処理時間を測定するためのコンテキストマネージャを追加し、ベクトル検索および前処理の各ステップでの実行時間を計測するように修正。コードの可読性を向上させるために、不要なコメントを削除。

app.py

@@ -9,6 +9,16 @@ import os
 from fastapi import FastAPI
 from pymilvus import MilvusClient
 from dotenv import load_dotenv
+import time
+from contextlib import contextmanager
+
+@contextmanager
+def measure(label="処理"):
+    start = time.time()
+    yield
+    end = time.time()
+    print(f"{label} 実行時間: {end - start:.6f} 秒")
+
 
 # .envファイルを読み込む
 load_dotenv()
@@ -58,7 +68,8 @@ def vector_search(query_address, top_k):
     max_retries = 5
     for attempt in range(max_retries):
         try:
-            query_embeds = embed_via_multilingual_e5_large([query_address])
+            with measure('vector_search - embed_via_multilingual_e5_large'):
+                query_embeds = embed_via_multilingual_e5_large([query_address])
             break  # 成功した場合はループを抜ける
 
         except InferenceEndpointError as e:
@@ -75,17 +86,8 @@ def vector_search(query_address, top_k):
             elif e.code == InferenceEndpointErrorCode.UNKNOWN_ERROR:
                 raise gr.Error(f"{InferenceEndpointErrorCode.UNKNOWN_ERROR}: {e.message}")
 
-    '''
-    hits = search_via_milvus(query_embeds[0], top_k, VECTOR_SEARCH_COLLECTION_NAME_V2)
-
-    if hits:
-        normalized = hits[0][-1]
-
-    else:
+    with measure('vector_search - search_via_milvus'):
         hits = search_via_milvus(query_embeds[0], top_k, VECTOR_SEARCH_COLLECTION_NAME)
-        normalized = hits[0][-1]
-    '''
-    hits = search_via_milvus(query_embeds[0], top_k, VECTOR_SEARCH_COLLECTION_NAME)
     return hits
 
 def replace_circle(input_text):
@@ -200,8 +202,11 @@ def compare_two_addresses(address1, address2):
 
 @app.post("/normalize-address")
 def normalize_address(query_address):
-    preprocessed = preprocess(query_address)
-    return vector_search(preprocessed, top_k=1)[0][-1]
+    with measure('preprocess'):
+        preprocessed = preprocess(query_address)
+    with measure('vector_search'):
+        result = vector_search(preprocessed, top_k=1)[0][-1]
+    return result
 
 # =========================
 #  Gradio tabs definition
@@ -360,11 +365,14 @@ def create_vector_search_tab():
         search_result_df = gr.Dataframe(label="検索結果")
 
         def search_address(query_address, top_k):
-            preprocessed = preprocess(query_address)
-            hits = vector_search(preprocessed, top_k)
-            normalized = hits[0][-1]
-            search_result_df = pd.DataFrame(hits, columns=['Top-k', '類似度', '住所'])
-            splits = split_address(normalized)
+            with measure('preprocess'):
+                preprocessed = preprocess(query_address)
+            with measure('vector_search'):
+                hits = vector_search(preprocessed, top_k)
+                normalized = hits[0][-1]
+                search_result_df = pd.DataFrame(hits, columns=['Top-k', '類似度', '住所'])
+            with measure('split_address'):
+                splits = split_address(normalized)
 
             data = {
                 'pref': splits['pref'],