feat: The OpenAI interface supports the thought process

[shaohuzhang1]

feat: The OpenAI interface supports the thought process

[shaohuzhang1]

Regularity Checks:

1. Initialization of other_params:

   • Both methods use if other_params is None to initialize it. This is good practice as it checks whether the parameter is provided and provides default values accordingly.

2. Stream Response (to_stream_chunk_response):

   • The choices list remains unchanged, except for adding an additional field: reasoning_content with a default value if not provided in other_params.

Potential Issues:

1. Type Hinting:

   • Ensure that all type hints within the function definitions match their respective types. For instance, _status should be of type int.
   • Consider using more specific annotations like Union[None, Dict[str, Any]] instead of just Dict[str, Any].

2. Datetime Module Usage:

   • In Python 3.x, you can import datetime directly from the module without specifying datetime. at the beginning. This aligns well with PEP 8 guidelines but could vary slightly based on Python versions used.

Optimization Suggestions:

1. Use Type Annotations for Methods:

   from typing import Set
   
   class OpenaiToResponse(BaseToResponse):
       def to_block_response(
               self,
               chat_id: str,
               chat_record_id: str,
               content: str,
               is_end: bool,
               completion_tokens: int,
               prompt_tokens: int,
               other_params: dict = None,  # Default is an empty dict
               _status: int = status.HTTP_200_OK
       ) -> JsonResponse:
           pass
   
       def to_stream_chunk_response(
               self,
               chat_id: str,
               chat_record_id: str,
               node_id: str,
               up_node_id_list: Set[str],  # Assuming this expects a set of strings
               content: str,
               is_end: bool,
               completion_tokens: int,
               prompt_tokens: int,
               other_params: dict = None
       ) -> None:
           pass

2. Consider Using Named Tuples or Data Classes:

from dataclasses import dataclass

@dataclass
class CompletionData:
    id: str
    model: str
    object: str
    created: float
    choices: List[Choice]
    usage: 'CompletionUsage'

@dataclass
class Choice:
    delta: ChoiceDelta
    finish_reason: Union[None, str]

@dataclass
class ChoiceDelta:
    content: str
    reasoning_content: str

@dataclass
class CompletionUsage:
    completion_tokens: int
    prompt_tokens: int

By implementing these suggestions, code will become more robust, maintainable, and efficient while adhering to best practices.

[f2c-ci-robot]

[APPROVALNOTIFIER] This PR is NOT APPROVED

This pull-request has been approved by:

The full list of commands accepted by this bot can be found here.

Details

Needs approval from an approver in each of these files:

• OWNERS

Approvers can indicate their approval by writing /approve in a comment
Approvers can cancel approval by writing /approve cancel in a comment

[f2c-ci-robot]

[APPROVALNOTIFIER] This PR is NOT APPROVED

This pull-request has been approved by:

The full list of commands accepted by this bot can be found here.

Details

Needs approval from an approver in each of these files:

• OWNERS

Approvers can indicate their approval by writing /approve in a comment
Approvers can cancel approval by writing /approve cancel in a comment