fix(mistral): a named tool_choice is dropped, the model picks any tool

Mistral's tool_choice takes either the enum (none, auto, any, required)
or an object naming one function, and that object is how a caller pins
the model to a single tool. The param mapping only handled strings, so
an object value was filtered out before it could be mapped and never
reached the request body: the model got tools with no tool_choice and
answered however it liked, silently ignoring the pin.

Map the object through as well, keeping the enum mapping and the fall
back to "any" for shapes Mistral does not document.

The same mapping backs the codestral provider and the Mistral models on
vertex_ai, so all three surfaces were affected.

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
+from collections.abc import AsyncIterator, Coroutine, Iterator, Mapping
 from typing import TYPE_CHECKING, Any, Final, Literal, cast, get_type_hints, overload
 
 import httpx
@@ -107,13 +107,14 @@ class MistralConfig(OpenAIGPTConfig):
 
         return supported_params
 
-    def _map_tool_choice(self, tool_choice: str) -> str:
-        if tool_choice == "auto" or tool_choice == "none":
-            return tool_choice
-        elif tool_choice == "required":
-            return "any"
-        else:  # openai 'tool_choice' object param not supported by Mistral API
-            return "any"
+    def _map_tool_choice(self, tool_choice: str | Mapping[str, object]) -> str | Mapping[str, object]:
+        match tool_choice:
+            case "auto" | "none":
+                return tool_choice
+            case {"type": "function", "function": {"name": str()}}:
+                return tool_choice
+            case _:
+                return "any"
 
     @staticmethod
     def _get_mistral_reasoning_system_prompt() -> str:
@@ -165,7 +166,7 @@ class MistralConfig(OpenAIGPTConfig):
                 optional_params["top_p"] = value
             if param == "stop":
                 optional_params["stop"] = value
-            if param == "tool_choice" and isinstance(value, str):
+            if param == "tool_choice" and isinstance(value, (str, Mapping)):
                 optional_params["tool_choice"] = self._map_tool_choice(tool_choice=value)
             if param == "seed":
                 optional_params["extra_body"] = {"random_seed": value}

tests/test_litellm/llms/mistral/test_mistral_chat_transformation.py

@@ -1,10 +1,15 @@
-from typing import List, cast
+from collections.abc import Mapping
+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
+from litellm.types.llms.openai import (
+    AllMessageValues,
+    ChatCompletionToolChoiceObjectParam,
+    ChatCompletionToolParam,
+)
 
 
 from litellm.llms.mistral.chat.transformation import (
@@ -844,3 +849,47 @@ def test_mistral_transform_request_hoists_tool_message_image():
         {"type": "text", "text": TOOL_RESULT_IMAGE_BOUNDARY},
         {"type": "image_url", "image_url": {"url": data_uri}},
     ]
+
+
+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]] = [
+        {
+            "type": "function",
+            "function": {"name": "get_weather", "parameters": {"type": "object", "properties": {}}},
+        }
+    ]
+
+    def _transform(self, tool_choice: str | Mapping[str, object]) -> dict[str, object]:
+        config: Final = MistralConfig()
+        optional_params: Final = config.map_openai_params(
+            non_default_params={"tool_choice": tool_choice, "tools": self.TOOLS},
+            optional_params={},
+            model="mistral-large-latest",
+            drop_params=False,
+        )
+        return config.transform_request(
+            model="mistral-large-latest",
+            messages=[{"role": "user", "content": "what is the weather in Madrid"}],
+            optional_params=optional_params,
+            litellm_params={},
+            headers={},
+        )
+
+    def test_named_function_is_forwarded_to_mistral(self) -> None:
+        assert self._transform(self.NAMED_TOOL_CHOICE)["tool_choice"] == self.NAMED_TOOL_CHOICE
+
+    @pytest.mark.parametrize(
+        "tool_choice, expected",
+        [("auto", "auto"), ("none", "none"), ("required", "any")],
+    )
+    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"