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111 lines
4.0 KiB
Plaintext
111 lines
4.0 KiB
Plaintext
---
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title: "PerplexityChatGenerator"
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id: perplexitychatgenerator
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slug: "/perplexitychatgenerator"
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description: "`PerplexityChatGenerator` enables chat completion using models via the Perplexity Agent API."
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---
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# PerplexityChatGenerator
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`PerplexityChatGenerator` enables chat completion using models via the Perplexity Agent API.
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<div className="key-value-table">
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| --- | --- |
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| **Most common position in a pipeline** | After a [ChatPromptBuilder](../builders/chatpromptbuilder.mdx) |
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| **Mandatory init variables** | `api_key`: A Perplexity API key. Can be set with `PERPLEXITY_API_KEY` env var. |
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| **Mandatory run variables** | `messages`: A list of [`ChatMessage`](../../concepts/data-classes/chatmessage.mdx) objects representing the chat |
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| **Output variables** | `replies`: A list of alternative replies of the LLM to the input chat |
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| **API reference** | [Integrations](/reference/integrations-perplexity) |
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| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/blob/main/integrations/perplexity/src/haystack_integrations/components/generators/perplexity/chat/chat_generator.py |
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| **Package name** | `perplexity-haystack` |
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</div>
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## Overview
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`PerplexityChatGenerator` is built on top of `OpenAIResponsesChatGenerator` and communicates with the [Perplexity Agent API](https://docs.perplexity.ai/) (`POST /v1/agent`), which uses an OpenAI Responses-compatible interface.
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It supports the following models:
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- `openai/gpt-5.5`
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- `openai/gpt-5.4` (default)
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- `anthropic/claude-sonnet-4-6`
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- `xai/grok-4.3`
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- `google/gemini-3-flash-preview`
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See the [Perplexity Agent API models page](https://docs.perplexity.ai/docs/agent-api/models) for the current list.
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`PerplexityChatGenerator` needs a Perplexity API key to work. It uses a `PERPLEXITY_API_KEY` environment variable by default.
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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](#usage) section for an example.
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You can pass any parameters supported by the Perplexity Agent API using the `generation_kwargs` parameter, both at initialization and in the `run()` method.
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## Usage
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### On its own
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```python
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from haystack.dataclasses import ChatMessage
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from haystack_integrations.components.generators.perplexity import (
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PerplexityChatGenerator,
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)
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chat_generator = PerplexityChatGenerator()
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response = chat_generator.run(
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[ChatMessage.from_user("What's Natural Language Processing? Be brief.")],
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)
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print(response["replies"][0].text)
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```
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With streaming — pass any callable to `streaming_callback`, or use the built-in `print_streaming_chunk`:
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```python
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from haystack.dataclasses import ChatMessage
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from haystack.components.generators.utils import print_streaming_chunk
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from haystack_integrations.components.generators.perplexity import (
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PerplexityChatGenerator,
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)
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chat_generator = PerplexityChatGenerator(streaming_callback=print_streaming_chunk)
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response = chat_generator.run(
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[ChatMessage.from_user("What's Natural Language Processing? Be brief.")],
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)
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```
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### In a pipeline
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```python
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from haystack import Pipeline
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from haystack.components.builders import ChatPromptBuilder
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from haystack.dataclasses import ChatMessage
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from haystack.utils import Secret
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from haystack_integrations.components.generators.perplexity import (
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PerplexityChatGenerator,
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)
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prompt_builder = ChatPromptBuilder(
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template=[
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ChatMessage.from_system("You are a helpful assistant."),
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ChatMessage.from_user("Tell me about {{topic}}"),
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],
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required_variables="*",
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)
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llm = PerplexityChatGenerator(
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api_key=Secret.from_env_var("PERPLEXITY_API_KEY"),
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model="openai/gpt-5.4",
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)
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pipe = Pipeline()
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pipe.add_component("prompt_builder", prompt_builder)
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pipe.add_component("llm", llm)
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pipe.connect("prompt_builder.prompt", "llm.messages")
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result = pipe.run(
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data={"prompt_builder": {"topic": "large language models"}},
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)
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print(result["llm"]["replies"][0].text)
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```
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