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253
vllm/tool_parsers/granite_tool_parser.py
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253
vllm/tool_parsers/granite_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 partial_json_parser
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from partial_json_parser.core.options import Allow
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from vllm.entrypoints.chat_utils import make_tool_call_id
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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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from vllm.tool_parsers.utils import (
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consume_space,
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find_common_prefix,
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is_complete_json,
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partial_json_loads,
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)
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logger = init_logger(__name__)
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class GraniteToolParser(ToolParser):
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"""
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Tool call parser for the granite 3.0 models. Intended
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for use with the examples/tool_chat_template_granite.jinja
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template.
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Used when --enable-auto-tool-choice --tool-call-parser granite
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are all set
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"""
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def __init__(self, tokenizer: TokenizerLike):
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super().__init__(tokenizer)
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# for granite 3.0, the token `<|tool_call|>`
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self.bot_token = "<|tool_call|>"
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# for granite 3.1, the string `<tool_call>`
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self.bot_string = "<tool_call>"
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def extract_tool_calls(
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self, model_output: str, request: ChatCompletionRequest
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) -> ExtractedToolCallInformation:
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stripped = (
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model_output.strip()
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.removeprefix(self.bot_token)
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.removeprefix(self.bot_string)
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.lstrip()
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)
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if not stripped or stripped[0] != "[":
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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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try:
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raw_function_calls = json.loads(stripped)
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if not isinstance(raw_function_calls, list):
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raise Exception(
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f"Expected dict or list, got {type(raw_function_calls)}"
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)
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logger.debug("Extracted %d tool calls", len(raw_function_calls))
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tool_calls = [
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ToolCall(
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type="function",
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function=FunctionCall(
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name=function_call["name"],
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# function call args are JSON but as a string
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arguments=json.dumps(
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function_call["arguments"], ensure_ascii=False
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),
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),
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)
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for function_call in raw_function_calls
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]
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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=None,
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)
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except Exception as e:
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logger.error("Error in extracting tool call from response %s", e)
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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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start_idx = consume_space(0, current_text)
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if current_text[start_idx:].startswith(self.bot_token):
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start_idx = consume_space(start_idx + len(self.bot_token), current_text)
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if current_text[start_idx:].startswith(self.bot_string):
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start_idx = consume_space(start_idx + len(self.bot_string), current_text)
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if (
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not current_text
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or start_idx >= len(current_text)
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or current_text[start_idx] != "["
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):
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return DeltaMessage(content=delta_text)
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# bit mask flags for partial JSON parsing. If the name hasn't been
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# sent yet, don't allow sending
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# an incomplete string since OpenAI only ever (as far as I have
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# seen) allows sending the entire tool/ function name at once.
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flags = Allow.ALL if self.current_tool_name_sent else Allow.ALL & ~Allow.STR
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try:
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tool_call_arr = None
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is_complete = None
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try:
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tool_calls, end_idx = partial_json_loads(
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current_text[start_idx:], flags
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)
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if type(tool_calls) is list:
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tool_call_arr = tool_calls
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else:
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return DeltaMessage(content=delta_text)
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is_complete = [True] * len(tool_calls)
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if not is_complete_json(current_text[start_idx : start_idx + end_idx]):
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is_complete[-1] = False
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except partial_json_parser.core.exceptions.MalformedJSON:
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logger.debug("not enough tokens to parse into JSON yet")
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return None
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# case -- if no tokens have been streamed for the tool, e.g.
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# only the array brackets, stream nothing
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if not tool_call_arr:
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return None
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# select as the current tool call the one we're on the state at
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current_tool_call: dict = tool_call_arr[self.current_tool_id]
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delta = None
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# case: we are starting a new tool in the array
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# -> array has > 0 length AND length has moved past cursor
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if len(tool_call_arr) > self.current_tool_id + 1:
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# if we're moving on to a new call, first make sure we
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# haven't missed anything in the previous one that was
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# auto-generated due to JSON completions, but wasn't
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# streamed to the client yet.
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if self.current_tool_id >= 0:
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cur_arguments = current_tool_call.get("arguments")
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if cur_arguments:
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cur_args_json = json.dumps(cur_arguments, ensure_ascii=False)
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sent = len(self.streamed_args_for_tool[self.current_tool_id])
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argument_diff = cur_args_json[sent:]
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logger.debug("got arguments diff: %s", argument_diff)
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delta = DeltaMessage(
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tool_calls=[
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DeltaToolCall(
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index=self.current_tool_id,
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function=DeltaFunctionCall(
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arguments=argument_diff
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).model_dump(exclude_none=True),
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)
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]
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)
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self.streamed_args_for_tool[self.current_tool_id] += (
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argument_diff
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)
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# re-set stuff pertaining to progress in the current tool
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self.current_tool_id = len(tool_call_arr) - 1
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self.current_tool_name_sent = False
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self.streamed_args_for_tool.append("")
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logger.debug("starting on new tool %d", self.current_tool_id)
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return delta
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# if the current tool name hasn't been sent, send if available
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# - otherwise send nothing
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elif not self.current_tool_name_sent:
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function_name = current_tool_call.get("name")
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if function_name:
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delta = DeltaMessage(
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tool_calls=[
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DeltaToolCall(
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index=self.current_tool_id,
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type="function",
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id=make_tool_call_id(),
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function=DeltaFunctionCall(
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name=function_name
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).model_dump(exclude_none=True),
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)
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]
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)
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self.current_tool_name_sent = True
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# now we know we're on the same tool call and we're streaming
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# arguments
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else:
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cur_arguments = current_tool_call.get("arguments")
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if cur_arguments:
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sent = len(self.streamed_args_for_tool[self.current_tool_id])
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cur_args_json = json.dumps(cur_arguments, ensure_ascii=False)
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prev_arguments = self.prev_tool_call_arr[self.current_tool_id].get(
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"arguments"
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)
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argument_diff = None
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if is_complete[self.current_tool_id]:
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argument_diff = cur_args_json[sent:]
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elif prev_arguments:
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prev_args_json = json.dumps(prev_arguments, ensure_ascii=False)
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if cur_args_json != prev_args_json:
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prefix = find_common_prefix(prev_args_json, cur_args_json)
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argument_diff = prefix[sent:]
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if argument_diff is not None:
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delta = DeltaMessage(
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tool_calls=[
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DeltaToolCall(
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index=self.current_tool_id,
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function=DeltaFunctionCall(
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arguments=argument_diff
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).model_dump(exclude_none=True),
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)
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]
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)
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self.streamed_args_for_tool[self.current_tool_id] += (
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argument_diff
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)
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self.prev_tool_call_arr = tool_call_arr
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return delta
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except Exception as e:
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logger.error("Error trying to handle streaming tool call: %s", e)
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logger.debug(
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"Skipping chunk as a result of tool streaming extraction error"
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)
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return None
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