code/main.py

import chainlit.data as cl_data
import asyncio
from modules.config.constants import (
    LITERAL_API_KEY_LOGGING,
    LITERAL_API_URL,
)
from modules.chat_processor.literal_ai import CustomLiteralDataLayer

import json
import yaml
from typing import Any, Dict, no_type_check
import chainlit as cl
from modules.chat.llm_tutor import LLMTutor
from modules.chat.helpers import (
    get_sources,
    get_history_chat_resume,
    get_history_setup_llm,
    get_last_config,
)
import copy
from typing import Optional
from chainlit.types import ThreadDict
import time

USER_TIMEOUT = 60_000
SYSTEM = "System"
LLM = "AI Tutor"
AGENT = "Agent"
YOU = "User"
ERROR = "Error"

with open("modules/config/config.yml", "r") as f:
    config = yaml.safe_load(f)


async def setup_data_layer():
    """
    Set up the data layer for chat logging.
    """
    if config["chat_logging"]["log_chat"]:
        data_layer = CustomLiteralDataLayer(
            api_key=LITERAL_API_KEY_LOGGING, server=LITERAL_API_URL
        )
    else:
        data_layer = None

    return data_layer


class Chatbot:
    def __init__(self, config):
        """
        Initialize the Chatbot class.
        """
        self.config = config

    async 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.

        #TODO: Clean this up.
        """
        start_time = time.time()

        llm_settings = cl.user_session.get("llm_settings", {})
        (
            chat_profile,
            retriever_method,
            memory_window,
            llm_style,
            generate_follow_up,
            chunking_mode,
        ) = (
            llm_settings.get("chat_model"),
            llm_settings.get("retriever_method"),
            llm_settings.get("memory_window"),
            llm_settings.get("llm_style"),
            llm_settings.get("follow_up_questions"),
            llm_settings.get("chunking_mode"),
        )

        chain = cl.user_session.get("chain")
        memory_list = cl.user_session.get(
            "memory",
            (
                list(chain.store.values())[0].messages
                if len(chain.store.values()) > 0
                else []
            ),
        )
        conversation_list = get_history_setup_llm(memory_list)

        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.config["llm_params"]["generate_follow_up"] = generate_follow_up
        self.config["splitter_options"]["chunking_mode"] = chunking_mode

        self.llm_tutor.update_llm(
            old_config, self.config
        )  # update only llm attributes that are changed
        self.chain = self.llm_tutor.qa_bot(
            memory=conversation_list,
        )

        cl.user_session.set("chain", self.chain)
        cl.user_session.set("llm_tutor", self.llm_tutor)

        print("Time taken to setup LLM: ", time.time() - start_time)

    @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", "gpt-4o-mini"],
                    initial_index=[
                        "local_llm",
                        "gpt-3.5-turbo-1106",
                        "gpt-4",
                        "gpt-4o-mini",
                    ].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.Switch(
                    id="stream_response",
                    label="Stream response",
                    initial=config["llm_params"]["stream"],
                ),
                cl.input_widget.Select(
                    id="chunking_mode",
                    label="Chunking mode",
                    values=["fixed", "semantic"],
                    initial_index=1,
                ),
                cl.input_widget.Switch(
                    id="follow_up_questions",
                    label="Generate follow up questions",
                    initial=False,
                ),
                cl.input_widget.Select(
                    id="llm_style",
                    label="Type of Conversation (Default Normal)",
                    values=["Normal", "ELI5"],
                    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"),
            "follow_up_questions": llm_settings.get("follow_up_questions"),
        }
        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 Starters only if the chat is new

        try:
            thread = cl_data._data_layer.get_thread(
                cl.context.session.thread_id
            )  # see if the thread has any steps
            if thread.steps or len(thread.steps) > 0:
                return None
        except Exception as e:
            print(e)
            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": LLM}
        return rename_dict.get(orig_author, orig_author)

    async def start(self, config=None):
        """
        Start the chatbot, initialize settings widgets,
        and display and load previous conversation if chat logging is enabled.
        """

        start_time = time.time()

        self.config = (
            await self._load_config() if config is None else config
        )  # Reload the configuration on chat resume

        await self.make_llm_settings_widgets(self.config)  # Reload the settings widgets

        await self.make_llm_settings_widgets(self.config)
        user = cl.user_session.get("user")

        try:
            self.user = {
                "user_id": user.identifier,
                "session_id": cl.context.session.thread_id,
            }
        except Exception as e:
            print(e)
            self.user = {
                "user_id": "guest",
                "session_id": cl.context.session.thread_id,
            }

        memory = cl.user_session.get("memory", [])

        cl.user_session.set("user", self.user)
        self.llm_tutor = LLMTutor(self.config, user=self.user)

        self.chain = self.llm_tutor.qa_bot(
            memory=memory,
        )
        self.question_generator = self.llm_tutor.question_generator
        cl.user_session.set("llm_tutor", self.llm_tutor)
        cl.user_session.set("chain", self.chain)

        print("Time taken to start LLM: ", time.time() - start_time)

    async def stream_response(self, response):
        """
        Stream the response from the LLM.

        Args:
            response: The response from the LLM.
        """
        msg = cl.Message(content="")
        await msg.send()

        output = {}
        for chunk in response:
            if "answer" in chunk:
                await msg.stream_token(chunk["answer"])

            for key in chunk:
                if key not in output:
                    output[key] = chunk[key]
                else:
                    output[key] += chunk[key]
        return output

    async def main(self, message):
        """
        Process and Display the Conversation.

        Args:
            message: The incoming chat message.
        """

        start_time = time.time()

        chain = cl.user_session.get("chain")

        llm_settings = cl.user_session.get("llm_settings", {})
        view_sources = llm_settings.get("view_sources", False)
        stream = llm_settings.get("stream_response", False)
        stream = False  # Fix streaming
        user_query_dict = {"input": message.content}
        # Define the base configuration
        chain_config = {
            "configurable": {
                "user_id": self.user["user_id"],
                "conversation_id": self.user["session_id"],
                "memory_window": self.config["llm_params"]["memory_window"],
            },
            "callbacks": (
                [cl.LangchainCallbackHandler()]
                if cl_data._data_layer and self.config["chat_logging"]["callbacks"]
                else None
            ),
        }

        if stream:
            res = chain.stream(user_query=user_query_dict, config=chain_config)
            res = await self.stream_response(res)
        else:
            res = await chain.invoke(
                user_query=user_query_dict,
                config=chain_config,
            )

        answer = res.get("answer", res.get("result"))

        answer_with_sources, source_elements, sources_dict = get_sources(
            res, answer, stream=stream, view_sources=view_sources
        )
        answer_with_sources = answer_with_sources.replace("$$", "$")

        print("Time taken to process the message: ", time.time() - start_time)

        actions = []

        if self.config["llm_params"]["generate_follow_up"]:
            start_time = time.time()
            config = {
                "callbacks": (
                    [cl.LangchainCallbackHandler()]
                    if cl_data._data_layer and self.config["chat_logging"]["callbacks"]
                    else None
                )
            }

            list_of_questions = await self.question_generator.generate_questions(
                query=user_query_dict["input"],
                response=answer,
                chat_history=res.get("chat_history"),
                context=res.get("context"),
                config=config,
            )

            for question in list_of_questions:

                actions.append(
                    cl.Action(
                        name="follow up question",
                        value="example_value",
                        description=question,
                        label=question,
                    )
                )

            print("Time taken to generate questions: ", time.time() - start_time)

        await cl.Message(
            content=answer_with_sources,
            elements=source_elements,
            author=LLM,
            actions=actions,
            metadata=self.config,
        ).send()

    async def on_chat_resume(self, thread: ThreadDict):
        thread_config = None
        steps = thread["steps"]
        k = self.config["llm_params"][
            "memory_window"
        ]  # on resume, alwyas use the default memory window
        conversation_list = get_history_chat_resume(steps, k, SYSTEM, LLM)
        thread_config = get_last_config(
            steps
        )  # TODO: Returns None for now - which causes config to be reloaded with default values
        cl.user_session.set("memory", conversation_list)
        await self.start(config=thread_config)

    @cl.oauth_callback
    def auth_callback(
        provider_id: str,
        token: str,
        raw_user_data: Dict[str, str],
        default_user: cl.User,
    ) -> Optional[cl.User]:
        return default_user

    async def on_follow_up(self, action: cl.Action):
        message = await cl.Message(
            content=action.description,
            type="user_message",
            author=self.user["user_id"],
        ).send()
        async with cl.Step(
            name="on_follow_up", type="run", parent_id=message.id
        ) as step:
            await self.main(message)
            step.output = message.content


chatbot = Chatbot(config=config)


async def start_app():
    cl_data._data_layer = await setup_data_layer()
    chatbot.literal_client = cl_data._data_layer.client if cl_data._data_layer else None
    cl.set_starters(chatbot.set_starters)
    cl.author_rename(chatbot.rename)
    cl.on_chat_start(chatbot.start)
    cl.on_chat_resume(chatbot.on_chat_resume)
    cl.on_message(chatbot.main)
    cl.on_settings_update(chatbot.update_llm)
    cl.action_callback("follow up question")(chatbot.on_follow_up)


asyncio.run(start_app())