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add tool parser

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luukunn
2025-08-13 01:06:55 +08:00
committed by luukunn
parent 132a8ef425
commit bbd50c6717
23 changed files with 1050 additions and 32 deletions
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fastdeploy/entrypoints/openai/tool_parsers/ernie_x1_tool_parser.py Normal file
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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
import json
import re
import uuid
from collections.abc import Sequence
from typing import Union
import partial_json_parser
def random_tool_call_id() -> str:
"""Generate a random tool call ID"""
return f"chatcmpl-tool-{str(uuid.uuid4().hex)}"
from fastdeploy.entrypoints.openai.protocol import (
ChatCompletionRequest,
DeltaFunctionCall,
DeltaMessage,
DeltaToolCall,
ExtractedToolCallInformation,
FunctionCall,
ToolCall,
)
from fastdeploy.entrypoints.openai.tool_parsers.abstract_tool_parser import (
ToolParser,
ToolParserManager,
)
from fastdeploy.utils import data_processor_logger
@ToolParserManager.register_module("ernie_x1")
class ErnieX1ToolParser(ToolParser):
"""
Tool parser for Ernie model version 4.5.1.
This parser handles tool calls with newline formats.
"""
def __init__(self, tokenizer):
super().__init__(tokenizer)
self.prev_tool_call_arr: list[dict] = []
self.current_tool_id: int = -1
self.current_tool_name_sent: bool = False
self.streamed_args_for_tool: list[str] = [] # map what has been streamed for each tool so far to a list
self.buffer: str = "" # buffer for accumulating unprocessed streaming content
if not self.model_tokenizer:
raise ValueError(
"The model tokenizer must be passed to the ToolCallParser constructor during construction."
)
def extract_tool_calls(self, model_output: str, request: ChatCompletionRequest) -> ExtractedToolCallInformation:
"""
Extract the tool calls from a complete model response.
Supports XML-style formats with newlines:
- XML format: <think>\n...\n</think>\n\n\n<tool_call>\n{...}\n</tool_call>\n...
Handles boundary cases:
1. Only name and partial arguments: {"name": "get_weather", "arguments": {"location": "北京"
2. Only partial name: {"name": "get_we
3. Only name and arguments field without content: {"name": "get_weather", "argume
"""
try:
tool_calls = []
# Check for invalid <response> tags before tool calls
if re.search(r"<response>[\s\S]*?</response>\s*(?=<tool_call>)", model_output):
data_processor_logger.error("Invalid format: <response> tags found before <tool_call>")
return ExtractedToolCallInformation(tools_called=False, content=model_output)
function_call_arr = []
remaining_text = model_output
while True:
# 查找下一个tool_call块
tool_call_pos = remaining_text.find("<tool_call>")
if tool_call_pos == -1:
break
# 提取tool_call开始位置后的内容
tool_content_start = tool_call_pos + len("<tool_call>")
tool_content_end = remaining_text.find("</tool_call>", tool_content_start)
tool_json = ""
if tool_content_end == -1:
# 处理未闭合的tool_call块（截断情况）
tool_json = remaining_text[tool_content_start:].strip()
remaining_text = "" # 没有更多内容需要处理
else:
# 处理完整的tool_call块
tool_json = remaining_text[tool_content_start:tool_content_end].strip()
remaining_text = remaining_text[tool_content_end + len("</tool_call>") :]
if not tool_json:
continue
# 处理JSON内容
tool_json = tool_json.strip()
if not tool_json.startswith("{"):
tool_json = "{" + tool_json
if not tool_json.endswith("}"):
tool_json = tool_json + "}"
try:
# 首先尝试标准JSON解析
try:
tool_data = json.loads(tool_json)
if isinstance(tool_data, dict) and "name" in tool_data and "arguments" in tool_data:
function_call_arr.append(
{
"name": tool_data["name"],
"arguments": tool_data["arguments"],
"_is_complete": True, # 明确标记为完整解析
}
)
continue
except json.JSONDecodeError:
pass
# 标准解析失败时尝试partial_json_parser
from partial_json_parser.core.options import Allow
try:
tool_data = {}
flags = Allow.ALL & ~Allow.STR
# 解析name字段
name_match = re.search(r'"name"\s*:\s*"([^"]*)"', tool_json)
if name_match:
tool_data["name"] = name_match.group(1)
# 解析arguments字段
args_match = re.search(r'"arguments"\s*:\s*(\{.*)', tool_json)
if args_match:
try:
tool_data["arguments"] = partial_json_parser.loads(args_match.group(1), flags=flags)
except:
tool_data["arguments"] = None
if isinstance(tool_data, dict):
function_call_arr.append(
{
"name": tool_data.get("name", ""),
"arguments": tool_data.get("arguments", {}),
"_is_partial": True, # 标记为部分解析
}
)
except Exception as e:
data_processor_logger.debug(f"Failed to parse tool call: {str(e)}")
continue
except Exception as e:
data_processor_logger.debug(f"Failed to parse tool call: {str(e)}")
continue
if not function_call_arr:
data_processor_logger.error("No valid tool calls found")
return ExtractedToolCallInformation(tools_called=False, content=model_output)
tool_calls = []
all_complete = True # 初始设为True，只要有一个不完整就变为False
for tool_call in function_call_arr:
# 记录工具调用解析状态
is_complete = tool_call.get("_is_complete", False)
is_partial = tool_call.get("_is_partial", False)
# 只要有一个不完整就认为整体不完整
if not is_complete or is_partial:
all_complete = False
# 处理参数序列化
tool_args = tool_call.get("arguments", {})
if not isinstance(tool_args, dict):
tool_args = {}
try:
args_str = json.dumps(tool_args, ensure_ascii=False) if tool_args else "{}"
except:
args_str = "{}"
tool_calls.append(
ToolCall(
type="function",
id=random_tool_call_id(),
function=FunctionCall(
name=tool_call.get("name", ""),
arguments=args_str,
),
)
)
# 只有当所有工具调用都明确标记为complete时才返回tools_called=True
return ExtractedToolCallInformation(
tools_called=all_complete, tool_calls=tool_calls if tool_calls else None, content=""
)
except Exception as e:
data_processor_logger.error(f"Error in extracting tool call from response: {str(e)}")
return ExtractedToolCallInformation(tools_called=False, tool_calls=None, content=model_output)
def extract_tool_calls_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],
request: dict,
) -> Union[DeltaMessage, None]:
# 忽略空chunk
if len(delta_text.strip()) == 0:
return None
try:
delta = None
# 使用buffer累积delta_text内容
self.buffer += delta_text
# 处理增量中的新tool_call开始
if "<tool_call>" in delta_text and "<tool_call>" not in previous_text:
self.current_tool_id = (
max(self.current_tool_id, 0) if self.current_tool_id == -1 else self.current_tool_id + 1
)
self.current_tool_name_sent = False
if len(self.streamed_args_for_tool) <= self.current_tool_id:
self.streamed_args_for_tool.append("")
data_processor_logger.debug(f"New tool call started with ID: {self.current_tool_id}")
# 增量解析逻辑
# 1. 尝试解析name字段
if not self.current_tool_name_sent and '"name"' in self.buffer:
name_match = re.search(r'"name"\s*:\s*"([^"]*)"', self.buffer)
if name_match:
name = name_match.group(1)
if name:
delta = DeltaMessage(
tool_calls=[
DeltaToolCall(
index=self.current_tool_id,
type="function",
id=random_tool_call_id(),
function=DeltaFunctionCall(name=name).model_dump(exclude_none=True),
)
]
)
print("delta name:", delta)
# 删除已处理的name部分
self.buffer = self.buffer[name_match.end() :]
self.current_tool_name_sent = True
return delta
# 2. 尝试解析arguments字段
if '"arguments"' in self.buffer:
args_match = re.search(r'"arguments"\s*:\s*(\{.*)', self.buffer)
if args_match:
args_content = args_match.group(1)
# 处理多余的大括号
open_braces = args_content.count("{")
close_braces = args_content.count("}")
if close_braces > open_braces:
args_content = args_content[: args_content.rfind("}")]
try:
# 增量解析arguments
parsed_args = json.loads(args_content)
if isinstance(parsed_args, dict):
args_json = json.dumps(parsed_args, ensure_ascii=False)
if len(args_json) > len(self.streamed_args_for_tool[self.current_tool_id]):
argument_diff = args_json[len(self.streamed_args_for_tool[self.current_tool_id]) :]
delta = DeltaMessage(
tool_calls=[
DeltaToolCall(
index=self.current_tool_id,
function=DeltaFunctionCall(arguments=argument_diff).model_dump(
exclude_none=True
),
)
]
)
print("delta argument:", delta)
# 删除已处理部分
processed_pos = args_match.start() + len('"arguments":')
self.buffer = (
self.buffer[:processed_pos] + self.buffer[processed_pos + len(args_json) :]
)
self.streamed_args_for_tool[self.current_tool_id] = args_json
return delta
except Exception as e:
data_processor_logger.debug(f"Partial arguments parsing: {str(e)}")
if "</tool_call>" in self.buffer:
end_pos = self.buffer.find("</tool_call>")
self.buffer = self.buffer[end_pos + len("</tool_call>") :]
# 完成当前工具调用处理
self.current_tool_id += 1
self.current_tool_name_sent = False
self.streamed_args_for_tool.append("")
return delta
except Exception as e:
data_processor_logger.error(f"Error in streaming tool call extraction: {str(e)}")
return None