partners/ollama: fix tool calling with nested schemas (#28225)

## Description

This PR addresses the following:

**Fixes Issue #25343:**
- Adds additional logic to parse shallowly nested JSON-encoded strings
in tool call arguments, allowing for proper parsing of responses like
that of Llama3.1 and 3.2 with nested schemas.
 
**Adds Integration Test for Fix:**
- Adds a Ollama specific integration test to ensure the issue is
resolved and to prevent regressions in the future.

**Fixes Failing Integration Tests:**
- Fixes failing integration tests (even prior to changes) caused by
`llama3-groq-tool-use` model. Previously,
tests`test_structured_output_async` and
`test_structured_output_optional_param` failed due to the model not
issuing a tool call in the response. Resolved by switching to
`llama3.1`.

## Issue
Fixes #25343.

## Dependencies
No dependencies.

____

Done in collaboration with @ishaan-upadhyay @mirajismail @ZackSteine.

libs/partners/ollama/tests/integration_tests/chat_models/test_chat_models.py

@@ -0,0 +1,41 @@
+"""Ollama specific chat model integration tests"""
+
+from typing import List, Optional
+
+import pytest
+from pydantic import BaseModel, Field
+
+from langchain_ollama import ChatOllama
+
+
+@pytest.mark.parametrize(("model"), [("llama3.1")])
+def test_structured_output_deeply_nested(model: str) -> None:
+    """Test to verify structured output with a nested objects."""
+    llm = ChatOllama(model=model, temperature=0)
+
+    class Person(BaseModel):
+        """Information about a person."""
+
+        name: Optional[str] = Field(default=None, description="The name of the person")
+        hair_color: Optional[str] = Field(
+            default=None, description="The color of the person's hair if known"
+        )
+        height_in_meters: Optional[str] = Field(
+            default=None, description="Height measured in meters"
+        )
+
+    class Data(BaseModel):
+        """Extracted data about people."""
+
+        people: List[Person]
+
+    chat = llm.with_structured_output(Data)  # type: ignore[arg-type]
+    text = (
+        "Alan Smith is 6 feet tall and has blond hair."
+        "Alan Poe is 3 feet tall and has grey hair."
+    )
+    result = chat.invoke(text)
+    assert isinstance(result, Data)
+
+    for chunk in chat.stream(text):
+        assert isinstance(chunk, Data)

libs/partners/ollama/tests/integration_tests/chat_models/test_chat_models_standard.py

@@ -1,4 +1,4 @@
-"""Test chat model integration."""
+"""Test chat model integration using standard integration tests."""
 
 from typing import Type
 
@@ -16,7 +16,7 @@ class TestChatOllama(ChatModelIntegrationTests):
 
     @property
     def chat_model_params(self) -> dict:
-        return {"model": "llama3-groq-tool-use"}
+        return {"model": "llama3.1"}
 
     @property
     def supports_image_inputs(self) -> bool: