top_k and top_p transposed in vertexai (langchain-ai#5673)

Fix transposed properties in vertexai model


Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>

langchain/llms/vertexai.py

@@ -43,8 +43,8 @@ def _default_params(self) -> Dict[str, Any]:
         base_params = {
             "temperature": self.temperature,
             "max_output_tokens": self.max_output_tokens,
-            "top_k": self.top_p,
-            "top_p": self.top_k,
+            "top_k": self.top_k,
+            "top_p": self.top_p,
         }
         return {**base_params}
 

tests/integration_tests/chat_models/test_vertexai.py

@@ -7,6 +7,8 @@
 Your end-user credentials would be used to make the calls (make sure you've run 
 `gcloud auth login` first).
 """
+from unittest.mock import Mock, patch
+
 import pytest
 
 from langchain.chat_models import ChatVertexAI
@@ -86,3 +88,31 @@ def test_vertexai_single_call_failes_no_message() -> None:
         str(exc_info.value)
         == "You should provide at least one message to start the chat!"
     )
+
+
+def test_vertexai_args_passed() -> None:
+    response_text = "Goodbye"
+    user_prompt = "Hello"
+    prompt_params = {
+        "max_output_tokens": 1,
+        "temperature": 10000.0,
+        "top_k": 10,
+        "top_p": 0.5,
+    }
+
+    # Mock the library to ensure the args are passed correctly
+    with patch(
+        "vertexai.language_models._language_models.ChatSession.send_message"
+    ) as send_message:
+        mock_response = Mock(text=response_text)
+        send_message.return_value = mock_response
+
+        model = ChatVertexAI(**prompt_params)
+        message = HumanMessage(content=user_prompt)
+        response = model([message])
+
+        assert response.content == response_text
+        send_message.assert_called_once_with(
+            user_prompt,
+            **prompt_params,
+        )