wang.yuqiandgemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
05f3d714db
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>
59 lines
1.6 KiB
Python
59 lines
1.6 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import time
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from typing import Any, TypeAlias
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from pydantic import (
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Field,
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)
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from vllm.entrypoints.openai.engine.protocol import OpenAIBaseModel, UsageInfo
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from vllm.entrypoints.pooling.base.protocol import (
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ChatRequestMixin,
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CompletionRequestMixin,
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EmbedRequestMixin,
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PoolingBasicRequestMixin,
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)
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from vllm.utils import random_uuid
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class EmbeddingCompletionRequest(
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PoolingBasicRequestMixin, CompletionRequestMixin, EmbedRequestMixin
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):
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# Ordered by official OpenAI API documentation
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# https://platform.openai.com/docs/api-reference/embeddings
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pass
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class EmbeddingChatRequest(
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PoolingBasicRequestMixin, ChatRequestMixin, EmbedRequestMixin
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):
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mm_processor_kwargs: dict[str, Any] | None = Field(
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default=None,
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description=("Additional kwargs to pass to the HF processor."),
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)
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EmbeddingRequest: TypeAlias = EmbeddingCompletionRequest | EmbeddingChatRequest
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class EmbeddingResponseData(OpenAIBaseModel):
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index: int
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object: str = "embedding"
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embedding: list[float] | str
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class EmbeddingResponse(OpenAIBaseModel):
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id: str = Field(default_factory=lambda: f"embd-{random_uuid()}")
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object: str = "list"
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created: int = Field(default_factory=lambda: int(time.time()))
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model: str
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data: list[EmbeddingResponseData]
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usage: UsageInfo
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class EmbeddingBytesResponse(OpenAIBaseModel):
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content: list[bytes]
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headers: dict[str, str] | None = None
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media_type: str = "application/octet-stream"
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