File size: 4,247 Bytes
6158da4 b83cc65 6158da4 b83cc65 6d056d5 b83cc65 6158da4 b83cc65 6158da4 b83cc65 6158da4 6d056d5 6158da4 b83cc65 6158da4 b83cc65 6158da4 b83cc65 6158da4 6d056d5 b83cc65 6158da4 b83cc65 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 |
from langchain.document_loaders import PyPDFLoader, DirectoryLoader
from langchain import PromptTemplate
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains import RetrievalQA
from langchain.llms import CTransformers
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.llm_tutor import LLMTutor
from modules.constants import *
from modules.helpers import get_sources
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
# Console Handler
console_handler = logging.StreamHandler()
console_handler.setLevel(logging.INFO)
formatter = logging.Formatter("%(asctime)s - %(levelname)s - %(message)s")
console_handler.setFormatter(formatter)
logger.addHandler(console_handler)
# File Handler
log_file_path = "log_file.log" # Change this to your desired log file path
file_handler = logging.FileHandler(log_file_path)
file_handler.setLevel(logging.INFO)
file_handler.setFormatter(formatter)
logger.addHandler(file_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("code/config.yml", "r") as f:
config = yaml.safe_load(f)
print(config)
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()
model = config["llm_params"]["local_llm_params"]["model"]
msg = cl.Message(content=f"Starting the bot {model}...")
await msg.send()
msg.content = opening_message
await msg.update()
cl.user_session.set("chain", chain)
@cl.on_message
async def main(message):
user = cl.user_session.get("user")
chain = cl.user_session.get("chain")
# cb = cl.AsyncLangchainCallbackHandler(
# stream_final_answer=True, answer_prefix_tokens=["FINAL", "ANSWER"]
# )
# cb.answer_reached = True
# res=await chain.acall(message, callbacks=[cb])
res = await chain.acall(message.content)
print(f"response: {res}")
try:
answer = res["answer"]
except:
answer = res["result"]
print(f"answer: {answer}")
logger.info(f"Question: {res['question']}")
logger.info(f"History: {res['chat_history']}")
logger.info(f"Answer: {answer}\n")
answer_with_sources, source_elements = get_sources(res, answer)
await cl.Message(content=answer_with_sources, elements=source_elements).send()
|