Best Practices - Perplexity

Overview

This guide covers essential best practices for using the Perplexity SDKs in production environments. Following these practices will help you build robust, secure, and efficient applications.

Security Best Practices

Environment Variables

Always store API keys securely using environment variables:

Use environment variables

Store API keys in environment variables, never in source code.

import os
from perplexity import Perplexity

# Good: Use environment variables
client = Perplexity(
    api_key=os.environ.get("PERPLEXITY_API_KEY")
)

# Bad: Never hardcode API keys
# client = Perplexity(api_key="pplx-abc123...")  # DON'T DO THIS

Never commit API keys to version control. Use .env files locally and secure environment variable management in production.

Use .env files for local development

Create a .env file for local development (add it to .gitignore):

cat > .env << 'EOF'
PERPLEXITY_API_KEY=your_api_key_here
PERPLEXITY_MAX_RETRIES=3
PERPLEXITY_TIMEOUT=30000
EOF

from dotenv import load_dotenv
import os
from perplexity import Perplexity

# Load environment variables from .env file
load_dotenv()

client = Perplexity(
    api_key=os.getenv("PERPLEXITY_API_KEY"),
    max_retries=int(os.getenv("PERPLEXITY_MAX_RETRIES", "3"))
)

Validate environment variables

Check for required environment variables at startup.

import os
import sys
from perplexity import Perplexity

def create_client():
    api_key = os.getenv("PERPLEXITY_API_KEY")
    if not api_key:
        print("Error: PERPLEXITY_API_KEY environment variable is required")
        sys.exit(1)
    
    return Perplexity(api_key=api_key)

client = create_client()

API Key Rotation

Implement secure API key rotation:

import os
import logging
from perplexity import Perplexity
from typing import Optional

class SecurePerplexityClient:
    def __init__(self, primary_key: Optional[str] = None, fallback_key: Optional[str] = None):
        self.primary_key = primary_key or os.getenv("PERPLEXITY_API_KEY")
        self.fallback_key = fallback_key or os.getenv("PERPLEXITY_API_KEY_FALLBACK")
        self.current_client = Perplexity(api_key=self.primary_key)
        self.logger = logging.getLogger(__name__)
    
    def _switch_to_fallback(self):
        """Switch to fallback API key if available"""
        if self.fallback_key:
            self.logger.warning("Switching to fallback API key")
            self.current_client = Perplexity(api_key=self.fallback_key)
            return True
        return False
    
    def search(self, query: str, **kwargs):
        try:
            return self.current_client.search.create(query=query, **kwargs)
        except Exception as e:
            if "authentication" in str(e).lower() and self._switch_to_fallback():
                return self.current_client.search.create(query=query, **kwargs)
            raise e

# Usage
client = SecurePerplexityClient()

Rate Limiting and Efficiency

Intelligent Rate Limiting

Implement exponential backoff with jitter:

import time
import random
import asyncio
from typing import TypeVar, Callable, Any
import perplexity
from perplexity import Perplexity

T = TypeVar('T')

class RateLimitedClient:
    def __init__(self, client: Perplexity, max_retries: int = 5):
        self.client = client
        self.max_retries = max_retries
    
    def _calculate_delay(self, attempt: int) -> float:
        """Calculate delay with exponential backoff and jitter"""
        base_delay = 2 ** attempt
        jitter = random.uniform(0.1, 0.5)
        return min(base_delay + jitter, 60.0)  # Cap at 60 seconds
    
    def with_retry(self, func: Callable[[], T]) -> T:
        """Execute function with intelligent retry logic"""
        last_exception = None
        
        for attempt in range(self.max_retries):
            try:
                return func()
            except perplexity.RateLimitError as e:
                last_exception = e
                if attempt < self.max_retries - 1:
                    delay = self._calculate_delay(attempt)
                    print(f"Rate limited. Retrying in {delay:.2f}s (attempt {attempt + 1})")
                    time.sleep(delay)
                    continue
                raise e
            except perplexity.APIConnectionError as e:
                last_exception = e
                if attempt < self.max_retries - 1:
                    delay = min(2 ** attempt, 10.0)  # Shorter delay for connection errors
                    print(f"Connection error. Retrying in {delay:.2f}s")
                    time.sleep(delay)
                    continue
                raise e
        
        raise last_exception
    
    def search(self, query: str, **kwargs):
        return self.with_retry(
            lambda: self.client.search.create(query=query, **kwargs)
        )

# Usage
client = RateLimitedClient(Perplexity())
result = client.search("artificial intelligence")

Request Batching

Efficiently batch multiple requests:

import asyncio
from typing import Callable, Awaitable, List, TypeVar, Generic
from perplexity import AsyncPerplexity, DefaultAioHttpClient

T = TypeVar('T')

class BatchProcessor(Generic[T]):
    def __init__(self, batch_size: int = 5, delay_between_batches: float = 1.0):
        self.batch_size = batch_size
        self.delay_between_batches = delay_between_batches
    
    async def process_batch(
        self, 
        items: List[str], 
        process_func: Callable[[str], Awaitable[T]]
    ) -> List[T]:
        """Process items in batches with rate limiting"""
        results = []
        
        for i in range(0, len(items), self.batch_size):
            batch = items[i:i + self.batch_size]
            
            # Process batch concurrently
            tasks = [process_func(item) for item in batch]
            batch_results = await asyncio.gather(*tasks, return_exceptions=True)
            
            # Filter out exceptions and collect results
            for result in batch_results:
                if not isinstance(result, Exception):
                    results.append(result)
            
            # Delay between batches
            if i + self.batch_size < len(items):
                await asyncio.sleep(self.delay_between_batches)
        
        return results

# Usage
async def main():
    processor = BatchProcessor(batch_size=3, delay_between_batches=0.5)
    
    async with AsyncPerplexity(
        http_client=DefaultAioHttpClient()
    ) as client:
        
        async def search_query(query: str):
            return await client.search.create(query=query)
        
        queries = ["AI", "ML", "DL", "NLP", "CV"]
        results = await processor.process_batch(queries, search_query)
        
        print(f"Processed {len(results)} successful queries")

asyncio.run(main())

Production Configuration

Configuration Management

Use environment-based configuration for different deployment stages:

import os
from dataclasses import dataclass
from typing import Optional
import httpx
from perplexity import Perplexity, DefaultHttpxClient

@dataclass
class PerplexityConfig:
    api_key: str
    max_retries: int = 3
    timeout_seconds: float = 30.0
    max_connections: int = 100
    max_keepalive: int = 20
    environment: str = "production"
    
    @classmethod
    def from_env(cls) -> "PerplexityConfig":
        """Load configuration from environment variables"""
        api_key = os.getenv("PERPLEXITY_API_KEY")
        if not api_key:
            raise ValueError("PERPLEXITY_API_KEY environment variable is required")
        
        return cls(
            api_key=api_key,
            max_retries=int(os.getenv("PERPLEXITY_MAX_RETRIES", "3")),
            timeout_seconds=float(os.getenv("PERPLEXITY_TIMEOUT", "30.0")),
            max_connections=int(os.getenv("PERPLEXITY_MAX_CONNECTIONS", "100")),
            max_keepalive=int(os.getenv("PERPLEXITY_MAX_KEEPALIVE", "20")),
            environment=os.getenv("ENVIRONMENT", "production")
        )
    
    def create_client(self) -> Perplexity:
        """Create optimized client based on configuration"""
        timeout = httpx.Timeout(
            connect=5.0,
            read=self.timeout_seconds,
            write=10.0,
            pool=10.0
        )
        
        limits = httpx.Limits(
            max_keepalive_connections=self.max_keepalive,
            max_connections=self.max_connections,
            keepalive_expiry=60.0 if self.environment == "production" else 30.0
        )
        
        return Perplexity(
            api_key=self.api_key,
            max_retries=self.max_retries,
            timeout=timeout,
            http_client=DefaultHttpxClient(limits=limits)
        )

# Usage
config = PerplexityConfig.from_env()
client = config.create_client()

Monitoring and Logging

Implement comprehensive monitoring:

import logging
import time
import functools
from typing import Any, Callable
from perplexity import Perplexity
import perplexity

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)

class MonitoredPerplexityClient:
    def __init__(self, client: Perplexity):
        self.client = client
        self.request_count = 0
        self.error_count = 0
        self.total_response_time = 0.0
    
    def _log_request(self, method: str, **kwargs):
        """Log request details"""
        self.request_count += 1
        logger.info(f"Making {method} request #{self.request_count}")
        logger.debug(f"Request parameters: {kwargs}")
    
    def _log_response(self, method: str, duration: float, success: bool = True):
        """Log response details"""
        self.total_response_time += duration
        avg_response_time = self.total_response_time / self.request_count
        
        if success:
            logger.info(f"{method} completed in {duration:.2f}s (avg: {avg_response_time:.2f}s)")
        else:
            self.error_count += 1
            logger.error(f"{method} failed after {duration:.2f}s (errors: {self.error_count})")
    
    def search(self, query: str, **kwargs):
        self._log_request("search", query=query, **kwargs)
        start_time = time.time()
        
        try:
            result = self.client.search.create(query=query, **kwargs)
            duration = time.time() - start_time
            self._log_response("search", duration, success=True)
            return result
        except Exception as e:
            duration = time.time() - start_time
            self._log_response("search", duration, success=False)
            logger.error(f"Search error: {type(e).__name__}: {e}")
            raise
    
    def get_stats(self):
        """Get client statistics"""
        return {
            "total_requests": self.request_count,
            "error_count": self.error_count,
            "success_rate": (self.request_count - self.error_count) / max(self.request_count, 1),
            "avg_response_time": self.total_response_time / max(self.request_count, 1)
        }

# Usage
client = MonitoredPerplexityClient(Perplexity())
result = client.search("machine learning")
print(client.get_stats())

Error Handling Best Practices

Graceful Degradation

Implement fallback strategies for different error types:

from typing import Optional, Dict, Any
import perplexity
from perplexity import Perplexity

class ResilientPerplexityClient:
    def __init__(self, client: Perplexity):
        self.client = client
        self.circuit_breaker_threshold = 5
        self.circuit_breaker_count = 0
        self.circuit_breaker_open = False
    
    def _should_circuit_break(self) -> bool:
        """Check if circuit breaker should be triggered"""
        return self.circuit_breaker_count >= self.circuit_breaker_threshold
    
    def _record_failure(self):
        """Record a failure for circuit breaker"""
        self.circuit_breaker_count += 1
        if self._should_circuit_break():
            self.circuit_breaker_open = True
            print("Circuit breaker activated - temporarily disabling API calls")
    
    def _record_success(self):
        """Record a success - reset circuit breaker"""
        self.circuit_breaker_count = 0
        self.circuit_breaker_open = False
    
    def search_with_fallback(
        self, 
        query: str, 
        fallback_response: Optional[Dict[str, Any]] = None
    ):
        """Search with graceful degradation"""
        if self.circuit_breaker_open:
            print("Circuit breaker open - returning fallback response")
            return fallback_response or {
                "query": query,
                "results": [],
                "status": "service_unavailable"
            }
        
        try:
            result = self.client.search.create(query=query)
            self._record_success()
            return result
            
        except perplexity.RateLimitError:
            print("Rate limited - implementing backoff strategy")
            # Could implement intelligent backoff here
            raise
            
        except perplexity.APIConnectionError as e:
            print(f"Connection error: {e}")
            self._record_failure()
            return fallback_response or {
                "query": query,
                "results": [],
                "status": "connection_error"
            }
            
        except Exception as e:
            print(f"Unexpected error: {e}")
            self._record_failure()
            return fallback_response or {
                "query": query,
                "results": [],
                "status": "error"
            }

# Usage
client = ResilientPerplexityClient(Perplexity())
result = client.search_with_fallback("machine learning")

Testing Best Practices

Unit Testing with Mocking

Create testable code with proper mocking:

import unittest
from unittest.mock import Mock, patch
from perplexity import Perplexity, RateLimitError
from perplexity.types.search_create_response import SearchCreateResponse, Result

class TestPerplexityIntegration(unittest.TestCase):
    def setUp(self):
        self.mock_client = Mock(spec=Perplexity)

    def test_search_success(self):
        # Mock successful response
        mock_result = Result(
            title="Test Result",
            url="https://example.com",
            snippet="Test snippet"
        )

        mock_response = SearchCreateResponse(
            id="search_123",
            results=[mock_result]
        )

        self.mock_client.search.create.return_value = mock_response

        # Test your application logic
        result = self.mock_client.search.create(query="Perplexity Search API rate limit and retry handling example")

        self.assertEqual(result.id, "search_123")
        self.assertEqual(len(result.results), 1)
        self.assertEqual(result.results[0].title, "Test Result")

    @patch('perplexity.Perplexity')
    def test_rate_limit_handling(self, mock_perplexity_class):
        # Mock rate limit error
        mock_client = Mock()
        mock_perplexity_class.return_value = mock_client
        mock_client.search.create.side_effect = RateLimitError(
            "Rate limited",
            response=Mock(status_code=429),
            body={}
        )

        # Test your error handling logic here
        with self.assertRaises(RateLimitError):
            mock_client.search.create(query="Perplexity Search API Python SDK quickstart example")

if __name__ == '__main__':
    unittest.main()

Response — Perplexity Search API rate limit and retry handling example

{
  "id": "fc4a9702-2f8c-454c-bff5-04f63041aaa7",
  "results": [
    {
      "snippet": "The SDKs provide specific exception types for different error scenarios:\n* **APIConnectionError** - Network connection issues\n* **RateLimitError** - API rate limit exceeded\n* **APIStatusError** - HTTP status errors (4xx, 5xx)\n* **AuthenticationError** - Invalid API key or authentication issues\n* **ValidationError** - Invalid request parameters\n...\nHandle common API errors with try-catch blocks:\n<CodeGroup>\n```python Python theme={null}\nimport perplexity\nfrom perplexity import Perplexity\nclient = Perplexity()\ntry:\nsearch = client.search.create(query=\"machine learning\")\nprint(search.results)\nexcept perplexity.APIConnectionError as e:\nprint(\"Network connection failed\")\nprint(e.__cause__)\nexcept perplexity.RateLimitError as e:\nprint(\"Rate limit exceeded, please retry later\")\nexcept perplexity.APIStatusError as e:\nprint(f\"API error: {e.status_code}\")\nprint(e.response)\n```\n```typescript TypeScript theme={null}\nimport Perplexity from '@perplexity-ai/perplexity_ai';\nconst client = new Perplexity();\ntry {\nconst search = await client.search.create({ query: \"machine learning\" });\nconsole.log(search.results);\n} catch (error: any) {\nif (error.constructor.name === 'APIConnectionError') {\nconsole.log(\"Network connection failed\");\nconsole.log(error.cause);\n} else if (error.constructor.name === 'RateLimitError') {\nconsole.log(\"Rate limit exceeded, please retry later\");\n} else if (error.constructor.name === 'APIStatusError') {\nconsole.log(`API error: ${error.status}`);\nconsole.log(error.response);\n}\n}\n```\n</CodeGroup>\n...\n### Exponential Backoff for Rate Limits\nImplement intelligent retry logic for rate limit errors:\n<CodeGroup>\n```python Python theme={null}\nimport time\nimport random\nimport perplexity\nfrom perplexity import Perplexity\ndef search_with_retry(client, query, max_retries=3):\nfor attempt in range(max_retries):\ntry:\nreturn client.search.create(query=query)\nexcept perplexity.RateLimitError:\nif attempt == max_retries - 1:\nraise  # Re-raise on final attempt\n# Exponential backoff with jitter\ndelay = (2 ** attempt) + random.uniform(0, 1)\nprint(f\"Rate limited. Retrying in {delay:.2f} seconds...\")\n              time.sleep(delay)\nexcept perplexity.APIConnectionError:\nif attempt == max_retries - 1:\nraise\n# Shorter delay for connection errors\ndelay = 1 + random.uniform(0, 1)\nprint(f\"Connection error. Retrying in {delay:.2f} seconds...\")\n              time.sleep(delay)\n# Usage\nclient = Perplexity()\nresult = search_with_retry(client, \"artificial intelligence\")\n```\n```typescript TypeScript theme={null}\nimport Perplexity from '@perplexity-ai/perplexity_ai';\nasync function searchWithRetry(\nclient: Perplexity, \n      query: string, \n      maxRetries: number = 3\n) {\nfor (let attempt = 0; attempt < maxRetries; attempt++) {\ntry {\nreturn await client.search.create({ query });\n} catch (error: any) {\nif (attempt === maxRetries - 1) {\nthrow error; // Re-throw on final attempt\n}\nif (error.constructor.name === 'RateLimitError') {\n// Exponential backoff with jitter\nconst delay = (2 ** attempt + Math.random()) * 1000;\nconsole.log(`Rate limited. Retrying in ${delay}ms...`);\nawait new Promise(resolve => setTimeout(resolve, delay));\n} else if (error.constructor.name === 'APIConnectionError') {\n// Shorter delay for connection errors\nconst delay = (1 + Math.random()) * 1000;\nconsole.log(`Connection error. Retrying in ${delay}ms...`);\nawait new Promise(resolve => setTimeout(resolve, delay));\n} else {\nthrow error; // Don't retry other errors\n}\n}\n}\n}\n// Usage\nconst client = new Perplexity();\nconst result = await searchWithRetry(client, \"artificial intelligence\");\n```\n</CodeGroup>\n...\n```python Python theme={null}\nimport perplexity\nfrom perplexity import Perplexity\nclient = Perplexity()\ntry:\nchat = client.chat.completions.create(\nmodel=\"sonar-pro\",\nmessages=[{\"role\": \"user\", \"content\": \"What's the weather?\"}]\n)\nexcept perplexity.APIStatusError as e:\nprint(f\"Status Code: {e.status_code}\")\nprint(f\"Error Type: {e.type}\")\nprint(f\"Error Message: {e.message}\")\n# Access raw response for detailed debugging\nif hasattr(e, 'response'):\nprint(f\"Raw Response: {e.response.text}\")\nprint(f\"Request ID: {e.response.headers.get('X-Request-ID')}\")\nexcept perplexity.ValidationError as e:\nprint(f\"Validation Error: {e}\")\n# Handle parameter validation errors\nexcept Exception as e:\nprint(f\"Unexpected error: {type(e).__name__}: {e}\")\n```\n...\nImplement fallback mechanisms when API calls fail:\n<CodeGroup>\n```python Python theme={null}\nimport perplexity\nfrom perplexity import Perplexity\ndef get_ai_response(query, fallback_response=\"I'm sorry, I'm temporarily unavailable.\"):\nclient = Perplexity()\ntry:\n# Primary: Try online model\nresponse = client.chat.completions.create(\nmodel=\"sonar-pro\",\nmessages=[{\"role\": \"user\", \"content\": query}]\n)\nreturn response.choices[0].message.content\nexcept perplexity.RateLimitError:\ntry:\n# Fallback: Try offline model if rate limited\nresponse = client.chat.completions.create(\nmodel=\"llama-3.1-8b-instruct\",\nmessages=[{\"role\": \"user\", \"content\": query}]\n)\nreturn response.choices[0].message.content\nexcept Exception:\nreturn fallback_response\nexcept perplexity.APIConnectionError:\n# Network issues - return cached response or fallback\nreturn fallback_response\nexcept Exception as e:\nprint(f\"Unexpected error: {e}\")\nreturn fallback_response\n# Usage\nresponse = get_ai_response(\"What is machine learning?\")\n...\n<Step title=\"Always handle rate limits\">\nRate limiting is common with API usage.\nAlways implement retry logic with exponential backoff.\n<Warning>\nDon't implement aggressive retry loops without delays - this can worsen rate limiting.\n...\nInclude proper logging to track error patterns and API health.\n...\nCheck for invalid API keys and provide helpful error messages.\n<CodeGroup>",
      "title": "Error Handling",
      "url": "https://docs.perplexity.ai/docs/sdk/error-handling",
      "date": null,
      "last_updated": "2026-05-27"
    },
    {
      "snippet": "",
      "title": "Best Practices",
      "url": "https://docs.perplexity.ai/docs/search/best-practices",
      "date": null,
      "last_updated": "2026-05-26"
    },
    {
      "snippet": "",
      "title": "Perplexity API: Setup, Models, Integration & Best Practices",
      "url": "https://zuplo.com/learning-center/perplexity-api",
      "date": "2025-03-28",
      "last_updated": "2026-05-24"
    },
    {
      "snippet": "I am getting a \" [429] Request rate limit exceeded, please try again later.\" error from make.com.\n...\nYou can try placing a sleep module before each ai module to wait 3-5 seconds.",
      "title": "How can i optimise the number of api calls, and cell ...",
      "url": "https://community.make.com/t/how-can-i-optimise-the-number-of-api-calls-and-cell-updates-in-this-scenario/49923",
      "date": "2024-08-07",
      "last_updated": "2026-05-16"
    },
    {
      "snippet": "## Search API Rate Limits\nThe Search API has separate rate limits that apply to all usage tiers:\n| Endpoint       | Rate Limit             | Burst Capacity |\n| -------------- | ---------------------- | -------------- |\n| POST `/search` | 50 requests per second | 50 requests    |\n**Search Rate Limiter Behavior:**\n* **Burst**: Can handle 50 requests instantly\n* **Sustained**: Exactly 50 QPS average over time\n<Note>\nSearch rate limits are independent of your usage tier and apply consistently across all accounts using the same leaky bucket algorithm.\n</Note>\n...\nWhen you exceed your rate limits:\n1. **429 Error** - Your request gets rejected with \"Too Many Requests\"\n2. **Continuous Refill** - Tokens refill continuously based on your rate limit\n3. **Immediate Recovery** - New requests become available as soon as tokens refill\n**Example Recovery Times:**\n* **50 QPS limit**: 1 token refills every 20ms\n* **500 QPS limit**: 1 token refills every 2ms\n* **1,000 QPS limit**: 1 token refills every 1ms\n...\nVisit the [API Platform console](https://console.perplexity.ai) to see your current tier and total spending.\n...\nAdd credits to your account through the billing section.\nYour tier will automatically upgrade once you reach the spending threshold.\n...\nYour new rate limits take effect immediately after the tier upgrade.\nCheck your settings page to confirm.\n...\nIf you require custom rate limits beyond Tier 5, [fill out our rate limit increase request form](https://perplexity.typeform.com/to/yctmfyVT) and we'll review your use case to accommodate your needs.",
      "title": "Rate Limits & Usage Tiers - Perplexity API",
      "url": "https://docs.perplexity.ai/docs/admin/rate-limits-usage-tiers",
      "date": null,
      "last_updated": "2026-05-26"
    },
    {
      "snippet": "",
      "title": "Best Practices",
      "url": "https://docs.perplexity.ai/docs/sdk/best-practices",
      "date": null,
      "last_updated": "2026-05-27"
    },
    {
      "snippet": "Use highly specific queries for more targeted results.\nFor example, instead of searching for “AI”, use a detailed query like “artificial intelligence machine learning healthcare applications 2024”.\n```\n# Better: Specific query\nsearch = client.search.create(\nquery=\"artificial intelligence medical diagnosis accuracy 2024\",\nmax_results=10\n)\n# Avoid: Vague query\nsearch = client.search.create(\nquery=\"AI medical\",\n...\n)\n```\n...\nBreak your main topic into related sub-queries to cover all aspects of your research.\nUse the multi-query search feature to run multiple related queries in a single request for more comprehensive and relevant information.\n```\n...\nclient = Perplexity()\n# Comprehensive research with related queries\nsearch = client.search.create(\n...\n)\n...\n```\nYou can include up to 5 queries in a single multi-query request for efficient batch processing.\n3\nHandle rate limits efficiently\nImplement exponential backoff for rate limit errors and use appropriate batching strategies.\n```\nimport time\nimport random\nfrom perplexity import RateLimitError\ndef search_with_retry(client, query, max_retries=3):\nfor attempt in range(max_retries):\ntry:\nreturn client.search.create(query=query)\nexcept RateLimitError:\nif attempt < max_retries - 1:\n# Exponential backoff with jitter\ndelay = (2 ** attempt) + random.uniform(0, 1)\ntime.sleep(delay)\nelse:\nraise\n# Usage\ntry:\nsearch = search_with_retry(client, \"AI developments\")\nfor result in search.results:\nprint(f\"{result.title}: {result.url}\")\nexcept RateLimitError:\nprint(\"Maximum retries exceeded for search\")\n```\n4\nProcess concurrent searches efficiently\nUse async for concurrent requests while respecting rate limits.\n```\nimport asyncio\nfrom perplexity import AsyncPerplexity\nasync def batch_search(queries, batch_size=3, delay_ms=1000):\nasync with AsyncPerplexity() as client:\nresults = []\nfor i in range(0, len(queries), batch_size):\nbatch = queries[i:i + batch_size]\nbatch_tasks = [\nclient.search.create(query=query, max_results=5)\nfor query in batch\n]\nbatch_results = await asyncio.gather(*batch_tasks)\nresults.extend(batch_results)\n# Add delay between batches\nif i + batch_size < len(queries):\nawait asyncio.sleep(delay_ms / 1000)\nreturn results\n# Usage\nqueries = [\"AI developments\", \"climate change\", \"space exploration\"]\nresults = asyncio.run(batch_search(queries))\nprint(f\"Processed {len(results)} searches\")\n```\n...\n#### Rate-Limited Concurrent Processing\nFor large-scale applications, implement controlled concurrency with rate limiting:\n```\nimport asyncio\nfrom perplexity import AsyncPerplexity\nclass SearchManager:\ndef __init__(self, max_concurrent=5, delay_between_batches=1.0):\nself.max_concurrent = max_concurrent\nself.delay_between_batches = delay_between_batches\nself.semaphore = asyncio.Semaphore(max_concurrent)\nasync def search_single(self, client, query):\nasync with self.semaphore:\nreturn await client.search.create(query=query, max_results=5)\nasync def search_many(self, queries):\nasync with AsyncPerplexity() as client:\ntasks = [\nself.search_single(client, query)\nfor query in queries\n]\nresults = await asyncio.gather(*tasks, return_exceptions=True)\n# Filter out exceptions and return successful results\nsuccessful_results = [\nresult for result in results\nif not isinstance(result, Exception)\n]\nreturn successful_results\n# Usage\nasync def main():\nmanager = SearchManager(max_concurrent=3)\nqueries = [\n\"AI research 2024\",\n\"quantum computing advances\",\n\"renewable energy innovations\",\n\"biotechnology breakthroughs\",\n\"space exploration updates\"\n]\nresults = await manager.search_many(queries)\nprint(f\"Successfully processed {len(results)} out of {len(queries)} searches\")\nasyncio.run(main())\n```\n#### Error Handling in Async Operations\nImplement robust error handling for async search operations:\n```\nimport asyncio\nimport logging\nfrom perplexity import AsyncPerplexity, APIStatusError, RateLimitError\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger(__name__)\nasync def resilient_search(client, query, max_retries=3):\nfor attempt in range(max_retries):\ntry:\nresult = await client.search.create(query=query, max_results=5)\nlogger.info(f\"Search successful for: {query}\")\nreturn result\nexcept RateLimitError as e:\nif attempt < max_retries - 1:\ndelay = 2 ** attempt\nlogger.warning(f\"Rate limited for '{query}', retrying in {delay}s\")\nawait asyncio.sleep(delay)\nelse:\nlogger.error(f\"Max retries exceeded for: {query}\")\nreturn None\nexcept APIStatusError as e:\nlogger.error(f\"API error for '{query}': {e}\")\nreturn None\nexcept Exception as e:\nlogger.error(f\"Unexpected error for '{query}': {e}\")\nreturn None\nasync def main():\nasync with AsyncPerplexity() as client:\nqueries = [\"AI developments\", \"invalid query\", \"tech trends\"]\ntasks = [resilient_search(client, query) for query in queries]\nresults = await asyncio.gather(*tasks)\nsuccessful_results = [r for r in results if r is not None]\nprint(f\"Successful searches: {len(successful_results)}/{len(queries)}\")\nasyncio.run(main())\n```\n...\nRequest only the number of results you actually need.\nMore results = longer response times.\n...\nImplement caching for queries that don’t need real-time results.",
      "title": "Best Practices - Perplexity",
      "url": "https://docs.perplexity.ai/guides/search-best-practices",
      "date": "2025-10-09",
      "last_updated": "2026-05-10"
    },
    {
      "snippet": "The SDKs provide specific exception types for different error scenarios: - **APIConnectionError** - Network connection issues\n- **RateLimitError** - API rate limit exceeded\n- **APIStatusError** - HTTP status errors (4xx, 5xx)\n- **AuthenticationError** - Invalid API key or authentication issues\n- **ValidationError** - Invalid request parameters\n## ​ Basic Error Handling\nHandle common API errors with try-catch blocks:\n```\nimport perplexity\nfrom perplexity import Perplexity\nclient = Perplexity()\ntry:\nsearch = client.search.create(query=\"machine learning\")\nprint(search.results)\nexcept perplexity.APIConnectionError as e:\nprint(\"Network connection failed\")\nprint(e.__cause__)\nexcept perplexity.RateLimitError as e:\nprint(\"Rate limit exceeded, please retry later\")\nexcept perplexity.APIStatusError as e:\nprint(f\"API error: {e.status_code}\")\nprint(e.response)\n```\nCommon HTTP status codes: 400 (Bad Request), 401 (Authentication), 403 (Permission Denied), 404 (Not Found), 429 (Rate Limit), 500+ (Server Error).\n...\n### ​ Exponential Backoff for Rate Limits\nImplement intelligent retry logic for rate limit errors:\n```\nimport time\nimport random\nimport perplexity\nfrom perplexity import Perplexity\ndef search_with_retry(client, query, max_retries=3):\nfor attempt in range(max_retries):\ntry:\nreturn client.search.create(query=query)\nexcept perplexity.RateLimitError:\nif attempt == max_retries - 1:\nraise # Re-raise on final attempt\n# Exponential backoff with jitter\ndelay = (2 ** attempt) + random.uniform(0, 1)\nprint(f\"Rate limited. Retrying in {delay:.2f} seconds...\")\n time.sleep(delay)\nexcept perplexity.APIConnectionError:\nif attempt == max_retries - 1:\nraise\n# Shorter delay for connection errors\ndelay = 1 + random.uniform(0, 1)\nprint(f\"Connection error. Retrying in {delay:.2f} seconds...\")\n time.sleep(delay)\n# Usage\nclient = Perplexity()\nresult = search_with_retry(client, \"artificial intelligence\")\n```\n...\nImplement fallback mechanisms when API calls fail:\n```\nimport perplexity\nfrom perplexity import Perplexity\ndef get_ai_response(query, fallback_response=\"I'm sorry, I'm temporarily unavailable.\"):\nclient = Perplexity()\ntry:\n# Primary: Try online model\nresponse = client.chat.completions.create(\nmodel=\"sonar-pro\",\nmessages=[{\"role\": \"user\", \"content\": query}]\n)\nreturn response.choices[0].message.content\nexcept perplexity.RateLimitError:\ntry:\n# Fallback: Try offline model if rate limited\nresponse = client.chat.completions.create(\nmodel=\"llama-3.1-8b-instruct\",\nmessages=[{\"role\": \"user\", \"content\": query}]\n)\nreturn response.choices[0].message.content\nexcept Exception:\nreturn fallback_response\nexcept perplexity.APIConnectionError:\n# Network issues - return cached response or fallback\nreturn fallback_response\nexcept Exception as e:\nprint(f\"Unexpected error: {e}\")\nreturn fallback_response\n# Usage\nresponse = get_ai_response(\"What is machine learning?\")\nprint(response)\n```\n...\n1\nAlways handle rate limits\nRate limiting is common with API usage.\nAlways implement retry logic with exponential backoff.\n2\nLog errors for monitoring\nInclude proper logging to track error patterns and API health.\n...\n3\nSet appropriate timeouts\nConfigure timeouts to prevent hanging requests.\n```\n...\nCheck for invalid API keys and provide helpful error messages.\n```\ntry:\nresult = client.search.create(query=\"test\")\n...\nprint(\"Invalid API key. Please check your PERPLEXITY_API_KEY environment variable.\")\n```",
      "title": "Error Handling - Perplexity",
      "url": "https://docs.perplexity.ai/guides/perplexity-sdk-error-handling",
      "date": "2025-12-05",
      "last_updated": "2026-01-27"
    },
    {
      "snippet": "",
      "title": "Fix Perplexity API Errors 429, 405, 500 | Complete Tutorial 2025",
      "url": "https://www.hostingseekers.com/blog/fix-perplexity-api-errors-tutorial/",
      "date": "2025-11-21",
      "last_updated": "2026-05-16"
    }
  ],
  "server_time": null
}

Response — Perplexity Search API Python SDK quickstart example

{
  "id": "a3284d7c-f02c-45fc-82db-d368ad880edc",
  "results": [
    {
      "snippet": "",
      "title": "Quickstart - Perplexity API",
      "url": "https://docs.perplexity.ai/docs/getting-started/quickstart",
      "date": null,
      "last_updated": "2026-05-26"
    },
    {
      "snippet": "",
      "title": "Perplexity Search API",
      "url": "https://docs.perplexity.ai/docs/search/quickstart",
      "date": null,
      "last_updated": "2026-05-27"
    },
    {
      "snippet": "# Quickstart\n> Learn how to use the official Perplexity SDKs for Python and TypeScript to access the Perplexity APIs with type safety and async support.\n...\nThe official Perplexity SDKs provide convenient access to the Perplexity APIs from Python 3.8+ and Node.js applications.\nBoth SDKs include type definitions for all request parameters and response fields, with both synchronous and asynchronous clients.\nAccess four APIs: **Agent API** for third-party models with web search tools and presets, **Search** for ranked web search results, **Sonar** for web-grounded AI responses, and **Embeddings** for generating text embeddings.\n...\n<Card title=\"Search\" icon=\"search\" href=\"/docs/search/quickstart\">\nRanked web search results with filtering, multi-query support, and domain controls.\n...\n## Installation\nInstall the SDK for your preferred language:\n<CodeGroup>\n```bash Python theme={null}\npip install perplexityai\n```\n```bash TypeScript theme={null}\nnpm install @perplexity-ai/perplexity_ai\n```\n</CodeGroup>\n## Authentication\n<Card title=\"Get your Perplexity API Key\" icon=\"key\" arrow=\"True\" horizontal=\"True\" iconType=\"solid\" cta=\"Click here\" href=\"https://console.perplexity.ai\">\nNavigate to the **API Keys** tab in the API Portal and generate a new key.\n</Card>\nAfter generating the key, set it as an environment variable in your terminal:\n<Tabs>\n<Tab title=\"Windows\">\n```bash theme={null}\nsetx PERPLEXITY_API_KEY \"your_api_key_here\"\n```\n</Tab>\n<Tab title=\"MacOS/Linux\">\n```bash theme={null}\nexport PERPLEXITY_API_KEY=\"your_api_key_here\"\n```\n</Tab>\n</Tabs>\n### Using Environment Variables\nYou can use the environment variable directly:\n<CodeGroup>\n```python Python theme={null}\nimport os\nfrom perplexity import Perplexity\nclient = Perplexity() # Automatically uses PERPLEXITY_API_KEY\n```\n```typescript TypeScript theme={null}\nimport Perplexity from '@perplexity-ai/perplexity_ai';\nconst client = new Perplexity({\napiKey: process.env['PERPLEXITY_API_KEY'], // This is the default and can be omitted\n});\n```\n</CodeGroup>\nOr use [python-dotenv](https://pypi.org/project/python-dotenv/) (Python) or [dotenv](https://www.npmjs.com/package/dotenv) (Node.js) to load the environment variable from a `.env` file:\n<CodeGroup>\n```python Python theme={null}\nimport os\nfrom dotenv import load_dotenv\nfrom perplexity import Perplexity\nload_dotenv()\nclient = Perplexity() # Uses PERPLEXITY_API_KEY from .env file\n```\n```typescript TypeScript theme={null}\nimport Perplexity from '@perplexity-ai/perplexity_ai';\nimport dotenv from 'dotenv';\ndotenv.config();\nconst client = new Perplexity(); // Uses PERPLEXITY_API_KEY from .env file\n```\n</CodeGroup>\n<Tip>\nNow you're ready to start using the Perplexity APIs!\nChoose your API below for step-by-step usage guides.\n</Tip>\n<CardGroup cols={2}>\n<Card title=\"Agent API\" icon=\"code-circle\" href=\"/docs/agent-api/quickstart\">\nGet started with third-party models\n</Card>\n<Card title=\"Search\" icon=\"search\" href=\"/docs/search/quickstart\">\nGet started with web search\n</Card>\n...\nGet started with AI responses\n</Card>\n<Card title=\"Embeddings\" icon=\"cube\" href=\"/docs/embeddings/quickstart\">\nGet started with text embeddings\n...\nInstall from PyPI with pip\n</Card>\n<Card title=\"Node.js and TypeScript Package\" icon=\"brand-npm\" href=\"https://www.npmjs.com/package/@perplexity-ai/perplexity_ai\">\nInstall from npm registry\n</Card>\n</CardGroup>",
      "title": "Quickstart - Perplexity API",
      "url": "https://docs.perplexity.ai/docs/sdk/overview",
      "date": null,
      "last_updated": "2026-05-27"
    },
    {
      "snippet": "",
      "title": "Perplexity API",
      "url": "https://docs.perplexity.ai/docs/getting-started/overview",
      "date": null,
      "last_updated": "2026-05-26"
    },
    {
      "snippet": "The Perplexity Python library provides convenient access to the Perplexity REST API from any Python 3.8+\napplication.\nThe library includes type definitions for all request params and response fields,\nand offers both synchronous and asynchronous clients powered by httpx.\nIt is generated with Stainless.\n...\nThe REST API documentation can be found on docs.perplexity.ai.\nThe full API of this library can be found in api.md.\n## Installation\n```\n# install from PyPI\npip install perplexityai\n```\n## Search API\nGet web search results:\n```\nimport os\nfrom perplexity import Perplexity\nclient = Perplexity(\napi_key=os.environ.get(\"PERPLEXITY_API_KEY\"), # This is the default and can be omitted\n)\nsearch = client.search.create(\nquery=\"latest AI developments 2024\",\nmax_results=5\n)\nfor result in search.results:\nprint(f\"{result.title}: {result.url}\")\n```\n...\n```\n...\nfrom perplexity import Perplexity\nclient = Perplexity(\n...\n)\n...\nmessages=[\n{\n...\n\"content\": \"Tell me about the latest developments in AI\",\n}\n],\nmodel=\"sonar\",\n)\nprint(stream_chunk.id)\n```\n...\nSimply import `AsyncPerplexity` instead of `Perplexity` and use `await` with each API call:\n```\nimport os\nimport asyncio\nfrom perplexity import AsyncPerplexity\nclient = AsyncPerplexity(\napi_key=os.environ.get(\"PERPLEXITY_API_KEY\"), # This is the default and can be omitted\n)\nasync def main() -> None:\nstream_chunk = await client.chat.completions.create(\nmessages=[\n{\n\"role\": \"user\",\n\"content\": \"Tell me about the latest developments in AI\",\n}\n],\nmodel=\"sonar\",\n)\nprint(stream_chunk.id)\nasyncio.run(main())\n```\n...\nYou can enable this by installing `aiohttp`:\n```\n# install from PyPI\npip install perplexityai[aiohttp]\n```\nThen you can enable it by instantiating the client with `http_client=DefaultAioHttpClient()`:\n```\nimport asyncio\nfrom perplexity import DefaultAioHttpClient",
      "title": "perplexityai/perplexity-py - GitHub",
      "url": "https://github.com/perplexityai/perplexity-py",
      "date": "2025-09-25",
      "last_updated": "2025-10-19"
    },
    {
      "snippet": "## Get your Perplexity API Key\nNavigate to the **API Keys** tab in the API Portal and generate a new key.\nSee the API Groups page to set up an API group.\n**OpenAI SDK Compatible:** Perplexity’s API supports the OpenAI Chat Completions format.\nYou can use OpenAI client libraries by pointing to our endpoint.\n...\n## ​ Making Your First API Call\n- Python SDK\n- TypeScript SDK\n- cURL\n**Install the SDK first:** `pip install perplexityai`\n```\nfrom perplexity import Perplexity\n# Initialize the client (uses PERPLEXITY_API_KEY environment variable)\nclient = Perplexity()\n# Make the API call\ncompletion = client.chat.completions.create(\nmodel=\"sonar-pro\",\nmessages=[\n{\"role\": \"user\", \"content\": \"What were the results of the 2025 French Open Finals?\"}\n]\n)\n# Print the AI's response\nprint(completion.choices[0].message.content)\n```\nSet your API key as an environment variable: `export PERPLEXITY_API_KEY=\"your_api_key_here\"` (macOS/Linux) or `setx PERPLEXITY_API_KEY \"your_api_key_here\"` (Windows).",
      "title": "Quickstart",
      "url": "https://docs.perplexity.ai/getting-started/quickstart",
      "date": "2026-01-10",
      "last_updated": "2026-01-27"
    },
    {
      "snippet": "",
      "title": "Perplexity Search APIdocs.perplexity.ai › guides › search-quickstart",
      "url": "https://docs.perplexity.ai/docs/search/quickstart.md",
      "date": null,
      "last_updated": "2026-05-27"
    },
    {
      "snippet": "### 2.\nSearch API Quickstart (Python & TypeScript SDKs)\nThe docs recommend using official SDKs for safety and type-safety; you can also call the HTTP endpoint directly (POST https://api.perplexity.ai/search) with an Authorization header.\nBelow is a minimal Python example that mirrors the documented pattern.\n#### Basic Python example (client.search.create)\n# Example (conceptual) — mirrors docs pattern\nfrom perplexity import Client  # hypothetical SDK import style\nclient = Client(api_key=”YOUR_API_KEY”)\nresp = client.search.create(\nquery=”latest AI model research 2025″,\nmax_results=5\n)\n# Example response shape (simplified):\n# resp.results -> [ { “title”: “…”, “url”: “…”, “snippet”: “…”, “rank”: 1 }, … ]\nprint(resp.results[0][“title”], resp.results[0][“url”])\nThis call returns ranked results you can present to users or feed into an LLM for grounded synthesis.\nIf you prefer raw HTTP the docs provide a curl example for POST /search.\n...\nStart with the quickstart, use multi-query for depth, control content with max_tokens_per_page, and organize keys and billing via API Groups.",
      "title": "Mastering the Perplexity AI API Documentation",
      "url": "https://seabuckdigital.com/perplexity-ai-api-documentation/",
      "date": "2025-11-08",
      "last_updated": "2026-04-08"
    },
    {
      "snippet": "The Perplexity AI Toolkit makes it easy to use Perplexity Labs' `Sonar` language models (built on top of Meta's latest and most advanced model `LLama-3.1`) for creating chatbots, generating text, and searching the web (***in real-time** *).\nIt's designed for everyone, from beginners to experienced developers, allowing quick addition of AI features to projects with simple commands.\n...\n## Prerequisites\n- `Python 3.x`\n- An API key from Perplexity AI\n## Dependencies\nThe following Python packages are required:\n- `requests`: For making HTTP requests to the Perplexity API.\n...\n## Installation\nTo use the Perplexity AI Toolkit, clone the repository to your local machine and install the required Python packages.\nClone the repository:\n```\ngit clone https://github.com/RMNCLDYO/perplexity-ai-toolkit.git\n```\nNavigate to the repositories folder:\n```\ncd perplexity-ai-toolkit\n```\nInstall the required dependencies:\n```\npip install -r requirements.txt\n```\n## Configuration\n1. Obtain an API key from Perplexity.\n2. You have three options for managing your API key:\nClick here to view the API key configuration options - **Setting it as an environment variable on your device (recommended for everyday use)**\n- Navigate to your terminal.\n- Add your API key like so:\n```\nexport PERPLEXITY_API_KEY=your_api_key\n```\nThis method allows the API key to be loaded automatically when using the wrapper or CLI.\n- **Using an .env file (recommended for development):**\n- Install python-dotenv if you haven't already: `pip install python-dotenv`.\n- Create a .env file in the project's root directory.\n- Add your API key to the .env file like so:\n```\nPERPLEXITY_API_KEY=your_api_key\n```\nThis method allows the API key to be loaded automatically when using the wrapper or CLI, assuming you have python-dotenv installed and set up correctly.\n- **Direct Input:**\n- If you prefer not to use a `.env` file, you can directly pass your API key as an argument to the CLI or the wrapper functions.\n***CLI** *\n```\n--api_key \"your_api_key\"\n```\n***Wrapper** *\n```\napi_key=\"your_api_key\"\n```\n...\nThe Perplexity AI Toolkit can be used in two different modes: `Chat`, and `Search`.\n...\nChat mode is intended for chatting with an AI model (similar to a chatbot) or building conversational applications.\n#### Example Usage\n***CLI** *\n```\npython cli.py --chat\n```\n***Wrapper** *\n```\nfrom perplexity import Chat\nChat().run()\n```\n> An executable version of this example can be found here.\n(*You must move this file to the root folder before running the program.*)\n## Search Mode\nSearch mode is intended for searching online (in real-time) for a single query as perplexity does not support multi-turn conversations with their online models.\n#### Example Usage\n***CLI** *\n```\npython cli.py --search --query \"What is today's date?\"\n```\n***Wrapper** *\n```\nfrom perplexity import Search\nSearch().run(query=\"What is today's date?\")\n```\n> An executable version of this example can be found here.\n(*You must move this file to the root folder before running the program.*)\n*Search mode is limited to 'online' models, such as `llama-3.1-sonar-small-128k-online`, `llama-3.1-sonar-large-128k-online` and `llama-3.1-sonar-huge-128k-online`.*\n...\nA lightweight Python API wrapper and CLI for Perplexity’s Sonar language models.",
      "title": "RMNCLDYO/perplexity-ai-toolkit: A lightweight Python API ... - GitHub",
      "url": "https://github.com/RMNCLDYO/perplexity-ai-toolkit",
      "date": "2024-01-10",
      "last_updated": "2025-10-28"
    },
    {
      "snippet": "> Get started with Perplexity's Sonar API for web-grounded AI responses.\nMake your first API call in minutes.\n...\nPerplexity's Sonar API provides web-grounded AI responses with support for streaming, tools, search options, and more.\nYou can use it with OpenAI-compatible client libraries or our native SDKs for type safety and enhanced features.\nUse the Sonar API when you need web search capabilities built-in, streaming responses, or Perplexity's Sonar models.\n...\n## Installation\nInstall the SDK for your preferred language:\n<CodeGroup>\n```bash Python theme={null}\npip install perplexityai\n```\n```bash Typescript theme={null}\nnpm install @perplexity-ai/perplexity_ai\n```\n```bash OpenAI Python (Compatible) theme={null}\npip install openai\n```\n```bash OpenAI Typescript (Compatible) theme={null}\nnpm install openai\n```\n</CodeGroup>\n## Authentication\nSet your API key as an environment variable.\nThe SDK will automatically read it:\n<Tabs>\n<Tab title=\"macOS/Linux\">\n```bash theme={null}\nexport PERPLEXITY_API_KEY=\"your_api_key_here\"\n```\n</Tab>\n<Tab title=\"Windows\">\n```powershell theme={null}\nsetx PERPLEXITY_API_KEY \"your_api_key_here\"\n```\n</Tab>\n...\nAll SDK examples below automatically use the `PERPLEXITY_API_KEY` environment variable.\nYou can also pass the key explicitly if needed.\n...\n## Basic Usage\n### Non-Streaming Request\n<CodeGroup>\n```python Python SDK theme={null}\nfrom perplexity import Perplexity\nclient = Perplexity()\ncompletion = client.chat.completions.create(\nmodel=\"sonar-pro\",\nmessages=[\n{\"role\": \"user\", \"content\": \"What are the latest developments in quantum computing?\"}\n]\n)\nprint(completion.choices[0].message.content)\n```\n```typescript Typescript SDK theme={null}\nimport Perplexity from '@perplexity-ai/perplexity_ai';\nconst client = new Perplexity();\nconst completion = await client.chat.completions.create({\nmodel: \"sonar-pro\",\nmessages: [\n{ role: \"user\", content: \"What are the latest developments in quantum computing?\"\n}\n],\n});\nconsole.log(completion.choices[0].message.content);\n```\n```python OpenAI Python SDK theme={null}\nimport os\nfrom openai import OpenAI\nclient = OpenAI(\napi_key=os.environ.get(\"PERPLEXITY_API_KEY\"),\nbase_url=\"https://api.perplexity.ai\"\n)\nresp = client.chat.completions.create(\nmodel=\"sonar-pro\",\nmessages=[\n{\"role\": \"user\", \"content\": \"What are the latest developments in quantum computing?\"}\n]\n)\nprint(resp.choices[0].message.content)\n```\n```typescript OpenAI Typescript SDK theme={null}\nimport OpenAI from 'openai';\nconst client = new OpenAI({\napiKey: process.env.PERPLEXITY_API_KEY,\nbaseURL: \"https://api.perplexity.ai\"\n});\nconst resp = await client.chat.completions.create({\nmodel: \"sonar-pro\",\nmessages: [\n{ role: \"user\", content: \"What are the latest developments in quantum computing?\"\n}\n],\n});\nconsole.log(resp.choices[0].message.content);\n```\n```bash cURL theme={null}\ncurl https://api.perplexity.ai/v1/sonar \\\n-H \"Authorization: Bearer $PERPLEXITY_API_KEY\" \\\n-H \"Content-Type: application/json\" \\\n-d '{\n\"model\": \"sonar-pro\",\n\"messages\": [\n{\n\"role\": \"user\",\n\"content\": \"What are the latest developments in quantum computing?\"\n}\n]\n}' | jq\n```\n</CodeGroup>\n### Streaming Response\n<CodeGroup>\n```python Python SDK theme={null}\nfrom perplexity import Perplexity\nclient = Perplexity()\nstream = client.chat.completions.create(\nmodel=\"sonar-pro\",\nmessages=[\n{\"role\": \"user\", \"content\": \"What are the most popular open-source alternatives to OpenAI's GPT models?\"}\n],\nstream=True\n)\nfor chunk in stream:\nif chunk.choices[0].delta.content:\nprint(chunk.choices[0].delta.content, end=\"\")\n```\n```typescript Typescript SDK theme={null}\nimport Perplexity from '@perplexity-ai/perplexity_ai';\nconst client = new Perplexity();\nconst stream = await client.chat.completions.create({\nmodel: \"sonar-pro\",\nmessages: [\n{ role: \"user\", content: \"What are the most popular open-source alternatives to OpenAI's GPT models?\"\n}\n],\nstream: true,\n});\nfor await (const chunk of stream) {\nif (chunk.choices[0].delta.content) {\nprocess.stdout.write((chunk.choices[0]?.delta?.content ?? '') as string);\n}\n}\n```\n```bash cURL theme={null}\ncurl https://api.perplexity.ai/v1/sonar \\\n-H \"Authorization: Bearer $PERPLEXITY_API_KEY\" \\\n-H \"Content-Type: application/json\" \\\n-d '{\n\"model\": \"sonar-pro\",\n\"messages\": [\n{\n\"role\": \"user\",\n\"content\": \"What are the most popular open-source alternatives to OpenAI'\\''s GPT models?\"\n}\n],\n\"stream\": true\n}'\n```\n</CodeGroup>\n...\nGet raw search results with the Search API.",
      "title": "Sonar API - Perplexity",
      "url": "https://docs.perplexity.ai/docs/sonar/quickstart",
      "date": null,
      "last_updated": "2026-05-25"
    }
  ],
  "server_time": null
}

Performance Best Practices Summary

Use environment variables for configuration

Never hardcode API keys or configuration values.

Implement intelligent rate limiting

Use exponential backoff with jitter for retry strategies.

Configure connection pooling

Optimize HTTP client settings for your use case.

Monitor and log appropriately

Track performance metrics and error rates.

Implement graceful degradation

Provide fallback responses when APIs are unavailable.

Write testable code

Use dependency injection and mocking for unit tests.

Error Handling

Comprehensive error handling strategies

Performance

Async operations and optimization techniques

Configuration

Production-ready configuration patterns

Type Safety

Leveraging types for safer code

​Overview

​Security Best Practices

​Environment Variables

​API Key Rotation

​Rate Limiting and Efficiency

​Intelligent Rate Limiting

​Request Batching

​Production Configuration

​Configuration Management

​Monitoring and Logging

​Error Handling Best Practices

​Graceful Degradation

​Testing Best Practices

​Unit Testing with Mocking

​Performance Best Practices Summary

​Related Resources

Error Handling

Performance

Configuration

Type Safety

Overview

Security Best Practices

Environment Variables

API Key Rotation

Rate Limiting and Efficiency

Intelligent Rate Limiting

Request Batching

Production Configuration

Configuration Management

Monitoring and Logging

Error Handling Best Practices

Graceful Degradation

Testing Best Practices

Unit Testing with Mocking

Performance Best Practices Summary

Related Resources