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https://github.com/PaddlePaddle/FastDeploy.git
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371fb3f853
* add tool parser * add x1 enable_thinking * restart ci * fix vl reasoning parser * modify call style * modify call style * add offline enablethinking * fix completion * fix * fix unit test * fix unit test * fix unit test * fix vl reasoning parser * fix vl reasoning parser
348 lines
15 KiB
Python
348 lines
15 KiB
Python
# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import json
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import re
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import uuid
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from collections.abc import Sequence
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from typing import Union
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import partial_json_parser
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def random_tool_call_id() -> str:
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"""Generate a random tool call ID"""
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return f"chatcmpl-tool-{str(uuid.uuid4().hex)}"
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from fastdeploy.entrypoints.openai.protocol import (
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ChatCompletionRequest,
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DeltaFunctionCall,
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DeltaMessage,
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DeltaToolCall,
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ExtractedToolCallInformation,
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FunctionCall,
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ToolCall,
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)
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from fastdeploy.entrypoints.openai.tool_parsers.abstract_tool_parser import (
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ToolParser,
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ToolParserManager,
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)
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from fastdeploy.utils import data_processor_logger
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@ToolParserManager.register_module("ernie_x1")
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class ErnieX1ToolParser(ToolParser):
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"""
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Tool parser for Ernie model version 4.5.1.
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This parser handles tool calls with newline formats.
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"""
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def __init__(self, tokenizer):
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super().__init__(tokenizer)
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self.prev_tool_call_arr: list[dict] = []
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self.current_tool_id: int = -1
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self.current_tool_name_sent: bool = False
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self.streamed_args_for_tool: list[str] = [] # map what has been streamed for each tool so far to a list
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self.buffer: str = "" # buffer for accumulating unprocessed streaming content
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self.bracket_counts: dict = {"total_l": 0, "total_r": 0} # track bracket counts in streamed deltas
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self.tool_call_start_token: str = "<tool_call>"
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self.tool_call_end_token: str = "</tool_call>"
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self.tool_call_start_token_id = self.vocab.get(self.tool_call_start_token)
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self.tool_call_end_token_id = self.vocab.get(self.tool_call_end_token)
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if self.tool_call_start_token_id is None or self.tool_call_end_token_id is None:
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raise RuntimeError(
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"Hermes 2 Pro Tool parser could not locate tool call start/end " "tokens in the tokenizer!"
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)
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if not self.model_tokenizer:
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raise ValueError(
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"The model tokenizer must be passed to the ToolCallParser constructor during construction."
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)
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def extract_tool_calls(self, model_output: str, request: ChatCompletionRequest) -> ExtractedToolCallInformation:
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"""
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Extract the tool calls from a complete model response.
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Supports XML-style formats with newlines:
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- XML format: <think>\n...\n</think>\n\n\n<tool_call>\n{...}\n</tool_call>\n...
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Handles boundary cases:
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1. Only name and partial arguments: {"name": "get_weather", "arguments": {"location": "北京"
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2. Only partial name: {"name": "get_we
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3. Only name and arguments field without content: {"name": "get_weather", "argume
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"""
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try:
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tool_calls = []
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# Check for invalid <response> tags before tool calls
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if re.search(r"<response>[\s\S]*?</response>\s*(?=<tool_call>)", model_output):
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data_processor_logger.error("Invalid format: <response> tags found before <tool_call>")
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return ExtractedToolCallInformation(tools_called=False, content=model_output)
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function_call_arr = []
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remaining_text = model_output
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while True:
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# 查找下一个tool_call块
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tool_call_pos = remaining_text.find("<tool_call>")
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if tool_call_pos == -1:
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break
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# 提取tool_call开始位置后的内容
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tool_content_start = tool_call_pos + len("<tool_call>")
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tool_content_end = remaining_text.find("</tool_call>", tool_content_start)
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tool_json = ""
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if tool_content_end == -1:
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# 处理未闭合的tool_call块（截断情况）
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tool_json = remaining_text[tool_content_start:].strip()
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remaining_text = "" # 没有更多内容需要处理
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else:
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# 处理完整的tool_call块
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tool_json = remaining_text[tool_content_start:tool_content_end].strip()
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remaining_text = remaining_text[tool_content_end + len("</tool_call>") :]
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if not tool_json:
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continue
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# 处理JSON内容
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tool_json = tool_json.strip()
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if not tool_json.startswith("{"):
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tool_json = "{" + tool_json
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if not tool_json.endswith("}"):
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tool_json = tool_json + "}"
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try:
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# 首先尝试标准JSON解析
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try:
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tool_data = json.loads(tool_json)
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if isinstance(tool_data, dict) and "name" in tool_data and "arguments" in tool_data:
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function_call_arr.append(
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{
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"name": tool_data["name"],
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"arguments": tool_data["arguments"],
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"_is_complete": True, # 明确标记为完整解析
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}
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)
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continue
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except json.JSONDecodeError:
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pass
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# 标准解析失败时尝试partial_json_parser
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from partial_json_parser.core.options import Allow
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try:
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tool_data = {}
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flags = Allow.ALL & ~Allow.STR
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# 解析name字段
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name_match = re.search(r'"name"\s*:\s*"([^"]*)"', tool_json)
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if name_match:
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tool_data["name"] = name_match.group(1)
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# 解析arguments字段
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args_match = re.search(r'"arguments"\s*:\s*(\{.*)', tool_json)
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if args_match:
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try:
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tool_data["arguments"] = partial_json_parser.loads(args_match.group(1), flags=flags)
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except:
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tool_data["arguments"] = None
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if isinstance(tool_data, dict):
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function_call_arr.append(
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{
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"name": tool_data.get("name", ""),
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"arguments": tool_data.get("arguments", {}),
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"_is_partial": True, # 标记为部分解析
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}
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)
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except Exception as e:
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data_processor_logger.debug(f"Failed to parse tool call: {str(e)}")
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continue
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except Exception as e:
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data_processor_logger.debug(f"Failed to parse tool call: {str(e)}")
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continue
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if not function_call_arr:
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data_processor_logger.error("No valid tool calls found")
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return ExtractedToolCallInformation(tools_called=False, content=model_output)
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tool_calls = []
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all_complete = True # 初始设为True，只要有一个不完整就变为False
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for tool_call in function_call_arr:
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# 记录工具调用解析状态
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is_complete = tool_call.get("_is_complete", False)
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is_partial = tool_call.get("_is_partial", False)
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# 只要有一个不完整就认为整体不完整
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if not is_complete or is_partial:
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all_complete = False
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# 处理参数序列化
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tool_args = tool_call.get("arguments", {})
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if not isinstance(tool_args, dict):
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tool_args = {}
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try:
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args_str = json.dumps(tool_args, ensure_ascii=False) if tool_args else "{}"
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except:
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args_str = "{}"
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tool_calls.append(
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ToolCall(
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type="function",
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id=random_tool_call_id(),
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function=FunctionCall(
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name=tool_call.get("name", ""),
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arguments=args_str,
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),
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)
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)
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# 只有当所有工具调用都明确标记为complete时才返回tools_called=True
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return ExtractedToolCallInformation(
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tools_called=all_complete, tool_calls=tool_calls if tool_calls else None, content=""
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)
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except Exception as e:
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data_processor_logger.error(f"Error in extracting tool call from response: {str(e)}")
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return ExtractedToolCallInformation(tools_called=False, tool_calls=None, content=model_output)
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def extract_tool_calls_streaming(
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self,
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previous_text: str,
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current_text: str,
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delta_text: str,
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previous_token_ids: Sequence[int],
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current_token_ids: Sequence[int],
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delta_token_ids: Sequence[int],
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request: dict,
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) -> Union[DeltaMessage, None]:
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if self.tool_call_start_token_id not in current_token_ids:
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return DeltaMessage(content=delta_text)
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# 忽略空chunk
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if len(delta_text.strip()) == 0:
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return None
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try:
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delta = None
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# 使用buffer累积delta_text内容
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self.buffer += delta_text
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# 处理增量中的新tool_call开始
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if "<tool_call>" in delta_text:
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self.current_tool_id = (
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max(self.current_tool_id, 0) if self.current_tool_id == -1 else self.current_tool_id + 1
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)
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self.current_tool_name_sent = False
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if len(self.streamed_args_for_tool) <= self.current_tool_id:
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self.streamed_args_for_tool.append("")
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data_processor_logger.debug(f"New tool call started with ID: {self.current_tool_id}")
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# 1. 尝试解析name字段
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if not self.current_tool_name_sent and '"name"' in self.buffer:
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name_match = re.search(r'"name"\s*:\s*"([^"]*)"', self.buffer)
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if name_match:
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name = name_match.group(1)
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if name:
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delta = DeltaMessage(
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tool_calls=[
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DeltaToolCall(
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index=self.current_tool_id,
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type="function",
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id=random_tool_call_id(),
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function=DeltaFunctionCall(name=name).model_dump(exclude_none=True),
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)
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]
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)
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# 删除已处理的name部分
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self.buffer = self.buffer[name_match.end() :]
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self.current_tool_name_sent = True
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return delta
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# 2. 尝试解析arguments字段
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if '"arguments"' in self.buffer:
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args_match = re.search(r'"arguments"\s*:\s*(\{.*)', self.buffer)
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if args_match:
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args_content = args_match.group(1)
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try:
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# 检查是否到达arguments结尾(括号完全匹配)
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if "}}" in args_content:
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# 逐个字符检查括号匹配状态
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matched_pos = -1
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for i, ch in enumerate(delta_text):
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if ch == "{":
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self.bracket_counts["total_l"] += 1
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elif ch == "}":
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self.bracket_counts["total_r"] += 1
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if self.bracket_counts["total_l"] == self.bracket_counts["total_r"]: # 括号完全匹配
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matched_pos = i
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break
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if matched_pos >= 0:
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# 找到匹配点，清理buffer并返回
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truncate_text = delta_text[: matched_pos + 1]
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delta = DeltaMessage(
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tool_calls=[
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DeltaToolCall(
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index=self.current_tool_id,
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function=DeltaFunctionCall(arguments=truncate_text).model_dump(
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exclude_none=True
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),
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)
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]
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)
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self.buffer = self.buffer[args_match.end() :]
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return delta
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else:
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# 没有完全匹配，继续累积
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return None
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else:
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# 增量返回当前可解析的部分
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for ch in delta_text:
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if ch == "{":
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self.bracket_counts["total_l"] += 1
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elif ch == "}":
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self.bracket_counts["total_r"] += 1
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delta = DeltaMessage(
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tool_calls=[
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DeltaToolCall(
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index=self.current_tool_id,
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function=DeltaFunctionCall(arguments=delta_text).model_dump(exclude_none=True),
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)
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]
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)
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return delta
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except Exception as e:
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data_processor_logger.error(f"Error in streaming tool call extraction: {str(e)}")
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return None
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if "</tool_call>" in self.buffer:
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end_pos = self.buffer.find("</tool_call>")
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self.buffer = self.buffer[end_pos + len("</tool_call>") :]
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# 完成当前工具调用处理
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self.streamed_args_for_tool.append("")
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return delta
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except Exception as e:
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data_processor_logger.error(f"Error in streaming tool call extraction: {str(e)}")
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return None
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