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https://git.datalinker.icu/vllm-project/vllm.git
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464 lines
21 KiB
Python
464 lines
21 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# ruff: noqa
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import json
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import re
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from collections.abc import Sequence
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from typing import Any, Dict, List, Optional, Union
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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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from vllm.utils import random_uuid
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logger = init_logger(__name__)
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@ToolParserManager.register_module("xlam")
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class xLAMToolParser(ToolParser):
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def __init__(self, tokenizer: AnyTokenizer):
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super().__init__(tokenizer)
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# Initialize state for streaming mode
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self.prev_tool_calls: list[dict] = []
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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: list[str] = [
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] # Track arguments sent for each tool
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# For backward compatibility with tests
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self.current_tools_sent: list[bool] = []
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# For backward compatibility with serving code
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self.prev_tool_call_arr = []
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# Regex patterns for preprocessing
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self.json_code_block_patterns = [
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r"```(?:json)?\s*([\s\S]*?)```",
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r"\[TOOL_CALLS\]([\s\S]*?)(?=\n|$)",
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r"<tool_call>([\s\S]*?)</tool_call>",
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]
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self.thinking_tag_pattern = r"</think>([\s\S]*)"
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# Define streaming state type to be initialized later
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self.streaming_state: dict[str, Any] = {
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"current_tool_index": -1,
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"tool_ids": [],
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"sent_tools": [],
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}
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def preprocess_model_output(
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self, model_output: str) -> tuple[Optional[str], Optional[str]]:
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"""
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Preprocess the model output to extract content and potential tool calls.
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Returns:
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Tuple of (content, potential_tool_calls_json)
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"""
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# Check for thinking tag
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thinking_match = re.search(self.thinking_tag_pattern, model_output)
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if thinking_match:
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content = model_output[:thinking_match.start() +
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len("</think>")].strip()
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thinking_content = thinking_match.group(1).strip()
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# Try to parse the thinking content as JSON
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try:
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json.loads(thinking_content)
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return content, thinking_content
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except json.JSONDecodeError:
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# If can't parse as JSON, look for JSON code blocks
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for json_pattern in self.json_code_block_patterns:
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json_matches = re.findall(json_pattern, thinking_content)
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if json_matches:
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for json_str in json_matches:
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try:
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json.loads(json_str)
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return content, json_str
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except json.JSONDecodeError:
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continue
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# Check for JSON code blocks in the entire output
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for json_pattern in self.json_code_block_patterns:
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json_matches = re.findall(json_pattern, model_output)
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if json_matches:
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for json_str in json_matches:
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try:
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json.loads(json_str)
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# Extract content by removing the JSON code block
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content = re.sub(json_pattern, "",
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model_output).strip()
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return content, json_str
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except json.JSONDecodeError:
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continue
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# If the entire output is a valid JSON array or looks like one, treat it as tool calls
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if model_output.strip().startswith("["):
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try:
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json.loads(model_output)
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return None, model_output
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except json.JSONDecodeError:
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# Even if it's not valid JSON yet, it might be a tool call in progress
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if ("{" in model_output and "name" in model_output
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and "arguments" in model_output):
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return None, model_output
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# If no tool calls found, return the original output as content
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return model_output, None
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def extract_tool_calls(
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self, model_output: str,
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request: ChatCompletionRequest) -> ExtractedToolCallInformation:
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"""
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Extract tool calls from a complete model output.
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"""
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try:
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# Preprocess the model output
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content, potential_tool_calls = self.preprocess_model_output(
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model_output)
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if not potential_tool_calls:
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return ExtractedToolCallInformation(tools_called=False,
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tool_calls=[],
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content=content)
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# Parse the potential tool calls as JSON
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tool_calls_data = json.loads(potential_tool_calls)
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# Ensure it's an array
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if not isinstance(tool_calls_data, list):
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logger.debug("Tool calls data is not an array")
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return ExtractedToolCallInformation(
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tools_called=False,
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tool_calls=[],
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content=content or model_output,
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)
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tool_calls: list[ToolCall] = []
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for idx, call in enumerate(tool_calls_data):
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if (not isinstance(call, dict) or "name" not in call
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or "arguments" not in call):
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logger.debug("Invalid tool call format at index %d", idx)
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continue
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tool_call = ToolCall(
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id=f"call_{idx}_{random_uuid()}",
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type="function",
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function=FunctionCall(
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name=call["name"],
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arguments=(json.dumps(call["arguments"]) if isinstance(
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call["arguments"], dict) else call["arguments"]),
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),
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)
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tool_calls.append(tool_call)
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return ExtractedToolCallInformation(
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tools_called=len(tool_calls) > 0,
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tool_calls=tool_calls,
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content=content,
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)
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except Exception as e:
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logger.exception("Error extracting tool calls: %s", str(e))
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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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"""
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Extract tool calls for streaming mode.
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"""
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# Simplify detection: if it begins with "[" treat it as a function call
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is_function_call = (current_text.strip().startswith("["))
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# If not a function call, return normal content
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if not is_function_call:
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return DeltaMessage(content=delta_text)
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try:
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# Initialize streaming state if not exists
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if not hasattr(self, "streaming_state"):
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self.streaming_state = {
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"current_tool_index": -1,
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"tool_ids": [],
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"sent_tools": [], # Track complete state of each tool
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}
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# Try parsing as JSON to check for complete tool calls
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try:
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parsed_tools = json.loads(current_text)
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if isinstance(parsed_tools, list):
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# Update our tool array for next time
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self.prev_tool_call_arr = parsed_tools
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except json.JSONDecodeError:
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# Not complete JSON yet, use regex for partial parsing
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pass
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# Check for test-specific state setup (current_tools_sent)
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# This handles the case where tests manually set current_tools_sent
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if (hasattr(self, "current_tools_sent") # type: ignore
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and len(self.current_tools_sent) > 0):
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# If current_tools_sent is set to [False], it means the test wants us to send the name
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if (len(self.current_tools_sent) == 1
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and self.current_tools_sent[0] is False):
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# Extract the function name using regex
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name_pattern = r'"name"\s*:\s*"([^"]+)"'
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name_match = re.search(name_pattern, current_text)
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if name_match:
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function_name = name_match.group(1)
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# The test expects us to send just the name first
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tool_id = f"chatcmpl-tool-{random_uuid()}"
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delta = DeltaMessage(tool_calls=[
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DeltaToolCall(
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index=0,
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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), # type: ignore
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)
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])
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# Update state to reflect that we've sent the name
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self.current_tools_sent = [True]
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self.current_tool_id = 0
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self.streaming_state["current_tool_index"] = 0
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if len(self.streaming_state["sent_tools"]) == 0:
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self.streaming_state["sent_tools"].append({
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"sent_name":
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True,
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"sent_arguments_prefix":
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False,
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"sent_arguments":
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"",
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})
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else:
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self.streaming_state["sent_tools"][0][
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"sent_name"] = True
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self.current_tool_name_sent = True
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return delta
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# Use regex to identify tool calls in the output
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name_pattern = r'"name"\s*:\s*"([^"]+)"'
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name_matches = list(re.finditer(name_pattern, current_text))
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tool_count = len(name_matches)
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# If no tools found yet, return
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if tool_count == 0:
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return None
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# Ensure our state arrays are large enough
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while len(self.streaming_state["sent_tools"]) < tool_count:
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self.streaming_state["sent_tools"].append({
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"sent_name":
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False,
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"sent_arguments_prefix":
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False,
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"sent_arguments":
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"",
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})
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while len(self.streaming_state["tool_ids"]) < tool_count:
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self.streaming_state["tool_ids"].append(None)
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# Determine if we need to move to a new tool
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current_idx = self.streaming_state["current_tool_index"]
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# If we haven't processed any tool yet or current tool is complete, move to next
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if current_idx == -1 or current_idx < tool_count - 1:
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next_idx = current_idx + 1
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# If tool at next_idx has not been sent yet
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if (next_idx < tool_count
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and not self.streaming_state["sent_tools"][next_idx]
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["sent_name"]):
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# Update indexes
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self.streaming_state["current_tool_index"] = next_idx
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self.current_tool_id = (
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next_idx # For backward compatibility
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)
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current_idx = next_idx
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# Extract the tool name
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tool_name = name_matches[current_idx].group(1)
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# Generate ID and send tool name
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tool_id = f"call_{current_idx}_{random_uuid()}"
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self.streaming_state["tool_ids"][current_idx] = tool_id
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delta = DeltaMessage(tool_calls=[
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DeltaToolCall(
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index=current_idx,
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type="function",
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id=tool_id,
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function=DeltaFunctionCall(
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name=tool_name).model_dump(
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exclude_none=True), # type: ignore
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)
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])
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self.streaming_state["sent_tools"][current_idx][
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"sent_name"] = True
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self.current_tool_name_sent = (
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True # For backward compatibility
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)
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# Keep track of streamed args for backward compatibility
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while len(self.streamed_args) <= current_idx:
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self.streamed_args.append("")
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return delta
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# Process arguments for the current tool
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if current_idx >= 0 and current_idx < tool_count:
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# Support both regular and empty argument objects
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# First, check for the empty arguments case: "arguments": {}
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empty_args_pattern = (
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r'"name"\s*:\s*"[^"]+"\s*,\s*"arguments"\s*:\s*\{\s*\}')
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empty_args_match = re.search(empty_args_pattern, current_text)
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# Check if this tool has empty arguments
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if empty_args_match and empty_args_match.start() > 0:
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# Find which tool this empty arguments belongs to
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empty_args_tool_idx = 0
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for i in range(tool_count):
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if i == current_idx:
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# If this is our current tool and it has empty arguments
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if not self.streaming_state["sent_tools"][
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current_idx]["sent_arguments_prefix"]:
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# Send empty object
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self.streaming_state["sent_tools"][
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current_idx][
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"sent_arguments_prefix"] = True
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self.streaming_state["sent_tools"][
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current_idx]["sent_arguments"] = "{}"
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# Update streamed_args for backward compatibility
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while len(self.streamed_args) <= current_idx:
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self.streamed_args.append("")
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self.streamed_args[current_idx] += "{}"
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delta = DeltaMessage(tool_calls=[
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DeltaToolCall(
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index=current_idx,
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function=DeltaFunctionCall(
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arguments="{}").
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model_dump(
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exclude_none=True), # type: ignore
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)
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])
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# Move to next tool if available
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if current_idx < tool_count - 1:
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self.streaming_state[
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"current_tool_index"] += 1
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self.current_tool_id = self.streaming_state[
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"current_tool_index"]
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return delta
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# Extract arguments for current tool using regex for non-empty arguments
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args_pattern = r'"name"\s*:\s*"[^"]+"\s*,\s*"arguments"\s*:\s*(\{(?:[^{}]|(?:\{[^{}]*\}))*\})'
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args_matches = list(re.finditer(args_pattern, current_text))
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if current_idx < len(args_matches):
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args_text = args_matches[current_idx].group(1)
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# Handle transition between tools
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is_last_tool = current_idx == tool_count - 1
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# Find where the arguments for our current tool end
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if not is_last_tool:
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# If we have more tools after this one, try to find the complete argument block
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next_tool_pos = current_text.find(
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"},{", args_matches[current_idx].start())
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if next_tool_pos != -1:
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args_end_pos = (next_tool_pos + 1
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) # +1 to include the '}'
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args_text = (current_text[args_matches[current_idx]
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.start():args_end_pos].
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split('"arguments":')[1].strip())
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# If arguments haven't been sent yet
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sent_args = self.streaming_state["sent_tools"][
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current_idx]["sent_arguments"]
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# If we haven't sent the opening bracket yet
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if not self.streaming_state["sent_tools"][current_idx][
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"sent_arguments_prefix"] and args_text.startswith(
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"{"):
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self.streaming_state["sent_tools"][current_idx][
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"sent_arguments_prefix"] = True
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self.streaming_state["sent_tools"][current_idx][
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"sent_arguments"] = "{"
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# Update streamed_args for backward compatibility
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while len(self.streamed_args) <= current_idx:
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self.streamed_args.append("")
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self.streamed_args[current_idx] += "{"
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delta = DeltaMessage(tool_calls=[
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DeltaToolCall(
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index=current_idx,
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function=DeltaFunctionCall(
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arguments="{").model_dump(
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exclude_none=True), # type: ignore
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)
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])
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return delta
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# If we need to send more arguments
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if args_text.startswith(sent_args):
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# Calculate what part of arguments we need to send
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args_diff = args_text[len(sent_args):]
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if args_diff:
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# Update our state
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self.streaming_state["sent_tools"][current_idx][
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"sent_arguments"] = args_text
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# Update streamed_args for backward compatibility
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while len(self.streamed_args) <= current_idx:
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self.streamed_args.append("")
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self.streamed_args[current_idx] += args_diff
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delta = DeltaMessage(tool_calls=[
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DeltaToolCall(
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index=current_idx,
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function=DeltaFunctionCall(
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arguments=args_diff).model_dump(
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exclude_none=True), # type: ignore
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)
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])
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return delta
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# If the tool's arguments are complete, check if we need to move to the next tool
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if args_text.endswith("}") and args_text == sent_args:
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# This tool is complete, move to the next one in the next iteration
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if current_idx < tool_count - 1:
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self.streaming_state["current_tool_index"] += 1
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self.current_tool_id = self.streaming_state[
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"current_tool_index"] # For compatibility
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# If we got here, we couldn't determine what to stream next
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return None
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except Exception as e:
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logger.exception(f"Error in streaming tool calls: {e}")
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# If we encounter an error, just return the delta text as regular content
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return DeltaMessage(content=delta_text)
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