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from langchain_community.chat_models import ChatPerplexity |
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from pydantic.v1 import SecretStr |
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from langflow.base.models.model import LCModelComponent |
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from langflow.field_typing import LanguageModel |
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from langflow.inputs.inputs import HandleInput |
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from langflow.io import DropdownInput, FloatInput, IntInput, SecretStrInput |
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class PerplexityComponent(LCModelComponent): |
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display_name = "Perplexity" |
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description = "Generate text using Perplexity LLMs." |
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documentation = "https://python.langchain.com/v0.2/docs/integrations/chat/perplexity/" |
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icon = "Perplexity" |
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name = "PerplexityModel" |
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inputs = [ |
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*LCModelComponent._base_inputs, |
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DropdownInput( |
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name="model_name", |
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display_name="Model Name", |
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advanced=False, |
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options=[ |
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"llama-3.1-sonar-small-128k-online", |
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"llama-3.1-sonar-large-128k-online", |
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"llama-3.1-sonar-huge-128k-online", |
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"llama-3.1-sonar-small-128k-chat", |
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"llama-3.1-sonar-large-128k-chat", |
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"llama-3.1-8b-instruct", |
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"llama-3.1-70b-instruct", |
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], |
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value="llama-3.1-sonar-small-128k-online", |
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), |
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IntInput( |
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name="max_output_tokens", display_name="Max Output Tokens", info="The maximum number of tokens to generate." |
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), |
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SecretStrInput( |
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name="api_key", |
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display_name="Perplexity API Key", |
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info="The Perplexity API Key to use for the Perplexity model.", |
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advanced=False, |
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), |
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FloatInput(name="temperature", display_name="Temperature", value=0.75), |
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FloatInput( |
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name="top_p", |
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display_name="Top P", |
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info="The maximum cumulative probability of tokens to consider when sampling.", |
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advanced=True, |
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), |
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IntInput( |
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name="n", |
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display_name="N", |
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info="Number of chat completions to generate for each prompt. " |
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"Note that the API may not return the full n completions if duplicates are generated.", |
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advanced=True, |
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), |
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IntInput( |
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name="top_k", |
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display_name="Top K", |
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info="Decode using top-k sampling: consider the set of top_k most probable tokens. Must be positive.", |
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advanced=True, |
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), |
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HandleInput( |
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name="output_parser", |
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display_name="Output Parser", |
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info="The parser to use to parse the output of the model", |
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advanced=True, |
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input_types=["OutputParser"], |
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), |
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] |
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def build_model(self) -> LanguageModel: |
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api_key = SecretStr(self.api_key).get_secret_value() |
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temperature = self.temperature |
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model = self.model_name |
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max_output_tokens = self.max_output_tokens |
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top_k = self.top_k |
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top_p = self.top_p |
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n = self.n |
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return ChatPerplexity( |
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model=model, |
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temperature=temperature or 0.75, |
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pplx_api_key=api_key, |
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top_k=top_k or None, |
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top_p=top_p or None, |
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n=n or 1, |
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max_output_tokens=max_output_tokens, |
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) |
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