Merge branch 'dev' into feature-remote-ocr-2

[ci skip]

docs/configuration.md

@@ -1800,3 +1800,23 @@ password. All of these options come from their similarly-named [Django settings]
 #### [`PAPERLESS_EMAIL_USE_SSL=<bool>`](#PAPERLESS_EMAIL_USE_SSL) {#PAPERLESS_EMAIL_USE_SSL}
 
 : Defaults to false.
+
+## Remote OCR
+
+#### [`PAPERLESS_REMOTE_OCR_ENGINE=<str>`](#PAPERLESS_REMOTE_OCR_ENGINE) {#PAPERLESS_REMOTE_OCR_ENGINE}
+
+: The remote OCR engine to use. Currently only Azure AI is supported as "azureai".
+
+    Defaults to None, which disables remote OCR.
+
+#### [`PAPERLESS_REMOTE_OCR_API_KEY=<str>`](#PAPERLESS_REMOTE_OCR_API_KEY) {#PAPERLESS_REMOTE_OCR_API_KEY}
+
+: The API key to use for the remote OCR engine.
+
+    Defaults to None.
+
+#### [`PAPERLESS_REMOTE_OCR_ENDPOINT=<str>`](#PAPERLESS_REMOTE_OCR_ENDPOINT) {#PAPERLESS_REMOTE_OCR_ENDPOINT}
+
+: The endpoint to use for the remote OCR engine. This is required for Azure AI.
+
+    Defaults to None.

docs/index.md

@@ -25,9 +25,10 @@ physical documents into a searchable online archive so you can keep, well, _less
 ## Features
 
 -   **Organize and index** your scanned documents with tags, correspondents, types, and more.
--   _Your_ data is stored locally on _your_ server and is never transmitted or shared in any way.
+-   _Your_ data is stored locally on _your_ server and is never transmitted or shared in any way, unless you explicitly choose to do so.
 -   Performs **OCR** on your documents, adding searchable and selectable text, even to documents scanned with only images.
--   Utilizes the open-source Tesseract engine to recognize more than 100 languages.
+    -   Utilizes the open-source Tesseract engine to recognize more than 100 languages.
+    -   _New!_ Supports remote OCR with Azure AI (opt-in).
 -   Documents are saved as PDF/A format which is designed for long term storage, alongside the unaltered originals.
 -   Uses machine-learning to automatically add tags, correspondents and document types to your documents.
 -   Supports PDF documents, images, plain text files, Office documents (Word, Excel, PowerPoint, and LibreOffice equivalents)[^1] and more.

docs/usage.md

@@ -850,6 +850,21 @@ how regularly you intend to scan documents and use paperless.
     performed the task associated with the document, move it to the
     inbox.
 
+## Remote OCR
+
+!!! important
+
+    This feature is disabled by default and will always remain strictly "opt-in".
+
+Paperless-ngx supports performing OCR on documents using remote services. At the moment, this is limited to
+[Microsoft's Azure "Document Intelligence" service](https://azure.microsoft.com/en-us/products/ai-services/ai-document-intelligence).
+This is of course a paid service (with a free tier) which requires an Azure account and subscription. Azure AI is not affiliated with
+Paperless-ngx in any way. When enabled, Paperless-ngx will automatically send appropriate documents to Azure for OCR processing, bypassing
+the local OCR engine. See the [configuration](configuration.md#PAPERLESS_REMOTE_OCR_ENGINE) options for more details.
+
+Additionally, when using a commercial service with this feature, consider both potential costs as well as any associated file size
+or page limitations (e.g. with a free tier).
+
 ## Architecture
 
 Paperless-ngx consists of the following components:

pyproject.toml

@@ -15,6 +15,7 @@ classifiers = [
 # This will allow testing to not install a webserver, mysql, etc
 
 dependencies = [
+  "azure-ai-documentintelligence>=1.0.2",
   "babel>=2.17",
   "bleach~=6.2.0",
   "celery[redis]~=5.5.1",
@@ -230,6 +231,7 @@ testpaths = [
   "src/paperless_tesseract/tests/",
   "src/paperless_tika/tests",
   "src/paperless_text/tests/",
+  "src/paperless_remote/tests/",
 ]
 addopts = [
   "--pythonwarnings=all",

src/paperless/settings.py

@@ -322,6 +322,7 @@ INSTALLED_APPS = [
     "paperless_tesseract.apps.PaperlessTesseractConfig",
     "paperless_text.apps.PaperlessTextConfig",
     "paperless_mail.apps.PaperlessMailConfig",
+    "paperless_remote.apps.PaperlessRemoteParserConfig",
     "django.contrib.admin",
     "rest_framework",
     "rest_framework.authtoken",
@@ -425,7 +426,7 @@ WHITENOISE_STATIC_PREFIX = "/static/"
 if machine().lower() == "aarch64":  # pragma: no cover
     _static_backend = "django.contrib.staticfiles.storage.StaticFilesStorage"
 else:
-    _static_backend = "paperless.staticfiles.DeduplicatedCompressedStaticFilesStorage"
+    _static_backend = "whitenoise.storage.CompressedStaticFilesStorage"
 
 STORAGES = {
     "staticfiles": {
@@ -1388,3 +1389,10 @@ WEBHOOKS_ALLOW_INTERNAL_REQUESTS = __get_boolean(
     "PAPERLESS_WEBHOOKS_ALLOW_INTERNAL_REQUESTS",
     "true",
 )
+
+###############################################################################
+# Remote Parser                                                               #
+###############################################################################
+REMOTE_OCR_ENGINE = os.getenv("PAPERLESS_REMOTE_OCR_ENGINE")
+REMOTE_OCR_API_KEY = os.getenv("PAPERLESS_REMOTE_OCR_API_KEY")
+REMOTE_OCR_ENDPOINT = os.getenv("PAPERLESS_REMOTE_OCR_ENDPOINT")

src/paperless/staticfiles.py

@@ -1,385 +0,0 @@
-import gzip
-import hashlib
-import logging
-import os
-import shutil
-import threading
-import time
-from collections import defaultdict
-from concurrent.futures import ThreadPoolExecutor
-from concurrent.futures import as_completed
-from dataclasses import dataclass
-from pathlib import Path
-
-import brotli
-import humanize
-from django.contrib.staticfiles.storage import StaticFilesStorage
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass(slots=True)
-class FileInfo:
-    file_path_str: str
-    file_path_path: Path
-    checksum: str
-    original_size: int
-    gzip_size: int | None = None
-    brotli_size: int | None = None
-
-
-class DeduplicatedCompressedStaticFilesStorage(StaticFilesStorage):
-    # File extensions that should be compressed
-    COMPRESSIBLE_EXTENSIONS = {
-        ".css",
-        ".js",
-        ".html",
-        ".htm",
-        ".xml",
-        ".json",
-        ".txt",
-        ".svg",
-        ".md",
-        ".rst",
-        ".csv",
-        ".tsv",
-        ".yaml",
-        ".yml",
-        ".map",
-    }
-
-    # Minimum file size to compress (bytes)
-    MIN_COMPRESS_SIZE = 1024  # 1KB
-
-    # Maximum number of threads for parallel processing
-    MAX_WORKERS = min(32, (os.cpu_count() or 1) + 4)
-
-    # Chunk size for file reading
-    CHUNK_SIZE = 64 * 1024  # 64KB
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        # --- MODIFIED: Added path_to_file_info for easy lookup ---
-        self.hash_to_files: dict[str, list[FileInfo]] = defaultdict(list)
-        self.path_to_file_info: dict[str, FileInfo] = {}
-        self.linked_files: set[Path] = set()
-        self.compression_stats = {
-            "brotli": 0,
-            "gzip": 0,
-            "skipped_linked": 0,
-            "skipped_other": 0,
-            "errors": 0,
-        }
-        self._lock = threading.Lock()
-
-    def post_process(self, paths: list[str], **options):
-        """
-        Post-process collected files: deduplicate first, then compress.
-        Django 5.2 compatible with proper options handling.
-        """
-        start_time = time.time()
-
-        # Step 1: Build hash map for deduplication (parallel)
-        self._build_file_hash_map_parallel(paths)
-
-        # Step 2: Create hard links for duplicate files
-        self._create_hard_links()
-
-        # Step 3: Compress files (parallel, skip linked duplicates)
-        self._compress_files_parallel(paths)
-
-        # Step 4: Provide user a summary of the compression
-        self._log_compression_summary()
-
-        processing_time = time.time() - start_time
-        logger.info(f"Post-processing complete in {processing_time:.2f}s.")
-
-        # Return list of processed files
-        processed_files = []
-        for path in paths:
-            processed_files.append((path, path, True))
-            # Add compressed variants
-            file_path = self.path(path)
-            if Path(file_path + ".br").exists():
-                processed_files.append((path + ".br", path + ".br", True))
-            if Path(file_path + ".gz").exists():
-                processed_files.append((path + ".gz", path + ".gz", True))
-
-        return processed_files
-
-    def _build_file_hash_map_parallel(self, file_paths: list[str]):
-        """Build a map of file hashes using parallel processing."""
-        logger.info(
-            f"Hashing {len(file_paths)} files with {self.MAX_WORKERS} workers...",
-        )
-
-        def hash_file(path: str):
-            """Hash a single file."""
-            try:
-                file_path = Path(self.path(path))
-                if not file_path.is_file():
-                    return None, None, None
-
-                file_hash = self._get_file_hash_fast(file_path)
-                file_size = file_path.stat().st_size
-                return path, file_hash, file_size
-            except Exception as e:
-                logger.warning(f"Error hashing file {path}: {e}")
-                return path, None, None
-
-        with ThreadPoolExecutor(max_workers=self.MAX_WORKERS) as executor:
-            future_to_path = {
-                executor.submit(hash_file, path): path for path in file_paths
-            }
-
-            for future in as_completed(future_to_path):
-                path, file_hash, file_size = future.result()
-                if path is not None and file_hash is not None and file_size is not None:
-                    with self._lock:
-                        file_info = FileInfo(
-                            file_path_str=path,
-                            file_path_path=Path(self.path(path)),
-                            checksum=file_hash,
-                            original_size=file_size,
-                        )
-                        self.hash_to_files[file_hash].append(file_info)
-                        self.path_to_file_info[path] = file_info
-
-        duplicates = sum(1 for files in self.hash_to_files.values() if len(files) > 1)
-        logger.info(f"Found {duplicates} sets of duplicate files")
-
-    def _get_file_hash_fast(self, file_path: Path):
-        """Calculate SHA-256 hash of file content with optimized reading."""
-        hash_sha256 = hashlib.sha256()
-        try:
-            with file_path.open("rb") as f:
-                while chunk := f.read(self.CHUNK_SIZE):
-                    hash_sha256.update(chunk)
-        except OSError as e:
-            logger.warning(f"Could not read file {file_path}: {e}")
-            raise
-        return hash_sha256.hexdigest()
-
-    def _create_hard_links(self):
-        """Create hard links for duplicate files."""
-        logger.info("Creating hard links for duplicate files...")
-
-        linked_count = 0
-        for file_info_list in self.hash_to_files.values():
-            if len(file_info_list) <= 1:
-                continue
-
-            # Sort by file size (desc) then path length (asc) to keep best original
-            file_info_list.sort(key=lambda x: (-x.original_size, len(x.file_path_str)))
-            original_file_info = file_info_list[0]
-            duplicate_info = file_info_list[1:]
-
-            for duplicate_file_info in duplicate_info:
-                try:
-                    # Remove duplicate file and create hard link
-                    if duplicate_file_info.file_path_path.exists():
-                        duplicate_file_info.file_path_path.unlink()
-
-                    # Create hard link
-                    os.link(
-                        original_file_info.file_path_path,
-                        duplicate_file_info.file_path_path,
-                    )
-
-                    with self._lock:
-                        self.linked_files.add(duplicate_file_info.file_path_path)
-
-                    linked_count += 1
-
-                    logger.info(
-                        f"Linked {duplicate_file_info.file_path_path} -> {original_file_info.file_path_path}",
-                    )
-
-                except OSError as e:
-                    logger.error(
-                        f"Hard link failed for {original_file_info.file_path_path}, copying instead: {e}",
-                    )
-                    # Fall back to copying if hard linking fails
-                    try:
-                        import shutil
-
-                        shutil.copy2(
-                            original_file_info.file_path_path,
-                            original_file_info.file_path_path,
-                        )
-                        logger.error(
-                            f"Copied {original_file_info.file_path_path} (hard link failed)",
-                        )
-                    except Exception as copy_error:
-                        logger.error(
-                            f"Failed to copy {original_file_info.file_path_path}: {copy_error}",
-                        )
-
-        if linked_count > 0:
-            logger.info(f"Created {linked_count} hard links")
-
-    def _compress_files_parallel(self, file_paths: list[str]):
-        """Compress files using parallel processing and update FileInfo objects."""
-        # Identify files to compress, excluding hard links
-        compressible_files = [
-            self.path_to_file_info[path]
-            for path in file_paths
-            if self.path_to_file_info[path].file_path_path not in self.linked_files
-            and self._should_compress_file(path)
-        ]
-
-        if not compressible_files:
-            logger.info("No new files to compress")
-            return
-
-        logger.info(
-            f"Compressing {len(compressible_files)} files with {self.MAX_WORKERS} workers...",
-        )
-
-        def compress_file(file_info: FileInfo):
-            """Compress a single file and update its FileInfo by side-effect."""
-            brotli_size = None
-            gzip_size = None
-            error = None
-            try:
-                brotli_size = self._compress_file_brotli(str(file_info.file_path_path))
-                gzip_size = self._compress_file_gzip(str(file_info.file_path_path))
-                # Store the compressed sizes
-                file_info.brotli_size = brotli_size
-                file_info.gzip_size = gzip_size
-            except Exception as e:
-                error = str(e)
-                logger.warning(f"Error compressing {file_info.file_path_str}: {e}")
-            return {
-                "brotli": brotli_size is not None,
-                "gzip": gzip_size is not None,
-                "error": error,
-            }
-
-        with ThreadPoolExecutor(max_workers=self.MAX_WORKERS) as executor:
-            future_to_info = {
-                executor.submit(compress_file, info): info
-                for info in compressible_files
-            }
-
-            for future in as_completed(future_to_info):
-                result = future.result()
-                with self._lock:
-                    if result["brotli"]:
-                        self.compression_stats["brotli"] += 1
-                    if result["gzip"]:
-                        self.compression_stats["gzip"] += 1
-                    if result["error"]:
-                        self.compression_stats["errors"] += 1
-                    if (
-                        not result["brotli"]
-                        and not result["gzip"]
-                        and not result["error"]
-                    ):
-                        self.compression_stats["skipped_other"] += 1
-
-        self.compression_stats["skipped_linked"] = len(self.linked_files)
-        logger.info(f"File count stats: {self.compression_stats}")
-
-    def _should_compress_file(self, path: str):
-        """Determine if a file should be compressed."""
-        file_ext = Path(path).suffix.lower()
-        if file_ext not in self.COMPRESSIBLE_EXTENSIONS:
-            return False
-        try:
-            if Path(self.path(path)).stat().st_size < self.MIN_COMPRESS_SIZE:
-                return False
-        except OSError:
-            return False
-        return True
-
-    def _compress_file_brotli(self, file_path: str) -> int | None:
-        """Compress file using Brotli, returns compressed size or None."""
-        brotli_path = Path(file_path + ".br")
-        try:
-            with Path(file_path).open("rb") as f_in:
-                original_data = f_in.read()
-            compressed_data = brotli.compress(
-                original_data,
-                quality=10,
-                lgwin=22,  # Window size
-                lgblock=0,  # Auto block size
-            )
-            if len(compressed_data) < len(original_data) * 0.95:
-                with brotli_path.open("wb") as f_out:
-                    f_out.write(compressed_data)
-                return len(compressed_data)
-            return None
-        except Exception as e:
-            logger.warning(f"Brotli compression failed for {file_path}: {e}")
-            return None
-
-    def _compress_file_gzip(self, file_path: str) -> int | None:
-        """Compress file using GZip, returns compressed size or None."""
-        gzip_path = Path(file_path + ".gz")
-        file_path_path = Path(file_path)
-        try:
-            original_size = file_path_path.stat().st_size
-            with (
-                file_path_path.open("rb") as f_in,
-                gzip.open(
-                    gzip_path,
-                    "wb",
-                    compresslevel=7,
-                ) as f_out,
-            ):
-                shutil.copyfileobj(f_in, f_out, length=self.CHUNK_SIZE)
-
-            compressed_size = gzip_path.stat().st_size
-            if compressed_size < original_size * 0.95:
-                return compressed_size
-            else:
-                gzip_path.unlink()
-                return None
-        except Exception as e:
-            logger.warning(f"GZip compression failed for {file_path}: {e}")
-            if gzip_path.exists():
-                try:
-                    gzip_path.unlink()
-                except OSError:
-                    pass
-            return None
-
-    def _log_compression_summary(self):
-        """Calculates and logs the total size savings from compression."""
-        total_original_size = 0
-        total_brotli_size = 0
-        total_gzip_size = 0
-
-        # Only consider the original files, not the duplicates, for size calculation
-        unique_files = {
-            file_list[0].checksum: file_list[0]
-            for file_list in self.hash_to_files.values()
-        }
-
-        for file_info in unique_files.values():
-            if self._should_compress_file(file_info.file_path_str):
-                total_original_size += file_info.original_size
-                if file_info.brotli_size:
-                    total_brotli_size += file_info.brotli_size
-                if file_info.gzip_size:
-                    total_gzip_size += file_info.gzip_size
-
-        def get_savings(original: int, compressed: int) -> str:
-            if original == 0:
-                return "0.00%"
-            return f"{(1 - compressed / original) * 100:.2f}%"
-
-        logger.info(
-            f"Total Original Size (compressible files): {humanize.naturalsize(total_original_size)}",
-        )
-        if total_brotli_size > 0:
-            logger.info(
-                f"Total Brotli Size: {humanize.naturalsize(total_brotli_size)} "
-                f"(Savings: {get_savings(total_original_size, total_brotli_size)})",
-            )
-        if total_gzip_size > 0:
-            logger.info(
-                f"Total Gzip Size:   {humanize.naturalsize(total_gzip_size)} "
-                f"(Savings: {get_savings(total_original_size, total_gzip_size)})",
-            )

src/paperless_remote/__init__.py

@@ -0,0 +1,4 @@
+# this is here so that django finds the checks.
+from paperless_remote.checks import check_remote_parser_configured
+
+__all__ = ["check_remote_parser_configured"]

src/paperless_remote/apps.py

@@ -0,0 +1,14 @@
+from django.apps import AppConfig
+
+from paperless_remote.signals import remote_consumer_declaration
+
+
+class PaperlessRemoteParserConfig(AppConfig):
+    name = "paperless_remote"
+
+    def ready(self):
+        from documents.signals import document_consumer_declaration
+
+        document_consumer_declaration.connect(remote_consumer_declaration)
+
+        AppConfig.ready(self)

src/paperless_remote/checks.py

@@ -0,0 +1,15 @@
+from django.conf import settings
+from django.core.checks import Error
+from django.core.checks import register
+
+
+@register()
+def check_remote_parser_configured(app_configs, **kwargs):
+    if settings.REMOTE_OCR_ENGINE == "azureai" and not settings.REMOTE_OCR_ENDPOINT:
+        return [
+            Error(
+                "Azure AI remote parser requires endpoint to be configured.",
+            ),
+        ]
+
+    return []

src/paperless_remote/parsers.py

@@ -0,0 +1,113 @@
+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. 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:
+        """
+        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:
+        """
+        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.
+        """
+        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)

src/paperless_remote/signals.py

@@ -0,0 +1,18 @@
+def get_parser(*args, **kwargs):
+    from paperless_remote.parsers import RemoteDocumentParser
+
+    return RemoteDocumentParser(*args, **kwargs)
+
+
+def get_supported_mime_types():
+    from paperless_remote.parsers import RemoteDocumentParser
+
+    return RemoteDocumentParser(None).supported_mime_types()
+
+
+def remote_consumer_declaration(sender, **kwargs):
+    return {
+        "parser": get_parser,
+        "weight": 5,
+        "mime_types": get_supported_mime_types(),
+    }

src/paperless_remote/tests/test_checks.py

@@ -0,0 +1,29 @@
+from django.test import TestCase
+from django.test import override_settings
+
+from paperless_remote import check_remote_parser_configured
+
+
+class TestChecks(TestCase):
+    @override_settings(REMOTE_OCR_ENGINE=None)
+    def test_no_engine(self):
+        msgs = check_remote_parser_configured(None)
+        self.assertEqual(len(msgs), 0)
+
+    @override_settings(REMOTE_OCR_ENGINE="azureai")
+    @override_settings(REMOTE_OCR_API_KEY="somekey")
+    @override_settings(REMOTE_OCR_ENDPOINT=None)
+    def test_azure_no_endpoint(self):
+        msgs = check_remote_parser_configured(None)
+        self.assertEqual(len(msgs), 1)
+        self.assertTrue(
+            msgs[0].msg.startswith(
+                "Azure AI remote parser requires endpoint to be configured.",
+            ),
+        )
+
+    @override_settings(REMOTE_OCR_ENGINE="something")
+    @override_settings(REMOTE_OCR_API_KEY="somekey")
+    def test_valid_configuration(self):
+        msgs = check_remote_parser_configured(None)
+        self.assertEqual(len(msgs), 0)

src/paperless_remote/tests/test_parser.py

@@ -0,0 +1,101 @@
+import uuid
+from pathlib import Path
+from unittest import mock
+
+from django.test import TestCase
+from django.test import override_settings
+
+from documents.tests.utils import DirectoriesMixin
+from documents.tests.utils import FileSystemAssertsMixin
+from paperless_remote.parsers import RemoteDocumentParser
+from paperless_remote.signals import get_parser
+
+
+class TestParser(DirectoriesMixin, FileSystemAssertsMixin, TestCase):
+    SAMPLE_FILES = Path(__file__).resolve().parent / "samples"
+
+    def assertContainsStrings(self, content, strings):
+        # Asserts that all strings appear in content, in the given order.
+        indices = []
+        for s in strings:
+            if s in content:
+                indices.append(content.index(s))
+            else:
+                self.fail(f"'{s}' is not in '{content}'")
+        self.assertListEqual(indices, sorted(indices))
+
+    @mock.patch("paperless_tesseract.parsers.run_subprocess")
+    @mock.patch("azure.ai.documentintelligence.DocumentIntelligenceClient")
+    def test_get_text_with_azure(self, mock_client_cls, mock_subprocess):
+        # Arrange mock Azure client
+        mock_client = mock.Mock()
+        mock_client_cls.return_value = mock_client
+
+        # Simulate poller result and its `.details`
+        mock_poller = mock.Mock()
+        mock_poller.wait.return_value = None
+        mock_poller.details = {"operation_id": "fake-op-id"}
+        mock_client.begin_analyze_document.return_value = mock_poller
+        mock_poller.result.return_value.content = "This is a test document."
+
+        # Return dummy PDF bytes
+        mock_client.get_analyze_result_pdf.return_value = [
+            b"%PDF-",
+            b"1.7 ",
+            b"FAKEPDF",
+        ]
+
+        # Simulate pdftotext by writing dummy text to sidecar file
+        def fake_run(cmd, *args, **kwargs):
+            with Path(cmd[-1]).open("w", encoding="utf-8") as f:
+                f.write("This is a test document.")
+
+        mock_subprocess.side_effect = fake_run
+
+        with override_settings(
+            REMOTE_OCR_ENGINE="azureai",
+            REMOTE_OCR_API_KEY="somekey",
+            REMOTE_OCR_ENDPOINT="https://endpoint.cognitiveservices.azure.com",
+        ):
+            parser = get_parser(uuid.uuid4())
+            parser.parse(
+                self.SAMPLE_FILES / "simple-digital.pdf",
+                "application/pdf",
+            )
+
+            self.assertContainsStrings(
+                parser.text.strip(),
+                ["This is a test document."],
+            )
+
+    @override_settings(
+        REMOTE_OCR_ENGINE="azureai",
+        REMOTE_OCR_API_KEY="key",
+        REMOTE_OCR_ENDPOINT="https://endpoint.cognitiveservices.azure.com",
+    )
+    def test_supported_mime_types_valid_config(self):
+        parser = RemoteDocumentParser(uuid.uuid4())
+        expected_types = {
+            "application/pdf": ".pdf",
+            "image/png": ".png",
+            "image/jpeg": ".jpg",
+            "image/tiff": ".tiff",
+            "image/bmp": ".bmp",
+            "image/gif": ".gif",
+            "image/webp": ".webp",
+        }
+        self.assertEqual(parser.supported_mime_types(), expected_types)
+
+    def test_supported_mime_types_invalid_config(self):
+        parser = get_parser(uuid.uuid4())
+        self.assertEqual(parser.supported_mime_types(), {})
+
+    @override_settings(
+        REMOTE_OCR_ENGINE=None,
+        REMOTE_OCR_API_KEY=None,
+        REMOTE_OCR_ENDPOINT=None,
+    )
+    def test_parse_with_invalid_config(self):
+        parser = get_parser(uuid.uuid4())
+        parser.parse(self.SAMPLE_FILES / "simple-digital.pdf", "application/pdf")
+        self.assertEqual(parser.text, "")

uv.lock

@@ -95,6 +95,34 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/af/cc/55a32a2c98022d88812b5986d2a92c4ff3ee087e83b712ebc703bba452bf/Automat-24.8.1-py3-none-any.whl", hash = "sha256:bf029a7bc3da1e2c24da2343e7598affaa9f10bf0ab63ff808566ce90551e02a", size = 42585, upload-time = "2024-08-19T17:31:56.729Z" },
 ]
 
+[[package]]
+name = "azure-ai-documentintelligence"
+version = "1.0.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "azure-core", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
+    { name = "isodate", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
+    { name = "typing-extensions", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/44/7b/8115cd713e2caa5e44def85f2b7ebd02a74ae74d7113ba20bdd41fd6dd80/azure_ai_documentintelligence-1.0.2.tar.gz", hash = "sha256:4d75a2513f2839365ebabc0e0e1772f5601b3a8c9a71e75da12440da13b63484", size = 170940 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d9/75/c9ec040f23082f54ffb1977ff8f364c2d21c79a640a13d1c1809e7fd6b1a/azure_ai_documentintelligence-1.0.2-py3-none-any.whl", hash = "sha256:e1fb446abbdeccc9759d897898a0fe13141ed29f9ad11fc705f951925822ed59", size = 106005 },
+]
+
+[[package]]
+name = "azure-core"
+version = "1.33.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "requests", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
+    { name = "six", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
+    { name = "typing-extensions", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/75/aa/7c9db8edd626f1a7d99d09ef7926f6f4fb34d5f9fa00dc394afdfe8e2a80/azure_core-1.33.0.tar.gz", hash = "sha256:f367aa07b5e3005fec2c1e184b882b0b039910733907d001c20fb08ebb8c0eb9", size = 295633 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/07/b7/76b7e144aa53bd206bf1ce34fa75350472c3f69bf30e5c8c18bc9881035d/azure_core-1.33.0-py3-none-any.whl", hash = "sha256:9b5b6d0223a1d38c37500e6971118c1e0f13f54951e6893968b38910bc9cda8f", size = 207071 },
+]
+
 [[package]]
 name = "babel"
 version = "2.17.0"
@@ -1402,6 +1430,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/c7/fc/4e5a141c3f7c7bed550ac1f69e599e92b6be449dd4677ec09f325cad0955/inotifyrecursive-0.3.5-py3-none-any.whl", hash = "sha256:7e5f4a2e1dc2bef0efa3b5f6b339c41fb4599055a2b54909d020e9e932cc8d2f", size = 8009, upload-time = "2020-11-20T12:38:46.981Z" },
 ]
 
+[[package]]
+name = "isodate"
+version = "0.7.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/54/4d/e940025e2ce31a8ce1202635910747e5a87cc3a6a6bb2d00973375014749/isodate-0.7.2.tar.gz", hash = "sha256:4cd1aa0f43ca76f4a6c6c0292a85f40b35ec2e43e315b59f06e6d32171a953e6", size = 29705 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/15/aa/0aca39a37d3c7eb941ba736ede56d689e7be91cab5d9ca846bde3999eba6/isodate-0.7.2-py3-none-any.whl", hash = "sha256:28009937d8031054830160fce6d409ed342816b543597cece116d966c6d99e15", size = 22320 },
+]
+
 [[package]]
 name = "jinja2"
 version = "3.1.6"
@@ -2010,6 +2047,7 @@ name = "paperless-ngx"
 version = "2.18.3"
 source = { virtual = "." }
 dependencies = [
+    { name = "azure-ai-documentintelligence", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "babel", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "bleach", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "celery", extra = ["redis"], marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
@@ -2144,6 +2182,7 @@ typing = [
 
 [package.metadata]
 requires-dist = [
+    { name = "azure-ai-documentintelligence", specifier = ">=1.0.2" },
     { name = "babel", specifier = ">=2.17" },
     { name = "bleach", specifier = "~=6.2.0" },
     { name = "celery", extras = ["redis"], specifier = "~=5.5.1" },