refactor: unify structured output with pydantic model

Signed-off-by: Stream <Stream_2@qq.com>

api/tests/unit_tests/utils/structured_output_parser/test_structured_output_parser.py

@@ -2,9 +2,13 @@ from decimal import Decimal
 from unittest.mock import MagicMock, patch
 
 import pytest
+from pydantic import BaseModel, ConfigDict
 
 from core.llm_generator.output_parser.errors import OutputParserError
-from core.llm_generator.output_parser.structured_output import invoke_llm_with_structured_output
+from core.llm_generator.output_parser.structured_output import (
+    invoke_llm_with_pydantic_model,
+    invoke_llm_with_structured_output,
+)
 from core.model_runtime.entities.llm_entities import (
     LLMResult,
     LLMResultChunk,
@@ -461,3 +465,68 @@ def test_model_specific_schema_preparation():
 
     # For Gemini, the schema should not have additionalProperties and boolean should be converted to string
     assert "json_schema" in call_args.kwargs["model_parameters"]
+
+
+class ExampleOutput(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    name: str
+
+
+def test_structured_output_with_pydantic_model():
+    model_schema = get_model_entity("openai", "gpt-4o", support_structure_output=True)
+    model_instance = get_model_instance()
+    model_instance.invoke_llm.return_value = LLMResult(
+        model="gpt-4o",
+        message=AssistantPromptMessage(content='{"name": "test"}'),
+        usage=create_mock_usage(prompt_tokens=8, completion_tokens=4),
+    )
+
+    prompt_messages = [UserPromptMessage(content="Return a JSON object with name.")]
+
+    result = invoke_llm_with_pydantic_model(
+        provider="openai",
+        model_schema=model_schema,
+        model_instance=model_instance,
+        prompt_messages=prompt_messages,
+        output_model=ExampleOutput,
+        stream=False,
+    )
+
+    assert isinstance(result, LLMResultWithStructuredOutput)
+    assert result.structured_output == {"name": "test"}
+
+
+def test_structured_output_with_pydantic_model_streaming_rejected():
+    model_schema = get_model_entity("openai", "gpt-4o", support_structure_output=True)
+    model_instance = get_model_instance()
+
+    with pytest.raises(ValueError):
+        invoke_llm_with_pydantic_model(
+            provider="openai",
+            model_schema=model_schema,
+            model_instance=model_instance,
+            prompt_messages=[UserPromptMessage(content="test")],
+            output_model=ExampleOutput,
+            stream=True,
+        )
+
+
+def test_structured_output_with_pydantic_model_validation_error():
+    model_schema = get_model_entity("openai", "gpt-4o", support_structure_output=True)
+    model_instance = get_model_instance()
+    model_instance.invoke_llm.return_value = LLMResult(
+        model="gpt-4o",
+        message=AssistantPromptMessage(content='{"name": 123}'),
+        usage=create_mock_usage(prompt_tokens=8, completion_tokens=4),
+    )
+
+    with pytest.raises(OutputParserError):
+        invoke_llm_with_pydantic_model(
+            provider="openai",
+            model_schema=model_schema,
+            model_instance=model_instance,
+            prompt_messages=[UserPromptMessage(content="test")],
+            output_model=ExampleOutput,
+            stream=False,
+        )