v1.0

reasoning/qwen3_reasoning_parser.py

@@ -0,0 +1,67 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+
+from vllm.entrypoints.openai.protocol import ChatCompletionRequest, ResponsesRequest
+from vllm.reasoning.basic_parsers import BaseThinkingReasoningParser
+
+
+class Qwen3ReasoningParser(BaseThinkingReasoningParser):
+    """
+    Reasoning parser for the Qwen3 model.
+
+    The Qwen3 model uses <think>...</think> tokens to denote reasoning text
+    within its output. The model provides a strict switch to disable reasoning
+    output via the 'enable_thinking=False' parameter. This parser extracts the
+    reasoning content enclosed by <think> and </think> tokens from the model's
+    output.
+    """
+
+    @property
+    def start_token(self) -> str:
+        """The token that starts reasoning content."""
+        return "<think>"
+
+    @property
+    def end_token(self) -> str:
+        """The token that ends reasoning content."""
+        return "</think>"
+
+    def extract_reasoning(
+        self, model_output: str, request: ChatCompletionRequest | ResponsesRequest
+    ) -> tuple[str | None, str | None]:
+        """
+        Extract reasoning content from the model output.
+
+        Qwen3 has stricter requirements - it needs both start and end tokens
+        to be present, unlike other models that work with just the end token.
+
+        For text <think>abc</think>xyz:
+        - 'abc' goes to reasoning
+        - 'xyz' goes to content
+
+        Returns:
+            tuple[Optional[str], Optional[str]]: reasoning content and content
+        """
+
+        # Check if the model output contains both <think> and </think> tokens.
+        if self.start_token not in model_output or self.end_token not in model_output:
+            return None, model_output
+
+        # Check if the <think> is present in the model output, remove it
+        # if it is present.
+        model_output_parts = model_output.partition(self.start_token)
+        model_output = (
+            model_output_parts[2] if model_output_parts[1] else model_output_parts[0]
+        )
+
+        # Check if the model output contains the </think> tokens.
+        # If the end token is not found, return the model output as is.
+        if self.end_token not in model_output:
+            return None, model_output
+
+        # Extract reasoning content from the model output.
+        reasoning, _, content = model_output.partition(self.end_token)
+
+        final_content = content or None
+        return reasoning, final_content