@ToolParserManager.register_module("ernie45")
class Ernie45ToolParser(ToolParser):
def __init__(self, tokenizer: AnyTokenizer):
"""
Ernie thinking model format:
abc\n</think>\n\n\n<tool_call>\ndef\n</tool_call>\n
"""
super().__init__(tokenizer)
self.current_tool_name_sent = False
self.prev_tool_call_arr: list[dict] = []
self.current_tool_id = -1
self.streamed_args_for_tool: list[str] = []
self.think_end_token = "</think>"
self.response_start_token: str = "<response>"
self.response_end_token: str = "</response>"
self.tool_call_start_token = "<tool_call>"
self.tool_call_end_token = "</tool_call>"
self.tool_calls_start_token = self.tool_call_start_token
self.newline_token: str = "<0x0A>"
self.tool_call_regex = re.compile(
r"<tool_call>\s*(?P<json>\{.*?\})\s*</tool_call>", re.DOTALL
)
if not self.model_tokenizer:
raise ValueError(
"The model tokenizer must be passed to the ToolParser "
"constructor during construction."
)
self.think_end_token_id = self.vocab.get(self.think_end_token)
self.response_start_token_id = self.vocab.get(self.response_start_token)
self.response_end_token_id = self.vocab.get(self.response_end_token)
self.tool_call_start_token_id = self.vocab.get(self.tool_call_start_token)
self.tool_call_end_token_id = self.vocab.get(self.tool_call_end_token)
self.newline_token_id = self.vocab.get(self.newline_token)
self.parser_token_ids = [
self.think_end_token_id,
self.response_start_token_id,
self.response_end_token_id,
]
self._buffer = ""
def extract_tool_calls(
self,
model_output: str,
request: ChatCompletionRequest,
) -> ExtractedToolCallInformation:
# sanity check; avoid unnecessary processing
if self.tool_calls_start_token not in model_output:
return ExtractedToolCallInformation(
tools_called=False, tool_calls=[], content=model_output
)
else:
try:
tool_call_json_list = self.tool_call_regex.findall(model_output)
tool_calls = []
for tool_call_json in tool_call_json_list:
tool_call_dict = json.loads(tool_call_json)
args_str = json.dumps(
tool_call_dict.get("arguments", {}), ensure_ascii=False
)
tool_calls.append(
ToolCall(
type="function",
function=FunctionCall(
name=tool_call_dict.get("name", ""),
arguments=args_str,
),
)
)
content = model_output[
: model_output.find(self.tool_calls_start_token)
].rstrip("\n")
return ExtractedToolCallInformation(
tools_called=True,
tool_calls=tool_calls,
content=content if content else None,
)
except Exception:
logger.exception("Error in extracting tool call from response.")
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,
) -> DeltaMessage | None:
self._buffer += delta_text
cur_text = self._buffer
start_idx = cur_text.find(self.tool_call_start_token)
if start_idx == -1:
self._buffer = ""
# At least one toolcall has been completed
if self.current_tool_id > 0:
cur_text = ""
if self.current_tool_id == -1 and all(
token_id == self.newline_token_id for token_id in previous_token_ids
):
cur_text = cur_text.strip("\n")
# handle <response> </response> when tool_call is not triggered
# cur_text === delta_text
content = cur_text
if self.response_start_token_id in delta_token_ids:
content = content.lstrip("\n")
response_start_idx = content.find(self.response_start_token)
content = content[response_start_idx + len(self.response_start_token) :]
# if have </response>, remove it
response_end_idx = content.rfind(self.response_end_token)
if response_end_idx != -1:
content = content[:response_end_idx]
elif self.response_end_token_id in delta_token_ids:
response_end_idx = content.rfind(self.response_end_token)
content = content[:response_end_idx]
# remove \n after </think> or <response> or </response>
if (
len(previous_token_ids) > 0
and previous_token_ids[-1] in self.parser_token_ids
) and (
len(delta_token_ids) > 0 and delta_token_ids[0] == self.newline_token_id
):
content = content.lstrip("\n")
return DeltaMessage(content=content if content else None)
logger.debug("cur_text = %s", cur_text)
end_idx = cur_text.find(self.tool_call_end_token)
if end_idx != -1:
if self.current_tool_id == -1:
self.current_tool_id = 0
self.prev_tool_call_arr = []
self.streamed_args_for_tool = []
while len(self.prev_tool_call_arr) <= self.current_tool_id:
self.prev_tool_call_arr.append({})
while len(self.streamed_args_for_tool) <= self.current_tool_id:
self.streamed_args_for_tool.append("")
extracted_tool_calls = self.extract_tool_calls(
cur_text[: end_idx + len(self.tool_call_end_token)], request
)
if len(extracted_tool_calls.tool_calls) == 0:
logger.warning("Failed to extract any tool calls.")
return None
tool_call = extracted_tool_calls.tool_calls[0]
self.prev_tool_call_arr[self.current_tool_id] = {
"name": tool_call.function.name,
"arguments": json.loads(tool_call.function.arguments),
}
self.streamed_args_for_tool[self.current_tool_id] = (
tool_call.function.arguments
)
delta = DeltaMessage(
content=extracted_tool_calls.content,
tool_calls=[
DeltaToolCall(
index=self.current_tool_id,
id=tool_call.id,
type=tool_call.type,
function=DeltaFunctionCall(
name=tool_call.function.name,
arguments=tool_call.function.arguments,
),
)
],
)
self.current_tool_id += 1
self._buffer = cur_text[end_idx + len(self.tool_call_end_token) :]
return delta
self._buffer = cur_text[start_idx:]
content = cur_text[:start_idx].rstrip("\n")
return DeltaMessage(content=content if content else None)