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# You can find this code for Chainlit python streaming here (https://docs.chainlit.io/concepts/streaming/python)
# OpenAI Chat completion
import openai #importing openai for API usage
import chainlit as cl #importing chainlit for our app
# You only need the api key inserted here if it's not in your .env file
#openai.api_key = "YOUR_API_KEY"
# We select our model. If you do not have access to GPT-4, please use GPT-3.5T (gpt-3.5-turbo)
model_name = "gpt-3.5-turbo"
# model_name = "gpt-4"
settings = {
"temperature": 0.7, # higher value increases output diveresity/randomness
"max_tokens": 500, # maximum length of output response
"top_p": 1, # choose only the top x% of possible words to return
"frequency_penalty": 0, # higher value will result in the model being more conservative in its use of repeated tokens.
"presence_penalty": 0, # higher value will result in the model being more likely to generate tokens that have not yet been included in the generated text
}
@cl.on_chat_start # marks a function that will be executed at the start of a user session
def start_chat():
cl.user_session.set(
"message_history",
[{"role": "system", "content": "You are a helpful assistant."}],
)
@cl.on_message # marks a function that should be run each time the chatbot receives a message from a user
async def main(message: str):
message_history = cl.user_session.get("message_history")
message_history.append({"role": "user", "content": message})
msg = cl.Message(content="")
async for stream_resp in await openai.ChatCompletion.acreate(
model=model_name, messages=message_history, stream=True, **settings
):
token = stream_resp.choices[0]["delta"].get("content", "")
await msg.stream_token(token)
message_history.append({"role": "assistant", "content": msg.content})
await msg.send()
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