Update app.py

app.py

@@ -1,28 +1,13 @@
-#------------------------------------------------------------------------
-# Import Modules
-#------------------------------------------------------------------------
-
 import streamlit as st
 import openai
 import random
-import os
-from pinecone import Pinecone
-from langchain.chat_models import ChatOpenAI
-from langsmith import Client
-from langchain.smith import RunEvalConfig, run_on_dataset
 
-#------------------------------------------------------------------------
-# Load API Keys From the .env File & Load the OpenAI, Pinecone, and LangSmith Client
-#------------------------------------------------------------------------
 
 # Fetch the OpenAI API key from Streamlit secrets
 OPENAI_API_KEY = st.secrets["OPENAI_API_KEY"]
 # Retrieve the OpenAI API Key from secrets
 openai.api_key = st.secrets["OPENAI_API_KEY"]
 
-# os.environ["OPENAI_API_KEY"] = st.secrets["OPENAI_API_KEY"]
-# openai.api_key = os.getenv("OPENAI_API_KEY")
-
 # # Fetch Pinecone API key and environment from Streamlit secrets
 PINECONE_API_KEY = st.secrets["PINECONE_API_KEY"]
 # # AUTHENTICATE/INITIALIZE PINCONE SERVICE
@@ -30,27 +15,12 @@ from pinecone import Pinecone
 # PINECONE_API_KEY = "555c0e70-331d-4b43-aac7-5b3aac5078d6"
 pc = Pinecone(api_key=PINECONE_API_KEY)
 
-# os.environ["OPENAI_API_KEY"] = st.secrets["api_keys"]["OPENAI_API_KEY"]
-# os.environ["LANGCHAIN_API_KEY"] = st.secrets["api_keys"]["LANGCHAIN_API_KEY"]
-# # Set other LangChain configurations as environment variables
-# os.environ["LANGCHAIN_TRACING_V2"] = "true"
-# os.environ["LANGCHAIN_ENDPOINT"] = "https://api.smith.langchain.com"
-# os.environ["LANGCHAIN_PROJECT"] = "Inkqa"
-
-# client = Client() # Langsmith client
-
-#------------------------------------------------------------------------
-# Initialize 
-#------------------------------------------------------------------------
-
 # # Define the name of the Pinecone index
 index_name = 'mimtssinkqa'
 
 # Initialize the OpenAI embeddings object 
 from langchain_openai import OpenAIEmbeddings
 embeddings = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)
-# embeddings = OpenAIEmbeddings()
-
 
 # LOAD VECTOR STORE FROM EXISTING INDEX
 from langchain_community.vectorstores import Pinecone
@@ -64,7 +34,6 @@ def ask_with_memory(vector_store, query, chat_history=[]):
     from langchain.prompts import ChatPromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate
 
     llm = ChatOpenAI(model_name='gpt-3.5-turbo', temperature=0.5, openai_api_key=OPENAI_API_KEY)
-    # llm = ChatOpenAI(model_name='gpt-3.5-turbo', temperature=0.5)
     
     retriever = vector_store.as_retriever(search_type='similarity', search_kwargs={'k': 3})