diff --git a/qa_amend.py b/qa_amend.py
new file mode 100644
index 0000000..a7c1769
--- /dev/null
+++ b/qa_amend.py
@@ -0,0 +1,109 @@
+# qa_operations.py
+import json
+from kb_config import logger
+from sentence_transformers import CrossEncoder
+from faiss_kb_service import FaissKBService
+from langchain.docstore.document import Document
+from base_kb import KnowledgeFile
+from pydantic import BaseModel
+
+class QAService():
+    def __init__(self, kb_name, device=None) -> None:
+        self.kb_name = kb_name
+        self.device = device
+        self.fkbs = FaissKBService(kb_name, embed_model_path='bge-large-zh-v1.5', device=device)
+        self.fkbs.do_create_kb()
+
+    def update_qa_doc(self, qa_file_id, doc_list, id_list):
+        self.delete_qa_file(qa_file_id)
+        doc_infos = self.fkbs.do_add_doc(doc_list, ids=id_list)
+        logger.info('faiss add docs: ' + str(len(doc_infos)))
+        self.fkbs.save_vector_store()
+
+    def delete_qa_file(self, qa_file_id):
+
+        kb_file = KnowledgeFile(qa_file_id, self.kb_name)
+        self.fkbs.do_delete_doc(kb_file, not_refresh_vs_cache=True)
+
+    def search(self, query, top_k=3, score_threshold=0.1, reranked=False):
+        docs = self.fkbs.do_search(query, top_k, score_threshold)
+        return docs
+
+
+def create_question_id(question_code, j, test_question):
+    return f"{question_code}@{j}@{test_question}"
+
+
+def load_testing_data(file_path):
+    question_list = []
+    id_list = []
+
+    with open(file_path, encoding='utf-8') as f:
+        data = json.load(f)
+        for item in data:
+            question_code = item['questionCode']
+            question_list.extend(item['questionList'])
+            id_list.extend([create_question_id(question_code, j, q) for j, q in enumerate(item['questionList'])])
+
+    return question_list, id_list
+
+
+def convert_to_doc_list(question_list, id_list, qa_file_id):
+    doc_list = []
+    for question, id in zip(question_list, id_list):
+        metadata = {'source': qa_file_id, 'id': id}
+        doc = Document(page_content=question, metadata=metadata)
+        doc_list.append(doc)
+    return doc_list
+
+
+def store_data(path):
+    kb_name = 'my_kb_test'
+    device = None
+    qa_service = QAService(kb_name, device)
+
+    question_list, id_list = load_testing_data(path)
+    print('Loaded data!')
+
+    qa_file_id = 'QA_TEST_1'  # the source of the qa, using for data cleaning, make sure to be unique
+
+    doc_list = convert_to_doc_list(question_list, id_list, qa_file_id)
+
+    qa_service.update_qa_doc(qa_file_id, doc_list, id_list)
+
+    print("Data stored in the knowledge base successfully!")
+    return True
+
+
+class MatchInfo(BaseModel):
+    matchQuestionCode: str
+    matchQuestion: str
+    matchScore: str
+
+
+def match_query(qa_service, query, top_k=3, score_threshold=0.1):
+    docs = qa_service.search(query, top_k, 1 - score_threshold)
+    response = []
+    if docs:
+        for doc, similarity_score in docs:
+            doc_id = doc.metadata['id']
+            question_code = doc_id.split('@')[0]
+            match_info = MatchInfo(
+                matchQuestionCode=question_code,
+                matchQuestion=doc.page_content,
+                matchScore=f"{1 - similarity_score:.3f}"
+            )
+            response.append(match_info)
+
+        return response
+
+
+if __name__ == "__main__":
+    store_data("test.json")
+    kb_name = 'my_kb_test'
+    device = None
+    qa_service = QAService(kb_name, device)
+    top_k = 3
+    score_threshold = 0.1
+    result = match_query(qa_service, "你好吗?", top_k, score_threshold)
+    print(result)
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