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# OpenAI-Compatible Server
vLLM provides an HTTP server that implements OpenAI's [Completions API](https://platform.openai.com/docs/api-reference/completions), [Chat API](https://platform.openai.com/docs/api-reference/chat), and more! This functionality lets you serve models and interact with them using an HTTP client.
In your terminal, you can [install](../getting_started/installation/README.md) vLLM, then start the server with the [`vllm serve`](../configuration/serve_args.md) command. (You can also use our [Docker](../deployment/docker.md) image.)
```bash
vllm serve NousResearch/Meta-Llama-3-8B-Instruct \
--dtype auto \
--api-key token-abc123
```
To call the server, in your preferred text editor, create a script that uses an HTTP client. Include any messages that you want to send to the model. Then run that script. Below is an example script using the [official OpenAI Python client](https://github.com/openai/openai-python).
??? code
```python
from openai import OpenAI
client = OpenAI(
base_url="http://localhost:8000/v1",
api_key="token-abc123",
)
completion = client.chat.completions.create(
model="NousResearch/Meta-Llama-3-8B-Instruct",
messages=[
{"role": "user", "content": "Hello!"},
],
)
print(completion.choices[0].message)
```
!!! tip
vLLM supports some parameters that are not supported by OpenAI, `top_k` for example.
You can pass these parameters to vLLM using the OpenAI client in the `extra_body` parameter of your requests, i.e. `extra_body={"top_k": 50}` for `top_k`.
!!! important
By default, the server applies `generation_config.json` from the Hugging Face model repository if it exists. This means the default values of certain sampling parameters can be overridden by those recommended by the model creator.
To disable this behavior, please pass `--generation-config vllm` when launching the server.
## Supported APIs
We currently support the following OpenAI APIs:
- [Completions API](#completions-api) (`/v1/completions`)
- Only applicable to [text generation models](../models/generative_models.md).
- *Note: `suffix` parameter is not supported.*
- [Chat Completions API](#chat-api) (`/v1/chat/completions`)
- Only applicable to [text generation models](../models/generative_models.md) with a [chat template](../serving/openai_compatible_server.md#chat-template).
- *Note: `user` parameter is ignored.*
- *Note:* Setting the `parallel_tool_calls` parameter to `false` ensures vLLM only returns zero or one tool call per request. Setting it to `true` (the default) allows returning more than one tool call per request. There is no guarantee more than one tool call will be returned if this is set to `true`, as that behavior is model dependent and not all models are designed to support parallel tool calls.
- [Embeddings API](#embeddings-api) (`/v1/embeddings`)
- Only applicable to [embedding models](../models/pooling_models.md).
- [Transcriptions API](#transcriptions-api) (`/v1/audio/transcriptions`)
- Only applicable to [Automatic Speech Recognition (ASR) models](../models/supported_models.md#transcription).
- [Translation API](#translations-api) (`/v1/audio/translations`)
- Only applicable to [Automatic Speech Recognition (ASR) models](../models/supported_models.md#transcription).
In addition, we have the following custom APIs:
- [Tokenizer API](#tokenizer-api) (`/tokenize`, `/detokenize`)
- Applicable to any model with a tokenizer.
- [Pooling API](#pooling-api) (`/pooling`)
- Applicable to all [pooling models](../models/pooling_models.md).
- [Classification API](#classification-api) (`/classify`)
- Only applicable to [classification models](../models/pooling_models.md).
- [Score API](#score-api) (`/score`)
- Applicable to [embedding models and cross-encoder models](../models/pooling_models.md).
- [Re-rank API](#re-rank-api) (`/rerank`, `/v1/rerank`, `/v2/rerank`)
- Implements [Jina AI's v1 re-rank API](https://jina.ai/reranker/)
- Also compatible with [Cohere's v1 & v2 re-rank APIs](https://docs.cohere.com/v2/reference/rerank)
- Jina and Cohere's APIs are very similar; Jina's includes extra information in the rerank endpoint's response.
- Only applicable to [cross-encoder models](../models/pooling_models.md).
## Chat Template
In order for the language model to support chat protocol, vLLM requires the model to include
a chat template in its tokenizer configuration. The chat template is a Jinja2 template that
specifies how roles, messages, and other chat-specific tokens are encoded in the input.
An example chat template for `NousResearch/Meta-Llama-3-8B-Instruct` can be found [here](https://github.com/meta-llama/llama3?tab=readme-ov-file#instruction-tuned-models)
Some models do not provide a chat template even though they are instruction/chat fine-tuned. For those models,
you can manually specify their chat template in the `--chat-template` parameter with the file path to the chat
template, or the template in string form. Without a chat template, the server will not be able to process chat
and all chat requests will error.
```bash
vllm serve <model> --chat-template ./path-to-chat-template.jinja
```
vLLM community provides a set of chat templates for popular models. You can find them under the [examples](../../examples) directory.
With the inclusion of multi-modal chat APIs, the OpenAI spec now accepts chat messages in a new format which specifies
both a `type` and a `text` field. An example is provided below:
```python
completion = client.chat.completions.create(
model="NousResearch/Meta-Llama-3-8B-Instruct",
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": "Classify this sentiment: vLLM is wonderful!"},
],
},
],
)
```
Most chat templates for LLMs expect the `content` field to be a string, but there are some newer models like
`meta-llama/Llama-Guard-3-1B` that expect the content to be formatted according to the OpenAI schema in the
request. vLLM provides best-effort support to detect this automatically, which is logged as a string like
*"Detected the chat template content format to be..."*, and internally converts incoming requests to match
the detected format, which can be one of:
- `"string"`: A string.
- Example: `"Hello world"`
- `"openai"`: A list of dictionaries, similar to OpenAI schema.
- Example: `[{"type": "text", "text": "Hello world!"}]`
If the result is not what you expect, you can set the `--chat-template-content-format` CLI argument
to override which format to use.
## Extra Parameters
vLLM supports a set of parameters that are not part of the OpenAI API.
In order to use them, you can pass them as extra parameters in the OpenAI client.
Or directly merge them into the JSON payload if you are using HTTP call directly.
```python
completion = client.chat.completions.create(
model="NousResearch/Meta-Llama-3-8B-Instruct",
messages=[
{"role": "user", "content": "Classify this sentiment: vLLM is wonderful!"},
],
extra_body={
"structured_outputs": {"choice": ["positive", "negative"]},
},
)
```
## Extra HTTP Headers
Only `X-Request-Id` HTTP request header is supported for now. It can be enabled
with `--enable-request-id-headers`.
??? code
```python
completion = client.chat.completions.create(
model="NousResearch/Meta-Llama-3-8B-Instruct",
messages=[
{"role": "user", "content": "Classify this sentiment: vLLM is wonderful!"},
],
extra_headers={
"x-request-id": "sentiment-classification-00001",
},
)
print(completion._request_id)
completion = client.completions.create(
model="NousResearch/Meta-Llama-3-8B-Instruct",
prompt="A robot may not injure a human being",
extra_headers={
"x-request-id": "completion-test",
},
)
print(completion._request_id)
```
## API Reference
### Completions API
Our Completions API is compatible with [OpenAI's Completions API](https://platform.openai.com/docs/api-reference/completions);
you can use the [official OpenAI Python client](https://github.com/openai/openai-python) to interact with it.
Code example: [examples/online_serving/openai_completion_client.py](../../examples/online_serving/openai_completion_client.py)
#### Extra parameters
The following [sampling parameters](../api/README.md#inference-parameters) are supported.
??? code
```python
--8<-- "vllm/entrypoints/openai/protocol.py:completion-sampling-params"
```
The following extra parameters are supported:
??? code
```python
--8<-- "vllm/entrypoints/openai/protocol.py:completion-extra-params"
```
### Chat API
Our Chat API is compatible with [OpenAI's Chat Completions API](https://platform.openai.com/docs/api-reference/chat);
you can use the [official OpenAI Python client](https://github.com/openai/openai-python) to interact with it.
We support both [Vision](https://platform.openai.com/docs/guides/vision)- and
[Audio](https://platform.openai.com/docs/guides/audio?audio-generation-quickstart-example=audio-in)-related parameters;
see our [Multimodal Inputs](../features/multimodal_inputs.md) guide for more information.
- *Note: `image_url.detail` parameter is not supported.*
Code example: [examples/online_serving/openai_chat_completion_client.py](../../examples/online_serving/openai_chat_completion_client.py)
#### Extra parameters
The following [sampling parameters](../api/README.md#inference-parameters) are supported.
??? code
```python
--8<-- "vllm/entrypoints/openai/protocol.py:chat-completion-sampling-params"
```
The following extra parameters are supported:
??? code
```python
--8<-- "vllm/entrypoints/openai/protocol.py:chat-completion-extra-params"
```
### Embeddings API
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/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:
??? code
```python
from openai import OpenAI
from openai._types import NOT_GIVEN, NotGiven
from openai.types.chat import ChatCompletionMessageParam
from openai.types.create_embedding_response import CreateEmbeddingResponse
def create_chat_embeddings(
client: OpenAI,
*,
messages: list[ChatCompletionMessageParam],
model: str,
encoding_format: Union[Literal["base64", "float"], NotGiven] = NOT_GIVEN,
) -> CreateEmbeddingResponse:
return client.post(
"/embeddings",
cast_to=CreateEmbeddingResponse,
body={"messages": messages, "model": model, "encoding_format": encoding_format},
)
```
#### 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.
=== "VLM2Vec"
To serve the model:
```bash
vllm serve TIGER-Lab/VLM2Vec-Full --runner pooling \
--trust-remote-code \
--max-model-len 4096 \
--chat-template examples/template_vlm2vec_phi3v.jinja
```
!!! important
Since VLM2Vec has the same model architecture as Phi-3.5-Vision, we have to explicitly pass `--runner pooling`
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: [examples/template_vlm2vec_phi3v.jinja](../../examples/template_vlm2vec_phi3v.jinja)
Since the request schema is not defined by OpenAI client, we post a request to the server using the lower-level `requests` library:
??? code
```python
from openai import OpenAI
client = OpenAI(
base_url="http://localhost:8000/v1",
api_key="EMPTY",
)
image_url = "https://vllm-public-assets.s3.us-west-2.amazonaws.com/vision_model_images/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
response = create_chat_embeddings(
client,
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",
)
print("Image embedding output:", response.data[0].embedding)
```
=== "DSE-Qwen2-MRL"
To serve the model:
```bash
vllm serve MrLight/dse-qwen2-2b-mrl-v1 --runner pooling \
--trust-remote-code \
--max-model-len 8192 \
--chat-template examples/template_dse_qwen2_vl.jinja
```
!!! important
Like with VLM2Vec, we have to explicitly pass `--runner pooling`.
Additionally, `MrLight/dse-qwen2-2b-mrl-v1` requires an EOS token for embeddings, which is handled
by a custom chat template: [examples/template_dse_qwen2_vl.jinja](../../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: [examples/pooling/embed/openai_chat_embedding_client_for_multimodal.py](../../examples/pooling/embed/openai_chat_embedding_client_for_multimodal.py)
#### Extra parameters
The following [pooling parameters][vllm.PoolingParams] are supported.
```python
--8<-- "vllm/pooling_params.py:common-pooling-params"
--8<-- "vllm/pooling_params.py:embedding-pooling-params"
```
The following extra parameters are supported by default:
??? code
```python
--8<-- "vllm/entrypoints/pooling/embed/protocol.py:embedding-extra-params"
```
For chat-like input (i.e. if `messages` is passed), these extra parameters are supported instead:
??? code
```python
--8<-- "vllm/entrypoints/pooling/embed/protocol.py:chat-embedding-extra-params"
```
### Transcriptions API
Our Transcriptions API is compatible with [OpenAI's Transcriptions API](https://platform.openai.com/docs/api-reference/audio/createTranscription);
you can use the [official OpenAI Python client](https://github.com/openai/openai-python) to interact with it.
!!! note
To use the Transcriptions API, please install with extra audio dependencies using `pip install vllm[audio]`.
Code example: [examples/online_serving/openai_transcription_client.py](../../examples/online_serving/openai_transcription_client.py)
#### API Enforced Limits
Set the maximum audio file size (in MB) that VLLM will accept, via the
`VLLM_MAX_AUDIO_CLIP_FILESIZE_MB` environment variable. Default is 25 MB.
#### Uploading Audio Files
The Transcriptions API supports uploading audio files in various formats including FLAC, MP3, MP4, MPEG, MPGA, M4A, OGG, WAV, and WEBM.
**Using OpenAI Python Client:**
??? code
```python
from openai import OpenAI
client = OpenAI(
base_url="http://localhost:8000/v1",
api_key="token-abc123",
)
# Upload audio file from disk
with open("audio.mp3", "rb") as audio_file:
transcription = client.audio.transcriptions.create(
model="openai/whisper-large-v3-turbo",
file=audio_file,
language="en",
response_format="verbose_json",
)
print(transcription.text)
```
**Using curl with multipart/form-data:**
??? code
```bash
curl -X POST "http://localhost:8000/v1/audio/transcriptions" \
-H "Authorization: Bearer token-abc123" \
-F "file=@audio.mp3" \
-F "model=openai/whisper-large-v3-turbo" \
-F "language=en" \
-F "response_format=verbose_json"
```
**Supported Parameters:**
- `file`: The audio file to transcribe (required)
- `model`: The model to use for transcription (required)
- `language`: The language code (e.g., "en", "zh") (optional)
- `prompt`: Optional text to guide the transcription style (optional)
- `response_format`: Format of the response ("json", "text") (optional)
- `temperature`: Sampling temperature between 0 and 1 (optional)
For the complete list of supported parameters including sampling parameters and vLLM extensions, see the [protocol definitions](https://github.com/vllm-project/vllm/blob/main/vllm/entrypoints/openai/protocol.py#L2182).
**Response Format:**
For `verbose_json` response format:
??? code
```json
{
"text": "Hello, this is a transcription of the audio file.",
"language": "en",
"duration": 5.42,
"segments": [
{
"id": 0,
"seek": 0,
"start": 0.0,
"end": 2.5,
"text": "Hello, this is a transcription",
"tokens": [50364, 938, 428, 307, 275, 28347],
"temperature": 0.0,
"avg_logprob": -0.245,
"compression_ratio": 1.235,
"no_speech_prob": 0.012
}
]
}
```
Currently “verbose_json” response format doesn’t support avg_logprob, compression_ratio, no_speech_prob.
#### Extra Parameters
The following [sampling parameters](../api/README.md#inference-parameters) are supported.
??? code
```python
--8<-- "vllm/entrypoints/openai/protocol.py:transcription-sampling-params"
```
The following extra parameters are supported:
??? code
```python
--8<-- "vllm/entrypoints/openai/protocol.py:transcription-extra-params"
```
### Translations API
Our Translation API is compatible with [OpenAI's Translations API](https://platform.openai.com/docs/api-reference/audio/createTranslation);
you can use the [official OpenAI Python client](https://github.com/openai/openai-python) to interact with it.
Whisper models can translate audio from one of the 55 non-English supported languages into English.
Please mind that the popular `openai/whisper-large-v3-turbo` model does not support translating.
!!! note
To use the Translation API, please install with extra audio dependencies using `pip install vllm[audio]`.
Code example: [examples/online_serving/openai_translation_client.py](../../examples/online_serving/openai_translation_client.py)
#### Extra Parameters
The following [sampling parameters](../api/README.md#inference-parameters) are supported.
```python
--8<-- "vllm/entrypoints/openai/protocol.py:translation-sampling-params"
```
The following extra parameters are supported:
```python
--8<-- "vllm/entrypoints/openai/protocol.py:translation-extra-params"
```
### Tokenizer API
Our Tokenizer API is a simple wrapper over [HuggingFace-style tokenizers](https://huggingface.co/docs/transformers/en/main_classes/tokenizer).
It consists of two endpoints:
- `/tokenize` corresponds to calling `tokenizer.encode()`.
- `/detokenize` corresponds to calling `tokenizer.decode()`.
### Pooling API
Our Pooling API encodes input prompts using a [pooling model](../models/pooling_models.md) and returns the corresponding hidden states.
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)
### Classification API
Our Classification API directly supports Hugging Face sequence-classification models such as [ai21labs/Jamba-tiny-reward-dev](https://huggingface.co/ai21labs/Jamba-tiny-reward-dev) and [jason9693/Qwen2.5-1.5B-apeach](https://huggingface.co/jason9693/Qwen2.5-1.5B-apeach).
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/pooling/classify/openai_classification_client.py](../../examples/pooling/classify/openai_classification_client.py)
#### Example Requests
You can classify multiple texts by passing an array of strings:
```bash
curl -v "http://127.0.0.1:8000/classify" \
-H "Content-Type: application/json" \
-d '{
"model": "jason9693/Qwen2.5-1.5B-apeach",
"input": [
"Loved the new café—coffee was great.",
"This update broke everything. Frustrating."
]
}'
```
??? console "Response"
```json
{
"id": "classify-7c87cac407b749a6935d8c7ce2a8fba2",
"object": "list",
"created": 1745383065,
"model": "jason9693/Qwen2.5-1.5B-apeach",
"data": [
{
"index": 0,
"label": "Default",
"probs": [
0.565970778465271,
0.4340292513370514
],
"num_classes": 2
},
{
"index": 1,
"label": "Spoiled",
"probs": [
0.26448777318000793,
0.7355121970176697
],
"num_classes": 2
}
],
"usage": {
"prompt_tokens": 20,
"total_tokens": 20,
"completion_tokens": 0,
"prompt_tokens_details": null
}
}
```
You can also pass a string directly to the `input` field:
```bash
curl -v "http://127.0.0.1:8000/classify" \
-H "Content-Type: application/json" \
-d '{
"model": "jason9693/Qwen2.5-1.5B-apeach",
"input": "Loved the new café—coffee was great."
}'
```
??? console "Response"
```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
}
}
```
#### Extra parameters
The following [pooling parameters][vllm.PoolingParams] are supported.
```python
--8<-- "vllm/pooling_params.py:common-pooling-params"
--8<-- "vllm/pooling_params.py:classification-pooling-params"
```
The following extra parameters are supported:
```python
--8<-- "vllm/entrypoints/pooling/classify/protocol.py:classification-extra-params"
```
### Score API
Our Score API can apply a cross-encoder model or an embedding model to predict scores for sentence or multimodal pairs. When using an embedding model the score corresponds to the cosine similarity between each embedding pair.
Usually, the score for a sentence pair refers to the similarity between two sentences, on a scale of 0 to 1.
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/pooling/score/openai_cross_encoder_score.py](../../examples/pooling/score/openai_cross_encoder_score.py)
#### Single inference
You can pass a string to both `text_1` and `text_2`, forming a single sentence pair.
```bash
curl -X 'POST' \
'http://127.0.0.1:8000/score' \
-H 'accept: application/json' \
-H 'Content-Type: application/json' \
-d '{
"model": "BAAI/bge-reranker-v2-m3",
"encoding_format": "float",
"text_1": "What is the capital of France?",
"text_2": "The capital of France is Paris."
}'
```
??? console "Response"
```json
{
"id": "score-request-id",
"object": "list",
"created": 693447,
"model": "BAAI/bge-reranker-v2-m3",
"data": [
{
"index": 0,
"object": "score",
"score": 1
}
],
"usage": {}
}
```
#### Batch inference
You can pass a string to `text_1` and a list to `text_2`, forming multiple sentence pairs
where each pair is built from `text_1` and a string in `text_2`.
The total number of pairs is `len(text_2)`.
??? console "Request"
```bash
curl -X 'POST' \
'http://127.0.0.1:8000/score' \
-H 'accept: application/json' \
-H 'Content-Type: application/json' \
-d '{
"model": "BAAI/bge-reranker-v2-m3",
"text_1": "What is the capital of France?",
"text_2": [
"The capital of Brazil is Brasilia.",
"The capital of France is Paris."
]
}'
```
??? console "Response"
```json
{
"id": "score-request-id",
"object": "list",
"created": 693570,
"model": "BAAI/bge-reranker-v2-m3",
"data": [
{
"index": 0,
"object": "score",
"score": 0.001094818115234375
},
{
"index": 1,
"object": "score",
"score": 1
}
],
"usage": {}
}
```
You can pass a list to both `text_1` and `text_2`, forming multiple sentence pairs
where each pair is built from a string in `text_1` and the corresponding string in `text_2` (similar to `zip()`).
The total number of pairs is `len(text_2)`.
??? console "Request"
```bash
curl -X 'POST' \
'http://127.0.0.1:8000/score' \
-H 'accept: application/json' \
-H 'Content-Type: application/json' \
-d '{
"model": "BAAI/bge-reranker-v2-m3",
"encoding_format": "float",
"text_1": [
"What is the capital of Brazil?",
"What is the capital of France?"
],
"text_2": [
"The capital of Brazil is Brasilia.",
"The capital of France is Paris."
]
}'
```
??? console "Response"
```json
{
"id": "score-request-id",
"object": "list",
"created": 693447,
"model": "BAAI/bge-reranker-v2-m3",
"data": [
{
"index": 0,
"object": "score",
"score": 1
},
{
"index": 1,
"object": "score",
"score": 1
}
],
"usage": {}
}
```
#### Multi-modal inputs
You can pass multi-modal inputs to scoring models by passing `content` including a list of multi-modal input (image, etc.) in the request. Refer to the examples below for illustration.
=== "JinaVL-Reranker"
To serve the model:
```bash
vllm serve jinaai/jina-reranker-m0
```
Since the request schema is not defined by OpenAI client, we post a request to the server using the lower-level `requests` library:
??? Code
```python
import requests
response = requests.post(
"http://localhost:8000/v1/score",
json={
"model": "jinaai/jina-reranker-m0",
"text_1": "slm markdown",
"text_2": {
"content": [
{
"type": "image_url",
"image_url": {
"url": "https://raw.githubusercontent.com/jina-ai/multimodal-reranker-test/main/handelsblatt-preview.png"
},
},
{
"type": "image_url",
"image_url": {
"url": "https://raw.githubusercontent.com/jina-ai/multimodal-reranker-test/main/paper-11.png"
},
},
],
},
},
)
response.raise_for_status()
response_json = response.json()
print("Scoring output:", response_json["data"][0]["score"])
print("Scoring output:", response_json["data"][1]["score"])
```
Full example: [examples/pooling/score/openai_cross_encoder_score_for_multimodal.py](../../examples/pooling/score/openai_cross_encoder_score_for_multimodal.py)
#### Extra parameters
The following [pooling parameters][vllm.PoolingParams] are supported.
```python
--8<-- "vllm/pooling_params.py:common-pooling-params"
--8<-- "vllm/pooling_params.py:classification-pooling-params"
```
The following extra parameters are supported:
```python
--8<-- "vllm/entrypoints/pooling/score/protocol.py:score-extra-params"
```
### Re-rank API
Our Re-rank API can apply an embedding model or a cross-encoder model to predict relevant scores between a single query, and
each of a list of documents. Usually, the score for a sentence pair refers to the similarity between two sentences or multi-modal inputs (image, etc.), on a scale of 0 to 1.
You can find the documentation for cross encoder models at [sbert.net](https://www.sbert.net/docs/package_reference/cross_encoder/cross_encoder.html).
The rerank endpoints support popular re-rank models such as `BAAI/bge-reranker-base` and other models supporting the
`score` task. Additionally, `/rerank`, `/v1/rerank`, and `/v2/rerank`
endpoints are compatible with both [Jina AI's re-rank API interface](https://jina.ai/reranker/) and
[Cohere's re-rank API interface](https://docs.cohere.com/v2/reference/rerank) to ensure compatibility with
popular open-source tools.
Code example: [examples/pooling/score/openai_reranker.py](../../examples/pooling/score/openai_reranker.py)
#### Example Request
Note that the `top_n` request parameter is optional and will default to the length of the `documents` field.
Result documents will be sorted by relevance, and the `index` property can be used to determine original order.
??? console "Request"
```bash
curl -X 'POST' \
'http://127.0.0.1:8000/v1/rerank' \
-H 'accept: application/json' \
-H 'Content-Type: application/json' \
-d '{
"model": "BAAI/bge-reranker-base",
"query": "What is the capital of France?",
"documents": [
"The capital of Brazil is Brasilia.",
"The capital of France is Paris.",
"Horses and cows are both animals"
]
}'
```
??? console "Response"
```json
{
"id": "rerank-fae51b2b664d4ed38f5969b612edff77",
"model": "BAAI/bge-reranker-base",
"usage": {
"total_tokens": 56
},
"results": [
{
"index": 1,
"document": {
"text": "The capital of France is Paris."
},
"relevance_score": 0.99853515625
},
{
"index": 0,
"document": {
"text": "The capital of Brazil is Brasilia."
},
"relevance_score": 0.0005860328674316406
}
]
}
```
#### Extra parameters
The following [pooling parameters][vllm.PoolingParams] are supported.
```python
--8<-- "vllm/pooling_params.py:common-pooling-params"
--8<-- "vllm/pooling_params.py:classification-pooling-params"
```
The following extra parameters are supported:
```python
--8<-- "vllm/entrypoints/pooling/score/protocol.py:rerank-extra-params"
```
## Ray Serve LLM
Ray Serve LLM enables scalable, production-grade serving of the vLLM engine. It integrates tightly with vLLM and extends it with features such as auto-scaling, load balancing, and back-pressure.
Key capabilities:
- Exposes an OpenAI-compatible HTTP API as well as a Pythonic API.
- Scales from a single GPU to a multi-node cluster without code changes.
- Provides observability and autoscaling policies through Ray dashboards and metrics.
The following example shows how to deploy a large model like DeepSeek R1 with Ray Serve LLM: [examples/online_serving/ray_serve_deepseek.py](../../examples/online_serving/ray_serve_deepseek.py).
Learn more about Ray Serve LLM with the official [Ray Serve LLM documentation](https://docs.ray.io/en/latest/serve/llm/serving-llms.html).