init. project

rag-web-ui/backend/app/schemas/testing.py

@@ -0,0 +1,59 @@
+from typing import Dict, List, Optional
+
+from pydantic import BaseModel, Field
+
+
+class TestItemEntry(BaseModel):
+    id: str
+    content: str
+
+
+class TestCaseEntry(BaseModel):
+    id: str
+    item_id: str
+    operation_steps: List[str]
+    test_content: str
+    expected_result_placeholder: str
+
+
+class ExpectedResultEntry(BaseModel):
+    id: str
+    case_id: str
+    result: str
+
+
+class StepLogEntry(BaseModel):
+    step_name: str
+    input_summary: str
+    output_summary: str
+    success: bool
+    fallback_used: bool
+    duration_ms: float
+
+
+class TestingPipelineRequest(BaseModel):
+    requirement_text: str = Field(..., min_length=1)
+    requirement_type: Optional[str] = None
+    knowledge_base_ids: List[int] = []
+    retrieval_top_k: int = Field(default=8, ge=1, le=20)
+    knowledge_context: Optional[str] = None
+    use_model_generation: bool = True
+    max_items_per_group: int = Field(default=12, ge=4, le=30)
+    cases_per_item: int = Field(default=2, ge=1, le=5)
+    max_focus_points: int = Field(default=6, ge=3, le=12)
+    max_llm_calls: int = Field(default=10, ge=0, le=100)
+    debug: bool = False
+
+
+class TestingPipelineResponse(BaseModel):
+    trace_id: str
+    requirement_type: str
+    reason: str
+    candidates: List[str]
+    test_items: Dict[str, List[TestItemEntry]]
+    test_cases: Dict[str, List[TestCaseEntry]]
+    expected_results: Dict[str, List[ExpectedResultEntry]]
+    formatted_output: str
+    pipeline_summary: str
+    knowledge_used: bool = False
+    step_logs: List[StepLogEntry] = []