class AnthropicServingMessages(OpenAIServingChat):
"""Handler for Anthropic Messages API requests"""
def __init__(
self,
engine_client: EngineClient,
models: OpenAIServingModels,
response_role: str,
*,
online_renderer: "OnlineRenderer",
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,
default_chat_template_kwargs: dict[str, Any] | None = None,
):
super().__init__(
engine_client=engine_client,
models=models,
response_role=response_role,
online_renderer=online_renderer,
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,
default_chat_template_kwargs=default_chat_template_kwargs,
)
self.stop_reason_map = {
"stop": "end_turn",
"length": "max_tokens",
"tool_calls": "tool_use",
}
self._merge_inline_system = self._detect_merge_inline_system(chat_template)
@staticmethod
def _detect_merge_inline_system(chat_template: str | None) -> bool:
"""Auto-detect whether the chat template requires system-first ordering.
Renders a [system, user, system, user] conversation against the
template; if it raises (e.g. Qwen's ``loop.first`` guard), the
model needs inline system messages merged into the leading block.
"""
if not chat_template:
return True
try:
env = jinja2.sandbox.ImmutableSandboxedEnvironment(
trim_blocks=True,
lstrip_blocks=True,
extensions=[jinja2.ext.loopcontrols],
)
env.from_string(chat_template).render(
messages=[
{"role": "system", "content": "t"},
{"role": "user", "content": "t"},
{"role": "system", "content": "t"},
{"role": "user", "content": "t"},
],
add_generation_prompt=False,
)
return False
except jinja2.TemplateError:
return True
@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,
*,
merge_inline_system: bool = False,
) -> ChatCompletionRequest:
"""Convert Anthropic message format to OpenAI format"""
openai_messages: list[dict[str, Any]] = []
cls._convert_system_message(
anthropic_request,
openai_messages,
merge_inline_system=merge_inline_system,
)
cls._convert_messages(
anthropic_request.messages,
openai_messages,
merge_inline_system=merge_inline_system,
)
req = cls._build_base_request(anthropic_request, openai_messages)
cls._handle_streaming_options(req, anthropic_request)
cls._handle_output_config(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]],
*,
merge_inline_system: bool = False,
) -> None:
"""Convert Anthropic system message to OpenAI format"""
system_parts: list[str] = []
# Top-level system field
if anthropic_request.system:
if isinstance(anthropic_request.system, str):
system_parts.append(anthropic_request.system)
else:
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_parts.append(block.text)
# When the template requires system-first ordering, extract inline
# system messages from the messages array and merge them into the
# top-level block so the template doesn't reject them.
if merge_inline_system:
for msg in anthropic_request.messages:
if msg.role != "system":
continue
text = cls._extract_system_text(msg)
if text:
system_parts.append(text)
if system_parts:
openai_messages.append({"role": "system", "content": "".join(system_parts)})
@classmethod
def _extract_system_text(cls, msg) -> str | None:
"""Extract text from a system message, stripping billing headers."""
if isinstance(msg.content, str):
text = msg.content
if text.startswith("x-anthropic-billing-header"):
return None
return text
parts: list[str] = []
for block in msg.content:
if block.type == "text" and block.text:
if block.text.startswith("x-anthropic-billing-header"):
continue
parts.append(block.text)
return "".join(parts) if parts else None
@classmethod
def _convert_messages(
cls,
messages: list,
openai_messages: list[dict[str, Any]],
*,
merge_inline_system: bool = False,
) -> None:
"""Convert Anthropic messages to OpenAI format"""
for msg in messages:
# Handle system messages in-place: extract text, strip billing
# headers, and only emit if there is real content. This avoids
# going through _convert_block / _convert_message_content which
# doesn't strip billing headers and may produce messages with
# no "content" key.
if msg.role == "system":
if merge_inline_system:
continue # already merged into top-level by _convert_system_message
text = cls._extract_system_text(msg)
if text:
openai_messages.append({"role": "system", "content": text})
continue
openai_msg: dict[str, Any] = {"role": msg.role} # type: ignore
if isinstance(msg.content, str):
openai_msg["content"] = msg.content
else:
cls._convert_message_content(msg, openai_msg, openai_messages)
if not (msg.role == "user" and "content" not in openai_msg):
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)
elif block.type == "tool_reference":
# Tool references are expanded during tool_result processing
# when they appear inside tool_result content.
pass
@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] = []
tool_reference: list[dict[str, Any]] = []
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)
elif item_type == "tool_reference":
ref_name = item.get("tool_name") or item.get("name")
if ref_name:
tool_reference.append(
{"type": "tool_reference", "name": ref_name}
)
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
],
}
)
if tool_reference:
openai_messages.append(
{
"role": "tool",
"tool_call_id": block.tool_use_id or "",
"content": tool_reference, # type: ignore[dict-item]
}
)
@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,
chat_template_kwargs=anthropic_request.chat_template_kwargs,
)
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,
chat_template_kwargs=anthropic_request.chat_template_kwargs,
)
@classmethod
def _handle_output_config(
cls,
req: ChatCompletionRequest,
anthropic_request: AnthropicMessagesRequest | AnthropicCountTokensRequest,
) -> None:
"""Handle output configuration such as output format and effort"""
if isinstance(anthropic_request, AnthropicCountTokensRequest):
return
output_config: AnthropicOutputConfig | None = anthropic_request.output_config
if output_config and output_config.format and output_config.format.json_schema:
req.response_format = ResponseFormat(
type=output_config.format.type,
json_schema=JsonSchemaResponseFormat(
schema=output_config.format.json_schema,
name=output_config.format.type,
),
)
if output_config and output_config.effort is not None:
req.reasoning_effort = output_config.effort
@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,
"strict": tool.strict,
"defer_loading": tool.defer_loading,
},
}
)
)
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,
merge_inline_system=self._merge_inline_system,
)
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=_build_anthropic_usage(
generator.usage.prompt_tokens,
generator.usage.completion_tokens,
generator.usage,
),
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]
# Anthropic's canonical shape for an empty completion is a single
# empty text block, not []. Some strict clients assume content[0]
# exists, so emit one here.
if not content:
content.append(AnthropicContentBlock(type="text", text=""))
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
self.pending_content: list[str] = []
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
self.pending_content.clear()
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
state = _ActiveBlockState()
# Map from tool call index to tool_use_id
tool_index_to_id: dict[int, str] = {}
def stop_active_block():
events: list[str] = []
if state.block_type is None:
return events
if (
state.block_type == "thinking"
and state.block_signature is not None
and not state.signature_emitted
):
chunk = AnthropicStreamEvent(
index=state.block_index,
type="content_block_delta",
delta=AnthropicDelta(
type="signature_delta",
signature=state.block_signature,
),
)
data = chunk.model_dump_json(exclude_unset=True)
events.append(wrap_data_with_event(data, "content_block_delta"))
state.signature_emitted = True
stop_chunk = AnthropicStreamEvent(
index=state.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"))
state.reset()
state.content_block_index += 1
return events
def start_block(block: AnthropicContentBlock):
chunk = AnthropicStreamEvent(
index=state.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")
state.start(block)
return event
def stop_and_flush() -> list[str]:
buffered = list(state.pending_content)
state.pending_content.clear()
events = stop_active_block()
if not buffered:
return events
text = "".join(buffered)
events.append(start_block(AnthropicContentBlock(type="text", text="")))
pc_chunk = AnthropicStreamEvent(
index=state.block_index,
type="content_block_delta",
delta=AnthropicDelta(type="text_delta", text=text),
)
pc_data = pc_chunk.model_dump_json(exclude_unset=True)
events.append(wrap_data_with_event(pc_data, "content_block_delta"))
events.extend(stop_active_block())
return events
async for item in generator:
if item.startswith("data:"):
data_str = item[5:].strip().rstrip("\n")
if data_str == "[DONE]":
for event in stop_and_flush():
yield event
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,
# Set explicitly: this event is serialized
# with exclude_unset=True, which drops
# default-valued fields, while strict
# Anthropic SDK clients require
# message.type/role (issue #45367).
type="message",
role="assistant",
content=[],
model=origin_chunk.model,
stop_reason=None,
stop_sequence=None,
usage=_build_anthropic_usage(
origin_chunk.usage.prompt_tokens
if origin_chunk.usage
else 0,
0,
origin_chunk.usage,
),
),
)
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_and_flush():
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=_build_anthropic_usage(
origin_chunk.usage.prompt_tokens
if origin_chunk.usage
else 0,
origin_chunk.usage.completion_tokens
if origin_chunk.usage
else 0,
origin_chunk.usage,
),
)
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 state.block_type != "thinking":
for event in stop_and_flush():
yield event
start_event = start_block(
AnthropicContentBlock(
type="thinking", thinking=""
)
)
yield start_event
chunk = AnthropicStreamEvent(
index=(
state.block_index
if state.block_index is not None
else state.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
elif state.block_type == "tool_use":
state.pending_content.append(
origin_chunk.choices[0].delta.content
)
else:
if state.block_type != "text":
for event in stop_and_flush():
yield event
start_event = start_block(
AnthropicContentBlock(type="text", text="")
)
yield start_event
chunk = AnthropicStreamEvent(
index=(
state.block_index
if state.block_index is not None
else state.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 (
state.tool_use_id != tool_call.id
and tool_name is not None
):
for event in stop_and_flush():
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 state.tool_use_id == tool_call.id
):
chunk = AnthropicStreamEvent(
index=(
state.block_index
if state.block_index is not None
else state.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 state.tool_use_id == tool_use_id
):
chunk = AnthropicStreamEvent(
index=(
state.block_index
if state.block_index is not None
else state.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=sanitize_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,
merge_inline_system=self._merge_inline_system,
)
result = await self.render_chat_request(chat_req)
if isinstance(result, ErrorResponse):
return result
_, engine_inputs = result
input_tokens = sum( # type: ignore
len(engine_input["prompt_token_ids"]) # type: ignore[typeddict-item, misc]
for engine_input in engine_inputs
if "prompt_token_ids" in engine_input
)
response = AnthropicCountTokensResponse(
input_tokens=input_tokens,
context_management=AnthropicContextManagement(
original_input_tokens=input_tokens
),
)
return response