paperless-ngx/src/paperless_remote/parsers.py

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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):
"""
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This parser uses a remote OCR engine to parse documents. Currently, it supports Azure AI Vision
as this is the only service that provides a remote OCR API with text-embedded PDF output.
"""
logging_name = "paperless.parsing.remote"
def get_settings(self) -> RemoteEngineConfig:
"""
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Returns the configuration for the remote OCR engine, loaded from Django settings.
"""
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": ".pdf",
"image/png": ".png",
"image/jpeg": ".jpg",
"image/tiff": ".tiff",
"image/bmp": ".bmp",
"image/gif": ".gif",
"image/webp": ".webp",
}
else:
return {}
def azure_ai_vision_parse(
self,
file: Path,
) -> str | None:
"""
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Uses Azure AI Vision to parse the document and return the text content.
It requests a searchable PDF output with embedded text.
The PDF is saved to the archive_path attribute.
Returns the text content extracted from the document.
If the parsing fails, it returns None.
"""
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from azure.ai.documentintelligence import DocumentIntelligenceClient
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from azure.ai.documentintelligence.models import AnalyzeDocumentRequest
from azure.ai.documentintelligence.models import AnalyzeOutputOption
from azure.ai.documentintelligence.models import DocumentContentFormat
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from azure.core.credentials import AzureKeyCredential
client = DocumentIntelligenceClient(
endpoint=self.settings.endpoint,
credential=AzureKeyCredential(self.settings.api_key),
)
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try:
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 = 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
except Exception as e:
self.log.error(f"Azure AI Vision parsing failed: {e}")
finally:
client.close()
return None
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 = ""
elif self.settings.engine == "azureai":
self.text = self.azure_ai_vision_parse(document_path)