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vLLM/vllm/entrypoints/pooling/embed/protocol.py
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wang.yuqiandgemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com> 05f3d714db [Frontend][3/n] Make pooling entrypoints request schema consensus | EmbedRequest & ClassifyRequest (#32905)
Signed-off-by: wang.yuqi <yuqi.wang@daocloud.io>
Signed-off-by: wang.yuqi <noooop@126.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
2026-01-23 12:03:44 +00:00

59 lines
1.6 KiB
Python

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import time
from typing import Any, TypeAlias
from pydantic import (
Field,
)
from vllm.entrypoints.openai.engine.protocol import OpenAIBaseModel, UsageInfo
from vllm.entrypoints.pooling.base.protocol import (
ChatRequestMixin,
CompletionRequestMixin,
EmbedRequestMixin,
PoolingBasicRequestMixin,
)
from vllm.utils import random_uuid
class EmbeddingCompletionRequest(
PoolingBasicRequestMixin, CompletionRequestMixin, EmbedRequestMixin
):
# Ordered by official OpenAI API documentation
# https://platform.openai.com/docs/api-reference/embeddings
pass
class EmbeddingChatRequest(
PoolingBasicRequestMixin, ChatRequestMixin, EmbedRequestMixin
):
mm_processor_kwargs: dict[str, Any] | None = Field(
default=None,
description=("Additional kwargs to pass to the HF processor."),
)
EmbeddingRequest: TypeAlias = EmbeddingCompletionRequest | EmbeddingChatRequest
class EmbeddingResponseData(OpenAIBaseModel):
index: int
object: str = "embedding"
embedding: list[float] | str
class EmbeddingResponse(OpenAIBaseModel):
id: str = Field(default_factory=lambda: f"embd-{random_uuid()}")
object: str = "list"
created: int = Field(default_factory=lambda: int(time.time()))
model: str
data: list[EmbeddingResponseData]
usage: UsageInfo
class EmbeddingBytesResponse(OpenAIBaseModel):
content: list[bytes]
headers: dict[str, str] | None = None
media_type: str = "application/octet-stream"