add Tool Parser (#3272)

* add tool-parser

* add tool-parser

* add tool parser

* add tool parser

* fix

* add offline

* add offline

* fix

* parsers:tool&reasoning

* 修改tool parser名称·

* update

* fix reasoning-parser

* add requirements

* fix finish reason

* fix

* fix reasoning-parser

* fix

* fix

* fix

* fix

* fix

---------

Co-authored-by: zhuzixuan <zhuzixuan@baidu.com>

fastdeploy/reasoning/__init__.py

@@ -16,6 +16,7 @@
 
 from .abs_reasoning_parsers import ReasoningParser, ReasoningParserManager
 from .ernie_vl_reasoning_parsers import ErnieVLReasoningParser
+from .ernie_x1_reasoning_parsers import ErnieX1ReasoningParser
 from .qwen3_reasoning_parsers import Qwen3ReasoningParser
 
 __all__ = [
@@ -23,4 +24,5 @@ __all__ = [
     "ReasoningParserManager",
     "ErnieVLReasoningParser",
     "Qwen3ReasoningParser",
+    "ErnieX1ReasoningParser",
 ]

fastdeploy/reasoning/ernie_x1_reasoning_parsers.py

@@ -0,0 +1,208 @@
+# Copyright (c) 2025  PaddlePaddle Authors. All Rights Reserved.
+#
+#
+from collections.abc import Sequence
+from typing import Tuple
+
+from fastdeploy.entrypoints.openai.protocol import ChatCompletionRequest
+from fastdeploy.reasoning import ReasoningParser, ReasoningParserManager
+
+#
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+@ReasoningParserManager.register_module("ernie_x1")
+class ErnieX1ReasoningParser(ReasoningParser):
+    """
+    Reasoning parser for ernie_x1 model with stricter boundary checking.
+
+    This implementation follows the user's proposed approach:
+    1. For thinking content: waits for \n then checks for </think> tag
+    2. For response content: checks for <response> tag first, then waits for \n
+    3. Handles newlines in content more precisely
+    """
+
+    def __init__(self, tokenizer):
+        super().__init__(tokenizer)
+        self.think_end_token = "</think>"
+        self.response_start_token = "<response>"
+        self.response_end_token = "</response>"
+        self.tool_call_start_token = "<tool_call>"
+        self.tool_call_end_token = "</tool_call>"
+
+        if not self.model_tokenizer:
+            raise ValueError("The model tokenizer must be passed to the ReasoningParser constructor.")
+
+        self.think_end_token_id = self.vocab.get("</think>")
+        if self.think_end_token_id is None:
+            raise RuntimeError("Could not find think end token id in tokenizer vocabulary")
+
+    def extract_reasoning_content_streaming(
+        self,
+        previous_text: str,
+        current_text: str,
+        delta_text: str,
+        previous_token_ids: Sequence[int],
+        current_token_ids: Sequence[int],
+        delta_token_ids: Sequence[int],
+    ) -> tuple[str, str]:
+        """
+        根据用户需求实现的流式解析方法:
+        1. 初始内容都视为思考内容
+        2. 当遇到\n时检查后续是否是</think>
+        3. 思考结束后检查是<response>还是<tool_call>
+        4. 对于<response>内容，处理换行和结束标记
+        """
+        # 如果还在思考阶段
+        if not previous_text.endswith(self.think_end_token):
+            # 如果遇到\n后接</think>或直接遇到</think>，思考结束
+            if (previous_text.endswith("\n") and delta_text == self.think_end_token) or (
+                not previous_text.endswith("\n") and delta_text == self.think_end_token
+            ):
+                return "", ""
+            # 否则继续返回思考内容
+            return delta_text, ""
+
+        # 思考结束后检查是tool_call还是response
+        remaining_text = previous_text + delta_text
+        after_think = remaining_text[remaining_text.find(self.think_end_token) + len(self.think_end_token) :]
+
+        # 跳过think后的换行
+        after_think = after_think.lstrip("\n")
+
+        # 处理tool_call情况
+        if after_think.startswith(self.tool_call_start_token):
+            return "", ""
+
+        # 处理response情况
+        if after_think.startswith(self.response_start_token):
+            response_content = after_think[len(self.response_start_token) :]
+            # 跳过response后的换行
+            response_content = response_content.lstrip("\n")
+
+            # 检查response是否结束
+            if response_content.endswith(self.response_end_token):
+                return "", ""
+
+            # 返回response内容(使用delta_text确保流式输出)
+            return "", delta_text
+
+        # 默认情况不返回内容
+        return "", ""
+
+    def extract_reasoning_content(self, model_output: str, request: ChatCompletionRequest) -> Tuple[str, str]:
+        """
+        Batch version of the enhanced parser.
+        Modified to preserve newlines in both reasoning and response content,
+        only removing the single newline before closing tags.
+        """
+        reasoning_content = ""
+        response_content = ""
+
+        think_end_pos = model_output.find(self.think_end_token)
+        if think_end_pos != -1:
+            # Extract thinking content - only remove the last newline before </think>
+            reasoning_content = model_output[:think_end_pos]
+            if think_end_pos > 0 and reasoning_content[-1] == "\n":
+                reasoning_content = reasoning_content[:-1]
+
+            remaining = model_output[think_end_pos + len(self.think_end_token) :]
+
+            # Skip newlines after </think>
+            remaining = remaining.lstrip("\n")
+
+            # Check for response or tool_call
+            if remaining.startswith(self.response_start_token):
+                response_pos = len(self.response_start_token)
+                remaining = remaining[response_pos:].lstrip("\n")
+                response_end_pos = remaining.find(self.response_end_token)
+                if response_end_pos != -1:
+                    # Only strip the last newline before </response>, not all
+                    if response_end_pos > 0 and remaining[response_end_pos - 1] == "\n":
+                        response_content = remaining[: response_end_pos - 1]
+                    else:
+                        response_content = remaining[:response_end_pos]
+                else:
+                    # If no </response> found, return the rest as response content
+                    response_content = remaining
+            elif remaining.startswith(self.tool_call_start_token):
+                pass  # No response content
+        else:
+            # No thinking content found, return the whole input as reasoning
+            reasoning_content = model_output
+            response_content = ""
+        return reasoning_content, response_content
+
+
+import unittest
+from unittest.mock import MagicMock
+
+
+class TestErnieX1ReasoningParser(unittest.TestCase):
+    def setUp(self):
+        self.tokenizer = MagicMock()
+        self.tokenizer.vocab = {
+            "\n</think>\n\n": 1001,
+            "<response>\n": 1002,
+            "\n</response>\n": 1003,
+            "<tool_call>\n": 1004,
+            "\n</tool_call>\n": 1005,
+        }
+        self.parser = ErnieX1ReasoningParser(self.tokenizer)
+
+    def test_streaming_with_think_and_response(self):
+        # 测试标准情况：\n</think>\n\n<response>\ncontent\n</response>\n
+        prev_text = "thinking"
+        delta_text = "\n</think>\n\n<response>\nanswer\n</response>\n"
+        result = self.parser.extract_reasoning_content_streaming(prev_text, "", delta_text, [], [], [])
+        self.assertEqual(result, ("thinking", "answer"))
+
+    def test_streaming_with_think_and_tool_call(self):
+        # 测试tool_call情况
+        prev_text = "thinking"
+        delta_text = "\n</think>\n\n<tool_call>\ndetails\n</tool_call>\n"
+        result = self.parser.extract_reasoning_content_streaming(prev_text, "", delta_text, [], [], [])
+        self.assertEqual(result, ("thinking", ""))
+
+    def test_streaming_with_think_no_newline(self):
+        # 测试没有前置换行的情况
+        prev_text = "thinking"
+        delta_text = "</think>\n\n<response>answer</response>\n"
+        result = self.parser.extract_reasoning_content_streaming(prev_text, "", delta_text, [], [], [])
+        self.assertEqual(result, ("thinking", "answer"))
+
+    def test_streaming_response_without_leading_newline(self):
+        # 测试response内容没有前置换行
+        prev_text = "thinking\n</think>\n\n"
+        delta_text = "<response>answer\n</response>\n"
+        result = self.parser.extract_reasoning_content_streaming(prev_text, "", delta_text, [1001], [], [])
+        self.assertEqual(result, ("thinking", "answer"))
+
+    def test_streaming_response_with_middle_newline(self):
+        # 测试response内容中间的换行符
+        prev_text = "thinking\n</think>\n\n<response>\n"
+        delta_text = "line1\nline2\n</response>\n"
+        result = self.parser.extract_reasoning_content_streaming(prev_text, "", delta_text, [1001], [], [])
+        self.assertEqual(result, ("thinking", "line1\nline2"))
+
+    def test_streaming_partial_response(self):
+        # 测试不完整的response流式输出
+        prev_text = "thinking\n</think>\n\n<response>\n"
+        delta_text = "partial answer"
+        result = self.parser.extract_reasoning_content_streaming(prev_text, "", delta_text, [1001], [], [])
+        self.assertEqual(result, ("thinking", "partial answer"))
+
+
+if __name__ == "__main__":
+    unittest.main()