[Doc] Move multimodal Embedding API example to Online Serving page (#14017)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

docs/source/serving/multimodal_inputs.md

@@ -16,7 +16,7 @@ To input multi-modal data, follow this schema in {class}`vllm.inputs.PromptType`
 - `prompt`: The prompt should follow the format that is documented on HuggingFace.
 - `multi_modal_data`: This is a dictionary that follows the schema defined in {class}`vllm.multimodal.inputs.MultiModalDataDict`.
 
-### Image
+### Image Inputs
 
 You can pass a single image to the `'image'` field of the multi-modal dictionary, as shown in the following examples:
 
@@ -120,20 +120,20 @@ for o in outputs:
     print(generated_text)
 ```
 
-### Video
+### Video Inputs
 
 You can pass a list of NumPy arrays directly to the `'video'` field of the multi-modal dictionary
 instead of using multi-image input.
 
 Full example: <gh-file:examples/offline_inference/vision_language.py>
 
-### Audio
+### Audio Inputs
 
 You can pass a tuple `(array, sampling_rate)` to the `'audio'` field of the multi-modal dictionary.
 
 Full example: <gh-file:examples/offline_inference/audio_language.py>
 
-### Embedding
+### Embedding Inputs
 
 To input pre-computed embeddings belonging to a data type (i.e. image, video, or audio) directly to the language model,
 pass a tensor of shape `(num_items, feature_size, hidden_size of LM)` to the corresponding field of the multi-modal dictionary.
@@ -211,7 +211,7 @@ The chat template can be inferred based on the documentation on the model's Hugg
 For example, LLaVA-1.5 (`llava-hf/llava-1.5-7b-hf`) requires a chat template that can be found here: <gh-file:examples/template_llava.jinja>
 :::
 
-### Image
+### Image Inputs
 
 Image input is supported according to [OpenAI Vision API](https://platform.openai.com/docs/guides/vision).
 Here is a simple example using Phi-3.5-Vision.
@@ -293,7 +293,7 @@ export VLLM_IMAGE_FETCH_TIMEOUT=<timeout>
 
 :::
 
-### Video
+### Video Inputs
 
 Instead of `image_url`, you can pass a video file via `video_url`. Here is a simple example using [LLaVA-OneVision](https://huggingface.co/llava-hf/llava-onevision-qwen2-0.5b-ov-hf).
 
@@ -356,7 +356,7 @@ export VLLM_VIDEO_FETCH_TIMEOUT=<timeout>
 
 :::
 
-### Audio
+### Audio Inputs
 
 Audio input is supported according to [OpenAI Audio API](https://platform.openai.com/docs/guides/audio?audio-generation-quickstart-example=audio-in).
 Here is a simple example using Ultravox-v0.5-1B.
@@ -460,77 +460,6 @@ export VLLM_AUDIO_FETCH_TIMEOUT=<timeout>
 
 :::
 
-### Embedding
+### Embedding Inputs
 
-vLLM's Embeddings API is a superset of OpenAI's [Embeddings API](https://platform.openai.com/docs/api-reference/embeddings),
-where a list of chat `messages` can be passed instead of batched `inputs`. This enables multi-modal inputs to be passed to embedding models.
-
-:::{tip}
-The schema of `messages` is exactly the same as in Chat Completions API.
-You can refer to the above tutorials for more details on how to pass each type of multi-modal data.
-:::
-
-Usually, embedding models do not expect chat-based input, so we need to use a custom chat template to format the text and images.
-Refer to the examples below for illustration.
-
-Here is an end-to-end example using VLM2Vec. To serve the model:
-
-```bash
-vllm serve TIGER-Lab/VLM2Vec-Full --task embed \
-  --trust-remote-code --max-model-len 4096 --chat-template examples/template_vlm2vec.jinja
-```
-
-:::{important}
-Since VLM2Vec has the same model architecture as Phi-3.5-Vision, we have to explicitly pass `--task embed`
-to run this model in embedding mode instead of text generation mode.
-
-The custom chat template is completely different from the original one for this model,
-and can be found here: <gh-file:examples/template_vlm2vec.jinja>
-:::
-
-Since the request schema is not defined by OpenAI client, we post a request to the server using the lower-level `requests` library:
-
-```python
-import requests
-
-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"
-
-response = requests.post(
-    "http://localhost:8000/v1/embeddings",
-    json={
-        "model": "TIGER-Lab/VLM2Vec-Full",
-        "messages": [{
-            "role": "user",
-            "content": [
-                {"type": "image_url", "image_url": {"url": image_url}},
-                {"type": "text", "text": "Represent the given image."},
-            ],
-        }],
-        "encoding_format": "float",
-    },
-)
-response.raise_for_status()
-response_json = response.json()
-print("Embedding output:", response_json["data"][0]["embedding"])
-```
-
-Below is another example, this time using the `MrLight/dse-qwen2-2b-mrl-v1` model.
-
-```bash
-vllm serve MrLight/dse-qwen2-2b-mrl-v1 --task embed \
-  --trust-remote-code --max-model-len 8192 --chat-template examples/template_dse_qwen2_vl.jinja
-```
-
-:::{important}
-Like with VLM2Vec, we have to explicitly pass `--task embed`.
-
-Additionally, `MrLight/dse-qwen2-2b-mrl-v1` requires an EOS token for embeddings, which is handled
-by a custom chat template: <gh-file:examples/template_dse_qwen2_vl.jinja>
-:::
-
-:::{important}
-Also important, `MrLight/dse-qwen2-2b-mrl-v1` requires a placeholder image of the minimum image size for text query embeddings. See the full code
-example below for details.
-:::
-
-Full example: <gh-file:examples/online_serving/openai_chat_embedding_client_for_multimodal.py>
+TBD

docs/source/serving/openai_compatible_server.md

@@ -266,11 +266,85 @@ you can use the [official OpenAI Python client](https://github.com/openai/openai
 If the model has a [chat template](#chat-template), you can replace `inputs` with a list of `messages` (same schema as [Chat API](#chat-api))
 which will be treated as a single prompt to the model.
 
-:::{tip}
-This enables multi-modal inputs to be passed to embedding models, see [this page](#multimodal-inputs) for details.
+Code example: <gh-file:examples/online_serving/openai_embedding_client.py>
+
+#### Multi-modal inputs
+
+You can pass multi-modal inputs to embedding models by defining a custom chat template for the server
+and passing a list of `messages` in the request. Refer to the examples below for illustration.
+
+:::::{tab-set}
+::::{tab-item} VLM2Vec
+
+To serve the model:
+
+```bash
+vllm serve TIGER-Lab/VLM2Vec-Full --task embed \
+  --trust-remote-code --max-model-len 4096 --chat-template examples/template_vlm2vec.jinja
+```
+
+:::{important}
+Since VLM2Vec has the same model architecture as Phi-3.5-Vision, we have to explicitly pass `--task embed`
+to run this model in embedding mode instead of text generation mode.
+
+The custom chat template is completely different from the original one for this model,
+and can be found here: <gh-file:examples/template_vlm2vec.jinja>
 :::
 
-Code example: <gh-file:examples/online_serving/openai_embedding_client.py>
+Since the request schema is not defined by OpenAI client, we post a request to the server using the lower-level `requests` library:
+
+```python
+import requests
+
+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"
+
+response = requests.post(
+    "http://localhost:8000/v1/embeddings",
+    json={
+        "model": "TIGER-Lab/VLM2Vec-Full",
+        "messages": [{
+            "role": "user",
+            "content": [
+                {"type": "image_url", "image_url": {"url": image_url}},
+                {"type": "text", "text": "Represent the given image."},
+            ],
+        }],
+        "encoding_format": "float",
+    },
+)
+response.raise_for_status()
+response_json = response.json()
+print("Embedding output:", response_json["data"][0]["embedding"])
+```
+
+::::
+
+::::{tab-item} DSE-Qwen2-MRL
+
+To serve the model:
+
+```bash
+vllm serve MrLight/dse-qwen2-2b-mrl-v1 --task embed \
+  --trust-remote-code --max-model-len 8192 --chat-template examples/template_dse_qwen2_vl.jinja
+```
+
+:::{important}
+Like with VLM2Vec, we have to explicitly pass `--task embed`.
+
+Additionally, `MrLight/dse-qwen2-2b-mrl-v1` requires an EOS token for embeddings, which is handled
+by a custom chat template: <gh-file:examples/template_dse_qwen2_vl.jinja>
+:::
+
+:::{important}
+`MrLight/dse-qwen2-2b-mrl-v1` requires a placeholder image of the minimum image size for text query embeddings. See the full code
+example below for details.
+:::
+
+::::
+
+:::::
+
+Full example: <gh-file:examples/online_serving/openai_chat_embedding_client_for_multimodal.py>
 
 #### Extra parameters
 

vllm/model_executor/models/registry.py

@@ -19,6 +19,7 @@ import cloudpickle
 import torch.nn as nn
 
 from vllm.logger import init_logger
+from vllm.utils import is_in_doc_build
 
 from .interfaces import (has_inner_state, is_attention_free, is_hybrid,
                          supports_cross_encoding, supports_multimodal,
@@ -368,7 +369,8 @@ class _ModelRegistry:
                 raise ValueError(msg)
 
             model = _LazyRegisteredModel(*split_str)
-        elif isinstance(model_cls, type) and issubclass(model_cls, nn.Module):
+        elif isinstance(model_cls, type) and (is_in_doc_build() or issubclass(
+                model_cls, nn.Module)):
             model = _RegisteredModel.from_model_cls(model_cls)
         else:
             msg = ("`model_cls` should be a string or PyTorch model class, "