Summary of Changes

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This pull request introduces a significant enhancement to the SglangEngine by adding robust parsing capabilities for Large Language Model responses. The core purpose is to enable the extraction and clear separation of a model's internal reasoning process, typically indicated by specific tags, from its direct answer. This improvement provides a more structured and transparent way to handle LLM outputs, making both the thought process and the final result readily accessible within the ChatMessage object.

Highlights

• Reasoning Content Extraction: A new utility function, extract_reasoning_content, has been introduced in sglang_engine.py. This function is designed to parse LLM responses, specifically looking for content enclosed within <think> and </think> tags to separate the model's reasoning from its final answer.
• SglangEngine Integration: The _create_chat_completion_response method within the SglangEngine has been updated to utilize the new extract_reasoning_content parser. This ensures that responses from 'thinking models' are processed to distinguish between the reasoning steps and the ultimate output.
• Enhanced ChatMessage Structure: The ChatMessage object now includes a new field, reasoning_content. This allows the extracted reasoning process to be stored separately from the final content of the message, providing a more granular and informative representation of the LLM's output.

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