Files
FastDeploy/fastdeploy/entrypoints/openai/tool_parsers/abstract_tool_parser.py
2025-08-14 21:08:49 +08:00

160 lines
6.0 KiB
Python

"""
# 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 os
from collections.abc import Sequence
from functools import cached_property
from typing import Callable, Optional, Union
from fastdeploy.entrypoints.openai.protocol import (
ChatCompletionRequest,
DeltaMessage,
ExtractedToolCallInformation,
)
from fastdeploy.utils import data_processor_logger, import_from_path, is_list_of
class ToolParser:
"""
Abstract ToolParser class that should not be used directly. Provided
properties and methods should be used in
derived classes.
"""
def __init__(self, tokenizer):
self.prev_tool_call_arr: list[dict] = []
# the index of the tool call that is currently being parsed
self.current_tool_id: int = -1
self.current_tool_name_sent: bool = False
self.streamed_args_for_tool: list[str] = []
self.model_tokenizer = tokenizer
@cached_property
def vocab(self) -> dict[str, int]:
# NOTE: Only PreTrainedTokenizerFast is guaranteed to have .vocab
# whereas all tokenizers have .get_vocab()
return self.model_tokenizer.get_vocab()
def adjust_request(self, request: ChatCompletionRequest) -> ChatCompletionRequest:
"""
Static method that used to adjust the request parameters.
"""
return request
def extract_tool_calls(self, model_output: str, request: ChatCompletionRequest) -> ExtractedToolCallInformation:
"""
Static method that should be implemented for extracting tool calls from
a complete model-generated string.
Used for non-streaming responses where we have the entire model response
available before sending to the client.
Static because it's stateless.
"""
raise NotImplementedError("AbstractToolParser.extract_tool_calls has not been implemented!")
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: ChatCompletionRequest,
) -> Union[DeltaMessage, None]:
"""
Instance method that should be implemented for extracting tool calls
from an incomplete response; for use when handling tool calls and
streaming. Has to be an instance method because it requires state -
the current tokens/diffs, but also the information about what has
previously been parsed and extracted (see constructor)
"""
raise NotImplementedError("AbstractToolParser.extract_tool_calls_streaming has not been " "implemented!")
class ToolParserManager:
tool_parsers: dict[str, type] = {}
@classmethod
def get_tool_parser(cls, name) -> type:
"""
Get tool parser by name which is registered by `register_module`.
Raise a KeyError exception if the name is not registered.
"""
if name in cls.tool_parsers:
return cls.tool_parsers[name]
raise KeyError(f"tool helper: '{name}' not found in tool_parsers")
@classmethod
def _register_module(
cls, module: type, module_name: Optional[Union[str, list[str]]] = None, force: bool = True
) -> None:
if not issubclass(module, ToolParser):
raise TypeError(f"module must be subclass of ToolParser, but got {type(module)}")
if module_name is None:
module_name = module.__name__
if isinstance(module_name, str):
module_name = [module_name]
for name in module_name:
if not force and name in cls.tool_parsers:
existed_module = cls.tool_parsers[name]
raise KeyError(f"{name} is already registered " f"at {existed_module.__module__}")
cls.tool_parsers[name] = module
@classmethod
def register_module(
cls, name: Optional[Union[str, list[str]]] = None, force: bool = True, module: Union[type, None] = None
) -> Union[type, Callable]:
"""
Register module with the given name or name list. it can be used as a
decoder(with module as None) or normal function(with module as not
None).
"""
if not isinstance(force, bool):
raise TypeError(f"force must be a boolean, but got {type(force)}")
# raise the error ahead of time
if not (name is None or isinstance(name, str) or is_list_of(name, str)):
raise TypeError("name must be None, an instance of str, or a sequence of str, " f"but got {type(name)}")
# use it as a normal method: x.register_module(module=SomeClass)
if module is not None:
cls._register_module(module=module, module_name=name, force=force)
return module
# use it as a decorator: @x.register_module()
def _register(module):
cls._register_module(module=module, module_name=name, force=force)
return module
return _register
@classmethod
def import_tool_parser(cls, plugin_path: str) -> None:
"""
Import a user-defined tool parser by the path of the tool parser define
file.
"""
module_name = os.path.splitext(os.path.basename(plugin_path))[0]
try:
import_from_path(module_name, plugin_path)
except Exception:
data_processor_logger.exception("Failed to load module '%s' from %s.", module_name, plugin_path)
return