app_6.py

import streamlit as st
import os
from langchain.embeddings import OpenAIEmbeddings
from langchain.chat_models import ChatOpenAI
from langchain.vectorstores import AstraDB
from langchain.schema.runnable import RunnableMap
from langchain.prompts import ChatPromptTemplate
from langchain.callbacks.base import BaseCallbackHandler

# Streaming call back handler for responses
class StreamHandler(BaseCallbackHandler):
    def __init__(self, container, initial_text=""):
        self.container = container
        self.text = initial_text

    def on_llm_new_token(self, token: str, **kwargs):
        self.text += token
        self.container.markdown(self.text + "▌")

# Cache prompt for future runs
@st.cache_data()
def load_prompt():
    template = """You're a helpful AI assistent tasked to answer the user's questions.
You're friendly and you answer extensively with multiple sentences. You prefer to use bulletpoints to summarize.

CONTEXT:
{context}

QUESTION:
{question}

YOUR ANSWER:"""
    return ChatPromptTemplate.from_messages([("system", template)])
prompt = load_prompt()

# Cache OpenAI Chat Model for future runs
@st.cache_resource()
def load_chat_model():
    return ChatOpenAI(
        temperature=0.3,
        model='gpt-3.5-turbo',
        streaming=True,
        verbose=True
    )
chat_model = load_chat_model()

# Cache the Astra DB Vector Store for future runs
@st.cache_resource(show_spinner='Connecting to Astra')
def load_retriever():
    # Connect to the Vector Store
    vector_store = AstraDB(
        embedding=OpenAIEmbeddings(),
        collection_name="my_store",
        api_endpoint=st.secrets['ASTRA_API_ENDPOINT'],
        token=st.secrets['ASTRA_TOKEN']
    )

    # Get the retriever for the Chat Model
    retriever = vector_store.as_retriever(
        search_kwargs={"k": 5}
    )
    return retriever
retriever = load_retriever()

# Start with empty messages, stored in session state
if 'messages' not in st.session_state:
    st.session_state.messages = []

# Draw a title and some markdown
st.title("Your personal Efficiency Booster")
st.markdown("""Generative AI is considered to bring the next Industrial Revolution.  
Why? Studies show a **37% efficiency boost** in day to day work activities!""")

# Draw all messages, both user and bot so far (every time the app reruns)
for message in st.session_state.messages:
    st.chat_message(message['role']).markdown(message['content'])

# Draw the chat input box
if question := st.chat_input("What's up?"):
    
    # Store the user's question in a session object for redrawing next time
    st.session_state.messages.append({"role": "human", "content": question})

    # Draw the user's question
    with st.chat_message('human'):
        st.markdown(question)

    # UI placeholder to start filling with agent response
    with st.chat_message('assistant'):
        response_placeholder = st.empty()

    # Generate the answer by calling OpenAI's Chat Model
    inputs = RunnableMap({
        'context': lambda x: retriever.get_relevant_documents(x['question']),
        'question': lambda x: x['question']
    })
    chain = inputs | prompt | chat_model
    response = chain.invoke({'question': question}, config={'callbacks': [StreamHandler(response_placeholder)]})
    answer = response.content

    # Store the bot's answer in a session object for redrawing next time
    st.session_state.messages.append({"role": "ai", "content": answer})

    # Write the final answer without the cursor
    response_placeholder.markdown(answer)