vllm.tool_parsers.openai_tool_parser ¶

class OpenAIToolParser(ToolParser):
    def __init__(self, tokenizer: "TokenizerLike"):
        super().__init__(tokenizer)

    def extract_tool_calls(
        self,
        model_output: str,
        request: ChatCompletionRequest,
        token_ids: Sequence[int] | None = None,
    ) -> ExtractedToolCallInformation:
        if token_ids is None:
            raise NotImplementedError(
                "OpenAIToolParser requires token IDs and does not support text-based extraction."  # noqa: E501
            )

        parser = parse_output_into_messages(token_ids)
        tool_calls = []
        final_content = None
        commentary_content = None

        if len(parser.messages) > 0:
            for msg in parser.messages:
                if len(msg.content) < 1:
                    continue
                msg_text = msg.content[0].text
                if msg.recipient and msg.recipient.startswith("functions."):
                    # If no content-type is given assume JSON, as that's the
                    # most common case with gpt-oss models.
                    if not msg.content_type or "json" in msg.content_type:
                        # load and dump the JSON text to check validity and
                        # remove any extra newlines or other odd formatting
                        try:
                            tool_args = json.dumps(json.loads(msg_text))
                        except json.JSONDecodeError:
                            logger.exception(
                                "Error decoding JSON tool call from response."
                            )
                            tool_args = msg_text
                    else:
                        tool_args = msg_text
                    tool_calls.append(
                        ToolCall(
                            type="function",
                            function=FunctionCall(
                                name=msg.recipient.split("functions.")[1],
                                arguments=tool_args,
                            ),
                        )
                    )
                elif msg.channel == "final":
                    final_content = msg_text
                elif msg.channel == "commentary" and not msg.recipient:
                    commentary_content = msg_text

        return ExtractedToolCallInformation(
            tools_called=len(tool_calls) > 0,
            tool_calls=tool_calls,
            # prefer final content over commentary content if both are present
            # commentary content is tool call preambles meant to be shown to the user
            content=final_content or commentary_content,
        )

    def extract_tool_calls_streaming(
        self,
        previous_text: str,
        current_text: str,
        delta_text: str,
        previous_token_ids: Sequence[int],
        current_token_ids: Sequence[int],
        delta_token_ids: Sequence[int],
        request: ChatCompletionRequest,
    ) -> DeltaMessage | None:
        raise NotImplementedError(
            "Not being used, manual parsing in serving_chat.py"  # noqa: E501
        )