Update app.py

app.py

@@ -15,34 +15,34 @@ df['embeding_context'] = df['embeding_context'].astype(str).fillna('')
 # Filter out any rows where 'embeding_context' might be empty or invalid
 df = df[df['embeding_context'] != '']
 
-# Encode the 'embeding_context' column
-embedding_contexts = df['embeding_context'].tolist()
-embeddings_csv = model.encode(embedding_contexts, batch_size=12, max_length=1024)['dense_vecs']
-
-# Convert embeddings to numpy array
-embeddings_np = np.array(embeddings_csv).astype('float32')
-
-# FAISS index file path
-index_file_path = 'vector_store_bge_m3.index'
-
-# Check if FAISS index file already exists
-if os.path.exists(index_file_path):
-    # Load the existing FAISS index from file
-    index = faiss.read_index(index_file_path)
-    print("FAISS index loaded from file.")
-else:
-    # Initialize FAISS index (for L2 similarity)
-    dim = embeddings_np.shape[1]
-    index = faiss.IndexFlatL2(dim)
-
-    # Add embeddings to the FAISS index
-    index.add(embeddings_np)
-
-    # Save the FAISS index to a file for future use
-    faiss.write_index(index, index_file_path)
-    print("FAISS index created and saved to file.")
-
-index = faiss.read_index(index_file_path)
+# # Encode the 'embeding_context' column
+# embedding_contexts = df['embeding_context'].tolist()
+# embeddings_csv = model.encode(embedding_contexts, batch_size=12, max_length=1024)['dense_vecs']
+
+# # Convert embeddings to numpy array
+# embeddings_np = np.array(embeddings_csv).astype('float32')
+
+# # FAISS index file path
+# index_file_path = 'vector_store_bge_m3.index'
+
+# # Check if FAISS index file already exists
+# if os.path.exists(index_file_path):
+#     # Load the existing FAISS index from file
+#     index = faiss.read_index(index_file_path)
+#     print("FAISS index loaded from file.")
+# else:
+#     # Initialize FAISS index (for L2 similarity)
+#     dim = embeddings_np.shape[1]
+#     index = faiss.IndexFlatL2(dim)
+
+#     # Add embeddings to the FAISS index
+#     index.add(embeddings_np)
+
+#     # Save the FAISS index to a file for future use
+#     faiss.write_index(index, index_file_path)
+#     print("FAISS index created and saved to file.")
+
+index = faiss.read_index('vector_store_bge_m3.index')
 
 
 # Function to perform search and return all columns