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[Frontend][2/n] Make pooling entrypoints request schema consensus | ChatRequest (#32574)

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Signed-off-by: wang.yuqi <yuqi.wang@daocloud.io>
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wang.yuqi
2026-01-22 18:32:44 +08:00
committed by GitHub
parent 64e3d67ac0
commit 328cbb2773
24 changed files with 456 additions and 205 deletions
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2
docs/design/io_processor_plugins.md
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@@ -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/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.
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_online.py](../../examples/pooling/plugin/prithvi_geospatial_mae_online.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

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docs/models/pooling_models.md
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@@ -273,7 +273,7 @@ outputs = llm.embed(
print(outputs[0].outputs)
```
A code example can be found here: [examples/pooling/embed/embed_matryoshka_fy.py](../../examples/pooling/embed/embed_matryoshka_fy.py)
A code example can be found here: [examples/pooling/embed/embed_matryoshka_fy_offline.py](../../examples/pooling/embed/embed_matryoshka_fy_offline.py)
### Online Inference
@@ -303,7 +303,7 @@ 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/pooling/embed/openai_embedding_matryoshka_fy.py](../../examples/pooling/embed/openai_embedding_matryoshka_fy.py)
An OpenAI client example can be found here: [examples/pooling/embed/openai_embedding_matryoshka_fy_client.py](../../examples/pooling/embed/openai_embedding_matryoshka_fy_client.py)
## Deprecated Features

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docs/models/supported_models.md
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@@ -619,7 +619,7 @@ 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/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).
Named Entity Recognition (NER) usage, please refer to [examples/pooling/token_classify/ner_offline.py](../../examples/pooling/token_classify/ner_offline.py), [examples/pooling/token_classify/ner_online.py](../../examples/pooling/token_classify/ner_online.py).
## List of Multimodal Language Models

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docs/serving/openai_compatible_server.md
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@@ -551,7 +551,7 @@ 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/pooling/pooling/openai_pooling_client.py](../../examples/pooling/pooling/openai_pooling_client.py)
Code example: [examples/pooling/pooling/pooling_online.py](../../examples/pooling/pooling/pooling_online.py)
### Classification API