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refactor: removed subclass override when the base class should be resolving the model name

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refactor: always disable "stream"
This commit is contained in:
Fahad
2025-10-04 10:35:32 +04:00
parent d184024820
commit 06d7701cc3
17 changed files with 210 additions and 260 deletions
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26
providers/openai_compatible.py
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@@ -462,10 +462,11 @@ class OpenAICompatibleProvider(ModelProvider):
Args:
prompt: User prompt to send to the model
model_name: Name of the model to use
model_name: Canonical model name or its alias
system_prompt: Optional system prompt for model behavior
temperature: Sampling temperature
max_output_tokens: Maximum tokens to generate
images: Optional list of image paths or data URLs to include with the prompt (for vision models)
**kwargs: Additional provider-specific parameters
Returns:
@@ -497,6 +498,9 @@ class OpenAICompatibleProvider(ModelProvider):
# Validate parameters with the effective temperature
self.validate_parameters(model_name, effective_temperature)
# Resolve to canonical model name
resolved_model = self._resolve_model_name(model_name)
# Prepare messages
messages = []
if system_prompt:
@@ -518,7 +522,7 @@ class OpenAICompatibleProvider(ModelProvider):
# Continue with other images and text
continue
elif images and (not capabilities or not capabilities.supports_images):
logging.warning(f"Model {model_name} does not support images, ignoring {len(images)} image(s)")
logging.warning(f"Model {resolved_model} does not support images, ignoring {len(images)} image(s)")
# Add user message
if len(user_content) == 1:
@@ -529,14 +533,14 @@ class OpenAICompatibleProvider(ModelProvider):
messages.append({"role": "user", "content": user_content})
# Prepare completion parameters
# Always disable streaming for OpenRouter
# MCP doesn't use streaming, and this avoids issues with O3 model access
completion_params = {
"model": model_name,
"model": resolved_model,
"messages": messages,
"stream": False,
}
# Check model capabilities once to determine parameter support
resolved_model = self._resolve_model_name(model_name)
# Use the effective temperature we calculated earlier
supports_sampling = effective_temperature is not None
@@ -553,7 +557,7 @@ class OpenAICompatibleProvider(ModelProvider):
for key, value in kwargs.items():
if key in ["top_p", "frequency_penalty", "presence_penalty", "seed", "stop", "stream"]:
# Reasoning models (those that don't support temperature) also don't support these parameters
if not supports_sampling and key in ["top_p", "frequency_penalty", "presence_penalty"]:
if not supports_sampling and key in ["top_p", "frequency_penalty", "presence_penalty", "stream"]:
continue # Skip unsupported parameters for reasoning models
completion_params[key] = value
@@ -585,7 +589,7 @@ class OpenAICompatibleProvider(ModelProvider):
return ModelResponse(
content=content,
usage=usage,
model_name=model_name,
model_name=resolved_model,
friendly_name=self.FRIENDLY_NAME,
provider=self.get_provider_type(),
metadata={
@@ -601,12 +605,12 @@ class OpenAICompatibleProvider(ModelProvider):
operation=_attempt,
max_attempts=max_retries,
delays=retry_delays,
log_prefix=f"{self.FRIENDLY_NAME} API ({model_name})",
log_prefix=f"{self.FRIENDLY_NAME} API ({resolved_model})",
)
except Exception as exc:
attempts = max(attempt_counter["value"], 1)
error_msg = (
f"{self.FRIENDLY_NAME} API error for model {model_name} after {attempts} attempt"
f"{self.FRIENDLY_NAME} API error for model {resolved_model} after {attempts} attempt"
f"{'s' if attempts > 1 else ''}: {exc}"
)
logging.error(error_msg)
@@ -618,7 +622,7 @@ class OpenAICompatibleProvider(ModelProvider):
For proxy providers, this may use generic capabilities.
Args:
model_name: Model to validate for
model_name: Canonical model name or its alias
temperature: Temperature to validate
**kwargs: Additional parameters to validate
"""