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The search_mode: "academic" parameter allows you to tailor your searches specifically to academic and scholarly sources, prioritizing peer-reviewed papers, journal articles, and research publications.

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Overview

The academic filter—sometimes referred to as “academic mode” or “Focus: Academic”—is a feature in Perplexity that allows users to target their searches specifically to academic and scholarly sources. This is especially useful for students, researchers, and professionals who require peer-reviewed papers, journal articles, and research-focused answers rather than general web content. When you activate the academic filter by setting search_mode: "academic", Perplexity prioritizes results from scholarly databases, journals, and reputable academic publications, filtering out non-academic or general web sources. This ensures that the answers you receive are grounded in research and scholarly consensus.

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Key Features and Functionality

  • Source Filtering: Prioritizes scholarly databases, academic journals, and research publications
  • Research Focus: Returns results based on peer-reviewed research rather than general web content
  • Enhanced Precision: Provides more technical and discipline-specific information for academic queries
  • Compatibility: Works with other search parameters like search_context_size to further refine results

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Usage Examples

This example shows how to perform a basic search using the academic filter. 
from perplexity import Perplexity

client = Perplexity()

completion = client.chat.completions.create(
    model="sonar-pro",
    messages=[{"role": "user", "content": "What is the scientific name of the lions mane mushroom?"}],
    search_mode="academic",
    web_search_options={"search_context_size": "low"}
)

print(completion.choices[0].message.content)

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Combining Academic Mode with Other Parameters

You can combine the academic filter with other parameters for more refined searches: 
from perplexity import Perplexity

client = Perplexity()

completion = client.chat.completions.create(
    model="sonar",
    messages=[{"role": "user", "content": "What are the latest findings on neural networks for image recognition?"}],
    search_mode="academic",
    search_after_date_filter="1/1/2023",
    web_search_options={"search_context_size": "high"}
)

print(completion.choices[0].message.content)
The academic filter is particularly valuable for:
  1. Research Literature Reviews: When you need to gather scholarly information on a specific topic
  2. Technical and Scientific Queries: For questions requiring scientifically accurate, peer-reviewed answers
  3. Academic Writing Assistance: When working on papers, theses, or dissertations that require scholarly sources
  4. Educational Support: For students and educators requiring academically rigorous information

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Best Practices

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Optimizing Academic Searches

  • Be Specific: Formulate clear, focused questions to receive more precise academic responses
  • Use Technical Terminology: Include field-specific terms to better target relevant academic literature
  • Combine with Date Filters: For the most recent research, combine with search_after_date_filter
  • Adjust Context Size: Use higher search_context_size values for more comprehensive academic responses

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Performance Considerations

  • Academic searches may sometimes take slightly longer due to the specialized nature of scholarly databases
  • Consider using models like sonar-deep-research for particularly complex academic inquiries
  • For more comprehensive literature reviews, set stream: false to receive complete responses

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Limitations

  • Availability of academic sources varies by field and topic
  • Very recent research (published within the last few months) may not always be included
  • Some paywalled or subscription-only academic content may not be fully accessible
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