Skip to content

Fix reasoning model support (gpt-5.2-pro) by making temperature conditional #472

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

Open
kanlanc wants to merge 2 commits into
base: main
Choose a base branch
from
+156 −86
Open

Fix reasoning model support (gpt-5.2-pro) by making temperature conditional #472

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
1d18c1c
Fix reasoning model support (gpt-5.2-pro) by making temperature condi…
kanlanc Feb 14, 2026
60cbcc6
Add Responses API support for gpt-5.2-pro
kanlanc Feb 14, 2026
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
6 changes: 6 additions & 0 deletions api/config/generator.json
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -53,6 +53,12 @@
"gpt-5-mini": {
"temperature": 1.0
},
"gpt-5.2": {
"temperature": 1.0
},
"gpt-5.2-pro": {
"use_responses_api": true
},
"gpt-4o": {
"temperature": 0.7,
"top_p": 0.8
Expand Down
117 changes: 74 additions & 43 deletions api/simple_chat.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -379,22 +379,27 @@ async def chat_completions_stream(request: ChatCompletionRequest):
logger.warning("OPENAI_API_KEY not configured, but continuing with request")
# We'll let the OpenAIClient handle this and return an error message

# Initialize Openai client
model = OpenAIClient()
model_kwargs = {
"model": request.model,
"stream": True,
"temperature": model_config["temperature"]
}
# Only add top_p if it exists in the model config
if "top_p" in model_config:
model_kwargs["top_p"] = model_config["top_p"]

api_kwargs = model.convert_inputs_to_api_kwargs(
input=prompt,
model_kwargs=model_kwargs,
model_type=ModelType.LLM
)
use_responses_api = model_config.get("use_responses_api", False)

if not use_responses_api:
# Initialize Openai client (Chat Completions API)
model = OpenAIClient()
model_kwargs = {
"model": request.model,
"stream": True,
}
# Only add temperature/top_p if they exist in the model config
# (reasoning models like gpt-5.2-pro do not support these parameters)
if "temperature" in model_config:
model_kwargs["temperature"] = model_config["temperature"]
if "top_p" in model_config:
model_kwargs["top_p"] = model_config["top_p"]

api_kwargs = model.convert_inputs_to_api_kwargs(
input=prompt,
model_kwargs=model_kwargs,
model_type=ModelType.LLM
)
elif request.provider == "bedrock":
logger.info(f"Using AWS Bedrock with model: {request.model}")

Expand Down Expand Up @@ -485,18 +490,31 @@ async def response_stream():
yield f"\nError with OpenRouter API: {str(e_openrouter)}\n\nPlease check that you have set the OPENROUTER_API_KEY environment variable with a valid API key."
elif request.provider == "openai":
try:
# Get the response and handle it properly using the previously created api_kwargs
logger.info("Making Openai API call")
response = await model.acall(api_kwargs=api_kwargs, model_type=ModelType.LLM)
# Handle streaming response from Openai
async for chunk in response:
choices = getattr(chunk, "choices", [])
if len(choices) > 0:
delta = getattr(choices[0], "delta", None)
if delta is not None:
text = getattr(delta, "content", None)
if text is not None:
yield text
if use_responses_api:
logger.info("Making OpenAI Responses API call")
from openai import OpenAI as OpenAISyncClient
responses_client = OpenAISyncClient()
stream = responses_client.responses.create(
model=request.model,
input=prompt,
stream=True,
)
for event in stream:
if event.type == "response.output_text.delta":
yield event.delta
else:
# Get the response and handle it properly using the previously created api_kwargs
logger.info("Making Openai API call")
response = await model.acall(api_kwargs=api_kwargs, model_type=ModelType.LLM)
# Handle streaming response from Openai
async for chunk in response:
choices = getattr(chunk, "choices", [])
if len(choices) > 0:
delta = getattr(choices[0], "delta", None)
if delta is not None:
text = getattr(delta, "content", None)
if text is not None:
yield text
except Exception as e_openai:
logger.error(f"Error with Openai API: {str(e_openai)}")
yield f"\nError with Openai API: {str(e_openai)}\n\nPlease check that you have set the OPENAI_API_KEY environment variable with a valid API key."
Expand Down Expand Up @@ -617,21 +635,34 @@ async def response_stream():
yield f"\nError with OpenRouter API fallback: {str(e_fallback)}\n\nPlease check that you have set the OPENROUTER_API_KEY environment variable with a valid API key."
elif request.provider == "openai":
try:
# Create new api_kwargs with the simplified prompt
fallback_api_kwargs = model.convert_inputs_to_api_kwargs(
input=simplified_prompt,
model_kwargs=model_kwargs,
model_type=ModelType.LLM
)

# Get the response using the simplified prompt
logger.info("Making fallback Openai API call")
fallback_response = await model.acall(api_kwargs=fallback_api_kwargs, model_type=ModelType.LLM)

# Handle streaming fallback_response from Openai
async for chunk in fallback_response:
text = chunk if isinstance(chunk, str) else getattr(chunk, 'text', str(chunk))
yield text
if use_responses_api:
logger.info("Making fallback OpenAI Responses API call")
from openai import OpenAI as OpenAISyncClient
responses_client = OpenAISyncClient()
stream = responses_client.responses.create(
model=request.model,
input=simplified_prompt,
stream=True,
)
for event in stream:
if event.type == "response.output_text.delta":
yield event.delta
else:
# Create new api_kwargs with the simplified prompt
fallback_api_kwargs = model.convert_inputs_to_api_kwargs(
input=simplified_prompt,
model_kwargs=model_kwargs,
model_type=ModelType.LLM
)

# Get the response using the simplified prompt
logger.info("Making fallback Openai API call")
fallback_response = await model.acall(api_kwargs=fallback_api_kwargs, model_type=ModelType.LLM)

# Handle streaming fallback_response from Openai
async for chunk in fallback_response:
text = chunk if isinstance(chunk, str) else getattr(chunk, 'text', str(chunk))
yield text
except Exception as e_fallback:
logger.error(f"Error with Openai API fallback: {str(e_fallback)}")
yield f"\nError with Openai API fallback: {str(e_fallback)}\n\nPlease check that you have set the OPENAI_API_KEY environment variable with a valid API key."
Expand Down
119 changes: 76 additions & 43 deletions api/websocket_wiki.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -489,22 +489,27 @@ async def handle_websocket_chat(websocket: WebSocket):
logger.warning("OPENAI_API_KEY not configured, but continuing with request")
# We'll let the OpenAIClient handle this and return an error message

# Initialize Openai client
model = OpenAIClient()
model_kwargs = {
"model": request.model,
"stream": True,
"temperature": model_config["temperature"]
}
# Only add top_p if it exists in the model config
if "top_p" in model_config:
model_kwargs["top_p"] = model_config["top_p"]

api_kwargs = model.convert_inputs_to_api_kwargs(
input=prompt,
model_kwargs=model_kwargs,
model_type=ModelType.LLM
)
use_responses_api = model_config.get("use_responses_api", False)

if not use_responses_api:
# Initialize Openai client (Chat Completions API)
model = OpenAIClient()
model_kwargs = {
"model": request.model,
"stream": True,
}
# Only add temperature/top_p if they exist in the model config
# (reasoning models like gpt-5.2-pro do not support these parameters)
if "temperature" in model_config:
model_kwargs["temperature"] = model_config["temperature"]
if "top_p" in model_config:
model_kwargs["top_p"] = model_config["top_p"]

api_kwargs = model.convert_inputs_to_api_kwargs(
input=prompt,
model_kwargs=model_kwargs,
model_type=ModelType.LLM
)
elif request.provider == "bedrock":
logger.info(f"Using AWS Bedrock with model: {request.model}")

Expand Down Expand Up @@ -620,18 +625,33 @@ async def handle_websocket_chat(websocket: WebSocket):
await websocket.close()
elif request.provider == "openai":
try:
# Get the response and handle it properly using the previously created api_kwargs
logger.info("Making Openai API call")
response = await model.acall(api_kwargs=api_kwargs, model_type=ModelType.LLM)
# Handle streaming response from Openai
async for chunk in response:
choices = getattr(chunk, "choices", [])
if len(choices) > 0:
delta = getattr(choices[0], "delta", None)
if delta is not None:
text = getattr(delta, "content", None)
if text is not None:
await websocket.send_text(text)
if use_responses_api:
# Use the Responses API for models that don't support Chat Completions
# (e.g. gpt-5.2-pro)
logger.info("Making OpenAI Responses API call")
from openai import OpenAI as OpenAISyncClient
responses_client = OpenAISyncClient()
stream = responses_client.responses.create(
model=request.model,
input=prompt,
stream=True,
)
for event in stream:
if event.type == "response.output_text.delta":
await websocket.send_text(event.delta)
else:
# Get the response and handle it properly using the previously created api_kwargs
logger.info("Making Openai API call")
response = await model.acall(api_kwargs=api_kwargs, model_type=ModelType.LLM)
# Handle streaming response from Openai
async for chunk in response:
choices = getattr(chunk, "choices", [])
if len(choices) > 0:
delta = getattr(choices[0], "delta", None)
if delta is not None:
text = getattr(delta, "content", None)
if text is not None:
await websocket.send_text(text)
# Explicitly close the WebSocket connection after the response is complete
await websocket.close()
except Exception as e_openai:
Expand Down Expand Up @@ -774,21 +794,34 @@ async def handle_websocket_chat(websocket: WebSocket):
await websocket.send_text(error_msg)
elif request.provider == "openai":
try:
# Create new api_kwargs with the simplified prompt
fallback_api_kwargs = model.convert_inputs_to_api_kwargs(
input=simplified_prompt,
model_kwargs=model_kwargs,
model_type=ModelType.LLM
)

# Get the response using the simplified prompt
logger.info("Making fallback Openai API call")
fallback_response = await model.acall(api_kwargs=fallback_api_kwargs, model_type=ModelType.LLM)

# Handle streaming fallback_response from Openai
async for chunk in fallback_response:
text = chunk if isinstance(chunk, str) else getattr(chunk, 'text', str(chunk))
await websocket.send_text(text)
if use_responses_api:
logger.info("Making fallback OpenAI Responses API call")
from openai import OpenAI as OpenAISyncClient
responses_client = OpenAISyncClient()
stream = responses_client.responses.create(
model=request.model,
input=simplified_prompt,
stream=True,
)
for event in stream:
if event.type == "response.output_text.delta":
await websocket.send_text(event.delta)
else:
# Create new api_kwargs with the simplified prompt
fallback_api_kwargs = model.convert_inputs_to_api_kwargs(
input=simplified_prompt,
model_kwargs=model_kwargs,
model_type=ModelType.LLM
)

# Get the response using the simplified prompt
logger.info("Making fallback Openai API call")
fallback_response = await model.acall(api_kwargs=fallback_api_kwargs, model_type=ModelType.LLM)

# Handle streaming fallback_response from Openai
async for chunk in fallback_response:
text = chunk if isinstance(chunk, str) else getattr(chunk, 'text', str(chunk))
await websocket.send_text(text)
except Exception as e_fallback:
logger.error(f"Error with Openai API fallback: {str(e_fallback)}")
error_msg = f"\nError with Openai API fallback: {str(e_fallback)}\n\nPlease check that you have set the OPENAI_API_KEY environment variable with a valid API key."
Expand Down