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The Perplexity Search API returns real-time, grounded web search results that you can drop directly into a retrieval pipeline.

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Overview

This will help you get started with PerplexitySearchRetriever retrieval. For detailed documentation of all PerplexitySearchRetriever features and configurations head to the API reference.

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Integration details

RetrieverSourcePackagePY support
PerplexitySearchRetrieverThe web, via the Perplexity Search API.@langchain/perplexity✅

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Setup

You will need to populate a PERPLEXITY_API_KEY environment variable with your Perplexity API key, or pass it into the constructor as apiKey. Get a key from the Perplexity API key dashboard. 
process.env.PERPLEXITY_API_KEY = "your-api-key";
If you want to get automated tracing from individual queries, you can also set your LangSmith API key by uncommenting below: 
// process.env.LANGSMITH_API_KEY = "<YOUR API KEY HERE>";
// process.env.LANGSMITH_TRACING = "true";

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Installation

This retriever lives in the @langchain/perplexity package: 
npm install @langchain/perplexity @langchain/core

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Instantiation

import { PerplexitySearchRetriever } from "@langchain/perplexity";

const retriever = new PerplexitySearchRetriever({
  maxResults: 5,
  // searchDomainFilter: ["wikipedia.org"],
  // searchRecencyFilter: "week",
});
Constructor options include maxResults, country, searchDomainFilter, searchRecencyFilter, searchAfterDate, and searchBeforeDate. See the Perplexity Search API parameters for details.

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Usage

const query = "Who won the most recent FIFA World Cup?";

const docs = await retriever.invoke(query);
Each returned Document has the search result snippet as pageContent and { title, url, date, lastUpdated } in metadata.

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Use within a chain

Like other retrievers, PerplexitySearchRetriever can be incorporated into LLM applications via chains. 
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { ChatAnthropic } from "@langchain/anthropic";
import {
  RunnablePassthrough,
  RunnableSequence,
} from "@langchain/core/runnables";
import { StringOutputParser } from "@langchain/core/output_parsers";
import { Document } from "@langchain/core/documents";

const prompt = ChatPromptTemplate.fromTemplate(`
Answer the question based only on the context provided.

Context: {context}

Question: {question}`);

const llm = new ChatAnthropic({ model: "claude-3-5-haiku-latest" });

const formatDocs = (docs: Document[]) =>
  docs.map((doc) => doc.pageContent).join("\n\n");

const ragChain = RunnableSequence.from([
  {
    context: retriever.pipe(formatDocs),
    question: new RunnablePassthrough(),
  },
  prompt,
  llm,
  new StringOutputParser(),
]);

await ragChain.invoke("Who won the most recent FIFA World Cup?");

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API reference

For detailed documentation of all PerplexitySearchRetriever features and configurations head to the API reference.
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