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examples/online_serving/openai_chat_completion_with_reasoning.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+An example shows how to generate chat completions from reasoning models
+like DeepSeekR1.
+
+To run this example, you need to start the vLLM server
+with the reasoning parser:
+
+```bash
+vllm serve deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B \
+    --reasoning-parser deepseek_r1
+```
+
+This example demonstrates how to generate chat completions from reasoning models
+using the OpenAI Python client library.
+"""
+
+from openai import OpenAI
+
+# Modify OpenAI's API key and API base to use vLLM's API server.
+openai_api_key = "EMPTY"
+openai_api_base = "http://localhost:8000/v1"
+
+
+def main():
+    client = OpenAI(
+        api_key=openai_api_key,
+        base_url=openai_api_base,
+    )
+
+    models = client.models.list()
+    model = models.data[0].id
+
+    # Round 1
+    messages = [{"role": "user", "content": "9.11 and 9.8, which is greater?"}]
+    # ruff: noqa: E501
+    # For granite, add: `extra_body={"chat_template_kwargs": {"thinking": True}}`
+    response = client.chat.completions.create(model=model, messages=messages)
+
+    reasoning = response.choices[0].message.reasoning
+    content = response.choices[0].message.content
+
+    print("reasoning for Round 1:", reasoning)
+    print("content for Round 1:", content)
+
+    # Round 2
+    messages.append({"role": "assistant", "content": content})
+    messages.append(
+        {
+            "role": "user",
+            "content": "How many Rs are there in the word 'strawberry'?",
+        }
+    )
+    response = client.chat.completions.create(model=model, messages=messages)
+
+    reasoning = response.choices[0].message.reasoning
+    content = response.choices[0].message.content
+
+    print("reasoning for Round 2:", reasoning)
+    print("content for Round 2:", content)
+
+
+if __name__ == "__main__":
+    main()