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perplexity-sdk-patterns Apply production-ready Perplexity Sonar API patterns for TypeScript and Python.
Use when implementing Perplexity integrations, refactoring SDK usage,
or establishing team coding standards for search-augmented generation.
Trigger with phrases like "perplexity SDK patterns", "perplexity best practices",
"perplexity code patterns", "idiomatic perplexity", "perplexity wrapper".
Aller à l'installation Skills Marketplace Découvrez et explorez les compétences IA créées par la communauté.
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Afficher les détails du prompt Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
npx skills add https://github.com/jeremylongshore/claude-code-plugins-plus-skills --skill perplexity-sdk-patternsLa commande reste sur une seule ligne. Faites défiler horizontalement pour la vérifier avant de la copier.
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Télécharger Zip Téléchargement... Plus depuis ce dépôt Implement user sign-up and sign-in flows with Clerk.
Use when building authentication UI, customizing sign-in experience,
or implementing OAuth social login.
Trigger with phrases like "clerk sign-in", "clerk sign-up",
"clerk login flow", "clerk OAuth", "clerk social login".
Implement session management and middleware with Clerk.
Use when managing user sessions, configuring route protection,
or implementing token refresh and custom JWT templates.
Trigger with phrases like "clerk session", "clerk middleware",
"clerk route protection", "clerk token", "clerk JWT".
Configure enterprise SSO, role-based access control, and organization management.
Use when implementing SSO integration, configuring role-based permissions,
or setting up organization-level controls.
Trigger with phrases like "clerk SSO", "clerk RBAC",
"clerk enterprise", "clerk roles", "clerk permissions", "clerk organizations".
Métiers associés SOC
Basé sur la classification professionnelle SOC
name perplexity-sdk-patterns description Apply production-ready Perplexity Sonar API patterns for TypeScript and Python.
Use when implementing Perplexity integrations, refactoring SDK usage,
or establishing team coding standards for search-augmented generation.
Trigger with phrases like "perplexity SDK patterns", "perplexity best practices",
"perplexity code patterns", "idiomatic perplexity", "perplexity wrapper".
allowed-tools Read, Write, Edit version 1.12.0 license MIT author Jeremy Longshore <jeremy@intentsolutions.io> tags ["saas","perplexity","python","typescript"] compatibility Designed for Claude Code, also compatible with Codex and OpenClaw
Perplexity SDK Patterns
Overview
Production-ready patterns for Perplexity Sonar API. Since Perplexity uses the OpenAI wire format, you build wrappers around the openai client library with Perplexity-specific response handling (citations, search results, related questions).
Prerequisites
openai package installed (npm install openai or pip install openai)API key configured in PERPLEXITY_API_KEY
Understanding of OpenAI chat completions format
Instructions
Step 1: Typed Client Singleton (TypeScript)
import OpenAI from "openai" ;
export interface PerplexityChatCompletion extends OpenAI .ChatCompletion {
citations ?: string [];
search_results ?: Array <{
title : string ;
url : string ;
date ?: string ;
snippet : string ;
}>;
related_questions ?: string [];
}
export interface PerplexityUsage extends OpenAI .CompletionUsage {
citation_tokens ?: number ;
num_search_queries ?: number ;
reasoning_tokens ?: number ;
}
let instance
OpenAI
null
null
export
function
getClient
OpenAI
if
if
env
PERPLEXITY_API_KEY
throw
new
Error
"PERPLEXITY_API_KEY not set"
new
OpenAI
apiKey
env
PERPLEXITY_API_KEY
baseURL
"https://api.perplexity.ai"
return
Step 2: Search with Full Response Parsing
import { getClient, PerplexityChatCompletion } from "./client" ;
export type SearchModel = "sonar" | "sonar-pro" | "sonar-reasoning-pro" | "sonar-deep-research" ;
export type RecencyFilter = "hour" | "day" | "week" | "month" ;
export interface SearchOptions {
model ?: SearchModel ;
systemPrompt ?: string ;
maxTokens ?: number ;
temperature ?: number ;
searchRecencyFilter ?: RecencyFilter ;
searchDomainFilter ?: string [];
returnRelatedQuestions ?: boolean ;
returnImages ?: boolean ;
}
export interface SearchResult {
answer : string ;
citations : string [];
relatedQuestions : string [];
usage : {
promptTokens : number ;
completionTokens : number ;
totalTokens : number ;
citationTokens ?: number ;
searchQueries ?: number ;
};
model : string ;
}
export async function search (
query : string ,
opts : SearchOptions = {}
): Promise <SearchResult > {
const client = getClient ();
const response = (await client.chat .completions .create ({
model : opts.model || "sonar" ,
messages : [
...(opts.systemPrompt
? [{ role : "system" as const , content : opts.systemPrompt }]
: []),
{ role : "user" as const , content : query },
],
max_tokens : opts.maxTokens ,
temperature : opts.temperature ,
...(opts.searchRecencyFilter && { search_recency_filter : opts.searchRecencyFilter }),
...(opts.searchDomainFilter && { search_domain_filter : opts.searchDomainFilter }),
...(opts.returnRelatedQuestions && { return_related_questions : true }),
...(opts.returnImages && { return_images : true }),
} as any )) as unknown as PerplexityChatCompletion ;
return {
answer : response.choices [0 ].message .content || "" ,
citations : response.citations || [],
relatedQuestions : response.related_questions || [],
usage : {
promptTokens : response.usage ?.prompt_tokens || 0 ,
completionTokens : response.usage ?.completion_tokens || 0 ,
totalTokens : response.usage ?.total_tokens || 0 ,
citationTokens : (response.usage as any )?.citation_tokens ,
searchQueries : (response.usage as any )?.num_search_queries ,
},
model : response.model ,
};
}
Step 3: Retry with Exponential Backoff
export async function withRetry<T>(
operation : () => Promise <T>,
opts = { maxRetries : 3 , baseDelayMs : 1000 , maxDelayMs : 30000 }
): Promise <T> {
for (let attempt = 0 ; attempt <= opts.maxRetries ; attempt++) {
try {
return await operation ();
} catch (err : any ) {
if (attempt === opts.maxRetries ) throw err;
const status = err.status || err.response ?.status ;
if (status && status !== 429 && status !== 408 && status < 500 ) throw err;
const delay = Math .min (
opts.baseDelayMs * Math .pow (2 , attempt) + Math .random () * 500 ,
opts.maxDelayMs
);
await new Promise ((r ) => setTimeout (r, delay));
}
}
throw new Error ("Unreachable" );
}
const result = await withRetry (() =>
search ("latest AI developments" , { model : "sonar-pro" })
);
Step 4: Python Patterns
import os, hashlib, json
from openai import OpenAI
from functools import lru_cache
@lru_cache(maxsize=1 )
def get_client () -> OpenAI:
return OpenAI(
api_key=os.environ["PERPLEXITY_API_KEY" ],
base_url="https://api.perplexity.ai" ,
)
def search (
query: str ,
model: str = "sonar" ,
system_prompt: str | None = None ,
max_tokens: int | None = None ,
search_recency_filter: str | None = None ,
search_domain_filter: list [str ] | None = None ,
) -> dict :
client = get_client()
messages = []
if system_prompt:
messages.append({"role" : "system" , "content" : system_prompt})
messages.append({"role" : "user" , "content" : query})
kwargs = {"model" : model, "messages" : messages}
if max_tokens:
kwargs["max_tokens" ] = max_tokens
if search_recency_filter:
kwargs["search_recency_filter" ] = search_recency_filter
if search_domain_filter:
kwargs["search_domain_filter" ] = search_domain_filter
response = client.chat.completions.create(**kwargs)
raw = response.model_dump()
return {
"answer" : response.choices[0 ].message.content,
"citations" : raw.get("citations" , []),
"usage" : {
"prompt_tokens" : response.usage.prompt_tokens,
"completion_tokens" : response.usage.completion_tokens,
"total_tokens" : response.usage.total_tokens,
},
"model" : response.model,
}
Step 5: Citation Formatter
export function formatCitationsAsMarkdown (
answer : string ,
citations : string []
): string {
let formatted = answer;
citations.forEach ((url, i ) => {
const marker = `[${i + 1 } ]` ;
formatted = formatted.replaceAll (marker, `${i + 1 } ` );
});
return formatted;
}
export function formatCitationsAsFootnotes (
answer : string ,
citations : string []
): string {
const footnotes = citations
.map ((url, i ) => `[${i + 1 } ]: ${url} ` )
.join ("\n" );
return `${answer} \n\n---\n${footnotes} ` ;
}
Error Handling Pattern Use Case Benefit Typed response wrapper All API calls Access citations without any casts Retry with backoff Transient failures Handles 429 rate limits gracefully Citation formatter User-facing output Converts [1] markers to clickable links Python @lru_cache Client reuse Single client instance across calls
Output
Type-safe Perplexity client with full response typing
Search function with all Perplexity-specific parameters
Automatic retry with exponential backoff and jitter
Citation formatting utilities
Resources
Next Steps Apply patterns in perplexity-core-workflow-a for real-world usage.
