Perplexity Architecture Variants

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

Instructions

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

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

// 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.

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.

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)

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

Output

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

Resources

• Perplexity API Documentation
• Perplexity Model Pricing

Next Steps

For common pitfalls, see perplexity-known-pitfalls.