vllm/docs/source/serving/openai_compatible_server.md

# OpenAI Compatible Server

vLLM provides an HTTP server that implements OpenAI's [Completions](https://platform.openai.com/docs/api-reference/completions) and [Chat](https://platform.openai.com/docs/api-reference/chat) API.

You can start the server using Python, or using [Docker](deploying_with_docker.rst):
```bash
vllm serve NousResearch/Meta-Llama-3-8B-Instruct --dtype auto --api-key token-abc123
```

To call the server, you can use the official OpenAI Python client library, or any other HTTP client.
```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)
```

## API Reference
Please see the [OpenAI API Reference](https://platform.openai.com/docs/api-reference) for more information on the API. We support all parameters except:
- Chat: `tools`, and `tool_choice`.
- Completions: `suffix`.

vLLM also provides experimental support for OpenAI Vision API compatible inference. See more details in [Using VLMs](../models/vlm.rst).

## 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={
    "guided_choice": ["positive", "negative"]
  }
)
```

### Extra Parameters for Chat API
The following [sampling parameters (click through to see documentation)](../dev/sampling_params.rst) are supported.

```{literalinclude} ../../../vllm/entrypoints/openai/protocol.py
:language: python
:start-after: begin-chat-completion-sampling-params
:end-before: end-chat-completion-sampling-params
```

The following extra parameters are supported:

```{literalinclude} ../../../vllm/entrypoints/openai/protocol.py
:language: python
:start-after: begin-chat-completion-extra-params
:end-before: end-chat-completion-extra-params
```

### Extra Parameters for Completions API
The following [sampling parameters (click through to see documentation)](../dev/sampling_params.rst) are supported.

```{literalinclude} ../../../vllm/entrypoints/openai/protocol.py
:language: python
:start-after: begin-completion-sampling-params
:end-before: end-completion-sampling-params
```

The following extra parameters are supported:

```{literalinclude} ../../../vllm/entrypoints/openai/protocol.py
:language: python
:start-after: begin-completion-extra-params
:end-before: end-completion-extra-params
```

## 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 are 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 model,
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 in the examples
directory [here](https://github.com/vllm-project/vllm/tree/main/examples/)

## Command line arguments for the server

```{argparse}
:module: vllm.entrypoints.openai.cli_args
:func: create_parser_for_docs
:prog: vllm serve
```

### Config file

The `serve` module can also accept arguments from a config file in
`yaml` format. The arguments in the yaml must be specified using the 
long form of the argument outlined [here](https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html#command-line-arguments-for-the-server): 

For example:

```yaml
# config.yaml

host: "127.0.0.1"
port: 6379
uvicorn-log-level: "info"
```

```bash
$ vllm serve SOME_MODEL --config config.yaml
```
---
**NOTE**  
In case an argument is supplied using command line and the config file, the value from the commandline will take precedence.
The order of priorities is `command line > config file values > defaults`.

---

## Tool calling in the chat completion API
vLLM supports only named function calling in the chat completion API. The `tool_choice` options `auto` and `required` are **not yet supported** but on the roadmap.

To use a named function you need to define the function in the `tools` parameter and call it in the `tool_choice` parameter. 

It is the callers responsibility to prompt the model with the tool information, vLLM will not automatically manipulate the prompt. **This may change in the future.**

vLLM will use guided decoding to ensure the response matches the tool parameter object defined by the JSON schema in the `tools` parameter.

Please refer to the OpenAI API reference documentation for more information.