{
  "AI integration": "AI integration",
  "Add content": "Add content",
  "Add prompt": "Add prompt",
  "Frequency penalty description": "Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.",
  "Get models list failed, you can enter a model name manually.": "Get models list failed, you can enter a model name manually.",
  "Image": "Image",
  "LLM service": "LLM service",
  "LLM services": "LLM services",
  "Max completion tokens description": "An upper bound for the number of tokens that can be generated for a completion, including visible output tokens and reasoning tokens.",
  "Max retries": "Max retries",
  "Message": "Message",
  "Messages": "Messages",
  "Model": "Model",
  "Presence penalty description": "Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics.",
  "Provider": "Provider",
  "Response format description": "Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message.",
  "Role": "Role",
  "Structured output": "Structured output",
  "Temperature description": "What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.",
  "Text": "Text",
  "Timout (ms)": "Timout (ms)",
  "Top P description": "An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered.",
  "UID": "UID"
}