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runner_engine.py

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+# runner_engine.py
+
+import asyncio
+from hot_cache import LRUCache
+from web_search_autonomy import WebSearchAutonomySystem
+
+# NOTE: The mock objects previously in this file have been moved to `mock_objects.py`
+# for centralized test management. The main `RunnerEngine` class below is the
+# actual implementation.
+
+
+
+class RunnerEngine:
+    """推論実行エンジン（Layer 3）"""
+    
+    def __init__(self, llm_client, db_interface, web_search_system):
+        self.llm = llm_client
+        self.db = db_interface
+        self.web_search_system = web_search_system
+        self.hot_cache = LRUCache(max_size=20)
+
+    async def _fetch_db_coordinates(self, db_coordinates: list) -> dict:
+        """DB座標から知識を取得（ホットキャッシュ利用）"""
+        results = {}
+        for coord in db_coordinates[:5]:
+            if coord in self.hot_cache:
+                print(f"Cache: Hit for {coord}")
+                results[coord] = self.hot_cache[coord]
+                continue
+            
+            print(f"Cache: Miss for {coord}")
+            tile = await self.db.fetch_async(coord)
+            if tile:
+                self.hot_cache[coord] = tile
+                results[coord] = tile
+        return results
+
+    def _build_context(self, question: str, db_results: dict, session_context) -> str:
+        """LLMプロンプト用のコンテキストを構築"""
+        context_parts = []
+        if session_context: # この例では未使用
+            context_parts.append(f"セッション履歴: {session_context}")
+        
+        if db_results:
+            for coord, tile in db_results.items():
+                context_parts.append(f"【確実性{tile['certainty']}%】{tile['content']}")
+        
+        return "\n\n".join(context_parts)
+
+    def _format_prompt(self, question: str, context: str) -> str:
+        return f"情報: {context}\n\n質問: {question}\n\n指示: 提供された情報に基づき回答してください。"
+
+    async def generate_response_streaming(self, question: str, db_coordinates: list, session_context=None):
+        """ストリーミング形式での回答生成と動的なWeb検索判断"""
+        web_decision = self.web_search_system.should_search(question)
+        
+        db_task = asyncio.create_task(self._fetch_db_coordinates(db_coordinates))
+        web_task = asyncio.create_task(mock_web_search_api(question)) if web_decision["should_search"] else None
+
+        try:
+            db_results = await asyncio.wait_for(db_task, timeout=0.5)
+        except asyncio.TimeoutError:
+            db_results = {}
+        
+        context = self._build_context(question, db_results, session_context)
+        prompt = self._format_prompt(question, context)
+        
+        partial_response = ""
+        final_metadata = {}
+        
+        async for result in self.llm.generate_streaming(prompt):
+            if result['type'] == 'response_token':
+                token = result['token']
+                partial_response += token
+                yield result # トークンをそのまま中継
+                
+                # 推論中の動的Web検索判定
+                if len(partial_response) > 5 and len(partial_response) % 20 == 0 and not web_task:
+                    class MockInferenceState: partial_response = ""
+                    inference_state = MockInferenceState()
+                    inference_state.partial_response = partial_response
+                    dynamic_decision = self.web_search_system.should_search(question, inference_state=inference_state)
+                    if dynamic_decision["should_search"]:
+                        print("\n*** Dynamic Web Search Triggered! ***\n")
+                        web_task = asyncio.create_task(mock_web_search_api(question))
+            
+            elif result['type'] == 'completion':
+                # Judge層で必要となる構造化されたメタデータを準備
+                final_metadata = result['metadata']
+
+        web_results_content = []
+        if web_task:
+            try:
+                web_results_content = await asyncio.wait_for(web_task, timeout=2.0)
+                yield {"type": "web_results", "results": web_results_content}
+            except asyncio.TimeoutError:
+                yield {"type": "web_results", "results": [], "error": "timeout"}
+
+        # 最終的なメタデータを生成して終了
+        final_metadata["referenced_coords"] = db_coordinates
+        final_metadata["web_results"] = web_results_content
+        yield {
+            "type": "final_structured_response",
+            "is_complete": True,
+            "main_response": partial_response, 
+            **final_metadata # thinking_process, key_pointsなどを展開
+        }
+
+# --- 実行例 ---
+async def main():
+    # モックコンポーネントの初期化
+    llm = MockLLMClient()
+    db = MockDBInterface()
+    web_search = WebSearchAutonomySystem()
+    
+    runner = RunnerEngine(llm, db, web_search)
+    
+    question = "最新の心筋梗塞の診断について"
+    # Layer 1で抽出された想定の座標
+    db_coordinates = [(28, 35, 15)] 
+
+    print(f"--- Running pipeline for question: '{question}' ---")
+    final_response = {}
+    async for event in runner.generate_response_streaming(question, db_coordinates):
+        if event['type'] == 'response_token':
+            print(event['token'], end='', flush=True)
+        elif event['type'] == 'web_results':
+            print(f"\n\n--- Web Results Received ---")
+            print(event['results'])
+        elif event['type'] == 'final_structured_response':
+            final_response = event
+    
+    print("\n\n--- Final Structured Response (for Judge Layer) ---")
+    import json
+    print(json.dumps(final_response, indent=2, ensure_ascii=False))
+
+
+if __name__ == "__main__":
+    asyncio.run(main())