63 lines
2.5 KiB
Python
63 lines
2.5 KiB
Python
from typing import Literal
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from fastapi import APIRouter, Depends, HTTPException, Request, UploadFile
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from pydantic import BaseModel
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from private_gpt.server.ingest.ingest_service import IngestedDoc, IngestService
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from private_gpt.server.utils.auth import authenticated
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ingest_router = APIRouter(prefix="/v1", dependencies=[Depends(authenticated)])
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class IngestResponse(BaseModel):
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object: Literal["list"]
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model: Literal["private-gpt"]
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data: list[IngestedDoc]
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@ingest_router.post("/ingest", tags=["Ingestion"])
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def ingest(request: Request, file: UploadFile) -> IngestResponse:
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"""Ingests and processes a file, storing its chunks to be used as context.
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The context obtained from files is later used in
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`/chat/completions`, `/completions`, and `/chunks` APIs.
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Most common document
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formats are supported, but you may be prompted to install an extra dependency to
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manage a specific file type.
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A file can generate different Documents (for example a PDF generates one Document
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per page). All Documents IDs are returned in the response, together with the
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extracted Metadata (which is later used to improve context retrieval). Those IDs
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can be used to filter the context used to create responses in
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`/chat/completions`, `/completions`, and `/chunks` APIs.
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"""
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service = request.state.injector.get(IngestService)
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if file.filename is None:
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raise HTTPException(400, "No file name provided")
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ingested_documents = service.ingest(file.filename, file.file.read())
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return IngestResponse(object="list", model="private-gpt", data=ingested_documents)
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@ingest_router.get("/ingest/list", tags=["Ingestion"])
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def list_ingested(request: Request) -> IngestResponse:
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"""Lists already ingested Documents including their Document ID and metadata.
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Those IDs can be used to filter the context used to create responses
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in `/chat/completions`, `/completions`, and `/chunks` APIs.
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"""
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service = request.state.injector.get(IngestService)
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ingested_documents = service.list_ingested()
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return IngestResponse(object="list", model="private-gpt", data=ingested_documents)
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@ingest_router.delete("/ingest/{doc_id}", tags=["Ingestion"])
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def delete_ingested(request: Request, doc_id: str) -> None:
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"""Delete the specified ingested Document.
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The `doc_id` can be obtained from the `GET /ingest/list` endpoint.
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The document will be effectively deleted from your storage context.
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"""
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service = request.state.injector.get(IngestService)
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service.delete(doc_id)
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