PerplexityChatGenerator

PerplexityChatGenerator enables chat completion using models via the Perplexity Agent API.

Most common position in a pipeline	After a ChatPromptBuilder
Mandatory init variables	api_key: A Perplexity API key. Can be set with PERPLEXITY_API_KEY env var.
Mandatory run variables	messages: A list of ChatMessage objects representing the chat
Output variables	replies: A list of alternative replies of the LLM to the input chat
API reference	Integrations
GitHub link	https://github.com/deepset-ai/haystack-core-integrations/blob/main/integrations/perplexity/src/haystack_integrations/components/generators/perplexity/chat/chat_generator.py
Package name	perplexity-haystack

Overview

PerplexityChatGenerator is built on top of OpenAIResponsesChatGenerator and communicates with the Perplexity Agent API (POST /v1/agent), which uses an OpenAI Responses-compatible interface.

It supports the following models:

• openai/gpt-5.5
• openai/gpt-5.4 (default)
• anthropic/claude-sonnet-4-6
• xai/grok-4.3
• google/gemini-3-flash-preview

See the Perplexity Agent API models page for the current list.

PerplexityChatGenerator needs a Perplexity API key to work. It uses a PERPLEXITY_API_KEY environment variable by default.

The component accepts a list of ChatMessage objects to operate. ChatMessage is a data class that contains a message, a role (such as user, assistant, or system), and optional metadata. See the usage section for an example.

You can pass any parameters supported by the Perplexity Agent API using the generation_kwargs parameter, both at initialization and in the run() method.

Usage

On its own

python

from haystack.dataclasses import ChatMessage
from haystack_integrations.components.generators.perplexity import (
    PerplexityChatGenerator,
)

chat_generator = PerplexityChatGenerator()
response = chat_generator.run(
    [ChatMessage.from_user("What's Natural Language Processing? Be brief.")],
)
print(response["replies"][0].text)

With streaming — pass any callable to streaming_callback, or use the built-in print_streaming_chunk:

python

from haystack.dataclasses import ChatMessage
from haystack.components.generators.utils import print_streaming_chunk
from haystack_integrations.components.generators.perplexity import (
    PerplexityChatGenerator,
)

chat_generator = PerplexityChatGenerator(streaming_callback=print_streaming_chunk)
response = chat_generator.run(
    [ChatMessage.from_user("What's Natural Language Processing? Be brief.")],
)

In a pipeline

python

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

prompt_builder = ChatPromptBuilder(
    template=[
        ChatMessage.from_system("You are a helpful assistant."),
        ChatMessage.from_user("Tell me about {{topic}}"),
    ],
    required_variables="*",
)
llm = PerplexityChatGenerator(
    api_key=Secret.from_env_var("PERPLEXITY_API_KEY"),
    model="openai/gpt-5.4",
)

pipe = Pipeline()
pipe.add_component("prompt_builder", prompt_builder)
pipe.add_component("llm", llm)
pipe.connect("prompt_builder.prompt", "llm.messages")

result = pipe.run(
    data={"prompt_builder": {"topic": "large language models"}},
)
print(result["llm"]["replies"][0].text)