial intelligence 2024 trends” and prints the subject and prints AI analysis snippets. It then searches for a news for “technology progress”, listing the first three articles with sources and dates, and the handful ends by bringing and displaying the “Space Exploration 2024” 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, this section includes a Python “main” guard that first tests your serpent and gemini keys, asking them to achieve if they are still placedholders, and otherwise run the marktechpost -focused and normal demo sequences. It also defines two utility tasks: comparison_sch_NGines, which quer the multiple search engine (Google, Bing, Dakdukgo) by serpenty and return their raw JSOn results or errors, and trending_surches, which gain today’s trending trends using Google Trends. After these definitions, the script prints a brief status message that confirms that the tutorial is successfully loaded, reminds you of setting up your API keys, and releases new added methods for bringing marktackpost tutorials and trending materials.
In conclusion, following this tutorial, users will have a reusable, modular python class that will streamline web research and analysis, from keyword-operated search to automatically summarizing the conclusion using Gemini-powered AI. The combination of Sarpappy’s reliable search final points and Gemini’s natural language understanding enables unified “research-to-insights” workflow, ideal for content makers, developers and technical teams that need to be up to date with the latest tutorials and industry trends.
Check the notebook here. All credit for this research goes to researchers of this project. Also, feel free to follow us Twitter And don’t forget to join us 95K+ ML Subredit And subscribe Our newsletter.
Asif Razzaq is the CEO of MarketechPost Media Inc. as a visionary entrepreneur and engineer, Asif is committed to increasing the possibility of artificial intelligence for social good. Their most recent effort is the inauguration of the artificial intelligence media platform, MarktecPost, for its depth of machine learning and deep learning news for its depth of coverage .This is technically sound and easily understandable by a large audience. The platform has more than 2 million monthly views, showing its popularity among the audience.
