fix: align agent no-hit handling

ai_knowledge_base_v2/apps/admin-web/src/components/AgentManagementView.vue

@@ -74,7 +74,7 @@ const selectedModelName = computed(() => {
   return model?.displayName || model?.modelName || "未选择模型";
 });
 const selectedKnowledgeSummary = computed(() => {
-  if (!agentForm.knowledgeIds.length) return "未选择知识库";
+  if (!agentForm.knowledgeIds.length) return "默认使用全部正式开放知识库";
   if (agentForm.knowledgeIds.length === 1) {
     return knowledge.value.find((item) => item.id === agentForm.knowledgeIds[0])?.name || "1 个知识库";
   }

ai_knowledge_base_v2/apps/backend/app/services/agent_debug_service.py

@@ -23,12 +23,14 @@ class AgentDebugService:
             SimpleNamespace(id=index + 1, role=item.role, content=item.content)
             for index, item in enumerate(payload.history)
         ]
+        preview_knowledge_ids = payload.knowledgeIds or None
+        version_overrides = payload.knowledgeVersions or None
         rag_result = await KnowledgeAgentService.build_result(
             db,
             question=payload.question,
             history=history,
-            version_overrides=payload.knowledgeVersions,
+            version_overrides=version_overrides,
-            preview_knowledge_ids=payload.knowledgeIds,
+            preview_knowledge_ids=preview_knowledge_ids,
             prompt_override=payload.promptContent,
             response_depth=payload.responseDepth,
         )

ai_knowledge_base_v2/apps/backend/app/services/model_service.py

@@ -38,7 +38,7 @@ class ModelClientService:
             if model is None:
                 raise ExternalServiceError("未启用可用模型，请先在模型管理中启用一个模型。", provider="model")
             model_name = model.model_name
-            answer = _call_configured_model(model, rag_result, allow_no_hit=rag_result.allow_general_knowledge)
+            answer = _call_configured_model(model, rag_result, allow_no_hit=True)
         return ModelCompletion(
             answer=answer,
             model_id=model.id if model is not None else None,

ai_knowledge_base_v2/apps/backend/app/services/model_stream_service.py

@@ -30,7 +30,7 @@ from app.services.model_service import (
     _system_and_turn_messages,
     _system_config_bool,
 )
-from app.services.rag_service import NO_HIT_ANSWER, RagResult
+from app.services.rag_service import RagResult
 
 
 @dataclass(frozen=True)
@@ -64,14 +64,6 @@ class ModelStreamService:
                 chunks=_display_chunks(model, _mock_answer(rag_result)),
             )
 
-        if not rag_result.is_hit and not rag_result.allow_general_knowledge:
-            return StreamingModelResponse(
-                model_id=model.id if model is not None else None,
-                model_name=model.model_name if model is not None else "no-hit",
-                input_token=_rough_token_count(rag_result.prompt),
-                chunks=_display_chunks(model, NO_HIT_ANSWER),
-            )
-
         if model is None:
             raise ExternalServiceError("未启用可用模型，请先在模型管理中启用一个模型。", provider="model")
         if not (model.api_url or model.base_url) or not model.api_key:
@@ -98,14 +90,6 @@ class ModelStreamService:
                 chunks=_async_display_chunks(model, _mock_answer(rag_result)),
             )
 
-        if not rag_result.is_hit and not rag_result.allow_general_knowledge:
-            return AsyncStreamingModelResponse(
-                model_id=model.id if model is not None else None,
-                model_name=model.model_name if model is not None else "no-hit",
-                input_token=_rough_token_count(rag_result.prompt),
-                chunks=_async_display_chunks(model, NO_HIT_ANSWER),
-            )
-
         if model is None:
             raise ExternalServiceError("未启用可用模型，请先在模型管理中启用一个模型。", provider="model")
         if not (model.api_url or model.base_url) or not model.api_key:
@@ -147,19 +131,19 @@ def _get_enabled_model(db: Session) -> ModelConfig | None:
 def _stream_configured_model(model: ModelConfig, rag_result: RagResult) -> Iterator[str]:
     api_type = model.api_type or "openai_compatible"
     if model.stream_enabled != 1:
-        return iter((_call_configured_model(model, rag_result, allow_no_hit=rag_result.allow_general_knowledge),))
+        return iter((_call_configured_model(model, rag_result, allow_no_hit=True),))
     if api_type == "anthropic_messages":
         return _stream_anthropic_messages(model, rag_result)
     if api_type == "openai_compatible":
         return _stream_openai_compatible_model(model, rag_result)
-    return iter((_call_configured_model(model, rag_result, allow_no_hit=rag_result.allow_general_knowledge),))
+    return iter((_call_configured_model(model, rag_result, allow_no_hit=True),))
 
 
 async def _stream_configured_model_async(model: ModelConfig, rag_result: RagResult) -> AsyncIterator[str]:
     api_type = model.api_type or "openai_compatible"
     if model.stream_enabled != 1:
         answer = await asyncio.to_thread(
-            _call_configured_model, model, rag_result, allow_no_hit=rag_result.allow_general_knowledge
+            _call_configured_model, model, rag_result, allow_no_hit=True
         )
         yield answer
         return
@@ -175,7 +159,7 @@ async def _stream_configured_model_async(model: ModelConfig, rag_result: RagResu
         return
 
     answer = await asyncio.to_thread(
-        _call_configured_model, model, rag_result, allow_no_hit=rag_result.allow_general_knowledge
+        _call_configured_model, model, rag_result, allow_no_hit=True
     )
     yield answer
 

ai_knowledge_base_v2/apps/backend/tests/test_agent_runtime_config.py

@@ -16,6 +16,7 @@ from app.schemas.admin import AgentDebugRequest, AgentRuntimeConfigSaveRequest
 from app.services.agent_debug_service import AgentDebugService
 from app.services.model_stream_service import (
     AsyncStreamingModelResponse,
+    ModelStreamService,
     _openai_stream_payload,
     _stream_configured_model_async,
 )
@@ -194,12 +195,71 @@ def test_debug_preview_passes_conversation_history_and_replaces_saved_prompt():
 
     kwargs = build_result.await_args.kwargs
     assert [item.content for item in kwargs["history"]] == ["最开始的问题", "第一次回答"]
+    assert kwargs["preview_knowledge_ids"] is None
+    assert kwargs["version_overrides"] is None
     assert kwargs["prompt_override"] == "调试主提示词"
     assert kwargs["response_depth"] == 35
     assert result.messages == rag_result.messages
     assert result.prompt == "调试提示词渲染结果"
 
 
+def test_agent_debug_empty_knowledge_selection_uses_formal_open_catalog():
+    payload = AgentDebugRequest(
+        promptContent="调试主提示词",
+        modelId=1,
+        knowledgeIds=[],
+        knowledgeVersions={},
+        question="测试问题",
+    )
+    rag_result = RagResult(
+        question="测试问题",
+        knowledge_scopes=[],
+        chunks=[],
+        prompt="测试",
+        allow_general_knowledge=True,
+    )
+
+    with _database() as db:
+        build_result = AsyncMock(return_value=rag_result)
+        with patch("app.services.agent_debug_service.KnowledgeAgentService.build_result", build_result):
+            asyncio.run(AgentDebugService.build_result(db, payload))
+
+    kwargs = build_result.await_args.kwargs
+    assert kwargs["preview_knowledge_ids"] is None
+    assert kwargs["version_overrides"] is None
+
+
+def test_user_stream_no_hit_still_calls_model_for_cautious_answer():
+    async def configured_chunks(_model, _rag_result):
+        yield "我暂时没有可靠课程依据，但可以先帮你整理需要确认的点。"
+
+    async def collect():
+        with _database() as db:
+            model = _model()
+            db.add_all([
+                model,
+                SystemConfig(config_key="mock_model_enabled", config_value="false"),
+            ])
+            db.commit()
+            rag_result = RagResult(
+                question="课程里的特殊练习怎么做？",
+                knowledge_scopes=[],
+                chunks=[],
+                prompt="本轮没有可靠的正式知识章节。",
+                allow_general_knowledge=False,
+            )
+            with patch(
+                "app.services.model_stream_service._stream_configured_model_async",
+                configured_chunks,
+            ):
+                response = ModelStreamService.stream_async(db, rag_result)
+                return [chunk async for chunk in response.chunks]
+
+    chunks = asyncio.run(collect())
+
+    assert chunks == ["我暂时没有可靠课程依据，但可以先帮你整理需要确认的点。"]
+
+
 def test_prompt_includes_response_depth_instruction():
     with _database() as db:
         db.add(SystemConfig(config_key="agent_response_depth", config_value="20"))