add formatted_tools field

libs/partners/anthropic/langchain_anthropic/chat_models.py

@@ -84,6 +84,15 @@ _message_type_lookups = {
 }
 
 
+class AnthropicTool(TypedDict):
+    """Anthropic tool definition."""
+
+    name: str
+    description: str
+    input_schema: Dict[str, Any]
+    cache_control: NotRequired[Dict[str, str]]
+
+
 def _format_image(image_url: str) -> Dict:
     """
     Formats an image of format data:image/jpeg;base64,{b64_string}
@@ -604,6 +613,9 @@ class ChatAnthropic(BaseChatModel):
     message chunks will be generated during the stream including usage metadata.
     """
 
+    formatted_tools: List[AnthropicTool] = Field(default_factory=list)
+    """Tools in Anthropic format to be passed to model invocations."""
+
     @property
     def _llm_type(self) -> str:
         """Return type of chat model."""
@@ -690,6 +702,8 @@ class ChatAnthropic(BaseChatModel):
     ) -> Dict:
         messages = self._convert_input(input_).to_messages()
         system, formatted_messages = _format_messages(messages)
+        if self.formatted_tools and "tools" not in kwargs:
+            kwargs["tools"] = self.formatted_tools  # type: ignore[assignment]
         payload = {
             "model": self.model,
             "max_tokens": self.max_tokens,
@@ -955,6 +969,7 @@ class ChatAnthropic(BaseChatModel):
 
         """  # noqa: E501
         formatted_tools = [convert_to_anthropic_tool(tool) for tool in tools]
+        self.formatted_tools = formatted_tools
         if not tool_choice:
             pass
         elif isinstance(tool_choice, dict):
@@ -1120,43 +1135,24 @@ class ChatAnthropic(BaseChatModel):
         .. 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:
-            response = self._client.beta.messages.count_tokens(
-                betas=["token-counting-2024-11-01"],
-                model=self.model,
-                messages=formatted_messages,  # type: ignore[arg-type]
-            )
+            kwargs["system"] = formatted_system
+        if self.formatted_tools:
+            kwargs["tools"] = self.formatted_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
 
 
-class AnthropicTool(TypedDict):
-    """Anthropic tool definition."""
-
-    name: str
-    description: str
-    input_schema: Dict[str, Any]
-    cache_control: NotRequired[Dict[str, str]]
-
-
 def convert_to_anthropic_tool(
     tool: Union[Dict[str, Any], Type, Callable, BaseTool],
 ) -> AnthropicTool:

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

@@ -20,6 +20,7 @@ from langchain_core.tools import tool
 from pydantic import BaseModel, Field
 
 from langchain_anthropic import ChatAnthropic, ChatAnthropicMessages
+from langchain_anthropic.chat_models import convert_to_anthropic_tool
 from tests.unit_tests._utils import FakeCallbackHandler
 
 MODEL_NAME = "claude-3-sonnet-20240229"
@@ -368,18 +369,15 @@ async def test_astreaming() -> None:
 
 def test_tool_use() -> None:
     llm = ChatAnthropic(model=MODEL_NAME)  # type: ignore[call-arg]
-    llm_with_tools = llm.bind_tools(
-        [
-            {
-                "name": "get_weather",
-                "description": "Get weather report for a city",
-                "input_schema": {
-                    "type": "object",
-                    "properties": {"location": {"type": "string"}},
-                },
-            }
-        ]
-    )
+    tool_schema = {
+        "name": "get_weather",
+        "description": "Get weather report for a city",
+        "input_schema": {
+            "type": "object",
+            "properties": {"location": {"type": "string"}},
+        },
+    }
+    llm_with_tools = llm.bind_tools([tool_schema])
     response = llm_with_tools.invoke("what's the weather in san francisco, ca")
     assert isinstance(response, AIMessage)
     assert isinstance(response.content, list)
@@ -441,6 +439,31 @@ def test_tool_use() -> None:
             gathered = gathered + chunk  # type: ignore
     assert len(chunks) > 1
 
+    # Test via init
+    llm_with_tools = ChatAnthropic(model=MODEL_NAME, formatted_tools=[tool_schema])  # type: ignore
+    response = llm_with_tools.invoke("what's the weather in san francisco, ca")
+    assert isinstance(response, AIMessage)
+    assert isinstance(response.content, list)
+    assert isinstance(response.tool_calls, list)
+    assert len(response.tool_calls) == 1
+
+    # Test tool conversion
+    @tool
+    def get_weather(location: str) -> str:
+        """Get weather report for a city"""
+        return "Sunny"
+
+    formatted_tool = convert_to_anthropic_tool(get_weather)
+    llm_with_tools = ChatAnthropic(
+        model=MODEL_NAME,  # type: ignore[call-arg]
+        formatted_tools=[formatted_tool],
+    )
+    response = llm_with_tools.invoke("what's the weather in san francisco, ca")
+    assert isinstance(response, AIMessage)
+    assert isinstance(response.content, list)
+    assert isinstance(response.tool_calls, list)
+    assert len(response.tool_calls) == 1
+
 
 def test_anthropic_with_empty_text_block() -> None:
     """Anthropic SDK can return an empty text block."""
@@ -518,30 +541,25 @@ def test_get_num_tokens_from_messages() -> None:
     num_tokens = llm.get_num_tokens_from_messages(messages)
     assert num_tokens > 0
 
-    # Test tool use (not yet supported)
-    messages = [
-        AIMessage(
-            content=[
-                {"text": "Let's see.", "type": "text"},
-                {
-                    "id": "toolu_01V6d6W32QGGSmQm4BT98EKk",
-                    "input": {"location": "SF"},
-                    "name": "get_weather",
-                    "type": "tool_use",
-                },
-            ],
-            tool_calls=[
-                {
-                    "name": "get_weather",
-                    "args": {"location": "SF"},
-                    "id": "toolu_01V6d6W32QGGSmQm4BT98EKk",
-                    "type": "tool_call",
-                },
-            ],
-        )
-    ]
-    with pytest.raises(NotImplementedError):
-        num_tokens = llm.get_num_tokens_from_messages(messages)
+    # Test tool use
+    @tool
+    def get_weather(location: str) -> str:
+        """Get weather report for a city"""
+        return "Sunny"
+
+    ## via init
+    formatted_tool = convert_to_anthropic_tool(get_weather)
+    llm = ChatAnthropic(
+        model="claude-3-5-haiku-20241022",  # type: ignore[call-arg]
+        formatted_tools=[formatted_tool],
+    )
+    num_tokens = llm.get_num_tokens_from_messages(messages)
+    assert num_tokens > 0
+
+    ## via bind_tools
+    llm_with_tools = llm.bind_tools([get_weather])
+    num_tokens = llm_with_tools.get_num_tokens_from_messages(messages)  # type: ignore[attr-defined]
+    assert num_tokens > 0
 
 
 class GetWeather(BaseModel):