AGENT SKILL

perplexity-reference-architecture

Implement Perplexity reference architecture with best-practice project layout. Use when designing new Perplexity integrations, reviewing project structure, or establishing architecture standards for Perplexity applications. Trigger with phrases like "perplexity architecture", "perplexity best practices", "perplexity project structure", "how to organize perplexity", "perplexity layout".

License 许可
MIT
最近更新
2026-03-20
Stars
★ 4
Forks
0

01 前言 Front Matter

name
perplexity-reference-architecture
author
Jeremy Longshore <jeremy@intentsolutions.io>
license
MIT
version
1.0.0
description
Implement Perplexity reference architecture with best-practice project layout. Use when designing new Perplexity integrations, reviewing project structure, or establishing architecture standards for Perplexity applications. Trigger with phrases like "perplexity architecture", "perplexity best practices", "perplexity project structure", "how to organize perplexity", "perplexity layout".
allowed-tools
Read, Grep
compatible-with
claude-code, codex, openclaw

02 资源文件 1

03 SKILL.md 正文

Perplexity Reference Architecture

Contents

Overview

Production architecture for AI-powered research and search with Perplexity Sonar API. Covers model routing for cost/quality tradeoffs, citation extraction, multi-query research pipelines, and conversational search.

Prerequisites

  • Perplexity API key (Sonar access)
  • OpenAI-compatible client library
  • Understanding of search models (sonar, sonar-pro, sonar-reasoning)

Architecture

Application Layer (Research Agent, Fact Checker, Content Writer)
        |
Search Router (sonar/sonar-pro/sonar-reasoning)
        |
Citation Pipeline (Extract URLs, Validate, Store, Render)
        |
Cache Layer (Query Hash -> Result, TTL by freshness need)

Instructions

Step 1: Set Up Search Service with Model Routing

Use OpenAI-compatible client pointed at api.perplexity.ai. Route queries by depth: quick/standard (sonar), deep (sonar-pro), reasoning (sonar-reasoning).

Step 2: Build Citation Extraction Pipeline

Parse response text for [N] URL patterns and inline URLs. Deduplicate and store citations with metadata.

Step 3: Create Research Pipeline

Multi-phase: broad overview (fast model) -> identify subtopics -> deep dive each (sonar-pro) -> deduplicate citations.

Step 4: Implement Conversational Search

Maintain message history for follow-up questions that build on previous context.

See detailed implementation for search service, citation extraction, multi-query research pipeline, conversational session, and fact-check service code.

Output

  • Search service with model routing by query depth
  • Citation extraction from responses
  • Multi-query research pipeline
  • Conversational search with context

Error Handling

Issue Cause Solution
No citations returned Using basic sonar for complex query Upgrade to sonar-pro
Stale information Outdated sources Add recency preference in system prompt
High cost Using sonar-pro for simple queries Route simple queries to sonar
Rate limit Too many concurrent searches Add request queue with delays

Examples

Quick Fact Check

const result = await factCheck("The Earth is approximately 4.5 billion years old");
console.log(result.verdict);  // Accurate, with sources
console.log(result.sources);  // Citation URLs

Resources

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