forked from EngineX-Hygon/enginex-hygon-vllm
init src 0.9.2
This commit is contained in:
402
vllm/entrypoints/openai/tool_parsers/glm4_moe_tool_parser.py
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402
vllm/entrypoints/openai/tool_parsers/glm4_moe_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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# code modified from deepseekv3_tool_parser.py
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from collections.abc import Sequence
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from typing import Union
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import regex as re
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from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
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DeltaFunctionCall, DeltaMessage,
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DeltaToolCall,
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ExtractedToolCallInformation,
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FunctionCall, ToolCall)
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from vllm.entrypoints.openai.tool_parsers.abstract_tool_parser import (
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ToolParser, ToolParserManager)
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from vllm.logger import init_logger
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from vllm.transformers_utils.tokenizer import AnyTokenizer
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logger = init_logger(__name__)
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@ToolParserManager.register_module("glm4_moe")
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class Glm4MoeModelToolParser(ToolParser):
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def __init__(self, tokenizer: AnyTokenizer):
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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.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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# Updated regex for the XML-based format
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self.tool_call_regex = re.compile(
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r"<tool_call>\s*"
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r"(?P<function_name>[^\n<]+)\s*" # 函数名（到换行或 <）
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r"(?P<arguments>(?:\s*<arg_key>[^<]+</arg_key>\s*"
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r"<arg_value>[^<]*</arg_value>\s*)*)\s*"
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r"</tool_call>",
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re.DOTALL,
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)
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# Regex for parsing individual arguments
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self.arg_regex = re.compile(
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r"<arg_key>(?P<key>[^<]+)</arg_key>\s*<arg_value>(?P<value>[^<]*)</arg_value>",
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re.DOTALL,
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)
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# Streaming regex
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self.stream_tool_call_portion_regex = re.compile(
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r"(?P<function_name>[^\n<]+)\s*"
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r"(?P<arguments>(?:\s*<arg_key>[^<]+</arg_key>\s*"
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r"<arg_value>[^<]*</arg_value>\s*)*)",
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re.DOTALL,
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)
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# For streaming, we also need a regex to match just the function name
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self.stream_tool_call_name_regex = re.compile(
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r"(?P<function_name>[^\n<]+)",
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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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self.tool_call_start_token_id = self.vocab.get(
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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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def _parse_arguments(self, args_text: str) -> str:
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"""Parse XML-based arguments into JSON format."""
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if not args_text or not args_text.strip():
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return "{}"
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args_dict = {}
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matches = self.arg_regex.findall(args_text)
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for key, value in matches:
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args_dict[key.strip()] = value.strip()
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import json
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return json.dumps(args_dict, ensure_ascii=False)
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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(tools_called=False,
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tool_calls=[],
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content=model_output)
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try:
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# Find all tool calls in the output
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function_call_matches = self.tool_call_regex.findall(model_output)
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logger.debug("function_call_matches: %s", function_call_matches)
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if not function_call_matches:
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return ExtractedToolCallInformation(
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tools_called=False,
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tool_calls=[],
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content=model_output,
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)
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tool_calls = []
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for i, match in enumerate(function_call_matches):
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function_name, function_args_xml = match
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function_name = function_name.strip()
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# Parse XML arguments to JSON
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function_args_json = self._parse_arguments(function_args_xml)
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tool_calls.append(
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ToolCall(
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id=f"call_{i}",
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type='function',
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function=FunctionCall(name=function_name,
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arguments=function_args_json),
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))
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# Extract content before the first tool call
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content = model_output[:model_output.find(self.
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tool_calls_start_token)]
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return ExtractedToolCallInformation(
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tools_called=bool(tool_calls),
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tool_calls=tool_calls,
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content=content.strip() if content.strip() 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(tools_called=False,
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tool_calls=[],
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content=model_output)
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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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) -> Union[DeltaMessage, None]:
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logger.debug("delta_text: %s", delta_text)
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logger.debug("delta_token_ids: %s", delta_token_ids)
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# check to see if we should be streaming a tool call - is there a
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if self.tool_call_start_token_id not in current_token_ids:
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logger.debug("No tool call tokens found!")
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return DeltaMessage(content=delta_text)
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delta_text = delta_text.replace(self.tool_calls_start_token,
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"").replace(self.tool_call_end_token,
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"")
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try:
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# figure out where we are in the parsing by counting tool call
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# start & end tags
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prev_tool_start_count = previous_token_ids.count(
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self.tool_call_start_token_id)
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prev_tool_end_count = previous_token_ids.count(
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self.tool_call_end_token_id)
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cur_tool_start_count = current_token_ids.count(
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self.tool_call_start_token_id)
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cur_tool_end_count = current_token_ids.count(
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self.tool_call_end_token_id)
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tool_call_portion = None
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text_portion = None
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# case: if we're generating text, OR rounding out a tool call
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if (cur_tool_start_count == cur_tool_end_count
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and prev_tool_end_count == cur_tool_end_count
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and self.tool_call_end_token not in delta_text):
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logger.debug("Generating text content! skipping tool parsing.")
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return DeltaMessage(content=delta_text)
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if self.tool_call_end_token in delta_text:
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logger.debug("tool_call_end_token in delta_text")
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full_text = current_text + delta_text
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tool_call_portion = full_text.split(
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self.tool_call_start_token)[-1].split(
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self.tool_call_end_token)[0].rstrip()
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delta_text = delta_text.split(
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self.tool_call_end_token)[0].rstrip()
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text_portion = delta_text.split(
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self.tool_call_end_token)[-1].lstrip()
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# case -- we're starting a new tool call
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if (cur_tool_start_count > cur_tool_end_count
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and cur_tool_start_count > prev_tool_start_count):
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if len(delta_token_ids) > 1:
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tool_call_portion = current_text.split(
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self.tool_call_start_token)[-1]
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else:
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tool_call_portion = None
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delta = None
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text_portion = None
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# set cursors and state appropriately
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self.current_tool_id += 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 a new tool %s", self.current_tool_id)
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# case -- we're updating an existing tool call
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elif (cur_tool_start_count > cur_tool_end_count
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and cur_tool_start_count == prev_tool_start_count):
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# get the portion of the text that's the tool call
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tool_call_portion = current_text.split(
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self.tool_call_start_token)[-1]
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text_portion = None
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# case -- the current tool call is being closed.
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elif (cur_tool_start_count == cur_tool_end_count
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and cur_tool_end_count >= prev_tool_end_count):
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if self.prev_tool_call_arr is None or len(
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self.prev_tool_call_arr) == 0:
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logger.debug(
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"attempting to close tool call, but no tool call")
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return None
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diff = self.prev_tool_call_arr[self.current_tool_id].get(
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"arguments")
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if diff:
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diff = (diff.encode("utf-8").decode("unicode_escape")
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if diff is str else diff)
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if '"}' not in delta_text:
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return None
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end_loc = delta_text.rindex('"}')
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diff = delta_text[:end_loc] + '"}'
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logger.debug(
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"Finishing tool and found diff that had not "
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"been streamed yet: %s",
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diff,
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)
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self.streamed_args_for_tool[self.current_tool_id] += diff
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return DeltaMessage(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=diff).model_dump(exclude_none=True),
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)
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])
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# case -- otherwise we're just generating text
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else:
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text = delta_text.replace(self.tool_call_start_token, "")
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text = text.replace(self.tool_call_end_token, "")
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delta = DeltaMessage(tool_calls=[], content=text)
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return delta
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current_tool_call = dict()
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if tool_call_portion:
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current_tool_call_matches = (
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self.stream_tool_call_portion_regex.match(
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tool_call_portion))
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if current_tool_call_matches:
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tool_id, tool_args = (current_tool_call_matches.groups())
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tool_name = tool_id.split('.')[1].split(':')[0]
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current_tool_call['id'] = tool_id
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current_tool_call["name"] = tool_name
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current_tool_call["arguments"] = tool_args
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else:
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current_tool_call_name_matches = (
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self.stream_tool_call_name_regex.match(
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tool_call_portion))
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if current_tool_call_name_matches:
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tool_id_str, = current_tool_call_name_matches.groups()
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tool_name = tool_id_str.split('.')[1].split(':')[0]
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current_tool_call['id'] = tool_id_str
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current_tool_call["name"] = tool_name
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current_tool_call["arguments"] = ""
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else:
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logger.debug("Not enough token")
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return None
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# case - we haven't sent the tool name yet. If it's available, send
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# it. otherwise, wait until it's available.
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if not self.current_tool_name_sent:
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if current_tool_call is None:
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return None
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function_name: Union[str, None] = current_tool_call.get("name")
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tool_id = current_tool_call.get("id")
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if function_name:
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self.current_tool_name_sent = True
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return DeltaMessage(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=tool_id,
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function=DeltaFunctionCall(
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name=function_name).model_dump(
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exclude_none=True),
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)
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])
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else:
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return None
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# case -- otherwise, send the tool call delta
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# if the tool call portion is None, send the delta as text
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if tool_call_portion is None:
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# if there's text but not tool calls, send that -
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# otherwise None to skip chunk
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delta = (DeltaMessage(
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content=delta_text) if text_portion is not None else None)
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return delta
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# now, the nitty-gritty of tool calls
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# now we have the portion to parse as tool call.
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logger.debug("Trying to parse current tool call with ID %s",
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self.current_tool_id)
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# if we're starting a new tool call, push an empty object in as
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# a placeholder for the arguments
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if len(self.prev_tool_call_arr) <= self.current_tool_id:
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self.prev_tool_call_arr.append({})
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# main logic for tool parsing here - compare prev. partially-parsed
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# JSON to the current partially-parsed JSON
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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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cur_arguments = current_tool_call.get("arguments")
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logger.debug("diffing old arguments: %s", prev_arguments)
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logger.debug("against new ones: %s", cur_arguments)
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# case -- no arguments have been created yet. skip sending a delta.
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if not cur_arguments and not prev_arguments:
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logger.debug("Skipping text %s - no arguments", delta_text)
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delta = None
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# case -- prev arguments are defined, but non are now.
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# probably impossible, but not a fatal error - just keep going
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elif not cur_arguments and prev_arguments:
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logger.error("should be impossible to have arguments reset "
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"mid-call. skipping streaming anything.")
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delta = None
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# case -- we now have the first info about arguments available from
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# autocompleting the JSON
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elif cur_arguments and not prev_arguments:
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delta = DeltaMessage(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=cur_arguments).model_dump(
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exclude_none=True),
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)
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])
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self.streamed_args_for_tool[
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self.current_tool_id] = cur_arguments
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# last case -- we have an update to existing arguments.
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elif cur_arguments and prev_arguments:
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if (isinstance(delta_text, str)
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and cur_arguments != prev_arguments
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and len(cur_arguments) > len(prev_arguments)
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and cur_arguments.startswith(prev_arguments)):
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delta_arguments = cur_arguments[len(prev_arguments):]
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logger.debug("got diff %s", delta_text)
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delta = DeltaMessage(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=delta_arguments).model_dump(
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exclude_none=True),
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)
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])
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self.streamed_args_for_tool[
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self.current_tool_id] = cur_arguments
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else:
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delta = None
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# handle saving the state for the current tool into
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# the "prev" list for use in diffing for the next iteration
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if self.current_tool_id == len(self.prev_tool_call_arr) - 1:
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self.prev_tool_call_arr[
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self.current_tool_id] = current_tool_call
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else:
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self.prev_tool_call_arr.append(current_tool_call)
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return delta
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except Exception:
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logger.exception("Error trying to handle streaming tool call.")
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return None # do not stream a delta. skip this token ID.
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