sync: c44e5ea

src/inference/agent_manager.py

@@ -47,8 +47,8 @@ class AgentManager:
 
     Usage:
         manager = AgentManager("agents/")
-        persona = manager.get_agent("draft_response")
-        prompt = manager.build_prompt("draft_response", "민원 답변 초안을 작성해주세요.")
+        persona = manager.get_agent("domain_adapter")
+        prompt = manager.build_prompt("domain_adapter", "건축 허가 절차에 대해 안내해주세요.")
     """
 
     _FRONTMATTER_RE = re.compile(r"^---\s*\n(.*?)\n---\s*\n(.*)$", re.DOTALL)

src/inference/api_server.py

@@ -152,7 +152,7 @@ class vLLMEngineManager:
         self.session_store = SessionStore()
         self.agent_manager = AgentManager(AGENTS_DIR)
         self._api_lookup_action = None  # MinwonAnalysisAction (지연 초기화)
-        self._draft_response_fn = None  # 답변 초안 생성 클로저 (지연 초기화)
+        self._domain_adapter_fn = None  # Domain adapter generation closure (lazy init)
         self.graph = None  # LangGraph CompiledGraph (v2 엔드포인트용)
         self.graph_v3 = None  # v3 ReAct graph (v3 엔드포인트용)
         self._checkpointer_ctx = None  # AsyncSqliteSaver 컨텍스트 매니저 (lifespan에서 관리)
@@ -402,8 +402,8 @@ class vLLMEngineManager:
         gen_defaults = runtime_config.generation
 
         safe_message = self._escape_special_tokens(self._extract_query(request.prompt))
-        user_content = f"다음 민원에 대한 답변을 작성해 주세요.\n\n{safe_message}"
-        prompt = self._build_persona_prompt("draft_response", user_content)
+        user_content = f"다음 요청에 대해 해당 도메인의 전문 지식을 바탕으로 답변을 작성해 주세요.\n\n{safe_message}"
+        prompt = self._build_persona_prompt("domain_adapter", user_content)
 
         sampling_params = SamplingParams(
             temperature=request.temperature,
@@ -431,8 +431,8 @@ class vLLMEngineManager:
 
         safe_message = self._escape_special_tokens(self._extract_query(request.prompt))
         # 학습 데이터 형식: user = instruction + "\n\n" + input
-        user_content = f"다음 민원에 대한 답변을 작성해 주세요.\n\n{safe_message}"
-        prompt = self._build_persona_prompt("draft_response", user_content)
+        user_content = f"다음 요청에 대해 해당 도메인의 전문 지식을 바탕으로 답변을 작성해 주세요.\n\n{safe_message}"
+        prompt = self._build_persona_prompt("domain_adapter", user_content)
 
         sampling_params = SamplingParams(
             temperature=(
@@ -534,7 +534,7 @@ class vLLMEngineManager:
 
         engine_ref = self
 
-        async def _draft_response_tool(
+        async def _domain_adapter_tool(
             query: str,
             context: dict,
             session: SessionContext,
@@ -564,7 +564,7 @@ class vLLMEngineManager:
                     "text": "",
                     "draft_text": "",
                     "success": False,
-                    "error": "민원 답변 초안 생성 실패",
+                    "error": "Domain adapter response generation failed",
                     "results": [],
                     "context_text": "",
                 }
@@ -581,7 +581,7 @@ class vLLMEngineManager:
                 "completion_tokens": len(final_output.outputs[0].token_ids),
             }
 
-        self._draft_response_fn = _draft_response_tool
+        self._domain_adapter_fn = _domain_adapter_tool
 
     def _build_langgraph_tools(self) -> list:
         """LangGraph ToolNode용 도구 목록을 생성한다.
@@ -592,7 +592,7 @@ class vLLMEngineManager:
 
         return build_all_tools(
             api_lookup_action=self._api_lookup_action,
-            draft_response_fn=self._draft_response_fn,
+            domain_adapter_fn=self._domain_adapter_fn,
         )
 
     def _init_graph_with_async_checkpointer(self, checkpointer: object) -> None:

src/inference/graph/nodes.py

@@ -158,10 +158,16 @@ def _extractive_summarize(older_messages: list) -> str:
 
 
 _BASE_SYSTEM_PROMPT = (
-    "You are GovOn, a Korean civil complaint response assistant.\n"
-    "Analyze the user's request and actively use the available tools to provide accurate, data-backed answers.\n\n"
+    "You are GovOn, an agentic AI platform that transforms Korea's fragmented "
+    "government DX infrastructure into a unified AX (Agentic Transformation) system.\n"
+    "You orchestrate distributed government APIs — scattered across ministries, departments, "
+    "and agencies — as callable tools, enabling seamless cross-agency data access and "
+    "domain-specific response generation through a single intelligent interface.\n\n"
+    "Currently active domain adapters (MVP):\n"
+    "- public_admin_adapter: Public administration (civil complaints, permits, administrative procedures)\n"
+    "- legal_adapter: Legal domain (statutes, precedents, legal interpretation)\n\n"
     "Decision rules:\n"
-    "1. For complaint response drafts: ALWAYS call the appropriate adapter tool "
+    "1. For domain-specific response drafts: ALWAYS call the appropriate adapter tool "
     "(public_admin_adapter for administrative matters, legal_adapter for legal questions).\n"
     "2. For data or statistics: Use api_lookup, stats_lookup, issue_detector, "
     "keyword_analyzer, or demographics_lookup as needed.\n"

src/inference/graph/tools/__init__.py

@@ -15,7 +15,7 @@ from .search_tools import build_search_tools
 def build_all_tools(
     *,
     api_lookup_action: Optional[Any] = None,
-    draft_response_fn: Optional[Callable[..., Any]] = None,
+    domain_adapter_fn: Optional[Callable[..., Any]] = None,
 ) -> list:
     """전체 도구 목록을 동적으로 생성한다.
 
@@ -23,7 +23,7 @@ def build_all_tools(
     ----------
     api_lookup_action : Optional[MinwonAnalysisAction]
         공공데이터포털 API Action 인스턴스.
-    draft_response_fn : Optional[Callable]
+    domain_adapter_fn : Optional[Callable]
         답변 초안 생성 클로저. 있으면 adapter_tools를 추가.
 
     Returns
@@ -34,11 +34,11 @@ def build_all_tools(
     tools: list = []
     tools.extend(build_search_tools(api_lookup_action))
     tools.extend(build_analysis_tools(api_lookup_action))
-    if draft_response_fn:
+    if domain_adapter_fn:
         try:
             from .adapter_tools import build_adapter_tools
 
-            tools.extend(build_adapter_tools(draft_response_fn))
+            tools.extend(build_adapter_tools(domain_adapter_fn))
         except ImportError:
             import logging
 

src/inference/graph/tools/adapter_tools.py

@@ -22,12 +22,12 @@ class AdapterToolInput(BaseModel):
     query: str = Field(description="Civil complaint or legal query text")
 
 
-def build_adapter_tools(draft_response_fn: Callable) -> List[StructuredTool]:
+def build_adapter_tools(domain_adapter_fn: Callable) -> List[StructuredTool]:
     """AdapterRegistry의 모든 어댑터에 대해 StructuredTool을 동적 생성한다.
 
     Parameters
     ----------
-    draft_response_fn : Callable
+    domain_adapter_fn : Callable
         ``async (query, context, session) -> dict`` 시그니처의 답변 생성 함수.
 
     Returns
@@ -49,7 +49,7 @@ def build_adapter_tools(draft_response_fn: Callable) -> List[StructuredTool]:
                 try:
                     # session=None: ToolNode에서는 세션 컨텍스트가 불필요.
                     # persist_node가 별도로 세션 저장을 처리한다.
-                    result = await draft_response_fn(
+                    result = await domain_adapter_fn(
                         query=query,
                         context={"adapter": name},
                         session=None,

src/inference/runtime_config.py

@@ -149,10 +149,10 @@ class ModelConfig:
     - vLLM 서빙 옵션: --enable-auto-tool-choice --tool-call-parser hermes
 
     Multi-LoRA 어댑터:
-    - public_admin-adapter (LoRA #1): draft_response 용도
+    - public_admin-adapter (LoRA #1): domain_adapter 용도
       학습 데이터: umyunsang/govon-civil-response-data (74K건), QLoRA on AWQ base
       HF Hub: umyunsang/GovOn-EXAONE-LoRA-v2
-    - legal-adapter (LoRA #2): draft_response 용도
+    - legal-adapter (LoRA #2): domain_adapter 용도
       학습 데이터: umyunsang/govon-legal-response-data (243K건), QLoRA on AWQ base
       HuggingFace: siwo/govon-legal-adapter
     - 나머지 capability (api_lookup, synthesis 등)는 LoRA 없이 base model 사용