mirror of
https://github.com/hwchase17/langchain.git
synced 2025-06-19 05:13:46 +00:00
docs: anthropic api ref nit (#26591)
This commit is contained in:
Bagatur
GitHub
parent
e1d113ea84
commit
97b05d70e6
@ -807,14 +807,14 @@ class ChatAnthropic(BaseChatModel):
|
||||
Args:
|
||||
tools: A list of tool definitions to bind to this chat model.
|
||||
Supports Anthropic format tool schemas and any tool definition handled
|
||||
by :meth:`langchain_core.utils.function_calling.convert_to_openai_tool`.
|
||||
by :meth:`~langchain_core.utils.function_calling.convert_to_openai_tool`.
|
||||
tool_choice: Which tool to require the model to call. Options are:
|
||||
|
||||
- name of the tool as a string or as dict ``{"type": "tool", "name": "<<tool_name>>"}``: calls corresponding tool;
|
||||
- ``"auto"``, ``{"type: "auto"}``, or None: automatically selects a tool (including no tool);
|
||||
- ``"any"`` or ``{"type: "any"}``: force at least one tool to be called;
|
||||
kwargs: Any additional parameters are passed directly to
|
||||
``self.bind(**kwargs)``.
|
||||
:meth:`~langchain_anthropic.chat_models.ChatAnthropic.bind`.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
@ -833,7 +833,7 @@ class ChatAnthropic(BaseChatModel):
|
||||
product: str = Field(..., description="The product to look up.")
|
||||
|
||||
|
||||
llm = ChatAnthropic(model="claude-3-opus-20240229", temperature=0)
|
||||
llm = ChatAnthropic(model="claude-3-5-sonnet-20240620", temperature=0)
|
||||
llm_with_tools = llm.bind_tools([GetWeather, GetPrice])
|
||||
llm_with_tools.invoke("what is the weather like in San Francisco",)
|
||||
# -> AIMessage(
|
||||
@ -841,7 +841,7 @@ class ChatAnthropic(BaseChatModel):
|
||||
# {'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'},
|
||||
# {'text': None, 'type': 'tool_use', 'id': 'toolu_01SCgExKzQ7eqSkMHfygvYuu', 'name': 'GetWeather', 'input': {'location': 'San Francisco, CA'}}
|
||||
# ],
|
||||
# response_metadata={'id': 'msg_01GM3zQtoFv8jGQMW7abLnhi', 'model': 'claude-3-opus-20240229', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 487, 'output_tokens': 145}},
|
||||
# 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}},
|
||||
# id='run-87b1331e-9251-4a68-acef-f0a018b639cc-0'
|
||||
# )
|
||||
|
||||
@ -862,7 +862,7 @@ class ChatAnthropic(BaseChatModel):
|
||||
product: str = Field(..., description="The product to look up.")
|
||||
|
||||
|
||||
llm = ChatAnthropic(model="claude-3-opus-20240229", temperature=0)
|
||||
llm = ChatAnthropic(model="claude-3-5-sonnet-20240620", 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",)
|
||||
|
||||
@ -884,7 +884,7 @@ class ChatAnthropic(BaseChatModel):
|
||||
product: str = Field(..., description="The product to look up.")
|
||||
|
||||
|
||||
llm = ChatAnthropic(model="claude-3-opus-20240229", temperature=0)
|
||||
llm = ChatAnthropic(model="claude-3-5-sonnet-20240620", 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",)
|
||||
|
||||
@ -915,7 +915,7 @@ class ChatAnthropic(BaseChatModel):
|
||||
# We need to pass in extra headers to enable use of the beta cache
|
||||
# control API.
|
||||
llm = ChatAnthropic(
|
||||
model="claude-3-opus-20240229",
|
||||
model="claude-3-5-sonnet-20240620",
|
||||
temperature=0,
|
||||
extra_headers={"anthropic-beta": "prompt-caching-2024-07-31"}
|
||||
)
|
||||
@ -961,24 +961,20 @@ class ChatAnthropic(BaseChatModel):
|
||||
"""Model wrapper that returns outputs formatted to match the given schema.
|
||||
|
||||
Args:
|
||||
schema:
|
||||
The output schema. Can be passed in as:
|
||||
- an Anthropic tool schema,
|
||||
- an OpenAI function/tool schema,
|
||||
- a JSON Schema,
|
||||
- a TypedDict class (support added in 0.1.22),
|
||||
- or a Pydantic class.
|
||||
schema: The output schema. Can be passed in as:
|
||||
|
||||
- an Anthropic tool schema,
|
||||
- an OpenAI function/tool schema,
|
||||
- a JSON Schema,
|
||||
- a TypedDict class,
|
||||
- or a Pydantic class.
|
||||
|
||||
If ``schema`` is a Pydantic class then the model output will be a
|
||||
Pydantic instance of that class, and the model-generated fields will be
|
||||
validated by the Pydantic class. Otherwise the model output will be a
|
||||
dict and will not be validated. See :meth:`langchain_core.utils.function_calling.convert_to_openai_tool`
|
||||
dict and will not be validated. See :meth:`~langchain_core.utils.function_calling.convert_to_openai_tool`
|
||||
for more on how to properly specify types and descriptions of
|
||||
schema fields when specifying a Pydantic or TypedDict class.
|
||||
|
||||
.. versionchanged:: 0.1.22
|
||||
|
||||
Added support for TypedDict class.
|
||||
|
||||
include_raw:
|
||||
If False then only the parsed structured output is returned. If
|
||||
an error occurs during model output parsing it will be raised. If True
|
||||
@ -986,9 +982,10 @@ class ChatAnthropic(BaseChatModel):
|
||||
response will be returned. If an error occurs during output parsing it
|
||||
will be caught and returned as well. The final output is always a dict
|
||||
with keys "raw", "parsed", and "parsing_error".
|
||||
kwargs: Additional keyword arguments are ignored.
|
||||
|
||||
Returns:
|
||||
A Runnable that takes same inputs as a :class:`langchain_core.language_models.chat.BaseChatModel`.
|
||||
A Runnable that takes same inputs as a :class:`~langchain_core.language_models.chat.BaseChatModel`.
|
||||
|
||||
If ``include_raw`` is False and ``schema`` is a Pydantic class, Runnable outputs
|
||||
an instance of ``schema`` (i.e., a Pydantic object).
|
||||
@ -1011,7 +1008,7 @@ class ChatAnthropic(BaseChatModel):
|
||||
answer: str
|
||||
justification: str
|
||||
|
||||
llm = ChatAnthropic(model="claude-3-opus-20240229", temperature=0)
|
||||
llm = ChatAnthropic(model="claude-3-5-sonnet-20240620", temperature=0)
|
||||
structured_llm = llm.with_structured_output(AnswerWithJustification)
|
||||
|
||||
structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
|
||||
@ -1032,7 +1029,7 @@ class ChatAnthropic(BaseChatModel):
|
||||
answer: str
|
||||
justification: str
|
||||
|
||||
llm = ChatAnthropic(model="claude-3-opus-20240229", temperature=0)
|
||||
llm = ChatAnthropic(model="claude-3-5-sonnet-20240620", temperature=0)
|
||||
structured_llm = llm.with_structured_output(AnswerWithJustification, include_raw=True)
|
||||
|
||||
structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
|
||||
@ -1059,7 +1056,7 @@ class ChatAnthropic(BaseChatModel):
|
||||
"required": ["answer", "justification"]
|
||||
}
|
||||
}
|
||||
llm = ChatAnthropic(model="claude-3-opus-20240229", temperature=0)
|
||||
llm = ChatAnthropic(model="claude-3-5-sonnet-20240620", temperature=0)
|
||||
structured_llm = llm.with_structured_output(schema)
|
||||
|
||||
structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
|
||||
@ -1068,6 +1065,10 @@ class ChatAnthropic(BaseChatModel):
|
||||
# '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.'
|
||||
# }
|
||||
|
||||
.. versionchanged:: 0.1.22
|
||||
|
||||
Added support for TypedDict class as `schema`.
|
||||
|
||||
""" # noqa: E501
|
||||
|
||||
tool_name = convert_to_anthropic_tool(schema)["name"]
|
||||
|
||||
Loading…
Reference in New Issue
Block a user