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[Misc] Update openai client example file for multimodal (#25795)

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Signed-off-by: Roger Wang <hey@rogerw.io>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
This commit is contained in:
Roger Wang
2025-09-27 00:57:07 -07:00
committed by GitHub
parent 3939152069
commit 23b8ee672d
1 changed files with 36 additions and 27 deletions
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63
examples/online_serving/openai_chat_completion_client_for_multimodal.py
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@ -38,11 +38,13 @@ client = OpenAI(
base_url=openai_api_base,
)
headers = {"User-Agent": "vLLM Example Client"}
def encode_base64_content_from_url(content_url: str) -> str:
"""Encode a content retrieved from a remote url to base64 format."""
with requests.get(content_url) as response:
with requests.get(content_url, headers=headers) as response:
response.raise_for_status()
result = base64.b64encode(response.content).decode("utf-8")
@ -50,19 +52,19 @@ def encode_base64_content_from_url(content_url: str) -> str:
# Text-only inference
def run_text_only(model: str) -> None:
def run_text_only(model: str, max_completion_tokens: int) -> None:
chat_completion = client.chat.completions.create(
messages=[{"role": "user", "content": "What's the capital of France?"}],
model=model,
max_completion_tokens=64,
max_completion_tokens=max_completion_tokens,
)
result = chat_completion.choices[0].message.content
print("Chat completion output:", result)
print("Chat completion output:\n", result)
# Single-image input inference
def run_single_image(model: str) -> None:
def run_single_image(model: str, max_completion_tokens: int) -> None:
## Use image url in the payload
image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
chat_completion_from_url = client.chat.completions.create(
@ -79,11 +81,11 @@ def run_single_image(model: str) -> None:
}
],
model=model,
max_completion_tokens=64,
max_completion_tokens=max_completion_tokens,
)
result = chat_completion_from_url.choices[0].message.content
print("Chat completion output from image url:", result)
print("Chat completion output from image url:\n", result)
## Use base64 encoded image in the payload
image_base64 = encode_base64_content_from_url(image_url)
@ -101,7 +103,7 @@ def run_single_image(model: str) -> None:
}
],
model=model,
max_completion_tokens=64,
max_completion_tokens=max_completion_tokens,
)
result = chat_completion_from_base64.choices[0].message.content
@ -109,7 +111,7 @@ def run_single_image(model: str) -> None:
# Multi-image input inference
def run_multi_image(model: str) -> None:
def run_multi_image(model: str, max_completion_tokens: int) -> None:
image_url_duck = "https://upload.wikimedia.org/wikipedia/commons/d/da/2015_Kaczka_krzy%C5%BCowka_w_wodzie_%28samiec%29.jpg"
image_url_lion = "https://upload.wikimedia.org/wikipedia/commons/7/77/002_The_lion_king_Snyggve_in_the_Serengeti_National_Park_Photo_by_Giles_Laurent.jpg"
chat_completion_from_url = client.chat.completions.create(
@ -130,15 +132,15 @@ def run_multi_image(model: str) -> None:
}
],
model=model,
max_completion_tokens=64,
max_completion_tokens=max_completion_tokens,
)
result = chat_completion_from_url.choices[0].message.content
print("Chat completion output:", result)
print("Chat completion output:\n", result)
# Video input inference
def run_video(model: str) -> None:
def run_video(model: str, max_completion_tokens: int) -> None:
video_url = "http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/ForBiggerFun.mp4"
video_base64 = encode_base64_content_from_url(video_url)
@ -157,11 +159,11 @@ def run_video(model: str) -> None:
}
],
model=model,
max_completion_tokens=64,
max_completion_tokens=max_completion_tokens,
)
result = chat_completion_from_url.choices[0].message.content
print("Chat completion output from image url:", result)
print("Chat completion output from video url:\n", result)
## Use base64 encoded video in the payload
chat_completion_from_base64 = client.chat.completions.create(
@ -178,15 +180,15 @@ def run_video(model: str) -> None:
}
],
model=model,
max_completion_tokens=64,
max_completion_tokens=max_completion_tokens,
)
result = chat_completion_from_base64.choices[0].message.content
print("Chat completion output from base64 encoded image:", result)
print("Chat completion output from base64 encoded video:\n", result)
# Audio input inference
def run_audio(model: str) -> None:
def run_audio(model: str, max_completion_tokens: int) -> None:
from vllm.assets.audio import AudioAsset
audio_url = AudioAsset("winning_call").url
@ -211,11 +213,11 @@ def run_audio(model: str) -> None:
}
],
model=model,
max_completion_tokens=64,
max_completion_tokens=max_completion_tokens,
)
result = chat_completion_from_base64.choices[0].message.content
print("Chat completion output from input audio:", result)
print("Chat completion output from input audio:\n", result)
# HTTP URL
chat_completion_from_url = client.chat.completions.create(
@ -235,11 +237,11 @@ def run_audio(model: str) -> None:
}
],
model=model,
max_completion_tokens=64,
max_completion_tokens=max_completion_tokens,
)
result = chat_completion_from_url.choices[0].message.content
print("Chat completion output from audio url:", result)
print("Chat completion output from audio url:\n", result)
# base64 URL
chat_completion_from_base64 = client.chat.completions.create(
@ -259,14 +261,14 @@ def run_audio(model: str) -> None:
}
],
model=model,
max_completion_tokens=64,
max_completion_tokens=max_completion_tokens,
)
result = chat_completion_from_base64.choices[0].message.content
print("Chat completion output from base64 encoded audio:", result)
print("Chat completion output from base64 encoded audio:\n", result)
def run_multi_audio(model: str) -> None:
def run_multi_audio(model: str, max_completion_tokens: int) -> None:
from vllm.assets.audio import AudioAsset
# Two different audios to showcase batched inference.
@ -300,11 +302,11 @@ def run_multi_audio(model: str) -> None:
}
],
model=model,
max_completion_tokens=64,
max_completion_tokens=max_completion_tokens,
)
result = chat_completion_from_base64.choices[0].message.content
print("Chat completion output from input audio:", result)
print("Chat completion output from input audio:\n", result)
example_function_map = {
@ -330,13 +332,20 @@ def parse_args():
choices=list(example_function_map.keys()),
help="Conversation type with multimodal data.",
)
parser.add_argument(
"--max-completion-tokens",
"-n",
type=int,
default=128,
help="Maximum number of tokens to generate for each completion.",
)
return parser.parse_args()
def main(args) -> None:
chat_type = args.chat_type
model = get_first_model(client)
example_function_map[chat_type](model)
example_function_map[chat_type](model, args.max_completion_tokens)
if __name__ == "__main__":