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Documentation Index

Fetch the complete documentation index at: https://docs.perplexity.ai/llms.txt

Use this file to discover all available pages before exploring further.

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Overview

The perplexity-haystack package provides Haystack components for Perplexity’s Agent API, Embeddings API, and grounded Search API, so you can build retrieval-augmented and agentic pipelines that combine chat, embeddings, and live web search.
Haystack is an open-source Python framework by deepset for building production-ready LLM applications, including RAG pipelines and agentic workflows. Learn more at haystack.deepset.ai.
The integration includes:
  • PerplexityChatGenerator — Chat completions through the Agent API (OpenAI-compatible).
  • PerplexityTextEmbedder and PerplexityDocumentEmbedder — Embeddings through the Embeddings API.
  • PerplexityWebSearch — Ranked, grounded web results through the Search API.

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Installation

pip install perplexity-haystack

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API Key Setup

Set your Perplexity API key as an environment variable: 
import os

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

Get API Key

Generate your API key from the Perplexity dashboard.

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Quick Start: Chat (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_api_key_here"

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

print(response["replies"])

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Selecting a model

You can pick any of the supported Agent API models via the model parameter: 
client = PerplexityChatGenerator(model="anthropic/claude-sonnet-4-6")
Supported models include openai/gpt-5.4 (default), openai/gpt-5.5, openai/gpt-4o, anthropic/claude-sonnet-4-6, xai/grok-4-1, and google/gemini-3-flash-preview. See the Agent API models page for the full list.

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Quick Start: Embeddings

Embed a single query with PerplexityTextEmbedder: 
import os

from haystack_integrations.components.embedders.perplexity import PerplexityTextEmbedder

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

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

print(response["embedding"])
Embed a list of documents with 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)
Both embedders default to pplx-embed-v1-0.6b. The larger pplx-embed-v1-4b model is also available — set it via the model parameter.

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Quick Start: Web Search (Search API)

Use PerplexityWebSearch to get ranked, grounded web results inside a Haystack pipeline: 
import os

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

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

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)

Haystack Integrations

Catalog entry on haystack.deepset.ai

Source Code

perplexity-haystack on GitHub

PyPI Package

View on PyPI

Haystack Docs

Full Haystack documentation

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Support

Need help with the integration?