Update agent_service.py

agent_service.py

@@ -411,30 +411,35 @@ class AgentService:
     
     async def _execute_rag_search(self, query_params: Dict) -> str:
         """
-        Execute RAG search with Multi-Stage Fallback Strategy
+        Execute RAG search with Strict Relevance Filtering
         Called when LLM wants to search_events
         """
         query = query_params.get("query", "").strip()
         vibe = query_params.get("vibe", "").strip()
         time = query_params.get("time", "").strip()
         
-        # Strategy: Try specific -> broad
+        # Score threshold to filter weak/irrelevant matches
+        SCORE_THRESHOLD = 0.5  # Tune this value (0.4-0.6)
+        
+        # Build search strategies
         search_strategies = []
+        user_gave_specific_keyword = bool(query)  # If user gave a keyword, don't fallback to vibe
         
         # 1. Full combination (Specific)
-        if query or vibe:
-            full_query = f"{query} {vibe} {time}".strip()
+        full_query = f"{query} {vibe} {time}".strip()
+        if full_query:
             search_strategies.append(("Full Context", full_query))
             
         # 2. Main keyword only (Broad) - Critical for terms like "rượu"
-        if query and len(search_strategies) > 0 and query != full_query:
+        if query and query != full_query:
             search_strategies.append(("Keyword Only", query))
             
-        # 3. Vibe only (Fallback)
-        if vibe and len(search_strategies) > 0 and vibe != full_query:
+        # 3. Vibe only (Fallback) - ONLY if user didn't provide specific keyword
+        if vibe and not user_gave_specific_keyword and vibe != full_query:
             search_strategies.append(("Vibe Only", vibe))
             
-        print(f"🔍 RAG Search Plan: {[s[0] for s in search_strategies]}")
+        print(f"[SEARCH] Plan: {[s[0] for s in search_strategies]}")
+        print(f"[SEARCH] User gave specific keyword: {user_gave_specific_keyword}")
         
         final_results = []
         seen_ids = set()
@@ -443,23 +448,26 @@ class AgentService:
             if not search_text:
                 continue
                 
-            print(f"👉 Trying strategy: {strategy_name} ('{search_text}')")
+            print(f"[SEARCH] Trying: {strategy_name} ('{search_text}')")
             
-            # Use embedding + qdrant
+            # Use embedding + qdrant with score threshold
             embedding = self.embedding_service.encode_text(search_text)
             results = self.qdrant_service.search(
                 query_embedding=embedding,
-                limit=5
+                limit=5,
+                score_threshold=SCORE_THRESHOLD  # Filter weak matches
             )
             
             # Deduplicate and add results
             count = 0
             for res in results:
                 doc_id = res['id']
+                score = res.get('confidence', 0)
                 if doc_id not in seen_ids:
                     seen_ids.add(doc_id)
                     final_results.append(res)
                     count += 1
+                    print(f"   + Added: {doc_id[:20]}... (score: {score:.3f})")
             
             print(f"   Found {count} new results (Total: {len(final_results)})")