mistral[minor]: Function calling and with_structured_output (#18150)

![Screenshot 2024-02-26 at 2 07 06
PM](https://github.com/langchain-ai/langchain/assets/22008038/20cacb47-3b24-45b5-871b-dd169f1acd37)

libs/partners/mistralai/langchain_mistralai/chat_models.py

@@ -2,6 +2,7 @@ from __future__ import annotations
 
 import importlib.util
 import logging
+from operator import itemgetter
 from typing import (
     Any,
     AsyncIterator,
@@ -10,15 +11,19 @@ from typing import (
     Iterator,
     List,
     Optional,
+    Sequence,
     Tuple,
     Type,
     Union,
+    cast,
 )
 
+from langchain_core._api import beta
 from langchain_core.callbacks import (
     AsyncCallbackManagerForLLMRun,
     CallbackManagerForLLMRun,
 )
+from langchain_core.language_models import LanguageModelInput
 from langchain_core.language_models.chat_models import (
     BaseChatModel,
     agenerate_from_stream,
@@ -36,19 +41,22 @@ from langchain_core.messages import (
     HumanMessageChunk,
     SystemMessage,
     SystemMessageChunk,
+    ToolMessage,
 )
-from langchain_core.outputs import (
+from langchain_core.output_parsers.base import OutputParserLike
-    ChatGeneration,
+from langchain_core.output_parsers.openai_tools import (
-    ChatGenerationChunk,
+    JsonOutputKeyToolsParser,
-    ChatResult,
+    PydanticToolsParser,
 )
-from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
+from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
+from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator
+from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough
+from langchain_core.tools import BaseTool
 from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
+from langchain_core.utils.function_calling import convert_to_openai_tool
 from mistralai.async_client import MistralAsyncClient
 from mistralai.client import MistralClient
-from mistralai.constants import (
+from mistralai.constants import ENDPOINT as DEFAULT_MISTRAL_ENDPOINT
-    ENDPOINT as DEFAULT_MISTRAL_ENDPOINT,
-)
 from mistralai.exceptions import (
     MistralAPIException,
     MistralConnectionException,
@@ -57,12 +65,8 @@ from mistralai.exceptions import (
 from mistralai.models.chat_completion import (
     ChatCompletionResponse as MistralChatCompletionResponse,
 )
-from mistralai.models.chat_completion import (
+from mistralai.models.chat_completion import ChatMessage as MistralChatMessage
-    ChatMessage as MistralChatMessage,
+from mistralai.models.chat_completion import DeltaMessage as MistralDeltaMessage
-)
-from mistralai.models.chat_completion import (
-    DeltaMessage as MistralDeltaMessage,
-)
 
 logger = logging.getLogger(__name__)
 
@@ -89,14 +93,22 @@ def _convert_mistral_chat_message_to_message(
     _message: MistralChatMessage,
 ) -> BaseMessage:
     role = _message.role
+    content = cast(Union[str, List], _message.content)
     if role == "user":
-        return HumanMessage(content=_message.content)
+        return HumanMessage(content=content)
     elif role == "assistant":
-        return AIMessage(content=_message.content)
+        additional_kwargs: Dict = {}
+        if hasattr(_message, "tool_calls") and getattr(_message, "tool_calls"):
+            additional_kwargs["tool_calls"] = [
+                tc.model_dump() for tc in getattr(_message, "tool_calls")
+            ]
+        return AIMessage(content=content, additional_kwargs=additional_kwargs)
     elif role == "system":
-        return SystemMessage(content=_message.content)
+        return SystemMessage(content=content)
+    elif role == "tool":
+        return ToolMessage(content=content, name=_message.name)  # type: ignore[attr-defined]
     else:
-        return ChatMessage(content=_message.content, role=role)
+        return ChatMessage(content=content, role=role)
 
 
 async def acompletion_with_retry(
@@ -119,14 +131,19 @@ async def acompletion_with_retry(
 
 
 def _convert_delta_to_message_chunk(
-    _obj: MistralDeltaMessage, default_class: Type[BaseMessageChunk]
+    _delta: MistralDeltaMessage, default_class: Type[BaseMessageChunk]
 ) -> BaseMessageChunk:
-    role = getattr(_obj, "role")
+    role = getattr(_delta, "role")
-    content = getattr(_obj, "content", "")
+    content = getattr(_delta, "content", "")
     if role == "user" or default_class == HumanMessageChunk:
         return HumanMessageChunk(content=content)
     elif role == "assistant" or default_class == AIMessageChunk:
-        return AIMessageChunk(content=content)
+        additional_kwargs: Dict = {}
+        if hasattr(_delta, "tool_calls") and getattr(_delta, "tool_calls"):
+            additional_kwargs["tool_calls"] = [
+                tc.model_dump() for tc in getattr(_delta, "tool_calls")
+            ]
+        return AIMessageChunk(content=content, additional_kwargs=additional_kwargs)
     elif role == "system" or default_class == SystemMessageChunk:
         return SystemMessageChunk(content=content)
     elif role or default_class == ChatMessageChunk:
@@ -143,9 +160,26 @@ def _convert_message_to_mistral_chat_message(
     elif isinstance(message, HumanMessage):
         mistral_message = MistralChatMessage(role="user", content=message.content)
     elif isinstance(message, AIMessage):
-        mistral_message = MistralChatMessage(role="assistant", content=message.content)
+        if "tool_calls" in message.additional_kwargs:
+            from mistralai.models.chat_completion import (  # type: ignore[attr-defined]
+                ToolCall as MistralToolCall,
+            )
+
+            tool_calls = [
+                MistralToolCall.model_validate(tc)
+                for tc in message.additional_kwargs["tool_calls"]
+            ]
+        else:
+            tool_calls = None
+        mistral_message = MistralChatMessage(
+            role="assistant", content=message.content, tool_calls=tool_calls
+        )
     elif isinstance(message, SystemMessage):
         mistral_message = MistralChatMessage(role="system", content=message.content)
+    elif isinstance(message, ToolMessage):
+        mistral_message = MistralChatMessage(
+            role="tool", content=message.content, name=message.name
+        )
     else:
         raise ValueError(f"Got unknown type {message}")
     return mistral_message
@@ -368,6 +402,163 @@ class ChatMistralAI(BaseChatModel):
         )
         return self._create_chat_result(response)
 
+    def bind_tools(
+        self,
+        tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
+        **kwargs: Any,
+    ) -> Runnable[LanguageModelInput, BaseMessage]:
+        """Bind tool-like objects to this chat model.
+
+        Assumes model is compatible with OpenAI tool-calling API.
+
+        Args:
+            tools: A list of tool definitions to bind to this chat model.
+                Can be  a dictionary, pydantic model, callable, or BaseTool. Pydantic
+                models, callables, and BaseTools will be automatically converted to
+                their schema dictionary representation.
+            tool_choice: Which tool to require the model to call.
+                Must be the name of the single provided function or
+                "auto" to automatically determine which function to call
+                (if any), or a dict of the form:
+                {"type": "function", "function": {"name": <<tool_name>>}}.
+            **kwargs: Any additional parameters to pass to the
+                :class:`~langchain.runnable.Runnable` constructor.
+        """
+
+        formatted_tools = [convert_to_openai_tool(tool) for tool in tools]
+        return super().bind(tools=formatted_tools, **kwargs)
+
+    @beta()
+    def with_structured_output(
+        self,
+        schema: Union[Dict, Type[BaseModel]],
+        *,
+        include_raw: bool = False,
+        **kwargs: Any,
+    ) -> Runnable[LanguageModelInput, Union[Dict, BaseModel]]:
+        """Model wrapper that returns outputs formatted to match the given schema.
+
+        Args:
+            schema: The output schema as a dict or a Pydantic class. If a Pydantic class
+                then the model output will be an object of that class. If a dict then
+                the model output will be a dict. With a Pydantic class the returned
+                attributes will be validated, whereas with a dict they will not be. If
+                `method` is "function_calling" and `schema` is a dict, then the dict
+                must match the OpenAI function-calling spec.
+            include_raw: If False then only the parsed structured output is returned. If
+                an error occurs during model output parsing it will be raised. If True
+                then both the raw model response (a BaseMessage) and the parsed model
+                response will be returned. If an error occurs during output parsing it
+                will be caught and returned as well. The final output is always a dict
+                with keys "raw", "parsed", and "parsing_error".
+
+        Returns:
+            A Runnable that takes any ChatModel input and returns as output:
+
+                If include_raw is True then a dict with keys:
+                    raw: BaseMessage
+                    parsed: Optional[_DictOrPydantic]
+                    parsing_error: Optional[BaseException]
+
+                If include_raw is False then just _DictOrPydantic is returned,
+                where _DictOrPydantic depends on the schema:
+
+                If schema is a Pydantic class then _DictOrPydantic is the Pydantic
+                    class.
+
+                If schema is a dict then _DictOrPydantic is a dict.
+
+        Example: Function-calling, Pydantic schema (method="function_calling", include_raw=False):
+            .. code-block:: python
+
+                from langchain_mistralai import ChatMistralAI
+                from langchain_core.pydantic_v1 import BaseModel
+
+                class AnswerWithJustification(BaseModel):
+                    '''An answer to the user question along with justification for the answer.'''
+                    answer: str
+                    justification: str
+
+                llm = ChatMistralAI(model="mistral-large-latest", temperature=0)
+                structured_llm = llm.with_structured_output(AnswerWithJustification)
+
+                structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
+
+                # -> AnswerWithJustification(
+                #     answer='They weigh the same',
+                #     justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'
+                # )
+
+        Example: Function-calling, Pydantic schema (method="function_calling", include_raw=True):
+            .. code-block:: python
+
+                from langchain_mistralai import ChatMistralAI
+                from langchain_core.pydantic_v1 import BaseModel
+
+                class AnswerWithJustification(BaseModel):
+                    '''An answer to the user question along with justification for the answer.'''
+                    answer: str
+                    justification: str
+
+                llm = ChatMistralAI(model="mistral-large-latest", temperature=0)
+                structured_llm = llm.with_structured_output(AnswerWithJustification, include_raw=True)
+
+                structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
+                # -> {
+                #     'raw': AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Ao02pnFYXD6GN1yzc0uXPsvF', 'function': {'arguments': '{"answer":"They weigh the same.","justification":"Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ."}', 'name': 'AnswerWithJustification'}, 'type': 'function'}]}),
+                #     'parsed': AnswerWithJustification(answer='They weigh the same.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'),
+                #     'parsing_error': None
+                # }
+
+        Example: Function-calling, dict schema (method="function_calling", include_raw=False):
+            .. code-block:: python
+
+                from langchain_mistralai import ChatMistralAI
+                from langchain_core.pydantic_v1 import BaseModel
+                from langchain_core.utils.function_calling import convert_to_openai_tool
+
+                class AnswerWithJustification(BaseModel):
+                    '''An answer to the user question along with justification for the answer.'''
+                    answer: str
+                    justification: str
+
+                dict_schema = convert_to_openai_tool(AnswerWithJustification)
+                llm = ChatMistralAI(model="mistral-large-latest", temperature=0)
+                structured_llm = llm.with_structured_output(dict_schema)
+
+                structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
+                # -> {
+                #     'answer': 'They weigh the same',
+                #     'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.'
+                # }
+
+        """  # noqa: E501
+        if kwargs:
+            raise ValueError(f"Received unsupported arguments {kwargs}")
+        is_pydantic_schema = isinstance(schema, type) and issubclass(schema, BaseModel)
+        llm = self.bind_tools([schema], tool_choice="any")
+        if is_pydantic_schema:
+            output_parser: OutputParserLike = PydanticToolsParser(
+                tools=[schema], first_tool_only=True
+            )
+        else:
+            key_name = convert_to_openai_tool(schema)["function"]["name"]
+            output_parser = JsonOutputKeyToolsParser(
+                key_name=key_name, first_tool_only=True
+            )
+
+        if include_raw:
+            parser_assign = RunnablePassthrough.assign(
+                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"
+            )
+            return RunnableMap(raw=llm) | parser_with_fallback
+        else:
+            return llm | output_parser
+
     @property
     def _identifying_params(self) -> Dict[str, Any]:
         """Get the identifying parameters."""

libs/partners/mistralai/poetry.lock

@@ -16,13 +16,13 @@ typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""}
 
 [[package]]
 name = "anyio"
-version = "4.2.0"
+version = "4.3.0"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "anyio-4.2.0-py3-none-any.whl", hash = "sha256:745843b39e829e108e518c489b31dc757de7d2131d53fac32bd8df268227bfee"},
+    {file = "anyio-4.3.0-py3-none-any.whl", hash = "sha256:048e05d0f6caeed70d731f3db756d35dcc1f35747c8c403364a8332c630441b8"},
-    {file = "anyio-4.2.0.tar.gz", hash = "sha256:e1875bb4b4e2de1669f4bc7869b6d3f54231cdced71605e6e64c9be77e3be50f"},
+    {file = "anyio-4.3.0.tar.gz", hash = "sha256:f75253795a87df48568485fd18cdd2a3fa5c4f7c5be8e5e36637733fce06fed6"},
 ]
 
 [package.dependencies]
@@ -252,13 +252,13 @@ files = [
 
 [[package]]
 name = "httpcore"
-version = "1.0.2"
+version = "1.0.4"
 description = "A minimal low-level HTTP client."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "httpcore-1.0.2-py3-none-any.whl", hash = "sha256:096cc05bca73b8e459a1fc3dcf585148f63e534eae4339559c9b8a8d6399acc7"},
+    {file = "httpcore-1.0.4-py3-none-any.whl", hash = "sha256:ac418c1db41bade2ad53ae2f3834a3a0f5ae76b56cf5aa497d2d033384fc7d73"},
-    {file = "httpcore-1.0.2.tar.gz", hash = "sha256:9fc092e4799b26174648e54b74ed5f683132a464e95643b226e00c2ed2fa6535"},
+    {file = "httpcore-1.0.4.tar.gz", hash = "sha256:cb2839ccfcba0d2d3c1131d3c3e26dfc327326fbe7a5dc0dbfe9f6c9151bb022"},
 ]
 
 [package.dependencies]
@@ -269,7 +269,7 @@ h11 = ">=0.13,<0.15"
 asyncio = ["anyio (>=4.0,<5.0)"]
 http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
-trio = ["trio (>=0.22.0,<0.23.0)"]
+trio = ["trio (>=0.22.0,<0.25.0)"]
 
 [[package]]
 name = "httpx"
@@ -376,7 +376,7 @@ files = [
 
 [[package]]
 name = "langchain-core"
-version = "0.1.23"
+version = "0.1.26"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.8.1,<4.0"
@@ -386,7 +386,7 @@ develop = true
 [package.dependencies]
 anyio = ">=3,<5"
 jsonpatch = "^1.33"
-langsmith = "^0.0.87"
+langsmith = "^0.1.0"
 packaging = "^23.2"
 pydantic = ">=1,<3"
 PyYAML = ">=5.3"
@@ -402,16 +402,17 @@ url = "../../core"
 
 [[package]]
 name = "langsmith"
-version = "0.0.87"
+version = "0.1.8"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = ">=3.8.1,<4.0"
 files = [
-    {file = "langsmith-0.0.87-py3-none-any.whl", hash = "sha256:8903d3811b9fc89eb18f5961c8e6935fbd2d0f119884fbf30dc70b8f8f4121fc"},
+    {file = "langsmith-0.1.8-py3-none-any.whl", hash = "sha256:f4320fd80ec9d311a648e7d4c44e0814e6e5454772c5026f40db0307bc07e287"},
-    {file = "langsmith-0.0.87.tar.gz", hash = "sha256:36c4cc47e5b54be57d038036a30fb19ce6e4c73048cd7a464b8f25b459694d34"},
+    {file = "langsmith-0.1.8.tar.gz", hash = "sha256:ab5f1cdfb7d418109ea506d41928fb8708547db2f6c7f7da7cfe997f3c55767b"},
 ]
 
 [package.dependencies]
+orjson = ">=3.9.14,<4.0.0"
 pydantic = ">=1,<3"
 requests = ">=2,<3"
 
@@ -431,6 +432,24 @@ httpx = ">=0.25.2,<0.26.0"
 orjson = ">=3.9.10,<4.0.0"
 pydantic = ">=2.5.2,<3.0.0"
 
+[[package]]
+name = "mistralai"
+version = "0.1.2"
+description = ""
+optional = false
+python-versions = ">=3.9,<4.0"
+files = [
+    {file = "mistralai-0.1.2-py3-none-any.whl", hash = "sha256:5e74e5ef0c0f15058892d73b00c659e06e9882c00838a1ad9862d93c77336847"},
+    {file = "mistralai-0.1.2.tar.gz", hash = "sha256:eb915fd15075f71bdbfce9cb476bb647322b1ce1e93b19ab0047728067466397"},
+]
+
+[package.dependencies]
+httpx = ">=0.25.2,<0.26.0"
+orjson = ">=3.9.10,<4.0.0"
+pandas = ">=2.2.0,<3.0.0"
+pyarrow = ">=15.0.0,<16.0.0"
+pydantic = ">=2.5.2,<3.0.0"
+
 [[package]]
 name = "mypy"
 version = "0.991"
@@ -492,63 +511,108 @@ files = [
     {file = "mypy_extensions-1.0.0.tar.gz", hash = "sha256:75dbf8955dc00442a438fc4d0666508a9a97b6bd41aa2f0ffe9d2f2725af0782"},
 ]
 
+[[package]]
+name = "numpy"
+version = "1.26.4"
+description = "Fundamental package for array computing in Python"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "numpy-1.26.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:9ff0f4f29c51e2803569d7a51c2304de5554655a60c5d776e35b4a41413830d0"},
+    {file = "numpy-1.26.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:2e4ee3380d6de9c9ec04745830fd9e2eccb3e6cf790d39d7b98ffd19b0dd754a"},
+    {file = "numpy-1.26.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d209d8969599b27ad20994c8e41936ee0964e6da07478d6c35016bc386b66ad4"},
+    {file = "numpy-1.26.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ffa75af20b44f8dba823498024771d5ac50620e6915abac414251bd971b4529f"},
+    {file = "numpy-1.26.4-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:62b8e4b1e28009ef2846b4c7852046736bab361f7aeadeb6a5b89ebec3c7055a"},
+    {file = "numpy-1.26.4-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:a4abb4f9001ad2858e7ac189089c42178fcce737e4169dc61321660f1a96c7d2"},
+    {file = "numpy-1.26.4-cp310-cp310-win32.whl", hash = "sha256:bfe25acf8b437eb2a8b2d49d443800a5f18508cd811fea3181723922a8a82b07"},
+    {file = "numpy-1.26.4-cp310-cp310-win_amd64.whl", hash = "sha256:b97fe8060236edf3662adfc2c633f56a08ae30560c56310562cb4f95500022d5"},
+    {file = "numpy-1.26.4-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:4c66707fabe114439db9068ee468c26bbdf909cac0fb58686a42a24de1760c71"},
+    {file = "numpy-1.26.4-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:edd8b5fe47dab091176d21bb6de568acdd906d1887a4584a15a9a96a1dca06ef"},
+    {file = "numpy-1.26.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7ab55401287bfec946ced39700c053796e7cc0e3acbef09993a9ad2adba6ca6e"},
+    {file = "numpy-1.26.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:666dbfb6ec68962c033a450943ded891bed2d54e6755e35e5835d63f4f6931d5"},
+    {file = "numpy-1.26.4-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:96ff0b2ad353d8f990b63294c8986f1ec3cb19d749234014f4e7eb0112ceba5a"},
+    {file = "numpy-1.26.4-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:60dedbb91afcbfdc9bc0b1f3f402804070deed7392c23eb7a7f07fa857868e8a"},
+    {file = "numpy-1.26.4-cp311-cp311-win32.whl", hash = "sha256:1af303d6b2210eb850fcf03064d364652b7120803a0b872f5211f5234b399f20"},
+    {file = "numpy-1.26.4-cp311-cp311-win_amd64.whl", hash = "sha256:cd25bcecc4974d09257ffcd1f098ee778f7834c3ad767fe5db785be9a4aa9cb2"},
+    {file = "numpy-1.26.4-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:b3ce300f3644fb06443ee2222c2201dd3a89ea6040541412b8fa189341847218"},
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 testing = ["pytest", "pytest-benchmark"]
 
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+    {file = "pydantic_core-2.16.3-pp39-pypy39_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:ed25e1835c00a332cb10c683cd39da96a719ab1dfc08427d476bce41b92531fc"},
-    {file = "pydantic_core-2.16.2-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:459c0d338cc55d099798618f714b21b7ece17eb1a87879f2da20a3ff4c7628e2"},
+    {file = "pydantic_core-2.16.3-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:86b3d0033580bd6bbe07590152007275bd7af95f98eaa5bd36f3da219dcd93da"},
-    {file = "pydantic_core-2.16.2.tar.gz", hash = "sha256:0ba503850d8b8dcc18391f10de896ae51d37fe5fe43dbfb6a35c5c5cad271a06"},
+    {file = "pydantic_core-2.16.3.tar.gz", hash = "sha256:1cac689f80a3abab2d3c0048b29eea5751114054f032a941a32de4c852c59cad"},
 ]
 
 [package.dependencies]
@@ -727,6 +912,31 @@ pytest = ">=7.0.0"
 docs = ["sphinx (>=5.3)", "sphinx-rtd-theme (>=1.0)"]
 testing = ["coverage (>=6.2)", "flaky (>=3.5.0)", "hypothesis (>=5.7.1)", "mypy (>=0.931)", "pytest-trio (>=0.7.0)"]
 
+[[package]]
+name = "python-dateutil"
+version = "2.8.2"
+description = "Extensions to the standard Python datetime module"
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
+files = [
+    {file = "python-dateutil-2.8.2.tar.gz", hash = "sha256:0123cacc1627ae19ddf3c27a5de5bd67ee4586fbdd6440d9748f8abb483d3e86"},
+    {file = "python_dateutil-2.8.2-py2.py3-none-any.whl", hash = "sha256:961d03dc3453ebbc59dbdea9e4e11c5651520a876d0f4db161e8674aae935da9"},
+]
+
+[package.dependencies]
+six = ">=1.5"
+
+[[package]]
+name = "pytz"
+version = "2024.1"
+description = "World timezone definitions, modern and historical"
+optional = false
+python-versions = "*"
+files = [
+    {file = "pytz-2024.1-py2.py3-none-any.whl", hash = "sha256:328171f4e3623139da4983451950b28e95ac706e13f3f2630a879749e7a8b319"},
+    {file = "pytz-2024.1.tar.gz", hash = "sha256:2a29735ea9c18baf14b448846bde5a48030ed267578472d8955cd0e7443a9812"},
+]
+
 [[package]]
 name = "pyyaml"
 version = "6.0.1"
@@ -834,15 +1044,26 @@ files = [
     {file = "ruff-0.1.15.tar.gz", hash = "sha256:f6dfa8c1b21c913c326919056c390966648b680966febcb796cc9d1aaab8564e"},
 ]
 
+[[package]]
+name = "six"
+version = "1.16.0"
+description = "Python 2 and 3 compatibility utilities"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
+files = [
+    {file = "six-1.16.0-py2.py3-none-any.whl", hash = "sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254"},
+    {file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
+]
+
 [[package]]
 name = "sniffio"
-version = "1.3.0"
+version = "1.3.1"
 description = "Sniff out which async library your code is running under"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "sniffio-1.3.0-py3-none-any.whl", hash = "sha256:eecefdce1e5bbfb7ad2eeaabf7c1eeb404d7757c379bd1f7e5cce9d8bf425384"},
+    {file = "sniffio-1.3.1-py3-none-any.whl", hash = "sha256:2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2"},
-    {file = "sniffio-1.3.0.tar.gz", hash = "sha256:e60305c5e5d314f5389259b7f22aaa33d8f7dee49763119234af3755c55b9101"},
+    {file = "sniffio-1.3.1.tar.gz", hash = "sha256:f4324edc670a0f49750a81b895f35c3adb843cca46f0530f79fc1babb23789dc"},
 ]
 
 [[package]]
@@ -1019,24 +1240,35 @@ telegram = ["requests"]
 
 [[package]]
 name = "typing-extensions"
-version = "4.9.0"
+version = "4.10.0"
 description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "typing_extensions-4.9.0-py3-none-any.whl", hash = "sha256:af72aea155e91adfc61c3ae9e0e342dbc0cba726d6cba4b6c72c1f34e47291cd"},
+    {file = "typing_extensions-4.10.0-py3-none-any.whl", hash = "sha256:69b1a937c3a517342112fb4c6df7e72fc39a38e7891a5730ed4985b5214b5475"},
-    {file = "typing_extensions-4.9.0.tar.gz", hash = "sha256:23478f88c37f27d76ac8aee6c905017a143b0b1b886c3c9f66bc2fd94f9f5783"},
+    {file = "typing_extensions-4.10.0.tar.gz", hash = "sha256:b0abd7c89e8fb96f98db18d86106ff1d90ab692004eb746cf6eda2682f91b3cb"},
+]
+
+[[package]]
+name = "tzdata"
+version = "2024.1"
+description = "Provider of IANA time zone data"
+optional = false
+python-versions = ">=2"
+files = [
+    {file = "tzdata-2024.1-py2.py3-none-any.whl", hash = "sha256:9068bc196136463f5245e51efda838afa15aaeca9903f49050dfa2679db4d252"},
+    {file = "tzdata-2024.1.tar.gz", hash = "sha256:2674120f8d891909751c38abcdfd386ac0a5a1127954fbc332af6b5ceae07efd"},
 ]
 
 [[package]]
 name = "urllib3"
-version = "2.2.0"
+version = "2.2.1"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "urllib3-2.2.0-py3-none-any.whl", hash = "sha256:ce3711610ddce217e6d113a2732fafad960a03fd0318c91faa79481e35c11224"},
+    {file = "urllib3-2.2.1-py3-none-any.whl", hash = "sha256:450b20ec296a467077128bff42b73080516e71b56ff59a60a02bef2232c4fa9d"},
-    {file = "urllib3-2.2.0.tar.gz", hash = "sha256:051d961ad0c62a94e50ecf1af379c3aba230c66c710493493560c0c223c49f20"},
+    {file = "urllib3-2.2.1.tar.gz", hash = "sha256:d0570876c61ab9e520d776c38acbbb5b05a776d3f9ff98a5c8fd5162a444cf19"},
 ]
 
 [package.extras]
@@ -1048,4 +1280,4 @@ zstd = ["zstandard (>=0.18.0)"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8.1,<4.0"
-content-hash = "80ff38d21357b7d1d0dded7726d1d61b342307280838454ded0a7ed1b1220b40"
+content-hash = "1c452f2c7766fd2ea8327da8d028edc517354f8fbc62a3efa3dcb852c461d058"

libs/partners/mistralai/pyproject.toml

@@ -13,7 +13,7 @@ license = "MIT"
 [tool.poetry.dependencies]
 python = ">=3.8.1,<4.0"
 langchain-core = "^0.1"
-mistralai = ">=0.0.11,<0.1"
+mistralai = [{version = "^0.1", python = "^3.9"}, {version = ">=0.0.11,<0.2", python="3.8"}]
 tokenizers = "^0.15.1"
 
 [tool.poetry.group.test]