trying fix tenant issue

langchain_helper.py

@@ -33,16 +33,16 @@ current_model_id = os.getenv('model_id')
 def get_few_shot_db_chain(user_message):
     #llm = AzureOpenAI(deployment_name="gpt-35-turbo-instruct", temperature=0.2)
     llm = ChatOpenAI(model = current_model_id)
-    
+    print(llm)
     engine = create_engine("sqlite:///ecomm.db")
     db = SQLDatabase(engine=engine, sample_rows_in_table_info=3)
     
     embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
-
+    print(embeddings)
     to_vectorize = [" ".join(example.values()) for example in few_shots]
     
     vectorstore = Chroma.from_texts(to_vectorize, embeddings, metadatas=few_shots)
-    
+    print(vectorstore)
     example_selector = SemanticSimilarityExampleSelector(vectorstore=vectorstore, k=2)
     
     example_prompt = PromptTemplate(
@@ -59,7 +59,7 @@ def get_few_shot_db_chain(user_message):
                             )
     
     chain = SQLDatabaseChain.from_llm(llm, db, verbose=True, prompt=few_shot_prompt, return_intermediate_steps = True)
-    
+    print(chain)
     response_llm = chain.invoke(user_message)
     
     print(f"sql query : {response_llm['intermediate_steps'][1]}")
@@ -67,7 +67,8 @@ def get_few_shot_db_chain(user_message):
         intermediate_sql_query = response_llm['intermediate_steps'][2]['sql_cmd']
     
     result_df = pd.read_sql_query(intermediate_sql_query, engine)
-
+    print("Printing results")
+    print(result_df)
     output_dict = {
         "result_df" : result_df,
         "sql_command" : intermediate_sql_query,