技能 人工智能 Perplexity带引用来源搜索

Perplexity带引用来源搜索

v20260423
perplexity-core-workflow-a
本流程执行单次查询的Perplexity搜索,能够获取基于网络的答案,并自动附带行内引用来源。这对于构建高可信度的应用、事实核查工具或任何需要实时、可验证信息检索的系统至关重要。它涵盖了API调用、响应解析(答案、引用、搜索结果)和格式化处理,确保输出结果具有可追溯的来源链接。
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概览

Perplexity Core Workflow A: Search with Citations

Overview

Primary money-path workflow: send a search query to Perplexity Sonar, receive a web-grounded answer with inline citations, parse and display the results. This is the single-query pattern used for search widgets, fact-checking, and real-time information retrieval.

Prerequisites

  • Completed perplexity-install-auth setup
  • openai package installed
  • PERPLEXITY_API_KEY set

Instructions

Step 1: Initialize Client and Send Query

import OpenAI from "openai";

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

async function searchWithCitations(query: string) {
  const response = await perplexity.chat.completions.create({
    model: "sonar",
    messages: [
      {
        role: "system",
        content: "Provide accurate, well-sourced answers. Cite your sources inline.",
      },
      { role: "user", content: query },
    ],
    // Perplexity-specific parameters
    search_recency_filter: "week",  // hour | day | week | month
  } as any);

  return response;
}

Step 2: Parse Response with Citations

interface SearchResult {
  answer: string;
  citations: string[];
  searchResults: Array<{ title: string; url: string; snippet: string }>;
  tokensUsed: number;
}

function parseResponse(response: any): SearchResult {
  return {
    answer: response.choices[0].message.content,
    citations: response.citations || [],
    searchResults: response.search_results || [],
    tokensUsed: response.usage?.total_tokens || 0,
  };
}

Step 3: Format Citations for Display

function formatAnswer(result: SearchResult): string {
  let formatted = result.answer;

  // Replace [1], [2] markers with markdown links
  result.citations.forEach((url, i) => {
    formatted = formatted.replaceAll(`[${i + 1}]`, `[${i + 1}](${url})`);
  });

  // Append source list
  if (result.citations.length > 0) {
    formatted += "\n\n**Sources:**\n";
    result.citations.forEach((url, i) => {
      formatted += `${i + 1}. ${url}\n`;
    });
  }

  return formatted;
}

Step 4: Complete Workflow

async function main() {
  const query = "What are the latest advances in battery technology?";

  const response = await searchWithCitations(query);
  const result = parseResponse(response);
  const formatted = formatAnswer(result);

  console.log(formatted);
  console.log(`\n[${result.tokensUsed} tokens | ${result.citations.length} sources]`);
}

main().catch(console.error);

Step 5: Domain-Filtered Search

// Restrict search to trusted sources
async function domainFilteredSearch(query: string, domains: string[]) {
  const response = await perplexity.chat.completions.create({
    model: "sonar",
    messages: [{ role: "user", content: query }],
    search_domain_filter: domains,  // max 20 domains
  } as any);

  return parseResponse(response);
}

// Example: only search academic sources
const result = await domainFilteredSearch(
  "CRISPR gene editing latest trials",
  ["nature.com", "science.org", "nih.gov", "arxiv.org"]
);

Step 6: Python Implementation

from openai import OpenAI
import os, re

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

def search_with_citations(query: str, model: str = "sonar", recency: str = None) -> dict:
    kwargs = {
        "model": model,
        "messages": [
            {"role": "system", "content": "Provide accurate answers with cited sources."},
            {"role": "user", "content": query},
        ],
    }
    if recency:
        kwargs["search_recency_filter"] = recency

    response = client.chat.completions.create(**kwargs)
    raw = response.model_dump()

    return {
        "answer": response.choices[0].message.content,
        "citations": raw.get("citations", []),
        "tokens": response.usage.total_tokens,
    }

# Usage
result = search_with_citations(
    "What are the latest advances in battery technology?",
    recency="week"
)
print(result["answer"])
for i, url in enumerate(result["citations"], 1):
    print(f"  [{i}] {url}")

Error Handling

Error Cause Solution
401 Unauthorized Invalid API key Regenerate at perplexity.ai/settings/api
429 Too Many Requests Rate limit exceeded Implement exponential backoff
Empty citations Query too vague Make query more specific and factual
Stale information No recency filter Add search_recency_filter: "day"
Slow response (>10s) Using sonar-pro Switch to sonar for faster results

Output

  • Web-grounded answer text with inline citation markers
  • Parsed citation URLs for source verification
  • Formatted markdown with linked sources
  • Token usage for cost tracking

Resources

Next Steps

For multi-query research, see perplexity-core-workflow-b.

信息
Category 人工智能
Name perplexity-core-workflow-a
版本 v20260423
大小 5.61KB
更新时间 2026-04-28
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