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https://github.com/zylon-ai/private-gpt.git
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aa70d3d9f0
* Add simple Basic auth To enable the basic authentication, one must set `server.auth.enabled` to true. The static string defined in `server.auth.secret` must be set in the header `Authorization`. The health check endpoint will always be accessible, no matter the API auth configuration. * Fix linting and type check * Fighting with mypy being too restrictive Had to disable mypy in the `auth` as we are not using the same signature for the authenticated method. mypy was complaining that the signatures of `authenticated` must be identical, no matter in which logical branch we are. Given that fastapi is accomodating itself of method signatures (it will inject the dependencies in the method call), this warning of mypy is actually preventing us to do something legit. mypy doc: https://mypy.readthedocs.io/en/stable/common_issues.html * Write tests to verify that the simple auth is working
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, UploadFile
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from pydantic import BaseModel
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from private_gpt.di import root_injector
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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(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 = root_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() -> 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 = root_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(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 = root_injector.get(IngestService)
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service.delete(doc_id)
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