import json import yaml import os from typing import Any, Dict, no_type_check import chainlit as cl from modules.chat.llm_tutor import LLMTutor from modules.chat_processor.chat_processor import ChatProcessor from modules.config.constants import LLAMA_PATH from modules.chat.helpers import get_sources import copy from typing import Optional from dotenv import load_dotenv load_dotenv() print(os.environ.get("OAUTH_GOOGLE_CLIENT_ID")) USER_TIMEOUT = 60_000 SYSTEM = "System 🖥️" LLM = "LLM 🧠" AGENT = "Agent <>" YOU = "You 😃" ERROR = "Error 🚫" class Chatbot: def __init__(self): """ Initialize the Chatbot class. """ self.config = self._load_config() def _load_config(self): """ Load the configuration from a YAML file. """ with open("modules/config/config.yml", "r") as f: return yaml.safe_load(f) @no_type_check async def setup_llm(self): """ Set up the LLM with the provided settings. Update the configuration and initialize the LLM tutor. """ llm_settings = cl.user_session.get("llm_settings", {}) chat_profile, retriever_method, memory_window, llm_style = ( llm_settings.get("chat_model"), llm_settings.get("retriever_method"), llm_settings.get("memory_window"), llm_settings.get("llm_style"), ) chain = cl.user_session.get("chain") memory = chain.memory if chain else [] old_config = copy.deepcopy(self.config) self.config["vectorstore"]["db_option"] = retriever_method self.config["llm_params"]["memory_window"] = memory_window self.config["llm_params"]["llm_style"] = llm_style self.config["llm_params"]["llm_loader"] = chat_profile self.llm_tutor.update_llm( old_config, self.config ) # update only attributes that are changed self.chain = self.llm_tutor.qa_bot(memory=memory) tags = [chat_profile, self.config["vectorstore"]["db_option"]] self.chat_processor.config = self.config cl.user_session.set("chain", self.chain) cl.user_session.set("llm_tutor", self.llm_tutor) cl.user_session.set("chat_processor", self.chat_processor) @no_type_check async def update_llm(self, new_settings: Dict[str, Any]): """ Update the LLM settings and reinitialize the LLM with the new settings. Args: new_settings (Dict[str, Any]): The new settings to update. """ cl.user_session.set("llm_settings", new_settings) await self.inform_llm_settings() await self.setup_llm() async def make_llm_settings_widgets(self, config=None): """ Create and send the widgets for LLM settings configuration. Args: config: The configuration to use for setting up the widgets. """ config = config or self.config await cl.ChatSettings( [ cl.input_widget.Select( id="chat_model", label="Model Name (Default GPT-3)", values=["local_llm", "gpt-3.5-turbo-1106", "gpt-4"], initial_index=["local_llm", "gpt-3.5-turbo-1106", "gpt-4"].index(config["llm_params"]["llm_loader"]), ), cl.input_widget.Select( id="retriever_method", label="Retriever (Default FAISS)", values=["FAISS", "Chroma", "RAGatouille", "RAPTOR"], initial_index=["FAISS", "Chroma", "RAGatouille", "RAPTOR"].index(config["vectorstore"]["db_option"]) ), cl.input_widget.Slider( id="memory_window", label="Memory Window (Default 3)", initial=3, min=0, max=10, step=1, ), cl.input_widget.Switch( id="view_sources", label="View Sources", initial=False ), cl.input_widget.Select( id="llm_style", label="Type of Conversation (Default Normal)", values=["Normal", "ELI5", "Socratic"], initial_index=0, ), ] ).send() @no_type_check async def inform_llm_settings(self): """ Inform the user about the updated LLM settings and display them as a message. """ llm_settings: Dict[str, Any] = cl.user_session.get("llm_settings", {}) llm_tutor = cl.user_session.get("llm_tutor") settings_dict = { "model": llm_settings.get("chat_model"), "retriever": llm_settings.get("retriever_method"), "memory_window": llm_settings.get("memory_window"), "num_docs_in_db": ( len(llm_tutor.vector_db) if llm_tutor and hasattr(llm_tutor, "vector_db") else 0 ), "view_sources": llm_settings.get("view_sources"), } await cl.Message( author=SYSTEM, content="LLM settings have been updated. You can continue with your Query!", elements=[ cl.Text( name="settings", display="side", content=json.dumps(settings_dict, indent=4), language="json", ), ], ).send() async def set_starters(self): """ Set starter messages for the chatbot. """ return [ cl.Starter( label="recording on CNNs?", message="Where can I find the recording for the lecture on Transformers?", icon="/public/adv-screen-recorder-svgrepo-com.svg", ), cl.Starter( label="where's the slides?", message="When are the lectures? I can't find the schedule.", icon="/public/alarmy-svgrepo-com.svg", ), cl.Starter( label="Due Date?", message="When is the final project due?", icon="/public/calendar-samsung-17-svgrepo-com.svg", ), cl.Starter( label="Explain backprop.", message="I didn't understand the math behind backprop, could you explain it?", icon="/public/acastusphoton-svgrepo-com.svg", ), ] def rename(self, orig_author: str): """ Rename the original author to a more user-friendly name. Args: orig_author (str): The original author's name. Returns: str: The renamed author. """ rename_dict = {"Chatbot": "AI Tutor"} return rename_dict.get(orig_author, orig_author) async def start(self): """ Start the chatbot, initialize settings widgets, and display and load previous conversation if chat logging is enabled. """ await cl.Message(content="Welcome back! Setting up your session...").send() await self.make_llm_settings_widgets(self.config) user = cl.user_session.get("user") self.user = { "user_id": user.identifier, "session_id": "1234", } cl.user_session.set("user", self.user) self.chat_processor = ChatProcessor(self.config, self.user) self.llm_tutor = LLMTutor(self.config, user=self.user) if self.config["chat_logging"]["log_chat"]: # get previous conversation of the user memory = self.chat_processor.processor.prev_conv if len(self.chat_processor.processor.prev_conv) > 0: for idx, conv in enumerate(self.chat_processor.processor.prev_conv): await cl.Message( author="User", content=conv[0], type="user_message" ).send() await cl.Message(author="AI Tutor", content=conv[1]).send() else: memory = [] self.chain = self.llm_tutor.qa_bot(memory=memory) cl.user_session.set("llm_tutor", self.llm_tutor) cl.user_session.set("chain", self.chain) cl.user_session.set("chat_processor", self.chat_processor) async def on_chat_end(self): """ Handle the end of the chat session by sending a goodbye message. # TODO: Not used as of now - useful when the implementation for the conversation limiting is implemented """ await cl.Message(content="Sorry, I have to go now. Goodbye!").send() async def main(self, message): """ Process and Display the Conversation. Args: message: The incoming chat message. """ chain = cl.user_session.get("chain") llm_settings = cl.user_session.get("llm_settings", {}) view_sources = llm_settings.get("view_sources", False) processor = cl.user_session.get("chat_processor") res = await processor.rag(message.content, chain) answer = res.get("answer", res.get("result")) answer_with_sources, source_elements, sources_dict = get_sources( res, answer, view_sources=view_sources ) processor._process(message.content, answer, sources_dict) await cl.Message(content=answer_with_sources, elements=source_elements).send() def oauth_callback( provider_id: str, token: str, raw_user_data: Dict[str, str], default_user: cl.User, ) -> Optional[cl.User]: return default_user chatbot = Chatbot() cl.oauth_callback(chatbot.oauth_callback) cl.set_starters(chatbot.set_starters) cl.author_rename(chatbot.rename) cl.on_chat_start(chatbot.start) cl.on_chat_end(chatbot.on_chat_end) cl.on_message(chatbot.main) cl.on_settings_update(chatbot.update_llm)