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import chainlit as cl
from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain.schema import StrOutputParser
from langchain.chains import LLMChain
@cl.on_chat_start
async def on_chat_start():
##########################################################################
# Exercise 1a:
# Our Chainlit app should initialize the LLM chat via Langchain at the
# start of a chat session.
#
# First, we need to choose an LLM from OpenAI's list of models. Remember
# to set streaming=True for streaming tokens
##########################################################################
model = ChatOpenAI(
model="gpt-4-1106-preview",
streaming=True
)
##########################################################################
# Exercise 1b:
# Next, we will need to set the prompt template for chat. Prompt templates
# is how we set prompts and then inject informations into the prompt.
#
# Please create the prompt template using ChatPromptTemplate. Use variable
# name "question" as the variable in the template.
# Refer to the documentation listed in the README.md file for reference.
##########################################################################
prompt = ChatPromptTemplate.from_messages(
[
(
"system",
"You are Chainlit GPT, a helpful assistant.",
),
(
"human",
"{question}"
),
]
)
##########################################################################
# Exercise 1c:
# Now we have model and prompt, let's build our Chain. A Chain is one or a
# series of LLM calls.We will use the default StrOutputParser to parse the
# LLM outputs.
##########################################################################
chain = LLMChain(llm=model, prompt=prompt, output_parser=StrOutputParser())
# We are saving the chain in user_session, so we do not have to rebuild
# it every single time.
cl.user_session.set("chain", chain)
@cl.on_message
async def main(message: cl.Message):
# Let's load the chain from user_session
chain = cl.user_session.get("chain") # type: LLMChain
##########################################################################
# Exercise 1d:
# Everytime we receive a new user message, we will get the chain from
# user_session. We will run the chain with user's question and return LLM
# response to the user.
##########################################################################
response = await chain.arun(
question=message.content, callbacks=[cl.LangchainCallbackHandler()]
)
await cl.Message(content=response).send()
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