Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

[Misc] Update reasoning with stream example to use OpenAI library #14077

Open
wants to merge 2 commits into
base: main
Choose a base branch
from
+82 −57
Open

[Misc] Update reasoning with stream example to use OpenAI library #14077

Changes from 1 commit
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
e1c0782
Update stream reasoning example to use OpenAI client library
liuyanyi Mar 1, 2025
18a80aa
Update reasoning document
liuyanyi Mar 3, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
89 changes: 33 additions & 56 deletions examples/online_serving/openai_chat_completion_with_reasoning_streaming.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -19,73 +19,50 @@
where you want to display chat completions to the user as they are generated
by the model.

Here we do not use the OpenAI Python client library, because it does not support
`reasoning_content` fields in the response.
Remember to check content and reasoning_content exist in `ChatCompletionChunk`,
content may not exist leading to errors if you try to access it.
"""

import json

import requests
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"

models = requests.get(
f"{openai_api_base}/models",
headers={
"Authorization": f"Bearer {openai_api_key}"
},
).json()
model = models["data"][0]["id"]
client = OpenAI(
Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Could we fall back to requests-based method if ChoiceDelta is missing in the Python package

#14070 (comment)

Copy link
Contributor Author

@liuyanyi liuyanyi Mar 3, 2025 •
edited
Loading

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

I'm not quiet sure about the fallback, i think this comment means the reasoning_content is not define in ChoiceDelta. I think openai-python package can use this correct.

image

In this pr, hasattr is used to check whether content or reasoning_content exists.

Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

I’m curious—what would happen if users were working with an older version of the OpenAI package. Would it cause any compatibility issues or unexpected behavior?

Copy link
Contributor Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

i double check an old version (openai-python==1.0.0 because OpenAI client is not exist <1.0), eveything works fine.
OpenAI python package support extra filed in their pydantic models.

https://github.com/openai/openai-python/blob/64af9e8f06be4bfe02e0e5e9cb0aa7889a5db6d7/src/openai/_models.py#L86-L90

The key point to prevent error is use hasattr to check reasoning_content but not chunk.choices[0].delta.reasoning_content

Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

LGTM.

api_key=openai_api_key,
base_url=openai_api_base,
)

# Streaming chat completions
messages = [{"role": "user", "content": "9.11 and 9.8, which is greater?"}]
models = client.models.list()
model = models.data[0].id

response = requests.post(
f"{openai_api_base}/chat/completions",
headers={"Authorization": f"Bearer {openai_api_key}"},
json={
"model": model,
"messages": messages,
"stream": True
},
)
messages = [{"role": "user", "content": "9.11 and 9.8, which is greater?"}]
stream = client.chat.completions.create(model=model,
messages=messages,
stream=True)

print("client: Start streaming chat completions...")
printed_reasoning_content = False
printed_content = False
# Make the streaming request
if response.status_code == 200:
# Process the streaming response
for line in response.iter_lines():
if line: # Filter out keep-alive new lines
# Decode the line and parse the JSON
decoded_line = line.decode("utf-8")
if decoded_line.startswith("data:"):
data = decoded_line[5:].strip() # Remove "data:" prefix
if data == "[DONE]": # End of stream
print("\nclient: Stream completed.")
break
try:
# Parse the JSON data
chunk = json.loads(data)
reasoning_content = chunk["choices"][0]["delta"].get(
"reasoning_content", "")
content = chunk["choices"][0]["delta"].get("content", "")

if reasoning_content:
if not printed_reasoning_content:
printed_reasoning_content = True
print("reasoning_content:", end="", flush=True)
print(reasoning_content, end="", flush=True)
elif content:
if not printed_content:
printed_content = True
print("\ncontent:", end="", flush=True)
# Extract and print the content
print(content, end="", flush=True)
except json.JSONDecodeError:
print("Error decoding JSON:", decoded_line)
else:
print(f"Error: {response.status_code} - {response.text}")
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:
if not printed_reasoning_content:
printed_reasoning_content = True
print("reasoning_content:", end="", flush=True)
print(reasoning_content, end="", flush=True)
elif content:
if not printed_content:
printed_content = True
print("\ncontent:", end="", flush=True)
# Extract and print the content
print(content, end="", flush=True)