Create pinecone_utils.py

pinecone_utils.py

@@ -0,0 +1,34 @@
+# pinecone_utils.py
+
+import pinecone
+from config import PINECONE_API_KEY, PINECONE_ENVIRONMENT, INDEX_NAME, CONTEXT_FIELDS
+import torch
+
+# Conectar a Pinecone
+def connect_to_pinecone():
+    pinecone.init(api_key=PINECONE_API_KEY, environment=PINECONE_ENVIRONMENT)
+    index = pinecone.Index(INDEX_NAME)
+    return index
+
+# Realizar búsqueda vectorial
+def vector_search(query, embedding_model, index):
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    # Generar el embedding utilizando el modelo de embeddings
+    xq = embedding_model.encode(query, convert_to_tensor=True, device=device)
+
+    # Convertir el tensor a lista
+    xq = xq.cpu().tolist()
+
+    # Realizar búsqueda vectorial en el índice de Pinecone
+    res = index.query(vector=xq, top_k=3, include_metadata=True)
+    if res and res['matches']:
+        return [
+            {
+                'content': ' '.join(f"{k}: {v}" for k, v in match['metadata'].items() if k in CONTEXT_FIELDS and k != 'Tag'),
+                'metadata': match['metadata'],
+                'score': match.get('score', 0)
+            }
+            for match in res['matches']
+            if 'metadata' in match
+        ]
+    return []