perplexity-architecture-variants | perplexity-pack

Stats

Actions

Tags

perplexity-architecture-variants | perplexity-pack

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.

// 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

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.