136 lines
5.0 KiB
Python
136 lines
5.0 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import logging
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from collections.abc import Callable
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from openai.types.responses.response_function_tool_call import ResponseFunctionToolCall
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from openai.types.responses.response_output_message import ResponseOutputMessage
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from openai.types.responses.response_output_text import ResponseOutputText
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from openai.types.responses.response_reasoning_item import (
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Content,
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ResponseReasoningItem,
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)
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from vllm.entrypoints.openai.protocol import ResponseInputOutputItem, ResponsesRequest
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from vllm.outputs import CompletionOutput
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from vllm.reasoning.abs_reasoning_parsers import ReasoningParser
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from vllm.tokenizers.protocol import TokenizerLike
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from vllm.tool_parsers.abstract_tool_parser import ToolParser
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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 = logging.getLogger(__name__)
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class ResponsesParser:
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"""Incremental parser over completion tokens with reasoning support."""
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def __init__(
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self,
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*,
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tokenizer: AnyTokenizer,
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reasoning_parser_cls: Callable[[AnyTokenizer], ReasoningParser],
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response_messages: list[ResponseInputOutputItem],
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request: ResponsesRequest,
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tool_parser_cls: Callable[[TokenizerLike], ToolParser] | None,
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):
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self.response_messages: list[ResponseInputOutputItem] = (
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# TODO: initial messages may not be properly typed
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response_messages
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)
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self.num_init_messages = len(response_messages)
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self.tokenizer = tokenizer
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self.request = request
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self.reasoning_parser_instance = reasoning_parser_cls(tokenizer)
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self.tool_parser_instance = None
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if tool_parser_cls is not None:
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self.tool_parser_instance = tool_parser_cls(tokenizer)
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def process(self, output: CompletionOutput) -> "ResponsesParser":
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reasoning_content, content = self.reasoning_parser_instance.extract_reasoning(
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output.text, request=self.request
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)
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if reasoning_content:
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self.response_messages.append(
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ResponseReasoningItem(
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type="reasoning",
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id=f"rs_{random_uuid()}",
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summary=[],
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content=[
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Content(
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type="reasoning_text",
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text=reasoning_content,
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)
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],
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)
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)
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function_calls: list[ResponseFunctionToolCall] = []
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if self.tool_parser_instance is not None:
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tool_call_info = self.tool_parser_instance.extract_tool_calls(
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content if content is not None else "",
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request=self.request, # type: ignore
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)
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if tool_call_info is not None and tool_call_info.tools_called:
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# extract_tool_calls() returns a list of tool calls.
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function_calls.extend(
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ResponseFunctionToolCall(
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id=f"fc_{random_uuid()}",
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call_id=f"call_{random_uuid()}",
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type="function_call",
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status="completed",
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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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for tool_call in tool_call_info.tool_calls
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)
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content = tool_call_info.content
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if content and content.strip() == "":
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content = None
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if content:
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self.response_messages.append(
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ResponseOutputMessage(
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type="message",
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id=f"msg_{random_uuid()}",
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status="completed",
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role="assistant",
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content=[
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ResponseOutputText(
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annotations=[], # TODO
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type="output_text",
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text=content,
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logprobs=None, # TODO
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)
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],
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)
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)
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if len(function_calls) > 0:
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self.response_messages.extend(function_calls)
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return self
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def get_responses_parser_for_simple_context(
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*,
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tokenizer: AnyTokenizer,
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reasoning_parser_cls: Callable[[AnyTokenizer], ReasoningParser],
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response_messages: list[ResponseInputOutputItem],
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request: ResponsesRequest,
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tool_parser_cls,
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) -> ResponsesParser:
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"""Factory function to create a ResponsesParser with
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optional reasoning parser.
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Returns:
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ResponsesParser instance configured with the provided parser
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"""
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return ResponsesParser(
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tokenizer=tokenizer,
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reasoning_parser_cls=reasoning_parser_cls,
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response_messages=response_messages,
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request=request,
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tool_parser_cls=tool_parser_cls,
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)
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