feat: add models for gitee.ai (#9490)

api/core/model_runtime/model_providers/gitee_ai/_common.py

@@ -0,0 +1,47 @@
+from dashscope.common.error import (
+    AuthenticationError,
+    InvalidParameter,
+    RequestFailure,
+    ServiceUnavailableError,
+    UnsupportedHTTPMethod,
+    UnsupportedModel,
+)
+
+from core.model_runtime.errors.invoke import (
+    InvokeAuthorizationError,
+    InvokeBadRequestError,
+    InvokeConnectionError,
+    InvokeError,
+    InvokeRateLimitError,
+    InvokeServerUnavailableError,
+)
+
+
+class _CommonGiteeAI:
+    @property
+    def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
+        """
+        Map model invoke error to unified error
+        The key is the error type thrown to the caller
+        The value is the error type thrown by the model,
+        which needs to be converted into a unified error type for the caller.
+
+        :return: Invoke error mapping
+        """
+        return {
+            InvokeConnectionError: [
+                RequestFailure,
+            ],
+            InvokeServerUnavailableError: [
+                ServiceUnavailableError,
+            ],
+            InvokeRateLimitError: [],
+            InvokeAuthorizationError: [
+                AuthenticationError,
+            ],
+            InvokeBadRequestError: [
+                InvalidParameter,
+                UnsupportedModel,
+                UnsupportedHTTPMethod,
+            ],
+        }

api/core/model_runtime/model_providers/gitee_ai/gitee_ai.py

@@ -0,0 +1,25 @@
+import logging
+
+from core.model_runtime.entities.model_entities import ModelType
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.__base.model_provider import ModelProvider
+
+logger = logging.getLogger(__name__)
+
+
+class GiteeAIProvider(ModelProvider):
+    def validate_provider_credentials(self, credentials: dict) -> None:
+        """
+        Validate provider credentials
+        if validate failed, raise exception
+
+        :param credentials: provider credentials, credentials form defined in `provider_credential_schema`.
+        """
+        try:
+            model_instance = self.get_model_instance(ModelType.LLM)
+            model_instance.validate_credentials(model="Qwen2-7B-Instruct", credentials=credentials)
+        except CredentialsValidateFailedError as ex:
+            raise ex
+        except Exception as ex:
+            logger.exception(f"{self.get_provider_schema().provider} credentials validate failed")
+            raise ex

api/core/model_runtime/model_providers/gitee_ai/gitee_ai.yaml

@@ -0,0 +1,35 @@
+provider: gitee_ai
+label:
+  en_US: Gitee AI
+  zh_Hans: Gitee AI
+description:
+  en_US: 快速体验大模型，领先探索 AI 开源世界
+  zh_Hans: 快速体验大模型，领先探索 AI 开源世界
+icon_small:
+  en_US: Gitee-AI-Logo.svg
+icon_large:
+  en_US: Gitee-AI-Logo-full.svg
+help:
+  title:
+    en_US: Get your token from Gitee AI
+    zh_Hans: 从 Gitee AI 获取 token
+  url:
+    en_US: https://ai.gitee.com/dashboard/settings/tokens
+supported_model_types:
+  - llm
+  - text-embedding
+  - rerank
+  - speech2text
+  - tts
+configurate_methods:
+  - predefined-model
+provider_credential_schema:
+  credential_form_schemas:
+    - variable: api_key
+      label:
+        en_US: API Key
+      type: secret-input
+      required: true
+      placeholder:
+        zh_Hans: 在此输入您的 API Key
+        en_US: Enter your API Key

api/core/model_runtime/model_providers/gitee_ai/llm/Qwen2-72B-Instruct.yaml

@@ -0,0 +1,105 @@
+model: Qwen2-72B-Instruct
+label:
+  zh_Hans: Qwen2-72B-Instruct
+  en_US: Qwen2-72B-Instruct
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 6400
+parameter_rules:
+  - name: stream
+    use_template: boolean
+    label:
+      en_US: "Stream"
+      zh_Hans: "流式"
+    type: boolean
+    default: true
+    required: true
+    help:
+      en_US: "Whether to return the results in batches through streaming. If set to true, the generated text will be pushed to the user in real time during the generation process."
+      zh_Hans: "是否通过流式分批返回结果。如果设置为 true，生成过程中实时地向用户推送每一部分生成的文本。"
+
+  - name: max_tokens
+    use_template: max_tokens
+    label:
+      en_US: "Max Tokens"
+      zh_Hans: "最大Token数"
+    type: int
+    default: 512
+    min: 1
+    required: true
+    help:
+      en_US: "The maximum number of tokens that can be generated by the model varies depending on the model."
+      zh_Hans: "模型可生成的最大 token 个数，不同模型上限不同。"
+
+  - name: temperature
+    use_template: temperature
+    label:
+      en_US: "Temperature"
+      zh_Hans: "采样温度"
+    type: float
+    default: 0.7
+    min: 0.0
+    max: 1.0
+    precision: 1
+    required: true
+    help:
+      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."
+      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。"
+
+  - name: top_p
+    use_template: top_p
+    label:
+      en_US: "Top P"
+      zh_Hans: "Top P"
+    type: float
+    default: 0.7
+    min: 0.0
+    max: 1.0
+    precision: 1
+    required: true
+    help:
+      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."
+      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。"
+
+  - name: top_k
+    use_template: top_k
+    label:
+      en_US: "Top K"
+      zh_Hans: "Top K"
+    type: int
+    default: 50
+    min: 0
+    max: 100
+    required: true
+    help:
+      en_US: "The value range is [0,100], which limits the model to only select from the top k words with the highest probability when choosing the next word at each step. The larger the value, the more diverse text generation will be."
+      zh_Hans: "取值范围为 [0,100]，限制模型在每一步选择下一个词时，只从概率最高的前 k 个词中选取。数值越大，文本生成越多样。"
+
+  - name: frequency_penalty
+    use_template: frequency_penalty
+    label:
+      en_US: "Frequency Penalty"
+      zh_Hans: "频率惩罚"
+    type: float
+    default: 0
+    min: -1.0
+    max: 1.0
+    precision: 1
+    required: false
+    help:
+      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."
+      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。"
+
+  - name: user
+    use_template: text
+    label:
+      en_US: "User"
+      zh_Hans: "用户"
+    type: string
+    required: false
+    help:
+      en_US: "Used to track and differentiate conversation requests from different users."
+      zh_Hans: "用于追踪和区分不同用户的对话请求。"

api/core/model_runtime/model_providers/gitee_ai/llm/Qwen2-7B-Instruct.yaml

@@ -0,0 +1,105 @@
+model: Qwen2-7B-Instruct
+label:
+  zh_Hans: Qwen2-7B-Instruct
+  en_US: Qwen2-7B-Instruct
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 32768
+parameter_rules:
+  - name: stream
+    use_template: boolean
+    label:
+      en_US: "Stream"
+      zh_Hans: "流式"
+    type: boolean
+    default: true
+    required: true
+    help:
+      en_US: "Whether to return the results in batches through streaming. If set to true, the generated text will be pushed to the user in real time during the generation process."
+      zh_Hans: "是否通过流式分批返回结果。如果设置为 true，生成过程中实时地向用户推送每一部分生成的文本。"
+
+  - name: max_tokens
+    use_template: max_tokens
+    label:
+      en_US: "Max Tokens"
+      zh_Hans: "最大Token数"
+    type: int
+    default: 512
+    min: 1
+    required: true
+    help:
+      en_US: "The maximum number of tokens that can be generated by the model varies depending on the model."
+      zh_Hans: "模型可生成的最大 token 个数，不同模型上限不同。"
+
+  - name: temperature
+    use_template: temperature
+    label:
+      en_US: "Temperature"
+      zh_Hans: "采样温度"
+    type: float
+    default: 0.7
+    min: 0.0
+    max: 1.0
+    precision: 1
+    required: true
+    help:
+      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."
+      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。"
+
+  - name: top_p
+    use_template: top_p
+    label:
+      en_US: "Top P"
+      zh_Hans: "Top P"
+    type: float
+    default: 0.7
+    min: 0.0
+    max: 1.0
+    precision: 1
+    required: true
+    help:
+      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."
+      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。"
+
+  - name: top_k
+    use_template: top_k
+    label:
+      en_US: "Top K"
+      zh_Hans: "Top K"
+    type: int
+    default: 50
+    min: 0
+    max: 100
+    required: true
+    help:
+      en_US: "The value range is [0,100], which limits the model to only select from the top k words with the highest probability when choosing the next word at each step. The larger the value, the more diverse text generation will be."
+      zh_Hans: "取值范围为 [0,100]，限制模型在每一步选择下一个词时，只从概率最高的前 k 个词中选取。数值越大，文本生成越多样。"
+
+  - name: frequency_penalty
+    use_template: frequency_penalty
+    label:
+      en_US: "Frequency Penalty"
+      zh_Hans: "频率惩罚"
+    type: float
+    default: 0
+    min: -1.0
+    max: 1.0
+    precision: 1
+    required: false
+    help:
+      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."
+      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。"
+
+  - name: user
+    use_template: text
+    label:
+      en_US: "User"
+      zh_Hans: "用户"
+    type: string
+    required: false
+    help:
+      en_US: "Used to track and differentiate conversation requests from different users."
+      zh_Hans: "用于追踪和区分不同用户的对话请求。"

api/core/model_runtime/model_providers/gitee_ai/llm/Yi-1.5-34B-Chat.yaml

@@ -0,0 +1,105 @@
+model: Yi-1.5-34B-Chat
+label:
+  zh_Hans: Yi-1.5-34B-Chat
+  en_US: Yi-1.5-34B-Chat
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 4096
+parameter_rules:
+  - name: stream
+    use_template: boolean
+    label:
+      en_US: "Stream"
+      zh_Hans: "流式"
+    type: boolean
+    default: true
+    required: true
+    help:
+      en_US: "Whether to return the results in batches through streaming. If set to true, the generated text will be pushed to the user in real time during the generation process."
+      zh_Hans: "是否通过流式分批返回结果。如果设置为 true，生成过程中实时地向用户推送每一部分生成的文本。"
+
+  - name: max_tokens
+    use_template: max_tokens
+    label:
+      en_US: "Max Tokens"
+      zh_Hans: "最大Token数"
+    type: int
+    default: 512
+    min: 1
+    required: true
+    help:
+      en_US: "The maximum number of tokens that can be generated by the model varies depending on the model."
+      zh_Hans: "模型可生成的最大 token 个数，不同模型上限不同。"
+
+  - name: temperature
+    use_template: temperature
+    label:
+      en_US: "Temperature"
+      zh_Hans: "采样温度"
+    type: float
+    default: 0.7
+    min: 0.0
+    max: 1.0
+    precision: 1
+    required: true
+    help:
+      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."
+      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。"
+
+  - name: top_p
+    use_template: top_p
+    label:
+      en_US: "Top P"
+      zh_Hans: "Top P"
+    type: float
+    default: 0.7
+    min: 0.0
+    max: 1.0
+    precision: 1
+    required: true
+    help:
+      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."
+      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。"
+
+  - name: top_k
+    use_template: top_k
+    label:
+      en_US: "Top K"
+      zh_Hans: "Top K"
+    type: int
+    default: 50
+    min: 0
+    max: 100
+    required: true
+    help:
+      en_US: "The value range is [0,100], which limits the model to only select from the top k words with the highest probability when choosing the next word at each step. The larger the value, the more diverse text generation will be."
+      zh_Hans: "取值范围为 [0,100]，限制模型在每一步选择下一个词时，只从概率最高的前 k 个词中选取。数值越大，文本生成越多样。"
+
+  - name: frequency_penalty
+    use_template: frequency_penalty
+    label:
+      en_US: "Frequency Penalty"
+      zh_Hans: "频率惩罚"
+    type: float
+    default: 0
+    min: -1.0
+    max: 1.0
+    precision: 1
+    required: false
+    help:
+      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."
+      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。"
+
+  - name: user
+    use_template: text
+    label:
+      en_US: "User"
+      zh_Hans: "用户"
+    type: string
+    required: false
+    help:
+      en_US: "Used to track and differentiate conversation requests from different users."
+      zh_Hans: "用于追踪和区分不同用户的对话请求。"

api/core/model_runtime/model_providers/gitee_ai/llm/_position.yaml

@@ -0,0 +1,7 @@
+- Qwen2-7B-Instruct
+- Qwen2-72B-Instruct
+- Yi-1.5-34B-Chat
+- glm-4-9b-chat
+- deepseek-coder-33B-instruct-chat
+- deepseek-coder-33B-instruct-completions
+- codegeex4-all-9b

api/core/model_runtime/model_providers/gitee_ai/llm/codegeex4-all-9b.yaml

@@ -0,0 +1,105 @@
+model: codegeex4-all-9b
+label:
+  zh_Hans: codegeex4-all-9b
+  en_US: codegeex4-all-9b
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 40960
+parameter_rules:
+  - name: stream
+    use_template: boolean
+    label:
+      en_US: "Stream"
+      zh_Hans: "流式"
+    type: boolean
+    default: true
+    required: true
+    help:
+      en_US: "Whether to return the results in batches through streaming. If set to true, the generated text will be pushed to the user in real time during the generation process."
+      zh_Hans: "是否通过流式分批返回结果。如果设置为 true，生成过程中实时地向用户推送每一部分生成的文本。"
+
+  - name: max_tokens
+    use_template: max_tokens
+    label:
+      en_US: "Max Tokens"
+      zh_Hans: "最大Token数"
+    type: int
+    default: 512
+    min: 1
+    required: true
+    help:
+      en_US: "The maximum number of tokens that can be generated by the model varies depending on the model."
+      zh_Hans: "模型可生成的最大 token 个数，不同模型上限不同。"
+
+  - name: temperature
+    use_template: temperature
+    label:
+      en_US: "Temperature"
+      zh_Hans: "采样温度"
+    type: float
+    default: 0.7
+    min: 0.0
+    max: 1.0
+    precision: 1
+    required: true
+    help:
+      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."
+      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。"
+
+  - name: top_p
+    use_template: top_p
+    label:
+      en_US: "Top P"
+      zh_Hans: "Top P"
+    type: float
+    default: 0.7
+    min: 0.0
+    max: 1.0
+    precision: 1
+    required: true
+    help:
+      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."
+      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。"
+
+  - name: top_k
+    use_template: top_k
+    label:
+      en_US: "Top K"
+      zh_Hans: "Top K"
+    type: int
+    default: 50
+    min: 0
+    max: 100
+    required: true
+    help:
+      en_US: "The value range is [0,100], which limits the model to only select from the top k words with the highest probability when choosing the next word at each step. The larger the value, the more diverse text generation will be."
+      zh_Hans: "取值范围为 [0,100]，限制模型在每一步选择下一个词时，只从概率最高的前 k 个词中选取。数值越大，文本生成越多样。"
+
+  - name: frequency_penalty
+    use_template: frequency_penalty
+    label:
+      en_US: "Frequency Penalty"
+      zh_Hans: "频率惩罚"
+    type: float
+    default: 0
+    min: -1.0
+    max: 1.0
+    precision: 1
+    required: false
+    help:
+      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."
+      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。"
+
+  - name: user
+    use_template: text
+    label:
+      en_US: "User"
+      zh_Hans: "用户"
+    type: string
+    required: false
+    help:
+      en_US: "Used to track and differentiate conversation requests from different users."
+      zh_Hans: "用于追踪和区分不同用户的对话请求。"

api/core/model_runtime/model_providers/gitee_ai/llm/deepseek-coder-33B-instruct-chat.yaml

@@ -0,0 +1,105 @@
+model: deepseek-coder-33B-instruct-chat
+label:
+  zh_Hans: deepseek-coder-33B-instruct-chat
+  en_US: deepseek-coder-33B-instruct-chat
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 9000
+parameter_rules:
+  - name: stream
+    use_template: boolean
+    label:
+      en_US: "Stream"
+      zh_Hans: "流式"
+    type: boolean
+    default: true
+    required: true
+    help:
+      en_US: "Whether to return the results in batches through streaming. If set to true, the generated text will be pushed to the user in real time during the generation process."
+      zh_Hans: "是否通过流式分批返回结果。如果设置为 true，生成过程中实时地向用户推送每一部分生成的文本。"
+
+  - name: max_tokens
+    use_template: max_tokens
+    label:
+      en_US: "Max Tokens"
+      zh_Hans: "最大Token数"
+    type: int
+    default: 512
+    min: 1
+    required: true
+    help:
+      en_US: "The maximum number of tokens that can be generated by the model varies depending on the model."
+      zh_Hans: "模型可生成的最大 token 个数，不同模型上限不同。"
+
+  - name: temperature
+    use_template: temperature
+    label:
+      en_US: "Temperature"
+      zh_Hans: "采样温度"
+    type: float
+    default: 0.7
+    min: 0.0
+    max: 1.0
+    precision: 1
+    required: true
+    help:
+      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."
+      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。"
+
+  - name: top_p
+    use_template: top_p
+    label:
+      en_US: "Top P"
+      zh_Hans: "Top P"
+    type: float
+    default: 0.7
+    min: 0.0
+    max: 1.0
+    precision: 1
+    required: true
+    help:
+      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."
+      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。"
+
+  - name: top_k
+    use_template: top_k
+    label:
+      en_US: "Top K"
+      zh_Hans: "Top K"
+    type: int
+    default: 50
+    min: 0
+    max: 100
+    required: true
+    help:
+      en_US: "The value range is [0,100], which limits the model to only select from the top k words with the highest probability when choosing the next word at each step. The larger the value, the more diverse text generation will be."
+      zh_Hans: "取值范围为 [0,100]，限制模型在每一步选择下一个词时，只从概率最高的前 k 个词中选取。数值越大，文本生成越多样。"
+
+  - name: frequency_penalty
+    use_template: frequency_penalty
+    label:
+      en_US: "Frequency Penalty"
+      zh_Hans: "频率惩罚"
+    type: float
+    default: 0
+    min: -1.0
+    max: 1.0
+    precision: 1
+    required: false
+    help:
+      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."
+      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。"
+
+  - name: user
+    use_template: text
+    label:
+      en_US: "User"
+      zh_Hans: "用户"
+    type: string
+    required: false
+    help:
+      en_US: "Used to track and differentiate conversation requests from different users."
+      zh_Hans: "用于追踪和区分不同用户的对话请求。"

api/core/model_runtime/model_providers/gitee_ai/llm/deepseek-coder-33B-instruct-completions.yaml

@@ -0,0 +1,91 @@
+model: deepseek-coder-33B-instruct-completions
+label:
+  zh_Hans: deepseek-coder-33B-instruct-completions
+  en_US: deepseek-coder-33B-instruct-completions
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: completion
+  context_size: 9000
+parameter_rules:
+  - name: stream
+    use_template: boolean
+    label:
+      en_US: "Stream"
+      zh_Hans: "流式"
+    type: boolean
+    default: true
+    required: true
+    help:
+      en_US: "Whether to return the results in batches through streaming. If set to true, the generated text will be pushed to the user in real time during the generation process."
+      zh_Hans: "是否通过流式分批返回结果。如果设置为 true，生成过程中实时地向用户推送每一部分生成的文本。"
+
+  - name: max_tokens
+    use_template: max_tokens
+    label:
+      en_US: "Max Tokens"
+      zh_Hans: "最大Token数"
+    type: int
+    default: 512
+    min: 1
+    required: true
+    help:
+      en_US: "The maximum number of tokens that can be generated by the model varies depending on the model."
+      zh_Hans: "模型可生成的最大 token 个数，不同模型上限不同。"
+
+  - name: temperature
+    use_template: temperature
+    label:
+      en_US: "Temperature"
+      zh_Hans: "采样温度"
+    type: float
+    default: 0.7
+    min: 0.0
+    max: 1.0
+    precision: 1
+    required: true
+    help:
+      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."
+      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。"
+
+  - name: top_p
+    use_template: top_p
+    label:
+      en_US: "Top P"
+      zh_Hans: "Top P"
+    type: float
+    default: 0.7
+    min: 0.0
+    max: 1.0
+    precision: 1
+    required: true
+    help:
+      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."
+      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。"
+
+  - name: frequency_penalty
+    use_template: frequency_penalty
+    label:
+      en_US: "Frequency Penalty"
+      zh_Hans: "频率惩罚"
+    type: float
+    default: 0
+    min: -1.0
+    max: 1.0
+    precision: 1
+    required: false
+    help:
+      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."
+      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。"
+
+  - name: user
+    use_template: text
+    label:
+      en_US: "User"
+      zh_Hans: "用户"
+    type: string
+    required: false
+    help:
+      en_US: "Used to track and differentiate conversation requests from different users."
+      zh_Hans: "用于追踪和区分不同用户的对话请求。"

api/core/model_runtime/model_providers/gitee_ai/llm/glm-4-9b-chat.yaml

@@ -0,0 +1,105 @@
+model: glm-4-9b-chat
+label:
+  zh_Hans: glm-4-9b-chat
+  en_US: glm-4-9b-chat
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 32768
+parameter_rules:
+  - name: stream
+    use_template: boolean
+    label:
+      en_US: "Stream"
+      zh_Hans: "流式"
+    type: boolean
+    default: true
+    required: true
+    help:
+      en_US: "Whether to return the results in batches through streaming. If set to true, the generated text will be pushed to the user in real time during the generation process."
+      zh_Hans: "是否通过流式分批返回结果。如果设置为 true，生成过程中实时地向用户推送每一部分生成的文本。"
+
+  - name: max_tokens
+    use_template: max_tokens
+    label:
+      en_US: "Max Tokens"
+      zh_Hans: "最大Token数"
+    type: int
+    default: 512
+    min: 1
+    required: true
+    help:
+      en_US: "The maximum number of tokens that can be generated by the model varies depending on the model."
+      zh_Hans: "模型可生成的最大 token 个数，不同模型上限不同。"
+
+  - name: temperature
+    use_template: temperature
+    label:
+      en_US: "Temperature"
+      zh_Hans: "采样温度"
+    type: float
+    default: 0.7
+    min: 0.0
+    max: 1.0
+    precision: 1
+    required: true
+    help:
+      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."
+      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。"
+
+  - name: top_p
+    use_template: top_p
+    label:
+      en_US: "Top P"
+      zh_Hans: "Top P"
+    type: float
+    default: 0.7
+    min: 0.0
+    max: 1.0
+    precision: 1
+    required: true
+    help:
+      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."
+      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。"
+
+  - name: top_k
+    use_template: top_k
+    label:
+      en_US: "Top K"
+      zh_Hans: "Top K"
+    type: int
+    default: 50
+    min: 0
+    max: 100
+    required: true
+    help:
+      en_US: "The value range is [0,100], which limits the model to only select from the top k words with the highest probability when choosing the next word at each step. The larger the value, the more diverse text generation will be."
+      zh_Hans: "取值范围为 [0,100]，限制模型在每一步选择下一个词时，只从概率最高的前 k 个词中选取。数值越大，文本生成越多样。"
+
+  - name: frequency_penalty
+    use_template: frequency_penalty
+    label:
+      en_US: "Frequency Penalty"
+      zh_Hans: "频率惩罚"
+    type: float
+    default: 0
+    min: -1.0
+    max: 1.0
+    precision: 1
+    required: false
+    help:
+      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."
+      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。"
+
+  - name: user
+    use_template: text
+    label:
+      en_US: "User"
+      zh_Hans: "用户"
+    type: string
+    required: false
+    help:
+      en_US: "Used to track and differentiate conversation requests from different users."
+      zh_Hans: "用于追踪和区分不同用户的对话请求。"

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

@@ -0,0 +1,47 @@
+from collections.abc import Generator
+from typing import Optional, Union
+
+from core.model_runtime.entities.llm_entities import LLMMode, LLMResult
+from core.model_runtime.entities.message_entities import (
+    PromptMessage,
+    PromptMessageTool,
+)
+from core.model_runtime.model_providers.openai_api_compatible.llm.llm import OAIAPICompatLargeLanguageModel
+
+
+class GiteeAILargeLanguageModel(OAIAPICompatLargeLanguageModel):
+    MODEL_TO_IDENTITY: dict[str, str] = {
+        "Yi-1.5-34B-Chat": "Yi-34B-Chat",
+        "deepseek-coder-33B-instruct-completions": "deepseek-coder-33B-instruct",
+        "deepseek-coder-33B-instruct-chat": "deepseek-coder-33B-instruct",
+    }
+
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
+        self._add_custom_parameters(credentials, model, model_parameters)
+        return super()._invoke(model, credentials, prompt_messages, model_parameters, tools, stop, stream)
+
+    def validate_credentials(self, model: str, credentials: dict) -> None:
+        self._add_custom_parameters(credentials, model, None)
+        super().validate_credentials(model, credentials)
+
+    @staticmethod
+    def _add_custom_parameters(credentials: dict, model: str, model_parameters: dict) -> None:
+        if model is None:
+            model = "bge-large-zh-v1.5"
+
+        model_identity = GiteeAILargeLanguageModel.MODEL_TO_IDENTITY.get(model, model)
+        credentials["endpoint_url"] = f"https://ai.gitee.com/api/serverless/{model_identity}/"
+        if model.endswith("completions"):
+            credentials["mode"] = LLMMode.COMPLETION.value
+        else:
+            credentials["mode"] = LLMMode.CHAT.value

api/core/model_runtime/model_providers/gitee_ai/rerank/_position.yaml

@@ -0,0 +1 @@
+- bge-reranker-v2-m3

api/core/model_runtime/model_providers/gitee_ai/rerank/bge-reranker-v2-m3.yaml

@@ -0,0 +1,4 @@
+model: bge-reranker-v2-m3
+model_type: rerank
+model_properties:
+  context_size: 1024

api/core/model_runtime/model_providers/gitee_ai/rerank/rerank.py

@@ -0,0 +1,128 @@
+from typing import Optional
+
+import httpx
+
+from core.model_runtime.entities.common_entities import I18nObject
+from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelPropertyKey, ModelType
+from core.model_runtime.entities.rerank_entities import RerankDocument, RerankResult
+from core.model_runtime.errors.invoke import (
+    InvokeAuthorizationError,
+    InvokeBadRequestError,
+    InvokeConnectionError,
+    InvokeError,
+    InvokeRateLimitError,
+    InvokeServerUnavailableError,
+)
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.__base.rerank_model import RerankModel
+
+
+class GiteeAIRerankModel(RerankModel):
+    """
+    Model class for rerank model.
+    """
+
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        query: str,
+        docs: list[str],
+        score_threshold: Optional[float] = None,
+        top_n: Optional[int] = None,
+        user: Optional[str] = None,
+    ) -> RerankResult:
+        """
+        Invoke rerank model
+
+        :param model: model name
+        :param credentials: model credentials
+        :param query: search query
+        :param docs: docs for reranking
+        :param score_threshold: score threshold
+        :param top_n: top n documents to return
+        :param user: unique user id
+        :return: rerank result
+        """
+        if len(docs) == 0:
+            return RerankResult(model=model, docs=[])
+
+        base_url = credentials.get("base_url", "https://ai.gitee.com/api/serverless")
+        base_url = base_url.removesuffix("/")
+
+        try:
+            body = {"model": model, "query": query, "documents": docs}
+            if top_n is not None:
+                body["top_n"] = top_n
+            response = httpx.post(
+                f"{base_url}/{model}/rerank",
+                json=body,
+                headers={"Authorization": f"Bearer {credentials.get('api_key')}"},
+            )
+
+            response.raise_for_status()
+            results = response.json()
+
+            rerank_documents = []
+            for result in results["results"]:
+                rerank_document = RerankDocument(
+                    index=result["index"],
+                    text=result["document"]["text"],
+                    score=result["relevance_score"],
+                )
+                if score_threshold is None or result["relevance_score"] >= score_threshold:
+                    rerank_documents.append(rerank_document)
+            return RerankResult(model=model, docs=rerank_documents)
+        except httpx.HTTPStatusError as e:
+            raise InvokeServerUnavailableError(str(e))
+
+    def validate_credentials(self, model: str, credentials: dict) -> None:
+        """
+        Validate model credentials
+
+        :param model: model name
+        :param credentials: model credentials
+        :return:
+        """
+        try:
+            self._invoke(
+                model=model,
+                credentials=credentials,
+                query="What is the capital of the United States?",
+                docs=[
+                    "Carson City is the capital city of the American state of Nevada. At the 2010 United States "
+                    "Census, Carson City had a population of 55,274.",
+                    "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean that "
+                    "are a political division controlled by the United States. Its capital is Saipan.",
+                ],
+                score_threshold=0.01,
+            )
+        except Exception as ex:
+            raise CredentialsValidateFailedError(str(ex))
+
+    @property
+    def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
+        """
+        Map model invoke error to unified error
+        """
+        return {
+            InvokeConnectionError: [httpx.ConnectError],
+            InvokeServerUnavailableError: [httpx.RemoteProtocolError],
+            InvokeRateLimitError: [],
+            InvokeAuthorizationError: [httpx.HTTPStatusError],
+            InvokeBadRequestError: [httpx.RequestError],
+        }
+
+    def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity:
+        """
+        generate custom model entities from credentials
+        """
+        entity = AIModelEntity(
+            model=model,
+            label=I18nObject(en_US=model),
+            model_type=ModelType.RERANK,
+            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            model_properties={ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size"))},
+        )
+
+        return entity

api/core/model_runtime/model_providers/gitee_ai/speech2text/_position.yaml

@@ -0,0 +1,2 @@
+- whisper-base
+- whisper-large

api/core/model_runtime/model_providers/gitee_ai/speech2text/speech2text.py

@@ -0,0 +1,53 @@
+import os
+from typing import IO, Optional
+
+import requests
+
+from core.model_runtime.errors.invoke import InvokeBadRequestError
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.__base.speech2text_model import Speech2TextModel
+from core.model_runtime.model_providers.gitee_ai._common import _CommonGiteeAI
+
+
+class GiteeAISpeech2TextModel(_CommonGiteeAI, Speech2TextModel):
+    """
+    Model class for OpenAI Compatible Speech to text model.
+    """
+
+    def _invoke(self, model: str, credentials: dict, file: IO[bytes], user: Optional[str] = None) -> str:
+        """
+        Invoke speech2text model
+
+        :param model: model name
+        :param credentials: model credentials
+        :param file: audio file
+        :param user: unique user id
+        :return: text for given audio file
+        """
+        # doc: https://ai.gitee.com/docs/openapi/serverless#tag/serverless/POST/{service}/speech-to-text
+
+        endpoint_url = f"https://ai.gitee.com/api/serverless/{model}/speech-to-text"
+        files = [("file", file)]
+        _, file_ext = os.path.splitext(file.name)
+        headers = {"Content-Type": f"audio/{file_ext}", "Authorization": f"Bearer {credentials.get('api_key')}"}
+        response = requests.post(endpoint_url, headers=headers, files=files)
+        if response.status_code != 200:
+            raise InvokeBadRequestError(response.text)
+        response_data = response.json()
+        return response_data["text"]
+
+    def validate_credentials(self, model: str, credentials: dict) -> None:
+        """
+        Validate model credentials
+
+        :param model: model name
+        :param credentials: model credentials
+        :return:
+        """
+        try:
+            audio_file_path = self._get_demo_file_path()
+
+            with open(audio_file_path, "rb") as audio_file:
+                self._invoke(model, credentials, audio_file)
+        except Exception as ex:
+            raise CredentialsValidateFailedError(str(ex))

api/core/model_runtime/model_providers/gitee_ai/speech2text/whisper-base.yaml

@@ -0,0 +1,5 @@
+model: whisper-base
+model_type: speech2text
+model_properties:
+  file_upload_limit: 1
+  supported_file_extensions: flac,mp3,mp4,mpeg,mpga,m4a,ogg,wav,webm

api/core/model_runtime/model_providers/gitee_ai/speech2text/whisper-large.yaml

@@ -0,0 +1,5 @@
+model: whisper-large
+model_type: speech2text
+model_properties:
+  file_upload_limit: 1
+  supported_file_extensions: flac,mp3,mp4,mpeg,mpga,m4a,ogg,wav,webm

api/core/model_runtime/model_providers/gitee_ai/text_embedding/_position.yaml

@@ -0,0 +1,3 @@
+- bge-large-zh-v1.5
+- bge-small-zh-v1.5
+- bge-m3

api/core/model_runtime/model_providers/gitee_ai/text_embedding/bge-large-zh-v1.5.yaml

@@ -0,0 +1,8 @@
+model: bge-large-zh-v1.5
+label:
+  zh_Hans: bge-large-zh-v1.5
+  en_US: bge-large-zh-v1.5
+model_type: text-embedding
+model_properties:
+  context_size: 200000
+  max_chunks: 20

api/core/model_runtime/model_providers/gitee_ai/text_embedding/bge-m3.yaml

@@ -0,0 +1,8 @@
+model: bge-m3
+label:
+  zh_Hans: bge-m3
+  en_US: bge-m3
+model_type: text-embedding
+model_properties:
+  context_size: 200000
+  max_chunks: 20

api/core/model_runtime/model_providers/gitee_ai/text_embedding/bge-small-zh-v1.5.yaml

@@ -0,0 +1,8 @@
+model: bge-small-zh-v1.5
+label:
+  zh_Hans: bge-small-zh-v1.5
+  en_US: bge-small-zh-v1.5
+model_type: text-embedding
+model_properties:
+  context_size: 200000
+  max_chunks: 20

api/core/model_runtime/model_providers/gitee_ai/text_embedding/text_embedding.py

@@ -0,0 +1,31 @@
+from typing import Optional
+
+from core.entities.embedding_type import EmbeddingInputType
+from core.model_runtime.entities.text_embedding_entities import TextEmbeddingResult
+from core.model_runtime.model_providers.openai_api_compatible.text_embedding.text_embedding import (
+    OAICompatEmbeddingModel,
+)
+
+
+class GiteeAIEmbeddingModel(OAICompatEmbeddingModel):
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        texts: list[str],
+        user: Optional[str] = None,
+        input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
+    ) -> TextEmbeddingResult:
+        self._add_custom_parameters(credentials, model)
+        return super()._invoke(model, credentials, texts, user, input_type)
+
+    def validate_credentials(self, model: str, credentials: dict) -> None:
+        self._add_custom_parameters(credentials, None)
+        super().validate_credentials(model, credentials)
+
+    @staticmethod
+    def _add_custom_parameters(credentials: dict, model: str) -> None:
+        if model is None:
+            model = "bge-m3"
+
+        credentials["endpoint_url"] = f"https://ai.gitee.com/api/serverless/{model}/v1/"

api/core/model_runtime/model_providers/gitee_ai/tts/ChatTTS.yaml

@@ -0,0 +1,11 @@
+model: ChatTTS
+model_type: tts
+model_properties:
+  default_voice: 'default'
+  voices:
+    - mode: 'default'
+      name: 'Default'
+      language: [ 'zh-Hans', 'en-US', 'de-DE', 'fr-FR', 'es-ES', 'it-IT', 'th-TH', 'id-ID' ]
+  word_limit: 3500
+  audio_type: 'mp3'
+  max_workers: 5

api/core/model_runtime/model_providers/gitee_ai/tts/FunAudioLLM-CosyVoice-300M.yaml

@@ -0,0 +1,11 @@
+model: FunAudioLLM-CosyVoice-300M
+model_type: tts
+model_properties:
+  default_voice: 'default'
+  voices:
+    - mode: 'default'
+      name: 'Default'
+      language: [ 'zh-Hans', 'en-US', 'de-DE', 'fr-FR', 'es-ES', 'it-IT', 'th-TH', 'id-ID' ]
+  word_limit: 3500
+  audio_type: 'mp3'
+  max_workers: 5

api/core/model_runtime/model_providers/gitee_ai/tts/_position.yaml

@@ -0,0 +1,4 @@
+- speecht5_tts
+- ChatTTS
+- fish-speech-1.2-sft
+- FunAudioLLM-CosyVoice-300M

api/core/model_runtime/model_providers/gitee_ai/tts/fish-speech-1.2-sft.yaml

@@ -0,0 +1,11 @@
+model: fish-speech-1.2-sft
+model_type: tts
+model_properties:
+  default_voice: 'default'
+  voices:
+    - mode: 'default'
+      name: 'Default'
+      language: [ 'zh-Hans', 'en-US', 'de-DE', 'fr-FR', 'es-ES', 'it-IT', 'th-TH', 'id-ID' ]
+  word_limit: 3500
+  audio_type: 'mp3'
+  max_workers: 5

api/core/model_runtime/model_providers/gitee_ai/tts/speecht5_tts.yaml

@@ -0,0 +1,11 @@
+model: speecht5_tts
+model_type: tts
+model_properties:
+  default_voice: 'default'
+  voices:
+    - mode: 'default'
+      name: 'Default'
+      language: [ 'zh-Hans', 'en-US', 'de-DE', 'fr-FR', 'es-ES', 'it-IT', 'th-TH', 'id-ID' ]
+  word_limit: 3500
+  audio_type: 'mp3'
+  max_workers: 5

api/core/model_runtime/model_providers/gitee_ai/tts/tts.py

@@ -0,0 +1,79 @@
+from typing import Optional
+
+import requests
+
+from core.model_runtime.errors.invoke import InvokeBadRequestError
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.__base.tts_model import TTSModel
+from core.model_runtime.model_providers.gitee_ai._common import _CommonGiteeAI
+
+
+class GiteeAIText2SpeechModel(_CommonGiteeAI, TTSModel):
+    """
+    Model class for OpenAI Speech to text model.
+    """
+
+    def _invoke(
+        self, model: str, tenant_id: str, credentials: dict, content_text: str, voice: str, user: Optional[str] = None
+    ) -> any:
+        """
+        _invoke text2speech model
+
+        :param model: model name
+        :param tenant_id: user tenant id
+        :param credentials: model credentials
+        :param content_text: text content to be translated
+        :param voice: model timbre
+        :param user: unique user id
+        :return: text translated to audio file
+        """
+        return self._tts_invoke_streaming(model=model, credentials=credentials, content_text=content_text, voice=voice)
+
+    def validate_credentials(self, model: str, credentials: dict) -> None:
+        """
+        validate credentials text2speech model
+
+        :param model: model name
+        :param credentials: model credentials
+        :return: text translated to audio file
+        """
+        try:
+            self._tts_invoke_streaming(
+                model=model,
+                credentials=credentials,
+                content_text="Hello Dify!",
+                voice=self._get_model_default_voice(model, credentials),
+            )
+        except Exception as ex:
+            raise CredentialsValidateFailedError(str(ex))
+
+    def _tts_invoke_streaming(self, model: str, credentials: dict, content_text: str, voice: str) -> any:
+        """
+        _tts_invoke_streaming text2speech model
+        :param model: model name
+        :param credentials: model credentials
+        :param content_text: text content to be translated
+        :param voice: model timbre
+        :return: text translated to audio file
+        """
+        try:
+            # doc: https://ai.gitee.com/docs/openapi/serverless#tag/serverless/POST/{service}/text-to-speech
+            endpoint_url = "https://ai.gitee.com/api/serverless/" + model + "/text-to-speech"
+
+            headers = {"Content-Type": "application/json"}
+            api_key = credentials.get("api_key")
+            if api_key:
+                headers["Authorization"] = f"Bearer {api_key}"
+
+            payload = {"inputs": content_text}
+            response = requests.post(endpoint_url, headers=headers, json=payload)
+
+            if response.status_code != 200:
+                raise InvokeBadRequestError(response.text)
+
+            data = response.content
+
+            for i in range(0, len(data), 1024):
+                yield data[i : i + 1024]
+        except Exception as ex:
+            raise InvokeBadRequestError(str(ex))

api/pytest.ini

@@ -27,3 +27,4 @@ env =
     XINFERENCE_GENERATION_MODEL_UID = generate
     XINFERENCE_RERANK_MODEL_UID = rerank
     XINFERENCE_SERVER_URL = http://a.abc.com:11451
+    GITEE_AI_API_KEY = aaaaaaaaaaaaaaaaaaaa

api/tests/integration_tests/.env.example

@@ -83,3 +83,6 @@ VOLC_EMBEDDING_ENDPOINT_ID=
 
 # 360 AI Credentials
 ZHINAO_API_KEY=
+
+# Gitee AI Credentials
+GITEE_AI_API_KEY=

api/tests/integration_tests/model_runtime/gitee_ai/test_llm.py

@@ -0,0 +1,132 @@
+import os
+from collections.abc import Generator
+
+import pytest
+
+from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta
+from core.model_runtime.entities.message_entities import (
+    AssistantPromptMessage,
+    PromptMessageTool,
+    SystemPromptMessage,
+    UserPromptMessage,
+)
+from core.model_runtime.entities.model_entities import AIModelEntity
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.gitee_ai.llm.llm import GiteeAILargeLanguageModel
+
+
+def test_predefined_models():
+    model = GiteeAILargeLanguageModel()
+    model_schemas = model.predefined_models()
+
+    assert len(model_schemas) >= 1
+    assert isinstance(model_schemas[0], AIModelEntity)
+
+
+def test_validate_credentials_for_chat_model():
+    model = GiteeAILargeLanguageModel()
+
+    with pytest.raises(CredentialsValidateFailedError):
+        # model name to gpt-3.5-turbo because of mocking
+        model.validate_credentials(model="gpt-3.5-turbo", credentials={"api_key": "invalid_key"})
+
+    model.validate_credentials(
+        model="Qwen2-7B-Instruct",
+        credentials={"api_key": os.environ.get("GITEE_AI_API_KEY")},
+    )
+
+
+def test_invoke_chat_model():
+    model = GiteeAILargeLanguageModel()
+
+    result = model.invoke(
+        model="Qwen2-7B-Instruct",
+        credentials={"api_key": os.environ.get("GITEE_AI_API_KEY")},
+        prompt_messages=[
+            SystemPromptMessage(
+                content="You are a helpful AI assistant.",
+            ),
+            UserPromptMessage(content="Hello World!"),
+        ],
+        model_parameters={
+            "temperature": 0.0,
+            "top_p": 1.0,
+            "presence_penalty": 0.0,
+            "frequency_penalty": 0.0,
+            "max_tokens": 10,
+            "stream": False,
+        },
+        stop=["How"],
+        stream=False,
+        user="foo",
+    )
+
+    assert isinstance(result, LLMResult)
+    assert len(result.message.content) > 0
+
+
+def test_invoke_stream_chat_model():
+    model = GiteeAILargeLanguageModel()
+
+    result = model.invoke(
+        model="Qwen2-7B-Instruct",
+        credentials={"api_key": os.environ.get("GITEE_AI_API_KEY")},
+        prompt_messages=[
+            SystemPromptMessage(
+                content="You are a helpful AI assistant.",
+            ),
+            UserPromptMessage(content="Hello World!"),
+        ],
+        model_parameters={"temperature": 0.0, "max_tokens": 100, "stream": False},
+        stream=True,
+        user="foo",
+    )
+
+    assert isinstance(result, Generator)
+
+    for chunk in result:
+        assert isinstance(chunk, LLMResultChunk)
+        assert isinstance(chunk.delta, LLMResultChunkDelta)
+        assert isinstance(chunk.delta.message, AssistantPromptMessage)
+        assert len(chunk.delta.message.content) > 0 if chunk.delta.finish_reason is None else True
+        if chunk.delta.finish_reason is not None:
+            assert chunk.delta.usage is not None
+
+
+def test_get_num_tokens():
+    model = GiteeAILargeLanguageModel()
+
+    num_tokens = model.get_num_tokens(
+        model="Qwen2-7B-Instruct",
+        credentials={"api_key": os.environ.get("GITEE_AI_API_KEY")},
+        prompt_messages=[UserPromptMessage(content="Hello World!")],
+    )
+
+    assert num_tokens == 10
+
+    num_tokens = model.get_num_tokens(
+        model="Qwen2-7B-Instruct",
+        credentials={"api_key": os.environ.get("GITEE_AI_API_KEY")},
+        prompt_messages=[
+            SystemPromptMessage(
+                content="You are a helpful AI assistant.",
+            ),
+            UserPromptMessage(content="Hello World!"),
+        ],
+        tools=[
+            PromptMessageTool(
+                name="get_weather",
+                description="Determine weather in my location",
+                parameters={
+                    "type": "object",
+                    "properties": {
+                        "location": {"type": "string", "description": "The city and state e.g. San Francisco, CA"},
+                        "unit": {"type": "string", "enum": ["c", "f"]},
+                    },
+                    "required": ["location"],
+                },
+            ),
+        ],
+    )
+
+    assert num_tokens == 77

api/tests/integration_tests/model_runtime/gitee_ai/test_provider.py

@@ -0,0 +1,15 @@
+import os
+
+import pytest
+
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.gitee_ai.gitee_ai import GiteeAIProvider
+
+
+def test_validate_provider_credentials():
+    provider = GiteeAIProvider()
+
+    with pytest.raises(CredentialsValidateFailedError):
+        provider.validate_provider_credentials(credentials={"api_key": "invalid_key"})
+
+    provider.validate_provider_credentials(credentials={"api_key": os.environ.get("GITEE_AI_API_KEY")})

api/tests/integration_tests/model_runtime/gitee_ai/test_rerank.py

@@ -0,0 +1,47 @@
+import os
+
+import pytest
+
+from core.model_runtime.entities.rerank_entities import RerankResult
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.gitee_ai.rerank.rerank import GiteeAIRerankModel
+
+
+def test_validate_credentials():
+    model = GiteeAIRerankModel()
+
+    with pytest.raises(CredentialsValidateFailedError):
+        model.validate_credentials(
+            model="bge-reranker-v2-m3",
+            credentials={"api_key": "invalid_key"},
+        )
+
+    model.validate_credentials(
+        model="bge-reranker-v2-m3",
+        credentials={
+            "api_key": os.environ.get("GITEE_AI_API_KEY"),
+        },
+    )
+
+
+def test_invoke_model():
+    model = GiteeAIRerankModel()
+    result = model.invoke(
+        model="bge-reranker-v2-m3",
+        credentials={
+            "api_key": os.environ.get("GITEE_AI_API_KEY"),
+        },
+        query="What is the capital of the United States?",
+        docs=[
+            "Carson City is the capital city of the American state of Nevada. At the 2010 United States "
+            "Census, Carson City had a population of 55,274.",
+            "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean that "
+            "are a political division controlled by the United States. Its capital is Saipan.",
+        ],
+        top_n=1,
+        score_threshold=0.01,
+    )
+
+    assert isinstance(result, RerankResult)
+    assert len(result.docs) == 1
+    assert result.docs[0].score >= 0.01

api/tests/integration_tests/model_runtime/gitee_ai/test_speech2text.py

@@ -0,0 +1,45 @@
+import os
+
+import pytest
+
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.gitee_ai.speech2text.speech2text import GiteeAISpeech2TextModel
+
+
+def test_validate_credentials():
+    model = GiteeAISpeech2TextModel()
+
+    with pytest.raises(CredentialsValidateFailedError):
+        model.validate_credentials(
+            model="whisper-base",
+            credentials={"api_key": "invalid_key"},
+        )
+
+    model.validate_credentials(
+        model="whisper-base",
+        credentials={"api_key": os.environ.get("GITEE_AI_API_KEY")},
+    )
+
+
+def test_invoke_model():
+    model = GiteeAISpeech2TextModel()
+
+    # Get the directory of the current file
+    current_dir = os.path.dirname(os.path.abspath(__file__))
+
+    # Get assets directory
+    assets_dir = os.path.join(os.path.dirname(current_dir), "assets")
+
+    # Construct the path to the audio file
+    audio_file_path = os.path.join(assets_dir, "audio.mp3")
+
+    # Open the file and get the file object
+    with open(audio_file_path, "rb") as audio_file:
+        file = audio_file
+
+        result = model.invoke(
+            model="whisper-base", credentials={"api_key": os.environ.get("GITEE_AI_API_KEY")}, file=file
+        )
+
+        assert isinstance(result, str)
+        assert result == "1 2 3 4 5 6 7 8 9 10"

api/tests/integration_tests/model_runtime/gitee_ai/test_text_embedding.py

@@ -0,0 +1,46 @@
+import os
+
+import pytest
+
+from core.model_runtime.entities.text_embedding_entities import TextEmbeddingResult
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.gitee_ai.text_embedding.text_embedding import GiteeAIEmbeddingModel
+
+
+def test_validate_credentials():
+    model = GiteeAIEmbeddingModel()
+
+    with pytest.raises(CredentialsValidateFailedError):
+        model.validate_credentials(model="bge-large-zh-v1.5", credentials={"api_key": "invalid_key"})
+
+    model.validate_credentials(model="bge-large-zh-v1.5", credentials={"api_key": os.environ.get("GITEE_AI_API_KEY")})
+
+
+def test_invoke_model():
+    model = GiteeAIEmbeddingModel()
+
+    result = model.invoke(
+        model="bge-large-zh-v1.5",
+        credentials={
+            "api_key": os.environ.get("GITEE_AI_API_KEY"),
+        },
+        texts=["hello", "world"],
+        user="user",
+    )
+
+    assert isinstance(result, TextEmbeddingResult)
+    assert len(result.embeddings) == 2
+
+
+def test_get_num_tokens():
+    model = GiteeAIEmbeddingModel()
+
+    num_tokens = model.get_num_tokens(
+        model="bge-large-zh-v1.5",
+        credentials={
+            "api_key": os.environ.get("GITEE_AI_API_KEY"),
+        },
+        texts=["hello", "world"],
+    )
+
+    assert num_tokens == 2

api/tests/integration_tests/model_runtime/gitee_ai/test_tts.py

@@ -0,0 +1,23 @@
+import os
+
+from core.model_runtime.model_providers.gitee_ai.tts.tts import GiteeAIText2SpeechModel
+
+
+def test_invoke_model():
+    model = GiteeAIText2SpeechModel()
+
+    result = model.invoke(
+        model="speecht5_tts",
+        tenant_id="test",
+        credentials={
+            "api_key": os.environ.get("GITEE_AI_API_KEY"),
+        },
+        content_text="Hello, world!",
+        voice="",
+    )
+
+    content = b""
+    for chunk in result:
+        content += chunk
+
+    assert content != b""