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import os
from dotenv import load_dotenv
import chainlit as cl
from openai import AsyncOpenAI
# Load environment variables from .env file
load_dotenv()
# Default model settings
DEFAULT_SETTINGS = {
"model": "gpt-3.5-turbo",
"temperature": 0.7,
"max_tokens": 500,
"top_p": 1,
"frequency_penalty": 0,
"presence_penalty": 0,
}
SYSTEM_PROMPT = "You are a helpful, friendly AI assistant. Provide clear and concise responses."
@cl.on_chat_start
async def start():
"""
Initialize the chat session:
- Create OpenAI client
- Set up message history with system prompt
- Configure model settings
- Send welcome message
"""
# Initialize OpenAI client
client = AsyncOpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
cl.user_session.set("client", client)
# Initialize message history with system prompt
message_history = [{"role": "system", "content": SYSTEM_PROMPT}]
cl.user_session.set("message_history", message_history)
# Save model settings
cl.user_session.set("settings", DEFAULT_SETTINGS)
# Send welcome message
await cl.Message(
content="Hello! I'm your AI assistant powered by OpenAI. How can I help you today?"
).send()
@cl.on_message
async def main(user_message: cl.Message):
"""
Process user messages and generate AI responses:
- Update message history with user input
- Call OpenAI API with current conversation context
- Stream the response back to the user
- Update message history with AI response
Args:
user_message: The message sent by the user
"""
# Retrieve session data
client = cl.user_session.get("client")
message_history = cl.user_session.get("message_history")
settings = cl.user_session.get("settings")
# Add user message to history
message_history.append({"role": "user", "content": user_message.content})
# Prepare response message with loading state
response_message = cl.Message(content="")
await response_message.send()
try:
# Call OpenAI API to get response
stream = await client.chat.completions.create(
messages=message_history,
stream=True,
**settings
)
# Stream the response
full_response = ""
async for chunk in stream:
if chunk.choices[0].delta.content:
content_chunk = chunk.choices[0].delta.content
full_response += content_chunk
# Update message in real-time
response_message.content = full_response
await response_message.update()
# Add AI response to message history
message_history.append({"role": "assistant", "content": full_response})
cl.user_session.set("message_history", message_history)
except Exception as e:
# Handle errors
response_message.content = f"Error: {str(e)}"
await response_message.update() |