109 lines
3.5 KiB
Python

from pathlib import Path
from django.conf import settings
from paperless_tesseract.parsers import RasterisedDocumentParser
class RemoteEngineConfig:
def __init__(
self,
engine: str,
api_key: str | None = None,
endpoint: str | None = None,
):
self.engine = engine
self.api_key = api_key
self.endpoint = endpoint
def engine_is_valid(self):
valid = self.engine in ["azureai"] and self.api_key is not None
if self.engine == "azureai":
valid = valid and self.endpoint is not None
return valid
class RemoteDocumentParser(RasterisedDocumentParser):
"""
This parser uses a remote ocr engine to parse documents
"""
logging_name = "paperless.parsing.remote"
def get_settings(self) -> RemoteEngineConfig:
"""
This parser uses the OCR configuration settings to parse documents
"""
return RemoteEngineConfig(
engine=settings.REMOTE_OCR_ENGINE,
api_key=settings.REMOTE_OCR_API_KEY,
endpoint=settings.REMOTE_OCR_ENDPOINT,
)
def supported_mime_types(self):
if self.settings.engine_is_valid():
return [
"application/pdf",
"image/png",
"image/jpeg",
"image/tiff",
"image/bmp",
"image/gif",
"image/webp",
]
else:
return []
def azure_ai_vision_parse(
self,
file: Path,
) -> str | None:
"""
This method uses the Azure AI Vision API to parse documents
"""
from azure.ai.documentintelligence import DocumentIntelligenceClient
from azure.ai.documentintelligence.models import AnalyzeDocumentRequest
from azure.ai.documentintelligence.models import AnalyzeOutputOption
from azure.ai.documentintelligence.models import DocumentContentFormat
from azure.core.credentials import AzureKeyCredential
client = DocumentIntelligenceClient(
endpoint=self.settings.endpoint,
credential=AzureKeyCredential(self.settings.api_key),
)
with file.open("rb") as f:
analyze_request = AnalyzeDocumentRequest(bytes_source=f.read())
poller = client.begin_analyze_document(
model_id="prebuilt-read",
body=analyze_request,
output_content_format=DocumentContentFormat.TEXT,
output=[AnalyzeOutputOption.PDF], # request searchable PDF output
content_type="application/json",
)
poller.wait()
result_id = poller.details["operation_id"]
result = poller.result()
# Download the PDF with embedded text
self.archive_path = Path(self.tempdir) / "archive.pdf"
with self.archive_path.open("wb") as f:
for chunk in client.get_analyze_result_pdf(
model_id="prebuilt-read",
result_id=result_id,
):
f.write(chunk)
return result.content
def parse(self, document_path: Path, mime_type, file_name=None):
if not self.settings.engine_is_valid():
self.log.warning(
"No valid remote parser engine is configured, content will be empty.",
)
self.text = ""
return
elif self.settings.engine == "azureai":
self.text = self.azure_ai_vision_parse(document_path)