Update langchain_helper.py

langchain_helper.py

@@ -28,17 +28,18 @@ from plotly.express import bar, line, scatter, area, pie
 from dotenv import load_dotenv, find_dotenv
 _ = load_dotenv(find_dotenv()) # read local .env file
 
-current_model_id = os.getenv('model_id')
+current_model_id = "gpt-35-turbo-instruct"
 
 def get_few_shot_db_chain(user_message):
     chromadb.api.client.SharedSystemClient.clear_system_cache()
-    #llm = AzureOpenAI(deployment_name="gpt-35-turbo-instruct", temperature=0.2)
-    llm = ChatOpenAI(model = current_model_id)
+    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")
+
+    embeddings = AzureOpenAIEmbeddings(model="text-embedding-3-small")
+    #embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
     print(embeddings)
     to_vectorize = [" ".join(example.values()) for example in few_shots]
     
@@ -88,7 +89,8 @@ def get_few_shot_db_chain(user_message):
     
 
 def get_graph_details(usermessage:str, df=None):
-    llm = ChatOpenAI(deployment_name=current_model_id, temperature=0.15)
+    llm = AzureOpenAI(deployment_name=current_model_id, temperature=0.15)
+    #llm = ChatOpenAI(deployment_name=current_model_id, temperature=0.15)
     template = ChatPromptTemplate.from_messages(
         [("system", "You are a visualisation expert and plotly developer, your task is to come up with best suitable \
                      chart representing user ask for the given data. please use plotly express library in python for \