Separate agent tuning from model setup

ai_knowledge_base_v2/apps/backend/app/api/admin_settings.py

@@ -9,9 +9,19 @@ from app.core.dependencies import get_current_admin
 from app.core.responses import api_success
 from app.models.admin import Admin
 from app.models.ai_config import ModelConfig, Prompt, SystemConfig
-from app.schemas.admin import EnableModelRequest, ModelSaveRequest, PromptSaveRequest, SystemConfigSaveRequest
+from app.models.knowledge import Knowledge
+from app.schemas.admin import (
+    AgentDebugRequest,
+    EnableModelRequest,
+    ModelSaveRequest,
+    PromptSaveRequest,
+    SystemConfigSaveRequest,
+)
 from app.services.admin_service import OperationLogService
+from app.services.feishu_service import FeishuKnowledgeService
+from app.services.knowledge_service import KnowledgeScope
 from app.services.model_service import ModelClientService
+from app.services.rag_service import RagResult
 
 router = APIRouter()
 
@@ -48,6 +58,37 @@ def reset_prompt(db: Session = Depends(get_db), current_admin: Admin = Depends(g
     return api_success({"promptContent": prompt.prompt_content})
 
 
+@router.post("/agent/debug")
+def debug_agent(
+    payload: AgentDebugRequest,
+    db: Session = Depends(get_db),
+    current_admin: Admin = Depends(get_current_admin),
+) -> dict:
+    model = db.get(ModelConfig, payload.modelId)
+    if model is None:
+        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="模型不存在")
+
+    scopes = _agent_knowledge_scopes(db, payload.knowledgeIds)
+    chunks = FeishuKnowledgeService.retrieve(payload.question, scopes)
+    prompt = _build_agent_debug_prompt(payload.promptContent, payload.question, chunks)
+    rag_result = RagResult(question=payload.question, knowledge_scopes=scopes, chunks=chunks, prompt=prompt)
+    result = ModelClientService.debug_model(
+        model,
+        rag_result,
+        {
+            "temperature": payload.temperature,
+            "top_p": payload.topP,
+            "top_k": payload.topK,
+            "presence_penalty": payload.presencePenalty,
+            "frequency_penalty": payload.frequencyPenalty,
+            "max_token": payload.maxToken,
+        },
+    )
+    OperationLogService.write(db, admin_id=current_admin.id, module="agent", action="debug", target_id=model.id)
+    db.commit()
+    return api_success(result)
+
+
 @router.get("/model/list")
 def list_models(db: Session = Depends(get_db), current_admin: Admin = Depends(get_current_admin)) -> dict:
     models = db.scalars(select(ModelConfig).order_by(ModelConfig.id.desc())).all()
@@ -235,6 +276,32 @@ def _model_dict(model: ModelConfig) -> dict:
     }
 
 
+def _agent_knowledge_scopes(db: Session, knowledge_ids: list[int]) -> list[KnowledgeScope]:
+    query = select(Knowledge).where(Knowledge.status == 1)
+    if knowledge_ids:
+        query = query.where(Knowledge.id.in_(knowledge_ids))
+    rows = db.scalars(query.order_by(Knowledge.id.asc())).all()
+    return [
+        KnowledgeScope(
+            id=item.id,
+            name=item.name,
+            feishu_space_id=item.feishu_space_id,
+            feishu_node_id=item.feishu_node_id,
+        )
+        for item in rows
+    ]
+
+
+def _build_agent_debug_prompt(prompt_content: str, question: str, chunks: list) -> str:
+    context = "\n\n".join(
+        f"[知识片段 {index}] 来源：{chunk.knowledge_name} / {chunk.title}\n{chunk.content}"
+        for index, chunk in enumerate(chunks, start=1)
+    )
+    if not context:
+        context = "未检索到相关知识片段。"
+    return f"{prompt_content.strip()}\n\n{context}\n\n用户问题：{question.strip()}"
+
+
 def _config_dict(config: SystemConfig) -> dict:
     return {
         "id": config.id,