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mlc-ai--mlc-llm/examples/rest/python/sample_openai.py
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chore: import upstream snapshot with attribution
2026-07-13 13:23:58 +08:00

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Python

import openai
openai.api_key = "None"
openai.api_base = "http://127.0.0.1:8000/v1"
model = "vicuna-v1-7b"
class color:
PURPLE = "\033[95m"
CYAN = "\033[96m"
DARKCYAN = "\033[36m"
BLUE = "\033[94m"
GREEN = "\033[92m"
YELLOW = "\033[93m"
RED = "\033[91m"
BOLD = "\033[1m"
UNDERLINE = "\033[4m"
END = "\033[0m"
# Chat completion example without streaming
print(f"{color.BOLD}OpenAI chat completion example without streaming:{color.END}\n")
completion = openai.ChatCompletion.create(
model=model, messages=[{"role": "user", "content": "Write a poem about OpenAI"}]
)
print(f"{color.GREEN}{completion.choices[0].message.content}{color.END}\n\n")
# Chat completion example with streaming
print(f"{color.BOLD}OpenAI chat completion example with streaming:{color.END}\n")
res = openai.ChatCompletion.create(
model=model, messages=[{"role": "user", "content": "Write a poem about OpenAI"}], stream=True
)
for chunk in res:
content = chunk["choices"][0]["delta"].get("content", "")
print(f"{color.GREEN}{content}{color.END}", end="", flush=True)
print("\n")
# Completion example
print(f"{color.BOLD}OpenAI completion example:{color.END}\n")
res = openai.Completion.create(prompt="Write a poem about OpenAI", model=model)
print(f"{color.GREEN}{res.choices[0].text}{color.END}\n\n")