Added example for multimodal embedding (#4206)

Co-authored-by: Chayenne <zhaochen20@outlook.com>

examples/runtime/README.md

+# Runtime examples
+
+The below examples will mostly need you to start a server in a separate terminal before you can execute them. Please see in the code for detailed instruction.
+
+## Native API
+
+* `lora.py`: An example how to use LoRA adapters.
+* `multimodal_embedding.py`: An example how perform [multi modal embedding](Alibaba-NLP/gme-Qwen2-VL-2B-Instruct).
+* `openai_batch_chat.py`: An example how to process batch requests for chat completions.
+* `openai_batch_complete.py`: An example how to process batch requests for text completions.
+* `openai_chat_with_response_prefill.py`: An example how to [prefill](https://eugeneyan.com/writing/prompting/#prefill-claudes-responses) a response using OpenAI API.
+* `reward_model.py`: An example how to extract scores from a reward model.
+* `vertex_predict.py`: An example how to deploy a model to [Vertex AI](https://cloud.google.com/vertex-ai?hl=en).
+
+## Engine
+
+The `engine` folder contains that examples that show how to use [Offline Engine API](https://docs.sglang.ai/backend/offline_engine_api.html#Offline-Engine-API) for common workflows.
+
+* `custom_server.py`: An example how to deploy a custom server.
+* `embedding.py`: An example how to extract embeddings.
+* `launch_engine.py`: An example how to launch the Engine.
+* `offline_batch_inference_eagle.py`: An example how to perform speculative decoding using [EAGLE](https://docs.sglang.ai/backend/speculative_decoding.html).
+* `offline_batch_inference_torchrun.py`: An example how to perform inference using [torchrun](https://pytorch.org/docs/stable/elastic/run.html).
+* `offline_batch_inference_vlm.py`: An example how to use VLMs with the engine.
+* `offline_batch_inference.py`: An example how to use the engine to perform inference on a batch of examples.
+
+## Hidden States
+
+The `hidden_states` folder contains examples on how to extract hidden states using SGLang. Please note that this might degrade throughput due to cuda graph rebuilding.
+
+* `hidden_states_engine.py`: An example how to extract hidden states using the Engine API.
+* `hidden_states_server.py`: An example how to extract hidden states using the Server API.
+
+## LLaVA-NeXT
+
+SGLang support LLaVA-OneVision with single-image, multi-image and video are supported. The folder `llava_onevision` shows how to do this.
+
+## Token In, Token Out
+
+The folder `token_in_token_out` shows how to perform inference, where we provide tokens and get tokens as response.
+
+* `token_in_token_out_{llm|vlm}_{engine|server}.py`: Shows how to perform token in, token out workflow for llm/vlm using either the engine or native API.

examples/runtime/multimodal_embedding.py

+# launch server
+# python -m sglang.launch_server --model-path Alibaba-NLP/gme-Qwen2-VL-2B-Instruct --is-embedding --chat-template gme-qwen2-vl
+
+import requests
+
+url = "http://127.0.0.1:30000"
+
+text_input = "Represent this image in embedding space."
+image_path = "https://huggingface.co/datasets/liuhaotian/llava-bench-in-the-wild/resolve/main/images/023.jpg"
+
+payload = {
+    "model": "gme-qwen2-vl",
+    "input": [
+        {"type": "text", "text": text_input},
+        {"type": "image", "url": image_path},
+    ],
+}
+
+response = requests.post(url + "/v1/embeddings", json=payload).json()
+
+print("Embeddings:", [x.get("embedding") for x in response.get("data", [])])