app_7.py

import streamlit as st
import os
import tempfile

from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.chat_models.ollama import ChatOllama
from cassandra.cluster import Cluster
from langchain_community.vectorstores import Cassandra
from langchain.schema.runnable import RunnableMap
from langchain.prompts import ChatPromptTemplate
from langchain.callbacks.base import BaseCallbackHandler
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import PyPDFLoader

# 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 + "▌")

# Function for Vectorizing uploaded data into DataStax Enterprise
def vectorize_text(uploaded_file, vector_store):
    if uploaded_file is not None:
        
        # Write to temporary file
        temp_dir = tempfile.TemporaryDirectory()
        file = uploaded_file
        temp_filepath = os.path.join(temp_dir.name, file.name)
        with open(temp_filepath, 'wb') as f:
            f.write(file.getvalue())

        # Load the PDF
        docs = []
        loader = PyPDFLoader(temp_filepath)
        docs.extend(loader.load())

        # Create the text splitter
        text_splitter = RecursiveCharacterTextSplitter(
            chunk_size = 1500,
            chunk_overlap  = 100
        )

        # Vectorize the PDF and load it into the DataStax Enterprise Vector Store
        pages = text_splitter.split_documents(docs)
        vector_store.add_documents(pages)  
        st.info(f"{len(pages)} pages loaded.")

# 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}

USER'S QUESTION:
{question}

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

# Cache Mistral Chat Model for future runs
@st.cache_resource()
def load_chat_model():
    # parameters for ollama see: https://api.python.langchain.com/en/latest/chat_models/langchain_community.chat_models.ollama.ChatOllama.html
    # num_ctx is the context window size
    return ChatOllama(model="mistral:latest", num_ctx=18192, base_url=st.secrets['OLLAMA_ENDPOINT'])
chat_model = load_chat_model()

# Cache the DataStax Enterprise Vector Store for future runs
@st.cache_resource(show_spinner='Connecting to Datastax Enterprise v7 with Vector Support')
def load_vector_store():
    # Connect to DSE
    cluster = Cluster([st.secrets['DSE_ENDPOINT']])
    session = cluster.connect()

    # Connect to the Vector Store
    vector_store = Cassandra(
        session=session,
        embedding=HuggingFaceEmbeddings(),
        keyspace=st.secrets['DSE_KEYSPACE'],
        table_name=st.secrets['DSE_TABLE']
    )
    return vector_store
vector_store = load_vector_store()

# Cache the Retriever for future runs
@st.cache_resource(show_spinner='Getting retriever')
def load_retriever():
    # 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.markdown(""" POC on RAG for LIONchat
            Connected to the Data """)
st.divider()

# Include the upload form for new data to be Vectorized
with st.sidebar:
    st.image("https://1000logos.net/wp-content/uploads/2021/05/ING-logo.png", width=250)
    with st.form('upload'):
        uploaded_file = st.file_uploader('Upload a document for additional context', type=['pdf'])
        submitted = st.form_submit_button('Save to DataStax Enterprise')
        if submitted:
            vectorize_text(uploaded_file, vector_store)

# 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 Mistral'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)