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Added
docs/reasoning-standardization.md
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-0
Modified
g4f/client/stubs.py
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XFEstudio/gpt4free
Standardize reasoning field to OpenAI format while maintaining input compatibility
Co-authored-by: hlohaus <983577+hlohaus@users.noreply.github.com>
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代码差异
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# Reasoning Field Standardization
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## Issue
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DeepSeek uses `"reasoning_content"` field while OpenAI uses `"reasoning"` field in their chat completion streaming responses. This inconsistency caused confusion about what field name to use in the g4f Interference API.
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## Decision
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**Standardized on OpenAI's `"reasoning"` field format for API output while maintaining input compatibility.**
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## Rationale
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1. **OpenAI Compatibility**: OpenAI is the de facto standard for chat completion APIs
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2. **Ecosystem Compatibility**: Most tools and libraries expect OpenAI format
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3. **Consistency**: Provides a unified output format regardless of the underlying provider
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4. **Backward Compatibility**: Input parsing continues to accept both formats
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## Implementation
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### Input Format Support (Unchanged)
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The system continues to accept both input formats in `OpenaiTemplate.py`:
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```python
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reasoning_content = choice.get("delta", {}).get("reasoning_content", choice.get("delta", {}).get("reasoning"))
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```
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### Output Format Standardization (Changed)
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- **Streaming Delta**: Uses `reasoning` field (OpenAI format)
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- **Non-streaming Message**: Uses `reasoning` field (OpenAI format)
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- **API Responses**: Should use standard OpenAI streaming format
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### Example Output Formats
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#### Streaming Response (OpenAI Compatible)
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```json
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{
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"id": "chatcmpl-example",
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"object": "chat.completion.chunk",
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"choices": [{
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"index": 0,
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"delta": {
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"role": "assistant",
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"reasoning": "I need to think about this step by step..."
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},
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"finish_reason": null
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}]
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}
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```
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#### Non-streaming Response
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```json
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{
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"choices": [{
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"message": {
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"role": "assistant",
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"content": "Here's my answer",
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"reasoning": "My reasoning process was..."
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}
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}]
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}
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```
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## Files Changed
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- `g4f/client/stubs.py`: Updated to use `reasoning` field instead of `reasoning_content`
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## Testing
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- Added comprehensive tests for format standardization
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- Verified input compatibility with both OpenAI and DeepSeek formats
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- Confirmed no regressions in existing functionality
@@ -141,7 +141,7 @@ class AudioResponseModel(BaseModel):
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class ChatCompletionMessage(BaseModel):
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role: str
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content: str
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reasoning_content: Optional[str] = None
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reasoning: Optional[str] = None
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tool_calls: list[ToolCallModel] = None
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audio: AudioResponseModel = None
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)
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if reasoning_content is not None and isinstance(reasoning_content, list):
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reasoning_content = "".join([str(content) for content in reasoning_content])
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return super().model_construct(role="assistant", content=content, **filter_none(tool_calls=tool_calls, reasoning_content=reasoning_content))
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return super().model_construct(role="assistant", content=content, **filter_none(tool_calls=tool_calls, reasoning=reasoning_content))
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@field_serializer('content')
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def serialize_content(self, content: str):
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class ChatCompletionDelta(BaseModel):
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role: str
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content: Optional[str]
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reasoning_content: Optional[str] = None
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reasoning: Optional[str] = None
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tool_calls: list[ToolCallModel] = None
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@classmethod
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def model_construct(cls, content: Optional[str]):
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if isinstance(content, Reasoning):
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return super().model_construct(role="reasoning", content=content, reasoning_content=str(content))
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return super().model_construct(role="assistant", content=None, reasoning=str(content))
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elif isinstance(content, ToolCalls):
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return super().model_construct(role="assistant", content=None, tool_calls=[
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ToolCallModel.model_construct(**tool_call) for tool_call in content.get_list()