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from langchain.document_loaders import PyPDFLoader, DirectoryLoader |
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from langchain import PromptTemplate |
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from langchain.embeddings import HuggingFaceEmbeddings |
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from langchain.vectorstores import FAISS |
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from langchain.chains import RetrievalQA |
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from langchain.llms import CTransformers |
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import chainlit as cl |
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from langchain_community.chat_models import ChatOpenAI |
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from langchain_community.embeddings import OpenAIEmbeddings |
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import yaml |
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import logging |
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from dotenv import load_dotenv |
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from modules.llm_tutor import LLMTutor |
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from modules.constants import * |
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from modules.helpers import get_sources |
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logger = logging.getLogger(__name__) |
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logger.setLevel(logging.INFO) |
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console_handler = logging.StreamHandler() |
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console_handler.setLevel(logging.INFO) |
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formatter = logging.Formatter("%(asctime)s - %(levelname)s - %(message)s") |
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console_handler.setFormatter(formatter) |
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logger.addHandler(console_handler) |
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log_file_path = "log_file.log" |
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file_handler = logging.FileHandler(log_file_path) |
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file_handler.setLevel(logging.INFO) |
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file_handler.setFormatter(formatter) |
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logger.addHandler(file_handler) |
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@cl.set_chat_profiles |
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async def chat_profile(): |
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return [ |
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cl.ChatProfile( |
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name="Llama", |
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markdown_description="Use the local LLM: **Tiny Llama**.", |
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), |
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cl.ChatProfile( |
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name="gpt-3.5-turbo-1106", |
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markdown_description="Use OpenAI API for **gpt-3.5-turbo-1106**.", |
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), |
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cl.ChatProfile( |
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name="gpt-4", |
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markdown_description="Use OpenAI API for **gpt-4**.", |
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), |
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] |
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@cl.author_rename |
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def rename(orig_author: str): |
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rename_dict = {"Chatbot": "AI Tutor"} |
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return rename_dict.get(orig_author, orig_author) |
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@cl.on_chat_start |
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async def start(): |
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with open("code/config.yml", "r") as f: |
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config = yaml.safe_load(f) |
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print(config) |
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logger.info("Config file loaded") |
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logger.info(f"Config: {config}") |
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logger.info("Creating llm_tutor instance") |
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chat_profile = cl.user_session.get("chat_profile") |
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if chat_profile is not None: |
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if chat_profile.lower() in ["gpt-3.5-turbo-1106", "gpt-4"]: |
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config["llm_params"]["llm_loader"] = "openai" |
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config["llm_params"]["openai_params"]["model"] = chat_profile.lower() |
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elif chat_profile.lower() == "llama": |
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config["llm_params"]["llm_loader"] = "local_llm" |
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config["llm_params"]["local_llm_params"]["model"] = LLAMA_PATH |
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config["llm_params"]["local_llm_params"]["model_type"] = "llama" |
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elif chat_profile.lower() == "mistral": |
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config["llm_params"]["llm_loader"] = "local_llm" |
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config["llm_params"]["local_llm_params"]["model"] = MISTRAL_PATH |
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config["llm_params"]["local_llm_params"]["model_type"] = "mistral" |
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else: |
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pass |
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llm_tutor = LLMTutor(config, logger=logger) |
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chain = llm_tutor.qa_bot() |
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model = config["llm_params"]["local_llm_params"]["model"] |
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msg = cl.Message(content=f"Starting the bot {model}...") |
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await msg.send() |
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msg.content = f"Hey, What Can I Help You With?\n\nYou can me ask me questions about the course logistics, course content, about the final project, or anything else!" |
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await msg.update() |
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cl.user_session.set("chain", chain) |
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@cl.on_message |
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async def main(message): |
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user = cl.user_session.get("user") |
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chain = cl.user_session.get("chain") |
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res = await chain.acall(message.content) |
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print(f"response: {res}") |
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try: |
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answer = res["answer"] |
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except: |
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answer = res["result"] |
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print(f"answer: {answer}") |
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answer_with_sources, source_elements = get_sources(res, answer) |
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await cl.Message(content=answer_with_sources, elements=source_elements).send() |
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