[examples] Resettle pooling examples. (#29365)

Signed-off-by: wang.yuqi <yuqi.wang@daocloud.io>
Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
Co-authored-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>

.buildkite/test-pipeline.yaml

@@ -390,20 +390,24 @@ steps:
   - examples/
   commands:
     - pip install tensorizer # for tensorizer test
+    # for basic
+    - python3 offline_inference/basic/chat.py
     - python3 offline_inference/basic/generate.py --model facebook/opt-125m
     - python3 offline_inference/basic/generate.py --model meta-llama/Llama-2-13b-chat-hf --cpu-offload-gb 10
-    - python3 offline_inference/basic/chat.py
-    - python3 offline_inference/prefix_caching.py
-    - python3 offline_inference/llm_engine_example.py
+    - python3 offline_inference/basic/classify.py
+    - python3 offline_inference/basic/embed.py
+    - python3 offline_inference/basic/score.py
+    # for multi-modal models
     - python3 offline_inference/audio_language.py --seed 0
     - python3 offline_inference/vision_language.py --seed 0
-    - python3 offline_inference/vision_language_pooling.py --seed 0
     - python3 offline_inference/vision_language_multi_image.py --seed 0
-    - python3 others/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 others/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
     - python3 offline_inference/encoder_decoder_multimodal.py --model-type whisper --seed 0
-    - python3 offline_inference/basic/classify.py
-    - python3 offline_inference/basic/embed.py
-    - python3 offline_inference/basic/score.py
+    # for pooling models
+    - python3 pooling/pooling/vision_language_pooling.py --seed 0
+    # for features demo
+    - python3 offline_inference/prefix_caching.py
+    - python3 offline_inference/llm_engine_example.py
+    - python3 others/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 others/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
     - python3 offline_inference/spec_decode.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
     # https://github.com/vllm-project/vllm/pull/26682 uses slightly more memory in PyTorch 2.9+ causing this test to OOM in 1xL4 GPU
     - python3 offline_inference/spec_decode.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 1536

.github/CODEOWNERS

@@ -146,10 +146,10 @@ mkdocs.yaml @hmellor
 /requirements/kv_connectors.txt @NickLucche
 
 # Pooling models
-/examples/*/pooling/ @noooop
+/examples/pooling @noooop
 /tests/models/*/pooling* @noooop
 /tests/entrypoints/pooling @noooop
-/vllm/entrypoints/pooling @aarnphm @chaunceyjiang @noooop
+/vllm/entrypoints/pooling @noooop
 /vllm/config/pooler.py @noooop
 /vllm/pooling_params.py @noooop
 /vllm/model_executor/layers/pooler.py @noooop

docs/.nav.yml

@@ -5,11 +5,7 @@ nav:
     - Getting Started:
       - getting_started/quickstart.md
       - getting_started/installation
-    - Examples:
-      - examples/README.md
-      - Offline Inference: examples/offline_inference
-      - Online Serving: examples/online_serving
-      - Others: examples/others
+      - Examples: examples
     - General:
       - usage/v1_guide.md
       - usage/*

docs/design/io_processor_plugins.md

@@ -79,7 +79,7 @@ The `post_process*` methods take `PoolingRequestOutput` objects as input and gen
 The `validate_or_generate_params` method is used for validating with the plugin any `SamplingParameters`/`PoolingParameters` received with the user request, or to generate new ones if none are specified. The function always returns the validated/generated parameters.
 The `output_to_response` method is used only for online serving and converts the plugin output to the `IOProcessorResponse` type that is then returned by the API Server. The implementation of the `/pooling` serving endpoint is available here [vllm/entrypoints/openai/serving_pooling.py](../../vllm/entrypoints/pooling/pooling/serving.py).
 
-An example implementation of a plugin that enables generating geotiff images with the PrithviGeospatialMAE model is available [here](https://github.com/IBM/terratorch/tree/main/terratorch/vllm/plugins/segmentation). Please, also refer to our online ([examples/online_serving/pooling/prithvi_geospatial_mae.py](../../examples/online_serving/pooling/prithvi_geospatial_mae.py)) and offline ([examples/offline_inference/pooling/prithvi_geospatial_mae_io_processor.py](../../examples/offline_inference/pooling/prithvi_geospatial_mae_io_processor.py)) inference examples.
+An example implementation of a plugin that enables generating geotiff images with the PrithviGeospatialMAE model is available [here](https://github.com/IBM/terratorch/tree/main/terratorch/vllm/plugins/segmentation). Please, also refer to our online ([examples/pooling/plugin/prithvi_geospatial_mae_client.py](../../examples/pooling/plugin/prithvi_geospatial_mae_client.py)) and offline ([examples/pooling/plugin/prithvi_geospatial_mae_io_processor.py](../../examples/pooling/plugin/prithvi_geospatial_mae_io_processor.py)) inference examples.
 
 ## Using an IO Processor plugin
 

docs/mkdocs/hooks/generate_examples.py

@@ -2,7 +2,8 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import itertools
 import logging
-from dataclasses import dataclass, field
+from dataclasses import dataclass
+from functools import cached_property
 from pathlib import Path
 from typing import Literal
 
@@ -16,13 +17,18 @@ EXAMPLE_DIR = ROOT_DIR / "examples"
 EXAMPLE_DOC_DIR = ROOT_DIR / "docs/examples"
 
 
-def fix_case(text: str) -> str:
+def title(text: str) -> str:
+    # Default title case
+    text = text.replace("_", " ").replace("/", " - ").title()
+    # Custom substitutions
     subs = {
+        "io": "IO",
         "api": "API",
         "cli": "CLI",
         "cpu": "CPU",
         "llm": "LLM",
         "mae": "MAE",
+        "ner": "NER",
         "tpu": "TPU",
         "gguf": "GGUF",
         "lora": "LoRA",
@@ -48,71 +54,65 @@ class Example:
     Attributes:
         path (Path): The path to the main directory or file.
         category (str): The category of the document.
-        main_file (Path): The main file in the directory.
-        other_files (list[Path]): list of other files in the directory.
-        title (str): The title of the document.
+
+    Properties::
+        main_file() -> Path | None: Determines the main file in the given path.
+        other_files() -> list[Path]: Determines other files in the directory excluding
+        the main file.
+        title() -> str: Determines the title of the document.
 
     Methods:
-        __post_init__(): Initializes the main_file, other_files, and title attributes.
-        determine_main_file() -> Path: Determines the main file in the given path.
-        determine_other_files() -> list[Path]: Determines other files in the directory excluding the main file.
-        determine_title() -> str: Determines the title of the document.
         generate() -> str: Generates the documentation content.
-    """  # noqa: E501
+    """
 
     path: Path
-    category: str = None
-    main_file: Path = field(init=False)
-    other_files: list[Path] = field(init=False)
-    title: str = field(init=False)
-
-    def __post_init__(self):
-        self.main_file = self.determine_main_file()
-        self.other_files = self.determine_other_files()
-        self.title = self.determine_title()
-
-    @property
-    def is_code(self) -> bool:
-        return self.main_file.suffix != ".md"
+    category: str
 
-    def determine_main_file(self) -> Path:
-        """
-        Determines the main file in the given path.
-        If the path is a file, it returns the path itself. Otherwise, it searches
-        for Markdown files (*.md) in the directory and returns the first one found.
-        Returns:
-            Path: The main file path, either the original path if it's a file or the first
-            Markdown file found in the directory.
-        Raises:
-            IndexError: If no Markdown files are found in the directory.
-        """  # noqa: E501
-        return self.path if self.path.is_file() else list(self.path.glob("*.md")).pop()
-
-    def determine_other_files(self) -> list[Path]:
-        """
-        Determine other files in the directory excluding the main file.
+    @cached_property
+    def main_file(self) -> Path | None:
+        """Determines the main file in the given path.
 
-        This method checks if the given path is a file. If it is, it returns an empty list.
-        Otherwise, it recursively searches through the directory and returns a list of all
-        files that are not the main file.
-
-        Returns:
-            list[Path]: A list of Path objects representing the other files in the directory.
-        """  # noqa: E501
+        If path is a file, it returns the path itself. If path is a directory, it
+        searches for Markdown files (*.md) in the directory and returns the first one
+        found. If no Markdown files are found, it returns None."""
+        # Single file example
+        if self.path.is_file():
+            return self.path
+        # Multi file example with a README
+        if md_paths := list(self.path.glob("*.md")):
+            return md_paths[0]
+        # Multi file example without a README
+        return None
+
+    @cached_property
+    def other_files(self) -> list[Path]:
+        """Determine other files in the directory excluding the main file.
+
+        If path is a file, it returns an empty list. Otherwise, it returns every file
+        in the directory except the main file in a list."""
+        # Single file example
         if self.path.is_file():
             return []
+        # Multi file example
         is_other_file = lambda file: file.is_file() and file != self.main_file
-        return [file for file in self.path.rglob("*") if is_other_file(file)]
+        return sorted(file for file in self.path.rglob("*") if is_other_file(file))
+
+    @cached_property
+    def is_code(self) -> bool:
+        return self.main_file is not None and self.main_file.suffix != ".md"
 
-    def determine_title(self) -> str:
-        if not self.is_code:
+    @cached_property
+    def title(self) -> str:
+        # Generate title from filename if no main md file found
+        if self.main_file is None or self.is_code:
+            return title(self.path.stem)
         # Specify encoding for building on Windows
         with open(self.main_file, encoding="utf-8") as f:
             first_line = f.readline().strip()
         match = re.match(r"^#\s+(?P<title>.+)$", first_line)
         if match:
             return match.group("title")
-        return fix_case(self.path.stem.replace("_", " ").title())
+        raise ValueError(f"Title not found in {self.main_file}")
 
     def fix_relative_links(self, content: str) -> str:
         """
@@ -156,6 +156,8 @@ class Example:
         # included files containing code fences too
         code_fence = "``````"
 
+        if self.main_file is not None:
+            # Single file example or multi file example with a README
             if self.is_code:
                 content += (
                     f"{code_fence}{self.main_file.suffix[1:]}\n"
@@ -163,17 +165,26 @@ class Example:
                     f"{code_fence}\n"
                 )
             else:
-            with open(self.main_file) as f:
+                with open(self.main_file, encoding="utf-8") as f:
                     # Skip the title from md snippets as it's been included above
                     main_content = f.readlines()[1:]
                 content += self.fix_relative_links("".join(main_content))
             content += "\n"
+        else:
+            # Multi file example without a README
+            for file in self.other_files:
+                file_title = title(str(file.relative_to(self.path).with_suffix("")))
+                content += f"## {file_title}\n\n"
+                content += (
+                    f'{code_fence}{file.suffix[1:]}\n--8<-- "{file}"\n{code_fence}\n\n'
+                )
+            return content
 
         if not self.other_files:
             return content
 
         content += "## Example materials\n\n"
-        for file in sorted(self.other_files):
+        for file in self.other_files:
             content += f'??? abstract "{file.relative_to(self.path)}"\n'
             if file.suffix != ".md":
                 content += f"    {code_fence}{file.suffix[1:]}\n"
@@ -200,11 +211,13 @@ def on_startup(command: Literal["build", "gh-deploy", "serve"], dirty: bool):
     glob_patterns = ["*.py", "*.md", "*.sh"]
     # Find categorised examples
     for category in categories:
+        logger.info("Processing category: %s", category.stem)
         globs = [category.glob(pattern) for pattern in glob_patterns]
         for path in itertools.chain(*globs):
             examples.append(Example(path, category.stem))
         # Find examples in subdirectories
-        for path in category.glob("*/*.md"):
+        globs = [category.glob(f"*/{pattern}") for pattern in glob_patterns]
+        for path in itertools.chain(*globs):
             examples.append(Example(path.parent, category.stem))
 
     # Generate the example documentation
@@ -217,3 +230,4 @@ def on_startup(command: Literal["build", "gh-deploy", "serve"], dirty: bool):
         with open(doc_path, "w+", encoding="utf-8") as f:
             f.write(example.generate())
         logger.debug("Example generated: %s", doc_path.relative_to(ROOT_DIR))
+    logger.info("Total examples generated: %d", len(examples))

docs/models/pooling_models.md

@@ -274,7 +274,7 @@ outputs = llm.embed(
 print(outputs[0].outputs)
 ```
 
-A code example can be found here: [examples/offline_inference/pooling/embed_matryoshka_fy.py](../../examples/offline_inference/pooling/embed_matryoshka_fy.py)
+A code example can be found here: [examples/pooling/embed/embed_matryoshka_fy.py](../../examples/pooling/embed/embed_matryoshka_fy.py)
 
 ### Online Inference
 
@@ -304,7 +304,7 @@ Expected output:
 {"id":"embd-5c21fc9a5c9d4384a1b021daccaf9f64","object":"list","created":1745476417,"model":"jinaai/jina-embeddings-v3","data":[{"index":0,"object":"embedding","embedding":[-0.3828125,-0.1357421875,0.03759765625,0.125,0.21875,0.09521484375,-0.003662109375,0.1591796875,-0.130859375,-0.0869140625,-0.1982421875,0.1689453125,-0.220703125,0.1728515625,-0.2275390625,-0.0712890625,-0.162109375,-0.283203125,-0.055419921875,-0.0693359375,0.031982421875,-0.04052734375,-0.2734375,0.1826171875,-0.091796875,0.220703125,0.37890625,-0.0888671875,-0.12890625,-0.021484375,-0.0091552734375,0.23046875]}],"usage":{"prompt_tokens":8,"total_tokens":8,"completion_tokens":0,"prompt_tokens_details":null}}
 ```
 
-An OpenAI client example can be found here: [examples/online_serving/pooling/openai_embedding_matryoshka_fy.py](../../examples/online_serving/pooling/openai_embedding_matryoshka_fy.py)
+An OpenAI client example can be found here: [examples/pooling/embed/openai_embedding_matryoshka_fy.py](../../examples/pooling/embed/openai_embedding_matryoshka_fy.py)
 
 ## Deprecated Features
 

docs/models/supported_models.md

@@ -568,7 +568,7 @@ These models primarily support the [`LLM.score`](./pooling_models.md#llmscore) A
     ```
 
 !!! note
-    Load the official original `Qwen3 Reranker` by using the following command. More information can be found at: [examples/offline_inference/pooling/qwen3_reranker.py](../../examples/offline_inference/pooling/qwen3_reranker.py).
+    Load the official original `Qwen3 Reranker` by using the following command. More information can be found at: [examples/pooling/score/qwen3_reranker.py](../../examples/pooling/score/qwen3_reranker.py).
 
     ```bash
     vllm serve Qwen/Qwen3-Reranker-0.6B --hf_overrides '{"architectures": ["Qwen3ForSequenceClassification"],"classifier_from_token": ["no", "yes"],"is_original_qwen3_reranker": true}'
@@ -606,7 +606,7 @@ These models primarily support the [`LLM.encode`](./pooling_models.md#llmencode)
 | `ModernBertForTokenClassification` | ModernBERT-based | `disham993/electrical-ner-ModernBERT-base` |  |  |
 
 !!! note
-    Named Entity Recognition (NER) usage, please refer to [examples/offline_inference/pooling/ner.py](../../examples/offline_inference/pooling/ner.py), [examples/online_serving/pooling/ner_client.py](../../examples/online_serving/pooling/ner_client.py).
+    Named Entity Recognition (NER) usage, please refer to [examples/pooling/token_classify/ner.py](../../examples/pooling/token_classify/ner.py), [examples/pooling/token_classify/ner_client.py](../../examples/pooling/token_classify/ner_client.py).
 
 ## List of Multimodal Language Models
 

docs/serving/openai_compatible_server.md

@@ -234,7 +234,7 @@ The following extra parameters are supported:
 Our Embeddings API is compatible with [OpenAI's Embeddings API](https://platform.openai.com/docs/api-reference/embeddings);
 you can use the [official OpenAI Python client](https://github.com/openai/openai-python) to interact with it.
 
-Code example: [examples/online_serving/pooling/openai_embedding_client.py](../../examples/online_serving/pooling/openai_embedding_client.py)
+Code example: [examples/pooling/embed/openai_embedding_client.py](../../examples/pooling/embed/openai_embedding_client.py)
 
 If the model has a [chat template](../serving/openai_compatible_server.md#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. Here is a convenience function for calling the API while retaining OpenAI's type annotations:
@@ -335,7 +335,7 @@ and passing a list of `messages` in the request. Refer to the examples below for
         `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: [examples/online_serving/pooling/openai_chat_embedding_client_for_multimodal.py](../../examples/online_serving/pooling/openai_chat_embedding_client_for_multimodal.py)
+Full example: [examples/pooling/embed/openai_chat_embedding_client_for_multimodal.py](../../examples/pooling/embed/openai_chat_embedding_client_for_multimodal.py)
 
 #### Extra parameters
 
@@ -516,7 +516,7 @@ Our Pooling API encodes input prompts using a [pooling model](../models/pooling_
 
 The input format is the same as [Embeddings API](#embeddings-api), but the output data can contain an arbitrary nested list, not just a 1-D list of floats.
 
-Code example: [examples/online_serving/pooling/openai_pooling_client.py](../../examples/online_serving/pooling/openai_pooling_client.py)
+Code example: [examples/pooling/pooling/openai_pooling_client.py](../../examples/pooling/pooling/openai_pooling_client.py)
 
 ### Classification API
 
@@ -524,7 +524,7 @@ Our Classification API directly supports Hugging Face sequence-classification mo
 
 We automatically wrap any other transformer via `as_seq_cls_model()`, which pools on the last token, attaches a `RowParallelLinear` head, and applies a softmax to produce per-class probabilities.
 
-Code example: [examples/online_serving/pooling/openai_classification_client.py](../../examples/online_serving/pooling/openai_classification_client.py)
+Code example: [examples/pooling/classify/openai_classification_client.py](../../examples/pooling/classify/openai_classification_client.py)
 
 #### Example Requests
 
@@ -640,7 +640,7 @@ Usually, the score for a sentence pair refers to the similarity between two sent
 
 You can find the documentation for cross encoder models at [sbert.net](https://www.sbert.net/docs/package_reference/cross_encoder/cross_encoder.html).
 
-Code example: [examples/online_serving/pooling/openai_cross_encoder_score.py](../../examples/online_serving/pooling/openai_cross_encoder_score.py)
+Code example: [examples/pooling/score/openai_cross_encoder_score.py](../../examples/pooling/score/openai_cross_encoder_score.py)
 
 #### Single inference
 
@@ -821,7 +821,7 @@ You can pass multi-modal inputs to scoring models by passing `content` including
         print("Scoring output:", response_json["data"][0]["score"])
         print("Scoring output:", response_json["data"][1]["score"])
         ```
-Full example: [examples/online_serving/pooling/openai_cross_encoder_score_for_multimodal.py](../../examples/online_serving/pooling/openai_cross_encoder_score_for_multimodal.py)
+Full example: [examples/pooling/score/openai_cross_encoder_score_for_multimodal.py](../../examples/pooling/score/openai_cross_encoder_score_for_multimodal.py)
 
 #### Extra parameters
 
@@ -851,7 +851,7 @@ endpoints are compatible with both [Jina AI's re-rank API interface](https://jin
 [Cohere's re-rank API interface](https://docs.cohere.com/v2/reference/rerank) to ensure compatibility with
 popular open-source tools.
 
-Code example: [examples/online_serving/pooling/jinaai_rerank_client.py](../../examples/online_serving/pooling/jinaai_rerank_client.py)
+Code example: [examples/pooling/score/jinaai_rerank_client.py](../../examples/pooling/score/jinaai_rerank_client.py)
 
 #### Example Request
 

examples/offline_inference/pooling/README.md

-# Pooling models
-
-## Convert llm model to seq cls
-
-```bash
-# for BAAI/bge-reranker-v2-gemma
-# Caution: "Yes" and "yes" are two different tokens
-python examples/offline_inference/pooling/convert_model_to_seq_cls.py --model_name BAAI/bge-reranker-v2-gemma --classifier_from_tokens '["Yes"]' --method no_post_processing --path ./bge-reranker-v2-gemma-seq-cls
-# for mxbai-rerank-v2
-python examples/offline_inference/pooling/convert_model_to_seq_cls.py --model_name mixedbread-ai/mxbai-rerank-base-v2 --classifier_from_tokens '["0", "1"]' --method from_2_way_softmax --path ./mxbai-rerank-base-v2-seq-cls
-# for Qwen3-Reranker
-python examples/offline_inference/pooling/convert_model_to_seq_cls.py --model_name Qwen/Qwen3-Reranker-0.6B --classifier_from_tokens '["no", "yes"]' --method from_2_way_softmax --path ./Qwen3-Reranker-0.6B-seq-cls
-```
-
-## Embed jina_embeddings_v3 usage
-
-Only text matching task is supported for now. See <https://github.com/vllm-project/vllm/pull/16120>
-
-```bash
-python examples/offline_inference/pooling/embed_jina_embeddings_v3.py
-```
-
-## Embed matryoshka dimensions usage
-
-```bash
-python examples/offline_inference/pooling/embed_matryoshka_fy.py
-```
-
-## Multi vector retrieval usage
-
-```bash
-python examples/offline_inference/pooling/multi_vector_retrieval.py
-```
-
-## Named Entity Recognition (NER) usage
-
-```bash
-python examples/offline_inference/pooling/ner.py
-```
-
-## Prithvi Geospatial MAE usage
-
-```bash
-python examples/offline_inference/pooling/prithvi_geospatial_mae.py
-```
-
-## IO Processor Plugins for Prithvi Geospatial MAE
-
-```bash
-python examples/offline_inference/pooling/prithvi_geospatial_mae_io_processor.py
-```
-
-## Qwen3 reranker usage
-
-```bash
-python examples/offline_inference/pooling/qwen3_reranker.py
-```

examples/online_serving/pooling/README.md

-# Pooling models
-
-## Cohere rerank usage
-
-```bash
-# vllm serve BAAI/bge-reranker-base
-python examples/online_serving/pooling/cohere_rerank_client.py
-```
-
-## Embedding requests base64 encoding_format usage
-
-```bash
-# vllm serve intfloat/e5-small
-python examples/online_serving/pooling/embedding_requests_base64_client.py
-```
-
-## Embedding requests bytes encoding_format usage
-
-```bash
-# vllm serve intfloat/e5-small
-python examples/online_serving/pooling/embedding_requests_bytes_client.py
-```
-
-## Jinaai rerank usage
-
-```bash
-# vllm serve BAAI/bge-reranker-base
-python examples/online_serving/pooling/jinaai_rerank_client.py
-```
-
-## Multi vector retrieval usage
-
-```bash
-# vllm serve BAAI/bge-m3
-python examples/online_serving/pooling/multi_vector_retrieval_client.py
-```
-
-## Named Entity Recognition (NER) usage
-
-```bash
-# vllm serve boltuix/NeuroBERT-NER
-python examples/online_serving/pooling/ner_client.py
-```
-
-## OpenAI chat embedding for multimodal usage
-
-```bash
-python examples/online_serving/pooling/openai_chat_embedding_client_for_multimodal.py
-```
-
-## OpenAI classification usage
-
-```bash
-# vllm serve jason9693/Qwen2.5-1.5B-apeach
-python examples/online_serving/pooling/openai_classification_client.py
-```
-
-## OpenAI cross_encoder score usage
-
-```bash
-# vllm serve BAAI/bge-reranker-v2-m3
-python examples/online_serving/pooling/openai_cross_encoder_score.py
-```
-
-## OpenAI cross_encoder score for multimodal usage
-
-```bash
-# vllm serve jinaai/jina-reranker-m0
-python examples/online_serving/pooling/openai_cross_encoder_score_for_multimodal.py
-```
-
-## OpenAI embedding usage
-
-```bash
-# vllm serve intfloat/e5-small
-python examples/online_serving/pooling/openai_embedding_client.py
-```
-
-## OpenAI embedding matryoshka dimensions usage
-
-```bash
-# vllm serve jinaai/jina-embeddings-v3 --trust-remote-code
-python examples/online_serving/pooling/openai_embedding_matryoshka_fy.py
-```
-
-## OpenAI pooling usage
-
-```bash
-# vllm serve internlm/internlm2-1_8b-reward --trust-remote-code
-python examples/online_serving/pooling/openai_pooling_client.py
-```
-
-## Online Prithvi Geospatial MAE usage
-
-```bash
-python examples/online_serving/pooling/prithvi_geospatial_mae.py
-```