In this tutorial, we show how to combine the power of Serpapi Google Look for capabilities with the Gemini-1.5-Flash model from Google to create an advanced end-to-end research and analysis workflow in a Google Colarb laptop. By defining an Advancedserpapi Python class, users have access to improved research methods that cover general web results, press articles and images, while taking advantage of Gemini to carry out in -depth analyzes of these results. The code provides specialized public services to target Marktechpost tutorials, aggregating the content in categories like Langchain, Chatgpt and Mlops, then synthesizing usable information using a carefully constructed 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 Python packages required for Serpapi research, Langchain utility room and Google SDK Gemini. The following imports provide standard modules (OS, JSON, DateTime) for environmental configuration, JSON handling and horodatages, as well as the Serpapi Googlesearch class to make API and GENAI calls to interact 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 space chains reserved for your API Serpapi and Gemini keys, then define the Serpapi key as an environment variable (so that Serpapi calls automatically authenticate) and configure the gemini client with its API key so that you can invoke 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 2025 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, Advancedserpapi, encapsulates the research methods based on Serpapi (web, news and images) and assistance functions to cleanse the resulting JSON data. He also joined a Gemini-1.5-Flash model, via Analyze_with_Gemini, to generate a summary piloted by AI of all the research data collected. Additional utilities include specialized research on Marktechpost tutorial, a routine “obtaining trends” between categories, and a combined “smart research” work flow which covers 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 2025 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 2025 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 2025", 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_torials () initializes the AdvancedSerpapi class and prints Marktechpost trendy tutorials for a list of categories (Langchain, Chatgpt, Python, AI, Mops). He then performs an intelligent “deep diving” research on “Langchain”, showing tutorial counts and an overview of the analysis of Gemini. Finally, he recovers and lists the five main articles “Trends 2025-2025” recent by Marktechpost.
In addition, Demo_Advanced_Serpapi () creates an advancedserpapi instance but focuses on a broader workflow: he performs intelligent research on “trends of artificial intelligence 2025” and prints the subject's summary and the IA analysis extract. He then carried out a news search for the “technological breakthrough”, lists the first three articles with sources and dates, and ends by recovering and posting a handful of image results “Space Exploration 2025”.
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 includes a “main” python goalkeeper who first checks your keys Serpapi and Gemini, encouraging you to obtain them if they are always reserved spaces, and otherwise manages the focused and general focused demos of Marktechpost in sequence. It also defines two utility functions: compare_search_engines, which questions several search engines (Google, Bing, DuckduckGo) via Serpapi and returns their JSON Brut's results or errors, and Trending_Searches, which recovers today's trend subjects using Google Trends. After these definitions, the script prints a brief state message confirming that the successful tutorial, reminds you of defining your API keys and highlights the newly added methods to recover the Marktechpost didactics and the trendy content.
In conclusion, by following this tutorial, users will have a reusable modular python class which rationalizes research and web analysis, the execution of research focused on keywords to automatically summarize the results using an AI supplied with Gemini. The combination of the reliable research ending points of Serpapi and the understanding of the natural language of Gemini allows a flawless “research” workflow, ideal for content creators, developers and technical teams that must remain up to date with the latest didactics and industry trends.
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Asif Razzaq is the CEO of Marktechpost Media Inc .. as a visionary entrepreneur and engineer, AIF undertakes to exploit the potential of artificial intelligence for social good. His most recent company is the launch of an artificial intelligence media platform, Marktechpost, which stands out from its in-depth coverage of automatic learning and in-depth learning news which are both technically solid and easily understandable by a large audience. The platform has more than 2 million monthly views, illustrating its popularity with the public.
