remove optional model get

open_notebook/domain/base.py

@@ -48,7 +48,7 @@ class ObjectModel(BaseModel):
             raise DatabaseOperationError(e)
 
     @classmethod
-    def get(cls: Type[T], id: str) -> Optional[T]:
+    def get(cls: Type[T], id: str) -> T:
         if not id:
             raise InvalidInputError("ID cannot be empty")
         try:

open_notebook/models/__init__.py

@@ -43,7 +43,7 @@ def get_model(model_id, model_type="language", **kwargs):
         **kwargs: Additional arguments to pass to the model constructor
     """
     assert model_id, "Model ID cannot be empty"
-    model = Model.get(model_id)
+    model: Model = Model.get(model_id)
 
     if not model:
         raise ValueError(f"Model with ID {model_id} not found")
@@ -65,77 +65,3 @@ def get_model(model_id, model_type="language", **kwargs):
         return model_instance.to_langchain()
 
     return model_instance
-
-
-# from open_notebook.domain.models import Model
-# from open_notebook.models.embedding_models import OpenAIEmbeddingModel
-# from open_notebook.models.llms import (
-#     AnthropicLanguageModel,
-#     GeminiLanguageModel,
-#     LiteLLMLanguageModel,
-#     OllamaLanguageModel,
-#     OpenAILanguageModel,
-#     OpenRouterLanguageModel,
-#     VertexAILanguageModel,
-#     VertexAnthropicLanguageModel,
-# )
-# from open_notebook.models.speech_to_text_models import OpenAISpeechToTextModel
-
-# SPEECH_TO_TEXT_CLASS_MAP = {
-#     "openai": OpenAISpeechToTextModel,
-# }
-
-
-# # todo: acho que dá pra juntar todos os get models em uma coisa só
-# def get_speech_to_text_model(model_id):
-#     assert model_id, "Model ID cannot be empty"
-#     model = Model.get(model_id)
-#     if not model:
-#         raise ValueError(f"Model with ID {model_id} not found")
-#     if model.provider not in SPEECH_TO_TEXT_CLASS_MAP.keys():
-#         raise ValueError(
-#             f"Provider {model.provider} not compatible with Embedding Models"
-#         )
-#     return SPEECH_TO_TEXT_CLASS_MAP[model.provider](model_name=model.name)
-
-
-# # Map provider names to classes
-# PROVIDER_CLASS_MAP = {
-#     "ollama": OllamaLanguageModel,
-#     "openrouter": OpenRouterLanguageModel,
-#     "vertexai-anthropic": VertexAnthropicLanguageModel,
-#     "litellm": LiteLLMLanguageModel,
-#     "vertexai": VertexAILanguageModel,
-#     "anthropic": AnthropicLanguageModel,
-#     "openai": OpenAILanguageModel,
-#     "gemini": GeminiLanguageModel,
-# }
-
-
-# # todo: make the provider check type specific
-# def get_langchain_model(model_id, json=False):
-#     model = Model.get(model_id)
-#     if not model:
-#         raise ValueError(f"Model {model_id} not found")
-#     if model.provider not in PROVIDER_CLASS_MAP.keys():
-#         raise ValueError(f"Provider {model.provider} not found")
-#     return PROVIDER_CLASS_MAP[model.provider](
-#         model_name=model.name, json=json
-#     ).to_langchain()
-
-
-# EMBEDDING_CLASS_MAP = {
-#     "openai": OpenAIEmbeddingModel,
-# }
-
-
-# def get_embedding_model(model_id):
-#     assert model_id, "Model ID cannot be empty"
-#     model = Model.get(model_id)
-#     if not model:
-#         raise ValueError(f"Model with ID {model_id} not found")
-#     if model.provider not in EMBEDDING_CLASS_MAP.keys():
-#         raise ValueError(
-#             f"Provider {model.provider} not compatible with Embedding Models"
-#         )
-#     return EMBEDDING_CLASS_MAP[model.provider](model_name=model.name)