For developers building AI tools for science and academia, Google Scholar is the holy grail of data. It contains citations, author profiles, and links to PDFs that standard Google Search misses.
However, scraping Google Scholar is notoriously difficult. It has aggressive rate limits and sophisticated bot detection that blocks even residential proxies quickly.
In this post, we explore how to reliably extract academic data for your RAG (Retrieval-Augmented Generation) pipelines using SearchCans.
Why “Science RAG” is the Next Big Thing
Standard LLMs hallucinate scientific facts. To build a trustworthy AI for researchers (like a “Chat with Papers” bot), you need to ground your model in real citations.
Use Cases:
Literature Review Agents
“Find all papers about Transformer architecture published after 2023.”
Citation Verification
Checking if an LLM’s claim is backed by a real study.
Trend Analysis
Tracking the citation growth of specific authors.
Research Discovery
Finding related work automatically
For building AI research agents, reliable access to academic data is crucial.
The Challenge: Scholar’s Anti-Bot Defense
If you try to scrape Scholar with a standard requests script, you will likely get blocked after the first request. Unlike standard search, Scholar is extremely sensitive to automated traffic patterns.
Common Issues:
- IP Bans: Faster than regular Google Search
- CAPTCHA Challenges: More frequent and harder to bypass
- Rate Limiting: Very aggressive throttling
- Cookie Requirements: Complex session management needed
The Solution: SearchCans Scholar API
SearchCans treats Google Scholar as a first-class citizen. We handle the complex cookie management and “patience” required to scrape Scholar without detection.
Data You Can Extract:
Paper Title & Snippet
Extract complete paper titles and abstracts.
Author Names
Get all author information for each publication.
Citation Count
“Cited by 405” - Track academic impact.
PDF Links
Direct download URLs to full papers.
Publication Year
Temporal filtering and trend analysis.
Journal/Conference
Publication venue information.
Related Papers
Discover connected research.
Python Example: Finding Papers on “LLM Hallucination”
import requests
API_KEY = "YOUR_SEARCHCANS_KEY"
def search_scholar(query):
url = "https://www.searchcans.com/api/search"
payload = {
"s": query,
"t": "scholar", # Dedicated Scholar engine
"d": 10
}
headers = {"Authorization": f"Bearer {API_KEY}"}
response = requests.post(url, json=payload, headers=headers)
return response.json()
data = search_scholar("LLM hallucination mitigation")
for paper in data.get('data', []):
print(f"Title: {paper['title']}")
print(f"Citations: {paper.get('citations', '0')}")
print(f"PDF: {paper.get('pdf_link', 'No PDF')}")
print("---")
Building a Research Assistant
1. Literature Review Tool
def literature_review(topic, min_citations=10, years=5):
query = f"{topic} after:{2026-years}"
results = search_scholar(query)
papers = []
for paper in results.get('data', []):
citations = int(paper.get('citations', '0').replace(',', ''))
if citations >= min_citations:
papers.append({
'title': paper['title'],
'authors': paper.get('authors', []),
'year': paper.get('year'),
'citations': citations,
'url': paper['url'],
'pdf': paper.get('pdf_link')
})
# Sort by citations
papers.sort(key=lambda x: x['citations'], reverse=True)
return papers
# Find highly-cited papers on transformers from last 5 years
important_papers = literature_review("transformer architecture", min_citations=100, years=5)
2. Citation Network Analysis
def build_citation_network(seed_paper_title):
# Search for the seed paper
seed_results = search_scholar(seed_paper_title)
if not seed_results.get('data'):
return None
seed_paper = seed_results['data'][0]
# Find papers that cite this one
cited_by_query = f'"{seed_paper_title}"'
citing_papers = search_scholar(cited_by_query)
# Find papers cited by this one (from references)
network = {
'seed': seed_paper,
'cited_by': citing_papers.get('data', []),
'references': [] # Would need to extract from paper
}
return network
3. Author Profile Analysis
def analyze_author(author_name):
query = f"author:{author_name}"
papers = search_scholar(query)
total_citations = 0
publications = []
for paper in papers.get('data', []):
citations = int(paper.get('citations', '0').replace(',', ''))
total_citations += citations
publications.append({
'title': paper['title'],
'year': paper.get('year'),
'citations': citations
})
# Calculate h-index
citations_list = sorted([p['citations'] for p in publications], reverse=True)
h_index = 0
for i, citations in enumerate(citations_list):
if citations >= i + 1:
h_index = i + 1
return {
'author': author_name,
'total_papers': len(publications),
'total_citations': total_citations,
'h_index': h_index,
'top_papers': publications[:5]
}
Integration with Vector Databases
Build a searchable knowledge base:
from sentence_transformers import SentenceTransformer
import numpy as np
model = SentenceTransformer('all-MiniLM-L6-v2')
def index_papers(papers):
embeddings = []
metadata = []
for paper in papers:
# Create text for embedding
text = f"{paper['title']} {paper.get('snippet', '')}"
# Generate embedding
embedding = model.encode(text)
embeddings.append(embedding)
metadata.append({
'title': paper['title'],
'authors': paper.get('authors', []),
'year': paper.get('year'),
'citations': paper.get('citations'),
'url': paper['url']
})
return np.array(embeddings), metadata
def semantic_search(query, embeddings, metadata, top_k=5):
query_embedding = model.encode(query)
# Cosine similarity
similarities = np.dot(embeddings, query_embedding) / (
np.linalg.norm(embeddings, axis=1) * np.linalg.norm(query_embedding)
)
top_indices = np.argsort(similarities)[-top_k:][::-1]
results = []
for idx in top_indices:
results.append({
'metadata': metadata[idx],
'similarity': similarities[idx]
})
return results
Building a “Chat with Papers” Bot
from openai import OpenAI
client = OpenAI()
def chat_with_papers(user_question, topic):
# 1. Find relevant papers
papers = search_scholar(topic)
# 2. Create context from papers
context = "Relevant research papers:\n\n"
for paper in papers.get('data', [])[:5]:
context += f"Title: {paper['title']}\n"
context += f"Authors: {', '.join(paper.get('authors', []))}\n"
context += f"Summary: {paper.get('snippet', 'No summary')}\n"
context += f"Citations: {paper.get('citations', '0')}\n\n"
# 3. Query LLM with context
response = client.chat.completions.create(
model="gpt-4",
messages=[
{"role": "system", "content": "You are a research assistant. Answer questions based on the provided academic papers. Always cite your sources."},
{"role": "user", "content": f"Context:\n{context}\n\nQuestion: {user_question}"}
]
)
return response.choices[0].message.content
# Usage
answer = chat_with_papers(
"What are the latest techniques to reduce hallucination in LLMs?",
"LLM hallucination mitigation 2024"
)
print(answer)
Advanced: PDF Download and Processing
import requests
from PyPDF2 import PdfReader
import io
def download_and_extract_text(pdf_url):
response = requests.get(pdf_url)
if response.status_code == 200:
pdf_file = io.BytesIO(response.content)
reader = PdfReader(pdf_file)
text = ""
for page in reader.pages:
text += page.extract_text()
return text
return None
def enrich_paper_with_content(paper):
if paper.get('pdf_link'):
full_text = download_and_extract_text(paper['pdf_link'])
if full_text:
paper['full_text'] = full_text
paper['has_full_text'] = True
return paper
Monitoring Research Trends
from datetime import datetime, timedelta
def track_emerging_topics(topics, days=30):
trends = {}
for topic in topics:
# Search for recent papers
cutoff_date = datetime.now() - timedelta(days=days)
year = cutoff_date.year
query = f"{topic} after:{year}"
results = search_scholar(query)
trends[topic] = {
'recent_papers': len(results.get('data', [])),
'total_citations': sum(
int(p.get('citations', '0').replace(',', ''))
for p in results.get('data', [])
),
'top_paper': results['data'][0] if results.get('data') else None
}
return trends
# Monitor AI safety topics
topics = [
"AI alignment",
"adversarial robustness",
"explainable AI",
"AI safety"
]
trends = track_emerging_topics(topics, days=90)
Best Practices
- Deduplication: Papers may appear in multiple searches
- Citation Formatting: Parse citation counts correctly (handle commas)
- Author Disambiguation: Same name doesn’t mean same person
- Rate Limiting: Even with API, be respectful with query frequency
- Data Validation: Check for None values and missing fields
For more on building RAG applications, see our comprehensive guide.
Cost Analysis
| Task | Monthly Volume | SearchCans Cost |
|---|---|---|
| Daily Literature Scan | 30 searches | $0.017 |
| Weekly Author Profiles | 120 searches | $0.067 |
| Continuous Monitoring | 1,000 searches | $0.56 |
| Large-Scale Analysis | 10,000 searches | $5.60 |
Integration with Research Tools
Zotero Export
def export_to_zotero_format(papers):
zotero_items = []
for paper in papers:
item = {
'itemType': 'journalArticle',
'title': paper['title'],
'creators': [{'creatorType': 'author', 'name': a} for a in paper.get('authors', [])],
'date': paper.get('year'),
'url': paper['url'],
'extra': f"Citations: {paper.get('citations', '0')}"
}
zotero_items.append(item)
return zotero_items
Mendeley Integration
def create_mendeley_bibliography(papers):
bibliography = []
for paper in papers:
authors = ', '.join(paper.get('authors', []))
citation = f"{authors}. ({paper.get('year')}). {paper['title']}."
bibliography.append(citation)
return '\n'.join(bibliography)
For more implementation examples, check out our Python scraping tutorial.
Conclusion
Don’t let IP bans slow down your research. Whether you are analyzing citation networks or building the next great academic AI, SearchCans provides the stable pipeline you need.
Unlock academic data today. For more on building AI tools, explore our complete documentation or check out our pricing.