mistralai: support method="json_schema" in structured output (#29461)

https://docs.mistral.ai/capabilities/structured-output/custom_structured_output/

libs/partners/mistralai/tests/integration_tests/test_chat_models.py

@@ -3,6 +3,7 @@
 import json
 from typing import Any, Optional
 
+import pytest
 from langchain_core.messages import (
     AIMessage,
     AIMessageChunk,
@@ -10,6 +11,7 @@ from langchain_core.messages import (
     HumanMessage,
 )
 from pydantic import BaseModel
+from typing_extensions import TypedDict
 
 from langchain_mistralai.chat_models import ChatMistralAI
 
@@ -176,6 +178,65 @@ def test_streaming_structured_output() -> None:
         chunk_num += 1
 
 
+class Book(BaseModel):
+    name: str
+    authors: list[str]
+
+
+class BookDict(TypedDict):
+    name: str
+    authors: list[str]
+
+
+def _check_parsed_result(result: Any, schema: Any) -> None:
+    if schema == Book:
+        assert isinstance(result, Book)
+    elif schema == BookDict:
+        assert all(key in ["name", "authors"] for key in result.keys())
+
+
+@pytest.mark.parametrize("schema", [Book, BookDict, Book.model_json_schema()])
+def test_structured_output_json_schema(schema: Any) -> None:
+    llm = ChatMistralAI(model="ministral-8b-latest")  # type: ignore[call-arg]
+    structured_llm = llm.with_structured_output(schema, method="json_schema")
+
+    messages = [
+        {"role": "system", "content": "Extract the book's information."},
+        {
+            "role": "user",
+            "content": "I recently read 'To Kill a Mockingbird' by Harper Lee.",
+        },
+    ]
+    # Test invoke
+    result = structured_llm.invoke(messages)
+    _check_parsed_result(result, schema)
+
+    # Test stream
+    for chunk in structured_llm.stream(messages):
+        _check_parsed_result(chunk, schema)
+
+
+@pytest.mark.parametrize("schema", [Book, BookDict, Book.model_json_schema()])
+async def test_structured_output_json_schema_async(schema: Any) -> None:
+    llm = ChatMistralAI(model="ministral-8b-latest")  # type: ignore[call-arg]
+    structured_llm = llm.with_structured_output(schema, method="json_schema")
+
+    messages = [
+        {"role": "system", "content": "Extract the book's information."},
+        {
+            "role": "user",
+            "content": "I recently read 'To Kill a Mockingbird' by Harper Lee.",
+        },
+    ]
+    # Test invoke
+    result = await structured_llm.ainvoke(messages)
+    _check_parsed_result(result, schema)
+
+    # Test stream
+    async for chunk in structured_llm.astream(messages):
+        _check_parsed_result(chunk, schema)
+
+
 def test_tool_call() -> None:
     llm = ChatMistralAI(model="mistral-large-latest", temperature=0)  # type: ignore[call-arg]