changed default params for gemini (#16044)

Replace this entire comment with:
- **Description:** changed default values for Vertex LLMs (to be handled
on the SDK's side)

libs/partners/google-vertexai/langchain_google_vertexai/llms.py

@@ -41,6 +41,11 @@ from langchain_google_vertexai._utils import (
     is_gemini_model,
 )
 
+_PALM_DEFAULT_MAX_OUTPUT_TOKENS = TextGenerationModel._DEFAULT_MAX_OUTPUT_TOKENS
+_PALM_DEFAULT_TEMPERATURE = 0.0
+_PALM_DEFAULT_TOP_P = 0.95
+_PALM_DEFAULT_TOP_K = 40
+
 
 def _completion_with_retry(
     llm: VertexAI,
@@ -118,14 +123,14 @@ class _VertexAICommon(_VertexAIBase):
     client_preview: Any = None  #: :meta private:
     model_name: str
     "Underlying model name."
-    temperature: float = 0.0
+    temperature: Optional[float] = None
     "Sampling temperature, it controls the degree of randomness in token selection."
-    max_output_tokens: int = 128
+    max_output_tokens: Optional[int] = None
     "Token limit determines the maximum amount of text output from one prompt."
-    top_p: float = 0.95
+    top_p: Optional[float] = None
     "Tokens are selected from most probable to least until the sum of their "
     "probabilities equals the top-p value. Top-p is ignored for Codey models."
-    top_k: int = 40
+    top_k: Optional[int] = None
     "How the model selects tokens for output, the next token is selected from "
     "among the top-k most probable tokens. Top-k is ignored for Codey models."
     credentials: Any = Field(default=None, exclude=True)
@@ -156,6 +161,15 @@ class _VertexAICommon(_VertexAIBase):
 
     @property
     def _default_params(self) -> Dict[str, Any]:
+        if self._is_gemini_model:
+            default_params = {}
+        else:
+            default_params = {
+                "temperature": _PALM_DEFAULT_TEMPERATURE,
+                "max_output_tokens": _PALM_DEFAULT_MAX_OUTPUT_TOKENS,
+                "top_p": _PALM_DEFAULT_TOP_P,
+                "top_k": _PALM_DEFAULT_TOP_K,
+            }
         params = {
             "temperature": self.temperature,
             "max_output_tokens": self.max_output_tokens,
@@ -168,7 +182,14 @@ class _VertexAICommon(_VertexAIBase):
                     "top_p": self.top_p,
                 }
             )
-        return params
+        updated_params = {}
+        for param_name, param_value in params.items():
+            default_value = default_params.get(param_name)
+            if param_value or default_value:
+                updated_params[param_name] = (
+                    param_value if param_value else default_value
+                )
+        return updated_params
 
     @classmethod
     def _init_vertexai(cls, values: Dict) -> None:
@@ -314,7 +335,7 @@ class VertexAI(_VertexAICommon, BaseLLM):
         **kwargs: Any,
     ) -> LLMResult:
         params = self._prepare_params(stop=stop, **kwargs)
-        generations = []
+        generations: List[List[Generation]] = []
         for prompt in prompts:
             res = await _acompletion_with_retry(
                 self,

libs/partners/google-vertexai/tests/unit_tests/test_chat_models.py

@@ -68,7 +68,7 @@ def test_vertexai_args_passed(stop: Optional[str]) -> None:
         mock_model.start_chat = mock_start_chat
         mg.return_value = mock_model
 
-        model = ChatVertexAI(**prompt_params)
+        model = ChatVertexAI(**prompt_params)  # type: ignore
         message = HumanMessage(content=user_prompt)
         if stop:
             response = model([message], stop=[stop])
@@ -110,3 +110,52 @@ def test_parse_chat_history_correct() -> None:
         ChatMessage(content=text_question, author="user"),
         ChatMessage(content=text_answer, author="bot"),
     ]
+
+
+def test_default_params_palm() -> None:
+    user_prompt = "Hello"
+
+    with patch("vertexai._model_garden._model_garden_models._from_pretrained") as mg:
+        mock_response = MagicMock()
+        mock_response.candidates = [Mock(text="Goodbye")]
+        mock_chat = MagicMock()
+        mock_send_message = MagicMock(return_value=mock_response)
+        mock_chat.send_message = mock_send_message
+
+        mock_model = MagicMock()
+        mock_start_chat = MagicMock(return_value=mock_chat)
+        mock_model.start_chat = mock_start_chat
+        mg.return_value = mock_model
+
+        model = ChatVertexAI(model_name="text-bison@001")
+        message = HumanMessage(content=user_prompt)
+        _ = model([message])
+        mock_start_chat.assert_called_once_with(
+            context=None,
+            message_history=[],
+            max_output_tokens=128,
+            top_k=40,
+            top_p=0.95,
+            stop_sequences=None,
+        )
+
+
+def test_default_params_gemini() -> None:
+    user_prompt = "Hello"
+
+    with patch("langchain_google_vertexai.chat_models.GenerativeModel") as gm:
+        mock_response = MagicMock()
+        content = Mock(parts=[Mock(function_call=None)])
+        mock_response.candidates = [Mock(text="Goodbye", content=content)]
+        mock_chat = MagicMock()
+        mock_send_message = MagicMock(return_value=mock_response)
+        mock_chat.send_message = mock_send_message
+
+        mock_model = MagicMock()
+        mock_start_chat = MagicMock(return_value=mock_chat)
+        mock_model.start_chat = mock_start_chat
+        gm.return_value = mock_model
+        model = ChatVertexAI(model_name="gemini-pro")
+        message = HumanMessage(content=user_prompt)
+        _ = model([message])
+        mock_start_chat.assert_called_once_with(history=[])