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import requests |
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from pydantic.v1 import SecretStr |
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from typing_extensions import override |
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from langflow.base.models.groq_constants import GROQ_MODELS |
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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, MessageTextInput, SecretStrInput |
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class GroqModel(LCModelComponent): |
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display_name: str = "Groq" |
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description: str = "Generate text using Groq." |
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icon = "Groq" |
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name = "GroqModel" |
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inputs = [ |
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*LCModelComponent._base_inputs, |
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SecretStrInput(name="groq_api_key", display_name="Groq API Key", info="API key for the Groq API."), |
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MessageTextInput( |
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name="groq_api_base", |
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display_name="Groq API Base", |
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info="Base URL path for API requests, leave blank if not using a proxy or service emulator.", |
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advanced=True, |
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value="https://api.groq.com", |
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), |
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IntInput( |
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name="max_tokens", |
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display_name="Max Output Tokens", |
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info="The maximum number of tokens to generate.", |
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advanced=True, |
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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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info="Run inference with this temperature. Must by in the closed interval [0.0, 1.0].", |
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value=0.1, |
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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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DropdownInput( |
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name="model_name", |
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display_name="Model", |
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info="The name of the model to use.", |
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options=GROQ_MODELS, |
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value="llama-3.1-8b-instant", |
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refresh_button=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 get_models(self) -> list[str]: |
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api_key = self.groq_api_key |
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base_url = self.groq_api_base or "https://api.groq.com" |
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url = f"{base_url}/openai/v1/models" |
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headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"} |
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try: |
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response = requests.get(url, headers=headers, timeout=10) |
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response.raise_for_status() |
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model_list = response.json() |
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return [model["id"] for model in model_list.get("data", [])] |
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except requests.RequestException as e: |
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self.status = f"Error fetching models: {e}" |
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return GROQ_MODELS |
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@override |
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def update_build_config(self, build_config: dict, field_value: str, field_name: str | None = None): |
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if field_name in {"groq_api_key", "groq_api_base", "model_name"}: |
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models = self.get_models() |
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build_config["model_name"]["options"] = models |
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return build_config |
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def build_model(self) -> LanguageModel: |
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try: |
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from langchain_groq import ChatGroq |
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except ImportError as e: |
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msg = "langchain-groq is not installed. Please install it with `pip install langchain-groq`." |
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raise ImportError(msg) from e |
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groq_api_key = self.groq_api_key |
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model_name = self.model_name |
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max_tokens = self.max_tokens |
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temperature = self.temperature |
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groq_api_base = self.groq_api_base |
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n = self.n |
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stream = self.stream |
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return ChatGroq( |
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model=model_name, |
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max_tokens=max_tokens or None, |
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temperature=temperature, |
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base_url=groq_api_base, |
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n=n or 1, |
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api_key=SecretStr(groq_api_key).get_secret_value(), |
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streaming=stream, |
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) |
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