Perplexity

tip

This provider is an extra of the lfx-bundles metapackage. It is opt-in for both Langflow and standalone LFX installs:

uv pip install "lfx-bundles[<bundle>]"

Replace <bundle> with this page's provider name, for example qdrant. For a torch-free full Langflow install, run uv pip install "langflow[bundles]". For standalone LFX with every provider in lfx-bundles, including PyTorch-based providers, run uv pip install "lfx[bundles]". See Additional bundles for the exact extra name.

Bundles contain custom components that support specific third-party integrations with Langflow.

This page describes the components that are available in the Perplexity bundle.

For more information about Perplexity features and functionality used by Perplexity components, see the Perplexity documentation.

Perplexity text generation

This component generates text using Perplexity's language models.

It can output either a Model Response (Message) or a Language Model (LanguageModel).

Use the Language Model output when you want to use a Perplexity model as the LLM for another LLM-driven component, such as an Agent or Smart Transform component.

For more information, see Language model components.

Perplexity text generation parameters

Some parameters are hidden by default in the visual editor. You can modify all component parameters through the component inspection panel that appears when you select a component.

Name	Type	Description
model_name	String	Input parameter. The name of the Perplexity model to use. Options include various Llama 3.1 models.
max_tokens	Integer	Input parameter. The maximum number of tokens to generate.
api_key	SecretString	Input parameter. The Perplexity API Key for authentication.
temperature	Float	Input parameter. Controls randomness in the output. Default: 0.75.
top_p	Float	Input parameter. The maximum cumulative probability of tokens to consider when sampling.
n	Integer	Input parameter. Number of chat completions to generate for each prompt.