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# OpenAI Chat completion
import os
import openai
import chainlit as cl
from chainlit.prompt import Prompt, PromptMessage
from chainlit.playground.providers import ChatOpenAI
from dotenv import load_dotenv
load_dotenv()
# ChatOpenAI Templates
system_template = """You are a highly analytical assistant who has in-depth knowledge of science!
"""
user_template = """{input}
Think through your response step by step.
Be concise in your responses
Do not make up stuff. If you don't know something, let the user know.
"""
@cl.on_chat_start
async def start_chat():
settings = {
"model": "gpt-3.5-turbo",
"temperature": 0.7, # Adjust to a valid range
"max_tokens": 500,
"top_p": 1,
"frequency_penalty": 0,
"presence_penalty": 0,
}
cl.user_session.set("settings", settings)
@cl.on_message
async def main(message: cl.Message):
settings = cl.user_session.get("settings")
# Set up the OpenAI client with the API key
openai.api_key = os.getenv("OPENAI_API_KEY")
prompt = Prompt(
provider=ChatOpenAI.id,
messages=[
PromptMessage(
role="system",
template=system_template,
formatted=system_template,
),
PromptMessage(
role="user",
template=user_template,
formatted=user_template.format(input=message.content),
),
],
inputs={"input": message.content},
settings=settings,
)
msg = cl.Message(content="")
# Call OpenAI and stream response
async for stream_resp in await openai.ChatCompletion.acreate(
model=settings['model'],
messages=[m.to_openai() for m in prompt.messages],
stream=True,
**settings
):
token = stream_resp.choices[0].delta.get("content", "")
await msg.stream_token(token)
# Update the prompt object with the completion
prompt.completion = msg.content
msg.prompt = prompt
# Send and close the message stream
await msg.send()
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