Build multi-turn conversations and manage context

from openai import OpenAI
from datetime import datetime

# Initialize Stepfun client
STEPFUN_KEY = ""

client = OpenAI(
  base_url="https://api.stepfun.ai/v1",
  api_key=STEPFUN_KEY
)

# Data structures
class Chat:
  id: str
  user_id: str

class Message:
  chat_id: str
  role : str # system, user, assistant, tool
  content: str # user input or model output
  created_at: datetime

# Fetch messages from the database

# messages_from_db = orm.order_by("created_at","asc").first(5)

messages_from_db = [
  {
    "chat_id":"chat_1",
    "role":"system",
    "content":"You are the Stepfun assistant",
    "created_at": "2024-01-01 10:01:00"
  },
  {
    "chat_id":"chat_1",
    "role":"user",
    "content":"How is the weather today?",
    "created_at": "2024-01-01 10:02:00"
  },
  {
    "chat_id":"chat_1",
    "role":"assistant",
    "content":"Sorry, I can’t answer weather questions.",
    "created_at": "2024-01-01 10:03:00"
  },
  {
    "chat_id":"chat_1",
    "role":"user",
    "content":"Is Beijing a good place to travel?",
    "created_at": "2024-01-01 10:04:00"
  }
]

def clean_msg(msg):
  del msg["chat_id"]
  del msg["created_at"]
  return msg

messages_for_chat = [clean_msg(item) for item in messages_from_db]

# Call the completion API

stream = client.chat.completions.create(
  model="step-3.7-flash",
  messages=messages_for_chat,
  stream=True,
)

# Render streaming output

for chunk in stream:
  if chunk.choices[0].delta.content is not None:
    print(chunk.choices[0].delta.content, end="")