revert generator

Author: fm1320

adalflow/adalflow/core/generator.py

@@ -70,11 +70,22 @@ class Generator(GradComponent, CachedEngine, CallbackManager):
         template (Optional[str], optional): The template for the prompt.  Defaults to :ref:`DEFAULT_ADALFLOW_SYSTEM_PROMPT<core-default_prompt_template>`.
         prompt_kwargs (Optional[Dict], optional): The preset prompt kwargs to fill in the variables in the prompt. Defaults to None.
         output_processors (Optional[Component], optional):  The output processors after model call. It can be a single component or a chained component via ``Sequential``. Defaults to None.
+        trainable_params (Optional[List[str]], optional): The list of trainable parameters. Defaults to [].
+    Note:
+        The output_processors will be applied to the string output of the model completion. And the result will be stored in the data field of the output.
+        And we encourage you to only use it to parse the response to data format you will use later.
         name (Optional[str], optional): The name of the generator. Defaults to None.
         cache_path (Optional[str], optional): The path to save the cache. Defaults to None.
         use_cache (bool, optional): Whether to use cache. Defaults to False.
     """
 
+    model_type: ModelType = ModelType.LLM
+    model_client: ModelClient  # for better type checking
+    _use_cache: bool = False
+    _kwargs: Dict[str, Any] = (
+        {}
+    )  # to create teacher generator from student TODO: might reaccess this
+
     def __init__(
         self,
         *,
@@ -90,6 +101,7 @@ def __init__(
         # args for the cache
         cache_path: Optional[str] = None,
         use_cache: bool = False,
+        trainable_params: Optional[List[str]] = [],
     ) -> None:
         r"""The default prompt is set to the DEFAULT_ADALFLOW_SYSTEM_PROMPT. It has the following variables:
         - task_desc_str
@@ -122,7 +134,6 @@ def __init__(
         CallbackManager.__init__(self)
 
         self.name = name or self.__class__.__name__
-        self.model_type = model_client.model_type  # Get model type from client
 
         self._init_prompt(template, prompt_kwargs)
 
@@ -153,6 +164,7 @@ def __init__(
             "name": name,
             "cache_path": cache_path,
             "use_cache": use_cache,
+            "trainable_params": trainable_params,
         }
         self._teacher: Optional["Generator"] = None
         self._trace_api_kwargs: Dict[str, Any] = (
@@ -326,6 +338,7 @@ def _pre_call(self, prompt_kwargs: Dict, model_kwargs: Dict) -> Dict[str, Any]:
         api_kwargs = self.model_client.convert_inputs_to_api_kwargs(
             input=prompt_str,
             model_kwargs=composed_model_kwargs,
+            model_type=self.model_type,
         )
         return api_kwargs