update anthropic

libs/partners/anthropic/langchain_anthropic/chat_models.py

@@ -1114,36 +1114,37 @@ class ChatAnthropic(BaseChatModel):
             return llm | output_parser
 
     @beta()
-    def get_num_tokens_from_messages(self, messages: List[BaseMessage]) -> int:
+    def get_num_tokens_from_messages(
+        self,
+        messages: List[BaseMessage],
+        tools: Optional[
+            Sequence[Union[Dict[str, Any], Type, Callable, BaseTool]]
+        ] = None,
+    ) -> int:
         """Count tokens in a sequence of input messages.
 
+        Args:
+            messages: The message inputs to tokenize.
+            tools: If provided, sequence of dict, BaseModel, function, or BaseTools
+                to be converted to tool schemas.
+
         .. versionchanged:: 0.2.5
 
                 Uses Anthropic's token counting API to count tokens in messages. See:
-                https://docs.anthropic.com/en/api/messages-count-tokens
+                https://docs.anthropic.com/en/docs/build-with-claude/token-counting
         """
-        if any(
-            isinstance(tool, ToolMessage)
-            or (isinstance(tool, AIMessage) and tool.tool_calls)
-            for tool in messages
-        ):
-            raise NotImplementedError(
-                "get_num_tokens_from_messages does not yet support counting tokens "
-                "in tool calls."
-            )
         formatted_system, formatted_messages = _format_messages(messages)
+        kwargs: Dict[str, Any] = {}
         if isinstance(formatted_system, str):
-            response = self._client.beta.messages.count_tokens(
-                betas=["token-counting-2024-11-01"],
-                model=self.model,
-                system=formatted_system,
-                messages=formatted_messages,  # type: ignore[arg-type]
-            )
-        else:
+            kwargs["system"] = formatted_system
+        if tools:
+            kwargs["tools"] = [convert_to_anthropic_tool(tool) for tool in tools]
+
         response = self._client.beta.messages.count_tokens(
             betas=["token-counting-2024-11-01"],
             model=self.model,
             messages=formatted_messages,  # type: ignore[arg-type]
+            **kwargs,
         )
         return response.input_tokens
 

libs/partners/anthropic/tests/integration_tests/test_chat_models.py

@@ -508,18 +508,34 @@ def test_with_structured_output() -> None:
 
 
 def test_get_num_tokens_from_messages() -> None:
-    llm = ChatAnthropic(model="claude-3-5-haiku-20241022")  # type: ignore[call-arg]
+    llm = ChatAnthropic(model="claude-3-5-sonnet-20241022")  # type: ignore[call-arg]
 
     # Test simple case
     messages = [
-        SystemMessage(content="You are an assistant."),
-        HumanMessage(content="What is the weather in SF?"),
+        SystemMessage(content="You are a scientist"),
+        HumanMessage(content="Hello, Claude"),
     ]
     num_tokens = llm.get_num_tokens_from_messages(messages)
     assert num_tokens > 0
 
-    # Test tool use (not yet supported)
+    # Test tool use
+    @tool(parse_docstring=True)
+    def get_weather(location: str) -> str:
+        """Get the current weather in a given location
+
+        Args:
+            location: The city and state, e.g. San Francisco, CA
+        """
+        return "Sunny"
+
     messages = [
+        HumanMessage(content="What's the weather like in San Francisco?"),
+    ]
+    num_tokens = llm.get_num_tokens_from_messages(messages, tools=[get_weather])
+    assert num_tokens > 0
+
+    messages = [
+        HumanMessage(content="What's the weather like in San Francisco?"),
         AIMessage(
             content=[
                 {"text": "Let's see.", "type": "text"},
@@ -538,10 +554,11 @@ def test_get_num_tokens_from_messages() -> None:
                     "type": "tool_call",
                 },
             ],
-        )
+        ),
+        ToolMessage(content="Sunny", tool_call_id="toolu_01V6d6W32QGGSmQm4BT98EKk"),
     ]
-    with pytest.raises(NotImplementedError):
-        num_tokens = llm.get_num_tokens_from_messages(messages)
+    num_tokens = llm.get_num_tokens_from_messages(messages, tools=[get_weather])
+    assert num_tokens > 0
 
 
 class GetWeather(BaseModel):