fix: LLM - Fixed parameters set in ChatModel.start_chat being ignored

PiperOrigin-RevId: 537204205

tests/unit/aiplatform/test_language_models.py

@@ -638,6 +638,51 @@ def test_chat(self):
             )
             assert len(chat._history) == 2
 
+        # Validating the parameters
+        chat_temperature = 0.1
+        chat_max_output_tokens = 100
+        chat_top_k = 1
+        chat_top_p = 0.1
+        message_temperature = 0.2
+        message_max_output_tokens = 200
+        message_top_k = 2
+        message_top_p = 0.2
+
+        chat2 = model.start_chat(
+            temperature=chat_temperature,
+            max_output_tokens=chat_max_output_tokens,
+            top_k=chat_top_k,
+            top_p=chat_top_p,
+        )
+
+        gca_predict_response3 = gca_prediction_service.PredictResponse()
+        gca_predict_response3.predictions.append(_TEST_CHAT_GENERATION_PREDICTION1)
+
+        with mock.patch.object(
+            target=prediction_service_client.PredictionServiceClient,
+            attribute="predict",
+            return_value=gca_predict_response3,
+        ) as mock_predict3:
+            chat2.send_message("Are my favorite movies based on a book series?")
+            prediction_parameters = mock_predict3.call_args[1]["parameters"]
+            assert prediction_parameters["temperature"] == chat_temperature
+            assert prediction_parameters["maxDecodeSteps"] == chat_max_output_tokens
+            assert prediction_parameters["topK"] == chat_top_k
+            assert prediction_parameters["topP"] == chat_top_p
+
+            chat2.send_message(
+                "Are my favorite movies based on a book series?",
+                temperature=message_temperature,
+                max_output_tokens=message_max_output_tokens,
+                top_k=message_top_k,
+                top_p=message_top_p,
+            )
+            prediction_parameters = mock_predict3.call_args[1]["parameters"]
+            assert prediction_parameters["temperature"] == message_temperature
+            assert prediction_parameters["maxDecodeSteps"] == message_max_output_tokens
+            assert prediction_parameters["topK"] == message_top_k
+            assert prediction_parameters["topP"] == message_top_p
+
     def test_text_embedding(self):
         """Tests the text embedding model."""
         aiplatform.init(

vertexai/language_models/_language_models.py

@@ -460,19 +460,23 @@ def send_message(
         self,
         message: str,
         *,
-        max_output_tokens: int = TextGenerationModel._DEFAULT_MAX_OUTPUT_TOKENS,
-        temperature: float = TextGenerationModel._DEFAULT_TEMPERATURE,
-        top_k: int = TextGenerationModel._DEFAULT_TOP_K,
-        top_p: float = TextGenerationModel._DEFAULT_TOP_P,
+        max_output_tokens: Optional[int] = None,
+        temperature: Optional[float] = None,
+        top_k: Optional[int] = None,
+        top_p: Optional[float] = None,
     ) -> "TextGenerationResponse":
         """Sends message to the language model and gets a response.
 
         Args:
             message: Message to send to the model
             max_output_tokens: Max length of the output text in tokens.
+                Uses the value specified when calling `ChatModel.start_chat` by default.
             temperature: Controls the randomness of predictions. Range: [0, 1].
+                Uses the value specified when calling `ChatModel.start_chat` by default.
             top_k: The number of highest probability vocabulary tokens to keep for top-k-filtering.
+                Uses the value specified when calling `ChatModel.start_chat` by default.
             top_p: The cumulative probability of parameter highest probability vocabulary tokens to keep for nucleus sampling. Range: [0, 1].
+                Uses the value specified when calling `ChatModel.start_chat` by default.
 
         Returns:
             A `TextGenerationResponse` object that contains the text produced by the model.
@@ -484,10 +488,12 @@ def send_message(
 
         response_obj = self._model.predict(
             prompt=new_history_text,
-            max_output_tokens=max_output_tokens or self._max_output_tokens,
-            temperature=temperature or self._temperature,
-            top_k=top_k or self._top_k,
-            top_p=top_p or self._top_p,
+            max_output_tokens=max_output_tokens
+            if max_output_tokens is not None
+            else self._max_output_tokens,
+            temperature=temperature if temperature is not None else self._temperature,
+            top_k=top_k if top_k is not None else self._top_k,
+            top_p=top_p if top_p is not None else self._top_p,
         )
         response_text = response_obj.text
 
@@ -636,28 +642,36 @@ def send_message(
         self,
         message: str,
         *,
-        max_output_tokens: int = TextGenerationModel._DEFAULT_MAX_OUTPUT_TOKENS,
-        temperature: float = TextGenerationModel._DEFAULT_TEMPERATURE,
-        top_k: int = TextGenerationModel._DEFAULT_TOP_K,
-        top_p: float = TextGenerationModel._DEFAULT_TOP_P,
+        max_output_tokens: Optional[int] = None,
+        temperature: Optional[float] = None,
+        top_k: Optional[int] = None,
+        top_p: Optional[float] = None,
     ) -> "TextGenerationResponse":
         """Sends message to the language model and gets a response.
 
         Args:
             message: Message to send to the model
             max_output_tokens: Max length of the output text in tokens.
+                Uses the value specified when calling `ChatModel.start_chat` by default.
             temperature: Controls the randomness of predictions. Range: [0, 1].
+                Uses the value specified when calling `ChatModel.start_chat` by default.
             top_k: The number of highest probability vocabulary tokens to keep for top-k-filtering.
+                Uses the value specified when calling `ChatModel.start_chat` by default.
             top_p: The cumulative probability of parameter highest probability vocabulary tokens to keep for nucleus sampling. Range: [0, 1].
+                Uses the value specified when calling `ChatModel.start_chat` by default.
 
         Returns:
             A `TextGenerationResponse` object that contains the text produced by the model.
         """
         prediction_parameters = {
-            "temperature": temperature,
-            "maxDecodeSteps": max_output_tokens,
-            "topP": top_p,
-            "topK": top_k,
+            "temperature": temperature
+            if temperature is not None
+            else self._temperature,
+            "maxDecodeSteps": max_output_tokens
+            if max_output_tokens is not None
+            else self._max_output_tokens,
+            "topP": top_p if top_p is not None else self._top_p,
+            "topK": top_k if top_k is not None else self._top_k,
         }
         messages = []
         for input_text, output_text in self._history: