Skip to main content Startseite Ersteller jeremylongshore claude-code-plugins-plus-skills perplexity-sdk-patterns
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".
Zur Installation springen Skills Marktplatz Entdecken und erkunden Sie KI-Skills, die von der Community erstellt wurden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Prompt-Details anzeigen Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
npx skills add https://github.com/jeremylongshore/claude-code-plugins-plus-skills --skill perplexity-sdk-patternsDer Befehl bleibt in einer Zeile. Scrollen Sie horizontal, um ihn vor dem Kopieren vollständig zu prüfen.
Sie bevorzugen eine lokale Kopie? Laden Sie die Dateien herunter, die SkillsMP derzeit vorliegen.
ZIP herunterladen Herunterladen... Mehr aus diesem Repository 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".
Verwandte Berufe SOC
Basierend auf der SOC-Berufsklassifikation
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.
