Update agent.py

agent.py

@@ -1,4 +1,4 @@
-# agent.py — AGENTE SEMÁNTICO CON FILTRADO DE FRASES COHERENTES
+# agent.py — AGENTE SEMÁNTICO CON SÍNTESIS INTELIGENTE (versión estable)
 import os
 import time
 import logging
@@ -99,61 +99,42 @@ class ImprovedSemanticAgent:
             logger.info(f"🔍 Analizando: '{user_prompt}'")
             query_embedding = self.embedding_model.encode([user_prompt], convert_to_numpy=True, normalize_embeddings=True)[0]
             query_embedding = query_embedding.astype('float32').reshape(1, -1)
-            distances, indices = self.index.search(query_embedding, 8)
-
-            # Extraer primer sustantivo del usuario (sujeto principal)
-            user_doc = NLP(user_prompt.lower())
-            user_first_noun = None
-            for token in user_doc:
-                if token.pos_ in ("NOUN", "PROPN"):
-                    user_first_noun = token.lemma_
-                    break
-
-            user_words = set(user_prompt.lower().split())
-            candidate_phrases = []
+            distances, indices = self.index.search(query_embedding, 5)
 
+            candidates = []
             for idx in indices[0]:
-                if idx >= len(self.indexed_examples):
-                    continue
-                caption = self.indexed_examples[idx]['caption']
-                caption_lower = caption.lower()
-
-                # Saltar si el caption comienza con el mismo sujeto
-                if user_first_noun and caption_lower.startswith(user_first_noun):
-                    continue
+                if idx < len(self.indexed_examples):
+                    candidates.append(self.indexed_examples[idx]['caption'])
+            if not candidates:
+                return self._structural_fallback(user_prompt, category), "🔧 Fallback estructural"
 
-                # Dividir por comas y procesar partes
-                parts = [p.strip() for p in caption.split(',') if len(p.strip()) > 12 and len(p.strip()) < 120]
+            user_words = set(user_prompt.lower().split())
+            all_parts = []
+            for caption in candidates:
+                parts = [p.strip() for p in caption.split(',') if 8 <= len(p) <= 120]
                 for part in parts:
                     part_lower = part.lower()
-                    part_words = set(part_lower.split())
-                    # Añadir solo si aporta al menos 3 palabras nuevas y no contiene el sujeto del usuario
-                    if (
-                        len(part_words - user_words) >= 3
-                        and (not user_first_noun or user_first_noun not in part_lower)
-                    ):
-                        candidate_phrases.append(part)
-
-            # Eliminar duplicados
+                    if len(set(part_lower.split()) - user_words) >= 2:
+                        all_parts.append(part)
+
             seen = set()
-            unique_phrases = []
-            for p in candidate_phrases:
+            unique_parts = []
+            for p in all_parts:
                 if p not in seen:
-                    unique_phrases.append(p)
+                    unique_parts.append(p)
                     seen.add(p)
 
-            # Seleccionar hasta 3 frases
-            selected = unique_phrases[:3]
+            selected = unique_parts[:6]
             if selected:
                 additions = ", ".join(selected)
                 enhanced = f"{user_prompt}, {additions}"
-                return enhanced, f"✨ Enriquecido con {len(selected)} frases coherentes"
+                return enhanced, f"✨ Prompt sintetizado con {len(candidates)} ejemplos"
             else:
-                return self._structural_fallback(user_prompt, category), "🔧 Fallback estructural"
+                return self._structural_fallback(user_prompt, category), "🔧 Fallback estructural (sin frases útiles)"
 
         except Exception as e:
             logger.error(f"❌ Error en _do_enhancement: {e}")
-            return self._structural_fallback(user_prompt, category), f"❌ Error: {str(e)}"
+            return user_prompt, f"❌ Error: {str(e)}"
 
     def _structural_fallback(self, prompt: str, category: str) -> str:
         enhancements = {