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style: repo linting pass (#33089)

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enable docstring-code-format
This commit is contained in:
Mason Daugherty
2025-09-24 15:25:55 -04:00
committed by GitHub
parent 083bb3cdd7
commit b92b394804
33 changed files with 687 additions and 283 deletions
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322
libs/partners/anthropic/langchain_anthropic/chat_models.py
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@@ -577,14 +577,32 @@ class ChatAnthropic(BaseChatModel):
.. code-block:: python
messages = [
("system", "You are a helpful translator. Translate the user sentence to French."),
(
"system",
"You are a helpful translator. Translate the user sentence to French.",
),
("human", "I love programming."),
]
llm.invoke(messages)
.. code-block:: python
AIMessage(content="J'aime la programmation.", response_metadata={'id': 'msg_01Trik66aiQ9Z1higrD5XFx3', 'model': 'claude-3-7-sonnet-20250219', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 25, 'output_tokens': 11}}, id='run-5886ac5f-3c2e-49f5-8a44-b1e92808c929-0', usage_metadata={'input_tokens': 25, 'output_tokens': 11, 'total_tokens': 36})
AIMessage(
content="J'aime la programmation.",
response_metadata={
"id": "msg_01Trik66aiQ9Z1higrD5XFx3",
"model": "claude-3-7-sonnet-20250219",
"stop_reason": "end_turn",
"stop_sequence": None,
"usage": {"input_tokens": 25, "output_tokens": 11},
},
id="run-5886ac5f-3c2e-49f5-8a44-b1e92808c929-0",
usage_metadata={
"input_tokens": 25,
"output_tokens": 11,
"total_tokens": 36,
},
)
Stream:
.. code-block:: python
@@ -594,14 +612,14 @@ class ChatAnthropic(BaseChatModel):
.. code-block:: python
AIMessageChunk(content='J', id='run-272ff5f9-8485-402c-b90d-eac8babc5b25')
AIMessageChunk(content="'", id='run-272ff5f9-8485-402c-b90d-eac8babc5b25')
AIMessageChunk(content='a', id='run-272ff5f9-8485-402c-b90d-eac8babc5b25')
AIMessageChunk(content='ime', id='run-272ff5f9-8485-402c-b90d-eac8babc5b25')
AIMessageChunk(content=' la', id='run-272ff5f9-8485-402c-b90d-eac8babc5b25')
AIMessageChunk(content=' programm', id='run-272ff5f9-8485-402c-b90d-eac8babc5b25')
AIMessageChunk(content='ation', id='run-272ff5f9-8485-402c-b90d-eac8babc5b25')
AIMessageChunk(content='.', id='run-272ff5f9-8485-402c-b90d-eac8babc5b25')
AIMessageChunk(content="J", id="run-272ff5f9-8485-402c-b90d-eac8babc5b25")
AIMessageChunk(content="'", id="run-272ff5f9-8485-402c-b90d-eac8babc5b25")
AIMessageChunk(content="a", id="run-272ff5f9-8485-402c-b90d-eac8babc5b25")
AIMessageChunk(content="ime", id="run-272ff5f9-8485-402c-b90d-eac8babc5b25")
AIMessageChunk(content=" la", id="run-272ff5f9-8485-402c-b90d-eac8babc5b25")
AIMessageChunk(content=" programm", id="run-272ff5f9-8485-402c-b90d-eac8babc5b25")
AIMessageChunk(content="ation", id="run-272ff5f9-8485-402c-b90d-eac8babc5b25")
AIMessageChunk(content=".", id="run-272ff5f9-8485-402c-b90d-eac8babc5b25")
.. code-block:: python
@@ -613,7 +631,7 @@ class ChatAnthropic(BaseChatModel):
.. code-block:: python
AIMessageChunk(content="J'aime la programmation.", id='run-b34faef0-882f-4869-a19c-ed2b856e6361')
AIMessageChunk(content="J'aime la programmation.", id="run-b34faef0-882f-4869-a19c-ed2b856e6361")
Async:
.. code-block:: python
@@ -628,41 +646,69 @@ class ChatAnthropic(BaseChatModel):
.. code-block:: python
AIMessage(content="J'aime la programmation.", response_metadata={'id': 'msg_01Trik66aiQ9Z1higrD5XFx3', 'model': 'claude-3-7-sonnet-20250219', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 25, 'output_tokens': 11}}, id='run-5886ac5f-3c2e-49f5-8a44-b1e92808c929-0', usage_metadata={'input_tokens': 25, 'output_tokens': 11, 'total_tokens': 36})
AIMessage(
content="J'aime la programmation.",
response_metadata={
"id": "msg_01Trik66aiQ9Z1higrD5XFx3",
"model": "claude-3-7-sonnet-20250219",
"stop_reason": "end_turn",
"stop_sequence": None,
"usage": {"input_tokens": 25, "output_tokens": 11},
},
id="run-5886ac5f-3c2e-49f5-8a44-b1e92808c929-0",
usage_metadata={
"input_tokens": 25,
"output_tokens": 11,
"total_tokens": 36,
},
)
Tool calling:
.. code-block:: python
from pydantic 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 GetPopulation(BaseModel):
'''Get the current population in a given location'''
location: str = Field(..., description="The city and state, e.g. San Francisco, CA")
llm_with_tools = llm.bind_tools([GetWeather, GetPopulation])
ai_msg = llm_with_tools.invoke("Which city is hotter today and which is bigger: LA or NY?")
ai_msg.tool_calls
.. code-block:: python
[{'name': 'GetWeather',
'args': {'location': 'Los Angeles, CA'},
'id': 'toolu_01KzpPEAgzura7hpBqwHbWdo'},
{'name': 'GetWeather',
'args': {'location': 'New York, NY'},
'id': 'toolu_01JtgbVGVJbiSwtZk3Uycezx'},
{'name': 'GetPopulation',
'args': {'location': 'Los Angeles, CA'},
'id': 'toolu_01429aygngesudV9nTbCKGuw'},
{'name': 'GetPopulation',
'args': {'location': 'New York, NY'},
'id': 'toolu_01JPktyd44tVMeBcPPnFSEJG'}]
[
{
"name": "GetWeather",
"args": {"location": "Los Angeles, CA"},
"id": "toolu_01KzpPEAgzura7hpBqwHbWdo",
},
{
"name": "GetWeather",
"args": {"location": "New York, NY"},
"id": "toolu_01JtgbVGVJbiSwtZk3Uycezx",
},
{
"name": "GetPopulation",
"args": {"location": "Los Angeles, CA"},
"id": "toolu_01429aygngesudV9nTbCKGuw",
},
{
"name": "GetPopulation",
"args": {"location": "New York, NY"},
"id": "toolu_01JPktyd44tVMeBcPPnFSEJG",
},
]
See ``ChatAnthropic.bind_tools()`` method for more.
@@ -673,6 +719,7 @@ class ChatAnthropic(BaseChatModel):
from pydantic import BaseModel, Field
class Joke(BaseModel):
'''Joke to tell user.'''
@@ -680,12 +727,17 @@ class ChatAnthropic(BaseChatModel):
punchline: str = Field(description="The punchline to the joke")
rating: Optional[int] = Field(description="How funny the joke is, from 1 to 10")
structured_llm = llm.with_structured_output(Joke)
structured_llm.invoke("Tell me a joke about cats")
.. code-block:: python
Joke(setup='Why was the cat sitting on the computer?', punchline='To keep an eye on the mouse!', rating=None)
Joke(
setup="Why was the cat sitting on the computer?",
punchline="To keep an eye on the mouse!",
rating=None,
)
See ``ChatAnthropic.with_structured_output()`` for more.
@@ -851,7 +903,14 @@ class ChatAnthropic(BaseChatModel):
.. code-block:: python
[{'signature': '...', 'thinking': "To find the cube root of 50.653...", 'type': 'thinking'}, {'text': 'The cube root of 50.653 is ...', 'type': 'text'}]
[
{
"signature": "...",
"thinking": "To find the cube root of 50.653...",
"type": "thinking",
},
{"text": "The cube root of 50.653 is ...", "type": "text"},
]
Citations:
Anthropic supports a
@@ -892,25 +951,39 @@ class ChatAnthropic(BaseChatModel):
.. code-block:: python
[{'text': 'Based on the document, ', 'type': 'text'},
{'text': 'the grass is green',
'type': 'text',
'citations': [{'type': 'char_location',
'cited_text': 'The grass is green. ',
'document_index': 0,
'document_title': 'My Document',
'start_char_index': 0,
'end_char_index': 20}]},
{'text': ', and ', 'type': 'text'},
{'text': 'the sky is blue',
'type': 'text',
'citations': [{'type': 'char_location',
'cited_text': 'The sky is blue.',
'document_index': 0,
'document_title': 'My Document',
'start_char_index': 20,
'end_char_index': 36}]},
{'text': '.', 'type': 'text'}]
[
{"text": "Based on the document, ", "type": "text"},
{
"text": "the grass is green",
"type": "text",
"citations": [
{
"type": "char_location",
"cited_text": "The grass is green. ",
"document_index": 0,
"document_title": "My Document",
"start_char_index": 0,
"end_char_index": 20,
}
],
},
{"text": ", and ", "type": "text"},
{
"text": "the sky is blue",
"type": "text",
"citations": [
{
"type": "char_location",
"cited_text": "The sky is blue.",
"document_index": 0,
"document_title": "My Document",
"start_char_index": 20,
"end_char_index": 36,
}
],
},
{"text": ".", "type": "text"},
]
Token usage:
.. code-block:: python
@@ -920,7 +993,7 @@ class ChatAnthropic(BaseChatModel):
.. code-block:: python
{'input_tokens': 25, 'output_tokens': 11, 'total_tokens': 36}
{"input_tokens": 25, "output_tokens": 11, "total_tokens": 36}
Message chunks containing token usage will be included during streaming by
default:
@@ -935,7 +1008,7 @@ class ChatAnthropic(BaseChatModel):
.. code-block:: python
{'input_tokens': 25, 'output_tokens': 11, 'total_tokens': 36}
{"input_tokens": 25, "output_tokens": 11, "total_tokens": 36}
These can be disabled by setting ``stream_usage=False`` in the stream method,
or by setting ``stream_usage=False`` when initializing ChatAnthropic.
@@ -981,7 +1054,7 @@ class ChatAnthropic(BaseChatModel):
.. code-block:: python
{'cache_read': 0, 'cache_creation': 1458}
{"cache_read": 0, "cache_creation": 1458}
Alternatively, you may enable prompt caching at invocation time. You may want to
conditionally cache based on runtime conditions, such as the length of the
@@ -1006,7 +1079,8 @@ class ChatAnthropic(BaseChatModel):
model="claude-3-7-sonnet-20250219",
)
messages = [{
messages = [
{
"role": "user",
"content": [
{
@@ -1015,7 +1089,8 @@ class ChatAnthropic(BaseChatModel):
"cache_control": {"type": "ephemeral", "ttl": "1h"},
},
],
}]
}
]
response = llm.invoke(messages)
@@ -1038,7 +1113,7 @@ class ChatAnthropic(BaseChatModel):
"cache_creation": 1000,
"ephemeral_1h_input_tokens": 750,
"ephemeral_5m_input_tokens": 250,
}
},
}
See `Claude documentation <https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching#1-hour-cache-duration-beta>`__
@@ -1128,12 +1203,14 @@ class ChatAnthropic(BaseChatModel):
llm = ChatAnthropic(model="claude-3-5-haiku-latest")
tool = {"type": "web_search_20250305", "name": "web_search", "max_uses": 3}
tool = {
"type": "web_search_20250305",
"name": "web_search",
"max_uses": 3,
}
llm_with_tools = llm.bind_tools([tool])
response = llm_with_tools.invoke(
"How do I update a web app to TypeScript 5.5?"
)
response = llm_with_tools.invoke("How do I update a web app to TypeScript 5.5?")
.. dropdown:: Web fetch (beta)
@@ -1146,12 +1223,14 @@ class ChatAnthropic(BaseChatModel):
betas=["web-fetch-2025-09-10"], # Enable web fetch beta
)
tool = {"type": "web_fetch_20250910", "name": "web_fetch", "max_uses": 3}
tool = {
"type": "web_fetch_20250910",
"name": "web_fetch",
"max_uses": 3,
}
llm_with_tools = llm.bind_tools([tool])
response = llm_with_tools.invoke(
"Please analyze the content at https://example.com/article"
)
response = llm_with_tools.invoke("Please analyze the content at https://example.com/article")
.. dropdown:: Code execution
@@ -1233,11 +1312,13 @@ class ChatAnthropic(BaseChatModel):
.. code-block:: python
{'id': 'msg_013xU6FHEGEq76aP4RgFerVT',
'model': 'claude-3-7-sonnet-20250219',
'stop_reason': 'end_turn',
'stop_sequence': None,
'usage': {'input_tokens': 25, 'output_tokens': 11}}
{
"id": "msg_013xU6FHEGEq76aP4RgFerVT",
"model": "claude-3-7-sonnet-20250219",
"stop_reason": "end_turn",
"stop_sequence": None,
"usage": {"input_tokens": 25, "output_tokens": 11},
}
""" # noqa: E501
@@ -1722,11 +1803,13 @@ class ChatAnthropic(BaseChatModel):
from langchain_anthropic import ChatAnthropic
from pydantic 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.'''
@@ -1735,7 +1818,9 @@ class ChatAnthropic(BaseChatModel):
llm = ChatAnthropic(model="claude-3-5-sonnet-latest", temperature=0)
llm_with_tools = llm.bind_tools([GetWeather, GetPrice])
llm_with_tools.invoke("what is the weather like in San Francisco",)
llm_with_tools.invoke(
"What is the weather like in San Francisco",
)
# -> AIMessage(
# content=[
# {'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'},
@@ -1752,11 +1837,13 @@ class ChatAnthropic(BaseChatModel):
from langchain_anthropic import ChatAnthropic
from pydantic 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.'''
@@ -1765,7 +1852,9 @@ class ChatAnthropic(BaseChatModel):
llm = ChatAnthropic(model="claude-3-5-sonnet-latest", 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",)
llm_with_tools.invoke(
"what is the weather like in San Francisco",
)
Example — force specific tool call with tool_choice ``'<name_of_tool>'``:
@@ -1775,11 +1864,13 @@ class ChatAnthropic(BaseChatModel):
from langchain_anthropic import ChatAnthropic
from pydantic 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.'''
@@ -1788,7 +1879,7 @@ class ChatAnthropic(BaseChatModel):
llm = ChatAnthropic(model="claude-3-5-sonnet-latest", 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",)
llm_with_tools.invoke("What is the weather like in San Francisco")
Example — cache specific tools:
@@ -1797,16 +1888,19 @@ class ChatAnthropic(BaseChatModel):
from langchain_anthropic import ChatAnthropic, convert_to_anthropic_tool
from pydantic 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.")
# We'll convert our pydantic class to the anthropic tool format
# before passing to bind_tools so that we can set the 'cache_control'
# field on our tool.
@@ -1822,19 +1916,97 @@ class ChatAnthropic(BaseChatModel):
temperature=0,
)
llm_with_tools = llm.bind_tools([GetWeather, cached_price_tool])
llm_with_tools.invoke("what is the weather like in San Francisco",)
llm_with_tools.invoke("What is the weather like in San Francisco")
This outputs:
.. code-block:: python
AIMessage(content=[{'text': "Certainly! I can help you find out the current weather in San Francisco. To get this information, I'll use the GetWeather function. Let me fetch that data for you right away.", 'type': 'text'}, {'id': 'toolu_01TS5h8LNo7p5imcG7yRiaUM', 'input': {'location': 'San Francisco, CA'}, 'name': 'GetWeather', 'type': 'tool_use'}], response_metadata={'id': 'msg_01Xg7Wr5inFWgBxE5jH9rpRo', 'model': 'claude-3-5-sonnet-latest', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 171, 'output_tokens': 96, 'cache_creation_input_tokens': 1470, 'cache_read_input_tokens': 0}}, id='run-b36a5b54-5d69-470e-a1b0-b932d00b089e-0', tool_calls=[{'name': 'GetWeather', 'args': {'location': 'San Francisco, CA'}, 'id': 'toolu_01TS5h8LNo7p5imcG7yRiaUM', 'type': 'tool_call'}], usage_metadata={'input_tokens': 171, 'output_tokens': 96, 'total_tokens': 267})
AIMessage(
content=[
{
"text": "Certainly! I can help you find out the current weather in San Francisco. To get this information, I'll use the GetWeather function. Let me fetch that data for you right away.",
"type": "text",
},
{
"id": "toolu_01TS5h8LNo7p5imcG7yRiaUM",
"input": {"location": "San Francisco, CA"},
"name": "GetWeather",
"type": "tool_use",
},
],
response_metadata={
"id": "msg_01Xg7Wr5inFWgBxE5jH9rpRo",
"model": "claude-3-5-sonnet-latest",
"stop_reason": "tool_use",
"stop_sequence": None,
"usage": {
"input_tokens": 171,
"output_tokens": 96,
"cache_creation_input_tokens": 1470,
"cache_read_input_tokens": 0,
},
},
id="run-b36a5b54-5d69-470e-a1b0-b932d00b089e-0",
tool_calls=[
{
"name": "GetWeather",
"args": {"location": "San Francisco, CA"},
"id": "toolu_01TS5h8LNo7p5imcG7yRiaUM",
"type": "tool_call",
}
],
usage_metadata={
"input_tokens": 171,
"output_tokens": 96,
"total_tokens": 267,
},
)
If we invoke the tool again, we can see that the "usage" information in the AIMessage.response_metadata shows that we had a cache hit:
.. code-block:: python
AIMessage(content=[{'text': 'To get the current weather in San Francisco, I can use the GetWeather function. Let me check that for you.', 'type': 'text'}, {'id': 'toolu_01HtVtY1qhMFdPprx42qU2eA', 'input': {'location': 'San Francisco, CA'}, 'name': 'GetWeather', 'type': 'tool_use'}], response_metadata={'id': 'msg_016RfWHrRvW6DAGCdwB6Ac64', 'model': 'claude-3-5-sonnet-latest', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 171, 'output_tokens': 82, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 1470}}, id='run-88b1f825-dcb7-4277-ac27-53df55d22001-0', tool_calls=[{'name': 'GetWeather', 'args': {'location': 'San Francisco, CA'}, 'id': 'toolu_01HtVtY1qhMFdPprx42qU2eA', 'type': 'tool_call'}], usage_metadata={'input_tokens': 171, 'output_tokens': 82, 'total_tokens': 253})
AIMessage(
content=[
{
"text": "To get the current weather in San Francisco, I can use the GetWeather function. Let me check that for you.",
"type": "text",
},
{
"id": "toolu_01HtVtY1qhMFdPprx42qU2eA",
"input": {"location": "San Francisco, CA"},
"name": "GetWeather",
"type": "tool_use",
},
],
response_metadata={
"id": "msg_016RfWHrRvW6DAGCdwB6Ac64",
"model": "claude-3-5-sonnet-latest",
"stop_reason": "tool_use",
"stop_sequence": None,
"usage": {
"input_tokens": 171,
"output_tokens": 82,
"cache_creation_input_tokens": 0,
"cache_read_input_tokens": 1470,
},
},
id="run-88b1f825-dcb7-4277-ac27-53df55d22001-0",
tool_calls=[
{
"name": "GetWeather",
"args": {"location": "San Francisco, CA"},
"id": "toolu_01HtVtY1qhMFdPprx42qU2eA",
"type": "tool_call",
}
],
usage_metadata={
"input_tokens": 171,
"output_tokens": 82,
"total_tokens": 253,
},
)
""" # noqa: E501
formatted_tools = [
@@ -1926,11 +2098,14 @@ class ChatAnthropic(BaseChatModel):
from langchain_anthropic import ChatAnthropic
from pydantic import BaseModel
class AnswerWithJustification(BaseModel):
'''An answer to the user question along with justification for the answer.'''
answer: str
justification: str
llm = ChatAnthropic(model="claude-3-5-sonnet-latest", temperature=0)
structured_llm = llm.with_structured_output(AnswerWithJustification)
@@ -1948,11 +2123,14 @@ class ChatAnthropic(BaseChatModel):
from langchain_anthropic import ChatAnthropic
from pydantic import BaseModel
class AnswerWithJustification(BaseModel):
'''An answer to the user question along with justification for the answer.'''
answer: str
justification: str
llm = ChatAnthropic(model="claude-3-5-sonnet-latest", temperature=0)
structured_llm = llm.with_structured_output(AnswerWithJustification, include_raw=True)
@@ -1978,8 +2156,8 @@ class ChatAnthropic(BaseChatModel):
"answer": {"type": "string"},
"justification": {"type": "string"},
},
"required": ["answer", "justification"]
}
"required": ["answer", "justification"],
},
}
llm = ChatAnthropic(model="claude-3-5-sonnet-latest", temperature=0)
structured_llm = llm.with_structured_output(schema)