完善skills；测试用例生成页面功能初步实现

rag-web-ui/backend/app/api/api_v1/testing.py

@@ -1,6 +1,8 @@
+import logging
+import asyncio
 from typing import Any, Dict, List
 
-from fastapi import APIRouter, Depends
+from fastapi import APIRouter, Depends, HTTPException
 from sqlalchemy.orm import Session
 
 from app.core.config import settings
@@ -15,6 +17,8 @@ from app.services.testing_pipeline import run_testing_pipeline
 from app.services.vector_store import VectorStoreFactory
 
 router = APIRouter()
+logger = logging.getLogger(__name__)
+MODEL_PIPELINE_TIMEOUT_SECONDS = 300
 
 
 async def _build_kb_vector_stores(db: Session, knowledge_bases: List[KnowledgeBase]) -> List[Dict[str, Any]]:
@@ -47,38 +51,75 @@ async def generate_testing_content(
 
     knowledge_context = (payload.knowledge_context or "").strip()
     if payload.knowledge_base_ids:
-        knowledge_bases = (
-            db.query(KnowledgeBase)
-            .filter(
-                KnowledgeBase.id.in_(payload.knowledge_base_ids),
-                KnowledgeBase.user_id == current_user.id,
+        try:
+            knowledge_bases = (
+                db.query(KnowledgeBase)
+                .filter(
+                    KnowledgeBase.id.in_(payload.knowledge_base_ids),
+                    KnowledgeBase.user_id == current_user.id,
+                )
+                .all()
             )
-            .all()
+
+            kb_vector_stores = await _build_kb_vector_stores(db, knowledge_bases)
+            if kb_vector_stores:
+                retriever = MultiKBRetriever(
+                    reranker_weight=settings.RERANKER_WEIGHT,
+                )
+                retrieval_rows = await retriever.retrieve(
+                    query=payload.requirement_text,
+                    kb_vector_stores=kb_vector_stores,
+                    fetch_k_per_kb=max(12, payload.retrieval_top_k * 2),
+                    top_k=payload.retrieval_top_k,
+                )
+                if retrieval_rows:
+                    knowledge_context = format_retrieval_context(retrieval_rows)
+        except Exception as exc:
+            logger.exception(
+                "Testing generation retrieval fallback triggered for user=%s knowledge_base_ids=%s: %s",
+                current_user.id,
+                payload.knowledge_base_ids,
+                exc,
+            )
+
+    pipeline_kwargs = {
+        "user_requirement_text": payload.requirement_text,
+        "requirement_type_input": payload.requirement_type,
+        "debug": payload.debug,
+        "knowledge_context": knowledge_context,
+        "use_model_generation": payload.use_model_generation,
+        "max_items_per_group": payload.max_items_per_group,
+        "cases_per_item": payload.cases_per_item,
+        "max_focus_points": payload.max_focus_points,
+        "max_llm_calls": payload.max_llm_calls,
+    }
+
+    try:
+        result = await asyncio.wait_for(
+            asyncio.to_thread(run_testing_pipeline, **pipeline_kwargs),
+            timeout=MODEL_PIPELINE_TIMEOUT_SECONDS,
         )
+    except asyncio.TimeoutError as exc:
+        logger.exception(
+            "Testing pipeline timed out for user=%s use_model_generation=%s after %s seconds",
+            current_user.id,
+            payload.use_model_generation,
+            MODEL_PIPELINE_TIMEOUT_SECONDS,
+        )
+        raise HTTPException(
+            status_code=504,
+            detail=f"LLM generation timed out after {MODEL_PIPELINE_TIMEOUT_SECONDS} seconds",
+        ) from exc
+    except Exception as exc:
+        logger.exception(
+            "Testing pipeline failed for user=%s use_model_generation=%s: %s",
+            current_user.id,
+            payload.use_model_generation,
+            exc,
+        )
+        raise HTTPException(
+            status_code=500,
+            detail=f"LLM generation failed: {exc}",
+        ) from exc
 
-        kb_vector_stores = await _build_kb_vector_stores(db, knowledge_bases)
-        if kb_vector_stores:
-            retriever = MultiKBRetriever(
-                reranker_weight=settings.RERANKER_WEIGHT,
-            )
-            retrieval_rows = await retriever.retrieve(
-                query=payload.requirement_text,
-                kb_vector_stores=kb_vector_stores,
-                fetch_k_per_kb=max(12, payload.retrieval_top_k * 2),
-                top_k=payload.retrieval_top_k,
-            )
-            if retrieval_rows:
-                knowledge_context = format_retrieval_context(retrieval_rows)
-
-    result = run_testing_pipeline(
-        user_requirement_text=payload.requirement_text,
-        requirement_type_input=payload.requirement_type,
-        debug=payload.debug,
-        knowledge_context=knowledge_context,
-        use_model_generation=payload.use_model_generation,
-        max_items_per_group=payload.max_items_per_group,
-        cases_per_item=payload.cases_per_item,
-        max_focus_points=payload.max_focus_points,
-        max_llm_calls=payload.max_llm_calls,
-    )
     return result