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# SPDX-License-Identifier: Apache-2.0
import json
import re
from collections.abc import Sequence
from typing import Union
import partial_json_parser
from partial_json_parser.core.options import Allow
from vllm.entrypoints.openai.protocol import (
ChatCompletionRequest,
DeltaFunctionCall, DeltaMessage,
DeltaToolCall,
ExtractedToolCallInformation,
FunctionCall,
ToolCall,
)
from vllm.entrypoints.openai.tool_parsers.abstract_tool_parser import (
ToolParser,
ToolParserManager,
)
from vllm.logger import init_logger
from vllm.transformers_utils.tokenizer import AnyTokenizer
from vllm.utils import random_uuid
logger = init_logger(__name__)
@ToolParserManager.register_module("llama_nemotron_json")
class LlamaNemotronJSONToolParser(ToolParser):
def __init__(self, tokenizer: AnyTokenizer):
super().__init__(tokenizer)
self.current_tool_name_sent: bool = False
self.prev_tool_call_arr: list[dict] = []
self.current_tool_id: int = -1
self.streamed_args_for_tool: list[str] = []
self.tool_call_start_token: str = "<TOOLCALL>"
self.tool_call_end_token: str = "</TOOLCALL>"
self.tool_call_regex = re.compile(r"<TOOLCALL>(.*?)</TOOLCALL>", re.DOTALL)
def extract_tool_calls(
self,
model_output: str,
request: ChatCompletionRequest,
) -> ExtractedToolCallInformation:
if self.tool_call_start_token not in model_output:
return ExtractedToolCallInformation(
tools_called=False,
tool_calls=[],
content=model_output,
)
else:
try:
str_tool_calls = self.tool_call_regex.findall(model_output)[0].strip()
if not str_tool_calls.startswith("["):
str_tool_calls = "[" + str_tool_calls
if not str_tool_calls.endswith("]"):
str_tool_calls = "]" + str_tool_calls
json_tool_calls = json.loads(str_tool_calls)
tool_calls = []
for tool_call in json_tool_calls:
try:
tool_calls.append(ToolCall(
type="function",
function=FunctionCall(
name=tool_call["name"],
arguments=json.dumps(tool_call["arguments"], ensure_ascii=False) \
if isinstance(tool_call["arguments"], dict) else tool_call["arguments"],
),
))
except:
continue
content = model_output[:model_output.rfind(self.tool_call_start_token)]
return ExtractedToolCallInformation(
tools_called=True,
tool_calls=tool_calls,
content=content if content else None,
)
except Exception:
logger.exception(f"Error in extracting tool call from response. Response: {model_output}")
return ExtractedToolCallInformation(
tools_called=False,
tool_calls=[],
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: ChatCompletionRequest,
) -> Union[DeltaMessage, None]:
raise NotImplementedError("Tool calling is not supported in streaming mode!")
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