langchain_google_vertexai : added logic to override get_num_tokens_from_messages() for ChatVertexAI (#16784)

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- **Description: added logic to override get_num_tokens_from_messages()
for ChatVertexAI. Currently ChatVertexAI was inheriting
get_num_tokens_from_messages() from BaseChatModel which in-turn was
calling GPT-2 tokenizer
  - **Issue: NA
  - **Dependencies: NA
  - **Twitter handle:@aditya_rane

@lkuligin for review

---------

Co-authored-by: adityarane@google.com <adityarane@google.com>
Co-authored-by: Leonid Kuligin <lkuligin@yandex.ru>

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

@@ -45,6 +45,12 @@ from vertexai.preview.generative_models import (  # type: ignore
     Image,
     Part,
 )
+from vertexai.preview.language_models import (  # type: ignore
+    ChatModel as PreviewChatModel,
+)
+from vertexai.preview.language_models import (
+    CodeChatModel as PreviewCodeChatModel,
+)
 
 from langchain_google_vertexai._utils import (
     get_generation_info,
@@ -316,12 +322,20 @@ class ChatVertexAI(_VertexAICommon, BaseChatModel):
             values["client"] = GenerativeModel(
                 model_name=values["model_name"], safety_settings=safety_settings
             )
+            values["client_preview"] = GenerativeModel(
+                model_name=values["model_name"], safety_settings=safety_settings
+            )
         else:
             if is_codey_model(values["model_name"]):
                 model_cls = CodeChatModel
+                model_cls_preview = PreviewCodeChatModel
             else:
                 model_cls = ChatModel
+                model_cls_preview = PreviewChatModel
             values["client"] = model_cls.from_pretrained(values["model_name"])
+            values["client_preview"] = model_cls_preview.from_pretrained(
+                values["model_name"]
+            )
         return values
 
     def _generate(

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

@@ -31,6 +31,12 @@ from vertexai.preview.generative_models import (  # type: ignore[import-untyped]
     Image,
 )
 from vertexai.preview.language_models import (  # type: ignore[import-untyped]
+    ChatModel as PreviewChatModel,
+)
+from vertexai.preview.language_models import (
+    CodeChatModel as PreviewCodeChatModel,
+)
+from vertexai.preview.language_models import (
     CodeGenerationModel as PreviewCodeGenerationModel,
 )
 from vertexai.preview.language_models import (
@@ -239,6 +245,27 @@ class _VertexAICommon(_VertexAIBase):
             params.pop("candidate_count")
         return params
 
+    def get_num_tokens(self, text: str) -> int:
+        """Get the number of tokens present in the text.
+
+        Useful for checking if an input will fit in a model's context window.
+
+        Args:
+            text: The string input to tokenize.
+
+        Returns:
+            The integer number of tokens in the text.
+        """
+        is_palm_chat_model = isinstance(
+            self.client_preview, PreviewChatModel
+        ) or isinstance(self.client_preview, PreviewCodeChatModel)
+        if is_palm_chat_model:
+            result = self.client_preview.start_chat().count_tokens(text)
+        else:
+            result = self.client_preview.count_tokens([text])
+
+        return result.total_tokens
+
 
 class VertexAI(_VertexAICommon, BaseLLM):
     """Google Vertex AI large language models."""
@@ -300,20 +327,6 @@ class VertexAI(_VertexAICommon, BaseLLM):
             raise ValueError("Only one candidate can be generated with streaming!")
         return values
 
-    def get_num_tokens(self, text: str) -> int:
-        """Get the number of tokens present in the text.
-
-        Useful for checking if an input will fit in a model's context window.
-
-        Args:
-            text: The string input to tokenize.
-
-        Returns:
-            The integer number of tokens in the text.
-        """
-        result = self.client_preview.count_tokens([text])
-        return result.total_tokens
-
     def _response_to_generation(
         self, response: TextGenerationResponse, *, stream: bool = False
     ) -> GenerationChunk:

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

@@ -225,6 +225,18 @@ def test_chat_vertexai_system_message(model_name: Optional[str]) -> None:
     assert isinstance(response.content, str)
 
 
+@pytest.mark.parametrize("model_name", model_names_to_test)
+def test_get_num_tokens_from_messages(model_name: str) -> None:
+    if model_name:
+        model = ChatVertexAI(model_name=model_name, temperature=0.0)
+    else:
+        model = ChatVertexAI(temperature=0.0)
+    message = HumanMessage(content="Hello")
+    token = model.get_num_tokens_from_messages(messages=[message])
+    assert isinstance(token, int)
+    assert token == 3
+
+
 def test_chat_vertexai_gemini_function_calling() -> None:
     class MyModel(BaseModel):
         name: str

libs/partners/google-vertexai/tests/integration_tests/test_tools.py

@@ -81,7 +81,6 @@ def test_tools() -> None:
     agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
 
     response = agent_executor.invoke({"input": "What is 6 raised to the 0.43 power?"})
-    print(response)
     assert isinstance(response, dict)
     assert response["input"] == "What is 6 raised to the 0.43 power?"
 
@@ -106,7 +105,6 @@ def test_stream() -> None:
     ]
     response = list(llm.stream("What is 6 raised to the 0.43 power?", functions=tools))
     assert len(response) == 1
-    # for chunk in response:
     assert isinstance(response[0], AIMessageChunk)
     assert "function_call" in response[0].additional_kwargs