chore(mistral): rebuild the tool_choice pin and cover malformed shapes

The previous commit forwarded the caller's tool_choice object by
reference. Mistral's schema forbids unknown fields, so any extra key
travelled with it and came back a 422, and an empty function name went
out as an unusable pin the API rejects. Both shapes pass the central
tool_choice validator, so callers can reach them.

Rebuild the pin from the extracted name instead, keeping only the type
and the function name, and require that name to be a non-empty string.
Anything else is left out, which is what the provider did before this
branch touched it.

Tests now assert against a literal written apart from the input, so an
in-place corruption of the pin cannot pass, and cover the reachable
malformed shapes: no name, an empty name, and a name that is not a
string.

litellm/llms/mistral/chat/transformation.py

@@ -6,7 +6,7 @@ Why separate file? Make it easy to see how transformation works
 Docs - https://docs.mistral.ai/api/
 """
 
-from collections.abc import AsyncIterator, Coroutine, Iterator, Mapping
+from collections.abc import AsyncIterator, Coroutine, Iterator
 from typing import TYPE_CHECKING, Any, Final, Literal, cast, get_type_hints, overload
 
 import httpx
@@ -22,7 +22,11 @@ from litellm.llms.openai.chat.gpt_transformation import (
 )
 from litellm.secret_managers.main import get_secret_str
 from litellm.types.llms.mistral import MistralThinkingBlock, MistralToolCallMessage
-from litellm.types.llms.openai import AllMessageValues
+from litellm.types.llms.openai import (
+    AllMessageValues,
+    ChatCompletionToolChoiceFunctionParam,
+    ChatCompletionToolChoiceObjectParam,
+)
 from litellm.types.utils import ModelResponse, ModelResponseStream
 from litellm.utils import convert_to_model_response_object
 
@@ -44,7 +48,7 @@ class MistralConfig(OpenAIGPTConfig):
 
     - `tools` (list or null): A list of available tools for the model. Use this to specify functions for which the model can generate JSON inputs.
 
-    - `tool_choice` (string - 'auto'/'any'/'none' or null): Specifies if/how functions are called. If set to none the model won't call a function and will generate a message instead. If set to auto the model can choose to either generate a message or call a function. If set to any the model is forced to call a function. Default - 'auto'.
+    - `tool_choice` (string - 'auto'/'any'/'none', an object naming one function such as {"type": "function", "function": {"name": "my_function"}}, or null): Specifies if/how functions are called. If set to none the model won't call a function and will generate a message instead. If set to auto the model can choose to either generate a message or call a function. If set to any the model is forced to call a function. Default - 'auto'.
 
     - `stop` (string or array of strings): Stop generation if this token is detected. Or if one of these tokens is detected when providing an array
 
@@ -59,7 +63,7 @@ class MistralConfig(OpenAIGPTConfig):
     top_p: int | None = None
     max_tokens: int | None = None
     tools: list | None = None
-    tool_choice: Literal["auto", "any", "none"] | None = None
+    tool_choice: Literal["auto", "any", "none"] | ChatCompletionToolChoiceObjectParam | None = None
     random_seed: int | None = None
     safe_prompt: bool | None = None
     response_format: dict | None = None
@@ -71,7 +75,7 @@ class MistralConfig(OpenAIGPTConfig):
         top_p: int | None = None,
         max_tokens: int | None = None,
         tools: list | None = None,
-        tool_choice: Literal["auto", "any", "none"] | None = None,
+        tool_choice: Literal["auto", "any", "none"] | ChatCompletionToolChoiceObjectParam | None = None,
         random_seed: int | None = None,
         safe_prompt: bool | None = None,
         response_format: dict | None = None,
@@ -107,14 +111,19 @@ class MistralConfig(OpenAIGPTConfig):
 
         return supported_params
 
-    def _map_tool_choice(self, tool_choice: str | Mapping[str, object]) -> str | Mapping[str, object]:
+    @staticmethod
+    def _map_tool_choice(tool_choice: str | dict[str, object]) -> str | ChatCompletionToolChoiceObjectParam | None:
         match tool_choice:
             case "auto" | "none":
                 return tool_choice
-            case {"type": "function", "function": {"name": str()}}:
-                return tool_choice
-            case _:
+            case {"type": "function", "function": {"name": str(name)}} if name:
+                return ChatCompletionToolChoiceObjectParam(
+                    type="function", function=ChatCompletionToolChoiceFunctionParam(name=name)
+                )
+            case str():
                 return "any"
+            case _:
+                return None
 
     @staticmethod
     def _get_mistral_reasoning_system_prompt() -> str:
@@ -166,8 +175,12 @@ class MistralConfig(OpenAIGPTConfig):
                 optional_params["top_p"] = value
             if param == "stop":
                 optional_params["stop"] = value
-            if param == "tool_choice" and isinstance(value, (str, Mapping)):
-                optional_params["tool_choice"] = self._map_tool_choice(tool_choice=value)
+            if (
+                param == "tool_choice"
+                and isinstance(value, (str, dict))
+                and (mapped_tool_choice := self._map_tool_choice(tool_choice=value)) is not None
+            ):
+                optional_params["tool_choice"] = mapped_tool_choice
             if param == "seed":
                 optional_params["extra_body"] = {"random_seed": value}
             if param == "response_format":

tests/test_litellm/llms/mistral/test_mistral_chat_transformation.py

@@ -1,15 +1,11 @@
-from collections.abc import Mapping
+from collections.abc import Mapping, Sequence
 from typing import Final, List, cast
 from unittest.mock import MagicMock, patch
 
 import pytest
 
 from litellm.litellm_core_utils.prompt_templates.common_utils import TOOL_RESULT_IMAGE_BOUNDARY
-from litellm.types.llms.openai import (
-    AllMessageValues,
-    ChatCompletionToolChoiceObjectParam,
-    ChatCompletionToolParam,
-)
+from litellm.types.llms.openai import AllMessageValues, ChatCompletionToolParam
 
 
 from litellm.llms.mistral.chat.transformation import (
@@ -854,21 +850,17 @@ def test_mistral_transform_request_hoists_tool_message_image():
 class TestMistralToolChoice:
     """Mistral's tool_choice is either an enum or a named function object: https://docs.mistral.ai/api/"""
 
-    NAMED_TOOL_CHOICE: Final[ChatCompletionToolChoiceObjectParam] = {
-        "type": "function",
-        "function": {"name": "get_weather"},
-    }
-    TOOLS: Final[list[ChatCompletionToolParam]] = [
+    TOOLS: Final[Sequence[ChatCompletionToolParam]] = [
         {
             "type": "function",
             "function": {"name": "get_weather", "parameters": {"type": "object", "properties": {}}},
         }
     ]
 
-    def _transform(self, tool_choice: str | Mapping[str, object]) -> dict[str, object]:
+    def _transform(self, tool_choice: str | Mapping[str, object]) -> Mapping[str, object]:
         config: Final = MistralConfig()
         optional_params: Final = config.map_openai_params(
-            non_default_params={"tool_choice": tool_choice, "tools": self.TOOLS},
+            non_default_params={"tool_choice": tool_choice, "tools": list(self.TOOLS)},
             optional_params={},
             model="mistral-large-latest",
             drop_params=False,
@@ -882,7 +874,20 @@ class TestMistralToolChoice:
         )
 
     def test_named_function_is_forwarded_to_mistral(self) -> None:
-        assert self._transform(self.NAMED_TOOL_CHOICE)["tool_choice"] == self.NAMED_TOOL_CHOICE
+        request: Final = self._transform({"type": "function", "function": {"name": "get_weather"}})
+
+        assert request["tool_choice"] == {"type": "function", "function": {"name": "get_weather"}}
+
+    def test_keys_mistral_forbids_are_not_replayed(self) -> None:
+        request: Final = self._transform(
+            {
+                "type": "function",
+                "function": {"name": "get_weather", "strict": True},
+                "cache_control": {"type": "ephemeral"},
+            }
+        )
+
+        assert request["tool_choice"] == {"type": "function", "function": {"name": "get_weather"}}
 
     @pytest.mark.parametrize(
         "tool_choice, expected",
@@ -891,5 +896,13 @@ class TestMistralToolChoice:
     def test_enum_tool_choice_is_mapped(self, tool_choice: str, expected: str) -> None:
         assert self._transform(tool_choice)["tool_choice"] == expected
 
-    def test_object_shapes_mistral_does_not_accept_fall_back_to_any(self) -> None:
-        assert self._transform({"type": "allowed_tools", "allowed_tools": {"mode": "auto"}})["tool_choice"] == "any"
+    @pytest.mark.parametrize(
+        "tool_choice",
+        [
+            {"type": "function", "function": {}},
+            {"type": "function", "function": {"name": ""}},
+            {"type": "function", "function": {"name": 123}},
+        ],
+    )
+    def test_function_objects_without_a_usable_name_are_left_out(self, tool_choice: Mapping[str, object]) -> None:
+        assert "tool_choice" not in self._transform(tool_choice)