fix(ai): force gemini flash usage and define impact_score in prompt

src/agentic_curator.py

@@ -129,6 +129,7 @@ async def evaluate_extracted_assets(raw_assets: List[Dict]) -> Dict[str, Dict]:
             "- Perform a real-time web search for each resource.\n"
             "- If the community (Reddit, Hacker News) reports the tool as 'unstable', 'abandoned', or 'vaporware', set reputation_penalty: true.\n"
             "PHASE 2: LINGUISTIC DIVERSITY & CLASSIFICATION\n"
+            "- Calculate 'impact_score' (0-100) based on architectural value, innovation, and technical depth (>= 80 is required for inclusion).\n"
             "- Identify TECHNICAL_HIERARCHY: List (max 10 strings) Area > Topic > Subtopics.\n"
             "PHASE 3: HIGH-DENSITY TECHNICAL SUMMARIES (Mandate 4)\n"
             "- Provide an 'en_summary' that is technical, professional and dense.\n"
@@ -142,7 +143,7 @@ async def evaluate_extracted_assets(raw_assets: List[Dict]) -> Dict[str, Dict]:
 
         try:
             # ENABLE GROUNDING FOR REPUTATION FILTER
-            results = await call_gemini_with_retry(prompt, use_grounding=True, role="Curator")
+            results = await call_gemini_with_retry(prompt, use_grounding=True, prefer_flash=True, role="Curator")
             if isinstance(results, list):
                 res_map = {normalize_url(r.get("url", "")): r for r in results}
                 for d in batch_data:

src/gemini_utils.py

@@ -295,16 +295,16 @@ async def call_gemini_with_retry(prompt: str, response_format: str = "json", max
     base_wait_time = 2.0
     
     # 1. Smart Filtering and Re-ordering
-    if use_grounding:
-        # For grounding, we MANDATE Pro models as they have superior search/reasoning capabilities
-        models = [m for m in models_pool if "pro" in m]
-        if not models:
-            models = ["gemini-1.5-pro", "gemini-1.5-pro-latest"]
-    elif prefer_flash:
+    if prefer_flash:
         # Strict filter: Only allow flash/lite models
         models = [m for m in models_pool if "flash" in m or "lite" in m]
         if not models:
             models = ["gemini-1.5-flash", "gemini-1.5-flash-latest"]
+    elif use_grounding:
+        # For grounding, we MANDATE Pro models as they have superior search/reasoning capabilities
+        models = [m for m in models_pool if "pro" in m]
+        if not models:
+            models = ["gemini-1.5-pro", "gemini-1.5-pro-latest"]
     else:
         models = models_pool