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from langchain_community.chat_models.litellm import ChatLiteLLM, ChatLiteLLMException |
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from langflow.base.constants import STREAM_INFO_TEXT |
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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.io import ( |
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BoolInput, |
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DictInput, |
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DropdownInput, |
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FloatInput, |
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IntInput, |
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MessageInput, |
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SecretStrInput, |
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StrInput, |
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) |
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class ChatLiteLLMModelComponent(LCModelComponent): |
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display_name = "LiteLLM" |
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description = "`LiteLLM` collection of large language models." |
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documentation = "https://python.langchain.com/docs/integrations/chat/litellm" |
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icon = "🚄" |
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inputs = [ |
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MessageInput(name="input_value", display_name="Input"), |
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StrInput( |
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name="model", |
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display_name="Model name", |
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advanced=False, |
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required=True, |
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info="The name of the model to use. For example, `gpt-3.5-turbo`.", |
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), |
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SecretStrInput( |
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name="api_key", |
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display_name="API Key", |
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advanced=False, |
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required=False, |
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), |
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DropdownInput( |
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name="provider", |
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display_name="Provider", |
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info="The provider of the API key.", |
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options=[ |
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"OpenAI", |
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"Azure", |
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"Anthropic", |
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"Replicate", |
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"Cohere", |
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"OpenRouter", |
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], |
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), |
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FloatInput( |
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name="temperature", |
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display_name="Temperature", |
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advanced=False, |
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required=False, |
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value=0.7, |
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), |
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DictInput( |
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name="kwargs", |
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display_name="Kwargs", |
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advanced=True, |
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required=False, |
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is_list=True, |
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value={}, |
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), |
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DictInput( |
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name="model_kwargs", |
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display_name="Model kwargs", |
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advanced=True, |
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required=False, |
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is_list=True, |
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value={}, |
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), |
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FloatInput(name="top_p", display_name="Top p", advanced=True, required=False, value=0.5), |
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IntInput(name="top_k", display_name="Top k", advanced=True, required=False, value=35), |
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IntInput( |
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name="n", |
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display_name="N", |
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advanced=True, |
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required=False, |
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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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value=1, |
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), |
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IntInput( |
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name="max_tokens", |
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display_name="Max tokens", |
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advanced=False, |
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value=256, |
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info="The maximum number of tokens to generate for each chat completion.", |
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), |
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IntInput( |
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name="max_retries", |
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display_name="Max retries", |
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advanced=True, |
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required=False, |
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value=6, |
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), |
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BoolInput( |
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name="verbose", |
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display_name="Verbose", |
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advanced=True, |
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required=False, |
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value=False, |
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), |
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BoolInput( |
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name="stream", |
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display_name="Stream", |
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info=STREAM_INFO_TEXT, |
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advanced=True, |
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), |
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StrInput( |
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name="system_message", |
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display_name="System Message", |
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info="System message to pass to the model.", |
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advanced=True, |
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), |
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] |
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def build_model(self) -> LanguageModel: |
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try: |
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import litellm |
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litellm.drop_params = True |
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litellm.set_verbose = self.verbose |
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except ImportError as e: |
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msg = "Could not import litellm python package. Please install it with `pip install litellm`" |
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raise ChatLiteLLMException(msg) from e |
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if "" in self.kwargs: |
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del self.kwargs[""] |
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if "" in self.model_kwargs: |
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del self.model_kwargs[""] |
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if self.provider == "Azure": |
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if "api_base" not in self.kwargs: |
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msg = "Missing api_base on kwargs" |
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raise ValueError(msg) |
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if "api_version" not in self.model_kwargs: |
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msg = "Missing api_version on model_kwargs" |
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raise ValueError(msg) |
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output = ChatLiteLLM( |
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model=f"{self.provider.lower()}/{self.model}", |
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client=None, |
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streaming=self.stream, |
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temperature=self.temperature, |
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model_kwargs=self.model_kwargs if self.model_kwargs is not None else {}, |
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top_p=self.top_p, |
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top_k=self.top_k, |
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n=self.n, |
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max_tokens=self.max_tokens, |
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max_retries=self.max_retries, |
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**self.kwargs, |
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) |
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output.client.api_key = self.api_key |
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return output |
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