add delete same query

application/nlq/business/vector_store.py

@@ -67,20 +67,29 @@ def get_all_agent_cot_samples(cls, profile_name):
     def add_sample(cls, profile_name, question, answer):
         logger.info(f'add sample question: {question} to profile {profile_name}')
         embedding = cls.create_vector_embedding_with_bedrock(question)
+        has_same_sample = cls.search_same_query(profile_name, 1, 'uba', embedding)
+        if has_same_sample:
+            logger.info(f'delete sample sample entity: {question} to profile {profile_name}')
         if cls.opensearch_dao.add_sample('uba', profile_name, question, answer, embedding):
             logger.info('Sample added')
 
     @classmethod
     def add_entity_sample(cls, profile_name, entity, comment):
         logger.info(f'add sample entity: {entity} to profile {profile_name}')
         embedding = cls.create_vector_embedding_with_bedrock(entity)
+        has_same_sample = cls.search_same_query(profile_name, 1, 'uba_ner', embedding)
+        if has_same_sample:
+            logger.info(f'delete sample sample entity: {entity} to profile {profile_name}')
         if cls.opensearch_dao.add_entity_sample('uba_ner', profile_name, entity, comment, embedding):
             logger.info('Sample added')
 
     @classmethod
     def add_agent_cot_sample(cls, profile_name, entity, comment):
-        logger.info(f'add sample entity: {entity} to profile {profile_name}')
+        logger.info(f'add agent sample query: {entity} to profile {profile_name}')
         embedding = cls.create_vector_embedding_with_bedrock(entity)
+        has_same_sample = cls.search_same_query(profile_name, 1, 'uba_agent', embedding)
+        if has_same_sample:
+            logger.info(f'delete agent sample sample query: {entity} to profile {profile_name}')
         if cls.opensearch_dao.add_agent_cot_sample('uba_agent', profile_name, entity, comment, embedding):
             logger.info('Sample added')
 
@@ -124,3 +133,29 @@ def search_sample(cls, profile_name, top_k, index_name, query):
         logger.info(f'search sample question: {query}  {index_name} from profile {profile_name}')
         sample_list = cls.opensearch_dao.search_sample(profile_name, top_k, index_name, query)
         return sample_list
+
+    @classmethod
+    def search_sample_with_embedding(cls, profile_name, top_k, index_name, query_embedding):
+        sample_list = cls.opensearch_dao.search_sample_with_embedding(profile_name, top_k, index_name, query_embedding)
+        return sample_list
+
+    @classmethod
+    def search_same_query(cls, profile_name, top_k, index_name, embedding):
+        search_res = cls.search_sample_with_embedding(profile_name, top_k, index_name, embedding)
+        if len(search_res) > 0:
+            similarity_sample = search_res[0]
+            similarity_score = similarity_sample["_score"]
+            similarity_id = similarity_sample['_id']
+            if similarity_score == 1.0:
+                if index_name == "uba":
+                    cls.delete_sample(profile_name, similarity_id)
+                    return True
+                elif index_name == "uba_ner":
+                    cls.delete_entity_sample(profile_name, similarity_id)
+                    return True
+                elif index_name == "uba_agent":
+                    cls.delete_agent_cot_sample(profile_name, similarity_id)
+                    return True
+                else:
+                    return False
+        return False

application/nlq/data_access/opensearch.py

@@ -191,6 +191,10 @@ def delete_sample(self, index_name, profile_name, doc_id):
 
     def search_sample(self, profile_name, top_k, index_name, query):
         records_with_embedding = create_vector_embedding_with_bedrock(query, index_name=index_name)
+        return self.search_sample_with_embedding(profile_name, top_k, index_name,  records_with_embedding['vector_field'])
+
+
+    def search_sample_with_embedding(self, profile_name, top_k, index_name, query_embedding):
         search_query = {
             "size": top_k,  # Adjust the size as needed to retrieve more or fewer results
             "query": {
@@ -205,7 +209,7 @@ def search_sample(self, profile_name, top_k, index_name, query):
                             "knn": {
                                 "vector_field": {
                                     # Make sure 'vector_field' is the name of your vector field in OpenSearch
-                                    "vector": records_with_embedding['vector_field'],
+                                    "vector": query_embedding,
                                     "k": top_k  # Adjust k as needed to retrieve more or fewer nearest neighbors
                                 }
                             }