refactor: replace OPRO with simple iterative refinement

Major changes:
- Remove fake OPRO evaluation (no more fake 0.5 scores)
- Add simple refinement based on user selection
- New endpoint: POST /opro/refine (selected + rejected instructions)
- Update prompt generation to focus on comprehensive coverage instead of style variety
- All generated instructions now start with role definition (你是一个...)
- Update README to reflect new approach and API endpoints

Technical details:
- Added refine_based_on_selection() in prompt_utils.py
- Added refine_instruction_candidates() in user_prompt_optimizer.py
- Added OPRORefineReq model and /opro/refine endpoint in api.py
- Updated frontend handleContinueOptimize() to use new refinement flow
- Changed prompt requirements from 'different styles' to 'comprehensive coverage'
- Added role definition requirement as first item in all prompt templates

_qwen_xinference_demo/api.py

@@ -24,7 +24,8 @@ from .opro.session_state import (
 from .opro.user_prompt_optimizer import generate_candidates
 from .opro.user_prompt_optimizer import (
     generate_system_instruction_candidates,
-    evaluate_system_instruction
+    evaluate_system_instruction,
+    refine_instruction_candidates
 )
 
 from .opro.ollama_client import call_qwen
@@ -159,6 +160,15 @@ class OPROExecuteReq(BaseModel):
     model_name: Optional[str] = None
 
 
+class OPRORefineReq(BaseModel):
+    """Request to refine based on selected instruction (simple iterative refinement, NOT OPRO)."""
+    run_id: str
+    selected_instruction: str
+    rejected_instructions: List[str]
+    top_k: Optional[int] = None
+    pool_size: Optional[int] = None
+
+
 # ============================================================================
 # LEGACY ENDPOINTS (Query Rewriting - NOT true OPRO)
 # ============================================================================
@@ -696,3 +706,44 @@ def opro_execute(req: OPROExecuteReq):
         })
     except Exception as e:
         raise AppException(500, f"Execution failed: {e}", "EXECUTION_ERROR")
+
+
+@app.post("/opro/refine", tags=["opro-true"])
+def opro_refine(req: OPRORefineReq):
+    """
+    Simple iterative refinement based on user selection (NOT OPRO).
+
+    This generates new candidates based on the selected instruction while avoiding rejected ones.
+    No scoring, no trajectory - just straightforward refinement based on user preference.
+    """
+    run = get_opro_run(req.run_id)
+    if not run:
+        raise AppException(404, "OPRO run not found", "RUN_NOT_FOUND")
+
+    top_k = req.top_k or config.TOP_K
+    pool_size = req.pool_size or config.GENERATION_POOL_SIZE
+
+    try:
+        candidates = refine_instruction_candidates(
+            task_description=run["task_description"],
+            selected_instruction=req.selected_instruction,
+            rejected_instructions=req.rejected_instructions,
+            top_k=top_k,
+            pool_size=pool_size,
+            model_name=run["model_name"]
+        )
+
+        # Update iteration counter
+        update_opro_iteration(req.run_id, candidates)
+
+        # Get updated run info
+        run = get_opro_run(req.run_id)
+
+        return ok({
+            "run_id": req.run_id,
+            "iteration": run["iteration"],
+            "candidates": [{"instruction": c, "score": None} for c in candidates],
+            "task_description": run["task_description"]
+        })
+    except Exception as e:
+        raise AppException(500, f"Refinement failed: {e}", "REFINEMENT_ERROR")