When to Use
Sonar Model Family
Quick Start
Chat Completions API
Citations and Sources
Search Configuration
Streaming
Error Handling
Best Practices
Anti-Patterns
Integration Checklist
When to Use Perplexity vs Others
CLI Quick Test
See Also

When to Use

This skill is loaded by backend-developer when:

openai package in requirements.txt or pyproject.toml with Perplexity base URL
Environment variables PERPLEXITY_API_KEY or PPLX_API_KEY present
User mentions "Perplexity", "Sonar", or "search-augmented" in task
Code uses api.perplexity.ai base URL

Minimum Detection Confidence: 0.8 (80%)

Sonar Model Family

Available Models

Model	Context	Search	Use Case	Speed
`sonar`	128K	Yes	General search-augmented	Fast
`sonar-pro`	200K	Yes	Complex research, deeper search	Medium
`sonar-reasoning`	128K	Yes	Multi-step reasoning with search	Slower
`sonar-reasoning-pro`	200K	Yes	Advanced reasoning, citations	Slowest

Model Selection Guide

Task Complexity -> Model Selection
+-- Simple factual queries     -> sonar (fast, affordable)
+-- Research with citations    -> sonar-pro (deeper search)
+-- Multi-step reasoning       -> sonar-reasoning
+-- Complex analysis           -> sonar-reasoning-pro

Pricing (per 1M tokens)

Model	Input	Output
sonar	$1.00	$1.00
sonar-pro	$3.00	$15.00
sonar-reasoning	$1.00	$5.00
sonar-reasoning-pro	$2.00	$8.00

Quick Start

Python Setup (Using OpenAI SDK)

from openai import OpenAI

client = OpenAI(
    api_key="pplx-...",  # or use PERPLEXITY_API_KEY env var
    base_url="https://api.perplexity.ai"
)

response = client.chat.completions.create(
    model="sonar",
    messages=[
        {"role": "system", "content": "Be precise and concise. Cite sources."},
        {"role": "user", "content": "What are the latest developments in AI?"}
    ]
)

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

# Access citations if available
if hasattr(response, 'citations'):
    for citation in response.citations:
        print(f"  - {citation}")

TypeScript Setup

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: process.env.PERPLEXITY_API_KEY,
  baseURL: 'https://api.perplexity.ai'
});

const response = await client.chat.completions.create({
  model: 'sonar',
  messages: [
    { role: 'system', content: 'Be precise and concise. Cite sources.' },
    { role: 'user', content: 'What are the latest developments in AI?' }
  ]
});

console.log(response.choices[0].message.content);

Environment Configuration

export PERPLEXITY_API_KEY="pplx-..."
# Alternative: export PPLX_API_KEY="pplx-..."

Chat Completions API

Common Parameters

Parameter	Type	Description
`model`	string	Model ID (required)
`messages`	array	Conversation messages (required)
`temperature`	float (0-2)	Randomness (0=deterministic)
`max_tokens`	int	Max response tokens
`stream`	boolean	Enable streaming

Perplexity-Specific Parameters

Parameter	Type	Description
`search_domain_filter`	array	Limit search to specific domains
`return_images`	boolean	Include relevant images
`return_related_questions`	boolean	Suggest follow-up questions
`search_recency_filter`	string	Filter by recency (day, week, month, year)

# Advanced search configuration
response = client.chat.completions.create(
    model="sonar-pro",
    messages=[{"role": "user", "content": "Recent tech layoffs?"}],
    extra_body={
        "search_domain_filter": ["techcrunch.com", "theverge.com"],
        "search_recency_filter": "week",
        "return_related_questions": True
    }
)

Citations and Sources

Perplexity automatically includes citations. Extract and display them:

def get_response_with_citations(client, query, model="sonar"):
    """Get response with structured citations."""
    response = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": query}]
    )

    content = response.choices[0].message.content
    citations = []

    if hasattr(response, 'citations'):
        citations = response.citations

    return content, citations

# Usage
answer, sources = get_response_with_citations(client, "Who won the latest Super Bowl?")
print("Answer:", answer)
for source in sources:
    print(f"  - {source}")

Search Configuration

Domain Filtering

response = client.chat.completions.create(
    model="sonar-pro",
    messages=[{"role": "user", "content": "Latest research on LLMs"}],
    extra_body={
        "search_domain_filter": ["arxiv.org", "openai.com", "anthropic.com"]
    }
)

Recency Filtering

response = client.chat.completions.create(
    model="sonar",
    messages=[{"role": "user", "content": "Stock market news"}],
    extra_body={
        "search_recency_filter": "day"  # day, week, month, year
    }
)

Academic Focus

response = client.chat.completions.create(
    model="sonar-pro",
    messages=[{
        "role": "system",
        "content": "Focus on peer-reviewed academic sources."
    }, {
        "role": "user",
        "content": "Research on sleep and memory consolidation?"
    }],
    extra_body={
        "search_domain_filter": [
            "pubmed.ncbi.nlm.nih.gov", "nature.com", "science.org"
        ]
    }
)

Streaming

Synchronous Streaming

stream = client.chat.completions.create(
    model="sonar",
    messages=[{"role": "user", "content": "Explain machine learning."}],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)

Async Streaming

import asyncio
from openai import AsyncOpenAI

async def stream_response(prompt: str):
    client = AsyncOpenAI(
        api_key="pplx-...",
        base_url="https://api.perplexity.ai"
    )

    stream = await client.chat.completions.create(
        model="sonar",
        messages=[{"role": "user", "content": prompt}],
        stream=True
    )

    async for chunk in stream:
        if chunk.choices[0].delta.content:
            print(chunk.choices[0].delta.content, end="", flush=True)

asyncio.run(stream_response("What's happening in AI today?"))

Error Handling

from openai import (
    OpenAI, RateLimitError, AuthenticationError, APIConnectionError
)
import time

def safe_search(query: str, max_retries: int = 3) -> str | None:
    client = OpenAI(api_key="pplx-...", base_url="https://api.perplexity.ai")

    for attempt in range(max_retries):
        try:
            response = client.chat.completions.create(
                model="sonar",
                messages=[{"role": "user", "content": query}]
            )
            return response.choices[0].message.content

        except AuthenticationError:
            raise  # Don't retry auth errors

        except RateLimitError:
            if attempt == max_retries - 1:
                raise
            wait_time = (2 ** attempt) + 1
            time.sleep(wait_time)

        except APIConnectionError:
            if attempt == max_retries - 1:
                raise
            time.sleep(1)

    return None

Best Practices

1. Model Selection for Task

def get_model_for_task(task_type: str) -> str:
    models = {
        "quick_lookup": "sonar",
        "research": "sonar-pro",
        "analysis": "sonar-reasoning",
        "complex": "sonar-reasoning-pro"
    }
    return models.get(task_type, "sonar")

2. Optimize for Search Quality

# BAD: vague query
messages = [{"role": "user", "content": "AI"}]

# GOOD: specific query with context
messages = [
    {"role": "system", "content": "You are a tech industry analyst."},
    {"role": "user", "content": "Top 3 AI startups that raised funding in 2024?"}
]

3. Use Recency Filters Appropriately

# Current events - use day/week filter
extra_body = {"search_recency_filter": "day"}

# General research - no filter needed
extra_body = {}

Anti-Patterns

Anti-Pattern	Problem	Solution
Using Perplexity for creative writing	Paying for unused search	Use OpenAI/Anthropic instead
Ignoring citations	Losing valuable source info	Always capture and display citations
Overly broad queries	Irrelevant search results	Be specific, add system context
Hardcoding API keys	Security risk	Use environment variables

Integration Checklist

Pre-Flight

PERPLEXITY_API_KEY environment variable set
Error handling with retry logic implemented
Appropriate model selected for task
Citation handling implemented

Production Readiness

Secrets in secret manager
Monitoring for API errors
Usage tracking and cost alerts
Fallback behavior for API failures

When to Use Perplexity vs Others

Use Case	Provider
Real-time information	Perplexity
Research with citations	Perplexity (Sonar Pro)
Current events	Perplexity
General chat/completion	OpenAI or Anthropic
Code generation	OpenAI or Anthropic
Creative writing	OpenAI or Anthropic

CLI Quick Test

# Test API access
curl -X POST https://api.perplexity.ai/chat/completions \
  -H "Authorization: Bearer $PERPLEXITY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "sonar", "messages": [{"role": "user", "content": "Hello"}]}'

When to Use

Sonar Model Family

Available Models

Model Selection Guide

Pricing (per 1M tokens)

Quick Start

Python Setup (Using OpenAI SDK)

TypeScript Setup

Environment Configuration

Chat Completions API

Common Parameters

Perplexity-Specific Parameters

Citations and Sources

Search Configuration

Domain Filtering

Recency Filtering

Academic Focus

Streaming

Synchronous Streaming

Async Streaming

Error Handling

Best Practices

1. Model Selection for Task

2. Optimize for Search Quality

3. Use Recency Filters Appropriately

Anti-Patterns

Integration Checklist

Pre-Flight

Production Readiness

When to Use Perplexity vs Others

CLI Quick Test

See Also

using-perplexity-platform

Perplexity Sonar API Development Skill

Quick Reference

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