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import json
import textwrap
from typing import Any, Callable, Dict, List, Literal, Optional, no_type_check
import chainlit as cl
from chainlit import run_sync
from chainlit.config import config
import yaml
import os
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
from chainlit.input_widget import Select, Switch, Slider
USER_TIMEOUT = 60_000
SYSTEM = "System 🖥️"
LLM = "LLM 🧠"
AGENT = "Agent <>"
YOU = "You 😃"
ERROR = "Error 🚫"
class Chatbot:
def __init__(self):
self.llm_tutor = None
self.chain = None
self.chat_processor = None
self.config = self._load_config()
def _load_config(self):
with open("modules/config/config.yml", "r") as f:
config = yaml.safe_load(f)
return config
async def ask_helper(func, **kwargs):
res = await func(**kwargs).send()
while not res:
res = await func(**kwargs).send()
return res
@no_type_check
async def setup_llm(self) -> None:
"""From the session `llm_settings`, create new LLMConfig and LLM objects,
save them in session state."""
llm_settings = cl.user_session.get("llm_settings", {})
chat_profile = llm_settings.get("chat_model")
retriever_method = llm_settings.get("retriever_method")
memory_window = llm_settings.get("memory_window")
self._configure_llm(chat_profile)
chain = cl.user_session.get("chain")
memory = chain.memory
self.config["vectorstore"][
"db_option"
] = retriever_method # update the retriever method in the config
memory.k = memory_window # set the memory window
self.llm_tutor = LLMTutor(self.config)
self.chain = self.llm_tutor.qa_bot(memory=memory)
tags = [chat_profile, self.config["vectorstore"]["db_option"]]
self.chat_processor = ChatProcessor(self.config, tags=tags)
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]) -> None:
"""Update LLMConfig and LLM from settings, and save in session state."""
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):
config = config or self.config
await cl.ChatSettings(
[
cl.input_widget.Select(
id="chat_model",
label="Model Name (Default GPT-3)",
values=["llama", "gpt-3.5-turbo-1106", "gpt-4"],
initial_index=0,
),
cl.input_widget.Select(
id="retriever_method",
label="Retriever (Default FAISS)",
values=["FAISS", "Chroma", "RAGatouille", "RAPTOR"],
initial_index=0,
),
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
),
]
).send() # type: ignore
@no_type_check
async def inform_llm_settings(self) -> None:
llm_settings: Dict[str, Any] = cl.user_session.get("llm_settings", {})
llm_tutor = cl.user_session.get("llm_tutor")
settings_dict = 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),
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):
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",
),
]
async def chat_profile(self):
return [
# cl.ChatProfile(
# name="gpt-3.5-turbo-1106",
# markdown_description="Use OpenAI API for **gpt-3.5-turbo-1106**.",
# ),
# cl.ChatProfile(
# name="gpt-4",
# markdown_description="Use OpenAI API for **gpt-4**.",
# ),
cl.ChatProfile(
name="Llama",
markdown_description="Use the local LLM: **Tiny Llama**.",
),
]
def rename(self, orig_author: str):
rename_dict = {"Chatbot": "AI Tutor"}
return rename_dict.get(orig_author, orig_author)
async def start(self):
await self.make_llm_settings_widgets(self.config)
chat_profile = cl.user_session.get("chat_profile")
if chat_profile:
self._configure_llm(chat_profile)
self.llm_tutor = LLMTutor(self.config)
self.chain = self.llm_tutor.qa_bot()
tags = [chat_profile, self.config["vectorstore"]["db_option"]]
self.chat_processor = ChatProcessor(self.config, tags=tags)
cl.user_session.set("llm_tutor", self.llm_tutor)
cl.user_session.set("chain", self.chain)
cl.user_session.set("counter", 0)
cl.user_session.set("chat_processor", self.chat_processor)
async def on_chat_end(self):
await cl.Message(content="Sorry, I have to go now. Goodbye!").send()
async def main(self, message):
user = cl.user_session.get("user")
chain = cl.user_session.get("chain")
counter = cl.user_session.get("counter")
llm_settings = cl.user_session.get("llm_settings")
counter += 1
cl.user_session.set("counter", counter)
cb = cl.AsyncLangchainCallbackHandler() # TODO: fix streaming here
cb.answer_reached = True
processor = cl.user_session.get("chat_processor")
res = await processor.rag(message.content, chain, cb)
answer = res.get("answer", res.get("result"))
answer_with_sources, source_elements, sources_dict = get_sources(
res, answer, view_sources=llm_settings.get("view_sources")
)
processor._process(message.content, answer, sources_dict)
await cl.Message(content=answer_with_sources, elements=source_elements).send()
def _configure_llm(self, chat_profile):
chat_profile = chat_profile.lower()
if chat_profile in ["gpt-3.5-turbo-1106", "gpt-4"]:
self.config["llm_params"]["llm_loader"] = "openai"
self.config["llm_params"]["openai_params"]["model"] = chat_profile
elif chat_profile == "llama":
self.config["llm_params"]["llm_loader"] = "local_llm"
self.config["llm_params"]["local_llm_params"]["model"] = LLAMA_PATH
self.config["llm_params"]["local_llm_params"]["model_type"] = "llama"
elif chat_profile == "mistral":
self.config["llm_params"]["llm_loader"] = "local_llm"
self.config["llm_params"]["local_llm_params"]["model"] = MISTRAL_PATH
self.config["llm_params"]["local_llm_params"]["model_type"] = "mistral"
chatbot = Chatbot()
# Register functions to Chainlit events
cl.set_starters(chatbot.set_starters)
cl.set_chat_profiles(chatbot.chat_profile)
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)
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