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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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GPG Key ID: B5690EEEBB952194
10 changed files with 214 additions and 143 deletions
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149
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]
@ -122,17 +121,15 @@ 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] return base62_str.rjust(9, "0")
else:
return base62_str.rjust(9, "0")
def _convert_mistral_chat_message_to_message( def _convert_mistral_chat_message_to_message(
@ -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,
) )
@ -220,10 +224,9 @@ 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()
return await _completion_with_retry(**kwargs) return await _completion_with_retry(**kwargs)
@ -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"):
@ -281,12 +284,11 @@ 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]
def _format_tool_call_for_mistral(tool_call: ToolCall) -> dict: def _format_tool_call_for_mistral(tool_call: ToolCall) -> dict:
@ -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,
@ -1041,8 +1055,7 @@ 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
def _identifying_params(self) -> dict[str, Any]: def _identifying_params(self) -> dict[str, Any]:
@ -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

42
libs/partners/mistralai/langchain_mistralai/embeddings.py
View File

@ -17,20 +17,20 @@ 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__)
MAX_TOKENS = 16_000 MAX_TOKENS = 16_000
"""A batching parameter for the Mistral API. This is NOT the maximum number of tokens """A batching parameter for the Mistral API. This is NOT the maximum number of tokens
accepted by the embedding model for each document/chunk, but rather the maximum number accepted by the embedding model for each document/chunk, but rather the maximum number
of tokens that can be sent in a single request to the Mistral API (across multiple of tokens that can be sent in a single request to the Mistral API (across multiple
documents/chunks)""" 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 = [
[[package]] [[package]]
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