PerplexitySearch 集成

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.

我们可以将其用作检索器。 它将展示此集成特有的功能。 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.

设置

安装

The integration lives in the langchain-perplexity package:

pip install -qU langchain-perplexity

凭证

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")

如果您希望从单个查询中获得自动追踪功能，还可以通过取消注释以下代码来设置 LangSmith API 密钥：

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

实例化

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)

用法

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")

在链中使用

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 参考

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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