[1/2][feature] support openai like classification api (#11618)

docs/supported_models/classify_models.md

@@ -0,0 +1,162 @@
+# Classification API
+
+This document describes the `/v1/classify` API endpoint implementation in SGLang, which is compatible with vLLM's classification API format.
+
+## Overview
+
+The classification API allows you to classify text inputs using classification models. This implementation follows the same format as vLLM's 0.7.0 classification API.
+
+## API Endpoint
+
+```
+POST /v1/classify
+```
+
+## Request Format
+
+```json
+{
+  "model": "model_name",
+  "input": "text to classify"
+}
+```
+
+### Parameters
+
+- `model` (string, required): The name of the classification model to use
+- `input` (string, required): The text to classify
+- `user` (string, optional): User identifier for tracking
+- `rid` (string, optional): Request ID for tracking
+- `priority` (integer, optional): Request priority
+
+## Response Format
+
+```json
+{
+  "id": "classify-9bf17f2847b046c7b2d5495f4b4f9682",
+  "object": "list",
+  "created": 1745383213,
+  "model": "jason9693/Qwen2.5-1.5B-apeach",
+  "data": [
+    {
+      "index": 0,
+      "label": "Default",
+      "probs": [0.565970778465271, 0.4340292513370514],
+      "num_classes": 2
+    }
+  ],
+  "usage": {
+    "prompt_tokens": 10,
+    "total_tokens": 10,
+    "completion_tokens": 0,
+    "prompt_tokens_details": null
+  }
+}
+```
+
+### Response Fields
+
+- `id`: Unique identifier for the classification request
+- `object`: Always "list"
+- `created`: Unix timestamp when the request was created
+- `model`: The model used for classification
+- `data`: Array of classification results
+  - `index`: Index of the result
+  - `label`: Predicted class label
+  - `probs`: Array of probabilities for each class
+  - `num_classes`: Total number of classes
+- `usage`: Token usage information
+  - `prompt_tokens`: Number of input tokens
+  - `total_tokens`: Total number of tokens
+  - `completion_tokens`: Number of completion tokens (always 0 for classification)
+  - `prompt_tokens_details`: Additional token details (optional)
+
+## Example Usage
+
+### Using curl
+
+```bash
+curl -v "http://127.0.0.1:8000/v1/classify" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "jason9693/Qwen2.5-1.5B-apeach",
+    "input": "Loved the new café—coffee was great."
+  }'
+```
+
+### Using Python
+
+```python
+import requests
+import json
+
+# Make classification request
+response = requests.post(
+    "http://127.0.0.1:8000/v1/classify",
+    headers={"Content-Type": "application/json"},
+    json={
+        "model": "jason9693/Qwen2.5-1.5B-apeach",
+        "input": "Loved the new café—coffee was great."
+    }
+)
+
+# Parse response
+result = response.json()
+print(json.dumps(result, indent=2))
+```
+
+## Supported Models
+
+The classification API works with any classification model supported by SGLang, including:
+
+### Classification Models (Multi-class)
+- `LlamaForSequenceClassification` - Multi-class classification
+- `Qwen2ForSequenceClassification` - Multi-class classification
+- `Qwen3ForSequenceClassification` - Multi-class classification
+- `BertForSequenceClassification` - Multi-class classification
+- `Gemma2ForSequenceClassification` - Multi-class classification
+
+**Label Mapping**: The API automatically uses the `id2label` mapping from the model's `config.json` file to provide meaningful label names instead of generic class names. If `id2label` is not available, it falls back to `LABEL_0`, `LABEL_1`, etc., or `Class_0`, `Class_1` as a last resort.
+
+### Reward Models (Single score)
+- `InternLM2ForRewardModel` - Single reward score
+- `Qwen2ForRewardModel` - Single reward score
+- `LlamaForSequenceClassificationWithNormal_Weights` - Special reward model
+
+**Note**: The `/classify` endpoint in SGLang was originally designed for reward models but now supports all non-generative models. Our `/v1/classify` endpoint provides a standardized vLLM-compatible interface for classification tasks.
+
+## Error Handling
+
+The API returns appropriate HTTP status codes and error messages:
+
+- `400 Bad Request`: Invalid request format or missing required fields
+- `500 Internal Server Error`: Server-side processing error
+
+Error response format:
+```json
+{
+  "error": "Error message",
+  "type": "error_type",
+  "code": 400
+}
+```
+
+## Implementation Details
+
+The classification API is implemented using:
+
+1. **Rust Router**: Handles routing and request/response models in `sgl-router/src/protocols/spec.rs`
+2. **Python HTTP Server**: Implements the actual endpoint in `python/sglang/srt/entrypoints/http_server.py`
+3. **Classification Service**: Handles the classification logic in `python/sglang/srt/entrypoints/openai/serving_classify.py`
+
+## Testing
+
+Use the provided test script to verify the implementation:
+
+```bash
+python test_classify_api.py
+```
+
+## Compatibility
+
+This implementation is compatible with vLLM's classification API format, allowing seamless migration from vLLM to SGLang for classification tasks.