PerplexityWebSearch

Search the web using the Perplexity Search API.

Most common position in a pipeline	Before a ChatPromptBuilder or at the beginning of an indexing pipeline
Mandatory init variables	api_key: A Perplexity API key. Can be set with PERPLEXITY_API_KEY env var.
Mandatory run variables	query: A string with your search query.
Output variables	documents: A list of Haystack Documents containing search result content and metadata. links: A list of strings of resulting URLs.
API reference	Integrations
GitHub link	https://github.com/deepset-ai/haystack-core-integrations/blob/main/integrations/perplexity/src/haystack_integrations/components/websearch/perplexity/perplexity_websearch.py
Package name	perplexity-haystack

Overview

When you give PerplexityWebSearch a query, it uses the Perplexity Search API to search the web and return relevant content as Haystack Document objects. It also returns a list of the source URLs.

Each returned Document contains a text snippet as its content and a meta dictionary with title, url, date, and last_updated fields.

PerplexityWebSearch requires a Perplexity API key to work. By default, it reads from the PERPLEXITY_API_KEY environment variable. You can also pass an api_key directly during initialization.

The top_k parameter controls the maximum number of results returned (between 1 and 20, default is 10).

You can filter and refine search results using search_params, which supports keys such as country, search_recency_filter, search_domain_filter, and date range filters. These can be set at initialization or overridden per run() call. See the Perplexity Search API reference for the full list of parameters.

PerplexityWebSearch supports both synchronous (run()) and asynchronous (run_async()) operation.

Usage

On its own

python

from haystack.utils import Secret
from haystack_integrations.components.websearch.perplexity import PerplexityWebSearch

web_search = PerplexityWebSearch(
    api_key=Secret.from_env_var("PERPLEXITY_API_KEY"),
    top_k=5,
)
result = web_search.run(query="What is Haystack by deepset?")

for doc in result["documents"]:
    print(doc.content)
    print(doc.meta["url"])

With search filters:

python

from haystack.utils import Secret
from haystack_integrations.components.websearch.perplexity import PerplexityWebSearch

web_search = PerplexityWebSearch(
    api_key=Secret.from_env_var("PERPLEXITY_API_KEY"),
    top_k=5,
    search_params={"country": "us", "search_recency_filter": "week"},
)
result = web_search.run(query="Latest AI research papers")

for doc in result["documents"]:
    print(doc.meta["title"], doc.meta["url"])

In a pipeline

Here is an example of a RAG pipeline that uses PerplexityWebSearch to look up an answer on the web.

python

from haystack import Pipeline
from haystack.utils import Secret
from haystack.components.builders.chat_prompt_builder import ChatPromptBuilder
from haystack.dataclasses import ChatMessage
from haystack_integrations.components.generators.perplexity import (
    PerplexityChatGenerator,
)
from haystack_integrations.components.websearch.perplexity import PerplexityWebSearch

web_search = PerplexityWebSearch(
    api_key=Secret.from_env_var("PERPLEXITY_API_KEY"),
    top_k=3,
)

prompt_template = [
    ChatMessage.from_system("You are a helpful assistant."),
    ChatMessage.from_user(
        "Given the information below:\n"
        "{% for document in documents %}{{ document.content }}\n{% endfor %}\n"
        "Answer the following question: {{ query }}.\nAnswer:",
    ),
]

prompt_builder = ChatPromptBuilder(
    template=prompt_template,
    required_variables=["query", "documents"],
)

llm = PerplexityChatGenerator(
    api_key=Secret.from_env_var("PERPLEXITY_API_KEY"),
)

pipe = Pipeline()
pipe.add_component("search", web_search)
pipe.add_component("prompt_builder", prompt_builder)
pipe.add_component("llm", llm)

pipe.connect("search.documents", "prompt_builder.documents")
pipe.connect("prompt_builder.prompt", "llm.messages")

query = "What is Haystack by deepset?"
result = pipe.run(data={"search": {"query": query}, "prompt_builder": {"query": query}})
print(result["llm"]["replies"][0].text)