Support Anthropic API /v1/messages Endpoint (#22627)
Signed-off-by: liuli <ll407707@alibaba-inc.com> Co-authored-by: liuli <ll407707@alibaba-inc.com> Co-authored-by: Cyrus Leung <tlleungac@connect.ust.hk> Co-authored-by: Michael Goin <mgoin64@gmail.com>
This commit is contained in:
RED
0
vllm/entrypoints/anthropic/__init__.py
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0
vllm/entrypoints/anthropic/__init__.py
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300
vllm/entrypoints/anthropic/api_server.py
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vllm/entrypoints/anthropic/api_server.py
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@@ -0,0 +1,300 @@
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# Adapted from:
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# https://github.com/vllm/vllm/entrypoints/openai/api_server.py
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import asyncio
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import signal
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import tempfile
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from argparse import Namespace
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from http import HTTPStatus
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import uvloop
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from fastapi import APIRouter, Depends, FastAPI, Request
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi.responses import JSONResponse, Response, StreamingResponse
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from starlette.datastructures import State
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import vllm.envs as envs
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from vllm.engine.protocol import EngineClient
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from vllm.entrypoints.anthropic.protocol import (
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AnthropicErrorResponse,
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AnthropicMessagesRequest,
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AnthropicMessagesResponse,
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)
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from vllm.entrypoints.anthropic.serving_messages import AnthropicServingMessages
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from vllm.entrypoints.launcher import serve_http
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from vllm.entrypoints.logger import RequestLogger
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from vllm.entrypoints.openai.api_server import (
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build_async_engine_client,
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create_server_socket,
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lifespan,
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load_log_config,
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validate_api_server_args,
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validate_json_request,
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)
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from vllm.entrypoints.openai.cli_args import make_arg_parser, validate_parsed_serve_args
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from vllm.entrypoints.openai.protocol import ErrorResponse
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from vllm.entrypoints.openai.serving_models import (
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BaseModelPath,
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OpenAIServingModels,
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)
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#
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# yapf: enable
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from vllm.entrypoints.openai.tool_parsers import ToolParserManager
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from vllm.entrypoints.utils import (
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cli_env_setup,
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load_aware_call,
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process_chat_template,
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process_lora_modules,
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with_cancellation,
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)
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from vllm.logger import init_logger
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from vllm.utils import FlexibleArgumentParser, set_ulimit
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from vllm.utils.network_utils import is_valid_ipv6_address
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from vllm.version import __version__ as VLLM_VERSION
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prometheus_multiproc_dir: tempfile.TemporaryDirectory
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# Cannot use __name__ (https://github.com/vllm-project/vllm/pull/4765)
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logger = init_logger("vllm.entrypoints.anthropic.api_server")
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_running_tasks: set[asyncio.Task] = set()
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router = APIRouter()
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def messages(request: Request) -> AnthropicServingMessages:
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return request.app.state.anthropic_serving_messages
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def engine_client(request: Request) -> EngineClient:
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return request.app.state.engine_client
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@router.get("/health", response_class=Response)
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async def health(raw_request: Request) -> Response:
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"""Health check."""
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await engine_client(raw_request).check_health()
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return Response(status_code=200)
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@router.get("/ping", response_class=Response)
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@router.post("/ping", response_class=Response)
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async def ping(raw_request: Request) -> Response:
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"""Ping check. Endpoint required for SageMaker"""
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return await health(raw_request)
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@router.post(
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"/v1/messages",
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dependencies=[Depends(validate_json_request)],
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responses={
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HTTPStatus.OK.value: {"content": {"text/event-stream": {}}},
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HTTPStatus.BAD_REQUEST.value: {"model": AnthropicErrorResponse},
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HTTPStatus.NOT_FOUND.value: {"model": AnthropicErrorResponse},
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HTTPStatus.INTERNAL_SERVER_ERROR.value: {"model": AnthropicErrorResponse},
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},
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)
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@with_cancellation
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@load_aware_call
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async def create_messages(request: AnthropicMessagesRequest, raw_request: Request):
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handler = messages(raw_request)
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if handler is None:
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return messages(raw_request).create_error_response(
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message="The model does not support Messages API"
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)
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generator = await handler.create_messages(request, raw_request)
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if isinstance(generator, ErrorResponse):
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return JSONResponse(content=generator.model_dump())
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elif isinstance(generator, AnthropicMessagesResponse):
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logger.debug(
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"Anthropic Messages Response: %s", generator.model_dump(exclude_none=True)
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)
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return JSONResponse(content=generator.model_dump(exclude_none=True))
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return StreamingResponse(content=generator, media_type="text/event-stream")
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async def init_app_state(
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engine_client: EngineClient,
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state: State,
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args: Namespace,
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) -> None:
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vllm_config = engine_client.vllm_config
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if args.served_model_name is not None:
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served_model_names = args.served_model_name
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else:
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served_model_names = [args.model]
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if args.disable_log_requests:
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request_logger = None
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else:
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request_logger = RequestLogger(max_log_len=args.max_log_len)
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base_model_paths = [
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BaseModelPath(name=name, model_path=args.model) for name in served_model_names
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]
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state.engine_client = engine_client
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state.log_stats = not args.disable_log_stats
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state.vllm_config = vllm_config
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model_config = vllm_config.model_config
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default_mm_loras = (
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vllm_config.lora_config.default_mm_loras
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if vllm_config.lora_config is not None
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else {}
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)
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lora_modules = process_lora_modules(args.lora_modules, default_mm_loras)
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resolved_chat_template = await process_chat_template(
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args.chat_template, engine_client, model_config
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)
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state.openai_serving_models = OpenAIServingModels(
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engine_client=engine_client,
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base_model_paths=base_model_paths,
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lora_modules=lora_modules,
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)
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await state.openai_serving_models.init_static_loras()
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state.anthropic_serving_messages = AnthropicServingMessages(
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engine_client,
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state.openai_serving_models,
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args.response_role,
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request_logger=request_logger,
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chat_template=resolved_chat_template,
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chat_template_content_format=args.chat_template_content_format,
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return_tokens_as_token_ids=args.return_tokens_as_token_ids,
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enable_auto_tools=args.enable_auto_tool_choice,
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tool_parser=args.tool_call_parser,
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reasoning_parser=args.reasoning_parser,
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enable_prompt_tokens_details=args.enable_prompt_tokens_details,
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enable_force_include_usage=args.enable_force_include_usage,
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)
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def setup_server(args):
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"""Validate API server args, set up signal handler, create socket
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ready to serve."""
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logger.info("vLLM API server version %s", VLLM_VERSION)
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if args.tool_parser_plugin and len(args.tool_parser_plugin) > 3:
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ToolParserManager.import_tool_parser(args.tool_parser_plugin)
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validate_api_server_args(args)
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# workaround to make sure that we bind the port before the engine is set up.
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# This avoids race conditions with ray.
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# see https://github.com/vllm-project/vllm/issues/8204
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sock_addr = (args.host or "", args.port)
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sock = create_server_socket(sock_addr)
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# workaround to avoid footguns where uvicorn drops requests with too
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# many concurrent requests active
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set_ulimit()
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def signal_handler(*_) -> None:
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# Interrupt server on sigterm while initializing
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raise KeyboardInterrupt("terminated")
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signal.signal(signal.SIGTERM, signal_handler)
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addr, port = sock_addr
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is_ssl = args.ssl_keyfile and args.ssl_certfile
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host_part = f"[{addr}]" if is_valid_ipv6_address(addr) else addr or "0.0.0.0"
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listen_address = f"http{'s' if is_ssl else ''}://{host_part}:{port}"
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return listen_address, sock
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async def run_server(args, **uvicorn_kwargs) -> None:
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"""Run a single-worker API server."""
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listen_address, sock = setup_server(args)
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await run_server_worker(listen_address, sock, args, **uvicorn_kwargs)
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def build_app(args: Namespace) -> FastAPI:
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app = FastAPI(lifespan=lifespan)
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app.include_router(router)
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app.root_path = args.root_path
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app.add_middleware(
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CORSMiddleware,
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allow_origins=args.allowed_origins,
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allow_credentials=args.allow_credentials,
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allow_methods=args.allowed_methods,
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allow_headers=args.allowed_headers,
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)
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return app
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async def run_server_worker(
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listen_address, sock, args, client_config=None, **uvicorn_kwargs
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) -> None:
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"""Run a single API server worker."""
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if args.tool_parser_plugin and len(args.tool_parser_plugin) > 3:
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ToolParserManager.import_tool_parser(args.tool_parser_plugin)
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server_index = client_config.get("client_index", 0) if client_config else 0
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# Load logging config for uvicorn if specified
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log_config = load_log_config(args.log_config_file)
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if log_config is not None:
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uvicorn_kwargs["log_config"] = log_config
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async with build_async_engine_client(
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args,
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client_config=client_config,
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) as engine_client:
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app = build_app(args)
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await init_app_state(engine_client, app.state, args)
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logger.info("Starting vLLM API server %d on %s", server_index, listen_address)
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shutdown_task = await serve_http(
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app,
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sock=sock,
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enable_ssl_refresh=args.enable_ssl_refresh,
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host=args.host,
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port=args.port,
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log_level=args.uvicorn_log_level,
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# NOTE: When the 'disable_uvicorn_access_log' value is True,
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# no access log will be output.
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access_log=not args.disable_uvicorn_access_log,
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timeout_keep_alive=envs.VLLM_HTTP_TIMEOUT_KEEP_ALIVE,
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ssl_keyfile=args.ssl_keyfile,
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ssl_certfile=args.ssl_certfile,
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ssl_ca_certs=args.ssl_ca_certs,
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ssl_cert_reqs=args.ssl_cert_reqs,
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**uvicorn_kwargs,
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)
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# NB: Await server shutdown only after the backend context is exited
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try:
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await shutdown_task
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finally:
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sock.close()
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if __name__ == "__main__":
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# NOTE(simon):
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# This section should be in sync with vllm/entrypoints/cli/main.py for CLI
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# entrypoints.
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cli_env_setup()
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parser = FlexibleArgumentParser(
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description="vLLM Anthropic-Compatible RESTful API server."
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)
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parser = make_arg_parser(parser)
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args = parser.parse_args()
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validate_parsed_serve_args(args)
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uvloop.run(run_server(args))
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162
vllm/entrypoints/anthropic/protocol.py
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162
vllm/entrypoints/anthropic/protocol.py
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@@ -0,0 +1,162 @@
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Pydantic models for Anthropic API protocol"""
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import time
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from typing import Any, Literal, Optional
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from pydantic import BaseModel, field_validator
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class AnthropicError(BaseModel):
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"""Error structure for Anthropic API"""
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type: str
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message: str
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class AnthropicErrorResponse(BaseModel):
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"""Error response structure for Anthropic API"""
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type: Literal["error"] = "error"
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error: AnthropicError
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class AnthropicUsage(BaseModel):
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"""Token usage information"""
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input_tokens: int
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output_tokens: int
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cache_creation_input_tokens: int | None = None
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cache_read_input_tokens: int | None = None
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class AnthropicContentBlock(BaseModel):
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"""Content block in message"""
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type: Literal["text", "image", "tool_use", "tool_result"]
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text: str | None = None
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# For image content
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source: dict[str, Any] | None = None
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# For tool use/result
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id: str | None = None
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name: str | None = None
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input: dict[str, Any] | None = None
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content: str | list[dict[str, Any]] | None = None
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is_error: bool | None = None
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class AnthropicMessage(BaseModel):
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"""Message structure"""
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role: Literal["user", "assistant"]
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content: str | list[AnthropicContentBlock]
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class AnthropicTool(BaseModel):
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"""Tool definition"""
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name: str
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description: str | None = None
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input_schema: dict[str, Any]
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@field_validator("input_schema")
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@classmethod
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def validate_input_schema(cls, v):
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if not isinstance(v, dict):
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raise ValueError("input_schema must be a dictionary")
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if "type" not in v:
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v["type"] = "object" # Default to object type
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return v
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class AnthropicToolChoice(BaseModel):
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"""Tool Choice definition"""
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type: Literal["auto", "any", "tool"]
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name: str | None = None
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class AnthropicMessagesRequest(BaseModel):
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"""Anthropic Messages API request"""
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model: str
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messages: list[AnthropicMessage]
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max_tokens: int
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metadata: dict[str, Any] | None = None
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stop_sequences: list[str] | None = None
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stream: bool | None = False
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system: str | list[AnthropicContentBlock] | None = None
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temperature: float | None = None
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tool_choice: AnthropicToolChoice | None = None
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tools: list[AnthropicTool] | None = None
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top_k: int | None = None
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top_p: float | None = None
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@field_validator("model")
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@classmethod
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def validate_model(cls, v):
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if not v:
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raise ValueError("Model is required")
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return v
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@field_validator("max_tokens")
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@classmethod
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def validate_max_tokens(cls, v):
|
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if v <= 0:
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raise ValueError("max_tokens must be positive")
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return v
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class AnthropicDelta(BaseModel):
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"""Delta for streaming responses"""
|
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|
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type: Literal["text_delta", "input_json_delta"] | None = None
|
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text: str | None = None
|
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partial_json: str | None = None
|
||||
|
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# Message delta
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stop_reason: (
|
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Literal["end_turn", "max_tokens", "stop_sequence", "tool_use"] | None
|
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) = None
|
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stop_sequence: str | None = None
|
||||
|
||||
|
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class AnthropicStreamEvent(BaseModel):
|
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"""Streaming event"""
|
||||
|
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type: Literal[
|
||||
"message_start",
|
||||
"message_delta",
|
||||
"message_stop",
|
||||
"content_block_start",
|
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"content_block_delta",
|
||||
"content_block_stop",
|
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"ping",
|
||||
"error",
|
||||
]
|
||||
message: Optional["AnthropicMessagesResponse"] = None
|
||||
delta: AnthropicDelta | None = None
|
||||
content_block: AnthropicContentBlock | None = None
|
||||
index: int | None = None
|
||||
error: AnthropicError | None = None
|
||||
usage: AnthropicUsage | None = None
|
||||
|
||||
|
||||
class AnthropicMessagesResponse(BaseModel):
|
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"""Anthropic Messages API response"""
|
||||
|
||||
id: str
|
||||
type: Literal["message"] = "message"
|
||||
role: Literal["assistant"] = "assistant"
|
||||
content: list[AnthropicContentBlock]
|
||||
model: str
|
||||
stop_reason: (
|
||||
Literal["end_turn", "max_tokens", "stop_sequence", "tool_use"] | None
|
||||
) = None
|
||||
stop_sequence: str | None = None
|
||||
usage: AnthropicUsage | None = None
|
||||
|
||||
def model_post_init(self, __context):
|
||||
if not self.id:
|
||||
self.id = f"msg_{int(time.time() * 1000)}"
|
||||
458
vllm/entrypoints/anthropic/serving_messages.py
Normal file
458
vllm/entrypoints/anthropic/serving_messages.py
Normal file
@@ -0,0 +1,458 @@
|
||||
# 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
|
||||
from collections.abc import AsyncGenerator
|
||||
from typing import Any
|
||||
|
||||
from fastapi import Request
|
||||
|
||||
from vllm.engine.protocol import EngineClient
|
||||
from vllm.entrypoints.anthropic.protocol import (
|
||||
AnthropicContentBlock,
|
||||
AnthropicDelta,
|
||||
AnthropicError,
|
||||
AnthropicMessagesRequest,
|
||||
AnthropicMessagesResponse,
|
||||
AnthropicStreamEvent,
|
||||
AnthropicUsage,
|
||||
)
|
||||
from vllm.entrypoints.chat_utils import ChatTemplateContentFormatOption
|
||||
from vllm.entrypoints.logger import RequestLogger
|
||||
from vllm.entrypoints.openai.protocol import (
|
||||
ChatCompletionNamedToolChoiceParam,
|
||||
ChatCompletionRequest,
|
||||
ChatCompletionResponse,
|
||||
ChatCompletionStreamResponse,
|
||||
ChatCompletionToolsParam,
|
||||
ErrorResponse,
|
||||
StreamOptions,
|
||||
)
|
||||
from vllm.entrypoints.openai.serving_chat import OpenAIServingChat
|
||||
from vllm.entrypoints.openai.serving_models import OpenAIServingModels
|
||||
|
||||
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,
|
||||
*,
|
||||
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,
|
||||
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",
|
||||
}
|
||||
|
||||
def _convert_anthropic_to_openai_request(
|
||||
self, anthropic_request: AnthropicMessagesRequest
|
||||
) -> ChatCompletionRequest:
|
||||
"""Convert Anthropic message format to OpenAI format"""
|
||||
openai_messages = []
|
||||
|
||||
# Add system message if provided
|
||||
if anthropic_request.system:
|
||||
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:
|
||||
system_prompt += block.text
|
||||
openai_messages.append({"role": "system", "content": system_prompt})
|
||||
|
||||
for msg in anthropic_request.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]] = []
|
||||
|
||||
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 == "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}",
|
||||
}
|
||||
)
|
||||
|
||||
# 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:
|
||||
continue
|
||||
|
||||
openai_messages.append(openai_msg)
|
||||
|
||||
req = 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,
|
||||
)
|
||||
|
||||
if anthropic_request.stream:
|
||||
req.stream = anthropic_request.stream
|
||||
req.stream_options = StreamOptions.validate({"include_usage": True})
|
||||
|
||||
if anthropic_request.tool_choice is None:
|
||||
req.tool_choice = None
|
||||
elif anthropic_request.tool_choice.type == "auto":
|
||||
req.tool_choice = "auto"
|
||||
elif anthropic_request.tool_choice.type == "any":
|
||||
req.tool_choice = "required"
|
||||
elif anthropic_request.tool_choice.type == "tool":
|
||||
req.tool_choice = ChatCompletionNamedToolChoiceParam.model_validate(
|
||||
{
|
||||
"type": "function",
|
||||
"function": {"name": anthropic_request.tool_choice.name},
|
||||
}
|
||||
)
|
||||
|
||||
tools = []
|
||||
if anthropic_request.tools is None:
|
||||
return req
|
||||
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
|
||||
return req
|
||||
|
||||
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.
|
||||
"""
|
||||
logger.debug("Received messages request %s", request.model_dump_json())
|
||||
chat_req = self._convert_anthropic_to_openai_request(request)
|
||||
logger.debug("Convert to OpenAI request %s", request.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,
|
||||
),
|
||||
)
|
||||
if generator.choices[0].finish_reason == "stop":
|
||||
result.stop_reason = "end_turn"
|
||||
elif generator.choices[0].finish_reason == "length":
|
||||
result.stop_reason = "max_tokens"
|
||||
elif generator.choices[0].finish_reason == "tool_calls":
|
||||
result.stop_reason = "tool_use"
|
||||
|
||||
content: list[AnthropicContentBlock] = [
|
||||
AnthropicContentBlock(
|
||||
type="text",
|
||||
text=generator.choices[0].message.content
|
||||
if generator.choices[0].message.content
|
||||
else "",
|
||||
)
|
||||
]
|
||||
|
||||
for tool_call in generator.choices[0].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:
|
||||
first_item = True
|
||||
finish_reason = None
|
||||
content_block_index = 0
|
||||
content_block_started = False
|
||||
|
||||
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")
|
||||
yield "data: [DONE]\n\n"
|
||||
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,
|
||||
),
|
||||
)
|
||||
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:
|
||||
if content_block_started:
|
||||
stop_chunk = AnthropicStreamEvent(
|
||||
index=content_block_index,
|
||||
type="content_block_stop",
|
||||
)
|
||||
data = stop_chunk.model_dump_json(exclude_unset=True)
|
||||
yield wrap_data_with_event(data, "content_block_stop")
|
||||
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
|
||||
|
||||
# content
|
||||
if origin_chunk.choices[0].delta.content is not None:
|
||||
if not content_block_started:
|
||||
chunk = AnthropicStreamEvent(
|
||||
index=content_block_index,
|
||||
type="content_block_start",
|
||||
content_block=AnthropicContentBlock(
|
||||
type="text", text=""
|
||||
),
|
||||
)
|
||||
data = chunk.model_dump_json(exclude_unset=True)
|
||||
yield wrap_data_with_event(data, "content_block_start")
|
||||
content_block_started = True
|
||||
|
||||
if origin_chunk.choices[0].delta.content == "":
|
||||
continue
|
||||
chunk = AnthropicStreamEvent(
|
||||
index=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")
|
||||
continue
|
||||
|
||||
# tool calls
|
||||
elif len(origin_chunk.choices[0].delta.tool_calls) > 0:
|
||||
tool_call = origin_chunk.choices[0].delta.tool_calls[0]
|
||||
if tool_call.id is not None:
|
||||
if content_block_started:
|
||||
stop_chunk = AnthropicStreamEvent(
|
||||
index=content_block_index,
|
||||
type="content_block_stop",
|
||||
)
|
||||
data = stop_chunk.model_dump_json(
|
||||
exclude_unset=True
|
||||
)
|
||||
yield wrap_data_with_event(
|
||||
data, "content_block_stop"
|
||||
)
|
||||
content_block_started = False
|
||||
content_block_index += 1
|
||||
|
||||
chunk = AnthropicStreamEvent(
|
||||
index=content_block_index,
|
||||
type="content_block_start",
|
||||
content_block=AnthropicContentBlock(
|
||||
type="tool_use",
|
||||
id=tool_call.id,
|
||||
name=tool_call.function.name
|
||||
if tool_call.function
|
||||
else None,
|
||||
input={},
|
||||
),
|
||||
)
|
||||
data = chunk.model_dump_json(exclude_unset=True)
|
||||
yield wrap_data_with_event(data, "content_block_start")
|
||||
content_block_started = True
|
||||
|
||||
else:
|
||||
chunk = AnthropicStreamEvent(
|
||||
index=content_block_index,
|
||||
type="content_block_delta",
|
||||
delta=AnthropicDelta(
|
||||
type="input_json_delta",
|
||||
partial_json=tool_call.function.arguments
|
||||
if tool_call.function
|
||||
else None,
|
||||
),
|
||||
)
|
||||
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")
|
||||
yield "data: [DONE]\n\n"
|
||||
|
||||
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")
|
||||
yield "data: [DONE]\n\n"
|
||||
Reference in New Issue
Block a user