fix(syntax): corrige errores de sintaxis y formato en Python

- Corrige paréntesis faltante en DeletionRequestActionSerializer (serialisers.py:2855)
- Elimina espacios en blanco en líneas vacías (W293)
- Elimina espacios finales en líneas (W291)
- Elimina imports no utilizados (F401)
- Normaliza comillas a comillas dobles (Q000)
- Agrega comas finales faltantes (COM812)
- Ordena imports según convenciones (I001)
- Actualiza anotaciones de tipo a PEP 585 (UP006)

Este commit resuelve el error de compilación en el job de CI/CD
que estaba causando que fallara el linting check.

Archivos afectados: 38
Líneas modificadas: ~2200

src/documents/ai_deletion_manager.py

@@ -14,14 +14,10 @@ According to agents.md requirements:
 from __future__ import annotations
 
 import logging
-from typing import TYPE_CHECKING
 from typing import Any
 
 from django.contrib.auth.models import User
 
-if TYPE_CHECKING:
-    pass
-
 logger = logging.getLogger("paperless.ai_deletion")
 
 

src/documents/ai_scanner.py

@@ -29,7 +29,6 @@ from __future__ import annotations
 import logging
 from typing import TYPE_CHECKING
 from typing import Any
-from typing import Dict
 from typing import TypedDict
 
 from django.conf import settings
@@ -142,34 +141,34 @@ class AIScanResult:
         """
         # Convert internal tuple format to TypedDict format
         result: AIScanResultDict = {
-            'tags': [{'tag_id': tag_id, 'confidence': conf} for tag_id, conf in self.tags],
-            'custom_fields': {
-                field_id: {'value': value, 'confidence': conf}
+            "tags": [{"tag_id": tag_id, "confidence": conf} for tag_id, conf in self.tags],
+            "custom_fields": {
+                field_id: {"value": value, "confidence": conf}
                 for field_id, (value, conf) in self.custom_fields.items()
             },
-            'workflows': [{'workflow_id': wf_id, 'confidence': conf} for wf_id, conf in self.workflows],
-            'extracted_entities': self.extracted_entities,
-            'metadata': self.metadata,
+            "workflows": [{"workflow_id": wf_id, "confidence": conf} for wf_id, conf in self.workflows],
+            "extracted_entities": self.extracted_entities,
+            "metadata": self.metadata,
         }
 
         # Add optional fields only if present
         if self.correspondent:
-            result['correspondent'] = {
-                'correspondent_id': self.correspondent[0],
-                'confidence': self.correspondent[1],
+            result["correspondent"] = {
+                "correspondent_id": self.correspondent[0],
+                "confidence": self.correspondent[1],
             }
         if self.document_type:
-            result['document_type'] = {
-                'type_id': self.document_type[0],
-                'confidence': self.document_type[1],
+            result["document_type"] = {
+                "type_id": self.document_type[0],
+                "confidence": self.document_type[1],
             }
         if self.storage_path:
-            result['storage_path'] = {
-                'path_id': self.storage_path[0],
-                'confidence': self.storage_path[1],
+            result["storage_path"] = {
+                "path_id": self.storage_path[0],
+                "confidence": self.storage_path[1],
             }
         if self.title_suggestion:
-            result['title_suggestion'] = self.title_suggestion
+            result["title_suggestion"] = self.title_suggestion
 
         return result
 
@@ -1054,7 +1053,7 @@ class AIDocumentScanner:
         warm_up_time = time.time() - start_time
         logger.info(f"ML model warm-up completed in {warm_up_time:.2f}s")
 
-    def get_cache_metrics(self) -> Dict[str, Any]:
+    def get_cache_metrics(self) -> dict[str, Any]:
         """
         Get cache performance metrics.
         

src/documents/consumer.py

@@ -310,7 +310,7 @@ class ConsumerPlugin(
             # Parsing phase
             document_parser = self._create_parser_instance(parser_class)
             text, date, thumbnail, archive_path, page_count = self._parse_document(
-                document_parser, mime_type
+                document_parser, mime_type,
             )
 
             # Storage phase
@@ -394,7 +394,7 @@ class ConsumerPlugin(
     def _attempt_pdf_recovery(
         self,
         tempdir: tempfile.TemporaryDirectory,
-        original_mime_type: str
+        original_mime_type: str,
     ) -> str:
         """
         Attempt to recover a PDF file with incorrect MIME type using qpdf.
@@ -438,7 +438,7 @@ class ConsumerPlugin(
     def _get_parser_class(
         self,
         mime_type: str,
-        tempdir: tempfile.TemporaryDirectory
+        tempdir: tempfile.TemporaryDirectory,
     ) -> type[DocumentParser]:
         """
         Determine which parser to use based on MIME type.
@@ -468,7 +468,7 @@ class ConsumerPlugin(
 
     def _create_parser_instance(
         self,
-        parser_class: type[DocumentParser]
+        parser_class: type[DocumentParser],
     ) -> DocumentParser:
         """
         Create a parser instance with progress callback.
@@ -496,7 +496,7 @@ class ConsumerPlugin(
     def _parse_document(
         self,
         document_parser: DocumentParser,
-        mime_type: str
+        mime_type: str,
     ) -> tuple[str, datetime.datetime | None, Path, Path | None, int | None]:
         """
         Parse the document and extract metadata.
@@ -670,7 +670,7 @@ class ConsumerPlugin(
         self,
         document: Document,
         thumbnail: Path,
-        archive_path: Path | None
+        archive_path: Path | None,
     ) -> None:
         """
         Store document files (source, thumbnail, archive) to disk.
@@ -949,7 +949,7 @@ class ConsumerPlugin(
             text: The extracted document text
         """
         # Check if AI scanner is enabled
-        if not getattr(settings, 'PAPERLESS_ENABLE_AI_SCANNER', True):
+        if not getattr(settings, "PAPERLESS_ENABLE_AI_SCANNER", True):
             self.log.debug("AI scanner is disabled, skipping AI analysis")
             return
 

src/documents/migrations/1075_add_performance_indexes.py

@@ -1,6 +1,7 @@
 # Generated manually for performance optimization
 
-from django.db import migrations, models
+from django.db import migrations
+from django.db import models
 
 
 class Migration(migrations.Migration):

src/documents/migrations/1076_add_deletion_request.py

@@ -1,9 +1,10 @@
 # Generated manually for DeletionRequest model
 # Based on model definition in documents/models.py
 
-from django.conf import settings
-from django.db import migrations, models
 import django.db.models.deletion
+from django.conf import settings
+from django.db import migrations
+from django.db import models
 
 
 class Migration(migrations.Migration):
@@ -48,7 +49,7 @@ class Migration(migrations.Migration):
                 (
                     "ai_reason",
                     models.TextField(
-                        help_text="Detailed explanation from AI about why deletion is recommended"
+                        help_text="Detailed explanation from AI about why deletion is recommended",
                     ),
                 ),
                 (

src/documents/migrations/1077_add_deletionrequest_performance_indexes.py

@@ -1,6 +1,7 @@
 # Generated manually for DeletionRequest performance optimization
 
-from django.db import migrations, models
+from django.db import migrations
+from django.db import models
 
 
 class Migration(migrations.Migration):

src/documents/migrations/1078_aisuggestionfeedback.py

@@ -1,9 +1,10 @@
 # Generated manually for AI Suggestions API
 
-from django.conf import settings
-from django.db import migrations, models
-import django.db.models.deletion
 import django.core.validators
+import django.db.models.deletion
+from django.conf import settings
+from django.db import migrations
+from django.db import models
 
 
 class Migration(migrations.Migration):

src/documents/ml/__init__.py

@@ -10,9 +10,9 @@ Provides AI/ML capabilities including:
 from __future__ import annotations
 
 __all__ = [
-    "TransformerDocumentClassifier",
     "DocumentNER",
     "SemanticSearch",
+    "TransformerDocumentClassifier",
 ]
 
 # Lazy imports to avoid loading heavy ML libraries unless needed

src/documents/ml/classifier.py

@@ -15,23 +15,16 @@ Logging levels used in this module:
 from __future__ import annotations
 
 import logging
-from pathlib import Path
-from typing import TYPE_CHECKING
 
 import torch
 from torch.utils.data import Dataset
-from transformers import (
-    AutoModelForSequenceClassification,
-    AutoTokenizer,
-    Trainer,
-    TrainingArguments,
-)
+from transformers import AutoModelForSequenceClassification
+from transformers import AutoTokenizer
+from transformers import Trainer
+from transformers import TrainingArguments
 
 from documents.ml.model_cache import ModelCacheManager
 
-if TYPE_CHECKING:
-    from documents.models import Document
-
 logger = logging.getLogger("paperless.ml.classifier")
 
 
@@ -141,7 +134,7 @@ class TransformerDocumentClassifier:
 
         logger.info(
             f"Initialized TransformerDocumentClassifier with {model_name} "
-            f"(caching: {use_cache})"
+            f"(caching: {use_cache})",
         )
 
     def train(

src/documents/ml/model_cache.py

@@ -24,8 +24,9 @@ import pickle
 import threading
 import time
 from collections import OrderedDict
+from collections.abc import Callable
 from pathlib import Path
-from typing import Any, Callable, Dict, Optional, Tuple
+from typing import Any
 
 logger = logging.getLogger("paperless.ml.model_cache")
 
@@ -58,7 +59,7 @@ class CacheMetrics:
         with self.lock:
             self.loads += 1
 
-    def get_stats(self) -> Dict[str, Any]:
+    def get_stats(self) -> dict[str, Any]:
         with self.lock:
             total = self.hits + self.misses
             hit_rate = (self.hits / total * 100) if total > 0 else 0.0
@@ -98,7 +99,7 @@ class LRUCache:
         self.lock = threading.Lock()
         self.metrics = CacheMetrics()
 
-    def get(self, key: str) -> Optional[Any]:
+    def get(self, key: str) -> Any | None:
         """
         Get item from cache.
         
@@ -153,7 +154,7 @@ class LRUCache:
         with self.lock:
             return len(self.cache)
 
-    def get_metrics(self) -> Dict[str, Any]:
+    def get_metrics(self) -> dict[str, Any]:
         """Get cache metrics."""
         return self.metrics.get_stats()
 
@@ -173,7 +174,7 @@ class ModelCacheManager:
         model = cache.get_or_load_model("classifier", loader_func)
     """
 
-    _instance: Optional[ModelCacheManager] = None
+    _instance: ModelCacheManager | None = None
     _lock = threading.Lock()
 
     def __new__(cls, *args, **kwargs):
@@ -187,7 +188,7 @@ class ModelCacheManager:
     def __init__(
         self,
         max_models: int = 3,
-        disk_cache_dir: Optional[str] = None,
+        disk_cache_dir: str | None = None,
     ):
         """
         Initialize model cache manager.
@@ -215,7 +216,7 @@ class ModelCacheManager:
     def get_instance(
         cls,
         max_models: int = 3,
-        disk_cache_dir: Optional[str] = None,
+        disk_cache_dir: str | None = None,
     ) -> ModelCacheManager:
         """
         Get singleton instance of ModelCacheManager.
@@ -278,7 +279,7 @@ class ModelCacheManager:
                 load_time = time.time() - start_time
                 logger.info(
                     f"Model loaded successfully: {model_key} "
-                    f"(took {load_time:.2f}s)"
+                    f"(took {load_time:.2f}s)",
                 )
 
                 return model
@@ -289,7 +290,7 @@ class ModelCacheManager:
     def save_embeddings_to_disk(
         self,
         key: str,
-        embeddings: Dict[int, Any],
+        embeddings: dict[int, Any],
     ) -> bool:
         """
         Save embeddings to disk cache.
@@ -311,7 +312,7 @@ class ModelCacheManager:
         cache_file = self.disk_cache_dir / f"{key}.pkl"
 
         try:
-            with open(cache_file, 'wb') as f:
+            with open(cache_file, "wb") as f:
                 pickle.dump(embeddings, f, protocol=pickle.HIGHEST_PROTOCOL)
             logger.info(f"Saved {len(embeddings)} embeddings to {cache_file}")
             return True
@@ -330,7 +331,7 @@ class ModelCacheManager:
     def load_embeddings_from_disk(
         self,
         key: str,
-    ) -> Optional[Dict[int, Any]]:
+    ) -> dict[int, Any] | None:
         """
         Load embeddings from disk cache.
         
@@ -393,7 +394,7 @@ class ModelCacheManager:
                 except Exception as e:
                     logger.error(f"Failed to delete {cache_file}: {e}")
 
-    def get_metrics(self) -> Dict[str, Any]:
+    def get_metrics(self) -> dict[str, Any]:
         """
         Get cache performance metrics.
         
@@ -416,7 +417,7 @@ class ModelCacheManager:
 
     def warm_up(
         self,
-        model_loaders: Dict[str, Callable[[], Any]],
+        model_loaders: dict[str, Callable[[], Any]],
     ) -> None:
         """
         Pre-load models on startup (warm-up).

src/documents/ml/ner.py

@@ -14,15 +14,11 @@ from __future__ import annotations
 
 import logging
 import re
-from typing import TYPE_CHECKING
 
 from transformers import pipeline
 
 from documents.ml.model_cache import ModelCacheManager
 
-if TYPE_CHECKING:
-    pass
-
 logger = logging.getLogger("paperless.ml.ner")
 
 

src/documents/ml/semantic_search.py

@@ -18,18 +18,14 @@ Examples:
 from __future__ import annotations
 
 import logging
-from pathlib import Path
-from typing import TYPE_CHECKING
 
 import numpy as np
 import torch
-from sentence_transformers import SentenceTransformer, util
+from sentence_transformers import SentenceTransformer
+from sentence_transformers import util
 
 from documents.ml.model_cache import ModelCacheManager
 
-if TYPE_CHECKING:
-    pass
-
 logger = logging.getLogger("paperless.ml.semantic_search")
 
 
@@ -67,7 +63,7 @@ class SemanticSearch:
         """
         logger.info(
             f"Initializing SemanticSearch with model: {model_name} "
-            f"(caching: {use_cache})"
+            f"(caching: {use_cache})",
         )
 
         self.model_name = model_name
@@ -127,11 +123,11 @@ class SemanticSearch:
                 if not isinstance(embedding, np.ndarray) and not isinstance(embedding, torch.Tensor):
                     logger.warning(f"Embedding for doc {doc_id} is not a numpy array or tensor")
                     return False
-                if hasattr(embedding, 'size'):
+                if hasattr(embedding, "size"):
                     if embedding.size == 0:
                         logger.warning(f"Embedding for doc {doc_id} is empty")
                         return False
-                elif hasattr(embedding, 'numel'):
+                elif hasattr(embedding, "numel"):
                     if embedding.numel() == 0:
                         logger.warning(f"Embedding for doc {doc_id} is empty")
                         return False
@@ -216,11 +212,11 @@ class SemanticSearch:
         try:
             result = self.cache_manager.save_embeddings_to_disk(
                 "document_embeddings",
-                self.document_embeddings
+                self.document_embeddings,
             )
             if result:
                 logger.info(
-                    f"Successfully saved {len(self.document_embeddings)} embeddings to disk"
+                    f"Successfully saved {len(self.document_embeddings)} embeddings to disk",
                 )
             else:
                 logger.error("Failed to save embeddings to disk (returned False)")

src/documents/models.py

@@ -1604,30 +1604,30 @@ class DeletionRequest(models.Model):
     # Requester (AI system)
     requested_by_ai = models.BooleanField(default=True)
     ai_reason = models.TextField(
-        help_text=_("Detailed explanation from AI about why deletion is recommended")
+        help_text=_("Detailed explanation from AI about why deletion is recommended"),
     )
 
     # User who must approve
     user = models.ForeignKey(
         User,
         on_delete=models.CASCADE,
-        related_name='deletion_requests',
+        related_name="deletion_requests",
         help_text=_("User who must approve this deletion"),
     )
 
     # Status tracking
-    STATUS_PENDING = 'pending'
-    STATUS_APPROVED = 'approved'
-    STATUS_REJECTED = 'rejected'
-    STATUS_CANCELLED = 'cancelled'
-    STATUS_COMPLETED = 'completed'
+    STATUS_PENDING = "pending"
+    STATUS_APPROVED = "approved"
+    STATUS_REJECTED = "rejected"
+    STATUS_CANCELLED = "cancelled"
+    STATUS_COMPLETED = "completed"
 
     STATUS_CHOICES = [
-        (STATUS_PENDING, _('Pending')),
-        (STATUS_APPROVED, _('Approved')),
-        (STATUS_REJECTED, _('Rejected')),
-        (STATUS_CANCELLED, _('Cancelled')),
-        (STATUS_COMPLETED, _('Completed')),
+        (STATUS_PENDING, _("Pending")),
+        (STATUS_APPROVED, _("Approved")),
+        (STATUS_REJECTED, _("Rejected")),
+        (STATUS_CANCELLED, _("Cancelled")),
+        (STATUS_COMPLETED, _("Completed")),
     ]
 
     status = models.CharField(
@@ -1639,7 +1639,7 @@ class DeletionRequest(models.Model):
     # Documents to be deleted
     documents = models.ManyToManyField(
         Document,
-        related_name='deletion_requests',
+        related_name="deletion_requests",
         help_text=_("Documents that would be deleted if approved"),
     )
 
@@ -1656,7 +1656,7 @@ class DeletionRequest(models.Model):
         on_delete=models.SET_NULL,
         null=True,
         blank=True,
-        related_name='reviewed_deletion_requests',
+        related_name="reviewed_deletion_requests",
         help_text=_("User who reviewed and approved/rejected"),
     )
     review_comment = models.TextField(
@@ -1672,21 +1672,21 @@ class DeletionRequest(models.Model):
     )
 
     class Meta:
-        ordering = ['-created_at']
+        ordering = ["-created_at"]
         verbose_name = _("deletion request")
         verbose_name_plural = _("deletion requests")
         indexes = [
             # Composite index for common listing queries (by user, filtered by status, sorted by date)
             # PostgreSQL can use this index for queries on: user, user+status, user+status+created_at
-            models.Index(fields=['user', 'status', 'created_at'], name='delreq_user_status_created_idx'),
+            models.Index(fields=["user", "status", "created_at"], name="delreq_user_status_created_idx"),
             # Index for queries filtering by status and date without user filter
-            models.Index(fields=['status', 'created_at'], name='delreq_status_created_idx'),
+            models.Index(fields=["status", "created_at"], name="delreq_status_created_idx"),
             # Index for queries filtering by user and date (common for user-specific views)
-            models.Index(fields=['user', 'created_at'], name='delreq_user_created_idx'),
+            models.Index(fields=["user", "created_at"], name="delreq_user_created_idx"),
             # Index for queries filtering by review date
-            models.Index(fields=['reviewed_at'], name='delreq_reviewed_at_idx'),
+            models.Index(fields=["reviewed_at"], name="delreq_reviewed_at_idx"),
             # Index for queries filtering by completion date
-            models.Index(fields=['completed_at'], name='delreq_completed_at_idx'),
+            models.Index(fields=["completed_at"], name="delreq_completed_at_idx"),
         ]
 
     def __str__(self):
@@ -1745,67 +1745,67 @@ class AISuggestionFeedback(models.Model):
     """
 
     # Suggestion types
-    TYPE_TAG = 'tag'
-    TYPE_CORRESPONDENT = 'correspondent'
-    TYPE_DOCUMENT_TYPE = 'document_type'
-    TYPE_STORAGE_PATH = 'storage_path'
-    TYPE_CUSTOM_FIELD = 'custom_field'
-    TYPE_WORKFLOW = 'workflow'
-    TYPE_TITLE = 'title'
+    TYPE_TAG = "tag"
+    TYPE_CORRESPONDENT = "correspondent"
+    TYPE_DOCUMENT_TYPE = "document_type"
+    TYPE_STORAGE_PATH = "storage_path"
+    TYPE_CUSTOM_FIELD = "custom_field"
+    TYPE_WORKFLOW = "workflow"
+    TYPE_TITLE = "title"
 
     SUGGESTION_TYPES = (
-        (TYPE_TAG, _('Tag')),
-        (TYPE_CORRESPONDENT, _('Correspondent')),
-        (TYPE_DOCUMENT_TYPE, _('Document Type')),
-        (TYPE_STORAGE_PATH, _('Storage Path')),
-        (TYPE_CUSTOM_FIELD, _('Custom Field')),
-        (TYPE_WORKFLOW, _('Workflow')),
-        (TYPE_TITLE, _('Title')),
+        (TYPE_TAG, _("Tag")),
+        (TYPE_CORRESPONDENT, _("Correspondent")),
+        (TYPE_DOCUMENT_TYPE, _("Document Type")),
+        (TYPE_STORAGE_PATH, _("Storage Path")),
+        (TYPE_CUSTOM_FIELD, _("Custom Field")),
+        (TYPE_WORKFLOW, _("Workflow")),
+        (TYPE_TITLE, _("Title")),
     )
 
     # Feedback status
-    STATUS_APPLIED = 'applied'
-    STATUS_REJECTED = 'rejected'
+    STATUS_APPLIED = "applied"
+    STATUS_REJECTED = "rejected"
 
     FEEDBACK_STATUS = (
-        (STATUS_APPLIED, _('Applied')),
-        (STATUS_REJECTED, _('Rejected')),
+        (STATUS_APPLIED, _("Applied")),
+        (STATUS_REJECTED, _("Rejected")),
     )
 
     document = models.ForeignKey(
         Document,
         on_delete=models.CASCADE,
-        related_name='ai_suggestion_feedbacks',
-        verbose_name=_('document'),
+        related_name="ai_suggestion_feedbacks",
+        verbose_name=_("document"),
     )
 
     suggestion_type = models.CharField(
-        _('suggestion type'),
+        _("suggestion type"),
         max_length=50,
         choices=SUGGESTION_TYPES,
     )
 
     suggested_value_id = models.IntegerField(
-        _('suggested value ID'),
+        _("suggested value ID"),
         null=True,
         blank=True,
-        help_text=_('ID of the suggested object (tag, correspondent, etc.)'),
+        help_text=_("ID of the suggested object (tag, correspondent, etc.)"),
     )
 
     suggested_value_text = models.TextField(
-        _('suggested value text'),
+        _("suggested value text"),
         blank=True,
-        help_text=_('Text representation of the suggested value'),
+        help_text=_("Text representation of the suggested value"),
     )
 
     confidence = models.FloatField(
-        _('confidence'),
-        help_text=_('AI confidence score (0.0 to 1.0)'),
+        _("confidence"),
+        help_text=_("AI confidence score (0.0 to 1.0)"),
         validators=[MinValueValidator(0.0), MaxValueValidator(1.0)],
     )
 
     status = models.CharField(
-        _('status'),
+        _("status"),
         max_length=20,
         choices=FEEDBACK_STATUS,
     )
@@ -1815,36 +1815,36 @@ class AISuggestionFeedback(models.Model):
         on_delete=models.SET_NULL,
         null=True,
         blank=True,
-        related_name='ai_suggestion_feedbacks',
-        verbose_name=_('user'),
-        help_text=_('User who applied or rejected the suggestion'),
+        related_name="ai_suggestion_feedbacks",
+        verbose_name=_("user"),
+        help_text=_("User who applied or rejected the suggestion"),
     )
 
     created_at = models.DateTimeField(
-        _('created at'),
+        _("created at"),
         auto_now_add=True,
     )
 
     applied_at = models.DateTimeField(
-        _('applied/rejected at'),
+        _("applied/rejected at"),
         auto_now=True,
     )
 
     metadata = models.JSONField(
-        _('metadata'),
+        _("metadata"),
         default=dict,
         blank=True,
-        help_text=_('Additional metadata about the suggestion'),
+        help_text=_("Additional metadata about the suggestion"),
     )
 
     class Meta:
-        verbose_name = _('AI suggestion feedback')
-        verbose_name_plural = _('AI suggestion feedbacks')
-        ordering = ['-created_at']
+        verbose_name = _("AI suggestion feedback")
+        verbose_name_plural = _("AI suggestion feedbacks")
+        ordering = ["-created_at"]
         indexes = [
-            models.Index(fields=['document', 'suggestion_type']),
-            models.Index(fields=['status', 'created_at']),
-            models.Index(fields=['suggestion_type', 'status']),
+            models.Index(fields=["document", "suggestion_type"]),
+            models.Index(fields=["status", "created_at"]),
+            models.Index(fields=["suggestion_type", "status"]),
         ]
 
     def __str__(self):

src/documents/ocr/__init__.py

@@ -11,21 +11,21 @@ Lazy imports are used to avoid loading heavy dependencies unless needed.
 """
 
 __all__ = [
-    'TableExtractor',
-    'HandwritingRecognizer',
-    'FormFieldDetector',
+    "FormFieldDetector",
+    "HandwritingRecognizer",
+    "TableExtractor",
 ]
 
 
 def __getattr__(name):
     """Lazy import to avoid loading heavy ML models on startup."""
-    if name == 'TableExtractor':
+    if name == "TableExtractor":
         from .table_extractor import TableExtractor
         return TableExtractor
-    elif name == 'HandwritingRecognizer':
+    elif name == "HandwritingRecognizer":
         from .handwriting import HandwritingRecognizer
         return HandwritingRecognizer
-    elif name == 'FormFieldDetector':
+    elif name == "FormFieldDetector":
         from .form_detector import FormFieldDetector
         return FormFieldDetector
     raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

src/documents/ocr/form_detector.py

@@ -8,8 +8,8 @@ This module provides capabilities to:
 """
 
 import logging
-from pathlib import Path
-from typing import List, Dict, Any, Optional, Tuple
+from typing import Any
+
 import numpy as np
 from PIL import Image
 
@@ -59,8 +59,8 @@ class FormFieldDetector:
         self,
         image: Image.Image,
         min_size: int = 10,
-        max_size: int = 50
-    ) -> List[Dict[str, Any]]:
+        max_size: int = 50,
+    ) -> list[dict[str, Any]]:
         """
         Detect checkboxes in a form image.
         
@@ -114,9 +114,9 @@ class FormFieldDetector:
                     checked, confidence = self._is_checkbox_checked(checkbox_region)
 
                     checkboxes.append({
-                        'bbox': [x, y, x+w, y+h],
-                        'checked': checked,
-                        'confidence': confidence
+                        "bbox": [x, y, x+w, y+h],
+                        "checked": checked,
+                        "confidence": confidence,
                     })
 
             logger.info(f"Detected {len(checkboxes)} checkboxes")
@@ -129,7 +129,7 @@ class FormFieldDetector:
             logger.error(f"Error detecting checkboxes: {e}")
             return []
 
-    def _is_checkbox_checked(self, checkbox_image: np.ndarray) -> Tuple[bool, float]:
+    def _is_checkbox_checked(self, checkbox_image: np.ndarray) -> tuple[bool, float]:
         """
         Determine if a checkbox is checked.
         
@@ -167,8 +167,8 @@ class FormFieldDetector:
     def detect_text_fields(
         self,
         image: Image.Image,
-        min_width: int = 100
-    ) -> List[Dict[str, Any]]:
+        min_width: int = 100,
+    ) -> list[dict[str, Any]]:
         """
         Detect text input fields in a form.
         
@@ -202,14 +202,14 @@ class FormFieldDetector:
                 cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1],
                 cv2.MORPH_OPEN,
                 horizontal_kernel,
-                iterations=2
+                iterations=2,
             )
 
             # Find contours of horizontal lines
             contours, _ = cv2.findContours(
                 detect_horizontal,
                 cv2.RETR_EXTERNAL,
-                cv2.CHAIN_APPROX_SIMPLE
+                cv2.CHAIN_APPROX_SIMPLE,
             )
 
             text_fields = []
@@ -221,8 +221,8 @@ class FormFieldDetector:
                     # Expand upward to include text area
                     text_bbox = [x, max(0, y-30), x+w, y+h]
                     text_fields.append({
-                        'bbox': text_bbox,
-                        'type': 'line'
+                        "bbox": text_bbox,
+                        "type": "line",
                     })
 
             # Detect rectangular boxes (bordered text fields)
@@ -236,8 +236,8 @@ class FormFieldDetector:
                 aspect_ratio = w / h if h > 0 else 0
                 if w >= min_width and 20 <= h <= 100 and aspect_ratio > 2:
                     text_fields.append({
-                        'bbox': [x, y, x+w, y+h],
-                        'type': 'box'
+                        "bbox": [x, y, x+w, y+h],
+                        "type": "box",
                     })
 
             logger.info(f"Detected {len(text_fields)} text fields")
@@ -253,8 +253,8 @@ class FormFieldDetector:
     def detect_labels(
         self,
         image: Image.Image,
-        field_bboxes: List[List[int]]
-    ) -> List[Dict[str, Any]]:
+        field_bboxes: list[list[int]],
+    ) -> list[dict[str, Any]]:
         """
         Detect labels near form fields.
         
@@ -271,17 +271,17 @@ class FormFieldDetector:
             # Get all text with bounding boxes
             ocr_data = pytesseract.image_to_data(
                 image,
-                output_type=pytesseract.Output.DICT
+                output_type=pytesseract.Output.DICT,
             )
 
             # Group text into potential labels
             labels = []
-            for i, text in enumerate(ocr_data['text']):
+            for i, text in enumerate(ocr_data["text"]):
                 if text.strip() and len(text.strip()) > 2:
-                    x = ocr_data['left'][i]
-                    y = ocr_data['top'][i]
-                    w = ocr_data['width'][i]
-                    h = ocr_data['height'][i]
+                    x = ocr_data["left"][i]
+                    y = ocr_data["top"][i]
+                    w = ocr_data["width"][i]
+                    h = ocr_data["height"][i]
 
                     label_bbox = [x, y, x+w, y+h]
 
@@ -289,9 +289,9 @@ class FormFieldDetector:
                     closest_field_idx = self._find_closest_field(label_bbox, field_bboxes)
 
                     labels.append({
-                        'text': text.strip(),
-                        'bbox': label_bbox,
-                        'field_index': closest_field_idx
+                        "text": text.strip(),
+                        "bbox": label_bbox,
+                        "field_index": closest_field_idx,
                     })
 
             return labels
@@ -305,9 +305,9 @@ class FormFieldDetector:
 
     def _find_closest_field(
         self,
-        label_bbox: List[int], 
-        field_bboxes: List[List[int]]
-    ) -> Optional[int]:
+        label_bbox: list[int],
+        field_bboxes: list[list[int]],
+    ) -> int | None:
         """
         Find the closest field to a label.
         
@@ -325,7 +325,7 @@ class FormFieldDetector:
         label_center_x = (label_bbox[0] + label_bbox[2]) / 2
         label_center_y = (label_bbox[1] + label_bbox[3]) / 2
 
-        min_distance = float('inf')
+        min_distance = float("inf")
         closest_idx = 0
 
         for i, field_bbox in enumerate(field_bboxes):
@@ -336,7 +336,7 @@ class FormFieldDetector:
             # Euclidean distance
             distance = np.sqrt(
                 (label_center_x - field_center_x)**2 +
-                (label_center_y - field_center_y)**2
+                (label_center_y - field_center_y)**2,
             )
 
             if distance < min_distance:
@@ -348,8 +348,8 @@ class FormFieldDetector:
     def detect_form_fields(
         self,
         image_path: str,
-        extract_values: bool = True
-    ) -> List[Dict[str, Any]]:
+        extract_values: bool = True,
+    ) -> list[dict[str, Any]]:
         """
         Detect all form fields and extract their values.
         
@@ -372,14 +372,14 @@ class FormFieldDetector:
         """
         try:
             # Load image
-            image = Image.open(image_path).convert('RGB')
+            image = Image.open(image_path).convert("RGB")
 
             # Detect different field types
             text_fields = self.detect_text_fields(image)
             checkboxes = self.detect_checkboxes(image)
 
             # Combine all field bboxes for label detection
-            all_field_bboxes = [f['bbox'] for f in text_fields] + [cb['bbox'] for cb in checkboxes]
+            all_field_bboxes = [f["bbox"] for f in text_fields] + [cb["bbox"] for cb in checkboxes]
 
             # Detect labels
             labels = self.detect_labels(image, all_field_bboxes)
@@ -393,20 +393,20 @@ class FormFieldDetector:
                 label_text = self._find_label_for_field(i, labels, len(text_fields))
 
                 result = {
-                    'type': 'text',
-                    'label': label_text,
-                    'bbox': field['bbox'],
+                    "type": "text",
+                    "label": label_text,
+                    "bbox": field["bbox"],
                 }
 
                 # Extract value if requested
                 if extract_values:
-                    x1, y1, x2, y2 = field['bbox']
+                    x1, y1, x2, y2 = field["bbox"]
                     field_image = image.crop((x1, y1, x2, y2))
 
                     recognizer = self._get_handwriting_recognizer()
                     value = recognizer.recognize_from_image(field_image, preprocess=True)
-                    result['value'] = value.strip()
-                    result['confidence'] = recognizer._estimate_confidence(value)
+                    result["value"] = value.strip()
+                    result["confidence"] = recognizer._estimate_confidence(value)
 
                 results.append(result)
 
@@ -416,11 +416,11 @@ class FormFieldDetector:
                 label_text = self._find_label_for_field(field_idx, labels, len(all_field_bboxes))
 
                 results.append({
-                    'type': 'checkbox',
-                    'label': label_text,
-                    'value': checkbox['checked'],
-                    'bbox': checkbox['bbox'],
-                    'confidence': checkbox['confidence']
+                    "type": "checkbox",
+                    "label": label_text,
+                    "value": checkbox["checked"],
+                    "bbox": checkbox["bbox"],
+                    "confidence": checkbox["confidence"],
                 })
 
             logger.info(f"Detected {len(results)} form fields from {image_path}")
@@ -433,8 +433,8 @@ class FormFieldDetector:
     def _find_label_for_field(
         self,
         field_idx: int,
-        labels: List[Dict[str, Any]],
-        total_fields: int
+        labels: list[dict[str, Any]],
+        total_fields: int,
     ) -> str:
         """
         Find the label text for a specific field.
@@ -449,19 +449,19 @@ class FormFieldDetector:
         """
         matching_labels = [
             label for label in labels
-            if label['field_index'] == field_idx
+            if label["field_index"] == field_idx
         ]
 
         if matching_labels:
             # Combine multiple label parts if found
-            return ' '.join(label['text'] for label in matching_labels)
+            return " ".join(label["text"] for label in matching_labels)
 
         return f"Field_{field_idx + 1}"
 
     def extract_form_data(
         self,
         image_path: str,
-        output_format: str = 'dict'
+        output_format: str = "dict",
     ) -> Any:
         """
         Extract all form data as structured output.
@@ -476,16 +476,16 @@ class FormFieldDetector:
         # Detect and extract fields
         fields = self.detect_form_fields(image_path, extract_values=True)
 
-        if output_format == 'dict':
+        if output_format == "dict":
             # Return as dictionary
-            return {field['label']: field['value'] for field in fields}
+            return {field["label"]: field["value"] for field in fields}
 
-        elif output_format == 'json':
+        elif output_format == "json":
             import json
-            data = {field['label']: field['value'] for field in fields}
+            data = {field["label"]: field["value"] for field in fields}
             return json.dumps(data, indent=2)
 
-        elif output_format == 'dataframe':
+        elif output_format == "dataframe":
             import pandas as pd
             return pd.DataFrame(fields)
 

src/documents/ocr/handwriting.py

@@ -8,8 +8,8 @@ This module provides handwriting OCR capabilities using:
 """
 
 import logging
-from pathlib import Path
-from typing import List, Dict, Any, Optional, Tuple
+from typing import Any
+
 import numpy as np
 from PIL import Image
 
@@ -65,8 +65,9 @@ class HandwritingRecognizer:
             return
 
         try:
-            from transformers import TrOCRProcessor, VisionEncoderDecoderModel
             import torch
+            from transformers import TrOCRProcessor
+            from transformers import VisionEncoderDecoderModel
 
             logger.info(f"Loading handwriting recognition model: {self.model_name}")
 
@@ -90,7 +91,7 @@ class HandwritingRecognizer:
     def recognize_from_image(
         self,
         image: Image.Image,
-        preprocess: bool = True
+        preprocess: bool = True,
     ) -> str:
         """
         Recognize text from a single image.
@@ -142,11 +143,12 @@ class HandwritingRecognizer:
             Preprocessed PIL Image
         """
         try:
-            from PIL import ImageEnhance, ImageFilter
+            from PIL import ImageEnhance
+            from PIL import ImageFilter
 
             # Convert to grayscale
-            if image.mode != 'L':
-                image = image.convert('L')
+            if image.mode != "L":
+                image = image.convert("L")
 
             # Enhance contrast
             enhancer = ImageEnhance.Contrast(image)
@@ -156,7 +158,7 @@ class HandwritingRecognizer:
             image = image.filter(ImageFilter.MedianFilter(size=3))
 
             # Convert back to RGB (required by model)
-            image = image.convert('RGB')
+            image = image.convert("RGB")
 
             return image
 
@@ -164,7 +166,7 @@ class HandwritingRecognizer:
             logger.warning(f"Error preprocessing image: {e}")
             return image
 
-    def detect_text_lines(self, image: Image.Image) -> List[Dict[str, Any]]:
+    def detect_text_lines(self, image: Image.Image) -> list[dict[str, Any]]:
         """
         Detect individual text lines in an image.
         
@@ -208,12 +210,12 @@ class HandwritingRecognizer:
                     # Crop line from original image
                     line_img = image.crop((x, y, x+w, y+h))
                     lines.append({
-                        'bbox': [x, y, x+w, y+h],
-                        'image': line_img
+                        "bbox": [x, y, x+w, y+h],
+                        "image": line_img,
                     })
 
             # Sort lines top to bottom
-            lines.sort(key=lambda l: l['bbox'][1])
+            lines.sort(key=lambda l: l["bbox"][1])
 
             logger.info(f"Detected {len(lines)} text lines")
             return lines
@@ -228,8 +230,8 @@ class HandwritingRecognizer:
     def recognize_lines(
         self,
         image_path: str,
-        return_confidence: bool = True
-    ) -> List[Dict[str, Any]]:
+        return_confidence: bool = True,
+    ) -> list[dict[str, Any]]:
         """
         Recognize text from each line in an image.
         
@@ -250,7 +252,7 @@ class HandwritingRecognizer:
         """
         try:
             # Load image
-            image = Image.open(image_path).convert('RGB')
+            image = Image.open(image_path).convert("RGB")
 
             # Detect lines
             lines = self.detect_text_lines(image)
@@ -260,18 +262,18 @@ class HandwritingRecognizer:
             for i, line in enumerate(lines):
                 logger.debug(f"Recognizing line {i+1}/{len(lines)}")
 
-                text = self.recognize_from_image(line['image'], preprocess=True)
+                text = self.recognize_from_image(line["image"], preprocess=True)
 
                 result = {
-                    'text': text,
-                    'bbox': line['bbox'],
-                    'line_index': i
+                    "text": text,
+                    "bbox": line["bbox"],
+                    "line_index": i,
                 }
 
                 if return_confidence:
                     # Simple confidence based on text length and content
                     confidence = self._estimate_confidence(text)
-                    result['confidence'] = confidence
+                    result["confidence"] = confidence
 
                 results.append(result)
 
@@ -309,7 +311,7 @@ class HandwritingRecognizer:
             score += 0.1
 
         # Text with spaces (words) is more reliable
-        if ' ' in text:
+        if " " in text:
             score += 0.1
 
         # Penalize if too many special characters
@@ -322,8 +324,8 @@ class HandwritingRecognizer:
     def recognize_from_file(
         self,
         image_path: str,
-        mode: str = 'full'
-    ) -> Dict[str, Any]:
+        mode: str = "full",
+    ) -> dict[str, Any]:
         """
         Recognize handwriting from an image file.
         
@@ -337,30 +339,30 @@ class HandwritingRecognizer:
             Dictionary with recognized text and metadata
         """
         try:
-            if mode == 'full':
+            if mode == "full":
                 # Recognize entire image
-                image = Image.open(image_path).convert('RGB')
+                image = Image.open(image_path).convert("RGB")
                 text = self.recognize_from_image(image, preprocess=True)
 
                 return {
-                    'text': text,
-                    'mode': 'full',
-                    'confidence': self._estimate_confidence(text)
+                    "text": text,
+                    "mode": "full",
+                    "confidence": self._estimate_confidence(text),
                 }
 
-            elif mode == 'lines':
+            elif mode == "lines":
                 # Recognize line by line
                 lines = self.recognize_lines(image_path, return_confidence=True)
 
                 # Combine all lines
-                full_text = '\n'.join(line['text'] for line in lines)
-                avg_confidence = np.mean([line['confidence'] for line in lines]) if lines else 0.0
+                full_text = "\n".join(line["text"] for line in lines)
+                avg_confidence = np.mean([line["confidence"] for line in lines]) if lines else 0.0
 
                 return {
-                    'text': full_text,
-                    'lines': lines,
-                    'mode': 'lines',
-                    'confidence': float(avg_confidence)
+                    "text": full_text,
+                    "lines": lines,
+                    "mode": "lines",
+                    "confidence": float(avg_confidence),
                 }
 
             else:
@@ -369,17 +371,17 @@ class HandwritingRecognizer:
         except Exception as e:
             logger.error(f"Error recognizing from file {image_path}: {e}")
             return {
-                'text': '',
-                'mode': mode,
-                'confidence': 0.0,
-                'error': str(e)
+                "text": "",
+                "mode": mode,
+                "confidence": 0.0,
+                "error": str(e),
             }
 
     def recognize_form_fields(
         self,
         image_path: str,
-        field_regions: List[Dict[str, Any]]
-    ) -> Dict[str, str]:
+        field_regions: list[dict[str, Any]],
+    ) -> dict[str, str]:
         """
         Recognize text from specific form fields.
         
@@ -399,13 +401,13 @@ class HandwritingRecognizer:
         """
         try:
             # Load image
-            image = Image.open(image_path).convert('RGB')
+            image = Image.open(image_path).convert("RGB")
 
             # Extract and recognize each field
             results = {}
             for field in field_regions:
-                name = field['name']
-                bbox = field['bbox']
+                name = field["name"]
+                bbox = field["bbox"]
 
                 # Crop field region
                 x1, y1, x2, y2 = bbox
@@ -425,9 +427,9 @@ class HandwritingRecognizer:
 
     def batch_recognize(
         self,
-        image_paths: List[str],
-        mode: str = 'full'
-    ) -> List[Dict[str, Any]]:
+        image_paths: list[str],
+        mode: str = "full",
+    ) -> list[dict[str, Any]]:
         """
         Recognize handwriting from multiple images in batch.
         
@@ -442,7 +444,7 @@ class HandwritingRecognizer:
         for i, path in enumerate(image_paths):
             logger.info(f"Processing image {i+1}/{len(image_paths)}: {path}")
             result = self.recognize_from_file(path, mode=mode)
-            result['image_path'] = path
+            result["image_path"] = path
             results.append(result)
 
         return results

src/documents/ocr/table_extractor.py

@@ -8,9 +8,8 @@ This module uses various techniques to detect and extract tables from documents:
 """
 
 import logging
-from pathlib import Path
-from typing import List, Dict, Any, Optional, Tuple
-import numpy as np
+from typing import Any
+
 from PIL import Image
 
 logger = logging.getLogger(__name__)
@@ -59,8 +58,9 @@ class TableExtractor:
             return
 
         try:
-            from transformers import AutoImageProcessor, AutoModelForObjectDetection
             import torch
+            from transformers import AutoImageProcessor
+            from transformers import AutoModelForObjectDetection
 
             logger.info(f"Loading table detection model: {self.model_name}")
 
@@ -79,7 +79,7 @@ class TableExtractor:
             logger.error("Please install required packages: pip install transformers torch pillow")
             raise
 
-    def detect_tables(self, image: Image.Image) -> List[Dict[str, Any]]:
+    def detect_tables(self, image: Image.Image) -> list[dict[str, Any]]:
         """
         Detect tables in an image.
         
@@ -117,16 +117,16 @@ class TableExtractor:
             results = self._processor.post_process_object_detection(
                 outputs,
                 threshold=self.confidence_threshold,
-                target_sizes=target_sizes
+                target_sizes=target_sizes,
             )[0]
 
             # Convert to list of dicts
             tables = []
             for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
                 tables.append({
-                    'bbox': box.cpu().tolist(),
-                    'score': score.item(),
-                    'label': self._model.config.id2label[label.item()]
+                    "bbox": box.cpu().tolist(),
+                    "score": score.item(),
+                    "label": self._model.config.id2label[label.item()],
                 })
 
             logger.info(f"Detected {len(tables)} tables in image")
@@ -139,9 +139,9 @@ class TableExtractor:
     def extract_table_from_region(
         self,
         image: Image.Image,
-        bbox: List[float],
-        use_ocr: bool = True
-    ) -> Optional[Dict[str, Any]]:
+        bbox: list[float],
+        use_ocr: bool = True,
+    ) -> dict[str, Any] | None:
         """
         Extract table data from a specific region of an image.
         
@@ -166,7 +166,7 @@ class TableExtractor:
                 # Get detailed OCR data
                 ocr_data = pytesseract.image_to_data(
                     table_image,
-                    output_type=pytesseract.Output.DICT
+                    output_type=pytesseract.Output.DICT,
                 )
 
                 # Reconstruct table structure from OCR data
@@ -176,20 +176,20 @@ class TableExtractor:
                 raw_text = pytesseract.image_to_string(table_image)
 
                 return {
-                    'data': table_data,
-                    'raw_text': raw_text,
-                    'bbox': bbox,
-                    'image_size': table_image.size
+                    "data": table_data,
+                    "raw_text": raw_text,
+                    "bbox": bbox,
+                    "image_size": table_image.size,
                 }
             else:
                 # Fallback to basic OCR without structure
                 import pytesseract
                 raw_text = pytesseract.image_to_string(table_image)
                 return {
-                    'data': None,
-                    'raw_text': raw_text,
-                    'bbox': bbox,
-                    'image_size': table_image.size
+                    "data": None,
+                    "raw_text": raw_text,
+                    "bbox": bbox,
+                    "image_size": table_image.size,
                 }
 
         except ImportError:
@@ -199,7 +199,7 @@ class TableExtractor:
             logger.error(f"Error extracting table from region: {e}")
             return None
 
-    def _reconstruct_table_from_ocr(self, ocr_data: Dict) -> Optional[Any]:
+    def _reconstruct_table_from_ocr(self, ocr_data: dict) -> Any | None:
         """
         Reconstruct table structure from OCR output.
         
@@ -214,10 +214,10 @@ class TableExtractor:
 
             # Group text by vertical position (rows)
             rows = {}
-            for i, text in enumerate(ocr_data['text']):
+            for i, text in enumerate(ocr_data["text"]):
                 if text.strip():
-                    top = ocr_data['top'][i]
-                    left = ocr_data['left'][i]
+                    top = ocr_data["top"][i]
+                    left = ocr_data["left"][i]
 
                     # Group by approximate row (within 20 pixels)
                     row_key = round(top / 20) * 20
@@ -235,14 +235,14 @@ class TableExtractor:
             if table_rows:
                 # Pad rows to same length
                 max_cols = max(len(row) for row in table_rows)
-                table_rows = [row + [''] * (max_cols - len(row)) for row in table_rows]
+                table_rows = [row + [""] * (max_cols - len(row)) for row in table_rows]
 
                 # Create DataFrame
                 df = pd.DataFrame(table_rows)
 
                 # Try to use first row as header if it looks like one
                 if len(df) > 1:
-                    first_row_text = ' '.join(str(x) for x in df.iloc[0])
+                    first_row_text = " ".join(str(x) for x in df.iloc[0])
                     if not any(char.isdigit() for char in first_row_text):
                         df.columns = df.iloc[0]
                         df = df[1:].reset_index(drop=True)
@@ -261,8 +261,8 @@ class TableExtractor:
     def extract_tables_from_image(
         self,
         image_path: str,
-        output_format: str = 'dataframe'
-    ) -> List[Dict[str, Any]]:
+        output_format: str = "dataframe",
+    ) -> list[dict[str, Any]]:
         """
         Extract all tables from an image file.
         
@@ -275,7 +275,7 @@ class TableExtractor:
         """
         try:
             # Load image
-            image = Image.open(image_path).convert('RGB')
+            image = Image.open(image_path).convert("RGB")
 
             # Detect tables
             detections = self.detect_tables(image)
@@ -287,18 +287,18 @@ class TableExtractor:
 
                 table_data = self.extract_table_from_region(
                     image,
-                    detection['bbox']
+                    detection["bbox"],
                 )
 
                 if table_data:
-                    table_data['detection_score'] = detection['score']
-                    table_data['table_index'] = i
+                    table_data["detection_score"] = detection["score"]
+                    table_data["table_index"] = i
 
                     # Convert to requested format
-                    if output_format == 'csv' and table_data['data'] is not None:
-                        table_data['csv'] = table_data['data'].to_csv(index=False)
-                    elif output_format == 'json' and table_data['data'] is not None:
-                        table_data['json'] = table_data['data'].to_json(orient='records')
+                    if output_format == "csv" and table_data["data"] is not None:
+                        table_data["csv"] = table_data["data"].to_csv(index=False)
+                    elif output_format == "json" and table_data["data"] is not None:
+                        table_data["json"] = table_data["data"].to_json(orient="records")
 
                     tables.append(table_data)
 
@@ -312,8 +312,8 @@ class TableExtractor:
     def extract_tables_from_pdf(
         self,
         pdf_path: str,
-        page_numbers: Optional[List[int]] = None
-    ) -> Dict[int, List[Dict[str, Any]]]:
+        page_numbers: list[int] | None = None,
+    ) -> dict[int, list[dict[str, Any]]]:
         """
         Extract tables from a PDF document.
         
@@ -334,7 +334,7 @@ class TableExtractor:
                 images = convert_from_path(
                     pdf_path,
                     first_page=min(page_numbers),
-                    last_page=max(page_numbers)
+                    last_page=max(page_numbers),
                 )
             else:
                 images = convert_from_path(pdf_path)
@@ -352,11 +352,11 @@ class TableExtractor:
                 for detection in detections:
                     table_data = self.extract_table_from_region(
                         image,
-                        detection['bbox']
+                        detection["bbox"],
                     )
                     if table_data:
-                        table_data['detection_score'] = detection['score']
-                        table_data['page'] = page_num
+                        table_data["detection_score"] = detection["score"]
+                        table_data["page"] = page_num
                         tables.append(table_data)
 
                 if tables:
@@ -374,8 +374,8 @@ class TableExtractor:
 
     def save_tables_to_excel(
         self,
-        tables: List[Dict[str, Any]], 
-        output_path: str
+        tables: list[dict[str, Any]],
+        output_path: str,
     ) -> bool:
         """
         Save extracted tables to an Excel file.
@@ -390,17 +390,17 @@ class TableExtractor:
         try:
             import pandas as pd
 
-            with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
+            with pd.ExcelWriter(output_path, engine="openpyxl") as writer:
                 for i, table in enumerate(tables):
-                    if table.get('data') is not None:
+                    if table.get("data") is not None:
                         sheet_name = f"Table_{i+1}"
-                        if 'page' in table:
+                        if "page" in table:
                             sheet_name = f"Page_{table['page']}_Table_{i+1}"
 
-                        table['data'].to_excel(
+                        table["data"].to_excel(
                             writer,
                             sheet_name=sheet_name,
-                            index=False
+                            index=False,
                         )
 
             logger.info(f"Saved {len(tables)} tables to {output_path}")

src/documents/serialisers.py

@@ -46,7 +46,6 @@ if settings.AUDIT_LOG_ENABLED:
 from documents import bulk_edit
 from documents.data_models import DocumentSource
 from documents.filters import CustomFieldQueryParser
-from documents.models import AISuggestionFeedback
 from documents.models import Correspondent
 from documents.models import CustomField
 from documents.models import CustomFieldInstance
@@ -2788,9 +2787,9 @@ class DeletionRequestDetailSerializer(serializers.ModelSerializer):
     """Serializer for DeletionRequest model with document details."""
 
     document_details = serializers.SerializerMethodField()
-    user_username = serializers.CharField(source='user.username', read_only=True)
+    user_username = serializers.CharField(source="user.username", read_only=True)
     reviewed_by_username = serializers.CharField(
-        source='reviewed_by.username', 
+        source="reviewed_by.username",
         read_only=True,
         allow_null=True,
     )
@@ -2799,31 +2798,31 @@ class DeletionRequestDetailSerializer(serializers.ModelSerializer):
         from documents.models import DeletionRequest
         model = DeletionRequest
         fields = [
-            'id',
-            'created_at',
-            'updated_at',
-            'requested_by_ai',
-            'ai_reason',
-            'user',
-            'user_username',
-            'status',
-            'impact_summary',
-            'reviewed_at',
-            'reviewed_by',
-            'reviewed_by_username',
-            'review_comment',
-            'completed_at',
-            'completion_details',
-            'document_details',
+            "id",
+            "created_at",
+            "updated_at",
+            "requested_by_ai",
+            "ai_reason",
+            "user",
+            "user_username",
+            "status",
+            "impact_summary",
+            "reviewed_at",
+            "reviewed_by",
+            "reviewed_by_username",
+            "review_comment",
+            "completed_at",
+            "completion_details",
+            "document_details",
         ]
         read_only_fields = [
-            'id',
-            'created_at',
-            'updated_at',
-            'reviewed_at',
-            'reviewed_by',
-            'completed_at',
-            'completion_details',
+            "id",
+            "created_at",
+            "updated_at",
+            "reviewed_at",
+            "reviewed_by",
+            "completed_at",
+            "completion_details",
         ]
 
     def get_document_details(self, obj):
@@ -2831,12 +2830,12 @@ class DeletionRequestDetailSerializer(serializers.ModelSerializer):
         documents = obj.documents.all()
         return [
             {
-                'id': doc.id,
-                'title': doc.title,
-                'created': doc.created.isoformat() if doc.created else None,
-                'correspondent': doc.correspondent.name if doc.correspondent else None,
-                'document_type': doc.document_type.name if doc.document_type else None,
-                'tags': [tag.name for tag in doc.tags.all()],
+                "id": doc.id,
+                "title": doc.title,
+                "created": doc.created.isoformat() if doc.created else None,
+                "correspondent": doc.correspondent.name if doc.correspondent else None,
+                "document_type": doc.document_type.name if doc.document_type else None,
+                "tags": [tag.name for tag in doc.tags.all()],
             }
             for doc in documents
         ]
@@ -2852,6 +2851,9 @@ class DeletionRequestActionSerializer(serializers.Serializer):
         allow_blank=True,
         label="Review Comment",
         help_text="Optional comment when reviewing the deletion request",
+    )
+
+
 class AISuggestionsRequestSerializer(serializers.Serializer):
     """Serializer for requesting AI suggestions for a document."""
 

src/documents/serializers/__init__.py

@@ -1,17 +1,15 @@
 """Serializers package for documents app."""
 
-from .ai_suggestions import (
-    AISuggestionFeedbackSerializer,
-    AISuggestionsSerializer,
-    AISuggestionStatsSerializer,
-    ApplySuggestionSerializer,
-    RejectSuggestionSerializer,
-)
+from .ai_suggestions import AISuggestionFeedbackSerializer
+from .ai_suggestions import AISuggestionsSerializer
+from .ai_suggestions import AISuggestionStatsSerializer
+from .ai_suggestions import ApplySuggestionSerializer
+from .ai_suggestions import RejectSuggestionSerializer
 
 __all__ = [
-    'AISuggestionFeedbackSerializer',
-    'AISuggestionsSerializer',
-    'AISuggestionStatsSerializer',
-    'ApplySuggestionSerializer',
-    'RejectSuggestionSerializer',
+    "AISuggestionFeedbackSerializer",
+    "AISuggestionStatsSerializer",
+    "AISuggestionsSerializer",
+    "ApplySuggestionSerializer",
+    "RejectSuggestionSerializer",
 ]

src/documents/serializers/ai_suggestions.py

@@ -7,36 +7,33 @@ and handling user feedback on AI suggestions.
 
 from __future__ import annotations
 
-from typing import Any, Dict
+from typing import Any
 
 from rest_framework import serializers
 
-from documents.models import (
-    AISuggestionFeedback,
-    Correspondent,
-    CustomField,
-    DocumentType,
-    StoragePath,
-    Tag,
-    Workflow,
-)
-
+from documents.models import AISuggestionFeedback
+from documents.models import Correspondent
+from documents.models import CustomField
+from documents.models import DocumentType
+from documents.models import StoragePath
+from documents.models import Tag
+from documents.models import Workflow
 
 # Suggestion type choices - used across multiple serializers
 SUGGESTION_TYPE_CHOICES = [
-    'tag',
-    'correspondent',
-    'document_type',
-    'storage_path',
-    'custom_field',
-    'workflow',
-    'title',
+    "tag",
+    "correspondent",
+    "document_type",
+    "storage_path",
+    "custom_field",
+    "workflow",
+    "title",
 ]
 
 # Types that require value_id
-ID_REQUIRED_TYPES = ['tag', 'correspondent', 'document_type', 'storage_path', 'workflow']
+ID_REQUIRED_TYPES = ["tag", "correspondent", "document_type", "storage_path", "workflow"]
 # Types that require value_text
-TEXT_REQUIRED_TYPES = ['title']
+TEXT_REQUIRED_TYPES = ["title"]
 # Types that can use either (custom_field can be ID or text)
 
 
@@ -113,7 +110,7 @@ class AISuggestionsSerializer(serializers.Serializer):
     title_suggestion = TitleSuggestionSerializer(required=False, allow_null=True)
 
     @staticmethod
-    def from_scan_result(scan_result, document_id: int) -> Dict[str, Any]:
+    def from_scan_result(scan_result, document_id: int) -> dict[str, Any]:
         """
         Convert an AIScanResult object to serializer data.
         
@@ -133,25 +130,25 @@ class AISuggestionsSerializer(serializers.Serializer):
                 try:
                     tag = Tag.objects.get(pk=tag_id)
                     tag_suggestions.append({
-                        'id': tag.id,
-                        'name': tag.name,
-                        'color': getattr(tag, 'color', '#000000'),
-                        'confidence': confidence,
+                        "id": tag.id,
+                        "name": tag.name,
+                        "color": getattr(tag, "color", "#000000"),
+                        "confidence": confidence,
                     })
                 except Tag.DoesNotExist:
                     # Tag no longer exists in database; skip this suggestion
                     pass
-            data['tags'] = tag_suggestions
+            data["tags"] = tag_suggestions
 
         # Correspondent
         if scan_result.correspondent:
             corr_id, confidence = scan_result.correspondent
             try:
                 correspondent = Correspondent.objects.get(pk=corr_id)
-                data['correspondent'] = {
-                    'id': correspondent.id,
-                    'name': correspondent.name,
-                    'confidence': confidence,
+                data["correspondent"] = {
+                    "id": correspondent.id,
+                    "name": correspondent.name,
+                    "confidence": confidence,
                 }
             except Correspondent.DoesNotExist:
                 # Correspondent no longer exists in database; omit from suggestions
@@ -162,10 +159,10 @@ class AISuggestionsSerializer(serializers.Serializer):
             type_id, confidence = scan_result.document_type
             try:
                 doc_type = DocumentType.objects.get(pk=type_id)
-                data['document_type'] = {
-                    'id': doc_type.id,
-                    'name': doc_type.name,
-                    'confidence': confidence,
+                data["document_type"] = {
+                    "id": doc_type.id,
+                    "name": doc_type.name,
+                    "confidence": confidence,
                 }
             except DocumentType.DoesNotExist:
                 # Document type no longer exists in database; omit from suggestions
@@ -176,11 +173,11 @@ class AISuggestionsSerializer(serializers.Serializer):
             path_id, confidence = scan_result.storage_path
             try:
                 storage_path = StoragePath.objects.get(pk=path_id)
-                data['storage_path'] = {
-                    'id': storage_path.id,
-                    'name': storage_path.name,
-                    'path': storage_path.path,
-                    'confidence': confidence,
+                data["storage_path"] = {
+                    "id": storage_path.id,
+                    "name": storage_path.name,
+                    "path": storage_path.path,
+                    "confidence": confidence,
                 }
             except StoragePath.DoesNotExist:
                 # Storage path no longer exists in database; omit from suggestions
@@ -193,15 +190,15 @@ class AISuggestionsSerializer(serializers.Serializer):
                 try:
                     field = CustomField.objects.get(pk=field_id)
                     field_suggestions.append({
-                        'field_id': field.id,
-                        'field_name': field.name,
-                        'value': str(value),
-                        'confidence': confidence,
+                        "field_id": field.id,
+                        "field_name": field.name,
+                        "value": str(value),
+                        "confidence": confidence,
                     })
                 except CustomField.DoesNotExist:
                     # Custom field no longer exists in database; skip this suggestion
                     pass
-            data['custom_fields'] = field_suggestions
+            data["custom_fields"] = field_suggestions
 
         # Workflows
         if scan_result.workflows:
@@ -210,19 +207,19 @@ class AISuggestionsSerializer(serializers.Serializer):
                 try:
                     workflow = Workflow.objects.get(pk=workflow_id)
                     workflow_suggestions.append({
-                        'id': workflow.id,
-                        'name': workflow.name,
-                        'confidence': confidence,
+                        "id": workflow.id,
+                        "name": workflow.name,
+                        "confidence": confidence,
                     })
                 except Workflow.DoesNotExist:
                     # Workflow no longer exists in database; skip this suggestion
                     pass
-            data['workflows'] = workflow_suggestions
+            data["workflows"] = workflow_suggestions
 
         # Title suggestion
         if scan_result.title_suggestion:
-            data['title_suggestion'] = {
-                'title': scan_result.title_suggestion,
+            data["title_suggestion"] = {
+                "title": scan_result.title_suggestion,
             }
 
         return data
@@ -234,26 +231,26 @@ class SuggestionSerializerMixin:
     """
     def validate(self, attrs):
         """Validate that the correct value field is provided for the suggestion type."""
-        suggestion_type = attrs.get('suggestion_type')
-        value_id = attrs.get('value_id')
-        value_text = attrs.get('value_text')
+        suggestion_type = attrs.get("suggestion_type")
+        value_id = attrs.get("value_id")
+        value_text = attrs.get("value_text")
 
         # Types that require value_id
         if suggestion_type in ID_REQUIRED_TYPES and not value_id:
             raise serializers.ValidationError(
-                f"value_id is required for suggestion_type '{suggestion_type}'"
+                f"value_id is required for suggestion_type '{suggestion_type}'",
             )
 
         # Types that require value_text
         if suggestion_type in TEXT_REQUIRED_TYPES and not value_text:
             raise serializers.ValidationError(
-                f"value_text is required for suggestion_type '{suggestion_type}'"
+                f"value_text is required for suggestion_type '{suggestion_type}'",
             )
 
         # For custom_field, either is acceptable
-        if suggestion_type == 'custom_field' and not value_id and not value_text:
+        if suggestion_type == "custom_field" and not value_id and not value_text:
             raise serializers.ValidationError(
-                "Either value_id or value_text must be provided for custom_field"
+                "Either value_id or value_text must be provided for custom_field",
             )
 
         return attrs
@@ -295,19 +292,19 @@ class AISuggestionFeedbackSerializer(serializers.ModelSerializer):
     class Meta:
         model = AISuggestionFeedback
         fields = [
-            'id',
-            'document',
-            'suggestion_type',
-            'suggested_value_id',
-            'suggested_value_text',
-            'confidence',
-            'status',
-            'user',
-            'created_at',
-            'applied_at',
-            'metadata',
+            "id",
+            "document",
+            "suggestion_type",
+            "suggested_value_id",
+            "suggested_value_text",
+            "confidence",
+            "status",
+            "user",
+            "created_at",
+            "applied_at",
+            "metadata",
         ]
-        read_only_fields = ['id', 'created_at', 'applied_at']
+        read_only_fields = ["id", "created_at", "applied_at"]
 
 
 class AISuggestionStatsSerializer(serializers.Serializer):

src/documents/tests/test_ai_deletion_manager.py

@@ -18,13 +18,11 @@ from django.test import TestCase
 from django.utils import timezone
 
 from documents.ai_deletion_manager import AIDeletionManager
-from documents.models import (
-    Correspondent,
-    DeletionRequest,
-    Document,
-    DocumentType,
-    Tag,
-)
+from documents.models import Correspondent
+from documents.models import DeletionRequest
+from documents.models import Document
+from documents.models import DocumentType
+from documents.models import Tag
 
 
 class TestAIDeletionManagerCreateRequest(TestCase):

src/documents/tests/test_ai_permissions.py

@@ -10,24 +10,23 @@ Tests cover:
 - Permission assignment and verification
 """
 
-from django.contrib.auth.models import Group, Permission, User
+from django.contrib.auth.models import Group
+from django.contrib.auth.models import Permission
+from django.contrib.auth.models import User
 from django.contrib.contenttypes.models import ContentType
 from django.test import TestCase
 from rest_framework.test import APIRequestFactory
 
 from documents.models import Document
-from documents.permissions import (
-    CanApplyAISuggestionsPermission,
-    CanApproveDeletionsPermission,
-    CanConfigureAIPermission,
-    CanViewAISuggestionsPermission,
-)
+from documents.permissions import CanApplyAISuggestionsPermission
+from documents.permissions import CanApproveDeletionsPermission
+from documents.permissions import CanConfigureAIPermission
+from documents.permissions import CanViewAISuggestionsPermission
 
 
 class MockView:
     """Mock view for testing permissions."""
 
-    pass
 
 
 class TestCanViewAISuggestionsPermission(TestCase):
@@ -41,13 +40,13 @@ class TestCanViewAISuggestionsPermission(TestCase):
 
         # Create users
         self.superuser = User.objects.create_superuser(
-            username="admin", email="admin@test.com", password="admin123"
+            username="admin", email="admin@test.com", password="admin123",
         )
         self.regular_user = User.objects.create_user(
-            username="regular", email="regular@test.com", password="regular123"
+            username="regular", email="regular@test.com", password="regular123",
         )
         self.permitted_user = User.objects.create_user(
-            username="permitted", email="permitted@test.com", password="permitted123"
+            username="permitted", email="permitted@test.com", password="permitted123",
         )
 
         # Assign permission to permitted_user
@@ -107,13 +106,13 @@ class TestCanApplyAISuggestionsPermission(TestCase):
 
         # Create users
         self.superuser = User.objects.create_superuser(
-            username="admin", email="admin@test.com", password="admin123"
+            username="admin", email="admin@test.com", password="admin123",
         )
         self.regular_user = User.objects.create_user(
-            username="regular", email="regular@test.com", password="regular123"
+            username="regular", email="regular@test.com", password="regular123",
         )
         self.permitted_user = User.objects.create_user(
-            username="permitted", email="permitted@test.com", password="permitted123"
+            username="permitted", email="permitted@test.com", password="permitted123",
         )
 
         # Assign permission to permitted_user
@@ -173,13 +172,13 @@ class TestCanApproveDeletionsPermission(TestCase):
 
         # Create users
         self.superuser = User.objects.create_superuser(
-            username="admin", email="admin@test.com", password="admin123"
+            username="admin", email="admin@test.com", password="admin123",
         )
         self.regular_user = User.objects.create_user(
-            username="regular", email="regular@test.com", password="regular123"
+            username="regular", email="regular@test.com", password="regular123",
         )
         self.permitted_user = User.objects.create_user(
-            username="permitted", email="permitted@test.com", password="permitted123"
+            username="permitted", email="permitted@test.com", password="permitted123",
         )
 
         # Assign permission to permitted_user
@@ -239,13 +238,13 @@ class TestCanConfigureAIPermission(TestCase):
 
         # Create users
         self.superuser = User.objects.create_superuser(
-            username="admin", email="admin@test.com", password="admin123"
+            username="admin", email="admin@test.com", password="admin123",
         )
         self.regular_user = User.objects.create_user(
-            username="regular", email="regular@test.com", password="regular123"
+            username="regular", email="regular@test.com", password="regular123",
         )
         self.permitted_user = User.objects.create_user(
-            username="permitted", email="permitted@test.com", password="permitted123"
+            username="permitted", email="permitted@test.com", password="permitted123",
         )
 
         # Assign permission to permitted_user
@@ -345,7 +344,7 @@ class TestRoleBasedAccessControl(TestCase):
     def test_viewer_role_permissions(self):
         """Test that viewer role has appropriate permissions."""
         user = User.objects.create_user(
-            username="viewer", email="viewer@test.com", password="viewer123"
+            username="viewer", email="viewer@test.com", password="viewer123",
         )
         user.groups.add(self.viewer_group)
 
@@ -360,7 +359,7 @@ class TestRoleBasedAccessControl(TestCase):
     def test_editor_role_permissions(self):
         """Test that editor role has appropriate permissions."""
         user = User.objects.create_user(
-            username="editor", email="editor@test.com", password="editor123"
+            username="editor", email="editor@test.com", password="editor123",
         )
         user.groups.add(self.editor_group)
 
@@ -375,7 +374,7 @@ class TestRoleBasedAccessControl(TestCase):
     def test_admin_role_permissions(self):
         """Test that admin role has all permissions."""
         user = User.objects.create_user(
-            username="ai_admin", email="ai_admin@test.com", password="admin123"
+            username="ai_admin", email="ai_admin@test.com", password="admin123",
         )
         user.groups.add(self.admin_group)
 
@@ -390,7 +389,7 @@ class TestRoleBasedAccessControl(TestCase):
     def test_user_with_multiple_groups(self):
         """Test that user permissions accumulate from multiple groups."""
         user = User.objects.create_user(
-            username="multi_role", email="multi@test.com", password="multi123"
+            username="multi_role", email="multi@test.com", password="multi123",
         )
         user.groups.add(self.viewer_group, self.editor_group)
 
@@ -405,7 +404,7 @@ class TestRoleBasedAccessControl(TestCase):
     def test_direct_permission_assignment_overrides_group(self):
         """Test that direct permission assignment works alongside group permissions."""
         user = User.objects.create_user(
-            username="special", email="special@test.com", password="special123"
+            username="special", email="special@test.com", password="special123",
         )
         user.groups.add(self.viewer_group)
 
@@ -428,7 +427,7 @@ class TestPermissionAssignment(TestCase):
     def setUp(self):
         """Set up test user."""
         self.user = User.objects.create_user(
-            username="testuser", email="test@test.com", password="test123"
+            username="testuser", email="test@test.com", password="test123",
         )
         content_type = ContentType.objects.get_for_model(Document)
         self.view_permission, _ = Permission.objects.get_or_create(
@@ -500,7 +499,7 @@ class TestPermissionEdgeCases(TestCase):
     def test_inactive_user_with_permission(self):
         """Test that inactive users are denied even with permission."""
         user = User.objects.create_user(
-            username="inactive", email="inactive@test.com", password="inactive123"
+            username="inactive", email="inactive@test.com", password="inactive123",
         )
         user.is_active = False
         user.save()

src/documents/tests/test_ai_scanner.py

@@ -21,24 +21,21 @@ Tests cover:
 from unittest import mock
 
 from django.db import transaction
-from django.test import TestCase, override_settings
+from django.test import TestCase
+from django.test import override_settings
 
-from documents.ai_scanner import (
-    AIScanResult,
-    AIDocumentScanner,
-    get_ai_scanner,
-)
-from documents.models import (
-    Correspondent,
-    CustomField,
-    Document,
-    DocumentType,
-    StoragePath,
-    Tag,
-    Workflow,
-    WorkflowTrigger,
-    WorkflowAction,
-)
+from documents.ai_scanner import AIDocumentScanner
+from documents.ai_scanner import AIScanResult
+from documents.ai_scanner import get_ai_scanner
+from documents.models import Correspondent
+from documents.models import CustomField
+from documents.models import Document
+from documents.models import DocumentType
+from documents.models import StoragePath
+from documents.models import Tag
+from documents.models import Workflow
+from documents.models import WorkflowAction
+from documents.models import WorkflowTrigger
 
 
 class TestAIScanResult(TestCase):
@@ -100,7 +97,7 @@ class TestAIDocumentScannerInitialization(TestCase):
         """Test scanner initialization with custom confidence thresholds."""
         scanner = AIDocumentScanner(
             auto_apply_threshold=0.90,
-            suggest_threshold=0.70
+            suggest_threshold=0.70,
         )
 
         self.assertEqual(scanner.auto_apply_threshold, 0.90)
@@ -145,14 +142,14 @@ class TestAIDocumentScannerInitialization(TestCase):
 class TestAIDocumentScannerLazyLoading(TestCase):
     """Test lazy loading of ML components."""
 
-    @mock.patch('documents.ai_scanner.logger')
+    @mock.patch("documents.ai_scanner.logger")
     def test_get_classifier_loads_successfully(self, mock_logger):
         """Test successful lazy loading of classifier."""
         scanner = AIDocumentScanner()
 
         # Mock the import and class
         mock_classifier_instance = mock.MagicMock()
-        with mock.patch('documents.ai_scanner.TransformerDocumentClassifier', 
+        with mock.patch("documents.ai_scanner.TransformerDocumentClassifier",
                        return_value=mock_classifier_instance) as mock_classifier_class:
             classifier = scanner._get_classifier()
 
@@ -161,13 +158,13 @@ class TestAIDocumentScannerLazyLoading(TestCase):
             mock_classifier_class.assert_called_once()
             mock_logger.info.assert_called_with("ML classifier loaded successfully")
 
-    @mock.patch('documents.ai_scanner.logger')
+    @mock.patch("documents.ai_scanner.logger")
     def test_get_classifier_returns_cached_instance(self, mock_logger):
         """Test that classifier is only loaded once."""
         scanner = AIDocumentScanner()
 
         mock_classifier_instance = mock.MagicMock()
-        with mock.patch('documents.ai_scanner.TransformerDocumentClassifier',
+        with mock.patch("documents.ai_scanner.TransformerDocumentClassifier",
                        return_value=mock_classifier_instance):
             classifier1 = scanner._get_classifier()
             classifier2 = scanner._get_classifier()
@@ -175,12 +172,12 @@ class TestAIDocumentScannerLazyLoading(TestCase):
             self.assertEqual(classifier1, classifier2)
             self.assertIs(classifier1, classifier2)
 
-    @mock.patch('documents.ai_scanner.logger')
+    @mock.patch("documents.ai_scanner.logger")
     def test_get_classifier_handles_import_error(self, mock_logger):
         """Test that classifier loading handles import errors gracefully."""
         scanner = AIDocumentScanner()
 
-        with mock.patch('documents.ai_scanner.TransformerDocumentClassifier',
+        with mock.patch("documents.ai_scanner.TransformerDocumentClassifier",
                        side_effect=ImportError("Module not found")):
             classifier = scanner._get_classifier()
 
@@ -196,13 +193,13 @@ class TestAIDocumentScannerLazyLoading(TestCase):
 
         self.assertIsNone(classifier)
 
-    @mock.patch('documents.ai_scanner.logger')
+    @mock.patch("documents.ai_scanner.logger")
     def test_get_ner_extractor_loads_successfully(self, mock_logger):
         """Test successful lazy loading of NER extractor."""
         scanner = AIDocumentScanner()
 
         mock_ner_instance = mock.MagicMock()
-        with mock.patch('documents.ai_scanner.DocumentNER',
+        with mock.patch("documents.ai_scanner.DocumentNER",
                        return_value=mock_ner_instance) as mock_ner_class:
             ner = scanner._get_ner_extractor()
 
@@ -211,25 +208,25 @@ class TestAIDocumentScannerLazyLoading(TestCase):
             mock_ner_class.assert_called_once()
             mock_logger.info.assert_called_with("NER extractor loaded successfully")
 
-    @mock.patch('documents.ai_scanner.logger')
+    @mock.patch("documents.ai_scanner.logger")
     def test_get_ner_extractor_handles_error(self, mock_logger):
         """Test NER extractor handles loading errors."""
         scanner = AIDocumentScanner()
 
-        with mock.patch('documents.ai_scanner.DocumentNER',
+        with mock.patch("documents.ai_scanner.DocumentNER",
                        side_effect=Exception("Failed to load")):
             ner = scanner._get_ner_extractor()
 
             self.assertIsNone(ner)
             mock_logger.warning.assert_called()
 
-    @mock.patch('documents.ai_scanner.logger')
+    @mock.patch("documents.ai_scanner.logger")
     def test_get_semantic_search_loads_successfully(self, mock_logger):
         """Test successful lazy loading of semantic search."""
         scanner = AIDocumentScanner()
 
         mock_search_instance = mock.MagicMock()
-        with mock.patch('documents.ai_scanner.SemanticSearch',
+        with mock.patch("documents.ai_scanner.SemanticSearch",
                        return_value=mock_search_instance) as mock_search_class:
             search = scanner._get_semantic_search()
 
@@ -238,13 +235,13 @@ class TestAIDocumentScannerLazyLoading(TestCase):
             mock_search_class.assert_called_once()
             mock_logger.info.assert_called_with("Semantic search loaded successfully")
 
-    @mock.patch('documents.ai_scanner.logger')
+    @mock.patch("documents.ai_scanner.logger")
     def test_get_table_extractor_loads_successfully(self, mock_logger):
         """Test successful lazy loading of table extractor."""
         scanner = AIDocumentScanner()
 
         mock_extractor_instance = mock.MagicMock()
-        with mock.patch('documents.ai_scanner.TableExtractor',
+        with mock.patch("documents.ai_scanner.TableExtractor",
                        return_value=mock_extractor_instance) as mock_extractor_class:
             extractor = scanner._get_table_extractor()
 
@@ -276,7 +273,7 @@ class TestExtractEntities(TestCase):
             "dates": ["2024-01-01", "2024-12-31"],
             "amounts": ["$1,000", "$500"],
             "locations": ["New York"],
-            "misc": ["Invoice#123"]
+            "misc": ["Invoice#123"],
         }
 
         scanner._ner_extractor = mock_ner
@@ -320,7 +317,7 @@ class TestExtractEntities(TestCase):
 
         self.assertEqual(entities, {})
 
-    @mock.patch('documents.ai_scanner.logger')
+    @mock.patch("documents.ai_scanner.logger")
     def test_extract_entities_handles_exception(self, mock_logger):
         """Test that entity extraction handles exceptions gracefully."""
         scanner = AIDocumentScanner()
@@ -345,10 +342,10 @@ class TestSuggestTags(TestCase):
         self.tag3 = Tag.objects.create(name="Tax", matching_algorithm=Tag.MATCH_AUTO)
         self.document = Document.objects.create(
             title="Test Document",
-            content="Test content"
+            content="Test content",
         )
 
-    @mock.patch('documents.ai_scanner.match_tags')
+    @mock.patch("documents.ai_scanner.match_tags")
     def test_suggest_tags_with_matched_tags(self, mock_match_tags):
         """Test tag suggestions from matching."""
         scanner = AIDocumentScanner()
@@ -357,7 +354,7 @@ class TestSuggestTags(TestCase):
         suggestions = scanner._suggest_tags(
             self.document,
             "Invoice from ACME Corp",
-            {}
+            {},
         )
 
         # Should suggest both matched tags
@@ -370,14 +367,14 @@ class TestSuggestTags(TestCase):
         for _, confidence in suggestions:
             self.assertGreaterEqual(confidence, 0.6)
 
-    @mock.patch('documents.ai_scanner.match_tags')
+    @mock.patch("documents.ai_scanner.match_tags")
     def test_suggest_tags_with_organization_entities(self, mock_match_tags):
         """Test tag suggestions based on organization entities."""
         scanner = AIDocumentScanner()
         mock_match_tags.return_value = []
 
         entities = {
-            "organizations": [{"text": "ACME Corp"}]
+            "organizations": [{"text": "ACME Corp"}],
         }
 
         suggestions = scanner._suggest_tags(self.document, "text", entities)
@@ -386,7 +383,7 @@ class TestSuggestTags(TestCase):
         tag_ids = [tag_id for tag_id, _ in suggestions]
         self.assertIn(self.tag2.id, tag_ids)
 
-    @mock.patch('documents.ai_scanner.match_tags')
+    @mock.patch("documents.ai_scanner.match_tags")
     def test_suggest_tags_removes_duplicates(self, mock_match_tags):
         """Test that duplicate tags keep highest confidence."""
         scanner = AIDocumentScanner()
@@ -397,8 +394,8 @@ class TestSuggestTags(TestCase):
 
         # Implementation should remove duplicates in actual code
 
-    @mock.patch('documents.ai_scanner.match_tags')
-    @mock.patch('documents.ai_scanner.logger')
+    @mock.patch("documents.ai_scanner.match_tags")
+    @mock.patch("documents.ai_scanner.logger")
     def test_suggest_tags_handles_exception(self, mock_logger, mock_match_tags):
         """Test tag suggestion handles exceptions."""
         scanner = AIDocumentScanner()
@@ -417,18 +414,18 @@ class TestDetectCorrespondent(TestCase):
         """Set up test correspondents."""
         self.correspondent1 = Correspondent.objects.create(
             name="ACME Corporation",
-            matching_algorithm=Correspondent.MATCH_AUTO
+            matching_algorithm=Correspondent.MATCH_AUTO,
         )
         self.correspondent2 = Correspondent.objects.create(
             name="TechStart Inc",
-            matching_algorithm=Correspondent.MATCH_AUTO
+            matching_algorithm=Correspondent.MATCH_AUTO,
         )
         self.document = Document.objects.create(
             title="Test Document",
-            content="Test content"
+            content="Test content",
         )
 
-    @mock.patch('documents.ai_scanner.match_correspondents')
+    @mock.patch("documents.ai_scanner.match_correspondents")
     def test_detect_correspondent_with_match(self, mock_match):
         """Test correspondent detection with successful match."""
         scanner = AIDocumentScanner()
@@ -441,7 +438,7 @@ class TestDetectCorrespondent(TestCase):
         self.assertEqual(corr_id, self.correspondent1.id)
         self.assertEqual(confidence, 0.85)
 
-    @mock.patch('documents.ai_scanner.match_correspondents')
+    @mock.patch("documents.ai_scanner.match_correspondents")
     def test_detect_correspondent_without_match(self, mock_match):
         """Test correspondent detection without match."""
         scanner = AIDocumentScanner()
@@ -451,14 +448,14 @@ class TestDetectCorrespondent(TestCase):
 
         self.assertIsNone(result)
 
-    @mock.patch('documents.ai_scanner.match_correspondents')
+    @mock.patch("documents.ai_scanner.match_correspondents")
     def test_detect_correspondent_from_ner_entities(self, mock_match):
         """Test correspondent detection from NER organizations."""
         scanner = AIDocumentScanner()
         mock_match.return_value = []
 
         entities = {
-            "organizations": [{"text": "ACME Corporation"}]
+            "organizations": [{"text": "ACME Corporation"}],
         }
 
         result = scanner._detect_correspondent(self.document, "text", entities)
@@ -468,8 +465,8 @@ class TestDetectCorrespondent(TestCase):
         self.assertEqual(corr_id, self.correspondent1.id)
         self.assertEqual(confidence, 0.70)
 
-    @mock.patch('documents.ai_scanner.match_correspondents')
-    @mock.patch('documents.ai_scanner.logger')
+    @mock.patch("documents.ai_scanner.match_correspondents")
+    @mock.patch("documents.ai_scanner.logger")
     def test_detect_correspondent_handles_exception(self, mock_logger, mock_match):
         """Test correspondent detection handles exceptions."""
         scanner = AIDocumentScanner()
@@ -488,18 +485,18 @@ class TestClassifyDocumentType(TestCase):
         """Set up test document types."""
         self.doc_type1 = DocumentType.objects.create(
             name="Invoice",
-            matching_algorithm=DocumentType.MATCH_AUTO
+            matching_algorithm=DocumentType.MATCH_AUTO,
         )
         self.doc_type2 = DocumentType.objects.create(
             name="Receipt",
-            matching_algorithm=DocumentType.MATCH_AUTO
+            matching_algorithm=DocumentType.MATCH_AUTO,
         )
         self.document = Document.objects.create(
             title="Test Document",
-            content="Test content"
+            content="Test content",
         )
 
-    @mock.patch('documents.ai_scanner.match_document_types')
+    @mock.patch("documents.ai_scanner.match_document_types")
     def test_classify_document_type_with_match(self, mock_match):
         """Test document type classification with match."""
         scanner = AIDocumentScanner()
@@ -512,7 +509,7 @@ class TestClassifyDocumentType(TestCase):
         self.assertEqual(type_id, self.doc_type1.id)
         self.assertEqual(confidence, 0.85)
 
-    @mock.patch('documents.ai_scanner.match_document_types')
+    @mock.patch("documents.ai_scanner.match_document_types")
     def test_classify_document_type_without_match(self, mock_match):
         """Test document type classification without match."""
         scanner = AIDocumentScanner()
@@ -522,8 +519,8 @@ class TestClassifyDocumentType(TestCase):
 
         self.assertIsNone(result)
 
-    @mock.patch('documents.ai_scanner.match_document_types')
-    @mock.patch('documents.ai_scanner.logger')
+    @mock.patch("documents.ai_scanner.match_document_types")
+    @mock.patch("documents.ai_scanner.logger")
     def test_classify_document_type_handles_exception(self, mock_logger, mock_match):
         """Test classification handles exceptions."""
         scanner = AIDocumentScanner()
@@ -543,14 +540,14 @@ class TestSuggestStoragePath(TestCase):
         self.storage_path1 = StoragePath.objects.create(
             name="Invoices",
             path="/documents/invoices",
-            matching_algorithm=StoragePath.MATCH_AUTO
+            matching_algorithm=StoragePath.MATCH_AUTO,
         )
         self.document = Document.objects.create(
             title="Test Document",
-            content="Test content"
+            content="Test content",
         )
 
-    @mock.patch('documents.ai_scanner.match_storage_paths')
+    @mock.patch("documents.ai_scanner.match_storage_paths")
     def test_suggest_storage_path_with_match(self, mock_match):
         """Test storage path suggestion with match."""
         scanner = AIDocumentScanner()
@@ -564,7 +561,7 @@ class TestSuggestStoragePath(TestCase):
         self.assertEqual(path_id, self.storage_path1.id)
         self.assertEqual(confidence, 0.80)
 
-    @mock.patch('documents.ai_scanner.match_storage_paths')
+    @mock.patch("documents.ai_scanner.match_storage_paths")
     def test_suggest_storage_path_without_match(self, mock_match):
         """Test storage path suggestion without match."""
         scanner = AIDocumentScanner()
@@ -575,8 +572,8 @@ class TestSuggestStoragePath(TestCase):
 
         self.assertIsNone(result)
 
-    @mock.patch('documents.ai_scanner.match_storage_paths')
-    @mock.patch('documents.ai_scanner.logger')
+    @mock.patch("documents.ai_scanner.match_storage_paths")
+    @mock.patch("documents.ai_scanner.logger")
     def test_suggest_storage_path_handles_exception(self, mock_logger, mock_match):
         """Test storage path suggestion handles exceptions."""
         scanner = AIDocumentScanner()
@@ -596,19 +593,19 @@ class TestExtractCustomFields(TestCase):
         """Set up test custom fields."""
         self.field_date = CustomField.objects.create(
             name="Invoice Date",
-            data_type=CustomField.FieldDataType.DATE
+            data_type=CustomField.FieldDataType.DATE,
         )
         self.field_amount = CustomField.objects.create(
             name="Total Amount",
-            data_type=CustomField.FieldDataType.STRING
+            data_type=CustomField.FieldDataType.STRING,
         )
         self.field_email = CustomField.objects.create(
             name="Contact Email",
-            data_type=CustomField.FieldDataType.STRING
+            data_type=CustomField.FieldDataType.STRING,
         )
         self.document = Document.objects.create(
             title="Test Document",
-            content="Test content"
+            content="Test content",
         )
 
     def test_extract_custom_fields_with_entities(self):
@@ -618,7 +615,7 @@ class TestExtractCustomFields(TestCase):
         entities = {
             "dates": [{"text": "2024-01-01"}],
             "amounts": [{"text": "$1,000"}],
-            "emails": ["test@example.com"]
+            "emails": ["test@example.com"],
         }
 
         fields = scanner._extract_custom_fields(self.document, "text", entities)
@@ -638,12 +635,12 @@ class TestExtractCustomFields(TestCase):
         # Should return empty dict
         self.assertEqual(fields, {})
 
-    @mock.patch('documents.ai_scanner.logger')
+    @mock.patch("documents.ai_scanner.logger")
     def test_extract_custom_fields_handles_exception(self, mock_logger):
         """Test custom field extraction handles exceptions."""
         scanner = AIDocumentScanner()
 
-        with mock.patch.object(CustomField.objects, 'all',
+        with mock.patch.object(CustomField.objects, "all",
                               side_effect=Exception("DB error")):
             fields = scanner._extract_custom_fields(self.document, "text", {})
 
@@ -658,31 +655,31 @@ class TestExtractFieldValue(TestCase):
         """Set up test fields."""
         self.field_date = CustomField.objects.create(
             name="Invoice Date",
-            data_type=CustomField.FieldDataType.DATE
+            data_type=CustomField.FieldDataType.DATE,
         )
         self.field_amount = CustomField.objects.create(
             name="Total Amount",
-            data_type=CustomField.FieldDataType.STRING
+            data_type=CustomField.FieldDataType.STRING,
         )
         self.field_invoice = CustomField.objects.create(
             name="Invoice Number",
-            data_type=CustomField.FieldDataType.STRING
+            data_type=CustomField.FieldDataType.STRING,
         )
         self.field_email = CustomField.objects.create(
             name="Contact Email",
-            data_type=CustomField.FieldDataType.STRING
+            data_type=CustomField.FieldDataType.STRING,
         )
         self.field_phone = CustomField.objects.create(
             name="Phone Number",
-            data_type=CustomField.FieldDataType.STRING
+            data_type=CustomField.FieldDataType.STRING,
         )
         self.field_person = CustomField.objects.create(
             name="Person Name",
-            data_type=CustomField.FieldDataType.STRING
+            data_type=CustomField.FieldDataType.STRING,
         )
         self.field_company = CustomField.objects.create(
             name="Company Name",
-            data_type=CustomField.FieldDataType.STRING
+            data_type=CustomField.FieldDataType.STRING,
         )
 
     def test_extract_field_value_date(self):
@@ -691,7 +688,7 @@ class TestExtractFieldValue(TestCase):
         entities = {"dates": [{"text": "2024-01-01"}]}
 
         value, confidence = scanner._extract_field_value(
-            self.field_date, "text", entities
+            self.field_date, "text", entities,
         )
 
         self.assertEqual(value, "2024-01-01")
@@ -703,7 +700,7 @@ class TestExtractFieldValue(TestCase):
         entities = {"amounts": [{"text": "$1,000"}]}
 
         value, confidence = scanner._extract_field_value(
-            self.field_amount, "text", entities
+            self.field_amount, "text", entities,
         )
 
         self.assertEqual(value, "$1,000")
@@ -715,7 +712,7 @@ class TestExtractFieldValue(TestCase):
         entities = {"invoice_numbers": ["INV-12345"]}
 
         value, confidence = scanner._extract_field_value(
-            self.field_invoice, "text", entities
+            self.field_invoice, "text", entities,
         )
 
         self.assertEqual(value, "INV-12345")
@@ -727,7 +724,7 @@ class TestExtractFieldValue(TestCase):
         entities = {"emails": ["test@example.com"]}
 
         value, confidence = scanner._extract_field_value(
-            self.field_email, "text", entities
+            self.field_email, "text", entities,
         )
 
         self.assertEqual(value, "test@example.com")
@@ -739,7 +736,7 @@ class TestExtractFieldValue(TestCase):
         entities = {"phones": ["+1-555-1234"]}
 
         value, confidence = scanner._extract_field_value(
-            self.field_phone, "text", entities
+            self.field_phone, "text", entities,
         )
 
         self.assertEqual(value, "+1-555-1234")
@@ -751,7 +748,7 @@ class TestExtractFieldValue(TestCase):
         entities = {"persons": [{"text": "John Doe"}]}
 
         value, confidence = scanner._extract_field_value(
-            self.field_person, "text", entities
+            self.field_person, "text", entities,
         )
 
         self.assertEqual(value, "John Doe")
@@ -763,7 +760,7 @@ class TestExtractFieldValue(TestCase):
         entities = {"organizations": [{"text": "ACME Corp"}]}
 
         value, confidence = scanner._extract_field_value(
-            self.field_company, "text", entities
+            self.field_company, "text", entities,
         )
 
         self.assertEqual(value, "ACME Corp")
@@ -775,7 +772,7 @@ class TestExtractFieldValue(TestCase):
         entities = {}
 
         value, confidence = scanner._extract_field_value(
-            self.field_date, "text", entities
+            self.field_date, "text", entities,
         )
 
         self.assertIsNone(value)
@@ -789,23 +786,23 @@ class TestSuggestWorkflows(TestCase):
         """Set up test workflows."""
         self.workflow1 = Workflow.objects.create(
             name="Invoice Processing",
-            enabled=True
+            enabled=True,
         )
         self.trigger1 = WorkflowTrigger.objects.create(
             workflow=self.workflow1,
-            type=WorkflowTrigger.WorkflowTriggerType.CONSUMPTION
+            type=WorkflowTrigger.WorkflowTriggerType.CONSUMPTION,
         )
         self.workflow2 = Workflow.objects.create(
             name="Document Archival",
-            enabled=True
+            enabled=True,
         )
         self.trigger2 = WorkflowTrigger.objects.create(
             workflow=self.workflow2,
-            type=WorkflowTrigger.WorkflowTriggerType.CONSUMPTION
+            type=WorkflowTrigger.WorkflowTriggerType.CONSUMPTION,
         )
         self.document = Document.objects.create(
             title="Test Document",
-            content="Test content"
+            content="Test content",
         )
 
     def test_suggest_workflows_with_matches(self):
@@ -820,7 +817,7 @@ class TestSuggestWorkflows(TestCase):
         # Create action for workflow
         WorkflowAction.objects.create(
             workflow=self.workflow1,
-            type=WorkflowAction.WorkflowActionType.ASSIGNMENT
+            type=WorkflowAction.WorkflowActionType.ASSIGNMENT,
         )
 
         suggestions = scanner._suggest_workflows(self.document, "text", scan_result)
@@ -841,17 +838,17 @@ class TestSuggestWorkflows(TestCase):
         # Should not suggest any (confidence too low)
         self.assertEqual(len(suggestions), 0)
 
-    @mock.patch('documents.ai_scanner.logger')
+    @mock.patch("documents.ai_scanner.logger")
     def test_suggest_workflows_handles_exception(self, mock_logger):
         """Test workflow suggestion handles exceptions."""
         scanner = AIDocumentScanner()
 
         scan_result = AIScanResult()
 
-        with mock.patch.object(Workflow.objects, 'filter',
+        with mock.patch.object(Workflow.objects, "filter",
                               side_effect=Exception("DB error")):
             suggestions = scanner._suggest_workflows(
-                self.document, "text", scan_result
+                self.document, "text", scan_result,
             )
 
             self.assertEqual(suggestions, [])
@@ -865,11 +862,11 @@ class TestEvaluateWorkflowMatch(TestCase):
         """Set up test workflow."""
         self.workflow = Workflow.objects.create(
             name="Test Workflow",
-            enabled=True
+            enabled=True,
         )
         self.document = Document.objects.create(
             title="Test Document",
-            content="Test content"
+            content="Test content",
         )
 
     def test_evaluate_workflow_match_base_confidence(self):
@@ -878,7 +875,7 @@ class TestEvaluateWorkflowMatch(TestCase):
         scan_result = AIScanResult()
 
         confidence = scanner._evaluate_workflow_match(
-            self.workflow, self.document, scan_result
+            self.workflow, self.document, scan_result,
         )
 
         self.assertEqual(confidence, 0.5)
@@ -892,11 +889,11 @@ class TestEvaluateWorkflowMatch(TestCase):
         # Create action for workflow
         WorkflowAction.objects.create(
             workflow=self.workflow,
-            type=WorkflowAction.WorkflowActionType.ASSIGNMENT
+            type=WorkflowAction.WorkflowActionType.ASSIGNMENT,
         )
 
         confidence = scanner._evaluate_workflow_match(
-            self.workflow, self.document, scan_result
+            self.workflow, self.document, scan_result,
         )
 
         self.assertGreater(confidence, 0.5)
@@ -908,7 +905,7 @@ class TestEvaluateWorkflowMatch(TestCase):
         scan_result.correspondent = (1, 0.90)
 
         confidence = scanner._evaluate_workflow_match(
-            self.workflow, self.document, scan_result
+            self.workflow, self.document, scan_result,
         )
 
         self.assertGreater(confidence, 0.5)
@@ -920,7 +917,7 @@ class TestEvaluateWorkflowMatch(TestCase):
         scan_result.tags = [(1, 0.80), (2, 0.75)]
 
         confidence = scanner._evaluate_workflow_match(
-            self.workflow, self.document, scan_result
+            self.workflow, self.document, scan_result,
         )
 
         self.assertGreater(confidence, 0.5)
@@ -936,11 +933,11 @@ class TestEvaluateWorkflowMatch(TestCase):
         # Create action
         WorkflowAction.objects.create(
             workflow=self.workflow,
-            type=WorkflowAction.WorkflowActionType.ASSIGNMENT
+            type=WorkflowAction.WorkflowActionType.ASSIGNMENT,
         )
 
         confidence = scanner._evaluate_workflow_match(
-            self.workflow, self.document, scan_result
+            self.workflow, self.document, scan_result,
         )
 
         self.assertLessEqual(confidence, 1.0)
@@ -953,7 +950,7 @@ class TestSuggestTitle(TestCase):
         """Set up test document."""
         self.document = Document.objects.create(
             title="Test Document",
-            content="Test content"
+            content="Test content",
         )
 
     def test_suggest_title_with_all_entities(self):
@@ -963,7 +960,7 @@ class TestSuggestTitle(TestCase):
         entities = {
             "document_type": "Invoice",
             "organizations": [{"text": "ACME Corporation"}],
-            "dates": [{"text": "2024-01-01"}]
+            "dates": [{"text": "2024-01-01"}],
         }
 
         title = scanner._suggest_title(self.document, "text", entities)
@@ -978,7 +975,7 @@ class TestSuggestTitle(TestCase):
         scanner = AIDocumentScanner()
 
         entities = {
-            "organizations": [{"text": "TechStart Inc"}]
+            "organizations": [{"text": "TechStart Inc"}],
         }
 
         title = scanner._suggest_title(self.document, "text", entities)
@@ -1002,7 +999,7 @@ class TestSuggestTitle(TestCase):
         long_org = "A" * 100
         entities = {
             "organizations": [{"text": long_org}],
-            "dates": [{"text": "2024-01-01"}]
+            "dates": [{"text": "2024-01-01"}],
         }
 
         title = scanner._suggest_title(self.document, "text", entities)
@@ -1010,7 +1007,7 @@ class TestSuggestTitle(TestCase):
         self.assertIsNotNone(title)
         self.assertLessEqual(len(title), 127)
 
-    @mock.patch('documents.ai_scanner.logger')
+    @mock.patch("documents.ai_scanner.logger")
     def test_suggest_title_handles_exception(self, mock_logger):
         """Test title suggestion handles exceptions."""
         scanner = AIDocumentScanner()
@@ -1034,7 +1031,7 @@ class TestExtractTables(TestCase):
 
         mock_extractor = mock.MagicMock()
         mock_extractor.extract_tables_from_image.return_value = [
-            {"data": [[1, 2], [3, 4]], "headers": ["A", "B"]}
+            {"data": [[1, 2], [3, 4]], "headers": ["A", "B"]},
         ]
         scanner._table_extractor = mock_extractor
 
@@ -1053,7 +1050,7 @@ class TestExtractTables(TestCase):
 
         self.assertEqual(tables, [])
 
-    @mock.patch('documents.ai_scanner.logger')
+    @mock.patch("documents.ai_scanner.logger")
     def test_extract_tables_handles_exception(self, mock_logger):
         """Test table extraction handles exceptions."""
         scanner = AIDocumentScanner()
@@ -1075,17 +1072,17 @@ class TestScanDocument(TestCase):
         """Set up test document."""
         self.document = Document.objects.create(
             title="Test Document",
-            content="Invoice from ACME Corporation dated 2024-01-01"
+            content="Invoice from ACME Corporation dated 2024-01-01",
         )
 
-    @mock.patch.object(AIDocumentScanner, '_extract_entities')
-    @mock.patch.object(AIDocumentScanner, '_suggest_tags')
-    @mock.patch.object(AIDocumentScanner, '_detect_correspondent')
-    @mock.patch.object(AIDocumentScanner, '_classify_document_type')
-    @mock.patch.object(AIDocumentScanner, '_suggest_storage_path')
-    @mock.patch.object(AIDocumentScanner, '_extract_custom_fields')
-    @mock.patch.object(AIDocumentScanner, '_suggest_workflows')
-    @mock.patch.object(AIDocumentScanner, '_suggest_title')
+    @mock.patch.object(AIDocumentScanner, "_extract_entities")
+    @mock.patch.object(AIDocumentScanner, "_suggest_tags")
+    @mock.patch.object(AIDocumentScanner, "_detect_correspondent")
+    @mock.patch.object(AIDocumentScanner, "_classify_document_type")
+    @mock.patch.object(AIDocumentScanner, "_suggest_storage_path")
+    @mock.patch.object(AIDocumentScanner, "_extract_custom_fields")
+    @mock.patch.object(AIDocumentScanner, "_suggest_workflows")
+    @mock.patch.object(AIDocumentScanner, "_suggest_title")
     def test_scan_document_orchestrates_all_methods(
         self,
         mock_title,
@@ -1095,7 +1092,7 @@ class TestScanDocument(TestCase):
         mock_doc_type,
         mock_correspondent,
         mock_tags,
-        mock_entities
+        mock_entities,
     ):
         """Test that scan_document calls all extraction methods."""
         scanner = AIDocumentScanner()
@@ -1127,26 +1124,26 @@ class TestScanDocument(TestCase):
         self.assertEqual(result.correspondent, (1, 0.90))
         self.assertEqual(result.document_type, (1, 0.80))
 
-    @mock.patch.object(AIDocumentScanner, '_extract_tables')
+    @mock.patch.object(AIDocumentScanner, "_extract_tables")
     def test_scan_document_extracts_tables_when_enabled(self, mock_extract_tables):
         """Test that tables are extracted when OCR is enabled and file path provided."""
         scanner = AIDocumentScanner(enable_advanced_ocr=True)
         mock_extract_tables.return_value = [{"data": "test"}]
 
         # Mock other methods to avoid complexity
-        with mock.patch.object(scanner, '_extract_entities', return_value={}), \
-             mock.patch.object(scanner, '_suggest_tags', return_value=[]), \
-             mock.patch.object(scanner, '_detect_correspondent', return_value=None), \
-             mock.patch.object(scanner, '_classify_document_type', return_value=None), \
-             mock.patch.object(scanner, '_suggest_storage_path', return_value=None), \
-             mock.patch.object(scanner, '_extract_custom_fields', return_value={}), \
-             mock.patch.object(scanner, '_suggest_workflows', return_value=[]), \
-             mock.patch.object(scanner, '_suggest_title', return_value=None):
+        with mock.patch.object(scanner, "_extract_entities", return_value={}), \
+             mock.patch.object(scanner, "_suggest_tags", return_value=[]), \
+             mock.patch.object(scanner, "_detect_correspondent", return_value=None), \
+             mock.patch.object(scanner, "_classify_document_type", return_value=None), \
+             mock.patch.object(scanner, "_suggest_storage_path", return_value=None), \
+             mock.patch.object(scanner, "_extract_custom_fields", return_value={}), \
+             mock.patch.object(scanner, "_suggest_workflows", return_value=[]), \
+             mock.patch.object(scanner, "_suggest_title", return_value=None):
 
             result = scanner.scan_document(
                 self.document,
                 "Document text",
-                original_file_path="/path/to/file.pdf"
+                original_file_path="/path/to/file.pdf",
             )
 
             mock_extract_tables.assert_called_once_with("/path/to/file.pdf")
@@ -1156,15 +1153,15 @@ class TestScanDocument(TestCase):
         """Test that tables are not extracted when file path is not provided."""
         scanner = AIDocumentScanner(enable_advanced_ocr=True)
 
-        with mock.patch.object(scanner, '_extract_tables') as mock_extract_tables, \
-             mock.patch.object(scanner, '_extract_entities', return_value={}), \
-             mock.patch.object(scanner, '_suggest_tags', return_value=[]), \
-             mock.patch.object(scanner, '_detect_correspondent', return_value=None), \
-             mock.patch.object(scanner, '_classify_document_type', return_value=None), \
-             mock.patch.object(scanner, '_suggest_storage_path', return_value=None), \
-             mock.patch.object(scanner, '_extract_custom_fields', return_value={}), \
-             mock.patch.object(scanner, '_suggest_workflows', return_value=[]), \
-             mock.patch.object(scanner, '_suggest_title', return_value=None):
+        with mock.patch.object(scanner, "_extract_tables") as mock_extract_tables, \
+             mock.patch.object(scanner, "_extract_entities", return_value={}), \
+             mock.patch.object(scanner, "_suggest_tags", return_value=[]), \
+             mock.patch.object(scanner, "_detect_correspondent", return_value=None), \
+             mock.patch.object(scanner, "_classify_document_type", return_value=None), \
+             mock.patch.object(scanner, "_suggest_storage_path", return_value=None), \
+             mock.patch.object(scanner, "_extract_custom_fields", return_value={}), \
+             mock.patch.object(scanner, "_suggest_workflows", return_value=[]), \
+             mock.patch.object(scanner, "_suggest_title", return_value=None):
 
             result = scanner.scan_document(self.document, "Document text")
 
@@ -1183,11 +1180,11 @@ class TestApplyScanResults(TestCase):
         self.doc_type = DocumentType.objects.create(name="Invoice")
         self.storage_path = StoragePath.objects.create(
             name="Invoices",
-            path="/invoices"
+            path="/invoices",
         )
         self.document = Document.objects.create(
             title="Test Document",
-            content="Test content"
+            content="Test content",
         )
 
     def test_apply_scan_results_auto_applies_high_confidence(self):
@@ -1203,7 +1200,7 @@ class TestApplyScanResults(TestCase):
         result = scanner.apply_scan_results(
             self.document,
             scan_result,
-            auto_apply=True
+            auto_apply=True,
         )
 
         # Verify auto-applied
@@ -1222,7 +1219,7 @@ class TestApplyScanResults(TestCase):
         """Test that medium confidence items are suggested, not applied."""
         scanner = AIDocumentScanner(
             auto_apply_threshold=0.80,
-            suggest_threshold=0.60
+            suggest_threshold=0.60,
         )
 
         scan_result = AIScanResult()
@@ -1232,7 +1229,7 @@ class TestApplyScanResults(TestCase):
         result = scanner.apply_scan_results(
             self.document,
             scan_result,
-            auto_apply=True
+            auto_apply=True,
         )
 
         # Verify suggested but not applied
@@ -1255,7 +1252,7 @@ class TestApplyScanResults(TestCase):
         result = scanner.apply_scan_results(
             self.document,
             scan_result,
-            auto_apply=False
+            auto_apply=False,
         )
 
         # Verify nothing was applied
@@ -1268,21 +1265,21 @@ class TestApplyScanResults(TestCase):
         scan_result = AIScanResult()
         scan_result.correspondent = (self.correspondent.id, 0.90)
 
-        with mock.patch.object(self.document, 'save',
+        with mock.patch.object(self.document, "save",
                               side_effect=Exception("Save failed")):
             with self.assertRaises(Exception):
                 with transaction.atomic():
                     scanner.apply_scan_results(
                         self.document,
                         scan_result,
-                        auto_apply=True
+                        auto_apply=True,
                     )
 
         # Verify transaction was rolled back
         self.document.refresh_from_db()
         self.assertIsNone(self.document.correspondent)
 
-    @mock.patch('documents.ai_scanner.logger')
+    @mock.patch("documents.ai_scanner.logger")
     def test_apply_scan_results_handles_exception(self, mock_logger):
         """Test that apply_scan_results handles exceptions gracefully."""
         scanner = AIDocumentScanner()
@@ -1293,7 +1290,7 @@ class TestApplyScanResults(TestCase):
         scanner.apply_scan_results(
             self.document,
             scan_result,
-            auto_apply=True
+            auto_apply=True,
         )
 
         mock_logger.error.assert_called()
@@ -1323,21 +1320,21 @@ class TestEdgeCasesAndErrorHandling(TestCase):
         """Set up test document."""
         self.document = Document.objects.create(
             title="Test Document",
-            content="Test content"
+            content="Test content",
         )
 
     def test_scan_document_with_empty_text(self):
         """Test scanning document with empty text."""
         scanner = AIDocumentScanner()
 
-        with mock.patch.object(scanner, '_extract_entities', return_value={}), \
-             mock.patch.object(scanner, '_suggest_tags', return_value=[]), \
-             mock.patch.object(scanner, '_detect_correspondent', return_value=None), \
-             mock.patch.object(scanner, '_classify_document_type', return_value=None), \
-             mock.patch.object(scanner, '_suggest_storage_path', return_value=None), \
-             mock.patch.object(scanner, '_extract_custom_fields', return_value={}), \
-             mock.patch.object(scanner, '_suggest_workflows', return_value=[]), \
-             mock.patch.object(scanner, '_suggest_title', return_value=None):
+        with mock.patch.object(scanner, "_extract_entities", return_value={}), \
+             mock.patch.object(scanner, "_suggest_tags", return_value=[]), \
+             mock.patch.object(scanner, "_detect_correspondent", return_value=None), \
+             mock.patch.object(scanner, "_classify_document_type", return_value=None), \
+             mock.patch.object(scanner, "_suggest_storage_path", return_value=None), \
+             mock.patch.object(scanner, "_extract_custom_fields", return_value={}), \
+             mock.patch.object(scanner, "_suggest_workflows", return_value=[]), \
+             mock.patch.object(scanner, "_suggest_title", return_value=None):
 
             result = scanner.scan_document(self.document, "")
 
@@ -1349,14 +1346,14 @@ class TestEdgeCasesAndErrorHandling(TestCase):
         scanner = AIDocumentScanner()
         long_text = "A" * 100000
 
-        with mock.patch.object(scanner, '_extract_entities', return_value={}), \
-             mock.patch.object(scanner, '_suggest_tags', return_value=[]), \
-             mock.patch.object(scanner, '_detect_correspondent', return_value=None), \
-             mock.patch.object(scanner, '_classify_document_type', return_value=None), \
-             mock.patch.object(scanner, '_suggest_storage_path', return_value=None), \
-             mock.patch.object(scanner, '_extract_custom_fields', return_value={}), \
-             mock.patch.object(scanner, '_suggest_workflows', return_value=[]), \
-             mock.patch.object(scanner, '_suggest_title', return_value=None):
+        with mock.patch.object(scanner, "_extract_entities", return_value={}), \
+             mock.patch.object(scanner, "_suggest_tags", return_value=[]), \
+             mock.patch.object(scanner, "_detect_correspondent", return_value=None), \
+             mock.patch.object(scanner, "_classify_document_type", return_value=None), \
+             mock.patch.object(scanner, "_suggest_storage_path", return_value=None), \
+             mock.patch.object(scanner, "_extract_custom_fields", return_value={}), \
+             mock.patch.object(scanner, "_suggest_workflows", return_value=[]), \
+             mock.patch.object(scanner, "_suggest_title", return_value=None):
 
             result = scanner.scan_document(self.document, long_text)
 
@@ -1367,14 +1364,14 @@ class TestEdgeCasesAndErrorHandling(TestCase):
         scanner = AIDocumentScanner()
         special_text = "Test with émojis 😀 and special chars: <>{}[]|\\`~"
 
-        with mock.patch.object(scanner, '_extract_entities', return_value={}), \
-             mock.patch.object(scanner, '_suggest_tags', return_value=[]), \
-             mock.patch.object(scanner, '_detect_correspondent', return_value=None), \
-             mock.patch.object(scanner, '_classify_document_type', return_value=None), \
-             mock.patch.object(scanner, '_suggest_storage_path', return_value=None), \
-             mock.patch.object(scanner, '_extract_custom_fields', return_value={}), \
-             mock.patch.object(scanner, '_suggest_workflows', return_value=[]), \
-             mock.patch.object(scanner, '_suggest_title', return_value=None):
+        with mock.patch.object(scanner, "_extract_entities", return_value={}), \
+             mock.patch.object(scanner, "_suggest_tags", return_value=[]), \
+             mock.patch.object(scanner, "_detect_correspondent", return_value=None), \
+             mock.patch.object(scanner, "_classify_document_type", return_value=None), \
+             mock.patch.object(scanner, "_suggest_storage_path", return_value=None), \
+             mock.patch.object(scanner, "_extract_custom_fields", return_value={}), \
+             mock.patch.object(scanner, "_suggest_workflows", return_value=[]), \
+             mock.patch.object(scanner, "_suggest_title", return_value=None):
 
             result = scanner.scan_document(self.document, special_text)
 
@@ -1388,7 +1385,7 @@ class TestEdgeCasesAndErrorHandling(TestCase):
         result = scanner.apply_scan_results(
             self.document,
             scan_result,
-            auto_apply=True
+            auto_apply=True,
         )
 
         self.assertEqual(result["applied"]["tags"], [])
@@ -1403,12 +1400,12 @@ class TestEdgeCasesAndErrorHandling(TestCase):
         # Test extreme values
         scanner_low = AIDocumentScanner(
             auto_apply_threshold=0.01,
-            suggest_threshold=0.01
+            suggest_threshold=0.01,
         )
         self.assertEqual(scanner_low.auto_apply_threshold, 0.01)
 
         scanner_high = AIDocumentScanner(
             auto_apply_threshold=0.99,
-            suggest_threshold=0.80
+            suggest_threshold=0.80,
         )
         self.assertEqual(scanner_high.auto_apply_threshold, 0.99)

src/documents/tests/test_ai_scanner_integration.py

@@ -8,24 +8,21 @@ document consumption to metadata application.
 
 from unittest import mock
 
-from django.test import TestCase, TransactionTestCase
+from django.test import TestCase
+from django.test import TransactionTestCase
 
-from documents.ai_scanner import (
-    AIDocumentScanner,
-    AIScanResult,
-    get_ai_scanner,
-)
-from documents.models import (
-    Correspondent,
-    CustomField,
-    Document,
-    DocumentType,
-    StoragePath,
-    Tag,
-    Workflow,
-    WorkflowTrigger,
-    WorkflowAction,
-)
+from documents.ai_scanner import AIDocumentScanner
+from documents.ai_scanner import AIScanResult
+from documents.ai_scanner import get_ai_scanner
+from documents.models import Correspondent
+from documents.models import CustomField
+from documents.models import Document
+from documents.models import DocumentType
+from documents.models import StoragePath
+from documents.models import Tag
+from documents.models import Workflow
+from documents.models import WorkflowAction
+from documents.models import WorkflowTrigger
 
 
 class TestAIScannerIntegrationBasic(TestCase):
@@ -35,49 +32,49 @@ class TestAIScannerIntegrationBasic(TestCase):
         """Set up test data."""
         self.document = Document.objects.create(
             title="Invoice from ACME Corporation",
-            content="Invoice #12345 from ACME Corporation dated 2024-01-01. Total: $1,000"
+            content="Invoice #12345 from ACME Corporation dated 2024-01-01. Total: $1,000",
         )
 
         self.tag_invoice = Tag.objects.create(
             name="Invoice",
             matching_algorithm=Tag.MATCH_AUTO,
-            match="invoice"
+            match="invoice",
         )
         self.tag_important = Tag.objects.create(
             name="Important",
             matching_algorithm=Tag.MATCH_AUTO,
-            match="total"
+            match="total",
         )
 
         self.correspondent = Correspondent.objects.create(
             name="ACME Corporation",
             matching_algorithm=Correspondent.MATCH_AUTO,
-            match="acme"
+            match="acme",
         )
 
         self.doc_type = DocumentType.objects.create(
             name="Invoice",
             matching_algorithm=DocumentType.MATCH_AUTO,
-            match="invoice"
+            match="invoice",
         )
 
         self.storage_path = StoragePath.objects.create(
             name="Invoices",
             path="/invoices",
             matching_algorithm=StoragePath.MATCH_AUTO,
-            match="invoice"
+            match="invoice",
         )
 
-    @mock.patch('documents.ai_scanner.match_tags')
-    @mock.patch('documents.ai_scanner.match_correspondents')
-    @mock.patch('documents.ai_scanner.match_document_types')
-    @mock.patch('documents.ai_scanner.match_storage_paths')
+    @mock.patch("documents.ai_scanner.match_tags")
+    @mock.patch("documents.ai_scanner.match_correspondents")
+    @mock.patch("documents.ai_scanner.match_document_types")
+    @mock.patch("documents.ai_scanner.match_storage_paths")
     def test_full_scan_and_apply_workflow(
         self,
         mock_storage,
         mock_types,
         mock_correspondents,
-        mock_tags
+        mock_tags,
     ):
         """Test complete workflow from scan to application."""
         # Mock the matching functions to return our test data
@@ -91,7 +88,7 @@ class TestAIScannerIntegrationBasic(TestCase):
         # Scan the document
         scan_result = scanner.scan_document(
             self.document,
-            self.document.content
+            self.document.content,
         )
 
         # Verify scan results
@@ -105,7 +102,7 @@ class TestAIScannerIntegrationBasic(TestCase):
         result = scanner.apply_scan_results(
             self.document,
             scan_result,
-            auto_apply=True
+            auto_apply=True,
         )
 
         # Verify application
@@ -118,7 +115,7 @@ class TestAIScannerIntegrationBasic(TestCase):
         self.assertEqual(self.document.document_type, self.doc_type)
         self.assertEqual(self.document.storage_path, self.storage_path)
 
-    @mock.patch('documents.ai_scanner.match_tags')
+    @mock.patch("documents.ai_scanner.match_tags")
     def test_scan_with_no_matches(self, mock_tags):
         """Test scanning when no matches are found."""
         mock_tags.return_value = []
@@ -127,7 +124,7 @@ class TestAIScannerIntegrationBasic(TestCase):
 
         scan_result = scanner.scan_document(
             self.document,
-            "Some random text with no matches"
+            "Some random text with no matches",
         )
 
         # Should return empty results
@@ -143,24 +140,24 @@ class TestAIScannerIntegrationCustomFields(TestCase):
         """Set up test data with custom fields."""
         self.document = Document.objects.create(
             title="Invoice",
-            content="Invoice #INV-123 dated 2024-01-01. Amount: $1,500. Contact: john@example.com"
+            content="Invoice #INV-123 dated 2024-01-01. Amount: $1,500. Contact: john@example.com",
         )
 
         self.field_date = CustomField.objects.create(
             name="Invoice Date",
-            data_type=CustomField.FieldDataType.DATE
+            data_type=CustomField.FieldDataType.DATE,
         )
         self.field_number = CustomField.objects.create(
             name="Invoice Number",
-            data_type=CustomField.FieldDataType.STRING
+            data_type=CustomField.FieldDataType.STRING,
         )
         self.field_amount = CustomField.objects.create(
             name="Total Amount",
-            data_type=CustomField.FieldDataType.STRING
+            data_type=CustomField.FieldDataType.STRING,
         )
         self.field_email = CustomField.objects.create(
             name="Contact Email",
-            data_type=CustomField.FieldDataType.STRING
+            data_type=CustomField.FieldDataType.STRING,
         )
 
     def test_custom_field_extraction_integration(self):
@@ -173,7 +170,7 @@ class TestAIScannerIntegrationCustomFields(TestCase):
             "dates": [{"text": "2024-01-01"}],
             "amounts": [{"text": "$1,500"}],
             "invoice_numbers": ["INV-123"],
-            "emails": ["john@example.com"]
+            "emails": ["john@example.com"],
         }
         scanner._ner_extractor = mock_ner
 
@@ -196,29 +193,29 @@ class TestAIScannerIntegrationWorkflows(TestCase):
         """Set up test workflows."""
         self.document = Document.objects.create(
             title="Invoice",
-            content="Invoice document"
+            content="Invoice document",
         )
 
         self.workflow1 = Workflow.objects.create(
             name="Invoice Processing",
-            enabled=True
+            enabled=True,
         )
         self.trigger1 = WorkflowTrigger.objects.create(
             workflow=self.workflow1,
-            type=WorkflowTrigger.WorkflowTriggerType.CONSUMPTION
+            type=WorkflowTrigger.WorkflowTriggerType.CONSUMPTION,
         )
         self.action1 = WorkflowAction.objects.create(
             workflow=self.workflow1,
-            type=WorkflowAction.WorkflowActionType.ASSIGNMENT
+            type=WorkflowAction.WorkflowActionType.ASSIGNMENT,
         )
 
         self.workflow2 = Workflow.objects.create(
             name="Archive Documents",
-            enabled=True
+            enabled=True,
         )
         self.trigger2 = WorkflowTrigger.objects.create(
             workflow=self.workflow2,
-            type=WorkflowTrigger.WorkflowTriggerType.CONSUMPTION
+            type=WorkflowTrigger.WorkflowTriggerType.CONSUMPTION,
         )
 
     def test_workflow_suggestion_integration(self):
@@ -234,7 +231,7 @@ class TestAIScannerIntegrationWorkflows(TestCase):
         workflows = scanner._suggest_workflows(
             self.document,
             self.document.content,
-            scan_result
+            scan_result,
         )
 
         # Should suggest workflows
@@ -250,7 +247,7 @@ class TestAIScannerIntegrationTransactions(TransactionTestCase):
         """Set up test data."""
         self.document = Document.objects.create(
             title="Test Document",
-            content="Test content"
+            content="Test content",
         )
         self.tag = Tag.objects.create(name="TestTag")
         self.correspondent = Correspondent.objects.create(name="TestCorp")
@@ -273,12 +270,12 @@ class TestAIScannerIntegrationTransactions(TransactionTestCase):
                 raise Exception("Forced save failure")
             return original_save(self, *args, **kwargs)
 
-        with mock.patch.object(Document, 'save', failing_save):
+        with mock.patch.object(Document, "save", failing_save):
             with self.assertRaises(Exception):
                 scanner.apply_scan_results(
                     self.document,
                     scan_result,
-                    auto_apply=True
+                    auto_apply=True,
                 )
 
         # Verify changes were rolled back
@@ -297,19 +294,19 @@ class TestAIScannerIntegrationPerformance(TestCase):
         for i in range(5):
             doc = Document.objects.create(
                 title=f"Document {i}",
-                content=f"Content for document {i}"
+                content=f"Content for document {i}",
             )
             documents.append(doc)
 
         # Mock to avoid actual ML loading
-        with mock.patch.object(scanner, '_extract_entities', return_value={}), \
-             mock.patch.object(scanner, '_suggest_tags', return_value=[]), \
-             mock.patch.object(scanner, '_detect_correspondent', return_value=None), \
-             mock.patch.object(scanner, '_classify_document_type', return_value=None), \
-             mock.patch.object(scanner, '_suggest_storage_path', return_value=None), \
-             mock.patch.object(scanner, '_extract_custom_fields', return_value={}), \
-             mock.patch.object(scanner, '_suggest_workflows', return_value=[]), \
-             mock.patch.object(scanner, '_suggest_title', return_value=None):
+        with mock.patch.object(scanner, "_extract_entities", return_value={}), \
+             mock.patch.object(scanner, "_suggest_tags", return_value=[]), \
+             mock.patch.object(scanner, "_detect_correspondent", return_value=None), \
+             mock.patch.object(scanner, "_classify_document_type", return_value=None), \
+             mock.patch.object(scanner, "_suggest_storage_path", return_value=None), \
+             mock.patch.object(scanner, "_extract_custom_fields", return_value={}), \
+             mock.patch.object(scanner, "_suggest_workflows", return_value=[]), \
+             mock.patch.object(scanner, "_suggest_title", return_value=None):
 
             results = []
             for doc in documents:
@@ -329,16 +326,16 @@ class TestAIScannerIntegrationEntityMatching(TestCase):
         """Set up test data."""
         self.document = Document.objects.create(
             title="Business Invoice",
-            content="Invoice from ACME Corporation"
+            content="Invoice from ACME Corporation",
         )
 
         self.correspondent_acme = Correspondent.objects.create(
             name="ACME Corporation",
-            matching_algorithm=Correspondent.MATCH_AUTO
+            matching_algorithm=Correspondent.MATCH_AUTO,
         )
         self.correspondent_other = Correspondent.objects.create(
             name="Other Company",
-            matching_algorithm=Correspondent.MATCH_AUTO
+            matching_algorithm=Correspondent.MATCH_AUTO,
         )
 
     def test_correspondent_matching_with_ner_entities(self):
@@ -348,16 +345,16 @@ class TestAIScannerIntegrationEntityMatching(TestCase):
         # Mock NER to extract organization
         mock_ner = mock.MagicMock()
         mock_ner.extract_all.return_value = {
-            "organizations": [{"text": "ACME Corporation"}]
+            "organizations": [{"text": "ACME Corporation"}],
         }
         scanner._ner_extractor = mock_ner
 
         # Mock matching to return empty (so NER-based matching is used)
-        with mock.patch('documents.ai_scanner.match_correspondents', return_value=[]):
+        with mock.patch("documents.ai_scanner.match_correspondents", return_value=[]):
             result = scanner._detect_correspondent(
                 self.document,
                 self.document.content,
-                {"organizations": [{"text": "ACME Corporation"}]}
+                {"organizations": [{"text": "ACME Corporation"}]},
             )
 
         # Should detect ACME correspondent
@@ -375,13 +372,13 @@ class TestAIScannerIntegrationTitleGeneration(TestCase):
 
         document = Document.objects.create(
             title="document.pdf",
-            content="Invoice from ACME Corp dated 2024-01-15"
+            content="Invoice from ACME Corp dated 2024-01-15",
         )
 
         entities = {
             "document_type": "Invoice",
             "organizations": [{"text": "ACME Corp"}],
-            "dates": [{"text": "2024-01-15"}]
+            "dates": [{"text": "2024-01-15"}],
         }
 
         title = scanner._suggest_title(document, document.content, entities)
@@ -399,7 +396,7 @@ class TestAIScannerIntegrationConfidenceLevels(TestCase):
         """Set up test data."""
         self.document = Document.objects.create(
             title="Test",
-            content="Test"
+            content="Test",
         )
         self.tag_high = Tag.objects.create(name="HighConfidence")
         self.tag_medium = Tag.objects.create(name="MediumConfidence")
@@ -409,7 +406,7 @@ class TestAIScannerIntegrationConfidenceLevels(TestCase):
         """Test that only high confidence suggestions are auto-applied."""
         scanner = AIDocumentScanner(
             auto_apply_threshold=0.80,
-            suggest_threshold=0.60
+            suggest_threshold=0.60,
         )
 
         scan_result = AIScanResult()
@@ -422,7 +419,7 @@ class TestAIScannerIntegrationConfidenceLevels(TestCase):
         result = scanner.apply_scan_results(
             self.document,
             scan_result,
-            auto_apply=True
+            auto_apply=True,
         )
 
         # Verify high confidence was applied
@@ -448,17 +445,17 @@ class TestAIScannerIntegrationGlobalInstance(TestCase):
         # Should be functional
         document = Document.objects.create(
             title="Test",
-            content="Test content"
+            content="Test content",
         )
 
-        with mock.patch.object(scanner1, '_extract_entities', return_value={}), \
-             mock.patch.object(scanner1, '_suggest_tags', return_value=[]), \
-             mock.patch.object(scanner1, '_detect_correspondent', return_value=None), \
-             mock.patch.object(scanner1, '_classify_document_type', return_value=None), \
-             mock.patch.object(scanner1, '_suggest_storage_path', return_value=None), \
-             mock.patch.object(scanner1, '_extract_custom_fields', return_value={}), \
-             mock.patch.object(scanner1, '_suggest_workflows', return_value=[]), \
-             mock.patch.object(scanner1, '_suggest_title', return_value=None):
+        with mock.patch.object(scanner1, "_extract_entities", return_value={}), \
+             mock.patch.object(scanner1, "_suggest_tags", return_value=[]), \
+             mock.patch.object(scanner1, "_detect_correspondent", return_value=None), \
+             mock.patch.object(scanner1, "_classify_document_type", return_value=None), \
+             mock.patch.object(scanner1, "_suggest_storage_path", return_value=None), \
+             mock.patch.object(scanner1, "_extract_custom_fields", return_value={}), \
+             mock.patch.object(scanner1, "_suggest_workflows", return_value=[]), \
+             mock.patch.object(scanner1, "_suggest_title", return_value=None):
 
             result1 = scanner1.scan_document(document, document.content)
             result2 = scanner2.scan_document(document, document.content)
@@ -476,17 +473,17 @@ class TestAIScannerIntegrationEdgeCases(TestCase):
 
         document = Document.objects.create(
             title="",
-            content=""
+            content="",
         )
 
-        with mock.patch.object(scanner, '_extract_entities', return_value={}), \
-             mock.patch.object(scanner, '_suggest_tags', return_value=[]), \
-             mock.patch.object(scanner, '_detect_correspondent', return_value=None), \
-             mock.patch.object(scanner, '_classify_document_type', return_value=None), \
-             mock.patch.object(scanner, '_suggest_storage_path', return_value=None), \
-             mock.patch.object(scanner, '_extract_custom_fields', return_value={}), \
-             mock.patch.object(scanner, '_suggest_workflows', return_value=[]), \
-             mock.patch.object(scanner, '_suggest_title', return_value=None):
+        with mock.patch.object(scanner, "_extract_entities", return_value={}), \
+             mock.patch.object(scanner, "_suggest_tags", return_value=[]), \
+             mock.patch.object(scanner, "_detect_correspondent", return_value=None), \
+             mock.patch.object(scanner, "_classify_document_type", return_value=None), \
+             mock.patch.object(scanner, "_suggest_storage_path", return_value=None), \
+             mock.patch.object(scanner, "_extract_custom_fields", return_value={}), \
+             mock.patch.object(scanner, "_suggest_workflows", return_value=[]), \
+             mock.patch.object(scanner, "_suggest_title", return_value=None):
 
             result = scanner.scan_document(document, document.content)
 
@@ -498,7 +495,7 @@ class TestAIScannerIntegrationEdgeCases(TestCase):
 
         document = Document.objects.create(
             title="Test",
-            content="Test"
+            content="Test",
         )
 
         scan_result = AIScanResult()
@@ -509,7 +506,7 @@ class TestAIScannerIntegrationEdgeCases(TestCase):
         result = scanner.apply_scan_results(
             document,
             scan_result,
-            auto_apply=True
+            auto_apply=True,
         )
 
         # Should not crash, just log errors
@@ -521,17 +518,17 @@ class TestAIScannerIntegrationEdgeCases(TestCase):
 
         document = Document.objects.create(
             title="Factura - España 🇪🇸",
-            content="Société française • 日本語 • Ελληνικά • مرحبا"
+            content="Société française • 日本語 • Ελληνικά • مرحبا",
         )
 
-        with mock.patch.object(scanner, '_extract_entities', return_value={}), \
-             mock.patch.object(scanner, '_suggest_tags', return_value=[]), \
-             mock.patch.object(scanner, '_detect_correspondent', return_value=None), \
-             mock.patch.object(scanner, '_classify_document_type', return_value=None), \
-             mock.patch.object(scanner, '_suggest_storage_path', return_value=None), \
-             mock.patch.object(scanner, '_extract_custom_fields', return_value={}), \
-             mock.patch.object(scanner, '_suggest_workflows', return_value=[]), \
-             mock.patch.object(scanner, '_suggest_title', return_value=None):
+        with mock.patch.object(scanner, "_extract_entities", return_value={}), \
+             mock.patch.object(scanner, "_suggest_tags", return_value=[]), \
+             mock.patch.object(scanner, "_detect_correspondent", return_value=None), \
+             mock.patch.object(scanner, "_classify_document_type", return_value=None), \
+             mock.patch.object(scanner, "_suggest_storage_path", return_value=None), \
+             mock.patch.object(scanner, "_extract_custom_fields", return_value={}), \
+             mock.patch.object(scanner, "_suggest_workflows", return_value=[]), \
+             mock.patch.object(scanner, "_suggest_title", return_value=None):
 
             result = scanner.scan_document(document, document.content)
 

src/documents/tests/test_api_ai_endpoints.py

@@ -12,18 +12,17 @@ Tests cover:
 
 from unittest import mock
 
-from django.contrib.auth.models import Permission, User
+from django.contrib.auth.models import Permission
+from django.contrib.auth.models import User
 from django.contrib.contenttypes.models import ContentType
 from rest_framework import status
 from rest_framework.test import APITestCase
 
-from documents.models import (
-    Correspondent,
-    DeletionRequest,
-    Document,
-    DocumentType,
-    Tag,
-)
+from documents.models import Correspondent
+from documents.models import DeletionRequest
+from documents.models import Document
+from documents.models import DocumentType
+from documents.models import Tag
 from documents.tests.utils import DirectoriesMixin
 
 
@@ -36,13 +35,13 @@ class TestAISuggestionsEndpoint(DirectoriesMixin, APITestCase):
 
         # Create users
         self.superuser = User.objects.create_superuser(
-            username="admin", email="admin@test.com", password="admin123"
+            username="admin", email="admin@test.com", password="admin123",
         )
         self.user_with_permission = User.objects.create_user(
-            username="permitted", email="permitted@test.com", password="permitted123"
+            username="permitted", email="permitted@test.com", password="permitted123",
         )
         self.user_without_permission = User.objects.create_user(
-            username="regular", email="regular@test.com", password="regular123"
+            username="regular", email="regular@test.com", password="regular123",
         )
 
         # Assign view permission
@@ -57,7 +56,7 @@ class TestAISuggestionsEndpoint(DirectoriesMixin, APITestCase):
         # Create test document
         self.document = Document.objects.create(
             title="Test Document",
-            content="This is a test invoice from ACME Corporation"
+            content="This is a test invoice from ACME Corporation",
         )
 
         # Create test metadata objects
@@ -70,7 +69,7 @@ class TestAISuggestionsEndpoint(DirectoriesMixin, APITestCase):
         response = self.client.post(
             "/api/ai/suggestions/",
             {"document_id": self.document.id},
-            format="json"
+            format="json",
         )
 
         self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)
@@ -82,7 +81,7 @@ class TestAISuggestionsEndpoint(DirectoriesMixin, APITestCase):
         response = self.client.post(
             "/api/ai/suggestions/",
             {"document_id": self.document.id},
-            format="json"
+            format="json",
         )
 
         self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)
@@ -91,7 +90,7 @@ class TestAISuggestionsEndpoint(DirectoriesMixin, APITestCase):
         """Test that superusers can access the endpoint."""
         self.client.force_authenticate(user=self.superuser)
 
-        with mock.patch('documents.views.get_ai_scanner') as mock_scanner:
+        with mock.patch("documents.views.get_ai_scanner") as mock_scanner:
             # Mock the scanner response
             mock_scan_result = mock.MagicMock()
             mock_scan_result.tags = [(self.tag.id, 0.85)]
@@ -108,7 +107,7 @@ class TestAISuggestionsEndpoint(DirectoriesMixin, APITestCase):
             response = self.client.post(
                 "/api/ai/suggestions/",
                 {"document_id": self.document.id},
-                format="json"
+                format="json",
             )
 
         self.assertEqual(response.status_code, status.HTTP_200_OK)
@@ -119,7 +118,7 @@ class TestAISuggestionsEndpoint(DirectoriesMixin, APITestCase):
         """Test that users with permission can access the endpoint."""
         self.client.force_authenticate(user=self.user_with_permission)
 
-        with mock.patch('documents.views.get_ai_scanner') as mock_scanner:
+        with mock.patch("documents.views.get_ai_scanner") as mock_scanner:
             # Mock the scanner response
             mock_scan_result = mock.MagicMock()
             mock_scan_result.tags = []
@@ -136,7 +135,7 @@ class TestAISuggestionsEndpoint(DirectoriesMixin, APITestCase):
             response = self.client.post(
                 "/api/ai/suggestions/",
                 {"document_id": self.document.id},
-                format="json"
+                format="json",
             )
 
         self.assertEqual(response.status_code, status.HTTP_200_OK)
@@ -148,7 +147,7 @@ class TestAISuggestionsEndpoint(DirectoriesMixin, APITestCase):
         response = self.client.post(
             "/api/ai/suggestions/",
             {"document_id": 99999},
-            format="json"
+            format="json",
         )
 
         self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)
@@ -160,7 +159,7 @@ class TestAISuggestionsEndpoint(DirectoriesMixin, APITestCase):
         response = self.client.post(
             "/api/ai/suggestions/",
             {},
-            format="json"
+            format="json",
         )
 
         self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
@@ -175,10 +174,10 @@ class TestApplyAISuggestionsEndpoint(DirectoriesMixin, APITestCase):
 
         # Create users
         self.superuser = User.objects.create_superuser(
-            username="admin", email="admin@test.com", password="admin123"
+            username="admin", email="admin@test.com", password="admin123",
         )
         self.user_with_permission = User.objects.create_user(
-            username="permitted", email="permitted@test.com", password="permitted123"
+            username="permitted", email="permitted@test.com", password="permitted123",
         )
 
         # Assign apply permission
@@ -193,7 +192,7 @@ class TestApplyAISuggestionsEndpoint(DirectoriesMixin, APITestCase):
         # Create test document
         self.document = Document.objects.create(
             title="Test Document",
-            content="Test content"
+            content="Test content",
         )
 
         # Create test metadata
@@ -205,7 +204,7 @@ class TestApplyAISuggestionsEndpoint(DirectoriesMixin, APITestCase):
         response = self.client.post(
             "/api/ai/suggestions/apply/",
             {"document_id": self.document.id},
-            format="json"
+            format="json",
         )
 
         self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)
@@ -214,7 +213,7 @@ class TestApplyAISuggestionsEndpoint(DirectoriesMixin, APITestCase):
         """Test successfully applying tag suggestions."""
         self.client.force_authenticate(user=self.superuser)
 
-        with mock.patch('documents.views.get_ai_scanner') as mock_scanner:
+        with mock.patch("documents.views.get_ai_scanner") as mock_scanner:
             # Mock the scanner response
             mock_scan_result = mock.MagicMock()
             mock_scan_result.tags = [(self.tag.id, 0.85)]
@@ -233,9 +232,9 @@ class TestApplyAISuggestionsEndpoint(DirectoriesMixin, APITestCase):
                 "/api/ai/suggestions/apply/",
                 {
                     "document_id": self.document.id,
-                    "apply_tags": True
+                    "apply_tags": True,
                 },
-                format="json"
+                format="json",
             )
 
         self.assertEqual(response.status_code, status.HTTP_200_OK)
@@ -245,7 +244,7 @@ class TestApplyAISuggestionsEndpoint(DirectoriesMixin, APITestCase):
         """Test successfully applying correspondent suggestion."""
         self.client.force_authenticate(user=self.superuser)
 
-        with mock.patch('documents.views.get_ai_scanner') as mock_scanner:
+        with mock.patch("documents.views.get_ai_scanner") as mock_scanner:
             # Mock the scanner response
             mock_scan_result = mock.MagicMock()
             mock_scan_result.tags = []
@@ -264,9 +263,9 @@ class TestApplyAISuggestionsEndpoint(DirectoriesMixin, APITestCase):
                 "/api/ai/suggestions/apply/",
                 {
                     "document_id": self.document.id,
-                    "apply_correspondent": True
+                    "apply_correspondent": True,
                 },
-                format="json"
+                format="json",
             )
 
         self.assertEqual(response.status_code, status.HTTP_200_OK)
@@ -285,10 +284,10 @@ class TestAIConfigurationEndpoint(DirectoriesMixin, APITestCase):
 
         # Create users
         self.superuser = User.objects.create_superuser(
-            username="admin", email="admin@test.com", password="admin123"
+            username="admin", email="admin@test.com", password="admin123",
         )
         self.user_without_permission = User.objects.create_user(
-            username="regular", email="regular@test.com", password="regular123"
+            username="regular", email="regular@test.com", password="regular123",
         )
 
     def test_unauthorized_access_denied(self):
@@ -309,7 +308,7 @@ class TestAIConfigurationEndpoint(DirectoriesMixin, APITestCase):
         """Test getting AI configuration."""
         self.client.force_authenticate(user=self.superuser)
 
-        with mock.patch('documents.views.get_ai_scanner') as mock_scanner:
+        with mock.patch("documents.views.get_ai_scanner") as mock_scanner:
             mock_scanner_instance = mock.MagicMock()
             mock_scanner_instance.auto_apply_threshold = 0.80
             mock_scanner_instance.suggest_threshold = 0.60
@@ -331,9 +330,9 @@ class TestAIConfigurationEndpoint(DirectoriesMixin, APITestCase):
             "/api/ai/config/",
             {
                 "auto_apply_threshold": 0.90,
-                "suggest_threshold": 0.70
+                "suggest_threshold": 0.70,
             },
-            format="json"
+            format="json",
         )
 
         self.assertEqual(response.status_code, status.HTTP_200_OK)
@@ -346,9 +345,9 @@ class TestAIConfigurationEndpoint(DirectoriesMixin, APITestCase):
         response = self.client.post(
             "/api/ai/config/",
             {
-                "auto_apply_threshold": 1.5  # Invalid: > 1.0
+                "auto_apply_threshold": 1.5,  # Invalid: > 1.0
             },
-            format="json"
+            format="json",
         )
 
         self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
@@ -363,13 +362,13 @@ class TestDeletionApprovalEndpoint(DirectoriesMixin, APITestCase):
 
         # Create users
         self.superuser = User.objects.create_superuser(
-            username="admin", email="admin@test.com", password="admin123"
+            username="admin", email="admin@test.com", password="admin123",
         )
         self.user_with_permission = User.objects.create_user(
-            username="permitted", email="permitted@test.com", password="permitted123"
+            username="permitted", email="permitted@test.com", password="permitted123",
         )
         self.user_without_permission = User.objects.create_user(
-            username="regular", email="regular@test.com", password="regular123"
+            username="regular", email="regular@test.com", password="regular123",
         )
 
         # Assign approval permission
@@ -385,7 +384,7 @@ class TestDeletionApprovalEndpoint(DirectoriesMixin, APITestCase):
         self.deletion_request = DeletionRequest.objects.create(
             user=self.user_with_permission,
             requested_by_ai=True,
-            ai_reason="Document appears to be a duplicate"
+            ai_reason="Document appears to be a duplicate",
         )
 
     def test_unauthorized_access_denied(self):
@@ -394,9 +393,9 @@ class TestDeletionApprovalEndpoint(DirectoriesMixin, APITestCase):
             "/api/ai/deletions/approve/",
             {
                 "request_id": self.deletion_request.id,
-                "action": "approve"
+                "action": "approve",
             },
-            format="json"
+            format="json",
         )
 
         self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)
@@ -409,9 +408,9 @@ class TestDeletionApprovalEndpoint(DirectoriesMixin, APITestCase):
             "/api/ai/deletions/approve/",
             {
                 "request_id": self.deletion_request.id,
-                "action": "approve"
+                "action": "approve",
             },
-            format="json"
+            format="json",
         )
 
         self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)
@@ -424,9 +423,9 @@ class TestDeletionApprovalEndpoint(DirectoriesMixin, APITestCase):
             "/api/ai/deletions/approve/",
             {
                 "request_id": self.deletion_request.id,
-                "action": "approve"
+                "action": "approve",
             },
-            format="json"
+            format="json",
         )
 
         self.assertEqual(response.status_code, status.HTTP_200_OK)
@@ -436,7 +435,7 @@ class TestDeletionApprovalEndpoint(DirectoriesMixin, APITestCase):
         self.deletion_request.refresh_from_db()
         self.assertEqual(
             self.deletion_request.status,
-            DeletionRequest.STATUS_APPROVED
+            DeletionRequest.STATUS_APPROVED,
         )
 
     def test_reject_deletion_success(self):
@@ -448,9 +447,9 @@ class TestDeletionApprovalEndpoint(DirectoriesMixin, APITestCase):
             {
                 "request_id": self.deletion_request.id,
                 "action": "reject",
-                "reason": "Document is still needed"
+                "reason": "Document is still needed",
             },
-            format="json"
+            format="json",
         )
 
         self.assertEqual(response.status_code, status.HTTP_200_OK)
@@ -459,7 +458,7 @@ class TestDeletionApprovalEndpoint(DirectoriesMixin, APITestCase):
         self.deletion_request.refresh_from_db()
         self.assertEqual(
             self.deletion_request.status,
-            DeletionRequest.STATUS_REJECTED
+            DeletionRequest.STATUS_REJECTED,
         )
 
     def test_invalid_request_id(self):
@@ -470,9 +469,9 @@ class TestDeletionApprovalEndpoint(DirectoriesMixin, APITestCase):
             "/api/ai/deletions/approve/",
             {
                 "request_id": 99999,
-                "action": "approve"
+                "action": "approve",
             },
-            format="json"
+            format="json",
         )
 
         self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)
@@ -485,9 +484,9 @@ class TestDeletionApprovalEndpoint(DirectoriesMixin, APITestCase):
             "/api/ai/deletions/approve/",
             {
                 "request_id": self.deletion_request.id,
-                "action": "approve"
+                "action": "approve",
             },
-            format="json"
+            format="json",
         )
 
         self.assertEqual(response.status_code, status.HTTP_200_OK)
@@ -502,7 +501,7 @@ class TestEndpointPermissionIntegration(DirectoriesMixin, APITestCase):
 
         # Create user with all AI permissions
         self.power_user = User.objects.create_user(
-            username="power_user", email="power@test.com", password="power123"
+            username="power_user", email="power@test.com", password="power123",
         )
 
         content_type = ContentType.objects.get_for_model(Document)
@@ -525,7 +524,7 @@ class TestEndpointPermissionIntegration(DirectoriesMixin, APITestCase):
 
         self.document = Document.objects.create(
             title="Test Doc",
-            content="Test"
+            content="Test",
         )
 
     def test_power_user_can_access_all_endpoints(self):
@@ -533,7 +532,7 @@ class TestEndpointPermissionIntegration(DirectoriesMixin, APITestCase):
         self.client.force_authenticate(user=self.power_user)
 
         # Test suggestions endpoint
-        with mock.patch('documents.views.get_ai_scanner') as mock_scanner:
+        with mock.patch("documents.views.get_ai_scanner") as mock_scanner:
             mock_scan_result = mock.MagicMock()
             mock_scan_result.tags = []
             mock_scan_result.correspondent = None
@@ -553,7 +552,7 @@ class TestEndpointPermissionIntegration(DirectoriesMixin, APITestCase):
             response1 = self.client.post(
                 "/api/ai/suggestions/",
                 {"document_id": self.document.id},
-                format="json"
+                format="json",
             )
             self.assertEqual(response1.status_code, status.HTTP_200_OK)
 
@@ -562,9 +561,9 @@ class TestEndpointPermissionIntegration(DirectoriesMixin, APITestCase):
                 "/api/ai/suggestions/apply/",
                 {
                     "document_id": self.document.id,
-                    "apply_tags": False
+                    "apply_tags": False,
                 },
-                format="json"
+                format="json",
             )
             self.assertEqual(response2.status_code, status.HTTP_200_OK)
 

src/documents/tests/test_api_ai_suggestions.py

@@ -9,14 +9,12 @@ from rest_framework import status
 from rest_framework.test import APITestCase
 
 from documents.ai_scanner import AIScanResult
-from documents.models import (
-    AISuggestionFeedback,
-    Correspondent,
-    Document,
-    DocumentType,
-    StoragePath,
-    Tag,
-)
+from documents.models import AISuggestionFeedback
+from documents.models import Correspondent
+from documents.models import Document
+from documents.models import DocumentType
+from documents.models import StoragePath
+from documents.models import Tag
 from documents.tests.utils import DirectoriesMixin
 
 
@@ -64,12 +62,12 @@ class TestAISuggestionsAPI(DirectoriesMixin, APITestCase):
     def test_ai_suggestions_endpoint_exists(self):
         """Test that the ai-suggestions endpoint is accessible."""
         response = self.client.get(
-            f"/api/documents/{self.document.pk}/ai-suggestions/"
+            f"/api/documents/{self.document.pk}/ai-suggestions/",
         )
         # Should not be 404
         self.assertNotEqual(response.status_code, status.HTTP_404_NOT_FOUND)
 
-    @mock.patch('documents.ai_scanner.get_ai_scanner')
+    @mock.patch("documents.ai_scanner.get_ai_scanner")
     def test_get_ai_suggestions_success(self, mock_get_scanner):
         """Test successfully getting AI suggestions for a document."""
         # Create mock scan result
@@ -87,7 +85,7 @@ class TestAISuggestionsAPI(DirectoriesMixin, APITestCase):
 
         # Make request
         response = self.client.get(
-            f"/api/documents/{self.document.pk}/ai-suggestions/"
+            f"/api/documents/{self.document.pk}/ai-suggestions/",
         )
 
         # Verify response
@@ -95,23 +93,23 @@ class TestAISuggestionsAPI(DirectoriesMixin, APITestCase):
         data = response.json()
 
         # Check tags
-        self.assertIn('tags', data)
-        self.assertEqual(len(data['tags']), 2)
-        self.assertEqual(data['tags'][0]['id'], self.tag1.id)
-        self.assertEqual(data['tags'][0]['confidence'], 0.85)
+        self.assertIn("tags", data)
+        self.assertEqual(len(data["tags"]), 2)
+        self.assertEqual(data["tags"][0]["id"], self.tag1.id)
+        self.assertEqual(data["tags"][0]["confidence"], 0.85)
 
         # Check correspondent
-        self.assertIn('correspondent', data)
-        self.assertEqual(data['correspondent']['id'], self.correspondent.id)
-        self.assertEqual(data['correspondent']['confidence'], 0.90)
+        self.assertIn("correspondent", data)
+        self.assertEqual(data["correspondent"]["id"], self.correspondent.id)
+        self.assertEqual(data["correspondent"]["confidence"], 0.90)
 
         # Check document type
-        self.assertIn('document_type', data)
-        self.assertEqual(data['document_type']['id'], self.doc_type.id)
+        self.assertIn("document_type", data)
+        self.assertEqual(data["document_type"]["id"], self.doc_type.id)
 
         # Check title suggestion
-        self.assertIn('title_suggestion', data)
-        self.assertEqual(data['title_suggestion']['title'], "Suggested Title")
+        self.assertIn("title_suggestion", data)
+        self.assertEqual(data["title_suggestion"]["title"], "Suggested Title")
 
     def test_get_ai_suggestions_no_content(self):
         """Test getting AI suggestions for document without content."""
@@ -126,7 +124,7 @@ class TestAISuggestionsAPI(DirectoriesMixin, APITestCase):
         response = self.client.get(f"/api/documents/{doc.pk}/ai-suggestions/")
 
         self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
-        self.assertIn("no content", response.json()['detail'].lower())
+        self.assertIn("no content", response.json()["detail"].lower())
 
     def test_get_ai_suggestions_document_not_found(self):
         """Test getting AI suggestions for non-existent document."""
@@ -137,19 +135,19 @@ class TestAISuggestionsAPI(DirectoriesMixin, APITestCase):
     def test_apply_suggestion_tag(self):
         """Test applying a tag suggestion."""
         request_data = {
-            'suggestion_type': 'tag',
-            'value_id': self.tag1.id,
-            'confidence': 0.85,
+            "suggestion_type": "tag",
+            "value_id": self.tag1.id,
+            "confidence": 0.85,
         }
 
         response = self.client.post(
             f"/api/documents/{self.document.pk}/apply-suggestion/",
             data=request_data,
-            format='json',
+            format="json",
         )
 
         self.assertEqual(response.status_code, status.HTTP_200_OK)
-        self.assertEqual(response.json()['status'], 'success')
+        self.assertEqual(response.json()["status"], "success")
 
         # Verify tag was applied
         self.document.refresh_from_db()
@@ -158,7 +156,7 @@ class TestAISuggestionsAPI(DirectoriesMixin, APITestCase):
         # Verify feedback was recorded
         feedback = AISuggestionFeedback.objects.filter(
             document=self.document,
-            suggestion_type='tag',
+            suggestion_type="tag",
         ).first()
         self.assertIsNotNone(feedback)
         self.assertEqual(feedback.status, AISuggestionFeedback.STATUS_APPLIED)
@@ -169,15 +167,15 @@ class TestAISuggestionsAPI(DirectoriesMixin, APITestCase):
     def test_apply_suggestion_correspondent(self):
         """Test applying a correspondent suggestion."""
         request_data = {
-            'suggestion_type': 'correspondent',
-            'value_id': self.correspondent.id,
-            'confidence': 0.90,
+            "suggestion_type": "correspondent",
+            "value_id": self.correspondent.id,
+            "confidence": 0.90,
         }
 
         response = self.client.post(
             f"/api/documents/{self.document.pk}/apply-suggestion/",
             data=request_data,
-            format='json',
+            format="json",
         )
 
         self.assertEqual(response.status_code, status.HTTP_200_OK)
@@ -189,7 +187,7 @@ class TestAISuggestionsAPI(DirectoriesMixin, APITestCase):
         # Verify feedback was recorded
         feedback = AISuggestionFeedback.objects.filter(
             document=self.document,
-            suggestion_type='correspondent',
+            suggestion_type="correspondent",
         ).first()
         self.assertIsNotNone(feedback)
         self.assertEqual(feedback.status, AISuggestionFeedback.STATUS_APPLIED)
@@ -197,15 +195,15 @@ class TestAISuggestionsAPI(DirectoriesMixin, APITestCase):
     def test_apply_suggestion_document_type(self):
         """Test applying a document type suggestion."""
         request_data = {
-            'suggestion_type': 'document_type',
-            'value_id': self.doc_type.id,
-            'confidence': 0.88,
+            "suggestion_type": "document_type",
+            "value_id": self.doc_type.id,
+            "confidence": 0.88,
         }
 
         response = self.client.post(
             f"/api/documents/{self.document.pk}/apply-suggestion/",
             data=request_data,
-            format='json',
+            format="json",
         )
 
         self.assertEqual(response.status_code, status.HTTP_200_OK)
@@ -217,35 +215,35 @@ class TestAISuggestionsAPI(DirectoriesMixin, APITestCase):
     def test_apply_suggestion_title(self):
         """Test applying a title suggestion."""
         request_data = {
-            'suggestion_type': 'title',
-            'value_text': 'New Suggested Title',
-            'confidence': 0.80,
+            "suggestion_type": "title",
+            "value_text": "New Suggested Title",
+            "confidence": 0.80,
         }
 
         response = self.client.post(
             f"/api/documents/{self.document.pk}/apply-suggestion/",
             data=request_data,
-            format='json',
+            format="json",
         )
 
         self.assertEqual(response.status_code, status.HTTP_200_OK)
 
         # Verify title was applied
         self.document.refresh_from_db()
-        self.assertEqual(self.document.title, 'New Suggested Title')
+        self.assertEqual(self.document.title, "New Suggested Title")
 
     def test_apply_suggestion_invalid_type(self):
         """Test applying suggestion with invalid type."""
         request_data = {
-            'suggestion_type': 'invalid_type',
-            'value_id': 1,
-            'confidence': 0.85,
+            "suggestion_type": "invalid_type",
+            "value_id": 1,
+            "confidence": 0.85,
         }
 
         response = self.client.post(
             f"/api/documents/{self.document.pk}/apply-suggestion/",
             data=request_data,
-            format='json',
+            format="json",
         )
 
         self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
@@ -253,14 +251,14 @@ class TestAISuggestionsAPI(DirectoriesMixin, APITestCase):
     def test_apply_suggestion_missing_value(self):
         """Test applying suggestion without value_id or value_text."""
         request_data = {
-            'suggestion_type': 'tag',
-            'confidence': 0.85,
+            "suggestion_type": "tag",
+            "confidence": 0.85,
         }
 
         response = self.client.post(
             f"/api/documents/{self.document.pk}/apply-suggestion/",
             data=request_data,
-            format='json',
+            format="json",
         )
 
         self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
@@ -268,15 +266,15 @@ class TestAISuggestionsAPI(DirectoriesMixin, APITestCase):
     def test_apply_suggestion_nonexistent_object(self):
         """Test applying suggestion with non-existent object ID."""
         request_data = {
-            'suggestion_type': 'tag',
-            'value_id': 99999,
-            'confidence': 0.85,
+            "suggestion_type": "tag",
+            "value_id": 99999,
+            "confidence": 0.85,
         }
 
         response = self.client.post(
             f"/api/documents/{self.document.pk}/apply-suggestion/",
             data=request_data,
-            format='json',
+            format="json",
         )
 
         self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)
@@ -284,24 +282,24 @@ class TestAISuggestionsAPI(DirectoriesMixin, APITestCase):
     def test_reject_suggestion(self):
         """Test rejecting an AI suggestion."""
         request_data = {
-            'suggestion_type': 'tag',
-            'value_id': self.tag1.id,
-            'confidence': 0.65,
+            "suggestion_type": "tag",
+            "value_id": self.tag1.id,
+            "confidence": 0.65,
         }
 
         response = self.client.post(
             f"/api/documents/{self.document.pk}/reject-suggestion/",
             data=request_data,
-            format='json',
+            format="json",
         )
 
         self.assertEqual(response.status_code, status.HTTP_200_OK)
-        self.assertEqual(response.json()['status'], 'success')
+        self.assertEqual(response.json()["status"], "success")
 
         # Verify feedback was recorded
         feedback = AISuggestionFeedback.objects.filter(
             document=self.document,
-            suggestion_type='tag',
+            suggestion_type="tag",
         ).first()
         self.assertIsNotNone(feedback)
         self.assertEqual(feedback.status, AISuggestionFeedback.STATUS_REJECTED)
@@ -312,15 +310,15 @@ class TestAISuggestionsAPI(DirectoriesMixin, APITestCase):
     def test_reject_suggestion_with_text(self):
         """Test rejecting a suggestion with text value."""
         request_data = {
-            'suggestion_type': 'title',
-            'value_text': 'Bad Title Suggestion',
-            'confidence': 0.50,
+            "suggestion_type": "title",
+            "value_text": "Bad Title Suggestion",
+            "confidence": 0.50,
         }
 
         response = self.client.post(
             f"/api/documents/{self.document.pk}/reject-suggestion/",
             data=request_data,
-            format='json',
+            format="json",
         )
 
         self.assertEqual(response.status_code, status.HTTP_200_OK)
@@ -328,11 +326,11 @@ class TestAISuggestionsAPI(DirectoriesMixin, APITestCase):
         # Verify feedback was recorded
         feedback = AISuggestionFeedback.objects.filter(
             document=self.document,
-            suggestion_type='title',
+            suggestion_type="title",
         ).first()
         self.assertIsNotNone(feedback)
         self.assertEqual(feedback.status, AISuggestionFeedback.STATUS_REJECTED)
-        self.assertEqual(feedback.suggested_value_text, 'Bad Title Suggestion')
+        self.assertEqual(feedback.suggested_value_text, "Bad Title Suggestion")
 
     def test_ai_suggestion_stats_empty(self):
         """Test getting statistics when no feedback exists."""
@@ -341,17 +339,17 @@ class TestAISuggestionsAPI(DirectoriesMixin, APITestCase):
         self.assertEqual(response.status_code, status.HTTP_200_OK)
         data = response.json()
 
-        self.assertEqual(data['total_suggestions'], 0)
-        self.assertEqual(data['total_applied'], 0)
-        self.assertEqual(data['total_rejected'], 0)
-        self.assertEqual(data['accuracy_rate'], 0)
+        self.assertEqual(data["total_suggestions"], 0)
+        self.assertEqual(data["total_applied"], 0)
+        self.assertEqual(data["total_rejected"], 0)
+        self.assertEqual(data["accuracy_rate"], 0)
 
     def test_ai_suggestion_stats_with_data(self):
         """Test getting statistics with feedback data."""
         # Create some feedback entries
         AISuggestionFeedback.objects.create(
             document=self.document,
-            suggestion_type='tag',
+            suggestion_type="tag",
             suggested_value_id=self.tag1.id,
             confidence=0.85,
             status=AISuggestionFeedback.STATUS_APPLIED,
@@ -359,7 +357,7 @@ class TestAISuggestionsAPI(DirectoriesMixin, APITestCase):
         )
         AISuggestionFeedback.objects.create(
             document=self.document,
-            suggestion_type='tag',
+            suggestion_type="tag",
             suggested_value_id=self.tag2.id,
             confidence=0.70,
             status=AISuggestionFeedback.STATUS_APPLIED,
@@ -367,7 +365,7 @@ class TestAISuggestionsAPI(DirectoriesMixin, APITestCase):
         )
         AISuggestionFeedback.objects.create(
             document=self.document,
-            suggestion_type='correspondent',
+            suggestion_type="correspondent",
             suggested_value_id=self.correspondent.id,
             confidence=0.60,
             status=AISuggestionFeedback.STATUS_REJECTED,
@@ -380,25 +378,25 @@ class TestAISuggestionsAPI(DirectoriesMixin, APITestCase):
         data = response.json()
 
         # Check overall stats
-        self.assertEqual(data['total_suggestions'], 3)
-        self.assertEqual(data['total_applied'], 2)
-        self.assertEqual(data['total_rejected'], 1)
-        self.assertAlmostEqual(data['accuracy_rate'], 66.67, places=1)
+        self.assertEqual(data["total_suggestions"], 3)
+        self.assertEqual(data["total_applied"], 2)
+        self.assertEqual(data["total_rejected"], 1)
+        self.assertAlmostEqual(data["accuracy_rate"], 66.67, places=1)
 
         # Check by_type stats
-        self.assertIn('by_type', data)
-        self.assertIn('tag', data['by_type'])
-        self.assertEqual(data['by_type']['tag']['total'], 2)
-        self.assertEqual(data['by_type']['tag']['applied'], 2)
-        self.assertEqual(data['by_type']['tag']['rejected'], 0)
+        self.assertIn("by_type", data)
+        self.assertIn("tag", data["by_type"])
+        self.assertEqual(data["by_type"]["tag"]["total"], 2)
+        self.assertEqual(data["by_type"]["tag"]["applied"], 2)
+        self.assertEqual(data["by_type"]["tag"]["rejected"], 0)
 
         # Check confidence averages
-        self.assertGreater(data['average_confidence_applied'], 0)
-        self.assertGreater(data['average_confidence_rejected'], 0)
+        self.assertGreater(data["average_confidence_applied"], 0)
+        self.assertGreater(data["average_confidence_rejected"], 0)
 
         # Check recent suggestions
-        self.assertIn('recent_suggestions', data)
-        self.assertEqual(len(data['recent_suggestions']), 3)
+        self.assertIn("recent_suggestions", data)
+        self.assertEqual(len(data["recent_suggestions"]), 3)
 
     def test_ai_suggestion_stats_accuracy_calculation(self):
         """Test that accuracy rate is calculated correctly."""
@@ -406,7 +404,7 @@ class TestAISuggestionsAPI(DirectoriesMixin, APITestCase):
         for i in range(7):
             AISuggestionFeedback.objects.create(
                 document=self.document,
-                suggestion_type='tag',
+                suggestion_type="tag",
                 suggested_value_id=self.tag1.id,
                 confidence=0.80,
                 status=AISuggestionFeedback.STATUS_APPLIED,
@@ -416,7 +414,7 @@ class TestAISuggestionsAPI(DirectoriesMixin, APITestCase):
         for i in range(3):
             AISuggestionFeedback.objects.create(
                 document=self.document,
-                suggestion_type='tag',
+                suggestion_type="tag",
                 suggested_value_id=self.tag2.id,
                 confidence=0.60,
                 status=AISuggestionFeedback.STATUS_REJECTED,
@@ -428,10 +426,10 @@ class TestAISuggestionsAPI(DirectoriesMixin, APITestCase):
         self.assertEqual(response.status_code, status.HTTP_200_OK)
         data = response.json()
 
-        self.assertEqual(data['total_suggestions'], 10)
-        self.assertEqual(data['total_applied'], 7)
-        self.assertEqual(data['total_rejected'], 3)
-        self.assertEqual(data['accuracy_rate'], 70.0)
+        self.assertEqual(data["total_suggestions"], 10)
+        self.assertEqual(data["total_applied"], 7)
+        self.assertEqual(data["total_rejected"], 3)
+        self.assertEqual(data["accuracy_rate"], 70.0)
 
     def test_authentication_required(self):
         """Test that authentication is required for all endpoints."""
@@ -439,7 +437,7 @@ class TestAISuggestionsAPI(DirectoriesMixin, APITestCase):
 
         # Test ai-suggestions endpoint
         response = self.client.get(
-            f"/api/documents/{self.document.pk}/ai-suggestions/"
+            f"/api/documents/{self.document.pk}/ai-suggestions/",
         )
         self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)
 

src/documents/tests/test_api_deletion_requests.py

@@ -11,17 +11,14 @@ Tests cover:
 """
 
 from django.contrib.auth.models import User
-from django.test import override_settings
 from rest_framework import status
 from rest_framework.test import APITestCase
 
-from documents.models import (
-    Correspondent,
-    DeletionRequest,
-    Document,
-    DocumentType,
-    Tag,
-)
+from documents.models import Correspondent
+from documents.models import DeletionRequest
+from documents.models import Document
+from documents.models import DocumentType
+from documents.models import Tag
 
 
 class TestDeletionRequestAPI(APITestCase):

src/documents/tests/test_consumer.py

@@ -1561,7 +1561,6 @@ class TestConsumerAIScannerIntegration(
         Verifies that AI scanner respects database transactions and handles
         rollbacks correctly.
         """
-        from django.db import transaction as db_transaction
 
         tag = Tag.objects.create(name="Invoice")
 

src/documents/tests/test_deletion_request_model.py

@@ -15,13 +15,11 @@ from django.contrib.auth.models import User
 from django.test import TestCase
 from django.utils import timezone
 
-from documents.models import (
-    Correspondent,
-    DeletionRequest,
-    Document,
-    DocumentType,
-    Tag,
-)
+from documents.models import Correspondent
+from documents.models import DeletionRequest
+from documents.models import Document
+from documents.models import DocumentType
+from documents.models import Tag
 
 
 class TestDeletionRequestModelCreation(TestCase):

src/documents/tests/test_ml_cache.py

@@ -8,11 +8,9 @@ from unittest import mock
 
 from django.test import TestCase
 
-from documents.ml.model_cache import (
-    CacheMetrics,
-    LRUCache,
-    ModelCacheManager,
-)
+from documents.ml.model_cache import CacheMetrics
+from documents.ml.model_cache import LRUCache
+from documents.ml.model_cache import ModelCacheManager
 
 
 class TestCacheMetrics(TestCase):

src/documents/tests/test_ml_smoke.py

@@ -150,7 +150,6 @@ class TestMLCacheDirectory:
 
     def test_model_cache_writable(self, tmp_path):
         """Test that we can write to model cache directory."""
-        import pathlib
 
         # Use tmp_path fixture for testing
         cache_dir = tmp_path / ".cache" / "huggingface"
@@ -169,7 +168,6 @@ class TestMLCacheDirectory:
 
     def test_torch_cache_directory(self, tmp_path, monkeypatch):
         """Test that PyTorch can use a custom cache directory."""
-        import torch
 
         # Set custom cache directory
         cache_dir = tmp_path / ".cache" / "torch"
@@ -204,9 +202,10 @@ class TestMLPerformanceBasic:
 
     def test_numpy_performance_basic(self):
         """Test basic NumPy performance with larger arrays."""
-        import numpy as np
         import time
 
+        import numpy as np
+
         # Create large array (10 million elements)
         arr = np.random.rand(10_000_000)
 

src/documents/views.py

@@ -89,6 +89,8 @@ from rest_framework.viewsets import ViewSet
 
 from documents import bulk_edit
 from documents import index
+from documents.ai_scanner import AIDocumentScanner
+from documents.ai_scanner import get_ai_scanner
 from documents.bulk_download import ArchiveOnlyStrategy
 from documents.bulk_download import OriginalAndArchiveStrategy
 from documents.bulk_download import OriginalsOnlyStrategy
@@ -141,13 +143,10 @@ from documents.models import UiSettings
 from documents.models import Workflow
 from documents.models import WorkflowAction
 from documents.models import WorkflowTrigger
-from documents.ai_scanner import AIDocumentScanner
-from documents.ai_scanner import get_ai_scanner
 from documents.parsers import get_parser_class_for_mime_type
 from documents.parsers import parse_date_generator
 from documents.permissions import AcknowledgeTasksPermissions
 from documents.permissions import CanApplyAISuggestionsPermission
-from documents.permissions import CanApproveDeletionsPermission
 from documents.permissions import CanConfigureAIPermission
 from documents.permissions import CanViewAISuggestionsPermission
 from documents.permissions import PaperlessAdminPermissions
@@ -1388,7 +1387,7 @@ class UnifiedSearchViewSet(DocumentViewSet):
             scan_result = scanner.scan_document(
                 document=document,
                 document_text=document.content,
-                original_file_path=document.source_path if hasattr(document, 'source_path') else None,
+                original_file_path=document.source_path if hasattr(document, "source_path") else None,
             )
 
             # Convert scan result to serializable format
@@ -1424,43 +1423,43 @@ class UnifiedSearchViewSet(DocumentViewSet):
             serializer = ApplySuggestionSerializer(data=request.data)
             serializer.is_valid(raise_exception=True)
 
-            suggestion_type = serializer.validated_data['suggestion_type']
-            value_id = serializer.validated_data.get('value_id')
-            value_text = serializer.validated_data.get('value_text')
-            confidence = serializer.validated_data['confidence']
+            suggestion_type = serializer.validated_data["suggestion_type"]
+            value_id = serializer.validated_data.get("value_id")
+            value_text = serializer.validated_data.get("value_text")
+            confidence = serializer.validated_data["confidence"]
 
             # Apply the suggestion based on type
             applied = False
             result_message = ""
 
-            if suggestion_type == 'tag' and value_id:
+            if suggestion_type == "tag" and value_id:
                 tag = Tag.objects.get(pk=value_id)
                 document.tags.add(tag)
                 applied = True
                 result_message = f"Tag '{tag.name}' applied"
 
-            elif suggestion_type == 'correspondent' and value_id:
+            elif suggestion_type == "correspondent" and value_id:
                 correspondent = Correspondent.objects.get(pk=value_id)
                 document.correspondent = correspondent
                 document.save()
                 applied = True
                 result_message = f"Correspondent '{correspondent.name}' applied"
 
-            elif suggestion_type == 'document_type' and value_id:
+            elif suggestion_type == "document_type" and value_id:
                 doc_type = DocumentType.objects.get(pk=value_id)
                 document.document_type = doc_type
                 document.save()
                 applied = True
                 result_message = f"Document type '{doc_type.name}' applied"
 
-            elif suggestion_type == 'storage_path' and value_id:
+            elif suggestion_type == "storage_path" and value_id:
                 storage_path = StoragePath.objects.get(pk=value_id)
                 document.storage_path = storage_path
                 document.save()
                 applied = True
                 result_message = f"Storage path '{storage_path.name}' applied"
 
-            elif suggestion_type == 'title' and value_text:
+            elif suggestion_type == "title" and value_text:
                 document.title = value_text
                 document.save()
                 applied = True
@@ -1518,10 +1517,10 @@ class UnifiedSearchViewSet(DocumentViewSet):
             serializer = RejectSuggestionSerializer(data=request.data)
             serializer.is_valid(raise_exception=True)
 
-            suggestion_type = serializer.validated_data['suggestion_type']
-            value_id = serializer.validated_data.get('value_id')
-            value_text = serializer.validated_data.get('value_text')
-            confidence = serializer.validated_data['confidence']
+            suggestion_type = serializer.validated_data["suggestion_type"]
+            value_id = serializer.validated_data.get("value_id")
+            value_text = serializer.validated_data.get("value_text")
+            confidence = serializer.validated_data["confidence"]
 
             # Record feedback
             AISuggestionFeedback.objects.create(
@@ -1554,7 +1553,10 @@ class UnifiedSearchViewSet(DocumentViewSet):
         Returns aggregated data about applied vs rejected suggestions,
         accuracy rates, and confidence scores.
         """
-        from django.db.models import Avg, Count, Q
+        from django.db.models import Avg
+        from django.db.models import Count
+        from django.db.models import Q
+
         from documents.models import AISuggestionFeedback
         from documents.serializers.ai_suggestions import AISuggestionStatsSerializer
 
@@ -1562,61 +1564,63 @@ class UnifiedSearchViewSet(DocumentViewSet):
             # Get overall counts
             total_feedbacks = AISuggestionFeedback.objects.count()
             total_applied = AISuggestionFeedback.objects.filter(
-                status=AISuggestionFeedback.STATUS_APPLIED
+                status=AISuggestionFeedback.STATUS_APPLIED,
             ).count()
             total_rejected = AISuggestionFeedback.objects.filter(
-                status=AISuggestionFeedback.STATUS_REJECTED
+                status=AISuggestionFeedback.STATUS_REJECTED,
             ).count()
 
             # Calculate accuracy rate
             accuracy_rate = (total_applied / total_feedbacks * 100) if total_feedbacks > 0 else 0
 
             # Get statistics by suggestion type using a single aggregated query
-            stats_by_type = AISuggestionFeedback.objects.values('suggestion_type').annotate(
-                total=Count('id'),
-                applied=Count('id', filter=Q(status=AISuggestionFeedback.STATUS_APPLIED)),
-                rejected=Count('id', filter=Q(status=AISuggestionFeedback.STATUS_REJECTED))
+            stats_by_type = AISuggestionFeedback.objects.values("suggestion_type").annotate(
+                total=Count("id"),
+                applied=Count("id", filter=Q(status=AISuggestionFeedback.STATUS_APPLIED)),
+                rejected=Count("id", filter=Q(status=AISuggestionFeedback.STATUS_REJECTED)),
             )
 
             # Build the by_type dictionary using the aggregated results
             by_type = {}
             for stat in stats_by_type:
-                suggestion_type = stat['suggestion_type']
-                type_total = stat['total']
-                type_applied = stat['applied']
-                type_rejected = stat['rejected']
+                suggestion_type = stat["suggestion_type"]
+                type_total = stat["total"]
+                type_applied = stat["applied"]
+                type_rejected = stat["rejected"]
 
                 by_type[suggestion_type] = {
-                    'total': type_total,
-                    'applied': type_applied,
-                    'rejected': type_rejected,
-                    'accuracy_rate': (type_applied / type_total * 100) if type_total > 0 else 0,
+                    "total": type_total,
+                    "applied": type_applied,
+                    "rejected": type_rejected,
+                    "accuracy_rate": (type_applied / type_total * 100) if type_total > 0 else 0,
                 }
 
             # Get average confidence scores
             avg_confidence_applied = AISuggestionFeedback.objects.filter(
-                status=AISuggestionFeedback.STATUS_APPLIED
-            ).aggregate(Avg('confidence'))['confidence__avg'] or 0.0
+                status=AISuggestionFeedback.STATUS_APPLIED,
+            ).aggregate(Avg("confidence"))["confidence__avg"] or 0.0
 
             avg_confidence_rejected = AISuggestionFeedback.objects.filter(
-                status=AISuggestionFeedback.STATUS_REJECTED
-            ).aggregate(Avg('confidence'))['confidence__avg'] or 0.0
+                status=AISuggestionFeedback.STATUS_REJECTED,
+            ).aggregate(Avg("confidence"))["confidence__avg"] or 0.0
 
             # Get recent suggestions (last 10)
-            recent_suggestions = AISuggestionFeedback.objects.order_by('-created_at')[:10]
+            recent_suggestions = AISuggestionFeedback.objects.order_by("-created_at")[:10]
 
             # Build response data
-            from documents.serializers.ai_suggestions import AISuggestionFeedbackSerializer
+            from documents.serializers.ai_suggestions import (
+                AISuggestionFeedbackSerializer,
+            )
             data = {
-                'total_suggestions': total_feedbacks,
-                'total_applied': total_applied,
-                'total_rejected': total_rejected,
-                'accuracy_rate': accuracy_rate,
-                'by_type': by_type,
-                'average_confidence_applied': avg_confidence_applied,
-                'average_confidence_rejected': avg_confidence_rejected,
-                'recent_suggestions': AISuggestionFeedbackSerializer(
-                    recent_suggestions, many=True
+                "total_suggestions": total_feedbacks,
+                "total_applied": total_applied,
+                "total_rejected": total_rejected,
+                "accuracy_rate": accuracy_rate,
+                "by_type": by_type,
+                "average_confidence_applied": avg_confidence_applied,
+                "average_confidence_rejected": avg_confidence_rejected,
+                "recent_suggestions": AISuggestionFeedbackSerializer(
+                    recent_suggestions, many=True,
                 ).data,
             }
 
@@ -3571,21 +3575,21 @@ class AISuggestionsView(GenericAPIView):
         request_serializer = AISuggestionsRequestSerializer(data=request.data)
         request_serializer.is_valid(raise_exception=True)
 
-        document_id = request_serializer.validated_data['document_id']
+        document_id = request_serializer.validated_data["document_id"]
 
         try:
             document = Document.objects.get(pk=document_id)
         except Document.DoesNotExist:
             return Response(
                 {"error": "Document not found or you don't have permission to view it"},
-                status=status.HTTP_404_NOT_FOUND
+                status=status.HTTP_404_NOT_FOUND,
             )
 
         # Check if user has permission to view this document
-        if not has_perms_owner_aware(request.user, 'documents.view_document', document):
+        if not has_perms_owner_aware(request.user, "documents.view_document", document):
             return Response(
                 {"error": "Permission denied"},
-                status=status.HTTP_403_FORBIDDEN
+                status=status.HTTP_403_FORBIDDEN,
             )
 
         # Get AI scanner and scan document
@@ -3600,7 +3604,7 @@ class AISuggestionsView(GenericAPIView):
             "document_type": None,
             "storage_path": None,
             "title_suggestion": scan_result.title_suggestion,
-            "custom_fields": {}
+            "custom_fields": {},
         }
 
         # Format tag suggestions
@@ -3610,7 +3614,7 @@ class AISuggestionsView(GenericAPIView):
                 response_data["tags"].append({
                     "id": tag.id,
                     "name": tag.name,
-                    "confidence": confidence
+                    "confidence": confidence,
                 })
             except Tag.DoesNotExist:
                 # Tag was suggested by AI but no longer exists; skip it
@@ -3624,7 +3628,7 @@ class AISuggestionsView(GenericAPIView):
                 response_data["correspondent"] = {
                     "id": correspondent.id,
                     "name": correspondent.name,
-                    "confidence": confidence
+                    "confidence": confidence,
                 }
             except Correspondent.DoesNotExist:
                 # Correspondent was suggested but no longer exists; skip it
@@ -3638,7 +3642,7 @@ class AISuggestionsView(GenericAPIView):
                 response_data["document_type"] = {
                     "id": doc_type.id,
                     "name": doc_type.name,
-                    "confidence": confidence
+                    "confidence": confidence,
                 }
             except DocumentType.DoesNotExist:
                 # Document type was suggested but no longer exists; skip it
@@ -3652,7 +3656,7 @@ class AISuggestionsView(GenericAPIView):
                 response_data["storage_path"] = {
                     "id": storage_path.id,
                     "name": storage_path.name,
-                    "confidence": confidence
+                    "confidence": confidence,
                 }
             except StoragePath.DoesNotExist:
                 # Storage path was suggested but no longer exists; skip it
@@ -3662,7 +3666,7 @@ class AISuggestionsView(GenericAPIView):
         for field_id, (value, confidence) in scan_result.custom_fields.items():
             response_data["custom_fields"][str(field_id)] = {
                 "value": value,
-                "confidence": confidence
+                "confidence": confidence,
             }
 
         return Response(response_data)
@@ -3683,21 +3687,21 @@ class ApplyAISuggestionsView(GenericAPIView):
         serializer = ApplyAISuggestionsSerializer(data=request.data)
         serializer.is_valid(raise_exception=True)
 
-        document_id = serializer.validated_data['document_id']
+        document_id = serializer.validated_data["document_id"]
 
         try:
             document = Document.objects.get(pk=document_id)
         except Document.DoesNotExist:
             return Response(
                 {"error": "Document not found"},
-                status=status.HTTP_404_NOT_FOUND
+                status=status.HTTP_404_NOT_FOUND,
             )
 
         # Check if user has permission to change this document
-        if not has_perms_owner_aware(request.user, 'documents.change_document', document):
+        if not has_perms_owner_aware(request.user, "documents.change_document", document):
             return Response(
                 {"error": "Permission denied"},
-                status=status.HTTP_403_FORBIDDEN
+                status=status.HTTP_403_FORBIDDEN,
             )
 
         # Get AI scanner and scan document
@@ -3707,8 +3711,8 @@ class ApplyAISuggestionsView(GenericAPIView):
         # Apply suggestions based on user selections
         applied = []
 
-        if serializer.validated_data.get('apply_tags'):
-            selected_tags = serializer.validated_data.get('selected_tags', [])
+        if serializer.validated_data.get("apply_tags"):
+            selected_tags = serializer.validated_data.get("selected_tags", [])
             if selected_tags:
                 # Apply only selected tags
                 tags_to_apply = [tag_id for tag_id, _ in scan_result.tags if tag_id in selected_tags]
@@ -3725,7 +3729,7 @@ class ApplyAISuggestionsView(GenericAPIView):
                     # Tag not found; skip applying this tag
                     pass
 
-        if serializer.validated_data.get('apply_correspondent') and scan_result.correspondent:
+        if serializer.validated_data.get("apply_correspondent") and scan_result.correspondent:
             corr_id, confidence = scan_result.correspondent
             try:
                 correspondent = Correspondent.objects.get(pk=corr_id)
@@ -3735,7 +3739,7 @@ class ApplyAISuggestionsView(GenericAPIView):
                 # Correspondent not found; skip applying
                 pass
 
-        if serializer.validated_data.get('apply_document_type') and scan_result.document_type:
+        if serializer.validated_data.get("apply_document_type") and scan_result.document_type:
             type_id, confidence = scan_result.document_type
             try:
                 doc_type = DocumentType.objects.get(pk=type_id)
@@ -3745,7 +3749,7 @@ class ApplyAISuggestionsView(GenericAPIView):
                 # Document type not found; skip applying
                 pass
 
-        if serializer.validated_data.get('apply_storage_path') and scan_result.storage_path:
+        if serializer.validated_data.get("apply_storage_path") and scan_result.storage_path:
             path_id, confidence = scan_result.storage_path
             try:
                 storage_path = StoragePath.objects.get(pk=path_id)
@@ -3755,7 +3759,7 @@ class ApplyAISuggestionsView(GenericAPIView):
                 # Storage path not found; skip applying
                 pass
 
-        if serializer.validated_data.get('apply_title') and scan_result.title_suggestion:
+        if serializer.validated_data.get("apply_title") and scan_result.title_suggestion:
             document.title = scan_result.title_suggestion
             applied.append(f"title: {scan_result.title_suggestion}")
 
@@ -3765,7 +3769,7 @@ class ApplyAISuggestionsView(GenericAPIView):
         return Response({
             "status": "success",
             "document_id": document.id,
-            "applied": applied
+            "applied": applied,
         })
 
 
@@ -3805,14 +3809,14 @@ class AIConfigurationView(GenericAPIView):
 
         # Create new scanner with updated configuration
         config = {}
-        if 'auto_apply_threshold' in serializer.validated_data:
-            config['auto_apply_threshold'] = serializer.validated_data['auto_apply_threshold']
-        if 'suggest_threshold' in serializer.validated_data:
-            config['suggest_threshold'] = serializer.validated_data['suggest_threshold']
-        if 'ml_enabled' in serializer.validated_data:
-            config['enable_ml_features'] = serializer.validated_data['ml_enabled']
-        if 'advanced_ocr_enabled' in serializer.validated_data:
-            config['enable_advanced_ocr'] = serializer.validated_data['advanced_ocr_enabled']
+        if "auto_apply_threshold" in serializer.validated_data:
+            config["auto_apply_threshold"] = serializer.validated_data["auto_apply_threshold"]
+        if "suggest_threshold" in serializer.validated_data:
+            config["suggest_threshold"] = serializer.validated_data["suggest_threshold"]
+        if "ml_enabled" in serializer.validated_data:
+            config["enable_ml_features"] = serializer.validated_data["ml_enabled"]
+        if "advanced_ocr_enabled" in serializer.validated_data:
+            config["enable_advanced_ocr"] = serializer.validated_data["advanced_ocr_enabled"]
 
         # Update global scanner instance
         # WARNING: Not thread-safe. Consider storing configuration in database
@@ -3822,7 +3826,7 @@ class AIConfigurationView(GenericAPIView):
 
         return Response({
             "status": "success",
-            "message": "AI configuration updated. Changes may require server restart for consistency."
+            "message": "AI configuration updated. Changes may require server restart for consistency.",
         })
 
 

src/documents/views/deletion_request.py

@@ -76,7 +76,7 @@ class DeletionRequestViewSet(ModelViewSet):
         # Check permissions
         if not self._can_manage_request(deletion_request):
             return HttpResponseForbidden(
-                "You don't have permission to approve this deletion request."
+                "You don't have permission to approve this deletion request.",
             )
 
         # Validate status
@@ -114,11 +114,11 @@ class DeletionRequestViewSet(ModelViewSet):
                         deleted_count += 1
                         logger.info(
                             f"Deleted document {doc_id} ('{doc_title}') "
-                            f"as part of deletion request {deletion_request.id}"
+                            f"as part of deletion request {deletion_request.id}",
                         )
                     except Exception as e:
                         logger.error(
-                            f"Failed to delete document {doc.id}: {str(e)}"
+                            f"Failed to delete document {doc.id}: {e!s}",
                         )
                         failed_deletions.append({
                             "id": doc.id,
@@ -138,14 +138,14 @@ class DeletionRequestViewSet(ModelViewSet):
 
                 logger.info(
                     f"Deletion request {deletion_request.id} completed. "
-                    f"Deleted {deleted_count}/{len(documents)} documents."
+                    f"Deleted {deleted_count}/{len(documents)} documents.",
                 )
         except Exception as e:
             logger.error(
-                f"Error executing deletion request {deletion_request.id}: {str(e)}"
+                f"Error executing deletion request {deletion_request.id}: {e!s}",
             )
             return Response(
-                {"error": f"Failed to execute deletion: {str(e)}"},
+                {"error": f"Failed to execute deletion: {e!s}"},
                 status=status.HTTP_500_INTERNAL_SERVER_ERROR,
             )
 
@@ -176,7 +176,7 @@ class DeletionRequestViewSet(ModelViewSet):
         # Check permissions
         if not self._can_manage_request(deletion_request):
             return HttpResponseForbidden(
-                "You don't have permission to reject this deletion request."
+                "You don't have permission to reject this deletion request.",
             )
 
         # Validate status
@@ -199,7 +199,7 @@ class DeletionRequestViewSet(ModelViewSet):
             )
 
         logger.info(
-            f"Deletion request {deletion_request.id} rejected by user {request.user.username}"
+            f"Deletion request {deletion_request.id} rejected by user {request.user.username}",
         )
 
         serializer = self.get_serializer(deletion_request)
@@ -228,7 +228,7 @@ class DeletionRequestViewSet(ModelViewSet):
         # Check permissions
         if not self._can_manage_request(deletion_request):
             return HttpResponseForbidden(
-                "You don't have permission to cancel this deletion request."
+                "You don't have permission to cancel this deletion request.",
             )
 
         # Validate status
@@ -249,7 +249,7 @@ class DeletionRequestViewSet(ModelViewSet):
         deletion_request.save()
 
         logger.info(
-            f"Deletion request {deletion_request.id} cancelled by user {request.user.username}"
+            f"Deletion request {deletion_request.id} cancelled by user {request.user.username}",
         )
 
         serializer = self.get_serializer(deletion_request)

src/paperless/middleware.py

@@ -160,7 +160,7 @@ class SecurityHeadersMiddleware:
 
         # Store nonce in request for use in templates
         # Templates can access this via {{ request.csp_nonce }}
-        if hasattr(request, '_csp_nonce'):
+        if hasattr(request, "_csp_nonce"):
             request._csp_nonce = nonce
 
         # Prevent clickjacking attacks

src/paperless/security.py

@@ -9,7 +9,6 @@ from __future__ import annotations
 
 import hashlib
 import logging
-import mimetypes
 import os
 import re
 from pathlib import Path
@@ -26,39 +25,39 @@ logger = logging.getLogger("paperless.security")
 # Lista explícita de tipos MIME permitidos
 ALLOWED_MIME_TYPES = {
     # Documentos
-    'application/pdf',
-    'application/vnd.oasis.opendocument.text',
-    'application/msword',
-    'application/vnd.openxmlformats-officedocument.wordprocessingml.document',
-    'application/vnd.ms-excel',
-    'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet',
-    'application/vnd.ms-powerpoint',
-    'application/vnd.openxmlformats-officedocument.presentationml.presentation',
-    'application/vnd.oasis.opendocument.spreadsheet',
-    'application/vnd.oasis.opendocument.presentation',
-    'application/rtf',
-    'text/rtf',
+    "application/pdf",
+    "application/vnd.oasis.opendocument.text",
+    "application/msword",
+    "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
+    "application/vnd.ms-excel",
+    "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
+    "application/vnd.ms-powerpoint",
+    "application/vnd.openxmlformats-officedocument.presentationml.presentation",
+    "application/vnd.oasis.opendocument.spreadsheet",
+    "application/vnd.oasis.opendocument.presentation",
+    "application/rtf",
+    "text/rtf",
 
     # Imágenes
-    'image/jpeg',
-    'image/png',
-    'image/gif',
-    'image/tiff',
-    'image/bmp',
-    'image/webp',
+    "image/jpeg",
+    "image/png",
+    "image/gif",
+    "image/tiff",
+    "image/bmp",
+    "image/webp",
 
     # Texto
-    'text/plain',
-    'text/html',
-    'text/csv',
-    'text/markdown',
+    "text/plain",
+    "text/html",
+    "text/csv",
+    "text/markdown",
 }
 
 # Maximum file size (100MB by default)
 # Can be overridden by settings.MAX_UPLOAD_SIZE
 try:
     from django.conf import settings
-    MAX_FILE_SIZE = getattr(settings, 'MAX_UPLOAD_SIZE', 100 * 1024 * 1024)  # 100MB por defecto
+    MAX_FILE_SIZE = getattr(settings, "MAX_UPLOAD_SIZE", 100 * 1024 * 1024)  # 100MB por defecto
 except ImportError:
     MAX_FILE_SIZE = 100 * 1024 * 1024  # 100MB in bytes
 
@@ -114,7 +113,6 @@ ALLOWED_JS_PATTERNS = [
 class FileValidationError(Exception):
     """Raised when file validation fails."""
 
-    pass
 
 
 def has_whitelisted_javascript(content: bytes) -> bool:
@@ -143,7 +141,7 @@ def validate_mime_type(mime_type: str) -> None:
     if mime_type not in ALLOWED_MIME_TYPES:
         raise FileValidationError(
             f"MIME type '{mime_type}' is not allowed. "
-            f"Allowed types: {', '.join(sorted(ALLOWED_MIME_TYPES))}"
+            f"Allowed types: {', '.join(sorted(ALLOWED_MIME_TYPES))}",
         )
 
 

src/paperless/urls.py

@@ -86,7 +86,7 @@ api_router.register(r"config", ApplicationConfigurationViewSet)
 api_router.register(r"processed_mail", ProcessedMailViewSet)
 api_router.register(r"deletion_requests", DeletionRequestViewSet)
 api_router.register(
-    r"deletion-requests", DeletionRequestViewSet, basename="deletion-requests"
+    r"deletion-requests", DeletionRequestViewSet, basename="deletion-requests",
 )