- **Bootstrap IoC-based architecture with modular services.**

- **Implement `MlxModelService` for local LLM backend.**
- **Introduce `DatabaseService` for MySQL integration.**
- **Add `HistoryService` to manage conversation context.**
- **Set up CLI interface via `CliUiService`.**
- **Establish EventBus for token streaming.**
- **Include conversation repository for data persistence.**
- **Add environment-based configuration management.**
- **Draft IoC architectural plan.**

services/chat/compact_service.py

@@ -0,0 +1,18 @@
+from services.model.base import AbstractModelService
+
+
+class CompactService:
+    """오래된 대화 턴을 LLM으로 요약하는 서비스."""
+
+    def __init__(self, model: AbstractModelService, max_tokens: int = 512):
+        self._model = model
+        self._max_tokens = max_tokens
+
+    def summarize(self, turns: list[dict]) -> str:
+        text = "\n".join(f"{t['role']}: {t['content']}" for t in turns)
+        prompt_history = [
+            {"role": "system", "content": "당신은 대화 요약 전문가입니다."},
+            {"role": "user", "content": f"다음 대화의 핵심 내용을 한국어로 간결하게 요약해주세요:\n\n{text}"},
+        ]
+        prompt = self._model.build_prompt(prompt_history)
+        return "".join(self._model.stream(prompt, self._max_tokens))