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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
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Mason Daugherty 2025-07-07 18:32:27 -04:00 committed by GitHub
parent e7eac27241
commit 2a7645300c
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18 changed files with 430 additions and 311 deletions
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14
libs/langchain/pyproject.toml
View File

@ -180,13 +180,13 @@ select = [
"YTT", # flake8-2020
]
ignore = [
"D100",
"D101",
"D102",
"D103",
"D104",
"D105",
"D107",
"D100", # Missing docstring in public module
"D101", # Missing docstring in public class
"D102", # Missing docstring in public method
"D103", # Missing docstring in public function
"D104", # Missing docstring in public package
"D105", # Missing docstring in magic method
"D107", # Missing docstring in __init__
"COM812", # Messes with the formatter
"ISC001", # Messes with the formatter
"PERF203", # Rarely useful

6
libs/partners/anthropic/langchain_anthropic/__init__.py
View File

@ -6,9 +6,9 @@ from langchain_anthropic.chat_models import (
from langchain_anthropic.llms import Anthropic, AnthropicLLM
__all__ = [
"ChatAnthropicMessages",
"ChatAnthropic",
"convert_to_anthropic_tool",
"Anthropic",
"AnthropicLLM",
"ChatAnthropic",
"ChatAnthropicMessages",
"convert_to_anthropic_tool",
]

6
libs/partners/anthropic/langchain_anthropic/_client_utils.py
View File

@ -6,6 +6,8 @@ for each instance of ChatAnthropic.
Logic is largely replicated from anthropic._base_client.
"""
from __future__ import annotations
import asyncio
import os
from functools import lru_cache
@ -17,7 +19,7 @@ _NOT_GIVEN: Any = object()
class _SyncHttpxClientWrapper(anthropic.DefaultHttpxClient):
"""Borrowed from anthropic._base_client"""
"""Borrowed from anthropic._base_client."""
def __del__(self) -> None:
if self.is_closed:
@ -30,7 +32,7 @@ class _SyncHttpxClientWrapper(anthropic.DefaultHttpxClient):
class _AsyncHttpxClientWrapper(anthropic.DefaultAsyncHttpxClient):
"""Borrowed from anthropic._base_client"""
"""Borrowed from anthropic._base_client."""
def __del__(self) -> None:
if self.is_closed:

202
libs/partners/anthropic/langchain_anthropic/chat_models.py
View File

@ -1,3 +1,5 @@
from __future__ import annotations
import copy
import json
import re
@ -111,18 +113,19 @@ def _is_builtin_tool(tool: Any) -> bool:
def _format_image(url: str) -> dict:
"""
Converts part["image_url"]["url"] strings (OpenAI format)
to the correct Anthropic format:
"""Convert part["image_url"]["url"] strings (OpenAI format) to Anthropic format.
{
"type": "base64",
"media_type": "image/jpeg",
"data": "/9j/4AAQSkZJRg...",
"type": "base64",
"media_type": "image/jpeg",
"data": "/9j/4AAQSkZJRg...",
}
Or
{
"type": "url",
"url": "https://example.com/image.jpg",
"type": "url",
"url": "https://example.com/image.jpg",
}
"""
# Base64 encoded image
@ -146,11 +149,14 @@ def _format_image(url: str) -> dict:
"url": url,
}
raise ValueError(
msg = (
"Malformed url parameter."
" Must be either an image URL (https://example.com/image.jpg)"
" or base64 encoded string (data:image/png;base64,'/9j/4AAQSk'...)"
)
raise ValueError(
msg,
)
def _merge_messages(
@ -177,8 +183,8 @@ def _merge_messages(
"content": curr.content,
"tool_use_id": curr.tool_call_id,
"is_error": curr.status == "error",
}
]
},
],
)
last = merged[-1] if merged else None
if any(
@ -187,7 +193,7 @@ def _merge_messages(
):
if isinstance(cast(BaseMessage, last).content, str):
new_content: list = [
{"type": "text", "text": cast(BaseMessage, last).content}
{"type": "text", "text": cast(BaseMessage, last).content},
]
else:
new_content = copy.copy(cast(list, cast(BaseMessage, last).content))
@ -234,10 +240,13 @@ def _format_data_content_block(block: dict) -> dict:
},
}
else:
raise ValueError(
msg = (
"Anthropic only supports 'url' and 'base64' source_type for image "
"content blocks."
)
raise ValueError(
msg,
)
elif block["type"] == "file":
if block["source_type"] == "url":
@ -276,7 +285,8 @@ def _format_data_content_block(block: dict) -> dict:
}
else:
raise ValueError(f"Block of type {block['type']} is not supported.")
msg = f"Block of type {block['type']} is not supported."
raise ValueError(msg)
if formatted_block:
for key in ["cache_control", "citations", "title", "context"]:
@ -292,7 +302,6 @@ def _format_messages(
messages: Sequence[BaseMessage],
) -> tuple[Union[str, list[dict], None], list[dict]]:
"""Format messages for anthropic."""
"""
[
{
@ -308,8 +317,9 @@ def _format_messages(
for i, message in enumerate(merged_messages):
if message.type == "system":
if system is not None:
raise ValueError("Received multiple non-consecutive system messages.")
elif isinstance(message.content, list):
msg = "Received multiple non-consecutive system messages."
raise ValueError(msg)
if isinstance(message.content, list):
system = [
(
block
@ -328,8 +338,9 @@ def _format_messages(
if not isinstance(message.content, str):
# parse as dict
if not isinstance(message.content, list):
msg = "Anthropic message content must be str or list of dicts"
raise ValueError(
"Anthropic message content must be str or list of dicts"
msg,
)
# populate content
@ -339,8 +350,9 @@ def _format_messages(
content.append({"type": "text", "text": block})
elif isinstance(block, dict):
if "type" not in block:
raise ValueError("Dict content block must have a type key")
elif block["type"] == "image_url":
msg = "Dict content block must have a type key"
raise ValueError(msg)
if block["type"] == "image_url":
# convert format
source = _format_image(block["image_url"]["url"])
content.append({"type": "image", "source": source})
@ -358,7 +370,9 @@ def _format_messages(
if tc["id"] == block["id"]
]
content.extend(
_lc_tool_calls_to_anthropic_tool_use_blocks(overlapping)
_lc_tool_calls_to_anthropic_tool_use_blocks(
overlapping,
),
)
else:
block.pop("text", None)
@ -398,7 +412,7 @@ def _format_messages(
for k, v in block.items()
if k
in ("type", "text", "cache_control", "citations")
}
},
)
elif block["type"] == "thinking":
content.append(
@ -407,7 +421,7 @@ def _format_messages(
for k, v in block.items()
if k
in ("type", "thinking", "cache_control", "signature")
}
},
)
elif block["type"] == "redacted_thinking":
content.append(
@ -415,13 +429,13 @@ def _format_messages(
k: v
for k, v in block.items()
if k in ("type", "cache_control", "data")
}
},
)
elif block["type"] == "tool_result":
tool_content = _format_messages(
[HumanMessage(block["content"])]
[HumanMessage(block["content"])],
)[1][0]["content"]
content.append({**block, **{"content": tool_content}})
content.append({**block, "content": tool_content})
elif block["type"] in (
"code_execution_tool_result",
"mcp_tool_result",
@ -439,15 +453,18 @@ def _format_messages(
"is_error", # for mcp_tool_result
"cache_control",
)
}
},
)
else:
content.append(block)
else:
raise ValueError(
msg = (
f"Content blocks must be str or dict, instead was: "
f"{type(block)}"
)
raise ValueError(
msg,
)
else:
content = message.content
@ -468,7 +485,7 @@ def _format_messages(
tc for tc in message.tool_calls if tc["id"] not in tool_use_ids
]
cast(list, content).extend(
_lc_tool_calls_to_anthropic_tool_use_blocks(missing_tool_calls)
_lc_tool_calls_to_anthropic_tool_use_blocks(missing_tool_calls),
)
formatted_messages.append({"role": role, "content": content})
@ -481,8 +498,7 @@ def _handle_anthropic_bad_request(e: anthropic.BadRequestError) -> None:
message = "Received only system message(s). "
warnings.warn(message)
raise e
else:
raise
raise
class ChatAnthropic(BaseChatModel):
@ -635,17 +651,17 @@ class ChatAnthropic(BaseChatModel):
.. code-block:: python
[{'name': 'GetWeather',
'args': {'location': 'Los Angeles, CA'},
'id': 'toolu_01KzpPEAgzura7hpBqwHbWdo'},
'args': {'location': 'Los Angeles, CA'},
'id': 'toolu_01KzpPEAgzura7hpBqwHbWdo'},
{'name': 'GetWeather',
'args': {'location': 'New York, NY'},
'id': 'toolu_01JtgbVGVJbiSwtZk3Uycezx'},
'args': {'location': 'New York, NY'},
'id': 'toolu_01JtgbVGVJbiSwtZk3Uycezx'},
{'name': 'GetPopulation',
'args': {'location': 'Los Angeles, CA'},
'id': 'toolu_01429aygngesudV9nTbCKGuw'},
'args': {'location': 'Los Angeles, CA'},
'id': 'toolu_01429aygngesudV9nTbCKGuw'},
{'name': 'GetPopulation',
'args': {'location': 'New York, NY'},
'id': 'toolu_01JPktyd44tVMeBcPPnFSEJG'}]
'args': {'location': 'New York, NY'},
'id': 'toolu_01JPktyd44tVMeBcPPnFSEJG'}]
See ``ChatAnthropic.bind_tools()`` method for more.
@ -673,7 +689,7 @@ class ChatAnthropic(BaseChatModel):
See ``ChatAnthropic.with_structured_output()`` for more.
Image input:
See `multimodal guides <https://python.langchain.com/docs/how_to/multimodal_inputs/>`_
See `multimodal guides <https://python.langchain.com/docs/how_to/multimodal_inputs/>`__
for more detail.
.. code-block:: python
@ -717,7 +733,7 @@ class ChatAnthropic(BaseChatModel):
.. dropdown:: Files API
You can also pass in files that are managed through Anthropic's
`Files API <https://docs.anthropic.com/en/docs/build-with-claude/files>`_:
`Files API <https://docs.anthropic.com/en/docs/build-with-claude/files>`__:
.. code-block:: python
@ -744,7 +760,7 @@ class ChatAnthropic(BaseChatModel):
llm.invoke([input_message])
PDF input:
See `multimodal guides <https://python.langchain.com/docs/how_to/multimodal_inputs/>`_
See `multimodal guides <https://python.langchain.com/docs/how_to/multimodal_inputs/>`__
for more detail.
.. code-block:: python
@ -782,7 +798,7 @@ class ChatAnthropic(BaseChatModel):
.. dropdown:: Files API
You can also pass in files that are managed through Anthropic's
`Files API <https://docs.anthropic.com/en/docs/build-with-claude/files>`_:
`Files API <https://docs.anthropic.com/en/docs/build-with-claude/files>`__:
.. code-block:: python
@ -810,7 +826,7 @@ class ChatAnthropic(BaseChatModel):
Extended thinking:
Claude 3.7 Sonnet supports an
`extended thinking <https://docs.anthropic.com/en/docs/build-with-claude/extended-thinking>`_
`extended thinking <https://docs.anthropic.com/en/docs/build-with-claude/extended-thinking>`__
feature, which will output the step-by-step reasoning process that led to its
final answer.
@ -838,10 +854,10 @@ class ChatAnthropic(BaseChatModel):
Citations:
Anthropic supports a
`citations <https://docs.anthropic.com/en/docs/build-with-claude/citations>`_
`citations <https://docs.anthropic.com/en/docs/build-with-claude/citations>`__
feature that lets Claude attach context to its answers based on source
documents supplied by the user. When
`document content blocks <https://docs.anthropic.com/en/docs/build-with-claude/citations#document-types>`_
`document content blocks <https://docs.anthropic.com/en/docs/build-with-claude/citations#document-types>`__
with ``"citations": {"enabled": True}`` are included in a query, Claude may
generate citations in its response.
@ -924,7 +940,7 @@ class ChatAnthropic(BaseChatModel):
or by setting ``stream_usage=False`` when initializing ChatAnthropic.
Prompt caching:
See LangChain `docs <https://python.langchain.com/docs/integrations/chat/anthropic/#built-in-tools>`_
See LangChain `docs <https://python.langchain.com/docs/integrations/chat/anthropic/#built-in-tools>`__
for more detail.
.. code-block:: python
@ -1000,11 +1016,11 @@ class ChatAnthropic(BaseChatModel):
}
}
See `Claude documentation <https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching#1-hour-cache-duration-beta>`_
See `Claude documentation <https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching#1-hour-cache-duration-beta>`__
for detail.
Token-efficient tool use (beta):
See LangChain `docs <https://python.langchain.com/docs/integrations/chat/anthropic/>`_
See LangChain `docs <https://python.langchain.com/docs/integrations/chat/anthropic/>`__
for more detail.
.. code-block:: python
@ -1041,7 +1057,7 @@ class ChatAnthropic(BaseChatModel):
Total tokens: 408
Built-in tools:
See LangChain `docs <https://python.langchain.com/docs/integrations/chat/anthropic/>`_
See LangChain `docs <https://python.langchain.com/docs/integrations/chat/anthropic/>`__
for more detail.
.. dropdown:: Web search
@ -1266,7 +1282,9 @@ class ChatAnthropic(BaseChatModel):
}
def _get_ls_params(
self, stop: Optional[list[str]] = None, **kwargs: Any
self,
stop: Optional[list[str]] = None,
**kwargs: Any,
) -> LangSmithParams:
"""Get standard params for tracing."""
params = self._get_invocation_params(stop=stop, **kwargs)
@ -1286,8 +1304,7 @@ class ChatAnthropic(BaseChatModel):
@classmethod
def build_extra(cls, values: dict) -> Any:
all_required_field_names = get_pydantic_field_names(cls)
values = _build_model_kwargs(values, all_required_field_names)
return values
return _build_model_kwargs(values, all_required_field_names)
@cached_property
def _client_params(self) -> dict[str, Any]:
@ -1361,14 +1378,12 @@ class ChatAnthropic(BaseChatModel):
def _create(self, payload: dict) -> Any:
if "betas" in payload:
return self._client.beta.messages.create(**payload)
else:
return self._client.messages.create(**payload)
return self._client.messages.create(**payload)
async def _acreate(self, payload: dict) -> Any:
if "betas" in payload:
return await self._async_client.beta.messages.create(**payload)
else:
return await self._async_client.messages.create(**payload)
return await self._async_client.messages.create(**payload)
def _stream(
self,
@ -1496,7 +1511,10 @@ class ChatAnthropic(BaseChatModel):
) -> ChatResult:
if self.streaming:
stream_iter = self._stream(
messages, stop=stop, run_manager=run_manager, **kwargs
messages,
stop=stop,
run_manager=run_manager,
**kwargs,
)
return generate_from_stream(stream_iter)
payload = self._get_request_payload(messages, stop=stop, **kwargs)
@ -1515,7 +1533,10 @@ class ChatAnthropic(BaseChatModel):
) -> ChatResult:
if self.streaming:
stream_iter = self._astream(
messages, stop=stop, run_manager=run_manager, **kwargs
messages,
stop=stop,
run_manager=run_manager,
**kwargs,
)
return await agenerate_from_stream(stream_iter)
payload = self._get_request_payload(messages, stop=stop, **kwargs)
@ -1558,7 +1579,7 @@ class ChatAnthropic(BaseChatModel):
tools: Sequence[Union[dict[str, Any], type, Callable, BaseTool]],
*,
tool_choice: Optional[
Union[dict[str, str], Literal["any", "auto"], str]
Union[dict[str, str], Literal["any", "auto"], str] # noqa: PYI051
] = None,
parallel_tool_calls: Optional[bool] = None,
**kwargs: Any,
@ -1716,10 +1737,13 @@ class ChatAnthropic(BaseChatModel):
elif isinstance(tool_choice, str):
kwargs["tool_choice"] = {"type": "tool", "name": tool_choice}
else:
raise ValueError(
msg = (
f"Unrecognized 'tool_choice' type {tool_choice=}. Expected dict, "
f"str, or None."
)
raise ValueError(
msg,
)
if parallel_tool_calls is not None:
disable_parallel_tool_use = not parallel_tool_calls
@ -1861,7 +1885,8 @@ class ChatAnthropic(BaseChatModel):
tool_name = formatted_tool["name"]
if self.thinking is not None and self.thinking.get("type") == "enabled":
llm = self._get_llm_for_structured_output_when_thinking_is_enabled(
schema, formatted_tool
schema,
formatted_tool,
)
else:
llm = self.bind_tools(
@ -1875,24 +1900,27 @@ class ChatAnthropic(BaseChatModel):
if isinstance(schema, type) and is_basemodel_subclass(schema):
output_parser: OutputParserLike = PydanticToolsParser(
tools=[schema], first_tool_only=True
tools=[schema],
first_tool_only=True,
)
else:
output_parser = JsonOutputKeyToolsParser(
key_name=tool_name, first_tool_only=True
key_name=tool_name,
first_tool_only=True,
)
if include_raw:
parser_assign = RunnablePassthrough.assign(
parsed=itemgetter("raw") | output_parser, parsing_error=lambda _: None
parsed=itemgetter("raw") | output_parser,
parsing_error=lambda _: None,
)
parser_none = RunnablePassthrough.assign(parsed=lambda _: None)
parser_with_fallback = parser_assign.with_fallbacks(
[parser_none], exception_key="parsing_error"
[parser_none],
exception_key="parsing_error",
)
return RunnableMap(raw=llm) | parser_with_fallback
else:
return llm | output_parser
return llm | output_parser
@beta()
def get_num_tokens_from_messages(
@ -1909,6 +1937,8 @@ class ChatAnthropic(BaseChatModel):
messages: The message inputs to tokenize.
tools: If provided, sequence of dict, BaseModel, function, or BaseTools
to be converted to tool schemas.
kwargs: Additional keyword arguments are passed to the
:meth:`~langchain_anthropic.chat_models.ChatAnthropic.bind` method.
Basic usage:
@ -1985,7 +2015,7 @@ def convert_to_anthropic_tool(
if isinstance(tool, dict) and all(
k in tool for k in ("name", "description", "input_schema")
):
anthropic_formatted = AnthropicTool(tool) # type: ignore
anthropic_formatted = AnthropicTool(tool) # type: ignore[misc]
else:
oai_formatted = convert_to_openai_tool(tool)["function"]
anthropic_formatted = AnthropicTool(
@ -2032,17 +2062,15 @@ class _AnthropicToolUse(TypedDict):
def _lc_tool_calls_to_anthropic_tool_use_blocks(
tool_calls: list[ToolCall],
) -> list[_AnthropicToolUse]:
blocks = []
for tool_call in tool_calls:
blocks.append(
_AnthropicToolUse(
type="tool_use",
name=tool_call["name"],
input=tool_call["args"],
id=cast(str, tool_call["id"]),
)
return [
_AnthropicToolUse(
type="tool_use",
name=tool_call["name"],
input=tool_call["args"],
id=cast(str, tool_call["id"]),
)
return blocks
for tool_call in tool_calls
]
def _make_message_chunk_from_anthropic_event(
@ -2107,7 +2135,7 @@ def _make_message_chunk_from_anthropic_event(
tool_call_chunks = []
message_chunk = AIMessageChunk(
content=[content_block],
tool_call_chunks=tool_call_chunks, # type: ignore
tool_call_chunks=tool_call_chunks,
)
block_start_event = event
elif event.type == "content_block_delta":
@ -2122,14 +2150,10 @@ def _make_message_chunk_from_anthropic_event(
if "citation" in content_block:
content_block["citations"] = [content_block.pop("citation")]
message_chunk = AIMessageChunk(content=[content_block])
elif event.delta.type == "thinking_delta":
content_block = event.delta.model_dump()
if "text" in content_block and content_block["text"] is None:
content_block.pop("text")
content_block["index"] = event.index
content_block["type"] = "thinking"
message_chunk = AIMessageChunk(content=[content_block])
elif event.delta.type == "signature_delta":
elif (
event.delta.type == "thinking_delta"
or event.delta.type == "signature_delta"
):
content_block = event.delta.model_dump()
if "text" in content_block and content_block["text"] is None:
content_block.pop("text")
@ -2155,7 +2179,7 @@ def _make_message_chunk_from_anthropic_event(
tool_call_chunks = []
message_chunk = AIMessageChunk(
content=[content_block],
tool_call_chunks=tool_call_chunks, # type: ignore
tool_call_chunks=tool_call_chunks,
)
elif event.type == "message_delta" and stream_usage:
usage_metadata = UsageMetadata(

36
libs/partners/anthropic/langchain_anthropic/experimental.py
View File

@ -1,3 +1,5 @@
from __future__ import annotations
import json
from typing import (
Any,
@ -66,7 +68,7 @@ def get_system_message(tools: list[dict]) -> str:
parameter_description=parameter.get("description"),
)
for name, parameter in tool["parameters"]["properties"].items()
]
],
),
}
for tool in tools
@ -79,7 +81,7 @@ def get_system_message(tools: list[dict]) -> str:
formatted_parameters=tool["formatted_parameters"],
)
for tool in tools_data
]
],
)
return SYSTEM_PROMPT_FORMAT.format(formatted_tools=tools_formatted)
@ -111,18 +113,20 @@ def _xml_to_function_call(invoke: Any, tools: list[dict]) -> dict[str, Any]:
if len(filtered_tools) > 0 and not isinstance(arguments, str):
tool = filtered_tools[0]
for key, value in arguments.items():
if key in tool["parameters"]["properties"]:
if "type" in tool["parameters"]["properties"][key]:
if tool["parameters"]["properties"][key][
"type"
] == "array" and not isinstance(value, list):
arguments[key] = [value]
if (
tool["parameters"]["properties"][key]["type"] != "object"
and isinstance(value, dict)
and len(value.keys()) == 1
):
arguments[key] = list(value.values())[0]
if (
key in tool["parameters"]["properties"]
and "type" in tool["parameters"]["properties"][key]
):
if tool["parameters"]["properties"][key][
"type"
] == "array" and not isinstance(value, list):
arguments[key] = [value]
if (
tool["parameters"]["properties"][key]["type"] != "object"
and isinstance(value, dict)
and len(value.keys()) == 1
):
arguments[key] = next(iter(value.values()))
return {
"function": {
@ -134,9 +138,7 @@ def _xml_to_function_call(invoke: Any, tools: list[dict]) -> dict[str, Any]:
def _xml_to_tool_calls(elem: Any, tools: list[dict]) -> list[dict[str, Any]]:
"""
Convert an XML element and its children into a dictionary of dictionaries.
"""
"""Convert an XML element and its children into a dictionary of dictionaries."""
invokes = elem.findall("invoke")
return [_xml_to_function_call(invoke, tools) for invoke in invokes]

54
libs/partners/anthropic/langchain_anthropic/llms.py
View File

@ -1,3 +1,5 @@
from __future__ import annotations
import re
import warnings
from collections.abc import AsyncIterator, Iterator, Mapping
@ -85,8 +87,7 @@ class _AnthropicCommon(BaseLanguageModel):
@classmethod
def build_extra(cls, values: dict) -> Any:
all_required_field_names = get_pydantic_field_names(cls)
values = _build_model_kwargs(values, all_required_field_names)
return values
return _build_model_kwargs(values, all_required_field_names)
@model_validator(mode="after")
def validate_environment(self) -> Self:
@ -125,11 +126,12 @@ class _AnthropicCommon(BaseLanguageModel):
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {**{}, **self._default_params}
return {**self._default_params}
def _get_anthropic_stop(self, stop: Optional[list[str]] = None) -> list[str]:
if not self.HUMAN_PROMPT or not self.AI_PROMPT:
raise NameError("Please ensure the anthropic package is loaded")
msg = "Please ensure the anthropic package is loaded"
raise NameError(msg)
if stop is None:
stop = []
@ -152,6 +154,7 @@ class AnthropicLLM(LLM, _AnthropicCommon):
from langchain_anthropic import AnthropicLLM
model = AnthropicLLM()
"""
model_config = ConfigDict(
@ -166,7 +169,7 @@ class AnthropicLLM(LLM, _AnthropicCommon):
warnings.warn(
"This Anthropic LLM is deprecated. "
"Please use `from langchain_anthropic import ChatAnthropic` "
"instead"
"instead",
)
return values
@ -199,7 +202,9 @@ class AnthropicLLM(LLM, _AnthropicCommon):
}
def _get_ls_params(
self, stop: Optional[list[str]] = None, **kwargs: Any
self,
stop: Optional[list[str]] = None,
**kwargs: Any,
) -> LangSmithParams:
"""Get standard params for tracing."""
params = super()._get_ls_params(stop=stop, **kwargs)
@ -213,7 +218,8 @@ class AnthropicLLM(LLM, _AnthropicCommon):
def _wrap_prompt(self, prompt: str) -> str:
if not self.HUMAN_PROMPT or not self.AI_PROMPT:
raise NameError("Please ensure the anthropic package is loaded")
msg = "Please ensure the anthropic package is loaded"
raise NameError(msg)
if prompt.startswith(self.HUMAN_PROMPT):
return prompt # Already wrapped.
@ -238,6 +244,8 @@ class AnthropicLLM(LLM, _AnthropicCommon):
Args:
prompt: The prompt to pass into the model.
stop: Optional list of stop words to use when generating.
run_manager: Optional callback manager for LLM run.
kwargs: Additional keyword arguments to pass to the model.
Returns:
The string generated by the model.
@ -253,7 +261,10 @@ class AnthropicLLM(LLM, _AnthropicCommon):
if self.streaming:
completion = ""
for chunk in self._stream(
prompt=prompt, stop=stop, run_manager=run_manager, **kwargs
prompt=prompt,
stop=stop,
run_manager=run_manager,
**kwargs,
):
completion += chunk.text
return completion
@ -281,7 +292,10 @@ class AnthropicLLM(LLM, _AnthropicCommon):
if self.streaming:
completion = ""
async for chunk in self._astream(
prompt=prompt, stop=stop, run_manager=run_manager, **kwargs
prompt=prompt,
stop=stop,
run_manager=run_manager,
**kwargs,
):
completion += chunk.text
return completion
@ -308,8 +322,12 @@ class AnthropicLLM(LLM, _AnthropicCommon):
Args:
prompt: The prompt to pass into the model.
stop: Optional list of stop words to use when generating.
run_manager: Optional callback manager for LLM run.
kwargs: Additional keyword arguments to pass to the model.
Returns:
A generator representing the stream of tokens from Anthropic.
Example:
.. code-block:: python
@ -319,12 +337,16 @@ class AnthropicLLM(LLM, _AnthropicCommon):
generator = anthropic.stream(prompt)
for token in generator:
yield token
"""
stop = self._get_anthropic_stop(stop)
params = {**self._default_params, **kwargs}
for token in self.client.completions.create(
prompt=self._wrap_prompt(prompt), stop_sequences=stop, stream=True, **params
prompt=self._wrap_prompt(prompt),
stop_sequences=stop,
stream=True,
**params,
):
chunk = GenerationChunk(text=token.completion)
@ -344,8 +366,12 @@ class AnthropicLLM(LLM, _AnthropicCommon):
Args:
prompt: The prompt to pass into the model.
stop: Optional list of stop words to use when generating.
run_manager: Optional callback manager for LLM run.
kwargs: Additional keyword arguments to pass to the model.
Returns:
A generator representing the stream of tokens from Anthropic.
Example:
.. code-block:: python
@ -354,6 +380,7 @@ class AnthropicLLM(LLM, _AnthropicCommon):
generator = anthropic.stream(prompt)
for token in generator:
yield token
"""
stop = self._get_anthropic_stop(stop)
params = {**self._default_params, **kwargs}
@ -372,15 +399,16 @@ class AnthropicLLM(LLM, _AnthropicCommon):
def get_num_tokens(self, text: str) -> int:
"""Calculate number of tokens."""
raise NotImplementedError(
msg = (
"Anthropic's legacy count_tokens method was removed in anthropic 0.39.0 "
"and langchain-anthropic 0.3.0. Please use "
"ChatAnthropic.get_num_tokens_from_messages instead."
)
raise NotImplementedError(
msg,
)
@deprecated(since="0.1.0", removal="1.0.0", alternative="AnthropicLLM")
class Anthropic(AnthropicLLM):
"""Anthropic large language model."""
pass

14
libs/partners/anthropic/langchain_anthropic/output_parsers.py
View File

@ -1,3 +1,5 @@
from __future__ import annotations
from typing import Any, Optional, Union, cast
from langchain_core.messages import AIMessage, ToolCall
@ -27,8 +29,12 @@ class ToolsOutputParser(BaseGenerationOutputParser):
Args:
result: A list of Generations to be parsed. The Generations are assumed
to be different candidate outputs for a single model input.
partial: (Not used) Whether the result is a partial result. If True, the
parser may return a partial result, which may not be complete or valid.
Returns:
Structured output.
"""
if not result or not isinstance(result[0], ChatGeneration):
return None if self.first_tool_only else []
@ -53,8 +59,7 @@ class ToolsOutputParser(BaseGenerationOutputParser):
if self.first_tool_only:
return tool_calls[0] if tool_calls else None
else:
return [tool_call for tool_call in tool_calls]
return list(tool_calls)
def _pydantic_parse(self, tool_call: dict) -> BaseModel:
cls_ = {schema.__name__: schema for schema in self.pydantic_schemas or []}[
@ -80,8 +85,7 @@ def extract_tool_calls(content: Union[str, list[Union[str, dict]]]) -> list[Tool
if block["type"] != "tool_use":
continue
tool_calls.append(
tool_call(name=block["name"], args=block["input"], id=block["id"])
tool_call(name=block["name"], args=block["input"], id=block["id"]),
)
return tool_calls
else:
return []
return []

55
libs/partners/anthropic/pyproject.toml
View File

@ -60,8 +60,58 @@ plugins = ['pydantic.mypy']
target-version = "py39"
[tool.ruff.lint]
select = ["E", "F", "I", "T201", "UP", "S"]
ignore = [ "UP007", ]
select = [
"A", # flake8-builtins
"ASYNC", # flake8-async
"C4", # flake8-comprehensions
"COM", # flake8-commas
"D", # pydocstyle
"DOC", # pydoclint
"E", # pycodestyle error
"EM", # flake8-errmsg
"F", # pyflakes
"FA", # flake8-future-annotations
"FBT", # flake8-boolean-trap
"FLY", # flake8-flynt
"I", # isort
"ICN", # flake8-import-conventions
"INT", # flake8-gettext
"ISC", # isort-comprehensions
"PGH", # pygrep-hooks
"PIE", # flake8-pie
"PERF", # flake8-perf
"PYI", # flake8-pyi
"Q", # flake8-quotes
"RET", # flake8-return
"RSE", # flake8-rst-docstrings
"RUF", # ruff
"S", # flake8-bandit
"SLF", # flake8-self
"SLOT", # flake8-slots
"SIM", # flake8-simplify
"T10", # flake8-debugger
"T20", # flake8-print
"TID", # flake8-tidy-imports
"UP", # pyupgrade
"W", # pycodestyle warning
"YTT", # flake8-2020
]
ignore = [
"D100", # Missing docstring in public module
"D101", # Missing docstring in public class
"D102", # Missing docstring in public method
"D103", # Missing docstring in public function
"D104", # Missing docstring in public package
"D105", # Missing docstring in magic method
"D107", # Missing docstring in __init__
"D214", # Section over-indented, doesn't play well with reStructuredText
"COM812", # Messes with the formatter
"ISC001", # Messes with the formatter
"PERF203", # Rarely useful
"UP007", # non-pep604-annotation-union
"UP045", # non-pep604-annotation-optional
"SIM105", # Rarely useful
]
[tool.coverage.run]
omit = ["tests/*"]
@ -78,4 +128,5 @@ asyncio_mode = "auto"
"tests/**/*.py" = [
"S101", # Tests need assertions
"S311", # Standard pseudo-random generators are not suitable for cryptographic purposes
"SLF001", # Private member access in tests
]

4
libs/partners/anthropic/tests/conftest.py
View File

@ -20,9 +20,7 @@ def remove_response_headers(response: dict) -> dict:
@pytest.fixture(scope="session")
def vcr_config(_base_vcr_config: dict) -> dict: # noqa: F811
"""
Extend the default configuration coming from langchain_tests.
"""
"""Extend the default configuration coming from langchain_tests."""
config = _base_vcr_config.copy()
config["before_record_request"] = remove_request_headers
config["before_record_response"] = remove_response_headers

143
libs/partners/anthropic/tests/integration_tests/test_chat_models.py
View File

@ -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)

1
libs/partners/anthropic/tests/integration_tests/test_compile.py
View File

@ -4,4 +4,3 @@ import pytest
@pytest.mark.compile
def test_placeholder() -> None:
"""Used for compiling integration tests without running any real tests."""
pass

32
libs/partners/anthropic/tests/integration_tests/test_experimental.py
View File

@ -1,5 +1,7 @@
"""Test ChatAnthropic chat model."""
from __future__ import annotations
from enum import Enum
from typing import Optional
@ -46,7 +48,8 @@ async def test_abatch_tags() -> None:
llm = ChatAnthropicTools(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)
@ -73,12 +76,12 @@ def test_invoke() -> None:
"""Test invoke tokens from ChatAnthropicTools."""
llm = ChatAnthropicTools(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 = ChatAnthropicTools(model_name=MODEL_NAME) # type: ignore[call-arg, call-arg]
prompt = ChatPromptTemplate.from_messages(
@ -89,7 +92,7 @@ def test_system_invoke() -> None:
"STAY IN CHARACTER",
),
("human", "Are you a mathematician?"),
]
],
)
chain = prompt | llm
@ -128,19 +131,24 @@ def test_anthropic_complex_structured_output() -> None:
"""Relevant information about an email."""
sender: Optional[str] = Field(
None, description="The sender's name, if available"
None,
description="The sender's name, if available",
)
sender_phone_number: Optional[str] = Field(
None, description="The sender's phone number, if available"
None,
description="The sender's phone number, if available",
)
sender_address: Optional[str] = Field(
None, description="The sender's address, if available"
None,
description="The sender's address, if available",
)
action_items: list[str] = Field(
..., description="A list of action items requested by the email"
...,
description="A list of action items requested by the email",
)
topic: str = Field(
..., description="High level description of what the email is about"
...,
description="High level description of what the email is about",
)
tone: ToneEnum = Field(..., description="The tone of the email.")
@ -150,7 +158,7 @@ def test_anthropic_complex_structured_output() -> None:
"human",
"What can you tell me about the following email? Make sure to answer in the correct format: {email}", # noqa: E501
),
]
],
)
llm = ChatAnthropicTools( # type: ignore[call-arg, call-arg]
@ -163,7 +171,7 @@ def test_anthropic_complex_structured_output() -> None:
response = extraction_chain.invoke(
{
"email": "From: Erick. The email is about the new project. The tone is positive. The action items are to send the report and to schedule a meeting." # noqa: E501
}
"email": "From: Erick. The email is about the new project. The tone is positive. The action items are to send the report and to schedule a meeting.", # noqa: E501
},
)
assert isinstance(response, Email)

24
libs/partners/anthropic/tests/integration_tests/test_standard.py
View File

@ -1,4 +1,4 @@
"""Standard LangChain interface tests"""
"""Standard LangChain interface tests."""
from pathlib import Path
from typing import Literal, cast
@ -87,9 +87,9 @@ class TestAnthropicStandard(ChatModelIntegrationTests):
"type": "text",
"text": input_,
"cache_control": {"type": "ephemeral"},
}
},
],
}
},
],
stream,
)
@ -118,9 +118,9 @@ class TestAnthropicStandard(ChatModelIntegrationTests):
"type": "text",
"text": input_,
"cache_control": {"type": "ephemeral"},
}
},
],
}
},
],
stream,
)
@ -134,22 +134,18 @@ class TestAnthropicStandard(ChatModelIntegrationTests):
"type": "text",
"text": input_,
"cache_control": {"type": "ephemeral"},
}
},
],
}
},
],
stream,
)
def _invoke(llm: ChatAnthropic, input_: list, stream: bool) -> AIMessage:
def _invoke(llm: ChatAnthropic, input_: list, stream: bool) -> AIMessage: # noqa: FBT001
if stream:
full = None
for chunk in llm.stream(input_):
if full is None:
full = cast(BaseMessageChunk, chunk)
else:
full = full + chunk
full = cast(BaseMessageChunk, chunk) if full is None else full + chunk
return cast(AIMessage, full)
else:
return cast(AIMessage, llm.invoke(input_))
return cast(AIMessage, llm.invoke(input_))

4
libs/partners/anthropic/tests/unit_tests/_utils.py
View File

@ -1,5 +1,7 @@
"""A fake callback handler for testing purposes."""
from __future__ import annotations
from typing import Any, Union
from langchain_core.callbacks import BaseCallbackHandler
@ -252,5 +254,5 @@ class FakeCallbackHandler(BaseCallbackHandler, BaseFakeCallbackHandlerMixin):
self.on_retriever_error_common()
# Overriding since BaseModel has __deepcopy__ method as well
def __deepcopy__(self, memo: dict) -> "FakeCallbackHandler": # type: ignore
def __deepcopy__(self, memo: dict) -> FakeCallbackHandler: # type: ignore[override]
return self

95
libs/partners/anthropic/tests/unit_tests/test_chat_models.py
View File

@ -1,5 +1,7 @@
"""Test chat model integration."""
from __future__ import annotations
import os
from typing import Any, Callable, Literal, Optional, cast
from unittest.mock import MagicMock, patch
@ -187,7 +189,7 @@ def test__merge_messages() -> None:
"text": None,
"name": "blah",
},
]
],
),
ToolMessage("buz output", tool_call_id="1", status="error"), # type: ignore[misc]
ToolMessage(
@ -234,7 +236,7 @@ def test__merge_messages() -> None:
"text": None,
"name": "blah",
},
]
],
),
HumanMessage( # type: ignore[misc]
[
@ -266,7 +268,7 @@ def test__merge_messages() -> None:
"is_error": False,
},
{"type": "text", "text": "next thing"},
]
],
),
]
actual = _merge_messages(messages)
@ -277,7 +279,7 @@ def test__merge_messages() -> None:
ToolMessage("buz output", tool_call_id="1"), # type: ignore[misc]
ToolMessage( # type: ignore[misc]
content=[
{"type": "tool_result", "content": "blah output", "tool_use_id": "2"}
{"type": "tool_result", "content": "blah output", "tool_use_id": "2"},
],
tool_call_id="2",
),
@ -292,8 +294,8 @@ def test__merge_messages() -> None:
"is_error": False,
},
{"type": "tool_result", "content": "blah output", "tool_use_id": "2"},
]
)
],
),
]
actual = _merge_messages(messages)
assert expected == actual
@ -310,7 +312,7 @@ def test__merge_messages_mutation() -> None:
]
expected = [
HumanMessage( # type: ignore[misc]
[{"type": "text", "text": "bar"}, {"type": "text", "text": "next thing"}]
[{"type": "text", "text": "bar"}, {"type": "text", "text": "next thing"}],
),
]
actual = _merge_messages(messages)
@ -327,7 +329,7 @@ def test__format_image() -> None:
@pytest.fixture()
def pydantic() -> type[BaseModel]:
class dummy_function(BaseModel):
"""dummy function"""
"""Dummy function."""
arg1: int = Field(..., description="foo")
arg2: Literal["bar", "baz"] = Field(..., description="one of 'bar', 'baz'")
@ -338,13 +340,14 @@ def pydantic() -> type[BaseModel]:
@pytest.fixture()
def function() -> Callable:
def dummy_function(arg1: int, arg2: Literal["bar", "baz"]) -> None:
"""dummy function
"""Dummy function.
Args:
----
arg1: foo
arg2: one of 'bar', 'baz'
"""
pass
""" # noqa: D401
return dummy_function
@ -358,7 +361,7 @@ def dummy_tool() -> BaseTool:
class DummyFunction(BaseTool): # type: ignore[override]
args_schema: type[BaseModel] = Schema
name: str = "dummy_function"
description: str = "dummy function"
description: str = "Dummy function."
def _run(self, *args: Any, **kwargs: Any) -> Any:
pass
@ -370,7 +373,7 @@ def dummy_tool() -> BaseTool:
def json_schema() -> dict:
return {
"title": "dummy_function",
"description": "dummy function",
"description": "Dummy function.",
"type": "object",
"properties": {
"arg1": {"description": "foo", "type": "integer"},
@ -388,7 +391,7 @@ def json_schema() -> dict:
def openai_function() -> dict:
return {
"name": "dummy_function",
"description": "dummy function",
"description": "Dummy function.",
"parameters": {
"type": "object",
"properties": {
@ -413,7 +416,7 @@ def test_convert_to_anthropic_tool(
) -> None:
expected = {
"name": "dummy_function",
"description": "dummy function",
"description": "Dummy function.",
"input_schema": {
"type": "object",
"properties": {
@ -429,7 +432,7 @@ def test_convert_to_anthropic_tool(
}
for fn in (pydantic, function, dummy_tool, json_schema, expected, openai_function):
actual = convert_to_anthropic_tool(fn) # type: ignore
actual = convert_to_anthropic_tool(fn)
assert actual == expected
@ -461,7 +464,7 @@ def test__format_messages_with_tool_calls() -> None:
"type": "base64",
"media_type": "image/jpeg",
},
}
},
],
tool_call_id="3",
)
@ -478,7 +481,7 @@ def test__format_messages_with_tool_calls() -> None:
"name": "bar",
"id": "1",
"input": {"baz": "buzz"},
}
},
],
},
{
@ -489,7 +492,7 @@ def test__format_messages_with_tool_calls() -> None:
"content": "blurb",
"tool_use_id": "1",
"is_error": False,
}
},
],
},
{
@ -500,7 +503,7 @@ def test__format_messages_with_tool_calls() -> None:
"name": "bar",
"id": "2",
"input": {"baz": "buzz"},
}
},
],
},
{
@ -516,7 +519,7 @@ def test__format_messages_with_tool_calls() -> None:
"type": "base64",
"media_type": "image/jpeg",
},
}
},
],
"tool_use_id": "2",
"is_error": False,
@ -531,7 +534,7 @@ def test__format_messages_with_tool_calls() -> None:
"type": "base64",
"media_type": "image/jpeg",
},
}
},
],
"tool_use_id": "3",
"is_error": False,
@ -579,7 +582,7 @@ def test__format_messages_with_str_content_and_tool_calls() -> None:
"content": "blurb",
"tool_use_id": "1",
"is_error": False,
}
},
],
},
],
@ -624,7 +627,7 @@ def test__format_messages_with_list_content_and_tool_calls() -> None:
"content": "blurb",
"tool_use_id": "1",
"is_error": False,
}
},
],
},
],
@ -676,7 +679,7 @@ def test__format_messages_with_tool_use_blocks_and_tool_calls() -> None:
"content": "blurb",
"tool_use_id": "1",
"is_error": False,
}
},
],
},
],
@ -690,7 +693,7 @@ def test__format_messages_with_cache_control() -> None:
SystemMessage(
[
{"type": "text", "text": "foo", "cache_control": {"type": "ephemeral"}},
]
],
),
HumanMessage(
[
@ -699,11 +702,11 @@ def test__format_messages_with_cache_control() -> None:
"type": "text",
"text": "foo",
},
]
],
),
]
expected_system = [
{"type": "text", "text": "foo", "cache_control": {"type": "ephemeral"}}
{"type": "text", "text": "foo", "cache_control": {"type": "ephemeral"}},
]
expected_messages = [
{
@ -712,7 +715,7 @@ def test__format_messages_with_cache_control() -> None:
{"type": "text", "text": "foo", "cache_control": {"type": "ephemeral"}},
{"type": "text", "text": "foo"},
],
}
},
]
actual_system, actual_messages = _format_messages(messages)
assert expected_system == actual_system
@ -733,8 +736,8 @@ def test__format_messages_with_cache_control() -> None:
"data": "<base64 data>",
"cache_control": {"type": "ephemeral"},
},
]
)
],
),
]
actual_system, actual_messages = _format_messages(messages)
assert actual_system is None
@ -756,7 +759,7 @@ def test__format_messages_with_cache_control() -> None:
"cache_control": {"type": "ephemeral"},
},
],
}
},
]
assert actual_messages == expected_messages
@ -773,8 +776,8 @@ def test__format_messages_with_citations() -> None:
"citations": {"enabled": True},
},
{"type": "text", "text": "What color is the grass and sky?"},
]
)
],
),
]
expected_messages = [
{
@ -791,7 +794,7 @@ def test__format_messages_with_citations() -> None:
},
{"type": "text", "text": "What color is the grass and sky?"},
],
}
},
]
actual_system, actual_messages = _format_messages(input_messages)
assert actual_system is None
@ -843,7 +846,7 @@ def test__format_messages_openai_image_format() -> None:
},
},
],
}
},
]
assert actual_messages == expected_messages
@ -856,7 +859,7 @@ def test__format_messages_with_multiple_system() -> None:
SystemMessage(
[
{"type": "text", "text": "foo", "cache_control": {"type": "ephemeral"}},
]
],
),
]
expected_system = [
@ -880,7 +883,8 @@ def test_anthropic_api_key_is_secret_string() -> None:
def test_anthropic_api_key_masked_when_passed_from_env(
monkeypatch: MonkeyPatch, capsys: CaptureFixture
monkeypatch: MonkeyPatch,
capsys: CaptureFixture,
) -> None:
"""Test that the API key is masked when passed from an environment variable."""
monkeypatch.setenv("ANTHROPIC_API_KEY ", "secret-api-key")
@ -920,7 +924,7 @@ def test_anthropic_uses_actual_secret_value_from_secretstr() -> 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")
@ -931,14 +935,16 @@ def test_anthropic_bind_tools_tool_choice() -> None:
anthropic_api_key="secret-api-key",
)
chat_model_with_tools = chat_model.bind_tools(
[GetWeather], tool_choice={"type": "tool", "name": "GetWeather"}
[GetWeather],
tool_choice={"type": "tool", "name": "GetWeather"},
)
assert cast(RunnableBinding, chat_model_with_tools).kwargs["tool_choice"] == {
"type": "tool",
"name": "GetWeather",
}
chat_model_with_tools = chat_model.bind_tools(
[GetWeather], tool_choice="GetWeather"
[GetWeather],
tool_choice="GetWeather",
)
assert cast(RunnableBinding, chat_model_with_tools).kwargs["tool_choice"] == {
"type": "tool",
@ -946,11 +952,11 @@ def test_anthropic_bind_tools_tool_choice() -> None:
}
chat_model_with_tools = chat_model.bind_tools([GetWeather], tool_choice="auto")
assert cast(RunnableBinding, chat_model_with_tools).kwargs["tool_choice"] == {
"type": "auto"
"type": "auto",
}
chat_model_with_tools = chat_model.bind_tools([GetWeather], tool_choice="any")
assert cast(RunnableBinding, chat_model_with_tools).kwargs["tool_choice"] == {
"type": "any"
"type": "any",
}
@ -1021,7 +1027,6 @@ class FakeTracer(BaseTracer):
def _persist_run(self, run: Run) -> None:
"""Persist a run."""
pass
def on_chat_model_start(self, *args: Any, **kwargs: Any) -> Run:
self.chat_model_start_inputs.append({"args": args, "kwargs": kwargs})
@ -1036,7 +1041,7 @@ def test_mcp_tracing() -> None:
"url": "https://mcp.deepwiki.com/mcp",
"name": "deepwiki",
"authorization_token": "PLACEHOLDER",
}
},
]
llm = ChatAnthropic(

5
libs/partners/anthropic/tests/unit_tests/test_output_parsers.py
View File

@ -95,7 +95,8 @@ def test_tools_output_parser_empty_content() -> None:
chart_type: Literal["pie", "line", "bar"]
output_parser = ToolsOutputParser(
first_tool_only=True, pydantic_schemas=[ChartType]
first_tool_only=True,
pydantic_schemas=[ChartType],
)
message = AIMessage(
"",
@ -105,7 +106,7 @@ def test_tools_output_parser_empty_content() -> None:
"args": {"chart_type": "pie"},
"id": "foo",
"type": "tool_call",
}
},
],
)
actual = output_parser.invoke(message)

2
libs/partners/anthropic/tests/unit_tests/test_standard.py
View File

@ -1,4 +1,4 @@
"""Standard LangChain interface tests"""
"""Standard LangChain interface tests."""
import pytest
from langchain_core.language_models import BaseChatModel

44
libs/partners/anthropic/uv.lock
View File

@ -477,7 +477,7 @@ requires-dist = [
[package.metadata.requires-dev]
codespell = [{ name = "codespell", specifier = ">=2.2.0,<3.0.0" }]
dev = [{ name = "langchain-core", editable = "../../core" }]
lint = [{ name = "ruff", specifier = ">=0.5,<1.0" }]
lint = [{ name = "ruff", specifier = ">=0.12.2,<0.13" }]
test = [
{ name = "defusedxml", specifier = ">=0.7.1,<1.0.0" },
{ name = "freezegun", specifier = ">=1.2.2,<2.0.0" },
@ -534,7 +534,7 @@ dev = [
{ name = "jupyter", specifier = ">=1.0.0,<2.0.0" },
{ name = "setuptools", specifier = ">=67.6.1,<68.0.0" },
]
lint = [{ name = "ruff", specifier = ">=0.11.2,<0.12.0" }]
lint = [{ name = "ruff", specifier = ">=0.12.2,<0.13" }]
test = [
{ name = "blockbuster", specifier = "~=1.5.18" },
{ name = "freezegun", specifier = ">=1.2.2,<2.0.0" },
@ -598,7 +598,7 @@ requires-dist = [
[package.metadata.requires-dev]
codespell = [{ name = "codespell", specifier = ">=2.2.0,<3.0.0" }]
lint = [{ name = "ruff", specifier = ">=0.9.2,<1.0.0" }]
lint = [{ name = "ruff", specifier = ">=0.12.2,<0.13" }]
test = [{ name = "langchain-core", editable = "../../core" }]
test-integration = []
typing = [
@ -1513,27 +1513,27 @@ wheels = [
[[package]]
name = "ruff"
version = "0.5.7"
version = "0.12.2"
source = { registry = "https://pypi.org/simple" }
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