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hejl
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<svg xmlns="http://www.w3.org/2000/svg" fill="currentColor" viewBox="0 0 24 24" aria-hidden="true" class="" focusable="false" style="fill:currentColor;height:28px;width:28px"><path d="m3.005 8.858 8.783 12.544h3.904L6.908 8.858zM6.905 15.825 3 21.402h3.907l1.951-2.788zM16.585 2l-6.75 9.64 1.953 2.79L20.492 2zM17.292 7.965v13.437h3.2V3.395z"></path></svg>
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Before Width: | Height: | Size: 356 B |
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model: grok-beta
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label:
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en_US: Grok beta
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model_type: llm
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features:
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- multi-tool-call
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model_properties:
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mode: chat
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context_size: 131072
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parameter_rules:
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- name: temperature
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label:
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en_US: "Temperature"
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zh_Hans: "采样温度"
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type: float
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default: 0.7
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min: 0.0
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max: 2.0
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precision: 1
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required: true
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help:
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en_US: "The randomness of the sampling temperature control output. The temperature value is within the range of [0.0, 1.0]. The higher the value, the more random and creative the output; the lower the value, the more stable it is. It is recommended to adjust either top_p or temperature parameters according to your needs to avoid adjusting both at the same time."
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zh_Hans: "采样温度控制输出的随机性。温度值在 [0.0, 1.0] 范围内，值越高，输出越随机和创造性；值越低，输出越稳定。建议根据需求调整 top_p 或 temperature 参数，避免同时调整两者。"
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- name: top_p
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label:
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en_US: "Top P"
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zh_Hans: "Top P"
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type: float
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default: 0.7
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min: 0.0
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max: 1.0
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precision: 1
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required: true
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help:
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en_US: "The value range of the sampling method is [0.0, 1.0]. The top_p value determines that the model selects tokens from the top p% of candidate words with the highest probability; when top_p is 0, this parameter is invalid. It is recommended to adjust either top_p or temperature parameters according to your needs to avoid adjusting both at the same time."
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zh_Hans: "采样方法的取值范围为 [0.0,1.0]。top_p 值确定模型从概率最高的前p%的候选词中选取 tokens；当 top_p 为 0 时，此参数无效。建议根据需求调整 top_p 或 temperature 参数，避免同时调整两者。"
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- name: frequency_penalty
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use_template: frequency_penalty
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label:
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en_US: "Frequency Penalty"
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zh_Hans: "频率惩罚"
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type: float
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default: 0
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min: 0
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max: 2.0
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precision: 1
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required: false
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help:
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en_US: "Used to adjust the frequency of repeated content in automatically generated text. Positive numbers reduce repetition, while negative numbers increase repetition. After setting this parameter, if a word has already appeared in the text, the model will decrease the probability of choosing that word for subsequent generation."
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zh_Hans: "用于调整自动生成文本中重复内容的频率。正数减少重复，负数增加重复。设置此参数后，如果一个词在文本中已经出现过，模型在后续生成中选择该词的概率会降低。"
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- name: user
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use_template: text
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label:
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en_US: "User"
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zh_Hans: "用户"
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type: string
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required: false
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help:
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en_US: "Used to track and differentiate conversation requests from different users."
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zh_Hans: "用于追踪和区分不同用户的对话请求。"
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from collections.abc import Generator
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from typing import Optional, Union
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from yarl import URL
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from core.model_runtime.entities.llm_entities import LLMMode, LLMResult
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from core.model_runtime.entities.message_entities import (
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PromptMessage,
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PromptMessageTool,
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)
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from core.model_runtime.model_providers.openai_api_compatible.llm.llm import OAIAPICompatLargeLanguageModel
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class XAILargeLanguageModel(OAIAPICompatLargeLanguageModel):
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def _invoke(
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self,
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model: str,
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credentials: dict,
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prompt_messages: list[PromptMessage],
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model_parameters: dict,
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tools: Optional[list[PromptMessageTool]] = None,
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stop: Optional[list[str]] = None,
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stream: bool = True,
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user: Optional[str] = None,
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) -> Union[LLMResult, Generator]:
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return super()._invoke(model, credentials, prompt_messages, model_parameters, tools, stop, stream)
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def validate_credentials(self, model: str, credentials: dict) -> None:
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self._add_custom_parameters(credentials)
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super().validate_credentials(model, credentials)
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@staticmethod
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def _add_custom_parameters(credentials) -> None:
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credentials["endpoint_url"] = str(URL(credentials["api_base"]) / "v1")
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credentials["mode"] = LLMMode.CHAT.value
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import logging
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from core.model_runtime.entities.model_entities import ModelType
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from core.model_runtime.errors.validate import CredentialsValidateFailedError
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from core.model_runtime.model_providers.__base.model_provider import ModelProvider
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logger = logging.getLogger(__name__)
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class XAIProvider(ModelProvider):
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def validate_provider_credentials(self, credentials: dict) -> None:
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"""
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Validate provider credentials
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if validate failed, raise exception
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:param credentials: provider credentials, credentials form defined in `provider_credential_schema`.
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"""
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try:
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model_instance = self.get_model_instance(ModelType.LLM)
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model_instance.validate_credentials(model="grok-beta", credentials=credentials)
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except CredentialsValidateFailedError as ex:
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raise ex
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except Exception as ex:
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logger.exception(f"{self.get_provider_schema().provider} credentials validate failed")
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raise ex
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provider: x
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label:
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en_US: xAI
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description:
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en_US: xAI is a company working on building artificial intelligence to accelerate human scientific discovery. We are guided by our mission to advance our collective understanding of the universe.
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icon_small:
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en_US: x-ai-logo.svg
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icon_large:
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en_US: x-ai-logo.svg
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help:
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title:
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en_US: Get your token from xAI
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zh_Hans: 从 xAI 获取 token
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url:
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en_US: https://x.ai/api
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supported_model_types:
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- llm
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configurate_methods:
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- predefined-model
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provider_credential_schema:
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credential_form_schemas:
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- variable: api_key
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label:
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en_US: API Key
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type: secret-input
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required: true
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placeholder:
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zh_Hans: 在此输入您的 API Key
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en_US: Enter your API Key
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- variable: api_base
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label:
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en_US: API Base
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type: text-input
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required: false
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default: https://api.x.ai
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placeholder:
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zh_Hans: 在此输入您的 API Base
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en_US: Enter your API Base
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