Update api.py

api.py

@@ -238,48 +238,42 @@ class SimilarityMemory:
     def __init__(self, n_components=100):
         self.memory_texts = []
         self.vectorizer = TfidfVectorizer()
-        self.svd = TruncatedSVD(n_components=n_components)
+        self.svd = None
         self.embeddings = None
-        self.fitted = False
+        self.n_components = n_components
 
     def add(self, text: str):
         self.memory_texts.append(text)
         self._update_embeddings()
 
     def _update_embeddings(self):
-        # 텍스트가 1개 이상일 때만 벡터화 및 차원 축소 진행
         if len(self.memory_texts) == 0:
             self.embeddings = None
-            self.fitted = False
+            self.svd = None
             return
 
-        # 벡터화
         X = self.vectorizer.fit_transform(self.memory_texts)
 
-        # 차원 축소
-        n_comp = min(self.svd.n_components, X.shape[1], len(self.memory_texts)-1)
+        n_comp = min(self.n_components, X.shape[1], len(self.memory_texts) - 1)
         if n_comp <= 0:
-            # 축소할 차원이 없으면 그냥 TF-IDF 벡터 사용
             self.embeddings = X.toarray()
-            self.fitted = True
+            self.svd = None
             return
 
-        self.svd = TruncatedSVD(n_components=n_comp)
-        self.embeddings = self.svd.fit_transform(X)
-        self.fitted = True
+        svd = TruncatedSVD(n_components=n_comp)
+        self.embeddings = svd.fit_transform(X)
+        self.svd = svd
 
     def retrieve(self, query: str, top_k=3):
-        if not self.fitted or self.embeddings is None or len(self.memory_texts) == 0:
+        if self.embeddings is None:
             return []
 
-        # 쿼리 벡터화 + 차원 축소 (fit_transform이 아니라 transform 해야 함)
         Xq = self.vectorizer.transform([query])
-        if self.svd.n_components > Xq.shape[1] or self.svd.n_components > len(self.memory_texts) - 1:
-            q_emb = Xq.toarray()
-        else:
+        if self.svd is not None:
             q_emb = self.svd.transform(Xq)
+        else:
+            q_emb = Xq.toarray()
 
-        # 코사인 유사도 계산
         sims = cosine_similarity(q_emb, self.embeddings)[0]
         top_indices = sims.argsort()[::-1][:top_k]
 
@@ -289,8 +283,9 @@ class SimilarityMemory:
 memory = SimilarityMemory()
 
 def merge_prompt_with_memory(prompt: str, memories: list):
-    context = "\n".join(f"{mem}" for mem in memories)
-    return f"{context} {prompt}"
+    context = "\n".join(memories)
+    return f"{context}\n{prompt}"
+
 
 def mismatch_tone(input_text, output_text):  
     if "ㅋㅋ" in input_text and not re.search(r'ㅋㅋ|ㅎ|재밌|놀|만나|맛집|여행', output_text):