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requirements.txt

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+python-dotenv
+langchain

streamlit-chat-context-in-session.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 dataclasses import dataclass
+from dotenv import load_dotenv
+# https://api.python.langchain.com/en/latest/llms/langchain_community.llms.cohere.Cohere.html#langchain_community.llms.cohere.Cohere
+from langchain_community.llms import Cohere
+
+### 2. Setup datastructure for holding the messages
+# Define a Message class for holding the query/response
+@dataclass
+class Message:
+    role: str       # identifies the actor (system, user or human, assistant or ai)
+    payload: str    # instructions, query, response
+
+# Streamlit knows about the common roles as a result, it is able to display the icons
+USER = "user"            # or human, 
+ASSISTANT = "assistant"  # or ai, 
+SYSTEM = "system"
+
+# 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('COHERE_API_KEY'):
+        st.session_state['COHERE_API_KEY'] = os.getenv('COHERE_API_KEY')
+except:
+    print("Environment file not found !! Copy & paste your Cohere API key.")
+
+
+### 3. Initialize the datastructure to hold the context
+MESSAGES='messages'
+if  MESSAGES not in st.session_state:
+    system_message  = Message(role=SYSTEM, payload='you are a polite assistant named "Ruby".')
+    st.session_state[MESSAGES] = [system_message]
+
+### 4. Setup the title & input text element for the Cohere API key
+#    Set the title
+#    Populate API key from session if it is available
+st.title("Multi-Turn conversation interface  !!!")
+
+# If the key is already available, initialize its value on the UI
+if 'COHERE_API_KEY' in st.session_state:
+    cohere_api_key = st.sidebar.text_input('Cohere API key',value=st.session_state['COHERE_API_KEY'])
+else:
+    cohere_api_key = st.sidebar.text_input('Cohere API key',placeholder='copy & paste your API key')
+
+
+
+
+### 5. Define utility functions to invoke the LLM
+
+# Create an instance of the LLM
+@st.cache_resource
+def  get_llm():
+     return Cohere(model="command", cohere_api_key=cohere_api_key) 
+
+# Create the context by concatenating the messages
+def get_chat_context():
+    context = ''
+    for msg in st.session_state[MESSAGES]:
+        context = context + '\n\n' + msg.role + ':' + msg.payload
+    return context
+
+# Generate the response and return
+def  get_llm_response(prompt):
+    llm = get_llm()
+
+    # 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
+
+### 6. 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[MESSAGES]:
+    st.chat_message(msg.role).write(msg.payload)
+
+### 7. Create the *chat_input* element to get the user query
+# Interface for user input
+prompt = st.chat_input(placeholder='Your input here')
+
+### 8. Process the query received from user
+if prompt:
+    # create user message and add to end of messages in the session
+    user_message = Message(role=USER, payload=prompt)
+    st.session_state[MESSAGES].append(user_message)
+
+    # Write the user prompt as chat message
+    st.chat_message(USER).write(prompt)
+
+    # Invoke the LLM
+    response = get_llm_response(prompt)
+
+    # Create message object representing the response
+    assistant_message = Message(role=ASSISTANT, payload=response)
+
+    # Add the response message to the mesages array in the session
+    st.session_state[MESSAGES].append(assistant_message)
+
+    # Write the response as chat_message
+    st.chat_message(ASSISTANT).write(response)
+
+### 9. Write out the current content of the context
+st.divider()
+st.subheader('st.session_state[MESSAGES] dump:')
+
+# Print the state of the buffer
+for msg in st.session_state[MESSAGES]:
+    st.text(msg.role + ' : ' + msg.payload)