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210
vllm/tool_parsers/ernie45_tool_parser.py
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210
vllm/tool_parsers/ernie45_tool_parser.py
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import json
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from collections.abc import Sequence
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import regex as re
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from vllm.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 vllm.logger import init_logger
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from vllm.tokenizers import TokenizerLike
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from vllm.tool_parsers.abstract_tool_parser import (
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ToolParser,
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)
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logger = init_logger(__name__)
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class Ernie45ToolParser(ToolParser):
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def __init__(self, tokenizer: TokenizerLike):
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"""
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Ernie thinking model format:
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abc\n</think>\n\n\n<tool_call>\ndef\n</tool_call>\n
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"""
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super().__init__(tokenizer)
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self.current_tool_name_sent = False
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self.prev_tool_call_arr: list[dict] = []
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self.current_tool_id = -1
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self.streamed_args_for_tool: list[str] = []
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self.think_end_token = "</think>"
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self.response_start_token: str = "<response>"
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self.response_end_token: str = "</response>"
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self.tool_call_start_token = "<tool_call>"
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self.tool_call_end_token = "</tool_call>"
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self.tool_calls_start_token = self.tool_call_start_token
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self.newline_token: str = "<0x0A>"
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self.tool_call_regex = re.compile(
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r"<tool_call>\s*(?P<json>\{.*?\})\s*</tool_call>", re.DOTALL
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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 ToolParser "
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"constructor during construction."
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)
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self.think_end_token_id = self.vocab.get(self.think_end_token)
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self.response_start_token_id = self.vocab.get(self.response_start_token)
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self.response_end_token_id = self.vocab.get(self.response_end_token)
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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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self.newline_token_id = self.vocab.get(self.newline_token)
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self.parser_token_ids = [
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self.think_end_token_id,
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self.response_start_token_id,
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self.response_end_token_id,
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]
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self._buffer = ""
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def extract_tool_calls(
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self,
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model_output: str,
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request: ChatCompletionRequest,
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) -> ExtractedToolCallInformation:
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# sanity check; avoid unnecessary processing
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if self.tool_calls_start_token not in model_output:
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return ExtractedToolCallInformation(
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tools_called=False, tool_calls=[], content=model_output
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)
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else:
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try:
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tool_call_json_list = self.tool_call_regex.findall(model_output)
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tool_calls = []
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for tool_call_json in tool_call_json_list:
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tool_call_dict = json.loads(tool_call_json)
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args_str = json.dumps(
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tool_call_dict.get("arguments", {}), ensure_ascii=False
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)
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tool_calls.append(
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ToolCall(
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type="function",
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function=FunctionCall(
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name=tool_call_dict.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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content = model_output[
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: model_output.find(self.tool_calls_start_token)
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].rstrip("\n")
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return ExtractedToolCallInformation(
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tools_called=True,
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tool_calls=tool_calls,
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content=content if content else None,
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)
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except Exception:
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logger.exception("Error in extracting tool call from response.")
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return ExtractedToolCallInformation(
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tools_called=False, tool_calls=[], content=model_output
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)
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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: ChatCompletionRequest,
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) -> DeltaMessage | None:
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self._buffer += delta_text
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cur_text = self._buffer
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start_idx = cur_text.find(self.tool_call_start_token)
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if start_idx == -1:
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self._buffer = ""
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# At least one toolcall has been completed
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if self.current_tool_id > 0:
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cur_text = ""
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if self.current_tool_id == -1 and all(
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token_id == self.newline_token_id for token_id in previous_token_ids
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):
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cur_text = cur_text.strip("\n")
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# handle <response> </response> when tool_call is not triggered
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# cur_text === delta_text
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content = cur_text
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if self.response_start_token_id in delta_token_ids:
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content = content.lstrip("\n")
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response_start_idx = content.find(self.response_start_token)
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content = content[response_start_idx + len(self.response_start_token) :]
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# if have </response>, remove it
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response_end_idx = content.rfind(self.response_end_token)
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if response_end_idx != -1:
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content = content[:response_end_idx]
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elif self.response_end_token_id in delta_token_ids:
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response_end_idx = content.rfind(self.response_end_token)
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content = content[:response_end_idx]
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# remove \n after </think> or <response> or </response>
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if (
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len(previous_token_ids) > 0
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and previous_token_ids[-1] in self.parser_token_ids
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) and (
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len(delta_token_ids) > 0 and delta_token_ids[0] == self.newline_token_id
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):
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content = content.lstrip("\n")
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return DeltaMessage(content=content if content else None)
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logger.debug("cur_text = %s", cur_text)
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end_idx = cur_text.find(self.tool_call_end_token)
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if end_idx != -1:
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if self.current_tool_id == -1:
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self.current_tool_id = 0
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self.prev_tool_call_arr = []
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self.streamed_args_for_tool = []
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while len(self.prev_tool_call_arr) <= self.current_tool_id:
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self.prev_tool_call_arr.append({})
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while len(self.streamed_args_for_tool) <= self.current_tool_id:
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self.streamed_args_for_tool.append("")
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extracted_tool_calls = self.extract_tool_calls(
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cur_text[: end_idx + len(self.tool_call_end_token)], request
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)
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if len(extracted_tool_calls.tool_calls) == 0:
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logger.warning("Failed to extract any tool calls.")
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return None
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tool_call = extracted_tool_calls.tool_calls[0]
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self.prev_tool_call_arr[self.current_tool_id] = {
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"name": tool_call.function.name,
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"arguments": json.loads(tool_call.function.arguments),
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}
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self.streamed_args_for_tool[self.current_tool_id] = (
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tool_call.function.arguments
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)
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delta = DeltaMessage(
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content=extracted_tool_calls.content,
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tool_calls=[
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DeltaToolCall(
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index=self.current_tool_id,
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id=tool_call.id,
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type=tool_call.type,
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function=DeltaFunctionCall(
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name=tool_call.function.name,
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arguments=tool_call.function.arguments,
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),
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)
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],
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)
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self.current_tool_id += 1
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self._buffer = cur_text[end_idx + len(self.tool_call_end_token) :]
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return delta
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self._buffer = cur_text[start_idx:]
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content = cur_text[:start_idx].rstrip("\n")
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return DeltaMessage(content=content if content else None)
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