455949675d
Signed-off-by: Marko Rosenmueller <5467316+dr75@users.noreply.github.com> Co-authored-by: Chauncey <chaunceyjiang@gmail.com>
102 lines
3.3 KiB
Python
102 lines
3.3 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""
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Harmony-specific streaming delta extraction for chat completions.
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This module handles the extraction of DeltaMessage objects from
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harmony parser state during streaming chat completions.
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"""
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from openai_harmony import StreamableParser
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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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DeltaFunctionCall,
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DeltaMessage,
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DeltaToolCall,
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)
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def extract_harmony_streaming_delta(
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harmony_parser: StreamableParser,
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cur_channel: str | None,
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cur_recipient: str | None,
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prev_recipient: str | None,
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delta_text: str,
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include_reasoning: bool,
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) -> tuple[DeltaMessage | None, bool]:
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"""
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Extract a DeltaMessage from harmony parser state during streaming.
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Args:
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harmony_parser: The StreamableParser instance tracking parse state
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cur_channel: Current channel ("final", "analysis", "commentary", etc.)
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cur_recipient: Current recipient (e.g., "functions.my_func")
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prev_recipient: Previous recipient for detecting tool call transitions
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delta_text: The text delta to include in the message
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include_reasoning: Whether to include reasoning content
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Returns:
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A tuple of (DeltaMessage or None, tools_streamed_flag)
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"""
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tools_streamed = False
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if cur_channel == "final":
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delta_message = DeltaMessage(content=delta_text)
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elif (
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(cur_channel == "commentary" or cur_channel == "analysis")
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and cur_recipient
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and cur_recipient.startswith("functions.")
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):
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# Count completed tool calls to determine index
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base_index = 0
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for msg in harmony_parser.messages:
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if (
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(msg.channel == "commentary" or msg.channel == "analysis")
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and msg.recipient
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and msg.recipient.startswith("functions.")
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):
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base_index += 1
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if prev_recipient != cur_recipient:
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tool_name = cur_recipient.split("functions.", 1)[1]
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delta_message = DeltaMessage(
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tool_calls=[
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DeltaToolCall(
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id=make_tool_call_id(),
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type="function",
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function=DeltaFunctionCall(
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name=tool_name,
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arguments="",
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),
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index=base_index,
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)
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]
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)
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elif delta_text:
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delta_message = DeltaMessage(
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tool_calls=[
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DeltaToolCall(
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index=base_index,
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function=DeltaFunctionCall(arguments=delta_text),
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)
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]
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)
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else:
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delta_message = None
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if delta_message is not None:
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tools_streamed = True
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elif cur_channel == "commentary":
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# Tool call preambles meant to be shown to the user
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delta_message = DeltaMessage(content=delta_text)
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elif cur_channel == "analysis":
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if include_reasoning:
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delta_message = DeltaMessage(reasoning=delta_text)
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else:
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delta_message = None
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else:
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delta_message = None
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return delta_message, tools_streamed
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