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from langchain_community.document_loaders import PyPDFLoader, DirectoryLoader
from langchain_core.prompts import PromptTemplate
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from langchain.chains import RetrievalQA
import chainlit as cl
from langchain_community.chat_models import ChatOpenAI
from langchain_community.embeddings import OpenAIEmbeddings
import yaml
import logging
from dotenv import load_dotenv
from modules.chat.llm_tutor import LLMTutor
from modules.config.constants import *
from modules.chat.helpers import get_sources
from modules.chat_processor.chat_processor import ChatProcessor
global logger
# Initialize logger
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
formatter = logging.Formatter("%(asctime)s - %(levelname)s - %(message)s")
# Console Handler
console_handler = logging.StreamHandler()
console_handler.setLevel(logging.INFO)
console_handler.setFormatter(formatter)
logger.addHandler(console_handler)
# Adding option to select the chat profile
@cl.set_chat_profiles
async def chat_profile():
return [
# cl.ChatProfile(
# name="Mistral",
# markdown_description="Use the local LLM: **Mistral**.",
# ),
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**.",
),
]
@cl.author_rename
def rename(orig_author: str):
rename_dict = {"Chatbot": "AI Tutor"}
return rename_dict.get(orig_author, orig_author)
# chainlit code
@cl.on_chat_start
async def start():
with open("modules/config/config.yml", "r") as f:
config = yaml.safe_load(f)
# Ensure log directory exists
log_directory = config["log_dir"]
if not os.path.exists(log_directory):
os.makedirs(log_directory)
# File Handler
log_file_path = (
f"{log_directory}/tutor.log" # Change this to your desired log file path
)
file_handler = logging.FileHandler(log_file_path, mode="w")
file_handler.setLevel(logging.INFO)
file_handler.setFormatter(formatter)
logger.addHandler(file_handler)
logger.info("Config file loaded")
logger.info(f"Config: {config}")
logger.info("Creating llm_tutor instance")
chat_profile = cl.user_session.get("chat_profile")
if chat_profile is not None:
if chat_profile.lower() in ["gpt-3.5-turbo-1106", "gpt-4"]:
config["llm_params"]["llm_loader"] = "openai"
config["llm_params"]["openai_params"]["model"] = chat_profile.lower()
elif chat_profile.lower() == "llama":
config["llm_params"]["llm_loader"] = "local_llm"
config["llm_params"]["local_llm_params"]["model"] = LLAMA_PATH
config["llm_params"]["local_llm_params"]["model_type"] = "llama"
elif chat_profile.lower() == "mistral":
config["llm_params"]["llm_loader"] = "local_llm"
config["llm_params"]["local_llm_params"]["model"] = MISTRAL_PATH
config["llm_params"]["local_llm_params"]["model_type"] = "mistral"
else:
pass
llm_tutor = LLMTutor(config, logger=logger)
chain = llm_tutor.qa_bot()
msg = cl.Message(content=f"Starting the bot {chat_profile}...")
await msg.send()
msg.content = opening_message
await msg.update()
tags = [chat_profile, config["vectorstore"]["db_option"]]
chat_processor = ChatProcessor(config["chat_logging"]["platform"], tags=tags)
cl.user_session.set("chain", chain)
cl.user_session.set("counter", 0)
cl.user_session.set("chat_processor", chat_processor)
@cl.on_chat_end
async def on_chat_end():
await cl.Message(content="Sorry, I have to go now. Goodbye!").send()
@cl.on_message
async def main(message):
global logger
user = cl.user_session.get("user")
chain = cl.user_session.get("chain")
counter = cl.user_session.get("counter")
counter += 1
cl.user_session.set("counter", counter)
# if counter >= 3: # Ensure the counter condition is checked
# await cl.Message(content="Your credits are up!").send()
# await on_chat_end() # Call the on_chat_end function to handle the end of the chat
# return # Exit the function to stop further processing
# else:
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)
try:
answer = res["answer"]
except:
answer = res["result"]
answer_with_sources, source_elements, sources_dict = get_sources(res, answer)
processor._process(message.content, answer, sources_dict)
await cl.Message(content=answer_with_sources, elements=source_elements).send()
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