# Reasoning Outputs

Reasoning models return an additional `reasoning_content` field in their output, which contains the reasoning steps that led to the final conclusion.

## Supported Models
| Model Name     | Parser Name    | Eable_thinking by Default |
|----------------|----------------|---------------------------|
| baidu/ERNIE-4.5-VL-424B-A47B-Paddle    | ernie-45-vl    | ✓                         |
| baidu/ERNIE-4.5-VL-28B-A3B-Paddle | ernie-45-vl    | ✓                         |

The reasoning model requires a specified parser to extract reasoning content. The reasoning mode can be disabled by setting the `"enable_thinking": false` parameter.

Interfaces that support toggling the reasoning mode:
1. `/v1/chat/completions` requests in OpenAI services.
2. `/v1/chat/completions` requests in the OpenAI Python client.
3. `llm.chat` requests in Offline interfaces.

For reasoning models, the length of the reasoning content can be controlled via `reasoning_max_tokens`. Add `"reasoning_max_tokens": 1024` to the request.

### Quick Start
When launching the model service, specify the parser name using the `--reasoning-parser` argument.
This parser will process the model's output and extract the `reasoning_content` field.

```bash
python -m fastdeploy.entrypoints.openai.api_server \
    --model /path/to/your/model \
    --enable-mm \
    --tensor-parallel-size 8 \
    --port 8192 \
    --quantization wint4 \
    --reasoning-parser ernie-45-vl
```

Next, make a request to the model that should return the reasoning content in the response.

```bash
curl -X POST "http://0.0.0.0:8192/v1/chat/completions" \
-H "Content-Type: application/json" \
-d '{
  "messages": [
    {"role": "user", "content": [
      {"type": "image_url", "image_url": {"url": "https://paddlenlp.bj.bcebos.com/datasets/paddlemix/demo_images/example2.jpg"}},
      {"type": "text", "text": "Which era does the cultural relic in the picture belong to"}
    ]}
  ],
  "chat_template_kwargs":{"enable_thinking": true},
  "reasoning_max_tokens": 1024
}'
```

The `reasoning_content` field contains the reasoning steps to reach the final conclusion, while the `content` field holds the conclusion itself.

### Streaming chat completions
Streaming chat completions are also supported for reasoning models. The `reasoning_content` field is available in the `delta` field in `chat completion response chunks`

```python
from openai import OpenAI
# Set OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8192/v1"
client = OpenAI(
    api_key=openai_api_key,
    base_url=openai_api_base,
)
chat_response = client.chat.completions.create(
    messages=[
        {"role": "user", "content": [ {"type": "image_url", "image_url": {"url": "https://paddlenlp.bj.bcebos.com/datasets/paddlemix/demo_images/example2.jpg"}},
        {"type": "text", "text": "Which era does the cultural relic in the picture belong to"}]}
    ],
    model="vl",
    stream=True,
    extra_body={
      "chat_template_kwargs":{"enable_thinking": True},
      "reasoning_max_tokens": 1024
    }
)
for chunk in chat_response:
    if chunk.choices[0].delta is not None:
        print(chunk.choices[0].delta, end='')
        print("\n")
```