Updated to use GPT-4o and removed PromptLayer

app_faiss.py

@@ -1,24 +1,16 @@
 import os, re, uuid
 from langchain_openai import OpenAI
-import promptlayer
 import streamlit as st
 from langchain_community.chat_models import ChatOpenAI
 from langchain.memory import ConversationBufferWindowMemory
 from langchain.chains import ConversationalRetrievalChain
 from langchain_openai import OpenAIEmbeddings
-from langchain_community.chat_models import PromptLayerChatOpenAI
 from langchain_community.vectorstores import FAISS
 from langchain.prompts.chat import ChatPromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate
 
-# gpt-3.5-turbo, gpt-4, and gpt-4-turbo-preview point to the latest model version
 #MODEL = "gpt-3.5-turbo" # 4K, Sept 2021. Legacy. Currently points to gpt-3.5-turbo-0613.
-#MODEL = "gpt-3.5-turbo-16k" # 16K, Sept 2021. Legacy. Snapshot of gpt-3.5-turbo from June 13th 2023. Will be deprecated on June 13, 2024
-MODEL = "gpt-3.5-turbo-1106" # 16K, Sept 2021. New Updated GPT 3.5 Turbo. The latest GPT-3.5 Turbo model with improved instruction following, JSON mode, reproducible outputs, parallel function calling, and more. Returns a maximum of 4,096 output tokens.
-#MODEL = "gpt-4" # 8K, Sept 2021
-#MODEL = "gpt-4-32k" # 32K, Sept 2021
-#MODEL = "gpt-4-turbo-preview" # 128K, Apr 2023
-#MODEL = "gpt-4-1106-preview" # 128K, Apr 2023
-#MODEL = "gpt-4o"
+#MODEL = "gpt-3.5-turbo-1106" # 16K, Sept 2021. New Updated GPT 3.5 Turbo. The latest GPT-3.5 Turbo model with improved instruction following, JSON mode, reproducible outputs, parallel function calling, and more. Returns a maximum of 4,096 output tokens.
+MODEL = "gpt-4o"
 
 if "session_id" not in st.session_state:
     st.session_state.session_id = str(uuid.uuid4())
@@ -36,7 +28,6 @@
 st.sidebar.markdown("Developed by Mark Craddock](https://twitter.com/mcraddock)", unsafe_allow_html=True)
 st.sidebar.markdown("Current Version: 1.1.0")
 st.sidebar.divider()
-st.sidebar.markdown("Using pt-3.5-turbo-1106 API")
 st.sidebar.markdown(st.session_state.session_id)
 st.sidebar.markdown("Wardley Mapping is provided courtesy of Simon Wardley and licensed Creative Commons Attribution Share-Alike.")
 st.sidebar.divider()
@@ -46,14 +37,12 @@
 
 if user_openai_api_key:
     os.environ["OPENAI_API_KEY"] = user_openai_api_key
-    
+
     if "vector_store" not in st.session_state:
         # If the user has provided an API key, use it
         # Swap out openai for promptlayer
-        promptlayer.api_key = st.secrets["PROMPTLAYER"]
-        openai = promptlayer.openai
         openai.api_key = user_openai_api_key
-        
+
         if os.path.exists(DATA_STORE_DIR):
             st.session_state.vector_store = FAISS.load_local(
                 DATA_STORE_DIR,
@@ -62,38 +51,38 @@
             )
         else:
             st.write(f"Missing files. Upload index.faiss and index.pkl files to {DATA_STORE_DIR} directory first")
-        
+
         custom_system_template="""
             You are SimonGPT with the style of a strategy researcher with well over twenty years research in strategy and cloud computing.
             You use complicated examples from Wardley Mapping in your answers.
             Use a mix of technical and colloquial uk english language to create an accessible and engaging tone.
             Your language should be for an 12 year old to understand.
             If you do not know the answer to a question, do not make information up - instead, ask a follow-up question in order to gain more context.
-            Your primary objective is to help the user formulate excellent answers by utilizing the context about the book and 
+            Your primary objective is to help the user formulate excellent answers by utilizing the context about the book and
             relevant details from your knowledge, along with insights from previous conversations.
             ----------------
             Reference Context and Knowledge from Similar Existing Services: {context}
             Previous Conversations: {chat_history}"""
-        
+
         custom_user_template = "Question:'''{question}'''"
-        
+
         prompt_messages = [
             SystemMessagePromptTemplate.from_template(custom_system_template),
             HumanMessagePromptTemplate.from_template(custom_user_template)
             ]
         prompt = ChatPromptTemplate.from_messages(prompt_messages)
-    
+
     if "memory" not in st.session_state:
         st.session_state.memory = ConversationBufferWindowMemory(memory_key="chat_history", return_messages=True, output_key='answer')
-    
+
     if "llm" not in st.session_state:
         st.session_state.llm = PromptLayerChatOpenAI(
             model_name=MODEL,
             temperature=0,
             max_tokens=300,
             pl_tags=["research2023", st.session_state.session_id],
         )  # Modify model_name if you have access to GPT-4
-        
+
     if "chain" not in st.session_state:
         st.session_state.chain = ConversationalRetrievalChain.from_llm(
             llm=st.session_state.llm,
@@ -109,20 +98,20 @@
             memory=st.session_state.memory,
             combine_docs_chain_kwargs={'prompt': prompt}
         )
-        
+
     if "messages" not in st.session_state:
         st.session_state.messages = []
-    
+
     for message in st.session_state.messages:
         if message["role"] in ["user", "assistant"]:
             with st.chat_message(message["role"]):
                 st.markdown(message["content"])
-    
+
     if query := st.chat_input("How is AI used in these maps?"):
         st.session_state.messages.append({"role": "user", "content": query})
         with st.chat_message("user"):
             st.markdown(query)
-          
+
         with st.spinner():
             with st.chat_message("assistant"):
                 response = st.session_state.chain(query)