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* Add sidecar.py module for User Story 0 (Paperless-ngx import) Implements the sidecar data model, schema validation, atomic writes, and I/O helpers per PRD section 7. - Sidecar and SidecarEvent dataclasses with full PRD schema - SidecarStatus and EventType enums - build_sidecar(), serialize(), deserialize() functions - validate_sidecar() with type and required field checking - get_sidecar_key() for .meta.json suffix generation - write_atomic() for crash-safe temp-key + copy + delete pattern - read_sidecar(), sidecar_exists(), matching_sha256() for storage I/O This is a reusable component for both the import script and main service. Co-authored-by: napnap75 <napnap75@users.noreply.github.com> * Add storage/ module with S3 and File backends Implements the storage layer abstraction for User Story 0. - StorageBackend ABC: defines interface for all storage implementations - S3Storage: boto3-based backend for Garage S3 (production) - FileStorage: filesystem-based backend for testing - get_storage_backend() factory function - Atomic operations: put, get, delete, copy, exists, list, head Both backends implement the same interface, allowing seamless switching between production S3 and local testing. Co-authored-by: napnap75 <napnap75@users.noreply.github.com> * Add period.py, extractor.py, and config.py reusable modules - period.py: Period parsing from filenames and computation from dates - parse_period_from_filename(): Extracts month-year, quarter, year, date patterns - compute_period_from_date(): Computes period per template rules (granularity + offset) - get_period(): Main entry point with filename fallback to rule-based computation - validate_period_consistency(): Checks period vs emission_date + rule - Supports French month names (août, janvier, etc.) - extractor.py: PDF text extraction using PyMuPDF - extract_first_page_text(): Extracts text from first PDF page - extract_and_validate(): Extracts and checks minimum text length - TextExtractor class for dependency injection and mocking - config.py: Configuration management - S3Config, PaperlessConfig, PollerConfig, ClassifierConfig dataclasses - AppConfig: Main application configuration from env vars - TemplateRegistry: Loads and manages template YAML configurations - load_config(), load_templates() helper functions All modules are reusable by both import script and main service. Co-authored-by: napnap75 <napnap75@users.noreply.github.com> * Add import_paperless.py script for User Story 0 Implements the standalone import script that: - Connects to Paperless-ngx REST API (PAPERLESS_URL + token from env) - Connects to Garage S3 bucket via boto3 (S3_ENDPOINT_URL, credentials, bucket) - Lists all documents from Paperless via paginated /api/documents/ - Fetches document metadata and maps to sidecar fields: - template: first path segment of storage_path - emission_date: document created field - emitting_company: correspondent name - document_type: document type name - period: parsed from filename tail, fallback to month-year from emission_date - extracted_text: first page text via PyMuPDF - optional_fields: from Paperless labels - Downloads PDF via /api/documents/{id}/download/ - Stores PDF at files/{template}/{original filename} - Writes sidecar {key}.meta.json with atomic temp-key + copy pattern - Supports resumability (skip if PDF + sidecar exist with matching SHA256) - Dry-run flag for testing - Limit flag for trial runs - Summary reporting at end - Exit code: 0 if all succeed, 1 if any failures Dependencies: requests, boto3, pymupdf, python-dateutil Usage: python scripts/import_paperless.py [--dry-run] [--limit N] [--verbose] Environment: PAPERLESS_URL, PAPERLESS_TOKEN, S3_ENDPOINT_URL, S3_ACCESS_KEY_ID, S3_SECRET_ACCESS_KEY, S3_BUCKET_NAME Co-authored-by: napnap75 <napnap75@users.noreply.github.com> * Add requirements.txt for import script dependencies Lists all Python dependencies needed to run scripts/import_paperless.py: - requests: Paperless-ngx API client - boto3: Garage S3 client - pymupdf: PDF text extraction - python-dateutil: Date parsing - PyYAML: YAML config parsing (optional) Co-authored-by: napnap75 <napnap75@users.noreply.github.com> * Update import script: add storage backend selection (s3/file) - Added --storage-backend argument (choices: s3, file, default: s3) - Added --file-base-dir argument for file storage - Storage backend validation based on selection - Updated help text and epilog with backend-specific env vars - Importer class now accepts storage_backend and file_base_dir params - _init_storage() method handles backend initialization - Summary now shows which storage backend was used Usage examples: # S3 storage (default) python scripts/import_paperless.py # File storage for testing python scripts/import_paperless.py --storage-backend file --file-base-dir ./test_storage # Explicit S3 python scripts/import_paperless.py --storage-backend s3 --s3-endpoint ... --s3-bucket ... Co-authored-by: napnap75 <napnap75@users.noreply.github.com> * Add README.md for scripts directory Comprehensive documentation for import_paperless.py script: - Overview and features - Requirements and installation - Usage examples (env vars + CLI args) - Storage backends (S3 vs file) - Metadata mapping table - Output structure - Resumability notes - Exit codes - Example output - Troubleshooting guide - Development/testing notes - Architecture overview Helps users understand how to use the import script for User Story 0. Co-authored-by: napnap75 <napnap75@users.noreply.github.com> * Fix Paperless API field mapping The Paperless-ngx API returns IDs for correspondent, document_type, and tags, not the actual string values. This commit fixes the import script to: - Use 'archived_file_name' instead of 'filename' for the actual filename - Fetch correspondent name from /api/correspondents/{id}/ endpoint - Fetch document type name from /api/document_types/{id}/ endpoint - Fetch tag names from /api/tags/{id}/ endpoint - Add caching for fetched entities to avoid duplicate API requests This ensures the sidecar metadata contains actual human-readable strings instead of numeric IDs. Changes: - PaperlessClient: Added get_correspondent_name(), get_document_type_name(), get_tag_name() - PaperlessClient: Added caches for fetched entities - get_all_documents(): Now resolves IDs to names for all fields - Updated module docstring to document this behavior Co-authored-by: napnap75 <napnap75@users.noreply.github.com> * Refactor PaperlessClient: change labels to list, add storage path caching, and improve document fetching logic --------- Co-authored-by: Vibe Nuage Agent <vibe@mistral.ai> Co-authored-by: napnap75 <napnap75@users.noreply.github.com>
120 lines
3.0 KiB
Python
120 lines
3.0 KiB
Python
"""Storage layer for Automatic PDF Renamer.
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Provides a unified interface for object storage operations.
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Implementations:
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- S3Storage: For Garage S3 (production)
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- FileStorage: For local filesystem (testing)
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"""
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from abc import ABC, abstractmethod
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from typing import Any
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class StorageBackend(ABC):
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"""Abstract base class for storage backends.
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All storage implementations must provide these methods.
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"""
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@abstractmethod
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def put_object(self, key: str, body: str | bytes) -> None:
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"""Upload an object to the storage.
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Args:
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key: The object key/path.
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body: The object content as string or bytes.
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"""
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pass
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@abstractmethod
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def get_object(self, key: str) -> str | bytes | None:
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"""Retrieve an object from storage.
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Args:
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key: The object key/path.
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Returns:
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The object content as string or bytes, or None if not found.
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"""
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pass
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@abstractmethod
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def delete_object(self, key: str) -> None:
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"""Delete an object from storage.
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Args:
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key: The object key/path.
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"""
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pass
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@abstractmethod
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def copy_object(self, src_key: str, dst_key: str) -> None:
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"""Copy an object within the storage.
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Args:
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src_key: Source object key/path.
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dst_key: Destination object key/path.
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"""
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pass
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@abstractmethod
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def object_exists(self, key: str) -> bool:
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"""Check if an object exists in storage.
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Args:
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key: The object key/path.
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Returns:
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True if the object exists, False otherwise.
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"""
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pass
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@abstractmethod
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def list_objects(self, prefix: str = "") -> list[str]:
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"""List all object keys under a prefix.
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Args:
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prefix: The prefix to filter by.
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Returns:
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List of object keys (full paths).
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"""
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pass
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@abstractmethod
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def head_object(self, key: str) -> dict[str, Any] | None:
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"""Get metadata for an object.
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Args:
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key: The object key/path.
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Returns:
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Dict with metadata (e.g., size, etag, content_type), or None if not found.
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"""
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pass
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# Convenience function to get the appropriate storage backend
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# This will be configured by the application
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def get_storage_backend(backend_type: str, **kwargs) -> StorageBackend:
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"""Factory function to get a storage backend instance.
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Args:
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backend_type: Either 's3' or 'file'.
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**kwargs: Backend-specific configuration.
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Returns:
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A StorageBackend instance.
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Raises:
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ValueError: If backend_type is not recognized.
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"""
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if backend_type == "s3":
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from .s3_storage import S3Storage
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return S3Storage(**kwargs)
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elif backend_type == "file":
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from .file_storage import FileStorage
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return FileStorage(**kwargs)
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else:
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raise ValueError(f"Unknown storage backend type: {backend_type}")
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