test: verify real kbqa workflow

backend/src/kbqa/agents/factory.py

@@ -26,6 +26,7 @@ def create_agent_bundle(settings: Settings) -> AgentBundle:
         system_prompt=RESEARCH_PROMPT,
         context_schema=ResearchContext,
         middleware=[
+            RequireResearchMiddleware(tool_name="search_knowledge_base"),
             ModelCallLimitMiddleware(run_limit=4, exit_behavior="error"),
             ToolCallLimitMiddleware(
                 tool_name="search_knowledge_base",

backend/src/kbqa/agents/llm.py

@@ -9,6 +9,7 @@ def create_chat_model(settings: Settings) -> ChatOpenAI:
         api_key=settings.dashscope_api_key,
         base_url=settings.dashscope_base_url,
         model=settings.chat_model,
+        extra_body={"enable_thinking": False},
         temperature=0.2,
         streaming=True,
         timeout=120.0,

backend/src/kbqa/agents/middleware.py

@@ -5,10 +5,13 @@ from langchain.messages import HumanMessage, ToolMessage
 
 
 class RequireResearchMiddleware(AgentMiddleware):
+    def __init__(self, tool_name: str = "research") -> None:
+        self.tool_name = tool_name
+
     @staticmethod
-    def _has_current_research(messages: list) -> bool:
+    def _has_current_tool(messages: list, tool_name: str) -> bool:
         for message in reversed(messages):
-            if isinstance(message, ToolMessage) and message.name == "research":
+            if isinstance(message, ToolMessage) and message.name == tool_name:
                 return True
             if isinstance(message, HumanMessage):
                 return False
@@ -19,5 +22,6 @@ class RequireResearchMiddleware(AgentMiddleware):
         request: ModelRequest,
         handler: Callable[[ModelRequest], Awaitable[ModelResponse]],
     ) -> ModelResponse:
-        tool_choice = "none" if self._has_current_research(request.messages) else "required"
+        has_tool_result = self._has_current_tool(request.messages, self.tool_name)
+        tool_choice = "none" if has_tool_result else "required"
         return await handler(request.override(tool_choice=tool_choice))

backend/src/kbqa/rag/vector_store.py

@@ -14,6 +14,7 @@ class MilvusVectorStore:
 
     def ensure_collection(self) -> None:
         if self.client.has_collection(self.collection_name):
+            self.client.load_collection(self.collection_name)
             return
         schema = self.client.create_schema(auto_id=False, enable_dynamic_field=False)
         schema.add_field(
@@ -44,6 +45,7 @@ class MilvusVectorStore:
             schema=schema,
             index_params=index_params,
         )
+        self.client.load_collection(self.collection_name)
 
     def upsert(self, chunks: list[PreparedChunk], vectors: list[list[float]]) -> None:
         if len(chunks) != len(vectors):
@@ -67,7 +69,7 @@ class MilvusVectorStore:
             data=[vector],
             anns_field="vector",
             limit=limit,
-            output_fields=["document_id", "order_index"],
+            output_fields=["chunk_id", "document_id", "order_index"],
             search_params={"metric_type": "COSINE"},
         )
         hits: list[VectorHit] = []
@@ -75,7 +77,7 @@ class MilvusVectorStore:
             entity = item.get("entity", {})
             hits.append(
                 VectorHit(
-                    chunk_id=str(item["id"]),
+                    chunk_id=str(entity["chunk_id"]),
                     document_id=str(entity["document_id"]),
                     order_index=int(entity["order_index"]),
                     score=float(item["distance"]),