diff --git a/org.html b/org.html
index 9f57b14..0b5d4a1 100644
--- a/org.html
+++ b/org.html
@@ -42,7 +42,8 @@
 <div class="container">
     <div class="chat-wrap">
         <div class="chat"></div>
-        <input type="text" class="prompt" value="재난관리부 부서장은 누구야?" placeholder="질문을 입력하고 Enter를 누르세요">
+        <input type="text" class="prompt" value="2직급 총 몇명이야?" placeholder="질문을 입력하고 Enter를 누르세요">
+<!--        <input type="text" class="prompt" value="재난관리부 부서장은 누구야?" placeholder="질문을 입력하고 Enter를 누르세요">-->
     </div>
 </div>
 <script>
diff --git a/org_offline.py b/org_offline.py
index d642c55..37dadfe 100644
--- a/org_offline.py
+++ b/org_offline.py
@@ -100,19 +100,31 @@ def get_qwen_model():
     return _model, _tokenizer
 
 
-def query_select(sessionId: str, query: str) :
+def query_select(sessionId: str, query: str, limit_unlock: bool) :
     keywords = extract_keywords_simple(query)
     print(keywords)
-    filter_list = [{"sessionId": sessionId}]
+    filter_conditions = [{"sessionId": sessionId}]
     if keywords['dept'] != '' :
-        filter_list.append({"deptCd": keywords['dept']})
+        filter_conditions.append({"deptCd": keywords['dept']})
     if keywords['rank'] != '' :
-        filter_list.append({"gradeCd": keywords['rank']})
-    print(filter_list)
-    results = collection.query(
-        query_texts=[keywords['keyword']],
-        where={"$and": filter_list},
-    )
+        filter_conditions.append({"gradeCd": keywords['rank']})
+
+    # 2. 조건이 1개보다 많을 때만 $and로 묶기
+    if len(filter_conditions) > -1:
+        where_clause = {"$and": filter_conditions}
+    else:
+        where_clause = filter_conditions[0]
+    print(where_clause)
+    if limit_unlock :
+        results = collection.get(
+            where=where_clause
+        )
+    else :
+        results = collection.query(
+            query_texts=[keywords['keyword']],
+            where=where_clause
+        )
+
     return results, keywords['keyword']
 
 
@@ -125,27 +137,34 @@ def query_select_summarize_stream(results, query, ai, min_similarity: float = 0.
         Generator: 스트리밍 응답 제너레이터
     """
 
-    if not results['documents'] or not results['documents'][0]:
-        def generate_empty():
-            yield json.dumps({"kind": "text", "text": "관련 문서를 찾을 수 없습니다."}) + "\n"
-        return generate_empty
+    if ai :
+        if not results['documents'] or not results['documents'][0]:
+            def generate_empty():
+                yield json.dumps({"kind": "text", "text": "관련 문서를 찾을 수 없습니다."}) + "\n"
+            return generate_empty
 
-    # 유사도 계산 및 필터링
-    filtered_docs = []
-    if results['distances'] and results['distances'][0]:
-        for doc, dist in zip(results['documents'][0], results['distances'][0]):
-            similarity = 1 - dist
-            if similarity >= min_similarity:
-                filtered_docs.append((doc, similarity))
-            if ai and len(filtered_docs) >= 5:
-                break
-    print(f"필터링된 문서: {filtered_docs}")
+        # 유사도 계산 및 필터링
+        filtered_docs = []
+        if results['distances'] and results['distances'][0]:
+            for doc, dist in zip(results['documents'][0], results['distances'][0]):
+                similarity = 1 - dist
+                if similarity >= min_similarity:
+                    filtered_docs.append((doc, similarity))
+                if ai and len(filtered_docs) >= 5:
+                    break
+        print(f"필터링된 문서: {filtered_docs}")
 
-    # 컨텍스트 생성
-    context_parts = []
-    for i, (doc, sim) in enumerate(filtered_docs):
-        context_parts.append(f"[청크 {i+1} | 유사도: {sim:.3f}]\n{doc}")
-    context = "\n\n".join(context_parts)
+        # 컨텍스트 생성
+        context_parts = []
+        for i, (doc, sim) in enumerate(filtered_docs):
+            context_parts.append(f"[유사도: {sim:.3f}]\n{doc}")
+        context = "\n\n".join(context_parts)
+    else :
+        print('일반')
+        context_parts = []
+        for doc in results.get('documents') :
+            context_parts.append(f"{doc}")
+        context = "\n".join(context_parts)
 
     # 모델 로드
     model, tokenizer = get_qwen_model()
@@ -183,7 +202,7 @@ def query_select_summarize_stream(results, query, ai, min_similarity: float = 0.
     generation_kwargs = dict(
         **model_inputs,
         streamer=streamer,
-        max_new_tokens=150,
+        max_new_tokens=3000,
         do_sample=True,
         temperature=0.3,
         top_p=0.9,
@@ -196,6 +215,7 @@ def query_select_summarize_stream(results, query, ai, min_similarity: float = 0.
 
     # 제너레이터 함수 정의
     def generate():
+
         for new_text in streamer:
             if new_text:
                 yield json.dumps({"kind": "text", "text": new_text}) + "\n"
@@ -235,8 +255,6 @@ def query_summarize_simple(query: str) :
         }
     ]
 
-    print(f'Messages: {messages}')
-
     # 토큰화
     text = tokenizer.apply_chat_template(
         messages,
@@ -320,12 +338,12 @@ def question(sessionId: str, query: str):
     """
     type = query_summarize_simple(query=query)
     if(type == '0') :
-        results, keyword = query_select(sessionId, query)
-        print('단순질문 AI')
+        results, keyword = query_select(sessionId, query, False)
+        print(f'단순질문 AI : {len(results)}')
         generate = query_select_summarize_stream(results, query=keyword, ai=True)
     else :
-        results, keyword = query_select(sessionId, query)
-        print('단순질문 데이터베이스조회')
+        results, keyword = query_select(sessionId, query, True)
+        print(f'개수 데이터베이스조회 : {len(results.get('ids'))}')
         generate = query_select_summarize_stream(results, query=keyword, ai=False)
 
 
diff --git a/util/org_filter.py b/util/org_filter.py
index cc78903..f5490d8 100644
--- a/util/org_filter.py
+++ b/util/org_filter.py
@@ -21,6 +21,9 @@ def extract_keywords_simple(query):
             keyword = query.replace(delete_paragraphs(query, code_dept), "").strip()
             break
 
+    if keyword == '':
+        keyword = query
+
     # 3. 직급 추출
     for k, v in ranks.items():
         code_rank = k