PerplexitySearch integration

Integrate with the PerplexitySearchRetriever using LangChain Python.

Perplexity Search is a web search API that returns ranked, source-attributed results designed for use by LLMs and agents. The Search API endpoint returns the underlying web results that power Perplexity’s answer engine.

We can use this as a retriever. It will show functionality specific to this integration. After going through, it may be useful to explore relevant use-case pages to learn how to use this retriever as part of a larger chain.

Setup

Installation

The integration lives in the langchain-perplexity package:

pip install -qU langchain-perplexity

Credentials

You’ll need a Perplexity API key to use this integration. Create one in the Perplexity API key dashboard, then set it as the PPLX_API_KEY environment variable (the integration also accepts PERPLEXITY_API_KEY).

import getpass
import os

if not os.environ.get("PPLX_API_KEY"):
    os.environ["PPLX_API_KEY"] = getpass.getpass("Perplexity API key:\n")

If you want to get automated tracing from individual queries, you can also set your LangSmith API key by uncommenting below:

# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
# os.environ["LANGSMITH_TRACING"] = "true"

Instantiation

PerplexitySearchRetriever accepts the following constructor arguments:

• k — max number of results (1–20). Defaults to 10.
• max_tokens — max total tokens across all results.
• max_tokens_per_page — max tokens returned per result page.
• country — ISO country code to bias results (e.g. "US").
• search_domain_filter — list of domains to include or exclude (max 20). Prefix a domain with - to exclude it. See the domain filter docs.
• search_recency_filter — one of "day", "week", "month", "year". See the date and time filter docs.
• search_after_date / search_before_date — date strings in MM/DD/YYYY format.

from langchain_perplexity import PerplexitySearchRetriever

retriever = PerplexitySearchRetriever(k=3)

Usage

query = "what year was breath of the wild released?"

retriever.invoke(query)

Each returned Document has the result snippet as its page_content, plus title, url, date, and last_updated in metadata.

Filtered usage

You can constrain the search with the same filters supported by the Perplexity Search API:

from langchain_perplexity import PerplexitySearchRetriever

retriever = PerplexitySearchRetriever(
    k=5,
    country="US",
    search_recency_filter="week",
    search_domain_filter=["nytimes.com", "reuters.com", "-pinterest.com"],
)

retriever.invoke("US Federal Reserve interest rate decisions")

from langchain_perplexity import PerplexitySearchRetriever

# Restrict results to a specific date range
retriever = PerplexitySearchRetriever(
    k=5,
    search_after_date="01/01/2025",
    search_before_date="06/30/2025",
)

retriever.invoke("US Federal Reserve interest rate decisions")

Use within a chain

We can easily combine this retriever into a chain to build a simple RAG pipeline.

from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import ChatOpenAI

prompt = ChatPromptTemplate.from_template(
    """Answer the question based only on the context provided.

Context: {context}

Question: {question}"""
)

llm = ChatOpenAI(model="gpt-5.5")


def format_docs(docs):
    return "\n\n".join(doc.page_content for doc in docs)


chain = (
    {"context": retriever | format_docs, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)

chain.invoke("What were the major outcomes of the most recent Federal Reserve meeting?")

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API reference

For detailed documentation of the Perplexity Search API and all of its options, see the Search API reference and the Perplexity API documentation.

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