Provide alwrity_rag_chatbot module
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lib/chatbot_custom/alwrity_rag_chatbot.py
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lib/chatbot_custom/alwrity_rag_chatbot.py
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import time
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import os
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import joblib
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import streamlit as st
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import google.generativeai as genai
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from dotenv import load_dotenv
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from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, ServiceContext, Document
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from llama_index.llms.openai import OpenAI
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import openai
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from pathlib import Path
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# Load environment variables
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load_dotenv()
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GOOGLE_API_KEY = os.environ.get('GOOGLE_API_KEY')
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genai.configure(api_key=os.environ.get('GEMINI_API_KEY'))
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load_dotenv(Path("../../.env"))
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openai.api_key = os.getenv("OPENAI_API_KEY")
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# Constants
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MODEL_ROLE = 'ai'
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AI_AVATAR_ICON = '👄'
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DATA_DIR = 'data/'
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def initialize_session_state():
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"""Initialize the chat message history in session state."""
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if "messages" not in st.session_state:
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st.session_state.messages = [
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{"role": "assistant", "content": "Ask me a question about documents from your local files or from the Web."}
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]
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@st.cache_resource(show_spinner=False)
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def load_data(input_dir):
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"""Load and index documents from the specified directory."""
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with st.spinner("Loading and indexing your docs – hang tight! This should take 1-2 minutes."):
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reader = SimpleDirectoryReader(input_dir=input_dir, recursive=True)
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docs = reader.load_data()
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service_context = ServiceContext.from_defaults(
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llm=OpenAI(
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model="gpt-3.5-turbo",
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temperature=0.5,
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system_prompt=(
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"You are an expert on content & digital marketing and your job is to answer technical questions."
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"Assume that all questions are related to provided documents, as context."
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"Keep your answers technical and based on facts – do not hallucinate features."
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)
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)
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)
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index = VectorStoreIndex.from_documents(docs, service_context=service_context)
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return index
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def display_chat_history():
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"""Display the chat message history."""
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for message in st.session_state.messages:
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with st.chat_message(message["role"]):
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st.write(message["content"])
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def generate_response(prompt, chat_engine):
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"""Generate a response from the chat engine and update the chat history."""
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if prompt:
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st.session_state.messages.append({"role": "user", "content": prompt})
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with st.chat_message("assistant"):
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with st.spinner("Thinking..."):
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response = chat_engine.chat(prompt)
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st.write(response.response)
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st.session_state.messages.append({"role": "assistant", "content": response.response})
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def history_chatbot():
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"""Main function to run the Streamlit app with history chat functionality."""
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# Ensure the data/ directory exists
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os.makedirs(DATA_DIR, exist_ok=True)
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# Generate a new chat ID
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new_chat_id = f'{time.time()}'
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# Load past chats if available
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try:
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past_chats = joblib.load(os.path.join(DATA_DIR, 'past_chats_list'))
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except FileNotFoundError:
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past_chats = {}
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# Sidebar for past chats
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with st.sidebar:
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st.write('# Past Chats')
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if 'chat_id' not in st.session_state:
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st.session_state.chat_id = st.selectbox(
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label='Pick a past chat',
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options=[new_chat_id] + list(past_chats.keys()),
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format_func=lambda x: past_chats.get(x, 'New Chat'),
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placeholder='_'
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)
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else:
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st.session_state.chat_id = st.selectbox(
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label='Pick a past chat',
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options=[new_chat_id, st.session_state.chat_id] + list(past_chats.keys()),
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index=1,
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format_func=lambda x: past_chats.get(x, 'New Chat' if x != st.session_state.chat_id else st.session_state.chat_title),
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placeholder='_'
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)
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st.session_state.chat_title = f'ChatSession-{st.session_state.chat_id}'
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# Load chat history if available
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try:
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st.session_state.messages = joblib.load(os.path.join(DATA_DIR, f'{st.session_state.chat_id}-st_messages'))
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st.session_state.gemini_history = joblib.load(os.path.join(DATA_DIR, f'{st.session_state.chat_id}-gemini_messages'))
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print('Loaded existing chat history')
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except FileNotFoundError:
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st.session_state.messages = []
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st.session_state.gemini_history = []
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print('Initialized new chat history')
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# Configure the AI model
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st.session_state.model = genai.GenerativeModel('gemini-pro')
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st.session_state.chat = st.session_state.model.start_chat(history=st.session_state.gemini_history)
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# Display past messages
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for message in st.session_state.messages:
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with st.chat_message(name=message['role'], avatar=message.get('avatar')):
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st.markdown(message['content'])
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# Handle user input
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if prompt := st.chat_input('Ask Alwrity...'):
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if st.session_state.chat_id not in past_chats:
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past_chats[st.session_state.chat_id] = st.session_state.chat_title
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joblib.dump(past_chats, os.path.join(DATA_DIR, 'past_chats_list'))
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# Display and save user message
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with st.chat_message('user'):
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st.markdown(prompt)
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st.session_state.messages.append({'role': 'user', 'content': prompt})
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# Send message to AI and stream the response
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response = st.session_state.chat.send_message(prompt, stream=True)
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full_response = ''
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with st.chat_message(name=MODEL_ROLE, avatar=AI_AVATAR_ICON):
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message_placeholder = st.empty()
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for chunk in response:
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for ch in chunk.text.split(' '):
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full_response += ch + ' '
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time.sleep(0.05)
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message_placeholder.write(full_response + '▌')
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message_placeholder.write(full_response)
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# Save the AI response
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st.session_state.messages.append({
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'role': MODEL_ROLE,
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'content': full_response,
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'avatar': AI_AVATAR_ICON
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})
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st.session_state.gemini_history = st.session_state.chat.history
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# Persist chat history to disk
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joblib.dump(st.session_state.messages, os.path.join(DATA_DIR, f'{st.session_state.chat_id}-st_messages'))
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joblib.dump(st.session_state.gemini_history, os.path.join(DATA_DIR, f'{st.session_state.chat_id}-gemini_messages'))
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def alwrity_chat_docqa():
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"""Main function to run the Streamlit app with document question answering functionality."""
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st.header("Ask Alwrity 💬 📚")
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initialize_session_state()
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option = st.radio(
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"Choose Data Source To Ask From:",
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("Ask Your Local Docs", "Ask Your PDFs", "Ask Your Videos", "Ask Your Audio Files")
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)
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if option == "Ask Your Local Docs":
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input_dir = st.text_input("Enter the path to the folder:")
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if input_dir:
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st.session_state.input_dir = input_dir
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elif option == "Ask Your PDFs":
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pdf_file = st.file_uploader("Upload a PDF file or enter a URL:", type=["pdf"])
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if pdf_file:
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st.session_state.input_file = pdf_file
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elif option == "Ask Your Videos":
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video_dir = st.text_input("Enter the path to the video folder:")
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if video_dir:
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st.session_state.input_dir = video_dir
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elif option == "Ask Your Audio Files":
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audio_dir = st.text_input("Enter the path to the audio folder:")
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if audio_dir:
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st.session_state.input_dir = audio_dir
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if 'input_dir' in st.session_state:
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index = load_data(st.session_state.input_dir)
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chat_engine = index.as_chat_engine(chat_mode="condense_question", verbose=True)
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display_chat_history()
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prompt = st.chat_input("Your question")
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if st.session_state.messages[-1]["role"] != "assistant":
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generate_response(prompt, chat_engine)
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elif 'input_file' in st.session_state:
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# Handle PDF file or URL input here
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st.write("Handling PDF file or URL input is not implemented yet.")
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def alwrity_rag_chatbot():
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"""Main function to run the combined Streamlit app."""
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st.sidebar.title("Alwrity RAG Chatbot")
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app_mode = st.sidebar.selectbox("Choose mode", ["History Chatbot", "Document QA Chatbot"])
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if app_mode == "History Chatbot":
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history_chatbot()
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elif app_mode == "Document QA Chatbot":
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alwrity_chat_docqa()
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if __name__ == "__main__":
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alwrity_rag_chatbot()
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