[Model] Improve multimodal pooling examples (#32085)

Signed-off-by: wang.yuqi <noooop@126.com>
Signed-off-by: wang.yuqi <yuqi.wang@daocloud.io>

examples/pooling/embed/vision_embedding_offline.py

@@ -0,0 +1,93 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# ruff: noqa: E501
+"""
+This example shows how to use vLLM for running offline inference with
+the correct prompt format on vision language models for multimodal embedding.
+
+For most models, the prompt format should follow corresponding examples
+on HuggingFace model repository.
+"""
+
+import argparse
+from dataclasses import asdict
+
+from vllm import LLM, EngineArgs
+from vllm.multimodal.utils import fetch_image
+
+image_url = "https://vllm-public-assets.s3.us-west-2.amazonaws.com/multimodal_asset/cat_snow.jpg"
+text = "A cat standing in the snow."
+multi_modal_data = {"image": fetch_image(image_url)}
+
+
+def print_embeddings(embeds):
+    embeds_trimmed = (str(embeds[:4])[:-1] + ", ...]") if len(embeds) > 4 else embeds
+    print(f"Embeddings: {embeds_trimmed} (size={len(embeds)})")
+
+
+def run_qwen3_vl():
+    engine_args = EngineArgs(
+        model="Qwen/Qwen3-VL-Embedding-2B",
+        runner="pooling",
+        max_model_len=8192,
+        limit_mm_per_prompt={"image": 1},
+    )
+    default_instruction = "Represent the user's input."
+    image_placeholder = "<|vision_start|><|image_pad|><|vision_end|>"
+    text_prompt = f"<|im_start|>system\n{default_instruction}<|im_end|>\n<|im_start|>user\n{text}<|im_end|>\n<|im_start|>assistant\n"
+    image_prompt = f"<|im_start|>system\n{default_instruction}<|im_end|>\n<|im_start|>user\n{image_placeholder}<|im_end|>\n<|im_start|>assistant\n"
+    image_text_prompt = f"<|im_start|>system\n{default_instruction}<|im_end|>\n<|im_start|>user\n{image_placeholder}{text}<|im_end|>\n<|im_start|>assistant\n"
+
+    llm = LLM(**asdict(engine_args))
+
+    print("Text embedding output:")
+    outputs = llm.embed(text_prompt, use_tqdm=False)
+    print_embeddings(outputs[0].outputs.embedding)
+
+    print("Image embedding output:")
+    outputs = llm.embed(
+        {
+            "prompt": image_prompt,
+            "multi_modal_data": multi_modal_data,
+        },
+        use_tqdm=False,
+    )
+    print_embeddings(outputs[0].outputs.embedding)
+
+    print("Image+Text embedding output:")
+    outputs = llm.embed(
+        {
+            "prompt": image_text_prompt,
+            "multi_modal_data": multi_modal_data,
+        },
+        use_tqdm=False,
+    )
+    print_embeddings(outputs[0].outputs.embedding)
+
+
+model_example_map = {
+    "qwen3_vl": run_qwen3_vl,
+}
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        "Script to run a specified VLM through vLLM offline api."
+    )
+    parser.add_argument(
+        "--model",
+        type=str,
+        choices=model_example_map.keys(),
+        required=True,
+        help="The name of the embedding model.",
+    )
+    return parser.parse_args()
+
+
+def main(args):
+    model_example_map[args.model]()
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    main(args)