anthropic[patch]: Release 0.1.13, tool_choice support (#21773)

libs/partners/anthropic/langchain_anthropic/chat_models.py

@@ -542,6 +542,10 @@ class ChatAnthropic(BaseChatModel):
     def bind_tools(
         self,
         tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
+        *,
+        tool_choice: Optional[
+            Union[Dict[str, str], Literal["any", "auto"], str]
+        ] = None,
         **kwargs: Any,
     ) -> Runnable[LanguageModelInput, BaseMessage]:
         """Bind tool-like objects to this chat model.
@@ -551,6 +555,15 @@ class ChatAnthropic(BaseChatModel):
                 Can be  a dictionary, pydantic model, callable, or BaseTool. Pydantic
                 models, callables, and BaseTools will be automatically converted to
                 their schema dictionary representation.
+            tool_choice: Which tool to require the model to call.
+                Options are:
+                    name of the tool (str): calls corresponding tool;
+                    "auto" or None: automatically selects a tool (including no tool);
+                    "any": force at least one tool to be called;
+                    or a dict of the form:
+                        {"type": "tool", "name": "tool_name"},
+                        or {"type: "any"},
+                        or {"type: "auto"};
             **kwargs: Any additional parameters to bind.
 
         Example:
@@ -564,9 +577,14 @@ class ChatAnthropic(BaseChatModel):
 
                     location: str = Field(..., description="The city and state, e.g. San Francisco, CA")
 
+                class GetPrice(BaseModel):
+                    '''Get the price of a specific product.'''
+
+                    product: str = Field(..., description="The product to look up.")
+
 
                 llm = ChatAnthropic(model="claude-3-opus-20240229", temperature=0)
-                llm_with_tools = llm.bind_tools([GetWeather])
+                llm_with_tools = llm.bind_tools([GetWeather, GetPrice])
                 llm_with_tools.invoke("what is the weather like in San Francisco",)
                 # -> AIMessage(
                 #     content=[
@@ -576,8 +594,64 @@ class ChatAnthropic(BaseChatModel):
                 #     response_metadata={'id': 'msg_01GM3zQtoFv8jGQMW7abLnhi', 'model': 'claude-3-opus-20240229', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 487, 'output_tokens': 145}},
                 #     id='run-87b1331e-9251-4a68-acef-f0a018b639cc-0'
                 # )
+
+        Example — force tool call with tool_choice 'any':
+            .. code-block:: python
+
+                from langchain_anthropic import ChatAnthropic
+                from langchain_core.pydantic_v1 import BaseModel, Field
+
+                class GetWeather(BaseModel):
+                    '''Get the current weather in a given location'''
+
+                    location: str = Field(..., description="The city and state, e.g. San Francisco, CA")
+
+                class GetPrice(BaseModel):
+                    '''Get the price of a specific product.'''
+
+                    product: str = Field(..., description="The product to look up.")
+
+
+                llm = ChatAnthropic(model="claude-3-opus-20240229", temperature=0)
+                llm_with_tools = llm.bind_tools([GetWeather, GetPrice], tool_choice="any")
+                llm_with_tools.invoke("what is the weather like in San Francisco",)
+
+
+        Example — force specific tool call with tool_choice '<name_of_tool>':
+            .. code-block:: python
+
+                from langchain_anthropic import ChatAnthropic
+                from langchain_core.pydantic_v1 import BaseModel, Field
+
+                class GetWeather(BaseModel):
+                    '''Get the current weather in a given location'''
+
+                    location: str = Field(..., description="The city and state, e.g. San Francisco, CA")
+
+                class GetPrice(BaseModel):
+                    '''Get the price of a specific product.'''
+
+                    product: str = Field(..., description="The product to look up.")
+
+
+                llm = ChatAnthropic(model="claude-3-opus-20240229", temperature=0)
+                llm_with_tools = llm.bind_tools([GetWeather, GetPrice], tool_choice="GetWeather")
+                llm_with_tools.invoke("what is the weather like in San Francisco",)
         """  # noqa: E501
         formatted_tools = [convert_to_anthropic_tool(tool) for tool in tools]
+        if not tool_choice:
+            pass
+        elif isinstance(tool_choice, dict):
+            kwargs["tool_choice"] = tool_choice
+        elif isinstance(tool_choice, str) and tool_choice in ("any", "auto"):
+            kwargs["tool_choice"] = {"type": tool_choice}
+        elif isinstance(tool_choice, str):
+            kwargs["tool_choice"] = {"type": "tool", "name": tool_choice}
+        else:
+            raise ValueError(
+                f"Unrecognized 'tool_choice' type {tool_choice=}. Expected dict, "
+                f"str, or None."
+            )
         return self.bind(tools=formatted_tools, **kwargs)
 
     def with_structured_output(
@@ -683,7 +757,7 @@ class ChatAnthropic(BaseChatModel):
                 # }
 
         """  # noqa: E501
-        llm = self.bind_tools([schema])
+        llm = self.bind_tools([schema], tool_choice="any")
         if isinstance(schema, type) and issubclass(schema, BaseModel):
             output_parser = ToolsOutputParser(
                 first_tool_only=True, pydantic_schemas=[schema]