Add model parameter translation (#8509)

Co-authored-by: swingchen01 <swings@126.com>
Co-authored-by: 陈长君 <chenchangjun@shuwen.com>

api/core/model_runtime/model_providers/ollama/llm/llm.py

@@ -472,12 +472,13 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
                 ParameterRule(
                     name=DefaultParameterName.TEMPERATURE.value,
                     use_template=DefaultParameterName.TEMPERATURE.value,
-                    label=I18nObject(en_US="Temperature"),
+                    label=I18nObject(en_US="Temperature", zh_Hans="温度"),
                     type=ParameterType.FLOAT,
                     help=I18nObject(
                         en_US="The temperature of the model. "
                         "Increasing the temperature will make the model answer "
-                        "more creatively. (Default: 0.8)"
+                        "more creatively. (Default: 0.8)",
+                        zh_Hans="模型的温度。增加温度将使模型的回答更具创造性。（默认值：0.8）",
                     ),
                     default=0.1,
                     min=0,
@@ -486,12 +487,13 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
                 ParameterRule(
                     name=DefaultParameterName.TOP_P.value,
                     use_template=DefaultParameterName.TOP_P.value,
-                    label=I18nObject(en_US="Top P"),
+                    label=I18nObject(en_US="Top P", zh_Hans="Top P"),
                     type=ParameterType.FLOAT,
                     help=I18nObject(
                         en_US="Works together with top-k. A higher value (e.g., 0.95) will lead to "
                         "more diverse text, while a lower value (e.g., 0.5) will generate more "
-                        "focused and conservative text. (Default: 0.9)"
+                        "focused and conservative text. (Default: 0.9)",
+                        zh_Hans="与top-k一起工作。较高的值（例如，0.95）会导致生成更多样化的文本，而较低的值（例如，0.5）会生成更专注和保守的文本。（默认值：0.9）",
                     ),
                     default=0.9,
                     min=0,
@@ -499,12 +501,13 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
                 ),
                 ParameterRule(
                     name="top_k",
-                    label=I18nObject(en_US="Top K"),
+                    label=I18nObject(en_US="Top K", zh_Hans="Top K"),
                     type=ParameterType.INT,
                     help=I18nObject(
                         en_US="Reduces the probability of generating nonsense. "
                         "A higher value (e.g. 100) will give more diverse answers, "
-                        "while a lower value (e.g. 10) will be more conservative. (Default: 40)"
+                        "while a lower value (e.g. 10) will be more conservative. (Default: 40)",
+                        zh_Hans="减少生成无意义内容的可能性。较高的值（例如100）将提供更多样化的答案，而较低的值（例如10）将更为保守。（默认值：40）",
                     ),
                     min=1,
                     max=100,
@@ -516,7 +519,8 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
                     help=I18nObject(
                         en_US="Sets how strongly to penalize repetitions. "
                         "A higher value (e.g., 1.5) will penalize repetitions more strongly, "
-                        "while a lower value (e.g., 0.9) will be more lenient. (Default: 1.1)"
+                        "while a lower value (e.g., 0.9) will be more lenient. (Default: 1.1)",
+                        zh_Hans="设置对重复内容的惩罚强度。一个较高的值（例如，1.5）会更强地惩罚重复内容，而一个较低的值（例如，0.9）则会相对宽容。（默认值：1.1）",
                     ),
                     min=-2,
                     max=2,
@@ -524,11 +528,12 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
                 ParameterRule(
                     name="num_predict",
                     use_template="max_tokens",
-                    label=I18nObject(en_US="Num Predict"),
+                    label=I18nObject(en_US="Num Predict", zh_Hans="最大令牌数预测"),
                     type=ParameterType.INT,
                     help=I18nObject(
                         en_US="Maximum number of tokens to predict when generating text. "
-                        "(Default: 128, -1 = infinite generation, -2 = fill context)"
+                        "(Default: 128, -1 = infinite generation, -2 = fill context)",
+                        zh_Hans="生成文本时预测的最大令牌数。（默认值：128，-1 = 无限生成，-2 = 填充上下文）",
                     ),
                     default=(512 if int(credentials.get("max_tokens", 4096)) >= 768 else 128),
                     min=-2,
@@ -536,121 +541,137 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
                 ),
                 ParameterRule(
                     name="mirostat",
-                    label=I18nObject(en_US="Mirostat sampling"),
+                    label=I18nObject(en_US="Mirostat sampling", zh_Hans="Mirostat 采样"),
                     type=ParameterType.INT,
                     help=I18nObject(
                         en_US="Enable Mirostat sampling for controlling perplexity. "
-                        "(default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)"
+                        "(default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)",
+                        zh_Hans="启用 Mirostat 采样以控制困惑度。"
+                        "（默认值：0，0 = 禁用，1 = Mirostat，2 = Mirostat 2.0）",
                     ),
                     min=0,
                     max=2,
                 ),
                 ParameterRule(
                     name="mirostat_eta",
-                    label=I18nObject(en_US="Mirostat Eta"),
+                    label=I18nObject(en_US="Mirostat Eta", zh_Hans="学习率"),
                     type=ParameterType.FLOAT,
                     help=I18nObject(
                         en_US="Influences how quickly the algorithm responds to feedback from "
                         "the generated text. A lower learning rate will result in slower adjustments, "
                         "while a higher learning rate will make the algorithm more responsive. "
-                        "(Default: 0.1)"
+                        "(Default: 0.1)",
+                        zh_Hans="影响算法对生成文本反馈响应的速度。较低的学习率会导致调整速度变慢，而较高的学习率会使得算法更加灵敏。（默认值：0.1）",
                     ),
                     precision=1,
                 ),
                 ParameterRule(
                     name="mirostat_tau",
-                    label=I18nObject(en_US="Mirostat Tau"),
+                    label=I18nObject(en_US="Mirostat Tau", zh_Hans="文本连贯度"),
                     type=ParameterType.FLOAT,
                     help=I18nObject(
                         en_US="Controls the balance between coherence and diversity of the output. "
-                        "A lower value will result in more focused and coherent text. (Default: 5.0)"
+                        "A lower value will result in more focused and coherent text. (Default: 5.0)",
+                        zh_Hans="控制输出的连贯性和多样性之间的平衡。较低的值会导致更专注和连贯的文本。（默认值：5.0）",
                     ),
                     precision=1,
                 ),
                 ParameterRule(
                     name="num_ctx",
-                    label=I18nObject(en_US="Size of context window"),
+                    label=I18nObject(en_US="Size of context window", zh_Hans="上下文窗口大小"),
                     type=ParameterType.INT,
                     help=I18nObject(
-                        en_US="Sets the size of the context window used to generate the next token. (Default: 2048)"
+                        en_US="Sets the size of the context window used to generate the next token. (Default: 2048)",
+                        zh_Hans="设置用于生成下一个标记的上下文窗口大小。（默认值：2048）",
                     ),
                     default=2048,
                     min=1,
                 ),
                 ParameterRule(
                     name="num_gpu",
-                    label=I18nObject(en_US="GPU Layers"),
+                    label=I18nObject(en_US="GPU Layers", zh_Hans="GPU 层数"),
                     type=ParameterType.INT,
                     help=I18nObject(
                         en_US="The number of layers to offload to the GPU(s). "
                         "On macOS it defaults to 1 to enable metal support, 0 to disable."
                         "As long as a model fits into one gpu it stays in one. "
-                        "It does not set the number of GPU(s). "
+                        "It does not set the number of GPU(s). ",
+                        zh_Hans="加载到 GPU 的层数。在 macOS 上，默认为 1 以启用 Metal 支持，设置为 0 则禁用。"
+                        "只要模型适合一个 GPU，它就保留在其中。它不设置 GPU 的数量。",
                     ),
                     min=-1,
                     default=1,
                 ),
                 ParameterRule(
                     name="num_thread",
-                    label=I18nObject(en_US="Num Thread"),
+                    label=I18nObject(en_US="Num Thread", zh_Hans="线程数"),
                     type=ParameterType.INT,
                     help=I18nObject(
                         en_US="Sets the number of threads to use during computation. "
                         "By default, Ollama will detect this for optimal performance. "
                         "It is recommended to set this value to the number of physical CPU cores "
-                        "your system has (as opposed to the logical number of cores)."
+                        "your system has (as opposed to the logical number of cores).",
+                        zh_Hans="设置计算过程中使用的线程数。默认情况下，Ollama会检测以获得最佳性能。建议将此值设置为系统拥有的物理CPU核心数（而不是逻辑核心数）。",
                     ),
                     min=1,
                 ),
                 ParameterRule(
                     name="repeat_last_n",
-                    label=I18nObject(en_US="Repeat last N"),
+                    label=I18nObject(en_US="Repeat last N", zh_Hans="回溯内容"),
                     type=ParameterType.INT,
                     help=I18nObject(
                         en_US="Sets how far back for the model to look back to prevent repetition. "
-                        "(Default: 64, 0 = disabled, -1 = num_ctx)"
+                        "(Default: 64, 0 = disabled, -1 = num_ctx)",
+                        zh_Hans="设置模型回溯多远的内容以防止重复。（默认值：64，0 = 禁用，-1 = num_ctx）",
                     ),
                     min=-1,
                 ),
                 ParameterRule(
                     name="tfs_z",
-                    label=I18nObject(en_US="TFS Z"),
+                    label=I18nObject(en_US="TFS Z", zh_Hans="减少标记影响"),
                     type=ParameterType.FLOAT,
                     help=I18nObject(
                         en_US="Tail free sampling is used to reduce the impact of less probable tokens "
                         "from the output. A higher value (e.g., 2.0) will reduce the impact more, "
-                        "while a value of 1.0 disables this setting. (default: 1)"
+                        "while a value of 1.0 disables this setting. (default: 1)",
+                        zh_Hans="用于减少输出中不太可能的标记的影响。较高的值（例如，2.0）会更多地减少这种影响，而1.0的值则会禁用此设置。（默认值：1）",
                     ),
                     precision=1,
                 ),
                 ParameterRule(
                     name="seed",
-                    label=I18nObject(en_US="Seed"),
+                    label=I18nObject(en_US="Seed", zh_Hans="随机数种子"),
                     type=ParameterType.INT,
                     help=I18nObject(
                         en_US="Sets the random number seed to use for generation. Setting this to "
                         "a specific number will make the model generate the same text for "
-                        "the same prompt. (Default: 0)"
+                        "the same prompt. (Default: 0)",
+                        zh_Hans="设置用于生成的随机数种子。将此设置为特定数字将使模型对相同的提示生成相同的文本。（默认值：0）",
                     ),
                 ),
                 ParameterRule(
                     name="keep_alive",
-                    label=I18nObject(en_US="Keep Alive"),
+                    label=I18nObject(en_US="Keep Alive", zh_Hans="模型存活时间"),
                     type=ParameterType.STRING,
                     help=I18nObject(
                         en_US="Sets how long the model is kept in memory after generating a response. "
                         "This must be a duration string with a unit (e.g., '10m' for 10 minutes or '24h' for 24 hours)."
                         " A negative number keeps the model loaded indefinitely, and '0' unloads the model"
                         " immediately after generating a response."
-                        " Valid time units are 's','m','h'. (Default: 5m)"
+                        " Valid time units are 's','m','h'. (Default: 5m)",
+                        zh_Hans="设置模型在生成响应后在内存中保留的时间。"
+                        "这必须是一个带有单位的持续时间字符串（例如，'10m' 表示10分钟，'24h' 表示24小时）。"
+                        "负数表示无限期地保留模型，'0'表示在生成响应后立即卸载模型。"
+                        "有效的时间单位有 's'（秒）、'm'（分钟）、'h'（小时）。（默认值：5m）",
                     ),
                 ),
                 ParameterRule(
                     name="format",
-                    label=I18nObject(en_US="Format"),
+                    label=I18nObject(en_US="Format", zh_Hans="返回格式"),
                     type=ParameterType.STRING,
                     help=I18nObject(
-                        en_US="the format to return a response in. Currently the only accepted value is json."
+                        en_US="the format to return a response in. Currently the only accepted value is json.",
+                        zh_Hans="返回响应的格式。目前唯一接受的值是json。",
                     ),
                     options=["json"],
                 ),

api/core/model_runtime/model_providers/openai_api_compatible/llm/llm.py

@@ -205,7 +205,13 @@ class OAIAPICompatLargeLanguageModel(_CommonOaiApiCompat, LargeLanguageModel):
             parameter_rules=[
                 ParameterRule(
                     name=DefaultParameterName.TEMPERATURE.value,
-                    label=I18nObject(en_US="Temperature"),
+                    label=I18nObject(en_US="Temperature", zh_Hans="温度"),
+                    help=I18nObject(
+                        en_US="Kernel sampling threshold. Used to determine the randomness of the results."
+                        "The higher the value, the stronger the randomness."
+                        "The higher the possibility of getting different answers to the same question.",
+                        zh_Hans="核采样阈值。用于决定结果随机性，取值越高随机性越强即相同的问题得到的不同答案的可能性越高。",
+                    ),
                     type=ParameterType.FLOAT,
                     default=float(credentials.get("temperature", 0.7)),
                     min=0,
@@ -214,7 +220,13 @@ class OAIAPICompatLargeLanguageModel(_CommonOaiApiCompat, LargeLanguageModel):
                 ),
                 ParameterRule(
                     name=DefaultParameterName.TOP_P.value,
-                    label=I18nObject(en_US="Top P"),
+                    label=I18nObject(en_US="Top P", zh_Hans="Top P"),
+                    help=I18nObject(
+                        en_US="The probability threshold of the nucleus sampling method during the generation process."
+                        "The larger the value is, the higher the randomness of generation will be."
+                        "The smaller the value is, the higher the certainty of generation will be.",
+                        zh_Hans="生成过程中核采样方法概率阈值。取值越大，生成的随机性越高；取值越小，生成的确定性越高。",
+                    ),
                     type=ParameterType.FLOAT,
                     default=float(credentials.get("top_p", 1)),
                     min=0,
@@ -223,7 +235,12 @@ class OAIAPICompatLargeLanguageModel(_CommonOaiApiCompat, LargeLanguageModel):
                 ),
                 ParameterRule(
                     name=DefaultParameterName.FREQUENCY_PENALTY.value,
-                    label=I18nObject(en_US="Frequency Penalty"),
+                    label=I18nObject(en_US="Frequency Penalty", zh_Hans="频率惩罚"),
+                    help=I18nObject(
+                        en_US="For controlling the repetition rate of words used by the model."
+                        "Increasing this can reduce the repetition of the same words in the model's output.",
+                        zh_Hans="用于控制模型已使用字词的重复率。 提高此项可以降低模型在输出中重复相同字词的重复度。",
+                    ),
                     type=ParameterType.FLOAT,
                     default=float(credentials.get("frequency_penalty", 0)),
                     min=-2,
@@ -231,7 +248,12 @@ class OAIAPICompatLargeLanguageModel(_CommonOaiApiCompat, LargeLanguageModel):
                 ),
                 ParameterRule(
                     name=DefaultParameterName.PRESENCE_PENALTY.value,
-                    label=I18nObject(en_US="Presence Penalty"),
+                    label=I18nObject(en_US="Presence Penalty", zh_Hans="存在惩罚"),
+                    help=I18nObject(
+                        en_US="Used to control the repetition rate when generating models."
+                        "Increasing this can reduce the repetition rate of model generation.",
+                        zh_Hans="用于控制模型生成时的重复度。提高此项可以降低模型生成的重复度。",
+                    ),
                     type=ParameterType.FLOAT,
                     default=float(credentials.get("presence_penalty", 0)),
                     min=-2,
@@ -239,7 +261,10 @@ class OAIAPICompatLargeLanguageModel(_CommonOaiApiCompat, LargeLanguageModel):
                 ),
                 ParameterRule(
                     name=DefaultParameterName.MAX_TOKENS.value,
-                    label=I18nObject(en_US="Max Tokens"),
+                    label=I18nObject(en_US="Max Tokens", zh_Hans="最大标记"),
+                    help=I18nObject(
+                        en_US="Maximum length of tokens for the model response.", zh_Hans="模型回答的tokens的最大长度。"
+                    ),
                     type=ParameterType.INT,
                     default=512,
                     min=1,