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app.py

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+# Manages user & assistant messages in the session state.
+
+### 1. Import the libraries
+import streamlit as st
+import time
+import os
+from dotenv import load_dotenv
+
+from langchain.memory import ConversationSummaryMemory
+from langchain.chains import ConversationChain
+from langchain_openai import ChatOpenAI
+from langchain.prompts import PromptTemplate
+from langchain_core.messages import HumanMessage, AIMessage
+
+# This is to simplify local development
+# Without this you will need to copy/paste the API key with every change
+try:
+    # CHANGE the location of the file
+    load_dotenv('C:\\Users\\raj\\.jupyter\\.env')
+    # Add the API key to the session - use it for populating the interface
+    if os.getenv('OPENAI_API_KEY'):
+        st.session_state['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY')
+except:
+    print("Environment file not found !! Copy & paste your OpenAI API key.")
+
+
+### 1. Setup the title & input text element for the OpenAI API key
+#    Set the title
+#    Populate API key from session if it is available
+st.title("LangChain ConversationSummaryMemory  !!!")
+
+# If the key is already available, initialize its value on the UI
+if 'OPENAI_API_KEY' in st.session_state:
+    openai_api_key = st.sidebar.text_input('OpenAI API key',value=st.session_state['OPENAI_API_KEY'])
+else:
+    openai_api_key = st.sidebar.text_input('OpenAI API key',placeholder='copy & paste your OpenAI API key')
+
+### 2. Define utility functions to invoke the LLM
+
+# Create an instance of the LLM for summarization
+@st.cache_resource
+def  get_summarization_llm():
+     model = 'gpt-3.5-turbo-0125'
+     return ChatOpenAI(model=model, openai_api_key=openai_api_key) 
+
+# Create an instance of the LLM for chatbot responses
+@st.cache_resource
+def  get_llm():
+     model = 'gpt-3.5-turbo-0125'
+     return ChatOpenAI(model=model, openai_api_key=openai_api_key) 
+
+@st.cache_resource
+def get_llm_chain():
+    memory = st.session_state['MEMORY']
+    conversation = ConversationChain(
+        llm=get_llm(),
+        # prompt=prompt_template,
+        # verbose=True,
+        memory=memory
+    )
+    return conversation
+
+# Create the context by concatenating the messages
+def get_chat_context():
+    memory = st.session_state['MEMORY']
+    return memory.buffer
+
+# Generate the response and return
+def  get_llm_response(prompt):
+    # llm = get_llm()
+    llm = get_llm_chain()
+
+    # Show spinner, while we are waiting for the response
+    with st.spinner('Invoking LLM ... '):
+        # get the context
+        chat_context = get_chat_context()
+
+        # Prefix the query with context
+        query_payload = chat_context +'\n\n Question: ' + prompt
+
+        response = llm.invoke(query_payload)
+
+        return response
+
+# Initialize the session state memory
+if 'MEMORY' not in st.session_state:
+    memory = ConversationSummaryMemory(
+        llm = get_summarization_llm(),
+        human_prefix='user',
+        ai_prefix = 'assistant',
+        return_messages=True
+    )
+    # add to the session
+    st.session_state['MEMORY'] = memory
+
+### 3. Write the messages to chat_message container
+# Write messages to the chat_message element
+# This is needed as streamlit re-runs the entire script when user provides input in a widget
+# https://docs.streamlit.io/develop/api-reference/chat/st.chat_message
+
+for msg in st.session_state['MEMORY'].chat_memory.messages:
+
+    if (isinstance(msg, HumanMessage)):
+        st.chat_message('user').write(msg.content)
+    elif (isinstance(msg, AIMessage)):
+        st.chat_message('ai').write(msg.content)
+    else:
+        print('System message: ', msg.content)
+    
+
+### 4. Create the *chat_input* element to get the user query
+# Interface for user input
+prompt = st.chat_input(placeholder='Your input here')
+
+### 5. Process the query received from user
+if prompt:
+
+    # Write the user prompt as chat message
+    st.chat_message('user').write(prompt)
+
+    # Invoke the LLM
+    response = get_llm_response(prompt)
+
+    # Write the response as chat_message
+    st.chat_message('ai').write(response['response'])
+
+### 6. Write out the current content of the context
+st.divider()
+st.subheader('Context/Summary:')
+
+# Print the state of the buffer
+st.session_state['MEMORY'].buffer

requirements.txt

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+python-dotenv
+langchain
+openai