Update app.py

app.py

@@ -43,7 +43,8 @@ class DocumentRetrievalAndGeneration:
         all_texts = [split.page_content for split in self.all_splits]
         embeddings = self.embeddings.encode(all_texts, convert_to_tensor=True).cpu().numpy()
         vectordb = FAISS.from_embeddings(
-            text_embeddings=list(zip(all_texts, embeddings)),  # Pairing texts with embeddings
+            texts=all_texts,  # Provide texts separately
+            embeddings=embeddings,  # Provide embeddings separately
             model=self.embeddings,
             metadatas=[{"source": f"doc_{i}"} for i in range(len(all_texts))]
         )