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anthropic[patch]: ruff fixes and rules (#31899)

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* bump ruff deps
* add more thorough ruff rules
* fix said rules
This commit is contained in:
Mason Daugherty
2025-07-07 18:32:27 -04:00
committed by GitHub
parent e7eac27241
commit 2a7645300c
18 changed files with 430 additions and 311 deletions
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143
libs/partners/anthropic/tests/integration_tests/test_chat_models.py
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@@ -1,5 +1,7 @@
"""Test ChatAnthropic chat model."""
from __future__ import annotations
import asyncio
import json
import os
@@ -43,10 +45,7 @@ def test_stream() -> None:
chunks_with_model_name = 0
for token in llm.stream("I'm Pickle Rick"):
assert isinstance(token.content, str)
if full is None:
full = cast(BaseMessageChunk, token)
else:
full = full + token
full = cast(BaseMessageChunk, token) if full is None else full + token
assert isinstance(token, AIMessageChunk)
if token.usage_metadata is not None:
if token.usage_metadata.get("input_tokens"):
@@ -55,11 +54,14 @@ def test_stream() -> None:
chunks_with_output_token_counts += 1
chunks_with_model_name += int("model_name" in token.response_metadata)
if chunks_with_input_token_counts != 1 or chunks_with_output_token_counts != 1:
raise AssertionError(
msg = (
"Expected exactly one chunk with input or output token counts. "
"AIMessageChunk aggregation adds counts. Check that "
"this is behaving properly."
)
raise AssertionError(
msg,
)
assert chunks_with_model_name == 1
# check token usage is populated
assert isinstance(full, AIMessageChunk)
@@ -85,10 +87,7 @@ async def test_astream() -> None:
chunks_with_output_token_counts = 0
async for token in llm.astream("I'm Pickle Rick"):
assert isinstance(token.content, str)
if full is None:
full = cast(BaseMessageChunk, token)
else:
full = full + token
full = cast(BaseMessageChunk, token) if full is None else full + token
assert isinstance(token, AIMessageChunk)
if token.usage_metadata is not None:
if token.usage_metadata.get("input_tokens"):
@@ -96,11 +95,14 @@ async def test_astream() -> None:
if token.usage_metadata.get("output_tokens"):
chunks_with_output_token_counts += 1
if chunks_with_input_token_counts != 1 or chunks_with_output_token_counts != 1:
raise AssertionError(
msg = (
"Expected exactly one chunk with input or output token counts. "
"AIMessageChunk aggregation adds counts. Check that "
"this is behaving properly."
)
raise AssertionError(
msg,
)
# check token usage is populated
assert isinstance(full, AIMessageChunk)
assert full.usage_metadata is not None
@@ -167,7 +169,8 @@ async def test_abatch_tags() -> None:
llm = ChatAnthropicMessages(model_name=MODEL_NAME) # type: ignore[call-arg, call-arg]
result = await llm.abatch(
["I'm Pickle Rick", "I'm not Pickle Rick"], config={"tags": ["foo"]}
["I'm Pickle Rick", "I'm not Pickle Rick"],
config={"tags": ["foo"]},
)
for token in result:
assert isinstance(token.content, str)
@@ -187,8 +190,8 @@ async def test_async_tool_use() -> None:
"type": "object",
"properties": {"location": {"type": "string"}},
},
}
]
},
],
)
response = await llm_with_tools.ainvoke("what's the weather in san francisco, ca")
assert isinstance(response, AIMessage)
@@ -202,16 +205,16 @@ async def test_async_tool_use() -> None:
# Test streaming
first = True
chunks = [] # type: ignore
chunks: list[BaseMessage | BaseMessageChunk] = []
async for chunk in llm_with_tools.astream(
"what's the weather in san francisco, ca"
"what's the weather in san francisco, ca",
):
chunks = chunks + [chunk]
chunks = [*chunks, chunk]
if first:
gathered = chunk
first = False
else:
gathered = gathered + chunk # type: ignore
gathered = gathered + chunk # type: ignore[assignment]
assert len(chunks) > 1
assert isinstance(gathered, AIMessageChunk)
assert isinstance(gathered.tool_call_chunks, list)
@@ -244,12 +247,12 @@ def test_invoke() -> None:
"""Test invoke tokens from ChatAnthropicMessages."""
llm = ChatAnthropicMessages(model_name=MODEL_NAME) # type: ignore[call-arg, call-arg]
result = llm.invoke("I'm Pickle Rick", config=dict(tags=["foo"]))
result = llm.invoke("I'm Pickle Rick", config={"tags": ["foo"]})
assert isinstance(result.content, str)
def test_system_invoke() -> None:
"""Test invoke tokens with a system message"""
"""Test invoke tokens with a system message."""
llm = ChatAnthropicMessages(model_name=MODEL_NAME) # type: ignore[call-arg, call-arg]
prompt = ChatPromptTemplate.from_messages(
@@ -260,7 +263,7 @@ def test_system_invoke() -> None:
"STAY IN CHARACTER",
),
("human", "Are you a mathematician?"),
]
],
)
chain = prompt | llm
@@ -282,7 +285,7 @@ def test_anthropic_generate() -> None:
"""Test generate method of anthropic."""
chat = ChatAnthropic(model=MODEL_NAME)
chat_messages: list[list[BaseMessage]] = [
[HumanMessage(content="How many toes do dogs have?")]
[HumanMessage(content="How many toes do dogs have?")],
]
messages_copy = [messages.copy() for messages in chat_messages]
result: LLMResult = chat.generate(chat_messages)
@@ -330,7 +333,7 @@ async def test_anthropic_async_streaming_callback() -> None:
verbose=True,
)
chat_messages: list[BaseMessage] = [
HumanMessage(content="How many toes do dogs have?")
HumanMessage(content="How many toes do dogs have?"),
]
async for token in chat.astream(chat_messages):
assert isinstance(token, AIMessageChunk)
@@ -352,8 +355,8 @@ def test_anthropic_multimodal() -> None:
},
},
{"type": "text", "text": "What is this a logo for?"},
]
)
],
),
]
response = chat.invoke(messages)
assert isinstance(response, AIMessage)
@@ -368,7 +371,9 @@ def test_streaming() -> None:
callback_manager = CallbackManager([callback_handler])
llm = ChatAnthropicMessages( # type: ignore[call-arg, call-arg]
model_name=MODEL_NAME, streaming=True, callback_manager=callback_manager
model_name=MODEL_NAME,
streaming=True,
callback_manager=callback_manager,
)
response = llm.generate([[HumanMessage(content="I'm Pickle Rick")]])
@@ -382,7 +387,9 @@ async def test_astreaming() -> None:
callback_manager = CallbackManager([callback_handler])
llm = ChatAnthropicMessages( # type: ignore[call-arg, call-arg]
model_name=MODEL_NAME, streaming=True, callback_manager=callback_manager
model_name=MODEL_NAME,
streaming=True,
callback_manager=callback_manager,
)
response = await llm.agenerate([[HumanMessage(content="I'm Pickle Rick")]])
@@ -421,19 +428,19 @@ def test_tool_use() -> None:
temperature=0,
# Add extra headers to also test token-efficient tools
model_kwargs={
"extra_headers": {"anthropic-beta": "token-efficient-tools-2025-02-19"}
"extra_headers": {"anthropic-beta": "token-efficient-tools-2025-02-19"},
},
)
llm_with_tools = llm.bind_tools([tool_definition])
first = True
chunks = [] # type: ignore
chunks: list[BaseMessage | BaseMessageChunk] = []
for chunk in llm_with_tools.stream(query):
chunks = chunks + [chunk]
chunks = [*chunks, chunk]
if first:
gathered = chunk
first = False
else:
gathered = gathered + chunk # type: ignore
gathered = gathered + chunk # type: ignore[assignment]
assert len(chunks) > 1
assert isinstance(gathered.content, list)
assert len(gathered.content) == 2
@@ -470,17 +477,17 @@ def test_tool_use() -> None:
query,
gathered,
ToolMessage(content="sunny and warm", tool_call_id=tool_call["id"]),
]
],
)
chunks = [] # type: ignore
chunks = []
first = True
for chunk in stream:
chunks = chunks + [chunk]
chunks = [*chunks, chunk]
if first:
gathered = chunk
first = False
else:
gathered = gathered + chunk # type: ignore
gathered = gathered + chunk # type: ignore[assignment]
assert len(chunks) > 1
@@ -489,14 +496,14 @@ def test_builtin_tools() -> None:
tool = {"type": "text_editor_20250124", "name": "str_replace_editor"}
llm_with_tools = llm.bind_tools([tool])
response = llm_with_tools.invoke(
"There's a syntax error in my primes.py file. Can you help me fix it?"
"There's a syntax error in my primes.py file. Can you help me fix it?",
)
assert isinstance(response, AIMessage)
assert response.tool_calls
class GenerateUsername(BaseModel):
"Get a username based on someone's name and hair color."
"""Get a username based on someone's name and hair color."""
name: str
hair_color: str
@@ -508,7 +515,7 @@ def test_disable_parallel_tool_calling() -> None:
result = llm_with_tools.invoke(
"Use the GenerateUsername tool to generate user names for:\n\n"
"Sally with green hair\n"
"Bob with blue hair"
"Bob with blue hair",
)
assert isinstance(result, AIMessage)
assert len(result.tool_calls) == 1
@@ -523,7 +530,7 @@ def test_anthropic_with_empty_text_block() -> None:
return "OK"
model = ChatAnthropic(model="claude-3-opus-20240229", temperature=0).bind_tools(
[type_letter]
[type_letter],
)
messages = [
@@ -531,7 +538,7 @@ def test_anthropic_with_empty_text_block() -> None:
content="Repeat the given string using the provided tools. Do not write "
"anything else or provide any explanations. For example, "
"if the string is 'abc', you must print the "
"letters 'a', 'b', and 'c' one at a time and in that order. "
"letters 'a', 'b', and 'c' one at a time and in that order. ",
),
HumanMessage(content="dog"),
AIMessage(
@@ -572,7 +579,7 @@ def test_with_structured_output() -> None:
"type": "object",
"properties": {"location": {"type": "string"}},
},
}
},
)
response = structured_llm.invoke("what's the weather in san francisco, ca")
assert isinstance(response, dict)
@@ -593,10 +600,11 @@ def test_get_num_tokens_from_messages() -> None:
# Test tool use
@tool(parse_docstring=True)
def get_weather(location: str) -> str:
"""Get the current weather in a given location
"""Get the current weather in a given location.
Args:
location: The city and state, e.g. San Francisco, CA
"""
return "Sunny"
@@ -634,7 +642,7 @@ def test_get_num_tokens_from_messages() -> None:
class GetWeather(BaseModel):
"""Get the current weather in a given location"""
"""Get the current weather in a given location."""
location: str = Field(..., description="The city and state, e.g. San Francisco, CA")
@@ -666,9 +674,9 @@ def test_pdf_document_input() -> None:
"media_type": "application/pdf",
},
},
]
)
]
],
),
],
)
assert isinstance(result, AIMessage)
assert isinstance(result.content, str)
@@ -694,7 +702,7 @@ def test_citations() -> None:
},
{"type": "text", "text": "What color is the grass and sky?"},
],
}
},
]
response = llm.invoke(messages)
assert isinstance(response, AIMessage)
@@ -704,10 +712,7 @@ def test_citations() -> None:
# Test streaming
full: Optional[BaseMessageChunk] = None
for chunk in llm.stream(messages):
if full is None:
full = cast(BaseMessageChunk, chunk)
else:
full = full + chunk
full = cast(BaseMessageChunk, chunk) if full is None else full + chunk
assert isinstance(full, AIMessageChunk)
assert isinstance(full.content, list)
assert any("citations" in block for block in full.content)
@@ -718,7 +723,7 @@ def test_citations() -> None:
"role": "user",
"content": "Can you comment on the citations you just made?",
}
_ = llm.invoke(messages + [full, next_message])
_ = llm.invoke([*messages, full, next_message])
@pytest.mark.vcr
@@ -742,10 +747,7 @@ def test_thinking() -> None:
# Test streaming
full: Optional[BaseMessageChunk] = None
for chunk in llm.stream([input_message]):
if full is None:
full = cast(BaseMessageChunk, chunk)
else:
full = full + chunk
full = cast(BaseMessageChunk, chunk) if full is None else full + chunk
assert isinstance(full, AIMessageChunk)
assert isinstance(full.content, list)
assert any("thinking" in block for block in full.content)
@@ -784,10 +786,7 @@ def test_redacted_thinking() -> None:
# Test streaming
full: Optional[BaseMessageChunk] = None
for chunk in llm.stream([input_message]):
if full is None:
full = cast(BaseMessageChunk, chunk)
else:
full = full + chunk
full = cast(BaseMessageChunk, chunk) if full is None else full + chunk
assert isinstance(full, AIMessageChunk)
assert isinstance(full.content, list)
stream_has_reasoning = False
@@ -864,7 +863,7 @@ def test_image_tool_calling() -> None:
"media_type": "image/jpeg",
"data": image_data,
},
}
},
)
messages = [
SystemMessage("you're a good assistant"),
@@ -878,7 +877,7 @@ def test_image_tool_calling() -> None:
"id": "foo",
"name": "color_picker",
},
]
],
),
HumanMessage(
[
@@ -889,12 +888,12 @@ def test_image_tool_calling() -> None:
{
"type": "text",
"text": "green is a great pick! that's my sister's favorite color", # noqa: E501
}
},
],
"is_error": False,
},
{"type": "text", "text": "what's my sister's favorite color"},
]
],
),
]
llm = ChatAnthropic(model="claude-3-5-sonnet-latest")
@@ -914,7 +913,7 @@ def test_web_search() -> None:
{
"type": "text",
"text": "How do I update a web app to TypeScript 5.5?",
}
},
],
}
response = llm_with_tools.invoke([input_message])
@@ -962,7 +961,7 @@ def test_code_execution() -> None:
"Calculate the mean and standard deviation of "
"[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]"
),
}
},
],
}
response = llm_with_tools.invoke([input_message])
@@ -999,7 +998,7 @@ def test_remote_mcp() -> None:
"name": "deepwiki",
"tool_configuration": {"enabled": True, "allowed_tools": ["ask_question"]},
"authorization_token": "PLACEHOLDER",
}
},
]
llm = ChatAnthropic(
@@ -1018,7 +1017,7 @@ def test_remote_mcp() -> None:
"What transport protocols does the 2025-03-26 version of the MCP "
"spec (modelcontextprotocol/modelcontextprotocol) support?"
),
}
},
],
}
response = llm.invoke([input_message])
@@ -1132,9 +1131,9 @@ def test_search_result_tool_message() -> None:
"To request vacation days, submit a leave request form "
"through the HR portal. Approval will be sent by email."
),
}
},
],
}
},
]
tool_call = {
@@ -1182,7 +1181,7 @@ def test_search_result_top_level() -> None:
"To request vacation days, submit a leave request form "
"through the HR portal. Approval will be sent by email."
),
}
},
],
},
{
@@ -1194,14 +1193,14 @@ def test_search_result_top_level() -> None:
{
"type": "text",
"text": "Managers have 3 days to approve a request.",
}
},
],
},
{
"type": "text",
"text": "How do I request vacation days?",
},
]
],
)
result = llm.invoke([input_message])
assert isinstance(result, AIMessage)