Files
paperless-ngx/src/documents/search/_schema.py
T
jurassicparkicecreamandshamoon 62cc31fbdb Feature: store barcode contents, list and search them (#14276)
* Feature: store barcode contents, list and search them

New setting PAPERLESS_CONSUMER_STORE_BARCODE_VALUES (off by default)
stores all barcodes found during consumption with the document: page,
type and content. They are listed on the metadata tab with a copy
button, returned by the documents API and searchable with barcodes:
in the advanced search. Versions keep their own barcodes, reprocessing
reads them again. Refs #9898

* Tests: cover the remaining barcode branches

Covers unchanged and failing barcode reads on reprocessing, unsupported files and DocumentBarcode.__str__, and uses toHaveLength in the barcode list spec as suggested by SonarCloud.

* Address review: keep barcodes when they can't be read, format choices

- Reprocessing and new versions keep the stored barcodes when the file
  can't be scanned or the scan fails, and replace them atomically.
- Format is a TextChoices of the zxing-cpp formats, with a test.
- Shared scan code, latest_version helper, TypedDict, serializer reuse.
- Barcodes in the split manifest, export/import tests, pytest-style tests.

* Barcode tests: TIFF reprocess, format check both ways, fixtures

- Reprocessing a TIFF with TIFF support off keeps the stored barcodes.
- The format test also fails when zxing-cpp drops a format.
- Fixtures in place of the sample dir mixin, plugin-level disabled test.
- Format labels aren't translated, OpenAPI enum named BarcodeFormatEnum.

* Review: module-level zxing reader, shorter barcode docs

- read_barcodes_zxing is a module-level function used by scan_pdf.
- Drop the trivial __str__ test, mark it no cover.
- Shorten the barcode docs and remove the duplicate in configuration.md.

* Fix header

* Use utility class

* Return barcodes from the metadata endpoint only

Drop the barcodes field and its prefetches from the document serializer,
as agreed in the review. Also remove the now empty component stylesheet.

---------

Co-authored-by: shamoon <4887959+shamoon@users.noreply.github.com>
2026-10-05 16:25:19 +00:00

351 lines
11 KiB
Python

from __future__ import annotations
import hashlib
import json
import logging
import shutil
from typing import TYPE_CHECKING
from typing import Final
from typing import NamedTuple
from typing import cast
import tantivy
from django.conf import settings
from whoosh_compat import FieldKind
from documents.search._fields import PUBLIC_FIELDS
if TYPE_CHECKING:
from pathlib import Path
logger = logging.getLogger("paperless.search")
# v1 - Initial tantivy schema format
# v2 - build_schema() derived from PUBLIC_FIELDS, changing the field declaration
# order, and the write-only correspondent/document_type/storage_path/tag id
# columns dropped. tantivy compares schemas by ordered field list, so an
# index built by v1 rejects every write against the v2 schema.
# v3 - barcodes JSON field for stored barcode contents
SCHEMA_VERSION: Final[int] = 3
class FieldDescriptor(NamedTuple):
"""One tantivy field, in declaration order.
The descriptor vocabulary is paperless', not tantivy-py's: it is both the
input to the SchemaBuilder and the input to schema_fingerprint(), so the
persisted fingerprint cannot move under a tantivy-py upgrade.
"""
name: str
kind: str
stored: bool
indexed: bool
fast: bool
tokenizer: str | None
# (schema kind, tokenizer) for the FieldKind -> FieldDescriptor mapping that
# doesn't need special-casing. JSON is handled separately below since it can
# emit a second, synthetic descriptor.
_KIND_TABLE: Final[dict[FieldKind, tuple[str, str | None]]] = {
FieldKind.TEXT: ("text", "paperless_text"),
FieldKind.KEYWORD: ("text", "raw"),
FieldKind.U64: ("u64", None),
FieldKind.DATE: ("date", None),
FieldKind.DATETIME: ("date", None),
}
# Kinds whose fast-field flag follows FieldSpec.fast rather than always False.
_FAST_FROM_FIELD: Final[frozenset[FieldKind]] = frozenset(
{FieldKind.U64, FieldKind.DATE, FieldKind.DATETIME},
)
def _public_field_descriptors() -> list[FieldDescriptor]:
"""Descriptors for the query-visible fields declared in PUBLIC_FIELDS."""
descriptors: list[FieldDescriptor] = []
for field in PUBLIC_FIELDS:
if field.kind is FieldKind.JSON:
descriptors.append(
FieldDescriptor(
field.name,
"json",
stored=True,
indexed=True,
fast=False,
tokenizer="paperless_text",
),
)
if field.name == "notes":
# Plain-text companion for snippet generation: tantivy's
# SnippetGenerator does not support JSON fields. Schema-only,
# no query-syntax meaning, not in PUBLIC_FIELDS.
descriptors.append(
FieldDescriptor(
"notes_text",
"text",
stored=True,
indexed=True,
fast=False,
tokenizer="paperless_text",
),
)
continue
schema_kind, tokenizer = _KIND_TABLE[field.kind]
descriptors.append(
FieldDescriptor(
field.name,
schema_kind,
stored=True,
indexed=True,
fast=field.fast if field.kind in _FAST_FROM_FIELD else False,
tokenizer=tokenizer,
),
)
return descriptors
def field_descriptors() -> list[FieldDescriptor]:
"""Every field of the document index, in the order tantivy declares them.
tantivy compares schemas by *ordered* field list, so the order here is
part of the on-disk contract: schema_fingerprint() hashes it and
needs_rebuild() acts on the result.
"""
return [
FieldDescriptor(
"id",
"u64",
stored=True,
indexed=True,
fast=True,
tokenizer=None,
),
*_public_field_descriptors(),
# Shadow sort fields - fast, not stored
*(
FieldDescriptor(
name,
"text",
stored=False,
indexed=True,
fast=True,
tokenizer="simple_analyzer",
)
for name in ("title_sort", "correspondent_sort", "type_sort")
),
# CJK support - not stored, indexed only
*(
FieldDescriptor(
name,
"text",
stored=False,
indexed=True,
fast=False,
tokenizer="bigram_analyzer",
)
for name in (
"bigram_content",
"bigram_title",
"bigram_correspondent",
"bigram_document_type",
"bigram_tag",
)
),
# Simple substring search support for title/content - not stored,
# indexed only
*(
FieldDescriptor(
name,
"text",
stored=False,
indexed=True,
fast=False,
tokenizer="simple_search_analyzer",
)
for name in ("simple_title", "simple_content")
),
# Autocomplete prefix scan via terms_with_prefix, which walks the
# field's term dictionary - so the field must be indexed (term dict),
# not stored. The stored value is never read back, so storing it only
# wastes space.
FieldDescriptor(
"autocomplete_word",
"text",
stored=False,
indexed=True,
fast=False,
tokenizer="raw",
),
# Permission filter columns, read by build_permission_filter.
*(
FieldDescriptor(
name,
"u64",
stored=False,
indexed=True,
fast=True,
tokenizer=None,
)
for name in ("owner_id", "viewer_id", "viewer_group_id")
),
]
def schema_fingerprint() -> str:
"""Hash of the field descriptors, stamped into .index_settings.json.
Changes whenever a field is added, removed, retyped, re-optioned or
reordered, so an index built from a different schema shape is detected
even when SCHEMA_VERSION was not bumped.
"""
payload = json.dumps([list(descriptor) for descriptor in field_descriptors()])
return hashlib.blake2b(payload.encode()).hexdigest()
def build_schema() -> tantivy.Schema:
"""
Build the Tantivy schema for the paperless document index.
Creates a comprehensive schema supporting full-text search, filtering,
sorting, and autocomplete functionality. Includes fields for document
content, metadata, permissions, custom fields, and notes.
Returns:
Configured Tantivy schema ready for index creation
"""
sb = tantivy.SchemaBuilder()
for descriptor in field_descriptors():
if descriptor.kind == "text":
sb.add_text_field(
descriptor.name,
stored=descriptor.stored,
fast=descriptor.fast,
tokenizer_name=cast("str", descriptor.tokenizer),
)
elif descriptor.kind == "json":
sb.add_json_field(
descriptor.name,
stored=descriptor.stored,
fast=descriptor.fast,
tokenizer_name=cast("str", descriptor.tokenizer),
)
elif descriptor.kind == "u64":
sb.add_unsigned_field(
descriptor.name,
stored=descriptor.stored,
indexed=descriptor.indexed,
fast=descriptor.fast,
)
elif descriptor.kind == "date":
sb.add_date_field(
descriptor.name,
stored=descriptor.stored,
indexed=descriptor.indexed,
fast=descriptor.fast,
)
else:
raise ValueError(f"Unknown schema field kind: {descriptor.kind}")
return sb.build()
def needs_rebuild(index_dir: Path) -> bool:
"""
Check if the search index needs rebuilding.
Reads .index_settings.json to compare the stored schema version, search
language and schema fingerprint against the current configuration. Returns
True if the file is missing, unparsable, or any value mismatches.
Args:
index_dir: Path to the search index directory
Returns:
True if the index needs rebuilding, False if it's up to date
"""
settings_file = index_dir / ".index_settings.json"
if not settings_file.exists():
return True
try:
data = json.loads(settings_file.read_text())
if data.get("schema_version") != SCHEMA_VERSION:
logger.info("Search index schema version mismatch - rebuilding.")
return True
if "language" not in data or data["language"] != settings.SEARCH_LANGUAGE:
logger.info("Search index language changed - rebuilding.")
return True
if data.get("schema_fingerprint") != schema_fingerprint():
logger.info("Search index schema fingerprint mismatch - rebuilding.")
return True
except ValueError:
return True
return False
def wipe_index(index_dir: Path) -> None:
"""
Delete all contents of the index directory to prepare for rebuild.
Recursively removes all files and subdirectories within the index
directory while preserving the directory itself.
Args:
index_dir: Path to the search index directory to clear
"""
for child in index_dir.iterdir():
if child.is_dir():
shutil.rmtree(child)
else:
child.unlink()
def _write_sentinels(index_dir: Path) -> None:
"""Write .index_settings.json so the next index open can skip rebuilding."""
settings_file = index_dir / ".index_settings.json"
settings_file.write_text(
json.dumps(
{
"schema_version": SCHEMA_VERSION,
"language": settings.SEARCH_LANGUAGE,
"schema_fingerprint": schema_fingerprint(),
},
),
)
def open_or_rebuild_index(index_dir: Path | None = None) -> tantivy.Index:
"""
Open the Tantivy index, creating or rebuilding as needed.
Checks if the index needs rebuilding due to schema version or language
changes. If rebuilding is needed, wipes the directory and creates a fresh
index with the current schema and configuration.
Args:
index_dir: Path to index directory (defaults to settings.INDEX_DIR)
Returns:
Opened Tantivy index (caller must register custom tokenizers)
"""
if index_dir is None:
index_dir = cast("Path", settings.INDEX_DIR)
if not index_dir.exists():
return tantivy.Index(build_schema())
if needs_rebuild(index_dir):
wipe_index(index_dir)
idx = tantivy.Index(build_schema(), path=str(index_dir))
_write_sentinels(index_dir)
return idx
try:
return tantivy.Index.open(str(index_dir))
except ValueError:
logger.exception(
"Search index is corrupted or incomplete - rebuilding from scratch.",
)
wipe_index(index_dir)
idx = tantivy.Index(build_schema(), path=str(index_dir))
_write_sentinels(index_dir)
return idx