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fix: specify dict type, fix bulk ingestion with metadata

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This commit is contained in:
Nathan Lenas 2024-07-23 09:18:27 +02:00
parent 8863154baa
commit 50388f6a33
6 changed files with 112 additions and 42 deletions
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25
private_gpt/server/ingest/ingest_router.py
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@ -1,6 +1,6 @@
from typing import Literal, Dict
from typing import Literal
from fastapi import APIRouter, Depends, HTTPException, Request, UploadFile, Form
from fastapi import APIRouter, Depends, Form, HTTPException, Request, UploadFile
from pydantic import BaseModel, Field
from private_gpt.server.ingest.ingest_service import IngestService
@ -20,14 +20,15 @@ class IngestTextBody(BaseModel):
"Chinese martial arts."
]
)
metadata: Dict = Field(None,
metadata: dict[str, str] = Field(
None,
examples=[
{
"title": "Avatar: The Last Airbender",
"author": "Michael Dante DiMartino, Bryan Konietzko",
"year": "2005",
}
]
],
)
@ -47,12 +48,14 @@ def ingest(request: Request, file: UploadFile) -> IngestResponse:
@ingest_router.post("/ingest/file", tags=["Ingestion"])
def ingest_file(request: Request, file: UploadFile, metadata: str = Form(None)) -> IngestResponse:
def ingest_file(
request: Request, file: UploadFile, metadata: str = Form(None)
) -> IngestResponse:
"""Ingests and processes a file, storing its chunks to be used as context.
metadata: Optional metadata to be associated with the file.
metadata: Optional metadata to be associated with the file.
You do not have to specify this field if not needed.
e.g. {"title": "Avatar: The Last Airbender", "author": "Michael Dante DiMartino, Bryan Konietzko", "year": "2005"}
e.g. {"title": "Avatar: The Last Airbender", "year": "2005"}
The context obtained from files is later used in
`/chat/completions`, `/completions`, and `/chunks` APIs.
@ -70,9 +73,11 @@ def ingest_file(request: Request, file: UploadFile, metadata: str = Form(None))
service = request.state.injector.get(IngestService)
if file.filename is None:
raise HTTPException(400, "No file name provided")
metadata_dict = None if metadata is None else eval(metadata)
ingested_documents = service.ingest_bin_data(file.filename, file.file, metadata_dict)
ingested_documents = service.ingest_bin_data(
file.filename, file.file, metadata_dict
)
return IngestResponse(object="list", model="private-gpt", data=ingested_documents)