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

Signed-off-by: Roger Wang <[email protected]>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
Signed-off-by: yewentao256 <[email protected]>

Author: 2 people

examples/online_serving/openai_chat_completion_client_for_multimodal.py

@@ -38,31 +38,33 @@
     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")
 
     return result
 
 
 # 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)
@@ -101,15 +103,15 @@ 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
     print("Chat completion output from base64 encoded image:", result)
 
 
 # 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__":