Update README.md

README.md

@@ -98,7 +98,7 @@ All these systems support the OpenAI Chat Completions API format, ensuring smoot
 vllm serve AXCXEPT/QwQ-32B-Distill-Qwen-1.5B-Alpha --max-model-len 32768 --enforce-eager
 ```
 
-### Call API:
+### Call API Without Streaming:
 ```python
 from openai import OpenAI
 client = OpenAI(
@@ -117,6 +117,79 @@ completion = client.chat.completions.create(
 print(completion.choices[0].message)
 ```
 
+### Call API With Streaming:
+```python
+# SPDX-License-Identifier: Apache-2.0
+"""
+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 \
+     --enable-reasoning --reasoning-parser deepseek_r1
+```
+
+Unlike openai_chat_completion_with_reasoning.py, this example demonstrates the
+streaming chat completions feature.
+
+The streaming chat completions feature allows you to receive chat completions
+in real-time as they are generated by the model. This is useful for scenarios
+where you want to display chat completions to the user as they are generated
+by the model.
+
+Remember to check content and reasoning_content exist in `ChatCompletionChunk`,
+content may not exist leading to errors if you try to access it.
+"""
+
+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"
+
+client = OpenAI(
+    api_key=openai_api_key,
+    base_url=openai_api_base,
+)
+
+models = client.models.list()
+model = models.data[0].id
+
+messages = [{"role": "user", "content": "9.11 and 9.8, which is greater?"}]
+# For granite, add: `extra_body={"chat_template_kwargs": {"thinking": True}}`
+stream = client.chat.completions.create(model=model,
+                                        messages=messages,
+                                        stream=True)
+
+print("client: Start streaming chat completions...")
+printed_reasoning_content = False
+printed_content = False
+
+for chunk in stream:
+    reasoning_content = None
+    content = None
+    # Check the content is reasoning_content or content
+    if hasattr(chunk.choices[0].delta, "reasoning_content"):
+        reasoning_content = chunk.choices[0].delta.reasoning_content
+    elif hasattr(chunk.choices[0].delta, "content"):
+        content = chunk.choices[0].delta.content
+
+    if reasoning_content is not None:
+        if not printed_reasoning_content:
+            printed_reasoning_content = True
+            print("reasoning_content:", end="", flush=True)
+        print(reasoning_content, end="", flush=True)
+    elif content is not None:
+        if not printed_content:
+            printed_content = True
+            print("\ncontent:", end="", flush=True)
+        # Extract and print the content
+        print(content, end="", flush=True)
+```
+
 ## License
 
 This project is released under the **MIT License**, reflecting our commitment to open and accessible AI. We firmly believe that cutting-edge AI research should be available for anyone to use, modify, and build upon.