Integration: Perplexity

Use the Perplexity Agent API, Embeddings API, and grounded Search API in Haystack pipelines.

Authors

deepset

Table of Contents

• Overview
• Installation
• Usage
• License

Overview

The perplexity-haystack package lets you use Perplexity’s Agent API, Embeddings API, and grounded Search API in Haystack pipelines through four components:

• PerplexityChatGenerator — chat completions through the Perplexity Agent API (OpenAI-compatible Responses API). Defaults to openai/gpt-5.4; other supported models include openai/gpt-5.5, openai/gpt-4o, anthropic/claude-sonnet-4-6, xai/grok-4-1, and google/gemini-3-flash-preview.
• PerplexityTextEmbedder and PerplexityDocumentEmbedder — embeddings through the Perplexity Embeddings API. Defaults to pplx-embed-v1-0.6b; pplx-embed-v1-4b is also available.
• PerplexityWebSearch — grounded web search results through the Perplexity Search API.

For more information about the Perplexity API, see the Perplexity docs.

In order to follow along with this guide, you’ll need a Perplexity API key. Add it as an environment variable, PERPLEXITY_API_KEY.

Installation

pip install perplexity-haystack

Usage

You can use the Perplexity components as standalone components or in Haystack pipelines.

Use Perplexity Chat Completions (Agent API)

PerplexityChatGenerator is powered by the Perplexity Agent API and defaults to openai/gpt-5.4.

import os

from haystack.dataclasses import ChatMessage
from haystack_integrations.components.generators.perplexity import PerplexityChatGenerator

os.environ["PERPLEXITY_API_KEY"] = "YOUR_PERPLEXITY_API_KEY"

client = PerplexityChatGenerator()
response = client.run(
    messages=[ChatMessage.from_user("What are Agentic Pipelines? Be brief.")]
)

print(response["replies"])

You can pick any of the supported Agent API models via the model parameter, for example:

client = PerplexityChatGenerator(model="anthropic/claude-sonnet-4-6")

Use Perplexity Embeddings

import os

from haystack_integrations.components.embedders.perplexity import PerplexityTextEmbedder

os.environ["PERPLEXITY_API_KEY"] = "YOUR_PERPLEXITY_API_KEY"

embedder = PerplexityTextEmbedder()
response = embedder.run(text="What is Haystack by deepset?")

print(response["embedding"])

For embedding a list of documents, use PerplexityDocumentEmbedder:

from haystack import Document
from haystack_integrations.components.embedders.perplexity import PerplexityDocumentEmbedder

docs = [Document(content="What is Haystack by deepset?")]
result = PerplexityDocumentEmbedder().run(documents=docs)

print(result["documents"][0].embedding)

Use Perplexity Web Search (Search API)

import os

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

os.environ["PERPLEXITY_API_KEY"] = "YOUR_PERPLEXITY_API_KEY"

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

documents = result["documents"]
links = result["links"]

print(documents)
print(links)

License

perplexity-haystack is distributed under the terms of the Apache-2.0 license.