> ## Documentation Index
> Fetch the complete documentation index at: https://docs.langchain.com/llms.txt
> Use this file to discover all available pages before exploring further.

# PerplexitySearch integration

> Integrate with the PerplexitySearchRetriever using LangChain Python.

> [Perplexity Search](https://docs.perplexity.ai/docs/search/quickstart) is a web search API that returns ranked, source-attributed results designed for use by LLMs and agents. The [Search API endpoint](https://docs.perplexity.ai/api-reference/search-post) returns the underlying web results that power Perplexity's answer engine.

We can use this as a [retriever](/oss/python/langchain/retrieval). It will show functionality specific to this integration. After going through, it may be useful to explore [relevant use-case pages](/oss/python/langchain/rag) to learn how to use this retriever as part of a larger chain.

## Setup

### Installation

The integration lives in the `langchain-perplexity` package:

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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](https://www.perplexity.ai/account/api/keys), then set it as the `PPLX_API_KEY` environment variable (the integration also accepts `PERPLEXITY_API_KEY`).

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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](/langsmith/observability) API key by uncommenting below:

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
# 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](https://docs.perplexity.ai/docs/search/filters/domain-filter).
* `search_recency_filter` — one of `"day"`, `"week"`, `"month"`, `"year"`. See the [date and time filter docs](https://docs.perplexity.ai/docs/search/filters/date-time-filters).
* `search_after_date` / `search_before_date` — date strings in `MM/DD/YYYY` format.

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_perplexity import PerplexitySearchRetriever

retriever = PerplexitySearchRetriever(k=3)
```

## Usage

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
query = "what year was breath of the wild released?"

retriever.invoke(query)
```

Each returned [`Document`](https://reference.langchain.com/python/langchain-core/documents/base/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:

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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")
```

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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.

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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?")
```

***

## API reference

For detailed documentation of the Perplexity Search API and all of its options, see the [Search API reference](https://docs.perplexity.ai/api-reference/search-post) and the [Perplexity API documentation](https://docs.perplexity.ai).

***

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