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from typing import Literal |
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from fastapi import APIRouter, Depends, Request |
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from pydantic import BaseModel |
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from private_gpt.server.embeddings.embeddings_service import ( |
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Embedding, |
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EmbeddingsService, |
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) |
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from private_gpt.server.utils.auth import authenticated |
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from private_gpt.server.utils.authentication import get_current_user |
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embeddings_router = APIRouter(prefix="/v1", dependencies=[Depends(get_current_user)]) |
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class EmbeddingsBody(BaseModel): |
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input: str | list[str] |
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class EmbeddingsResponse(BaseModel): |
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object: Literal["list"] |
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model: Literal["private-gpt"] |
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data: list[Embedding] |
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@embeddings_router.post("/embeddings", tags=["Embeddings"]) |
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def embeddings_generation(request: Request, body: EmbeddingsBody) -> EmbeddingsResponse: |
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"""Get a vector representation of a given input. |
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That vector representation can be easily consumed |
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by machine learning models and algorithms. |
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""" |
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service = request.state.injector.get(EmbeddingsService) |
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input_texts = body.input if isinstance(body.input, list) else [body.input] |
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embeddings = service.texts_embeddings(input_texts) |
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return EmbeddingsResponse(object="list", model="private-gpt", data=embeddings) |
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