serving.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Adapted from
# https://github.com/vllm/vllm/entrypoints/openai/serving_chat.py

"""Anthropic Messages API serving handler"""

import json
import logging
import time
import uuid
from collections.abc import AsyncGenerator
from typing import TYPE_CHECKING, Any

from fastapi import Request

from vllm.engine.protocol import EngineClient
from vllm.entrypoints.anthropic.protocol import (
    AnthropicContentBlock,
    AnthropicContextManagement,
    AnthropicCountTokensRequest,
    AnthropicCountTokensResponse,
    AnthropicDelta,
    AnthropicError,
    AnthropicMessagesRequest,
    AnthropicMessagesResponse,
    AnthropicStreamEvent,
    AnthropicUsage,
)
from vllm.entrypoints.chat_utils import ChatTemplateContentFormatOption
from vllm.entrypoints.logger import RequestLogger
from vllm.entrypoints.openai.chat_completion.protocol import (
    ChatCompletionNamedToolChoiceParam,
    ChatCompletionRequest,
    ChatCompletionResponse,
    ChatCompletionStreamResponse,
    ChatCompletionToolsParam,
)
from vllm.entrypoints.openai.chat_completion.serving import OpenAIServingChat
from vllm.entrypoints.openai.engine.protocol import (
    ErrorResponse,
    StreamOptions,
)
from vllm.entrypoints.openai.models.serving import OpenAIServingModels

if TYPE_CHECKING:
    from vllm.entrypoints.serve.render.serving import OpenAIServingRender

logger = logging.getLogger(__name__)


def wrap_data_with_event(data: str, event: str):
    return f"event: {event}\ndata: {data}\n\n"


class AnthropicServingMessages(OpenAIServingChat):
    """Handler for Anthropic Messages API requests"""

    def __init__(
        self,
        engine_client: EngineClient,
        models: OpenAIServingModels,
        response_role: str,
        *,
        openai_serving_render: "OpenAIServingRender",
        request_logger: RequestLogger | None,
        chat_template: str | None,
        chat_template_content_format: ChatTemplateContentFormatOption,
        return_tokens_as_token_ids: bool = False,
        reasoning_parser: str = "",
        enable_auto_tools: bool = False,
        tool_parser: str | None = None,
        enable_prompt_tokens_details: bool = False,
        enable_force_include_usage: bool = False,
    ):
        super().__init__(
            engine_client=engine_client,
            models=models,
            response_role=response_role,
            openai_serving_render=openai_serving_render,
            request_logger=request_logger,
            chat_template=chat_template,
            chat_template_content_format=chat_template_content_format,
            return_tokens_as_token_ids=return_tokens_as_token_ids,
            reasoning_parser=reasoning_parser,
            enable_auto_tools=enable_auto_tools,
            tool_parser=tool_parser,
            enable_prompt_tokens_details=enable_prompt_tokens_details,
            enable_force_include_usage=enable_force_include_usage,
        )
        self.stop_reason_map = {
            "stop": "end_turn",
            "length": "max_tokens",
            "tool_calls": "tool_use",
        }

    @staticmethod
    def _convert_image_source_to_url(source: dict[str, Any]) -> str:
        """Convert an Anthropic image source to an OpenAI-compatible URL.

        Anthropic supports two image source types:
        - base64: {"type": "base64", "media_type": "image/jpeg", "data": "..."}
        - url: {"type": "url", "url": "https://..."}

        For base64 sources, this constructs a proper data URI that
        downstream processors (e.g. vLLM's media connector) can handle.
        """
        source_type = source.get("type")
        if source_type == "url":
            return source.get("url", "")
        # Default to base64 processing if type is "base64"
        # or missing, ensuring a proper data URI is always
        # constructed for non-URL sources.
        media_type = source.get("media_type", "image/jpeg")
        data = source.get("data", "")
        return f"data:{media_type};base64,{data}"

    @classmethod
    def _convert_anthropic_to_openai_request(
        cls, anthropic_request: AnthropicMessagesRequest | AnthropicCountTokensRequest
    ) -> ChatCompletionRequest:
        """Convert Anthropic message format to OpenAI format"""
        openai_messages: list[dict[str, Any]] = []

        cls._convert_system_message(anthropic_request, openai_messages)
        cls._convert_messages(anthropic_request.messages, openai_messages)
        req = cls._build_base_request(anthropic_request, openai_messages)
        cls._handle_streaming_options(req, anthropic_request)
        cls._convert_tool_choice(anthropic_request, req)
        cls._convert_tools(anthropic_request, req)
        return req

    @classmethod
    def _convert_system_message(
        cls,
        anthropic_request: AnthropicMessagesRequest | AnthropicCountTokensRequest,
        openai_messages: list[dict[str, Any]],
    ) -> None:
        """Convert Anthropic system message to OpenAI format"""
        if not anthropic_request.system:
            return

        if isinstance(anthropic_request.system, str):
            openai_messages.append(
                {"role": "system", "content": anthropic_request.system}
            )
        else:
            system_prompt = ""
            for block in anthropic_request.system:
                if block.type == "text" and block.text:
                    # Strip Claude Code's attribution header which contains
                    # a per-request hash that defeats prefix caching.
                    if block.text.startswith("x-anthropic-billing-header"):
                        continue
                    system_prompt += block.text
            openai_messages.append({"role": "system", "content": system_prompt})

    @classmethod
    def _convert_messages(
        cls, messages: list, openai_messages: list[dict[str, Any]]
    ) -> None:
        """Convert Anthropic messages to OpenAI format"""
        for msg in messages:
            openai_msg: dict[str, Any] = {"role": msg.role}  # type: ignore

            if isinstance(msg.content, str):
                openai_msg["content"] = msg.content
            else:
                # Handle complex content blocks
                content_parts: list[dict[str, Any]] = []
                tool_calls: list[dict[str, Any]] = []
                reasoning_parts: list[str] = []

                for block in msg.content:
                    if block.type == "text" and block.text:
                        content_parts.append({"type": "text", "text": block.text})
                    elif block.type == "image" and block.source:
                        content_parts.append(
                            {
                                "type": "image_url",
                                "image_url": {"url": block.source.get("data", "")},
                            }
                        )
                    elif block.type == "thinking" and block.thinking is not None:
                        reasoning_parts.append(block.thinking)
                    elif block.type == "tool_use":
                        # Convert tool use to function call format
                        tool_call = {
                            "id": block.id or f"call_{int(time.time())}",
                            "type": "function",
                            "function": {
                                "name": block.name or "",
                                "arguments": json.dumps(block.input or {}),
                            },
                        }
                        tool_calls.append(tool_call)
                    elif block.type == "tool_result":
                        if msg.role == "user":
                            openai_messages.append(
                                {
                                    "role": "tool",
                                    "tool_call_id": block.id or "",
                                    "content": str(block.content)
                                    if block.content
                                    else "",
                                }
                            )
                        else:
                            # Assistant tool result becomes regular text
                            tool_result_text = (
                                str(block.content) if block.content else ""
                            )
                            content_parts.append(
                                {
                                    "type": "text",
                                    "text": f"Tool result: {tool_result_text}",
                                }
                            )

                if reasoning_parts:
                    openai_msg["reasoning"] = "".join(reasoning_parts)

                # Add tool calls to the message if any
                if tool_calls:
                    openai_msg["tool_calls"] = tool_calls  # type: ignore

                # Add content parts if any
                if content_parts:
                    if len(content_parts) == 1 and content_parts[0]["type"] == "text":
                        openai_msg["content"] = content_parts[0]["text"]
                    else:
                        openai_msg["content"] = content_parts  # type: ignore
                elif not tool_calls and not reasoning_parts:
                    continue

            openai_messages.append(openai_msg)

    @classmethod
    def _convert_message_content(
        cls,
        msg,
        openai_msg: dict[str, Any],
        openai_messages: list[dict[str, Any]],
    ) -> None:
        """Convert complex message content blocks"""
        content_parts: list[dict[str, Any]] = []
        tool_calls: list[dict[str, Any]] = []
        reasoning_parts: list[str] = []

        for block in msg.content:
            cls._convert_block(
                block,
                msg.role,
                content_parts,
                tool_calls,
                reasoning_parts,
                openai_messages,
            )

        if reasoning_parts:
            openai_msg["reasoning"] = "".join(reasoning_parts)

        if tool_calls:
            openai_msg["tool_calls"] = tool_calls  # type: ignore

        if content_parts:
            if len(content_parts) == 1 and content_parts[0]["type"] == "text":
                openai_msg["content"] = content_parts[0]["text"]
            else:
                openai_msg["content"] = content_parts  # type: ignore
        elif not tool_calls and not reasoning_parts:
            return

    @classmethod
    def _convert_block(
        cls,
        block,
        role: str,
        content_parts: list[dict[str, Any]],
        tool_calls: list[dict[str, Any]],
        reasoning_parts: list[str],
        openai_messages: list[dict[str, Any]],
    ) -> None:
        """Convert individual content block"""
        if block.type == "text" and block.text:
            content_parts.append({"type": "text", "text": block.text})
        elif block.type == "image" and block.source:
            image_url = cls._convert_image_source_to_url(block.source)
            content_parts.append({"type": "image_url", "image_url": {"url": image_url}})
        elif block.type == "thinking" and block.thinking is not None:
            reasoning_parts.append(block.thinking)
        elif block.type == "redacted_thinking":
            # Redacted thinking blocks contain safety-filtered reasoning.
            # We skip them as the content is opaque (base64 'data' field),
            # but accepting the block prevents a validation error when the
            # client echoes back the full assistant message.
            pass
        elif block.type == "tool_use":
            cls._convert_tool_use_block(block, tool_calls)
        elif block.type == "tool_result":
            cls._convert_tool_result_block(block, role, openai_messages, content_parts)

    @classmethod
    def _convert_tool_use_block(cls, block, tool_calls: list[dict[str, Any]]) -> None:
        """Convert tool_use block to OpenAI function call format"""
        tool_call = {
            "id": block.id or f"call_{int(time.time())}",
            "type": "function",
            "function": {
                "name": block.name or "",
                "arguments": json.dumps(block.input or {}),
            },
        }
        tool_calls.append(tool_call)

    @classmethod
    def _convert_tool_result_block(
        cls,
        block,
        role: str,
        openai_messages: list[dict[str, Any]],
        content_parts: list[dict[str, Any]],
    ) -> None:
        """Convert tool_result block to OpenAI format"""
        if role == "user":
            cls._convert_user_tool_result(block, openai_messages)
        else:
            tool_result_text = str(block.content) if block.content else ""
            content_parts.append(
                {"type": "text", "text": f"Tool result: {tool_result_text}"}
            )

    @classmethod
    def _convert_user_tool_result(
        cls, block, openai_messages: list[dict[str, Any]]
    ) -> None:
        """Convert user tool_result with text and image support"""
        tool_text = ""
        tool_image_urls: list[str] = []

        if isinstance(block.content, str):
            tool_text = block.content
        elif isinstance(block.content, list):
            text_parts: list[str] = []
            for item in block.content:
                if not isinstance(item, dict):
                    continue
                item_type = item.get("type")
                if item_type == "text":
                    text_parts.append(item.get("text", ""))
                elif item_type == "image":
                    source = item.get("source", {})
                    url = cls._convert_image_source_to_url(source)
                    if url:
                        tool_image_urls.append(url)
            tool_text = "\n".join(text_parts)

        openai_messages.append(
            {
                "role": "tool",
                "tool_call_id": block.tool_use_id or "",
                "content": tool_text or "",
            }
        )

        if tool_image_urls:
            openai_messages.append(
                {
                    "role": "user",
                    "content": [  # type: ignore[dict-item]
                        {"type": "image_url", "image_url": {"url": img}}
                        for img in tool_image_urls
                    ],
                }
            )

    @classmethod
    def _build_base_request(
        cls,
        anthropic_request: AnthropicMessagesRequest | AnthropicCountTokensRequest,
        openai_messages: list[dict[str, Any]],
    ) -> ChatCompletionRequest:
        """Build base ChatCompletionRequest"""
        if isinstance(anthropic_request, AnthropicCountTokensRequest):
            return ChatCompletionRequest(
                model=anthropic_request.model,
                messages=openai_messages,
            )

        return ChatCompletionRequest(
            model=anthropic_request.model,
            messages=openai_messages,
            max_tokens=anthropic_request.max_tokens,
            max_completion_tokens=anthropic_request.max_tokens,
            stop=anthropic_request.stop_sequences,
            temperature=anthropic_request.temperature,
            top_p=anthropic_request.top_p,
            top_k=anthropic_request.top_k,
            kv_transfer_params=anthropic_request.kv_transfer_params,
        )

    @classmethod
    def _handle_streaming_options(
        cls,
        req: ChatCompletionRequest,
        anthropic_request: AnthropicMessagesRequest | AnthropicCountTokensRequest,
    ) -> None:
        """Handle streaming configuration"""
        if isinstance(anthropic_request, AnthropicCountTokensRequest):
            return
        if anthropic_request.stream:
            req.stream = anthropic_request.stream
            req.stream_options = StreamOptions.model_validate(
                {"include_usage": True, "continuous_usage_stats": True}
            )

    @classmethod
    def _convert_tool_choice(
        cls,
        anthropic_request: AnthropicMessagesRequest | AnthropicCountTokensRequest,
        req: ChatCompletionRequest,
    ) -> None:
        """Convert Anthropic tool_choice to OpenAI format"""
        if anthropic_request.tool_choice is None:
            req.tool_choice = None
            return

        tool_choice_type = anthropic_request.tool_choice.type
        if tool_choice_type == "auto":
            req.tool_choice = "auto"
        elif tool_choice_type == "any":
            req.tool_choice = "required"
        elif tool_choice_type == "none":
            req.tool_choice = "none"
        elif tool_choice_type == "tool":
            req.tool_choice = ChatCompletionNamedToolChoiceParam.model_validate(
                {
                    "type": "function",
                    "function": {"name": anthropic_request.tool_choice.name},
                }
            )

    @classmethod
    def _convert_tools(
        cls,
        anthropic_request: AnthropicMessagesRequest | AnthropicCountTokensRequest,
        req: ChatCompletionRequest,
    ) -> None:
        """Convert Anthropic tools to OpenAI format"""
        if anthropic_request.tools is None:
            return

        tools = []
        for tool in anthropic_request.tools:
            tools.append(
                ChatCompletionToolsParam.model_validate(
                    {
                        "type": "function",
                        "function": {
                            "name": tool.name,
                            "description": tool.description,
                            "parameters": tool.input_schema,
                        },
                    }
                )
            )

        if req.tool_choice is None:
            req.tool_choice = "auto"
        req.tools = tools

    async def create_messages(
        self,
        request: AnthropicMessagesRequest,
        raw_request: Request | None = None,
    ) -> AsyncGenerator[str, None] | AnthropicMessagesResponse | ErrorResponse:
        """
        Messages API similar to Anthropic's API.

        See https://docs.anthropic.com/en/api/messages
        for the API specification. This API mimics the Anthropic messages API.
        """
        if logger.isEnabledFor(logging.DEBUG):
            logger.debug("Received messages request %s", request.model_dump_json())
        chat_req = self._convert_anthropic_to_openai_request(request)
        if logger.isEnabledFor(logging.DEBUG):
            logger.debug("Convert to OpenAI request %s", chat_req.model_dump_json())
        generator = await self.create_chat_completion(chat_req, raw_request)

        if isinstance(generator, ErrorResponse):
            return generator

        elif isinstance(generator, ChatCompletionResponse):
            return self.messages_full_converter(generator)

        return self.message_stream_converter(generator)

    def messages_full_converter(
        self,
        generator: ChatCompletionResponse,
    ) -> AnthropicMessagesResponse:
        result = AnthropicMessagesResponse(
            id=generator.id,
            content=[],
            model=generator.model,
            usage=AnthropicUsage(
                input_tokens=generator.usage.prompt_tokens,
                output_tokens=generator.usage.completion_tokens,
            ),
            kv_transfer_params=generator.kv_transfer_params,
        )
        choice = generator.choices[0]
        if choice.finish_reason == "stop":
            result.stop_reason = "end_turn"
        elif choice.finish_reason == "length":
            result.stop_reason = "max_tokens"
        elif choice.finish_reason == "tool_calls":
            result.stop_reason = "tool_use"

        content: list[AnthropicContentBlock] = []
        if choice.message.reasoning:
            content.append(
                AnthropicContentBlock(
                    type="thinking",
                    thinking=choice.message.reasoning,
                    signature=uuid.uuid4().hex,
                )
            )
        if choice.message.content:
            content.append(
                AnthropicContentBlock(
                    type="text",
                    text=choice.message.content,
                )
            )

        for tool_call in choice.message.tool_calls:
            anthropic_tool_call = AnthropicContentBlock(
                type="tool_use",
                id=tool_call.id,
                name=tool_call.function.name,
                input=json.loads(tool_call.function.arguments),
            )
            content += [anthropic_tool_call]

        result.content = content

        return result

    async def message_stream_converter(
        self,
        generator: AsyncGenerator[str, None],
    ) -> AsyncGenerator[str, None]:
        try:

            class _ActiveBlockState:
                def __init__(self) -> None:
                    self.content_block_index = 0
                    self.block_type: str | None = None
                    self.block_index: int | None = None
                    self.block_signature: str | None = None
                    self.signature_emitted: bool = False
                    self.tool_use_id: str | None = None

                def reset(self) -> None:
                    self.block_type = None
                    self.block_index = None
                    self.block_signature = None
                    self.signature_emitted = False
                    self.tool_use_id = None

                def start(self, block: AnthropicContentBlock) -> None:
                    self.block_type = block.type
                    self.block_index = self.content_block_index
                    if block.type == "thinking":
                        self.block_signature = uuid.uuid4().hex
                        self.signature_emitted = False
                        self.tool_use_id = None
                    elif block.type == "tool_use":
                        self.block_signature = None
                        self.signature_emitted = True
                        self.tool_use_id = block.id
                    else:
                        self.block_signature = None
                        self.signature_emitted = True
                        self.tool_use_id = None

            first_item = True
            finish_reason = None
            content_block_index = 0
            active_block_type: str | None = None
            active_block_index: int | None = None
            active_block_signature: str | None = None
            signature_emitted = False
            active_tool_use_id: str | None = None
            # Map from tool call index to tool_use_id
            tool_index_to_id: dict[int, str] = {}

            def stop_active_block():
                nonlocal active_block_type, active_block_index, content_block_index
                nonlocal active_block_signature, signature_emitted, active_tool_use_id
                events: list[str] = []
                if active_block_type is None:
                    return events
                if (
                    active_block_type == "thinking"
                    and active_block_signature is not None
                    and not signature_emitted
                ):
                    chunk = AnthropicStreamEvent(
                        index=active_block_index,
                        type="content_block_delta",
                        delta=AnthropicDelta(
                            type="signature_delta",
                            signature=active_block_signature,
                        ),
                    )
                    data = chunk.model_dump_json(exclude_unset=True)
                    events.append(wrap_data_with_event(data, "content_block_delta"))
                    signature_emitted = True
                stop_chunk = AnthropicStreamEvent(
                    index=active_block_index,
                    type="content_block_stop",
                )
                data = stop_chunk.model_dump_json(exclude_unset=True)
                events.append(wrap_data_with_event(data, "content_block_stop"))
                active_block_type = None
                active_block_index = None
                active_block_signature = None
                signature_emitted = False
                active_tool_use_id = None
                content_block_index += 1
                return events

            def start_block(block: AnthropicContentBlock):
                nonlocal active_block_type, active_block_index, content_block_index
                nonlocal active_block_signature, signature_emitted, active_tool_use_id
                chunk = AnthropicStreamEvent(
                    index=content_block_index,
                    type="content_block_start",
                    content_block=block,
                )
                data = chunk.model_dump_json(exclude_unset=True)
                event = wrap_data_with_event(data, "content_block_start")
                active_block_type = block.type
                active_block_index = content_block_index
                if block.type == "thinking":
                    active_block_signature = uuid.uuid4().hex
                    signature_emitted = False
                    active_tool_use_id = None
                elif block.type == "tool_use":
                    active_block_signature = None
                    signature_emitted = True
                    active_tool_use_id = block.id
                else:
                    active_block_signature = None
                    signature_emitted = True
                    active_tool_use_id = None
                return event

            async for item in generator:
                if item.startswith("data:"):
                    data_str = item[5:].strip().rstrip("\n")
                    if data_str == "[DONE]":
                        stop_message = AnthropicStreamEvent(
                            type="message_stop",
                        )
                        data = stop_message.model_dump_json(
                            exclude_unset=True, exclude_none=True
                        )
                        yield wrap_data_with_event(data, "message_stop")
                    else:
                        origin_chunk = ChatCompletionStreamResponse.model_validate_json(
                            data_str
                        )

                        if first_item:
                            chunk = AnthropicStreamEvent(
                                type="message_start",
                                message=AnthropicMessagesResponse(
                                    id=origin_chunk.id,
                                    content=[],
                                    model=origin_chunk.model,
                                    stop_reason=None,
                                    stop_sequence=None,
                                    usage=AnthropicUsage(
                                        input_tokens=origin_chunk.usage.prompt_tokens
                                        if origin_chunk.usage
                                        else 0,
                                        output_tokens=0,
                                    ),
                                ),
                            )
                            first_item = False
                            data = chunk.model_dump_json(exclude_unset=True)
                            yield wrap_data_with_event(data, "message_start")
                            continue

                        # last chunk including usage info
                        if len(origin_chunk.choices) == 0:
                            for event in stop_active_block():
                                yield event
                            stop_reason = self.stop_reason_map.get(
                                finish_reason or "stop"
                            )
                            chunk = AnthropicStreamEvent(
                                type="message_delta",
                                delta=AnthropicDelta(stop_reason=stop_reason),
                                usage=AnthropicUsage(
                                    input_tokens=origin_chunk.usage.prompt_tokens
                                    if origin_chunk.usage
                                    else 0,
                                    output_tokens=origin_chunk.usage.completion_tokens
                                    if origin_chunk.usage
                                    else 0,
                                ),
                            )
                            data = chunk.model_dump_json(exclude_unset=True)
                            yield wrap_data_with_event(data, "message_delta")
                            continue

                        if origin_chunk.choices[0].finish_reason is not None:
                            finish_reason = origin_chunk.choices[0].finish_reason
                            # continue

                        # thinking / text content
                        reasoning_delta = origin_chunk.choices[0].delta.reasoning
                        if reasoning_delta is not None:
                            if reasoning_delta == "":
                                pass
                            else:
                                if active_block_type != "thinking":
                                    for event in stop_active_block():
                                        yield event
                                    start_event = start_block(
                                        AnthropicContentBlock(
                                            type="thinking", thinking=""
                                        )
                                    )
                                    yield start_event
                                chunk = AnthropicStreamEvent(
                                    index=(
                                        active_block_index
                                        if active_block_index is not None
                                        else content_block_index
                                    ),
                                    type="content_block_delta",
                                    delta=AnthropicDelta(
                                        type="thinking_delta",
                                        thinking=reasoning_delta,
                                    ),
                                )
                                data = chunk.model_dump_json(exclude_unset=True)
                                yield wrap_data_with_event(data, "content_block_delta")

                        if origin_chunk.choices[0].delta.content is not None:
                            if origin_chunk.choices[0].delta.content == "":
                                pass
                            else:
                                if active_block_type != "text":
                                    for event in stop_active_block():
                                        yield event
                                    start_event = start_block(
                                        AnthropicContentBlock(type="text", text="")
                                    )
                                    yield start_event
                                chunk = AnthropicStreamEvent(
                                    index=(
                                        active_block_index
                                        if active_block_index is not None
                                        else content_block_index
                                    ),
                                    type="content_block_delta",
                                    delta=AnthropicDelta(
                                        type="text_delta",
                                        text=origin_chunk.choices[0].delta.content,
                                    ),
                                )
                                data = chunk.model_dump_json(exclude_unset=True)
                                yield wrap_data_with_event(data, "content_block_delta")

                        # tool calls - process all tool calls in the delta
                        if len(origin_chunk.choices[0].delta.tool_calls) > 0:
                            for tool_call in origin_chunk.choices[0].delta.tool_calls:
                                if tool_call.id is not None:
                                    # Update mapping for incremental updates
                                    tool_index_to_id[tool_call.index] = tool_call.id
                                    # Only create new block if different tool call
                                    # AND has a name
                                    tool_name = (
                                        tool_call.function.name
                                        if tool_call.function
                                        else None
                                    )
                                    if (
                                        active_tool_use_id != tool_call.id
                                        and tool_name is not None
                                    ):
                                        for event in stop_active_block():
                                            yield event
                                        start_event = start_block(
                                            AnthropicContentBlock(
                                                type="tool_use",
                                                id=tool_call.id,
                                                name=tool_name,
                                                input={},
                                            )
                                        )
                                        yield start_event
                                    # Handle initial arguments if present
                                    if (
                                        tool_call.function
                                        and tool_call.function.arguments
                                        and active_tool_use_id == tool_call.id
                                    ):
                                        chunk = AnthropicStreamEvent(
                                            index=(
                                                active_block_index
                                                if active_block_index is not None
                                                else content_block_index
                                            ),
                                            type="content_block_delta",
                                            delta=AnthropicDelta(
                                                type="input_json_delta",
                                                partial_json=tool_call.function.arguments,
                                            ),
                                        )
                                        data = chunk.model_dump_json(exclude_unset=True)
                                        yield wrap_data_with_event(
                                            data, "content_block_delta"
                                        )
                                else:
                                    # Incremental update - use index to find tool_use_id
                                    tool_use_id = tool_index_to_id.get(tool_call.index)
                                    if (
                                        tool_use_id is not None
                                        and tool_call.function
                                        and tool_call.function.arguments
                                        and active_tool_use_id == tool_use_id
                                    ):
                                        chunk = AnthropicStreamEvent(
                                            index=(
                                                active_block_index
                                                if active_block_index is not None
                                                else content_block_index
                                            ),
                                            type="content_block_delta",
                                            delta=AnthropicDelta(
                                                type="input_json_delta",
                                                partial_json=tool_call.function.arguments,
                                            ),
                                        )
                                        data = chunk.model_dump_json(exclude_unset=True)
                                        yield wrap_data_with_event(
                                            data, "content_block_delta"
                                        )
                            continue
                else:
                    error_response = AnthropicStreamEvent(
                        type="error",
                        error=AnthropicError(
                            type="internal_error",
                            message="Invalid data format received",
                        ),
                    )
                    data = error_response.model_dump_json(exclude_unset=True)
                    yield wrap_data_with_event(data, "error")

        except Exception as e:
            logger.exception("Error in message stream converter.")
            error_response = AnthropicStreamEvent(
                type="error",
                error=AnthropicError(type="internal_error", message=str(e)),
            )
            data = error_response.model_dump_json(exclude_unset=True)
            yield wrap_data_with_event(data, "error")

    async def count_tokens(
        self,
        request: AnthropicCountTokensRequest,
        raw_request: Request | None = None,
    ) -> AnthropicCountTokensResponse | ErrorResponse:
        """Implements Anthropic's messages.count_tokens endpoint."""
        chat_req = self._convert_anthropic_to_openai_request(request)
        result = await self.render_chat_request(chat_req)
        if isinstance(result, ErrorResponse):
            return result

        _, engine_prompts = result

        input_tokens = sum(  # type: ignore
            len(prompt["prompt_token_ids"])  # type: ignore[typeddict-item, misc]
            for prompt in engine_prompts
            if "prompt_token_ids" in prompt
        )

        response = AnthropicCountTokensResponse(
            input_tokens=input_tokens,
            context_management=AnthropicContextManagement(
                original_input_tokens=input_tokens
            ),
        )

        return response