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mistralai[patch]: ruff fixes and rules (#31918)

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* bump ruff deps
* add more thorough ruff rules
* fix said rules
This commit is contained in:
Mason Daugherty 2025-07-08 12:44:42 -04:00 committed by GitHub
parent ae210c1590
commit cbb418b4bf
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10 changed files with 214 additions and 143 deletions
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121
libs/partners/mistralai/langchain_mistralai/chat_models.py
View File

@ -94,8 +94,7 @@ def _create_retry_decorator(
Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun] Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun]
] = None, ] = None,
) -> Callable[[Any], Any]: ) -> Callable[[Any], Any]:
"""Returns a tenacity retry decorator, preconfigured to handle exceptions""" """Return a tenacity retry decorator, preconfigured to handle exceptions."""
errors = [httpx.RequestError, httpx.StreamError] errors = [httpx.RequestError, httpx.StreamError]
return create_base_retry_decorator( return create_base_retry_decorator(
error_types=errors, max_retries=llm.max_retries, run_manager=run_manager error_types=errors, max_retries=llm.max_retries, run_manager=run_manager
@ -103,12 +102,12 @@ def _create_retry_decorator(
def _is_valid_mistral_tool_call_id(tool_call_id: str) -> bool: def _is_valid_mistral_tool_call_id(tool_call_id: str) -> bool:
"""Check if tool call ID is nine character string consisting of a-z, A-Z, 0-9""" """Check if tool call ID is nine character string consisting of a-z, A-Z, 0-9."""
return bool(TOOL_CALL_ID_PATTERN.match(tool_call_id)) return bool(TOOL_CALL_ID_PATTERN.match(tool_call_id))
def _base62_encode(num: int) -> str: def _base62_encode(num: int) -> str:
"""Encodes a number in base62 and ensures result is of a specified length.""" """Encode a number in base62 and ensures result is of a specified length."""
base62 = "0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ" base62 = "0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ"
if num == 0: if num == 0:
return base62[0] return base62[0]
@ -122,16 +121,14 @@ def _base62_encode(num: int) -> str:
def _convert_tool_call_id_to_mistral_compatible(tool_call_id: str) -> str: def _convert_tool_call_id_to_mistral_compatible(tool_call_id: str) -> str:
"""Convert a tool call ID to a Mistral-compatible format""" """Convert a tool call ID to a Mistral-compatible format."""
if _is_valid_mistral_tool_call_id(tool_call_id): if _is_valid_mistral_tool_call_id(tool_call_id):
return tool_call_id return tool_call_id
else:
hash_bytes = hashlib.sha256(tool_call_id.encode()).digest() hash_bytes = hashlib.sha256(tool_call_id.encode()).digest()
hash_int = int.from_bytes(hash_bytes, byteorder="big") hash_int = int.from_bytes(hash_bytes, byteorder="big")
base62_str = _base62_encode(hash_int) base62_str = _base62_encode(hash_int)
if len(base62_str) >= 9: if len(base62_str) >= 9:
return base62_str[:9] return base62_str[:9]
else:
return base62_str.rjust(9, "0") return base62_str.rjust(9, "0")
@ -140,7 +137,8 @@ def _convert_mistral_chat_message_to_message(
) -> BaseMessage: ) -> BaseMessage:
role = _message["role"] role = _message["role"]
if role != "assistant": if role != "assistant":
raise ValueError(f"Expected role to be 'assistant', got {role}") msg = f"Expected role to be 'assistant', got {role}"
raise ValueError(msg)
content = cast(str, _message["content"]) content = cast(str, _message["content"])
additional_kwargs: dict = {} additional_kwargs: dict = {}
@ -170,9 +168,12 @@ def _raise_on_error(response: httpx.Response) -> None:
"""Raise an error if the response is an error.""" """Raise an error if the response is an error."""
if httpx.codes.is_error(response.status_code): if httpx.codes.is_error(response.status_code):
error_message = response.read().decode("utf-8") error_message = response.read().decode("utf-8")
raise httpx.HTTPStatusError( msg = (
f"Error response {response.status_code} " f"Error response {response.status_code} "
f"while fetching {response.url}: {error_message}", f"while fetching {response.url}: {error_message}"
)
raise httpx.HTTPStatusError(
msg,
request=response.request, request=response.request,
response=response, response=response,
) )
@ -182,9 +183,12 @@ async def _araise_on_error(response: httpx.Response) -> None:
"""Raise an error if the response is an error.""" """Raise an error if the response is an error."""
if httpx.codes.is_error(response.status_code): if httpx.codes.is_error(response.status_code):
error_message = (await response.aread()).decode("utf-8") error_message = (await response.aread()).decode("utf-8")
raise httpx.HTTPStatusError( msg = (
f"Error response {response.status_code} " f"Error response {response.status_code} "
f"while fetching {response.url}: {error_message}", f"while fetching {response.url}: {error_message}"
)
raise httpx.HTTPStatusError(
msg,
request=response.request, request=response.request,
response=response, response=response,
) )
@ -220,7 +224,6 @@ async def acompletion_with_retry(
llm.async_client, "POST", "/chat/completions", json=kwargs llm.async_client, "POST", "/chat/completions", json=kwargs
) )
return _aiter_sse(event_source) return _aiter_sse(event_source)
else:
response = await llm.async_client.post(url="/chat/completions", json=kwargs) response = await llm.async_client.post(url="/chat/completions", json=kwargs)
await _araise_on_error(response) await _araise_on_error(response)
return response.json() return response.json()
@ -237,7 +240,7 @@ def _convert_chunk_to_message_chunk(
content = _delta.get("content") or "" content = _delta.get("content") or ""
if role == "user" or default_class == HumanMessageChunk: if role == "user" or default_class == HumanMessageChunk:
return HumanMessageChunk(content=content) return HumanMessageChunk(content=content)
elif role == "assistant" or default_class == AIMessageChunk: if role == "assistant" or default_class == AIMessageChunk:
additional_kwargs: dict = {} additional_kwargs: dict = {}
response_metadata = {} response_metadata = {}
if raw_tool_calls := _delta.get("tool_calls"): if raw_tool_calls := _delta.get("tool_calls"):
@ -281,11 +284,10 @@ def _convert_chunk_to_message_chunk(
usage_metadata=usage_metadata, # type: ignore[arg-type] usage_metadata=usage_metadata, # type: ignore[arg-type]
response_metadata=response_metadata, response_metadata=response_metadata,
) )
elif role == "system" or default_class == SystemMessageChunk: if role == "system" or default_class == SystemMessageChunk:
return SystemMessageChunk(content=content) return SystemMessageChunk(content=content)
elif role or default_class == ChatMessageChunk: if role or default_class == ChatMessageChunk:
return ChatMessageChunk(content=content, role=role) return ChatMessageChunk(content=content, role=role)
else:
return default_class(content=content) # type: ignore[call-arg] return default_class(content=content) # type: ignore[call-arg]
@ -321,18 +323,24 @@ def _convert_message_to_mistral_chat_message(
message: BaseMessage, message: BaseMessage,
) -> dict: ) -> dict:
if isinstance(message, ChatMessage): if isinstance(message, ChatMessage):
return dict(role=message.role, content=message.content) return {"role": message.role, "content": message.content}
elif isinstance(message, HumanMessage): if isinstance(message, HumanMessage):
return dict(role="user", content=message.content) return {"role": "user", "content": message.content}
elif isinstance(message, AIMessage): if isinstance(message, AIMessage):
message_dict: dict[str, Any] = {"role": "assistant"} message_dict: dict[str, Any] = {"role": "assistant"}
tool_calls = [] tool_calls = []
if message.tool_calls or message.invalid_tool_calls: if message.tool_calls or message.invalid_tool_calls:
for tool_call in message.tool_calls: for tool_call in message.tool_calls:
tool_calls.append(_format_tool_call_for_mistral(tool_call)) tool_calls.extend(
[
_format_tool_call_for_mistral(tool_call)
for tool_call in message.tool_calls
]
)
for invalid_tool_call in message.invalid_tool_calls: for invalid_tool_call in message.invalid_tool_calls:
tool_calls.append( tool_calls.extend(
_format_invalid_tool_call_for_mistral(invalid_tool_call) _format_invalid_tool_call_for_mistral(invalid_tool_call)
for invalid_tool_call in message.invalid_tool_calls
) )
elif "tool_calls" in message.additional_kwargs: elif "tool_calls" in message.additional_kwargs:
for tc in message.additional_kwargs["tool_calls"]: for tc in message.additional_kwargs["tool_calls"]:
@ -359,9 +367,9 @@ def _convert_message_to_mistral_chat_message(
if "prefix" in message.additional_kwargs: if "prefix" in message.additional_kwargs:
message_dict["prefix"] = message.additional_kwargs["prefix"] message_dict["prefix"] = message.additional_kwargs["prefix"]
return message_dict return message_dict
elif isinstance(message, SystemMessage): if isinstance(message, SystemMessage):
return dict(role="system", content=message.content) return {"role": "system", "content": message.content}
elif isinstance(message, ToolMessage): if isinstance(message, ToolMessage):
return { return {
"role": "tool", "role": "tool",
"content": message.content, "content": message.content,
@ -370,8 +378,8 @@ def _convert_message_to_mistral_chat_message(
message.tool_call_id message.tool_call_id
), ),
} }
else: msg = f"Got unknown type {message}"
raise ValueError(f"Got unknown type {message}") raise ValueError(msg)
class ChatMistralAI(BaseChatModel): class ChatMistralAI(BaseChatModel):
@ -380,10 +388,10 @@ class ChatMistralAI(BaseChatModel):
# The type for client and async_client is ignored because the type is not # The type for client and async_client is ignored because the type is not
# an Optional after the model is initialized and the model_validator # an Optional after the model is initialized and the model_validator
# is run. # is run.
client: httpx.Client = Field( # type: ignore # : meta private: client: httpx.Client = Field( # type: ignore[assignment] # : meta private:
default=None, exclude=True default=None, exclude=True
) )
async_client: httpx.AsyncClient = Field( # type: ignore # : meta private: async_client: httpx.AsyncClient = Field( # type: ignore[assignment] # : meta private:
default=None, exclude=True default=None, exclude=True
) #: :meta private: ) #: :meta private:
mistral_api_key: Optional[SecretStr] = Field( mistral_api_key: Optional[SecretStr] = Field(
@ -417,8 +425,7 @@ class ChatMistralAI(BaseChatModel):
def build_extra(cls, values: dict[str, Any]) -> Any: def build_extra(cls, values: dict[str, Any]) -> Any:
"""Build extra kwargs from additional params that were passed in.""" """Build extra kwargs from additional params that were passed in."""
all_required_field_names = get_pydantic_field_names(cls) all_required_field_names = get_pydantic_field_names(cls)
values = _build_model_kwargs(values, all_required_field_names) return _build_model_kwargs(values, all_required_field_names)
return values
@property @property
def _default_params(self) -> dict[str, Any]: def _default_params(self) -> dict[str, Any]:
@ -432,8 +439,7 @@ class ChatMistralAI(BaseChatModel):
"safe_prompt": self.safe_mode, "safe_prompt": self.safe_mode,
**self.model_kwargs, **self.model_kwargs,
} }
filtered = {k: v for k, v in defaults.items() if v is not None} return {k: v for k, v in defaults.items() if v is not None}
return filtered
def _get_ls_params( def _get_ls_params(
self, stop: Optional[list[str]] = None, **kwargs: Any self, stop: Optional[list[str]] = None, **kwargs: Any
@ -481,13 +487,11 @@ class ChatMistralAI(BaseChatModel):
yield event.json() yield event.json()
return iter_sse() return iter_sse()
else:
response = self.client.post(url="/chat/completions", json=kwargs) response = self.client.post(url="/chat/completions", json=kwargs)
_raise_on_error(response) _raise_on_error(response)
return response.json() return response.json()
rtn = _completion_with_retry(**kwargs) return _completion_with_retry(**kwargs)
return rtn
def _combine_llm_outputs(self, llm_outputs: list[Optional[dict]]) -> dict: def _combine_llm_outputs(self, llm_outputs: list[Optional[dict]]) -> dict:
overall_token_usage: dict = {} overall_token_usage: dict = {}
@ -502,8 +506,7 @@ class ChatMistralAI(BaseChatModel):
overall_token_usage[k] += v overall_token_usage[k] += v
else: else:
overall_token_usage[k] = v overall_token_usage[k] = v
combined = {"token_usage": overall_token_usage, "model_name": self.model} return {"token_usage": overall_token_usage, "model_name": self.model}
return combined
@model_validator(mode="after") @model_validator(mode="after")
def validate_environment(self) -> Self: def validate_environment(self) -> Self:
@ -545,10 +548,12 @@ class ChatMistralAI(BaseChatModel):
) )
if self.temperature is not None and not 0 <= self.temperature <= 1: if self.temperature is not None and not 0 <= self.temperature <= 1:
raise ValueError("temperature must be in the range [0.0, 1.0]") msg = "temperature must be in the range [0.0, 1.0]"
raise ValueError(msg)
if self.top_p is not None and not 0 <= self.top_p <= 1: if self.top_p is not None and not 0 <= self.top_p <= 1:
raise ValueError("top_p must be in the range [0.0, 1.0]") msg = "top_p must be in the range [0.0, 1.0]"
raise ValueError(msg)
return self return self
@ -557,7 +562,7 @@ class ChatMistralAI(BaseChatModel):
messages: list[BaseMessage], messages: list[BaseMessage],
stop: Optional[list[str]] = None, stop: Optional[list[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None, run_manager: Optional[CallbackManagerForLLMRun] = None,
stream: Optional[bool] = None, stream: Optional[bool] = None, # noqa: FBT001
**kwargs: Any, **kwargs: Any,
) -> ChatResult: ) -> ChatResult:
should_stream = stream if stream is not None else self.streaming should_stream = stream if stream is not None else self.streaming
@ -669,7 +674,7 @@ class ChatMistralAI(BaseChatModel):
messages: list[BaseMessage], messages: list[BaseMessage],
stop: Optional[list[str]] = None, stop: Optional[list[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
stream: Optional[bool] = None, stream: Optional[bool] = None, # noqa: FBT001
**kwargs: Any, **kwargs: Any,
) -> ChatResult: ) -> ChatResult:
should_stream = stream if stream is not None else self.streaming should_stream = stream if stream is not None else self.streaming
@ -689,7 +694,7 @@ class ChatMistralAI(BaseChatModel):
def bind_tools( def bind_tools(
self, self,
tools: Sequence[Union[dict[str, Any], type, Callable, BaseTool]], tools: Sequence[Union[dict[str, Any], type, Callable, BaseTool]],
tool_choice: Optional[Union[dict, str, Literal["auto", "any"]]] = None, tool_choice: Optional[Union[dict, str, Literal["auto", "any"]]] = None, # noqa: PYI051
**kwargs: Any, **kwargs: Any,
) -> Runnable[LanguageModelInput, BaseMessage]: ) -> Runnable[LanguageModelInput, BaseMessage]:
"""Bind tool-like objects to this chat model. """Bind tool-like objects to this chat model.
@ -707,15 +712,15 @@ class ChatMistralAI(BaseChatModel):
{"type": "function", "function": {"name": <<tool_name>>}}. {"type": "function", "function": {"name": <<tool_name>>}}.
kwargs: Any additional parameters are passed directly to kwargs: Any additional parameters are passed directly to
``self.bind(**kwargs)``. ``self.bind(**kwargs)``.
"""
"""
formatted_tools = [convert_to_openai_tool(tool) for tool in tools] formatted_tools = [convert_to_openai_tool(tool) for tool in tools]
if tool_choice: if tool_choice:
tool_names = [] tool_names = []
for tool in formatted_tools: for tool in formatted_tools:
if "function" in tool and (name := tool["function"].get("name")): if ("function" in tool and (name := tool["function"].get("name"))) or (
tool_names.append(name) name := tool.get("name")
elif name := tool.get("name"): ):
tool_names.append(name) tool_names.append(name)
else: else:
pass pass
@ -738,7 +743,7 @@ class ChatMistralAI(BaseChatModel):
include_raw: bool = False, include_raw: bool = False,
**kwargs: Any, **kwargs: Any,
) -> Runnable[LanguageModelInput, Union[dict, BaseModel]]: ) -> Runnable[LanguageModelInput, Union[dict, BaseModel]]:
"""Model wrapper that returns outputs formatted to match the given schema. r"""Model wrapper that returns outputs formatted to match the given schema.
Args: Args:
schema: schema:
@ -785,6 +790,12 @@ class ChatMistralAI(BaseChatModel):
will be caught and returned as well. The final output is always a dict will be caught and returned as well. The final output is always a dict
with keys "raw", "parsed", and "parsing_error". with keys "raw", "parsed", and "parsing_error".
kwargs: Any additional parameters are passed directly to
``self.bind(**kwargs)``. This is useful for passing in
parameters such as ``tool_choice`` or ``tools`` to control
which tool the model should call, or to pass in parameters such as
``stop`` to control when the model should stop generating output.
Returns: Returns:
A Runnable that takes same inputs as a :class:`langchain_core.language_models.chat.BaseChatModel`. A Runnable that takes same inputs as a :class:`langchain_core.language_models.chat.BaseChatModel`.
@ -968,14 +979,16 @@ class ChatMistralAI(BaseChatModel):
""" # noqa: E501 """ # noqa: E501
_ = kwargs.pop("strict", None) _ = kwargs.pop("strict", None)
if kwargs: if kwargs:
raise ValueError(f"Received unsupported arguments {kwargs}") msg = f"Received unsupported arguments {kwargs}"
raise ValueError(msg)
is_pydantic_schema = isinstance(schema, type) and is_basemodel_subclass(schema) is_pydantic_schema = isinstance(schema, type) and is_basemodel_subclass(schema)
if method == "function_calling": if method == "function_calling":
if schema is None: if schema is None:
raise ValueError( msg = (
"schema must be specified when method is 'function_calling'. " "schema must be specified when method is 'function_calling'. "
"Received None." "Received None."
) )
raise ValueError(msg)
# TODO: Update to pass in tool name as tool_choice if/when Mistral supports # TODO: Update to pass in tool name as tool_choice if/when Mistral supports
# specifying a tool. # specifying a tool.
llm = self.bind_tools( llm = self.bind_tools(
@ -1014,10 +1027,11 @@ class ChatMistralAI(BaseChatModel):
) )
elif method == "json_schema": elif method == "json_schema":
if schema is None: if schema is None:
raise ValueError( msg = (
"schema must be specified when method is 'json_schema'. " "schema must be specified when method is 'json_schema'. "
"Received None." "Received None."
) )
raise ValueError(msg)
response_format = _convert_to_openai_response_format(schema, strict=True) response_format = _convert_to_openai_response_format(schema, strict=True)
llm = self.bind( llm = self.bind(
response_format=response_format, response_format=response_format,
@ -1041,7 +1055,6 @@ class ChatMistralAI(BaseChatModel):
[parser_none], exception_key="parsing_error" [parser_none], exception_key="parsing_error"
) )
return RunnableMap(raw=llm) | parser_with_fallback return RunnableMap(raw=llm) | parser_with_fallback
else:
return llm | output_parser return llm | output_parser
@property @property
@ -1072,7 +1085,7 @@ class ChatMistralAI(BaseChatModel):
def _convert_to_openai_response_format( def _convert_to_openai_response_format(
schema: Union[dict[str, Any], type], *, strict: Optional[bool] = None schema: Union[dict[str, Any], type], *, strict: Optional[bool] = None
) -> dict: ) -> dict:
"""Same as in ChatOpenAI, but don't pass through Pydantic BaseModels.""" """Perform same op as in ChatOpenAI, but do not pass through Pydantic BaseModels."""
if ( if (
isinstance(schema, dict) isinstance(schema, dict)
and "json_schema" in schema and "json_schema" in schema

40
libs/partners/mistralai/langchain_mistralai/embeddings.py
View File

@ -17,7 +17,7 @@ from pydantic import (
model_validator, model_validator,
) )
from tenacity import retry, retry_if_exception_type, stop_after_attempt, wait_fixed from tenacity import retry, retry_if_exception_type, stop_after_attempt, wait_fixed
from tokenizers import Tokenizer # type: ignore from tokenizers import Tokenizer # type: ignore[import]
from typing_extensions import Self from typing_extensions import Self
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
@ -30,7 +30,7 @@ documents/chunks)"""
class DummyTokenizer: class DummyTokenizer:
"""Dummy tokenizer for when tokenizer cannot be accessed (e.g., via Huggingface)""" """Dummy tokenizer for when tokenizer cannot be accessed (e.g., via Huggingface)."""
@staticmethod @staticmethod
def encode_batch(texts: list[str]) -> list[list[str]]: def encode_batch(texts: list[str]) -> list[list[str]]:
@ -126,9 +126,9 @@ class MistralAIEmbeddings(BaseModel, Embeddings):
# The type for client and async_client is ignored because the type is not # The type for client and async_client is ignored because the type is not
# an Optional after the model is initialized and the model_validator # an Optional after the model is initialized and the model_validator
# is run. # is run.
client: httpx.Client = Field(default=None) # type: ignore # : :meta private: client: httpx.Client = Field(default=None) # type: ignore[assignment] # :meta private:
async_client: httpx.AsyncClient = Field( # type: ignore # : meta private: async_client: httpx.AsyncClient = Field( # type: ignore[assignment] # :meta private:
default=None default=None
) )
mistral_api_key: SecretStr = Field( mistral_api_key: SecretStr = Field(
@ -153,7 +153,6 @@ class MistralAIEmbeddings(BaseModel, Embeddings):
@model_validator(mode="after") @model_validator(mode="after")
def validate_environment(self) -> Self: def validate_environment(self) -> Self:
"""Validate configuration.""" """Validate configuration."""
api_key_str = self.mistral_api_key.get_secret_value() api_key_str = self.mistral_api_key.get_secret_value()
# todo: handle retries # todo: handle retries
if not self.client: if not self.client:
@ -187,14 +186,14 @@ class MistralAIEmbeddings(BaseModel, Embeddings):
"Could not download mistral tokenizer from Huggingface for " "Could not download mistral tokenizer from Huggingface for "
"calculating batch sizes. Set a Huggingface token via the " "calculating batch sizes. Set a Huggingface token via the "
"HF_TOKEN environment variable to download the real tokenizer. " "HF_TOKEN environment variable to download the real tokenizer. "
"Falling back to a dummy tokenizer that uses `len()`." "Falling back to a dummy tokenizer that uses `len()`.",
stacklevel=2,
) )
self.tokenizer = DummyTokenizer() self.tokenizer = DummyTokenizer()
return self return self
def _get_batches(self, texts: list[str]) -> Iterable[list[str]]: def _get_batches(self, texts: list[str]) -> Iterable[list[str]]:
"""Split a list of texts into batches of less than 16k tokens for Mistral """Split list of texts into batches of less than 16k tokens for Mistral API."""
API."""
batch: list[str] = [] batch: list[str] = []
batch_tokens = 0 batch_tokens = 0
@ -224,6 +223,7 @@ class MistralAIEmbeddings(BaseModel, Embeddings):
Returns: Returns:
List of embeddings, one for each text. List of embeddings, one for each text.
""" """
try: try:
batch_responses = [] batch_responses = []
@ -238,16 +238,17 @@ class MistralAIEmbeddings(BaseModel, Embeddings):
def _embed_batch(batch: list[str]) -> Response: def _embed_batch(batch: list[str]) -> Response:
response = self.client.post( response = self.client.post(
url="/embeddings", url="/embeddings",
json=dict( json={
model=self.model, "model": self.model,
input=batch, "input": batch,
), },
) )
response.raise_for_status() response.raise_for_status()
return response return response
for batch in self._get_batches(texts): batch_responses = [
batch_responses.append(_embed_batch(batch)) _embed_batch(batch) for batch in self._get_batches(texts)
]
return [ return [
list(map(float, embedding_obj["embedding"])) list(map(float, embedding_obj["embedding"]))
for response in batch_responses for response in batch_responses
@ -265,16 +266,17 @@ class MistralAIEmbeddings(BaseModel, Embeddings):
Returns: Returns:
List of embeddings, one for each text. List of embeddings, one for each text.
""" """
try: try:
batch_responses = await asyncio.gather( batch_responses = await asyncio.gather(
*[ *[
self.async_client.post( self.async_client.post(
url="/embeddings", url="/embeddings",
json=dict( json={
model=self.model, "model": self.model,
input=batch, "input": batch,
), },
) )
for batch in self._get_batches(texts) for batch in self._get_batches(texts)
] ]
@ -296,6 +298,7 @@ class MistralAIEmbeddings(BaseModel, Embeddings):
Returns: Returns:
Embedding for the text. Embedding for the text.
""" """
return self.embed_documents([text])[0] return self.embed_documents([text])[0]
@ -307,5 +310,6 @@ class MistralAIEmbeddings(BaseModel, Embeddings):
Returns: Returns:
Embedding for the text. Embedding for the text.
""" """
return (await self.aembed_documents([text]))[0] return (await self.aembed_documents([text]))[0]

58
libs/partners/mistralai/pyproject.toml
View File

@ -48,8 +48,62 @@ disallow_untyped_defs = "True"
target-version = "py39" target-version = "py39"
[tool.ruff.lint] [tool.ruff.lint]
select = ["E", "F", "I", "T201", "UP", "S"] select = [
ignore = [ "UP007", ] "A", # flake8-builtins
"B", # flake8-bugbear
"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", # pydocstyle: Missing docstring in public module
"D101", # pydocstyle: Missing docstring in public class
"D102", # pydocstyle: Missing docstring in public method
"D103", # pydocstyle: Missing docstring in public function
"D104", # pydocstyle: Missing docstring in public package
"D105", # pydocstyle: Missing docstring in magic method
"D107", # pydocstyle: Missing docstring in __init__
"D203", # Messes with the formatter
"D407", # pydocstyle: Missing-dashed-underline-after-section
"COM812", # Messes with the formatter
"ISC001", # Messes with the formatter
"PERF203", # Rarely useful
"S112", # Rarely useful
"RUF012", # Doesn't play well with Pydantic
"SLF001", # Private member access
"UP007", # pyupgrade: non-pep604-annotation-union
"UP045", # pyupgrade: non-pep604-annotation-optional
]
[tool.coverage.run] [tool.coverage.run]
omit = ["tests/*"] omit = ["tests/*"]

23
libs/partners/mistralai/tests/integration_tests/test_chat_models.py
View File

@ -1,5 +1,7 @@
"""Test ChatMistral chat model.""" """Test ChatMistral chat model."""
from __future__ import annotations
import json import json
import logging import logging
import time import time
@ -43,11 +45,12 @@ async def test_astream() -> None:
if token.response_metadata: if token.response_metadata:
chunks_with_response_metadata += 1 chunks_with_response_metadata += 1
if chunks_with_token_counts != 1 or chunks_with_response_metadata != 1: if chunks_with_token_counts != 1 or chunks_with_response_metadata != 1:
raise AssertionError( msg = (
"Expected exactly one chunk with token counts or response_metadata. " "Expected exactly one chunk with token counts or response_metadata. "
"AIMessageChunk aggregation adds / appends counts and metadata. Check that " "AIMessageChunk aggregation adds / appends counts and metadata. Check that "
"this is behaving properly." "this is behaving properly."
) )
raise AssertionError(msg)
assert isinstance(full, AIMessageChunk) assert isinstance(full, AIMessageChunk)
assert full.usage_metadata is not None assert full.usage_metadata is not None
assert full.usage_metadata["input_tokens"] > 0 assert full.usage_metadata["input_tokens"] > 0
@ -61,7 +64,7 @@ async def test_astream() -> None:
async def test_abatch() -> None: async def test_abatch() -> None:
"""Test streaming tokens from ChatMistralAI""" """Test streaming tokens from ChatMistralAI."""
llm = ChatMistralAI() llm = ChatMistralAI()
result = await llm.abatch(["I'm Pickle Rick", "I'm not Pickle Rick"]) result = await llm.abatch(["I'm Pickle Rick", "I'm not Pickle Rick"])
@ -70,7 +73,7 @@ async def test_abatch() -> None:
async def test_abatch_tags() -> None: async def test_abatch_tags() -> None:
"""Test batch tokens from ChatMistralAI""" """Test batch tokens from ChatMistralAI."""
llm = ChatMistralAI() llm = ChatMistralAI()
result = await llm.abatch( result = await llm.abatch(
@ -81,7 +84,7 @@ async def test_abatch_tags() -> None:
def test_batch() -> None: def test_batch() -> None:
"""Test batch tokens from ChatMistralAI""" """Test batch tokens from ChatMistralAI."""
llm = ChatMistralAI() llm = ChatMistralAI()
result = llm.batch(["I'm Pickle Rick", "I'm not Pickle Rick"]) result = llm.batch(["I'm Pickle Rick", "I'm not Pickle Rick"])
@ -90,7 +93,7 @@ def test_batch() -> None:
async def test_ainvoke() -> None: async def test_ainvoke() -> None:
"""Test invoke tokens from ChatMistralAI""" """Test invoke tokens from ChatMistralAI."""
llm = ChatMistralAI() llm = ChatMistralAI()
result = await llm.ainvoke("I'm Pickle Rick", config={"tags": ["foo"]}) result = await llm.ainvoke("I'm Pickle Rick", config={"tags": ["foo"]})
@ -99,10 +102,10 @@ async def test_ainvoke() -> None:
def test_invoke() -> None: def test_invoke() -> None:
"""Test invoke tokens from ChatMistralAI""" """Test invoke tokens from ChatMistralAI."""
llm = ChatMistralAI() llm = ChatMistralAI()
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) assert isinstance(result.content, str)
@ -178,13 +181,11 @@ def test_streaming_structured_output() -> None:
structured_llm = llm.with_structured_output(Person) structured_llm = llm.with_structured_output(Person)
strm = structured_llm.stream("Erick, 27 years old") strm = structured_llm.stream("Erick, 27 years old")
chunk_num = 0 for chunk_num, chunk in enumerate(strm):
for chunk in strm:
assert chunk_num == 0, "should only have one chunk with model" assert chunk_num == 0, "should only have one chunk with model"
assert isinstance(chunk, Person) assert isinstance(chunk, Person)
assert chunk.name == "Erick" assert chunk.name == "Erick"
assert chunk.age == 27 assert chunk.age == 27
chunk_num += 1
class Book(BaseModel): class Book(BaseModel):
@ -201,7 +202,7 @@ def _check_parsed_result(result: Any, schema: Any) -> None:
if schema == Book: if schema == Book:
assert isinstance(result, Book) assert isinstance(result, Book)
else: else:
assert all(key in ["name", "authors"] for key in result.keys()) assert all(key in ["name", "authors"] for key in result)
@pytest.mark.parametrize("schema", [Book, BookDict, Book.model_json_schema()]) @pytest.mark.parametrize("schema", [Book, BookDict, Book.model_json_schema()])

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

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

2
libs/partners/mistralai/tests/integration_tests/test_embeddings.py
View File

@ -1,4 +1,4 @@
"""Test MistralAI Embedding""" """Test MistralAI Embedding."""
from langchain_mistralai import MistralAIEmbeddings from langchain_mistralai import MistralAIEmbeddings

2
libs/partners/mistralai/tests/integration_tests/test_standard.py
View File

@ -1,4 +1,4 @@
"""Standard LangChain interface tests""" """Standard LangChain interface tests."""
from langchain_core.language_models import BaseChatModel from langchain_core.language_models import BaseChatModel
from langchain_tests.integration_tests import ( # type: ignore[import-not-found] from langchain_tests.integration_tests import ( # type: ignore[import-not-found]

36
libs/partners/mistralai/tests/unit_tests/test_chat_models.py
View File

@ -84,23 +84,23 @@ def test_mistralai_initialization_baseurl_env(env_var_name: str) -> None:
[ [
( (
SystemMessage(content="Hello"), SystemMessage(content="Hello"),
dict(role="system", content="Hello"), {"role": "system", "content": "Hello"},
), ),
( (
HumanMessage(content="Hello"), HumanMessage(content="Hello"),
dict(role="user", content="Hello"), {"role": "user", "content": "Hello"},
), ),
( (
AIMessage(content="Hello"), AIMessage(content="Hello"),
dict(role="assistant", content="Hello"), {"role": "assistant", "content": "Hello"},
), ),
( (
AIMessage(content="{", additional_kwargs={"prefix": True}), AIMessage(content="{", additional_kwargs={"prefix": True}),
dict(role="assistant", content="{", prefix=True), {"role": "assistant", "content": "{", "prefix": True},
), ),
( (
ChatMessage(role="assistant", content="Hello"), ChatMessage(role="assistant", content="Hello"),
dict(role="assistant", content="Hello"), {"role": "assistant", "content": "Hello"},
), ),
], ],
) )
@ -112,17 +112,17 @@ def test_convert_message_to_mistral_chat_message(
def _make_completion_response_from_token(token: str) -> dict: def _make_completion_response_from_token(token: str) -> dict:
return dict( return {
id="abc123", "id": "abc123",
model="fake_model", "model": "fake_model",
choices=[ "choices": [
dict( {
index=0, "index": 0,
delta=dict(content=token), "delta": {"content": token},
finish_reason=None, "finish_reason": None,
) }
], ],
) }
def mock_chat_stream(*args: Any, **kwargs: Any) -> Generator: def mock_chat_stream(*args: Any, **kwargs: Any) -> Generator:
@ -275,8 +275,7 @@ def test_extra_kwargs() -> None:
def test_retry_with_failure_then_success() -> None: def test_retry_with_failure_then_success() -> None:
"""Test that retry mechanism works correctly when """Test retry mechanism works correctly when fiest request fails, second succeed."""
first request fails and second succeeds."""
# Create a real ChatMistralAI instance # Create a real ChatMistralAI instance
chat = ChatMistralAI(max_retries=3) chat = ChatMistralAI(max_retries=3)
@ -289,7 +288,8 @@ def test_retry_with_failure_then_success() -> None:
call_count += 1 call_count += 1
if call_count == 1: if call_count == 1:
raise httpx.RequestError("Connection error", request=MagicMock()) msg = "Connection error"
raise httpx.RequestError(msg, request=MagicMock())
mock_response = MagicMock() mock_response = MagicMock()
mock_response.status_code = 200 mock_response.status_code = 200

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

@ -1,4 +1,4 @@
"""Standard LangChain interface tests""" """Standard LangChain interface tests."""
from langchain_core.language_models import BaseChatModel from langchain_core.language_models import BaseChatModel
from langchain_tests.unit_tests import ( # type: ignore[import-not-found] from langchain_tests.unit_tests import ( # type: ignore[import-not-found]

42
libs/partners/mistralai/uv.lock
View File

@ -379,7 +379,7 @@ dev = [
{ name = "jupyter", specifier = ">=1.0.0,<2.0.0" }, { name = "jupyter", specifier = ">=1.0.0,<2.0.0" },
{ name = "setuptools", specifier = ">=67.6.1,<68.0.0" }, { 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 = [ test = [
{ name = "blockbuster", specifier = "~=1.5.18" }, { name = "blockbuster", specifier = "~=1.5.18" },
{ name = "freezegun", specifier = ">=1.2.2,<2.0.0" }, { name = "freezegun", specifier = ">=1.2.2,<2.0.0" },
@ -452,7 +452,7 @@ requires-dist = [
[package.metadata.requires-dev] [package.metadata.requires-dev]
codespell = [{ name = "codespell", specifier = ">=2.2.0,<3.0.0" }] codespell = [{ name = "codespell", specifier = ">=2.2.0,<3.0.0" }]
dev = [{ name = "langchain-core", editable = "../../core" }] dev = [{ name = "langchain-core", editable = "../../core" }]
lint = [{ name = "ruff", specifier = ">=0.5,<1.0" }] lint = [{ name = "ruff", specifier = ">=0.12.2,<0.13" }]
test = [ test = [
{ name = "langchain-core", editable = "../../core" }, { name = "langchain-core", editable = "../../core" },
{ name = "langchain-tests", editable = "../../standard-tests" }, { name = "langchain-tests", editable = "../../standard-tests" },
@ -1370,27 +1370,27 @@ wheels = [
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