from typing import Dict, Optional, Tuple, Type
class StreamingParseResult:
"""Result of streaming incremental parsing."""
def __init__(self, normal_text: str = "", reasoning_text: str = ""):
self.normal_text = normal_text
self.reasoning_text = reasoning_text
class BaseReasoningFormatDetector:
"""Base class providing two sets of interfaces: one-time and streaming incremental."""
def __init__(
self,
think_start_token: str,
think_end_token: str,
force_reasoning: bool = False,
stream_reasoning: bool = True,
):
self.think_start_token = think_start_token
self.think_end_token = think_end_token
self._in_reasoning = force_reasoning
self.stream_reasoning = stream_reasoning
self._buffer = ""
self.stripped_think_start = False
def detect_and_parse(self, text: str) -> StreamingParseResult:
"""
One-time parsing: Detects and parses reasoning sections in the provided text.
Returns both reasoning content and normal text separately.
"""
in_reasoning = self._in_reasoning or self.think_start_token in text
if not in_reasoning:
return StreamingParseResult(normal_text=text)
# The text is considered to be in a reasoning block.
processed_text = text.replace(self.think_start_token, "").strip()
if self.think_end_token not in processed_text:
# Assume reasoning was truncated before `` token
return StreamingParseResult(reasoning_text=processed_text)
# Extract reasoning content
splits = processed_text.split(self.think_end_token, maxsplit=1)
reasoning_text = splits[0]
normal_text = splits[1].strip()
return StreamingParseResult(
normal_text=normal_text, reasoning_text=reasoning_text
)
def parse_streaming_increment(self, new_text: str) -> StreamingParseResult:
"""
Streaming incremental parsing for reasoning content.
Handles partial reasoning tags and content.
If stream_reasoning is False:
Accumulates reasoning content until the end tag is found
If stream_reasoning is True:
Streams reasoning content as it arrives
"""
self._buffer += new_text
current_text = self._buffer
# If the current text is a prefix of the think token, keep buffering
if any(
token.startswith(current_text) and token != current_text
for token in [self.think_start_token, self.think_end_token]
):
return StreamingParseResult()
# Strip `` token if present
if not self.stripped_think_start and self.think_start_token in current_text:
current_text = current_text.replace(self.think_start_token, "")
self.stripped_think_start = True
self._in_reasoning = True
# Handle end of reasoning block
if self._in_reasoning and self.think_end_token in current_text:
end_idx = current_text.find(self.think_end_token)
reasoning_text = current_text[:end_idx]
self._buffer = ""
self._in_reasoning = False
normal_text = current_text[end_idx + len(self.think_end_token) :]
return StreamingParseResult(
normal_text=normal_text, reasoning_text=reasoning_text.rstrip()
)
# Continue with reasoning content
if self._in_reasoning:
if self.stream_reasoning:
# Stream the content immediately
self._buffer = ""
return StreamingParseResult(reasoning_text=current_text)
else:
return StreamingParseResult()
# If we're not in a reasoning block return as normal text
if not self._in_reasoning:
self._buffer = ""
return StreamingParseResult(normal_text=current_text)
return StreamingParseResult()
class DeepSeekR1Detector(BaseReasoningFormatDetector):
"""
Detector for DeepSeek-R1 model.
Assumes reasoning format:
()*(.*)
Returns all the text before the tag as `reasoning_text`
and the rest of the text as `normal_text`.
Supported models:
- DeepSeek-R1: Always generates thinking content without start tag
- DeepSeek-R1-0528: Generates thinking content with start tag
Format patterns:
- DeepSeek-R1: "I need to think about this...The answer is 42."
- DeepSeek-R1-0528: "I need to think about this...The answer is 42."
Args:
stream_reasoning (bool): If False, accumulates reasoning content until the end tag.
If True, streams reasoning content as it arrives.
"""
def __init__(self, stream_reasoning: bool = True, force_reasoning: bool = True):
# DeepSeek-R1 is assumed to be reasoning until `` token
super().__init__(
"",
"",
force_reasoning=True,
stream_reasoning=stream_reasoning,
)
# https://github.com/sgl-project/sglang/pull/3202#discussion_r1950153599
class Qwen3Detector(BaseReasoningFormatDetector):
"""
Detector for Qwen3 models (e.g., Qwen/Qwen3-235B-A22B).
Assumes reasoning format:
()*(.*)
Qwen3 models released before 07/2025 supports switching between thinking mode and normal
mode using `enable_thinking` parameter in the request parameter.
- enable_thinking=True: "reasoning contentThe answer is 42."
- enable_thinking=False: "The answer is 42." (no thinking tokens)
Args:
stream_reasoning (bool): If False, accumulates reasoning content until the end tag.
If True, streams reasoning content as it arrives.
"""
def __init__(self, stream_reasoning: bool = True, force_reasoning: bool = False):
super().__init__(
"",
"",
force_reasoning=force_reasoning,
stream_reasoning=stream_reasoning,
)
class KimiDetector(BaseReasoningFormatDetector):
"""
Detector for Kimi Thinking model.
Assumes reasoning format:
◁think▷*(.*)◁/think▷
Returns all the text before the ◁/think▷ tag as `reasoning_text`
and the rest of the text as `normal_text`.
"""
def __init__(self, stream_reasoning: bool = True, force_reasoning: bool = False):
super().__init__(
"◁think▷",
"◁/think▷",
force_reasoning=False,
stream_reasoning=stream_reasoning,
)
class ReasoningParser:
"""
Parser that handles both streaming and non-streaming scenarios for extracting
reasoning content from model outputs.
Args:
model_type (str): Type of model to parse reasoning from
stream_reasoning (bool): If False, accumulates reasoning content until complete.
If True, streams reasoning content as it arrives.
"""
DetectorMap: Dict[str, Type[BaseReasoningFormatDetector]] = {
"deepseek-r1": DeepSeekR1Detector,
"qwen3": Qwen3Detector,
"qwen3-thinking": Qwen3Detector,
"glm45": Qwen3Detector,
"kimi": KimiDetector,
"step3": DeepSeekR1Detector,
}
def __init__(
self,
model_type: Optional[str] = None,
stream_reasoning: bool = True,
force_reasoning: bool = False,
):
if not model_type:
raise ValueError("Model type must be specified")
detector_class = self.DetectorMap.get(model_type.lower())
if not detector_class:
raise ValueError(f"Unsupported model type: {model_type}")
if model_type.lower() == "qwen3-thinking":
force_reasoning = True
self.detector = detector_class(
stream_reasoning=stream_reasoning, force_reasoning=force_reasoning
)
def parse_non_stream(self, full_text: str) -> Tuple[str, str]:
"""Non-streaming call: one-time parsing"""
ret = self.detector.detect_and_parse(full_text)
return ret.reasoning_text, ret.normal_text
def parse_stream_chunk(self, chunk_text: str) -> Tuple[str, str]:
"""Streaming call: incremental parsing"""
ret = self.detector.parse_streaming_increment(chunk_text)
return ret.reasoning_text, ret.normal_text