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app/database/qdrant_vault.py

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+from qdrant_client import QdrantClient
+from qdrant_client.http import models
+import os
+
+class CognitiveVault:
+    def __init__(self):
+        # Conectamos al contenedor de Qdrant
+        self.client = QdrantClient(
+            host=os.getenv("QDRANT_HOST", "localhost"), 
+            port=int(os.getenv("QDRANT_PORT", 6333))
+        )
+        self.collection_name = "user_signatures"
+        self._ensure_collection()
+
+    def _ensure_collection(self):
+        """Crea la colección si no existe"""
+        collections = self.client.get_collections().collections
+        exists = any(c.name == self.collection_name for c in collections)
+        
+        if not exists:
+            self.client.recreate_collection(
+                collection_name=self.collection_name,
+                vectors_config=models.VectorParams(
+                    size=128, # Tamaño del vector de firma (ajustable)
+                    distance=models.Distance.COSINE
+                )
+            )
+
+    async def save_signature(self, user_id, vector, metadata):
+        """Guarda una nueva firma cognitiva"""
+        self.client.upsert(
+            collection_name=self.collection_name,
+            points=[
+                models.PointStruct(
+                    id=user_id, # Usamos el ID del usuario como punto
+                    vector=vector,
+                    payload=metadata
+                )
+            ]
+        )
+
+    async def verify_identity(self, vector):
+        """Busca la firma más parecida (Score de cercanía)"""
+        search_result = self.client.search(
+            collection_name=self.collection_name,
+            query_vector=vector,
+            limit=1
+        )
+        return search_result