gen: (GPT-5) #file:agent.py で LlmClassifier, MockClassifier を定義していますが、抽象クラスのインタフェースを作成してリファクタしてください

Author: ks6088ts

template_langgraph/agents/image_classifier_agent/agent.py

@@ -5,10 +5,14 @@
 from langgraph.graph import StateGraph
 from langgraph.types import Send
 
+from template_langgraph.agents.image_classifier_agent.classifiers import (
+    BaseClassifier,
+    LlmClassifier,
+    MockClassifier,
+)
 from template_langgraph.agents.image_classifier_agent.models import (
     AgentState,
     ClassifyImageState,
-    Result,
     Results,
 )
 from template_langgraph.llms.azure_openais import AzureOpenAiWrapper
@@ -28,62 +32,16 @@ def notify(self, id: str, body: dict) -> None:
         logger.info(f"Notification sent for request {id}: {body}")
 
 
-class MockClassifier:
-    def predict(
-        self,
-        prompt: str,
-        image: str,
-        llm=AzureOpenAiWrapper().chat_model,
-    ) -> Result:
-        """Simulate image classification."""
-        return Result(
-            title="Mocked Image Title",
-            summary=f"Mocked summary of the prompt: {prompt}",
-            labels=["mocked_label_1", "mocked_label_2"],
-            reliability=0.95,
-        )
-
-
-class LlmClassifier:
-    def predict(
-        self,
-        prompt: str,
-        image: str,
-        llm=AzureOpenAiWrapper().chat_model,
-    ) -> Result:
-        """Use the LLM to classify the image."""
-        logger.info(f"Classifying image with LLM: {prompt}")
-        return llm.with_structured_output(Result).invoke(
-            input=[
-                {
-                    "role": "user",
-                    "content": [
-                        {
-                            "type": "text",
-                            "text": prompt,
-                        },
-                        {
-                            "type": "image",
-                            "source_type": "base64",
-                            "data": image,
-                            "mime_type": "image/png",
-                        },
-                    ],
-                },
-            ]
-        )
-
-
 class ImageClassifierAgent:
     def __init__(
         self,
         llm=AzureOpenAiWrapper().chat_model,
         notifier=MockNotifier(),
-        classifier=MockClassifier(),
+        classifier: BaseClassifier = MockClassifier(),
     ):
         self.llm = llm
         self.notifier = notifier
-        self.classifier = classifier
+        self.classifier: BaseClassifier = classifier
 
     def create_graph(self):
         """Create the main graph for the agent."""

template_langgraph/agents/image_classifier_agent/classifiers.py

@@ -0,0 +1,80 @@
+"""Classifier interfaces and implementations for ImageClassifierAgent.
+
+This module defines an abstract base classifier interface so that different
+image classification strategies (mock, LLM-backed, future vision models, etc.)
+can be plugged into the agent without modifying the agent orchestration code.
+"""
+
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from typing import Any
+
+from langchain_core.language_models.chat_models import BaseChatModel
+
+from template_langgraph.agents.image_classifier_agent.models import Result
+from template_langgraph.loggers import get_logger
+
+logger = get_logger(__name__)
+
+
+class BaseClassifier(ABC):
+    """Abstract base class for image classifiers.
+
+    Implementations should return a structured ``Result`` object.
+    The ``llm`` argument is kept generic (Any) to avoid tight coupling
+    with a specific provider wrapper; callers supply a model instance
+    that offers the needed interface (e.g. ``with_structured_output``).
+    """
+
+    @abstractmethod
+    def predict(self, prompt: str, image: str, llm: BaseChatModel) -> Result:  # pragma: no cover - interface
+        """Classify an image.
+
+        Args:
+            prompt: Instruction or question guiding the classification.
+            image: Base64-encoded image string ("data" portion only).
+            llm: A language / vision model instance used (if needed) by the classifier.
+
+        Returns:
+            Result: Structured classification output.
+        """
+        raise NotImplementedError
+
+
+class MockClassifier(BaseClassifier):
+    """Simple mock classifier used for tests / offline development."""
+
+    def predict(self, prompt: str, image: str, llm: Any) -> Result:  # noqa: D401
+        import time
+
+        time.sleep(3)  # Simulate a long-running process
+        return Result(
+            title="Mocked Image Title",
+            summary=f"Mocked summary of the prompt: {prompt}",
+            labels=["mocked_label_1", "mocked_label_2"],
+            reliability=0.95,
+        )
+
+
+class LlmClassifier(BaseClassifier):
+    """LLM-backed classifier using the provided model's structured output capability."""
+
+    def predict(self, prompt: str, image: str, llm: BaseChatModel) -> Result:  # noqa: D401
+        logger.info(f"Classifying image with LLM: {prompt}")
+        return llm.with_structured_output(Result).invoke(
+            input=[
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "text", "text": prompt},
+                        {
+                            "type": "image",
+                            "source_type": "base64",
+                            "data": image,
+                            "mime_type": "image/png",
+                        },
+                    ],
+                }
+            ]
+        )