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docs: anthropic api ref nit (#26591)
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@ -807,14 +807,14 @@ class ChatAnthropic(BaseChatModel):
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Args:
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tools: A list of tool definitions to bind to this chat model.
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Supports Anthropic format tool schemas and any tool definition handled
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by :meth:`langchain_core.utils.function_calling.convert_to_openai_tool`.
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by :meth:`~langchain_core.utils.function_calling.convert_to_openai_tool`.
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tool_choice: Which tool to require the model to call. Options are:
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- name of the tool as a string or as dict ``{"type": "tool", "name": "<<tool_name>>"}``: calls corresponding tool;
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- ``"auto"``, ``{"type: "auto"}``, or None: automatically selects a tool (including no tool);
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- ``"any"`` or ``{"type: "any"}``: force at least one tool to be called;
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kwargs: Any additional parameters are passed directly to
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``self.bind(**kwargs)``.
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:meth:`~langchain_anthropic.chat_models.ChatAnthropic.bind`.
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Example:
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.. code-block:: python
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@ -833,7 +833,7 @@ class ChatAnthropic(BaseChatModel):
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product: str = Field(..., description="The product to look up.")
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llm = ChatAnthropic(model="claude-3-opus-20240229", temperature=0)
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llm = ChatAnthropic(model="claude-3-5-sonnet-20240620", temperature=0)
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llm_with_tools = llm.bind_tools([GetWeather, GetPrice])
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llm_with_tools.invoke("what is the weather like in San Francisco",)
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# -> AIMessage(
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@ -841,7 +841,7 @@ class ChatAnthropic(BaseChatModel):
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# {'text': '<thinking>\nBased on the user\'s question, the relevant function to call is GetWeather, which requires the "location" parameter.\n\nThe user has directly specified the location as "San Francisco". Since San Francisco is a well known city, I can reasonably infer they mean San Francisco, CA without needing the state specified.\n\nAll the required parameters are provided, so I can proceed with the API call.\n</thinking>', 'type': 'text'},
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# {'text': None, 'type': 'tool_use', 'id': 'toolu_01SCgExKzQ7eqSkMHfygvYuu', 'name': 'GetWeather', 'input': {'location': 'San Francisco, CA'}}
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# ],
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# response_metadata={'id': 'msg_01GM3zQtoFv8jGQMW7abLnhi', 'model': 'claude-3-opus-20240229', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 487, 'output_tokens': 145}},
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# response_metadata={'id': 'msg_01GM3zQtoFv8jGQMW7abLnhi', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 487, 'output_tokens': 145}},
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# id='run-87b1331e-9251-4a68-acef-f0a018b639cc-0'
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# )
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@ -862,7 +862,7 @@ class ChatAnthropic(BaseChatModel):
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product: str = Field(..., description="The product to look up.")
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llm = ChatAnthropic(model="claude-3-opus-20240229", temperature=0)
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llm = ChatAnthropic(model="claude-3-5-sonnet-20240620", temperature=0)
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llm_with_tools = llm.bind_tools([GetWeather, GetPrice], tool_choice="any")
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llm_with_tools.invoke("what is the weather like in San Francisco",)
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@ -884,7 +884,7 @@ class ChatAnthropic(BaseChatModel):
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product: str = Field(..., description="The product to look up.")
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llm = ChatAnthropic(model="claude-3-opus-20240229", temperature=0)
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llm = ChatAnthropic(model="claude-3-5-sonnet-20240620", temperature=0)
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llm_with_tools = llm.bind_tools([GetWeather, GetPrice], tool_choice="GetWeather")
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llm_with_tools.invoke("what is the weather like in San Francisco",)
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@ -915,7 +915,7 @@ class ChatAnthropic(BaseChatModel):
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# We need to pass in extra headers to enable use of the beta cache
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# control API.
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llm = ChatAnthropic(
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model="claude-3-opus-20240229",
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model="claude-3-5-sonnet-20240620",
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temperature=0,
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extra_headers={"anthropic-beta": "prompt-caching-2024-07-31"}
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)
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@ -961,24 +961,20 @@ class ChatAnthropic(BaseChatModel):
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"""Model wrapper that returns outputs formatted to match the given schema.
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Args:
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schema:
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The output schema. Can be passed in as:
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- an Anthropic tool schema,
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- an OpenAI function/tool schema,
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- a JSON Schema,
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- a TypedDict class (support added in 0.1.22),
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- or a Pydantic class.
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schema: The output schema. Can be passed in as:
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- an Anthropic tool schema,
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- an OpenAI function/tool schema,
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- a JSON Schema,
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- a TypedDict class,
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- or a Pydantic class.
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If ``schema`` is a Pydantic class then the model output will be a
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Pydantic instance of that class, and the model-generated fields will be
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validated by the Pydantic class. Otherwise the model output will be a
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dict and will not be validated. See :meth:`langchain_core.utils.function_calling.convert_to_openai_tool`
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dict and will not be validated. See :meth:`~langchain_core.utils.function_calling.convert_to_openai_tool`
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for more on how to properly specify types and descriptions of
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schema fields when specifying a Pydantic or TypedDict class.
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.. versionchanged:: 0.1.22
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Added support for TypedDict class.
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include_raw:
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If False then only the parsed structured output is returned. If
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an error occurs during model output parsing it will be raised. If True
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@ -986,9 +982,10 @@ class ChatAnthropic(BaseChatModel):
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response will be returned. If an error occurs during output parsing it
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will be caught and returned as well. The final output is always a dict
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with keys "raw", "parsed", and "parsing_error".
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kwargs: Additional keyword arguments are ignored.
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Returns:
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A Runnable that takes same inputs as a :class:`langchain_core.language_models.chat.BaseChatModel`.
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A Runnable that takes same inputs as a :class:`~langchain_core.language_models.chat.BaseChatModel`.
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If ``include_raw`` is False and ``schema`` is a Pydantic class, Runnable outputs
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an instance of ``schema`` (i.e., a Pydantic object).
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@ -1011,7 +1008,7 @@ class ChatAnthropic(BaseChatModel):
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answer: str
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justification: str
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llm = ChatAnthropic(model="claude-3-opus-20240229", temperature=0)
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llm = ChatAnthropic(model="claude-3-5-sonnet-20240620", temperature=0)
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structured_llm = llm.with_structured_output(AnswerWithJustification)
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structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
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@ -1032,7 +1029,7 @@ class ChatAnthropic(BaseChatModel):
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answer: str
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justification: str
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llm = ChatAnthropic(model="claude-3-opus-20240229", temperature=0)
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llm = ChatAnthropic(model="claude-3-5-sonnet-20240620", temperature=0)
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structured_llm = llm.with_structured_output(AnswerWithJustification, include_raw=True)
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structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
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@ -1059,7 +1056,7 @@ class ChatAnthropic(BaseChatModel):
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"required": ["answer", "justification"]
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}
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}
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llm = ChatAnthropic(model="claude-3-opus-20240229", temperature=0)
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llm = ChatAnthropic(model="claude-3-5-sonnet-20240620", temperature=0)
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structured_llm = llm.with_structured_output(schema)
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structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
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@ -1068,6 +1065,10 @@ class ChatAnthropic(BaseChatModel):
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# 'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.'
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# }
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.. versionchanged:: 0.1.22
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Added support for TypedDict class as `schema`.
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""" # noqa: E501
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tool_name = convert_to_anthropic_tool(schema)["name"]
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