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Perplexity AI API Guide 2026: Real-Time Web Search for AI Apps

Build AI apps with current web knowledge using Perplexity search API

By AI Skill Navigation Editorial TeamPublished May 28, 2026

Perplexity AI API Guide 2026: Real-Time Web Search for AI Apps

Perplexity combines LLMs with real-time web search, solving the stale data problem.

Perplexity vs Custom RAG

Use CasePerplexityCustom RAG

Current eventsBestStale Private documentsNoYour data Setup timeMinutesHours MaintenanceNoneOngoing

Getting Started

python

Perplexity uses OpenAI-compatible API

from openai import OpenAI

client = OpenAI( api_key='pplx-your-key', base_url='https://api.perplexity.ai' )

Basic Search Query

python
r = client.chat.completions.create(
    model='sonar-pro',
    messages=[
        {'role': 'system', 'content': 'Be precise and cite sources.'},
        {'role': 'user', 'content': 'Best AI agent frameworks in 2026?'}
    ]
)
print(r.choices[0].message.content)

Models

  • sonar: Fast, ~$1/1M tokens
  • sonar-pro: Higher quality + citations, ~$3/1M
  • sonar-reasoning: Step-by-step + search
  • sonar-reasoning-pro: Best reasoning + search
  • Streaming

    python
    stream = client.chat.completions.create(
        model='sonar-pro',
        messages=[{'role': 'user', 'content': 'AI chip news 2026'}],
        stream=True
    )
    for chunk in stream:
        if chunk.choices[0].delta.content:
            print(chunk.choices[0].delta.content, end='', flush=True)
    

    Multi-Turn Research Assistant

    python
    class ResearchBot:
        def __init__(self):
            self.history = []
        
        def ask(self, q: str) -> str:
            self.history.append({'role': 'user', 'content': q})
            r = client.chat.completions.create(
                model='sonar-pro',
                messages=[{'role': 'system', 'content': 'Research assistant.'}] + self.history
            )
            ans = r.choices[0].message.content
            self.history.append({'role': 'assistant', 'content': ans})
            return ans

    bot = ResearchBot() print(bot.ask('Top vector databases 2026?')) print(bot.ask('Compare pricing of those databases')) # remembers context

    Competitive Intelligence

    python
    from datetime import datetime

    def check_competitors(companies): return { c: client.chat.completions.create( model='sonar', messages=[{'role': 'user', 'content': f'Latest news from {c} this week?'}] ).choices[0].message.content for c in companies }

    report = check_competitors(['OpenAI', 'Anthropic', 'Mistral', 'Google DeepMind'])

    Conclusion

    Perplexity is the fastest way to build AI apps needing current information. For market research and competitive intelligence, it eliminates vector database maintenance.

    Also available in 中文.