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.