Agent skill

perplexity-architecture-variants

Choose and implement Perplexity architecture blueprints for different scales: direct search widget, cached research layer, and multi-query pipeline. Trigger with phrases like "perplexity architecture", "perplexity blueprint", "how to structure perplexity", "perplexity project layout".

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npx add-skill https://github.com/jeremylongshore/claude-code-plugins-plus-skills/tree/main/plugins/saas-packs/perplexity-pack/skills/perplexity-architecture-variants

SKILL.md

Perplexity Architecture Variants

Overview

Three validated architectures for Perplexity Sonar API at different scales. Each builds on the previous, adding caching and orchestration as volume grows.

Decision Matrix

Factor Direct Widget Cached Layer Research Pipeline
Volume <500/day 500-5K/day 5K+/day
Latency (p50) 2-5s 50ms (cached) / 2-5s (miss) 10-30s
Model sonar sonar + cache sonar + sonar-pro
Monthly Cost <$150 $50-$300 $300+
Complexity Minimal Moderate High

Instructions

Variant 1: Direct Search Widget (<500 queries/day)

Best for: Adding AI search to an existing app. No cache needed at this scale.

typescript
// Simple endpoint — add to any Express/Next.js app
import OpenAI from "openai";

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

app.post("/api/search", async (req, res) => {
  try {
    const response = await perplexity.chat.completions.create({
      model: "sonar",
      messages: [{ role: "user", content: req.body.query }],
      max_tokens: 1024,
    });

    res.json({
      answer: response.choices[0].message.content,
      citations: (response as any).citations || [],
    });
  } catch (err: any) {
    if (err.status === 429) {
      res.status(429).json({ error: "Rate limited. Try again shortly." });
    } else {
      res.status(500).json({ error: "Search unavailable" });
    }
  }
});

Variant 2: Cached Research Layer (500-5K queries/day)

Best for: Repeated queries, knowledge base search, FAQ bots. Cache eliminates duplicate API calls.

typescript
import { createHash } from "crypto";
import { LRUCache } from "lru-cache";

const cache = new LRUCache<string, any>({
  max: 5000,
  ttl: 4 * 3600_000,  // 4-hour TTL
});

class CachedSearchService {
  constructor(private client: OpenAI) {}

  async search(query: string, model = "sonar") {
    const key = this.cacheKey(query, model);
    const cached = cache.get(key);
    if (cached) return { ...cached, cached: true };

    const response = await this.client.chat.completions.create({
      model,
      messages: [{ role: "user", content: query }],
      max_tokens: 1024,
    });

    const result = {
      answer: response.choices[0].message.content || "",
      citations: (response as any).citations || [],
      model: response.model,
    };

    cache.set(key, result);
    return { ...result, cached: false };
  }

  private cacheKey(query: string, model: string): string {
    return createHash("sha256")
      .update(`${model}:${query.toLowerCase().trim()}`)
      .digest("hex");
  }

  get stats() {
    return { size: cache.size, max: 5000 };
  }
}

Variant 3: Multi-Query Research Pipeline (5K+ queries/day)

Best for: Automated research, report generation, competitive intelligence. Uses job queue for rate limiting and sonar-pro for deep analysis.

typescript
import PQueue from "p-queue";

class ResearchPipeline {
  private queue: PQueue;
  private cache: CachedSearchService;

  constructor(private client: OpenAI) {
    this.queue = new PQueue({
      concurrency: 3,
      interval: 60_000,
      intervalCap: 40,  // 40 RPM (safety margin)
    });
    this.cache = new CachedSearchService(client);
  }

  async researchTopic(topic: string): Promise<{
    overview: string;
    sections: Array<{ question: string; answer: string; citations: string[] }>;
    bibliography: string[];
  }> {
    // Phase 1: Decompose (sonar, fast)
    const decomposition = await this.cache.search(
      `Break "${topic}" into 4 focused research questions. One per line.`,
      "sonar"
    );
    const questions = decomposition.answer.split("\n").filter((q) => q.trim().length > 10);

    // Phase 2: Deep research each question (sonar-pro, queued)
    const sections = await Promise.all(
      questions.slice(0, 5).map((q) =>
        this.queue.add(async () => {
          const result = await this.cache.search(q.trim(), "sonar-pro");
          return { question: q.trim(), ...result };
        })
      )
    );

    // Phase 3: Compile
    const allCitations = new Set<string>();
    for (const s of sections) {
      if (s) s.citations.forEach((url: string) => allCitations.add(url));
    }

    return {
      overview: decomposition.answer,
      sections: sections.filter(Boolean).map((s) => ({
        question: s!.question,
        answer: s!.answer,
        citations: s!.citations,
      })),
      bibliography: [...allCitations],
    };
  }
}

Python Variant (Direct Widget)

python
from flask import Flask, request, jsonify
from openai import OpenAI
import os

app = Flask(__name__)
client = OpenAI(api_key=os.environ["PERPLEXITY_API_KEY"], base_url="https://api.perplexity.ai")

@app.route("/api/search", methods=["POST"])
def search():
    query = request.json["query"]
    response = client.chat.completions.create(
        model="sonar",
        messages=[{"role": "user", "content": query}],
        max_tokens=1024,
    )
    raw = response.model_dump()
    return jsonify({
        "answer": response.choices[0].message.content,
        "citations": raw.get("citations", []),
    })

Choosing the Right Variant

How many queries per day?
├─ <500 → Variant 1 (Direct Widget)
│   └─ Add retry with backoff
├─ 500-5K → Variant 2 (Cached Layer)
│   └─ Add LRU cache with 4-hour TTL
└─ 5K+ → Variant 3 (Research Pipeline)
    └─ Add job queue + sonar-pro for deep queries

Error Handling

Issue Cause Solution
Slow in UI No caching Add Variant 2 cache layer
High cost sonar-pro for all queries Route simple queries to sonar
Rate limited Burst traffic Add PQueue rate limiter
Stale answers Long cache TTL Reduce TTL for time-sensitive queries

Output

  • Selected architecture variant matching your scale
  • Implementation code for chosen variant
  • Cache strategy if applicable
  • Queue configuration if applicable

Resources

Next Steps

For common pitfalls, see perplexity-known-pitfalls.

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