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18 changed files with 1834 additions and 107 deletions
-8
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@@ -1209,14 +1209,6 @@ left unassigned, preventing low-confidence guesses from being applied.
Defaults to 0.6.
#### [`PAPERLESS_MATCH_REGEX_TIMEOUT_SECONDS=<float>`](#PAPERLESS_MATCH_REGEX_TIMEOUT_SECONDS) {#PAPERLESS_MATCH_REGEX_TIMEOUT_SECONDS}
: Sets the timeout, in seconds, for regular expression matching. Increase this
value if date parsing or user-defined matching rules time out when processing
long documents, especially on slower hardware.
Defaults to 0.1 seconds.
#### [`PAPERLESS_DATE_PARSER_LANGUAGES=<lang>`](#PAPERLESS_DATE_PARSER_LANGUAGES) {#PAPERLESS_DATE_PARSER_LANGUAGES}
: Specifies which language Paperless should use when parsing dates from documents.
+1
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@@ -77,6 +77,7 @@ dependencies = [
"torch~=2.13.0",
"watchfiles>=1.2",
"whitenoise~=6.11",
"whoosh-compat[tantivy]==0.1",
"zxing-cpp~=3.1.0",
]
[project.optional-dependencies]
+42
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@@ -0,0 +1,42 @@
from __future__ import annotations
from whoosh_compat import FieldKind
from whoosh_compat import FieldSpec
from whoosh_compat import SubpathSpec
# Internal-only schema fields with no query-syntax meaning of their own
# (sort shadow fields, bigram CJK fields, simple_title/simple_content,
# autocomplete_word, notes_text) are NOT represented here, they are
# declared in _schema.py's field_descriptors().
#
# analyzer/pattern_normalizer are deliberately left at FieldSpec's default
# (None): they're language-specific and only meaningful to whoosh-compat's
# parser, so _registry.py attaches them per-language via dataclasses.replace()
# rather than PUBLIC_FIELDS declaring them itself. _schema.py only reads
# name/kind/fast and never sees the analyzer at all.
PUBLIC_FIELDS: tuple[FieldSpec, ...] = (
FieldSpec("title", FieldKind.TEXT),
FieldSpec("content", FieldKind.TEXT),
FieldSpec("correspondent", FieldKind.TEXT),
FieldSpec("document_type", FieldKind.TEXT, aliases=("type",)),
FieldSpec("storage_path", FieldKind.TEXT, aliases=("path",)),
FieldSpec("original_filename", FieldKind.TEXT),
FieldSpec("tag", FieldKind.TEXT, comma_values=True),
FieldSpec("checksum", FieldKind.KEYWORD),
FieldSpec("asn", FieldKind.U64, fast=True),
FieldSpec("page_count", FieldKind.U64, fast=True),
FieldSpec("num_notes", FieldKind.U64, fast=True),
FieldSpec("created", FieldKind.DATE, date_only=True, fast=True),
FieldSpec("modified", FieldKind.DATETIME, fast=True),
FieldSpec("added", FieldKind.DATETIME, fast=True),
FieldSpec(
"notes",
FieldKind.JSON,
subpaths={"user": SubpathSpec(), "note": SubpathSpec(default=True)},
),
FieldSpec(
"custom_fields",
FieldKind.JSON,
subpaths={"name": SubpathSpec(), "value": SubpathSpec(default=True)},
),
)
+91
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@@ -0,0 +1,91 @@
from __future__ import annotations
import dataclasses
from typing import TYPE_CHECKING
from whoosh_compat import FieldKind
from whoosh_compat import FieldRegistry
from documents.search._fields import PUBLIC_FIELDS
from documents.search._tokenizer import ascii_fold
from documents.search._tokenizer import paperless_text_analyzer
from documents.search._tokenizer import stem_pattern_text
if TYPE_CHECKING:
from whoosh_compat import PatternNormalizer
_registry_cache: dict[str | None, FieldRegistry] = {}
def _identity_analyzer(text: str) -> list[str]:
"""Analyzer for KEYWORD fields indexed with the raw tokenizer (no splitting)."""
return [text]
def _fold_normalizer(text: str) -> str:
"""Wildcard/regex literal-run normalizer for fields indexed without stemming."""
return ascii_fold(text.lower())
def _make_pattern_normalizer(language: str | None) -> PatternNormalizer:
"""Build the wildcard/regex literal-run normalizer for a search language."""
def _pattern_normalizer(text: str) -> tuple[str, ...]:
"""Normalize a literal run into the forms a term may match.
TEXT index terms go through lowercase -> ascii_fold -> stem, so a
pattern that skips stemming can never match one: "invoice*" would look
for a term starting with "invoice" while the index holds "invoic". The
run is therefore offered stemmed as well. KEYWORD fields are indexed
raw and get _fold_normalizer instead, so their patterns stay literal.
Both forms are returned, as alternatives, because neither is a prefix
of the other in general: English stemming substitutes as well as
truncates ("copy" -> "copi"), so the stem alone loses the compounds
the typed run reaches ("copyright") while the typed run alone loses
the inflections the stem reaches ("copies"). whoosh-compat ORs the
alternatives per literal run and deduplicates them, so a run the
stemmer leaves alone costs exactly the one branch it did before.
Inside a bracket class the emitter calls this once per character and
uses the answer only if it is a single one-character form; two forms
there leave the character as typed. A stemmer does not change a lone
character, so the two forms deduplicate to one and the class body is
folded as before.
"""
folded = ascii_fold(text.lower())
stemmed = stem_pattern_text(folded, language)
return (folded, stemmed)
return _pattern_normalizer
def get_field_registry(language: str | None) -> FieldRegistry:
"""Build (or return the cached) FieldRegistry for the given search language.
Cached keyed by language, rebuilt on the same trigger register_tokenizers()
uses (settings.SEARCH_LANGUAGE change). A fresh call with a new language
builds and caches a new registry rather than mutating the old one.
"""
if language in _registry_cache:
return _registry_cache[language]
text_analyzer = paperless_text_analyzer(language).analyze
pattern_normalizer = _make_pattern_normalizer(language)
specs = [
dataclasses.replace(
field,
analyzer=_identity_analyzer
if field.kind is FieldKind.KEYWORD
else text_analyzer,
pattern_normalizer=_fold_normalizer
if field.kind is FieldKind.KEYWORD
else pattern_normalizer,
)
for field in PUBLIC_FIELDS
]
registry = FieldRegistry(specs)
_registry_cache[language] = registry
return registry
+222 -83
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@@ -1,14 +1,19 @@
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
@@ -16,7 +21,185 @@ if TYPE_CHECKING:
logger = logging.getLogger("paperless.search")
# v1 - Initial tantivy schema format
SCHEMA_VERSION: Final[int] = 1
# 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.
SCHEMA_VERSION: Final[int] = 2
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:
@@ -32,85 +215,37 @@ def build_schema() -> tantivy.Schema:
"""
sb = tantivy.SchemaBuilder()
sb.add_unsigned_field("id", stored=True, indexed=True, fast=True)
sb.add_text_field("checksum", stored=True, tokenizer_name="raw")
for field in (
"title",
"correspondent",
"document_type",
"storage_path",
"original_filename",
"content",
):
sb.add_text_field(field, stored=True, tokenizer_name="paperless_text")
# Shadow sort fields - fast, not stored/indexed
for field in ("title_sort", "correspondent_sort", "type_sort"):
sb.add_text_field(
field,
stored=False,
tokenizer_name="simple_analyzer",
fast=True,
)
# CJK support - not stored, indexed only
sb.add_text_field("bigram_content", stored=False, tokenizer_name="bigram_analyzer")
sb.add_text_field("bigram_title", stored=False, tokenizer_name="bigram_analyzer")
sb.add_text_field(
"bigram_correspondent",
stored=False,
tokenizer_name="bigram_analyzer",
)
sb.add_text_field(
"bigram_document_type",
stored=False,
tokenizer_name="bigram_analyzer",
)
sb.add_text_field("bigram_tag", stored=False, tokenizer_name="bigram_analyzer")
# Simple substring search support for title/content - not stored, indexed only
sb.add_text_field(
"simple_title",
stored=False,
tokenizer_name="simple_search_analyzer",
)
sb.add_text_field(
"simple_content",
stored=False,
tokenizer_name="simple_search_analyzer",
)
# 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.
sb.add_text_field("autocomplete_word", stored=False, tokenizer_name="raw")
sb.add_text_field("tag", stored=True, tokenizer_name="paperless_text")
# JSON fields — structured queries: notes.user:alice, custom_fields.name:invoice
sb.add_json_field("notes", stored=True, tokenizer_name="paperless_text")
# Plain-text companion for notes — tantivy's SnippetGenerator does not support
# JSON fields, so highlights require a text field with the same content.
sb.add_text_field("notes_text", stored=True, tokenizer_name="paperless_text")
sb.add_json_field("custom_fields", stored=True, tokenizer_name="paperless_text")
for field in (
"correspondent_id",
"document_type_id",
"storage_path_id",
"tag_id",
"owner_id",
"viewer_id",
"viewer_group_id",
):
sb.add_unsigned_field(field, stored=False, indexed=True, fast=True)
for field in ("created", "modified", "added"):
sb.add_date_field(field, stored=True, indexed=True, fast=True)
for field in ("asn", "page_count", "num_notes"):
sb.add_unsigned_field(field, stored=True, indexed=True, fast=True)
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()
@@ -119,9 +254,9 @@ def needs_rebuild(index_dir: Path) -> bool:
"""
Check if the search index needs rebuilding.
Reads .index_settings.json to compare the stored schema version and
search language against the current configuration. Returns True if the
file is missing, unparsable, or either value mismatches.
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
@@ -140,6 +275,9 @@ def needs_rebuild(index_dir: Path) -> bool:
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
@@ -170,6 +308,7 @@ def _write_sentinels(index_dir: Path) -> None:
{
"schema_version": SCHEMA_VERSION,
"language": settings.SEARCH_LANGUAGE,
"schema_fingerprint": schema_fingerprint(),
},
),
)
+51 -2
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@@ -1,6 +1,7 @@
from __future__ import annotations
import logging
from functools import cache
from typing import Final
import tantivy
@@ -71,7 +72,7 @@ def register_tokenizers(index: tantivy.Index, language: str | None) -> None:
use fast=True and Tantivy requires fast-field tokenizers to exist
even for documents that omit those fields.
"""
index.register_tokenizer("paperless_text", _paperless_text(language))
index.register_tokenizer("paperless_text", paperless_text_analyzer(language))
index.register_tokenizer("simple_analyzer", _simple_analyzer())
index.register_tokenizer("bigram_analyzer", _bigram_analyzer())
index.register_tokenizer("simple_search_analyzer", _simple_search_analyzer())
@@ -79,7 +80,7 @@ def register_tokenizers(index: tantivy.Index, language: str | None) -> None:
index.register_fast_field_tokenizer("simple_analyzer", _simple_analyzer())
def _paperless_text(language: str | None) -> tantivy.TextAnalyzer:
def paperless_text_analyzer(language: str | None) -> tantivy.TextAnalyzer:
"""Main full-text tokenizer for content, title, etc: simple -> remove_long(129) -> lowercase -> ascii_fold [-> stemmer]"""
builder = (
tantivy.TextAnalyzerBuilder(tantivy.Tokenizer.simple())
@@ -100,6 +101,54 @@ def _paperless_text(language: str | None) -> tantivy.TextAnalyzer:
return builder.build()
@cache
def _pattern_stemmer(language: str | None) -> tantivy.TextAnalyzer | None:
"""The stemming tail of paperless_text_analyzer, over a whole literal run.
Same language gate and same Snowball stemmer paperless_text_analyzer
applies at index time, so query patterns follow SEARCH_LANGUAGE. Returns
None when that gate disables stemming; paperless_text_analyzer already
warns about an unsupported language, so this stays quiet.
The raw tokenizer keeps the run whole (a wildcard literal is a fragment,
not necessarily a word), and remove_long is kept so an over-long run is
treated the same way the index treats it.
"""
if not language:
return None
tantivy_lang = _LANGUAGE_MAP.get(language.lower())
if tantivy_lang is None:
return None
return (
tantivy.TextAnalyzerBuilder(tantivy.Tokenizer.raw())
.filter(tantivy.Filter.remove_long(_TOKEN_REMOVE_LONG_LIMIT))
.filter(tantivy.Filter.stemmer(tantivy_lang))
.build()
)
def stem_pattern_text(text: str, language: str | None) -> str:
"""Stem an already lowercased/ascii-folded run the way index terms are.
Returns text unchanged when stemming is disabled for language, and also
when the stem step does not yield exactly one token: remove_long drops a run
past the length limit, leaving no stem to substitute. Falling back to the
text as typed is the safe direction for a pattern prefix, since it can only
be as narrow as it was before stemming was considered.
The raw tokenizer emits one token whatever the input and the stemmer is
1-to-1, so only the zero-token case can fire today; the guard covers both
counts so a tokenizer change cannot turn this into an IndexError.
"""
analyzer = _pattern_stemmer(language)
if analyzer is None:
return text
tokens = analyzer.analyze(text)
if len(tokens) != 1:
return text
return tokens[0]
def _simple_analyzer() -> tantivy.TextAnalyzer:
"""Tokenizer for shadow sort fields (title_sort, correspondent_sort, type_sort): simple -> lowercase -> ascii_fold."""
return (
@@ -0,0 +1,92 @@
"""Every declared JSON subpath must actually be written to the index.
PUBLIC_FIELDS declares each JSON field's subpaths (e.g. ``notes`` ->
{"user", "note"}), but nothing coupled that declaration to what
``_backend.py``'s document builder actually writes into the JSON blob at
index time. A subpath declared but never written would be
queryable-but-always-empty -- syntactically valid, silently matching
nothing -- with no test failure anywhere.
This indexes one real document carrying values for every JSON field
(a Note, a CustomFieldInstance) and inspects the document's own stored
JSON payload, rather than running field-specific queries: that way a
future JSON field's subpaths are covered automatically, without a new
per-subpath query having to be added by hand each time.
"""
from __future__ import annotations
from typing import TYPE_CHECKING
import pytest
import tantivy
from django.contrib.auth.models import User
from whoosh_compat import FieldKind
from documents.models import CustomField
from documents.models import CustomFieldInstance
from documents.models import Document
from documents.models import Note
from documents.search._fields import PUBLIC_FIELDS
if TYPE_CHECKING:
from documents.search._backend import TantivyBackend
pytestmark = [pytest.mark.search, pytest.mark.django_db]
class TestJsonSubpathsAreWrittenAtIndexTime:
def test_every_declared_json_subpath_appears_in_the_stored_document(
self,
backend: TantivyBackend,
) -> None:
"""
GIVEN:
- A document with a Note and a CustomFieldInstance attached
WHEN:
- The document is indexed via TantivyBackend.add_or_update
THEN:
- Every subpath PUBLIC_FIELDS declares for notes/custom_fields
is present as a key in the document's stored JSON payload
"""
user = User.objects.create_user(username="completeness-user")
field = CustomField.objects.create(
name="Completeness Field",
data_type=CustomField.FieldDataType.STRING,
)
doc = Document.objects.create(
title="Completeness doc",
content="x",
checksum="json-subpath-completeness",
)
Note.objects.create(document=doc, user=user, note="a note")
CustomFieldInstance.objects.create(
document=doc,
field=field,
value_text="a value",
)
backend.add_or_update(doc)
index = backend._index
searcher = index.searcher()
hits = searcher.search(
tantivy.Query.term_query(index.schema, "id", doc.pk),
limit=1,
).hits
assert hits, "the document was not indexed"
stored = searcher.doc(hits[0][1]).to_dict()
json_fields = [f for f in PUBLIC_FIELDS if f.kind is FieldKind.JSON]
assert json_fields, "no JSON fields declared - fixture is stale"
for field_spec in json_fields:
stored_values = stored.get(field_spec.name)
assert stored_values, (
f"{field_spec.name} was not written to the index at all"
)
written_keys = stored_values[0].keys()
for subpath in field_spec.subpaths:
assert subpath in written_keys, (
f"{field_spec.name}.{subpath} is declared in PUBLIC_FIELDS "
"but _backend.py's document builder never writes it - it "
"would be queryable but always empty"
)
@@ -0,0 +1,62 @@
"""Wildcard patterns on KEYWORD fields must stay literal.
``checksum`` is the only KEYWORD field: it is indexed with the raw tokenizer,
so its terms are never lowercased, folded or stemmed. Running its wildcard
patterns through the stemming normalizer rewrote hex prefixes ("ceded" ->
"cede") and returned documents whose checksum did not start with what the user
typed, which for an identity field is a wrong answer.
This covers only the registry-level normalizer, which is all that exists to
prove at this point in the stack: user queries are not yet routed through
whoosh-compat (that lands with the query-layer PR), so the same fact proven
end to end against real indexed documents lives in
``test_checksum_prefix_queries.py``.
"""
from __future__ import annotations
from typing import TYPE_CHECKING
import pytest
from documents.search._registry import get_field_registry
if TYPE_CHECKING:
from whoosh_compat import FieldRegistry
from whoosh_compat import PatternNormalizer
pytestmark = [pytest.mark.search, pytest.mark.django_db]
def _normalizer(registry: FieldRegistry, name: str) -> PatternNormalizer:
ref = registry.make_ref(name)
assert ref is not None
resolved = registry.resolve(ref)
assert resolved is not None
assert resolved.spec.pattern_normalizer is not None
return resolved.spec.pattern_normalizer
class TestKeywordPatternNormalizer:
@pytest.mark.parametrize(
"run",
[
pytest.param("ceded", id="stems_to_cede"),
pytest.param("added", id="stems_to_ad"),
pytest.param("cafed", id="stems_to_cafe"),
],
)
def test_keyword_runs_are_folded_not_stemmed(self, run: str) -> None:
"""
GIVEN:
- The "checksum" field's registered pattern normalizer
(KEYWORD kind, "en" registry)
WHEN:
- A wildcard pattern run is normalized
THEN:
- The run is returned unchanged, never widened to a stem (which
would return checksums that do not start with what the user
typed)
"""
normalize = _normalizer(get_field_registry("en"), "checksum")
assert normalize(run) == run
@@ -0,0 +1,156 @@
"""The pattern normalizer's stem-alternates contract, and its consistency
with the index-side analyzer.
Query patterns are normalized but were not stemmed, while index terms are
stemmed, so the natural spelling of a prefix search matched nothing:
``invoice*`` found no document although ``invoic*`` did. v2's index was
UNSTEMMED (whoosh ``TEXT()`` defaults to ``StandardAnalyzer``), so this
regressed against both baselines.
These are pure unit tests against ``_make_pattern_normalizer`` and
``stem_pattern_text`` directly, no query routing involved. The end-to-end
proof that a real wildcard query actually reaches a stemmed index term
lives in ``test_pattern_stemming.py``.
"""
from __future__ import annotations
from typing import TYPE_CHECKING
import pytest
from documents.search._registry import _make_pattern_normalizer
from documents.search._tokenizer import ascii_fold
from documents.search._tokenizer import paperless_text_analyzer
from documents.search._tokenizer import stem_pattern_text
if TYPE_CHECKING:
from whoosh_compat import PatternNormalizer
class TestStemsMatchTheIndexAnalyzer:
"""stem_pattern_text rebuilds paperless_text_analyzer's stemming tail rather
than sharing it, so a filter added to the index analyzer alone would silently
stop patterns from reaching the terms it produces.
"""
@pytest.mark.parametrize(
"language",
["en", "de", "fr", "es", "sv", None, "klingon"],
)
@pytest.mark.parametrize(
"word",
["Copies", "copyright", "Companies", "Invoices", "laufen", "casas", "Straße"],
)
def test_stem_equals_the_index_term(self, word: str, language: str | None) -> None:
"""
GIVEN:
- A word, across several representative index languages
("en", "de", "fr", "es", "sv"), no language, and an
unsupported language ("klingon")
WHEN:
- `stem_pattern_text` (the pattern-side stemmer) processes the
folded word, and `paperless_text_analyzer` (the index-side
analyzer) independently processes the same word
THEN:
- The two produce the identical term. `stem_pattern_text`
rebuilds `paperless_text_analyzer`'s stemming tail rather
than sharing it, so a filter added to the index analyzer
alone would silently stop patterns from reaching the terms
it produces; this pins the two staying in sync
"""
indexed = paperless_text_analyzer(language).analyze(word)[0]
assert stem_pattern_text(ascii_fold(word.lower()), language) == indexed
def _forms(normalize: PatternNormalizer, text: str) -> tuple[str, ...]:
"""The distinct forms a term may match, in order, the way the emitter reads
the normalizer's answer (see whoosh_compat.PatternNormalizer)."""
result = normalize(text)
if isinstance(result, str):
return (result,)
return tuple(dict.fromkeys(result))
class TestPatternNormalizer:
@pytest.mark.parametrize(
("text", "expected"),
[
("Invoice", ("invoice", "invoic")),
("companies", ("companies", "compani")),
# y -> i is a substitution, so both forms are needed: the index
# holds "librari" for "library" and "library" for "librarian".
("library", ("library", "librari")),
# A run the stemmer leaves alone collapses back to one form, so it
# costs exactly the one regex branch it did before.
("invoic", ("invoic",)),
("Universit", ("universit",)),
("Café", ("cafe",)),
],
)
def test_offers_the_typed_run_and_its_stem(
self,
text: str,
expected: tuple[str, ...],
) -> None:
"""
GIVEN:
- The "en" pattern normalizer
WHEN:
- It processes a literal run (e.g. "Invoice", "library",
"Café")
THEN:
- It returns the folded run and, where it differs, the
stemmed form, as distinct alternatives; a run the stemmer
leaves alone (e.g. "invoic") collapses back to the single
folded form. "library" needs both forms since y -> i is a
substitution: the index holds "librari" for "library" and
"library" for "librarian"
"""
assert _forms(_make_pattern_normalizer("en"), text) == expected
def test_run_that_yields_no_token_falls_back_to_the_typed_run(self) -> None:
"""
GIVEN:
- The "en" pattern normalizer
WHEN:
- It processes a run past the analyzer's remove_long limit
THEN:
- The run analyzes to zero tokens, so there is no stem to
offer, and only the folded run remains
"""
over_long = "invoices" * 20
assert _forms(_make_pattern_normalizer("en"), over_long) == (over_long,)
@pytest.mark.parametrize("language", [None, "klingon"])
def test_unstemmed_language_folds_only(self, language: str | None) -> None:
"""
GIVEN:
- A pattern normalizer with no language configured, or one
this build has no stemmer for ("klingon")
WHEN:
- It processes "Invoices"
THEN:
- Only the folded form ("invoices") is offered, since with no
stemmer configured the index holds surface forms and the
pattern must keep them too
"""
assert _forms(_make_pattern_normalizer(language), "Invoices") == ("invoices",)
@pytest.mark.parametrize("char", ["a", "Z", "é"])
def test_a_single_character_collapses_to_one_folded_form(self, char: str) -> None:
"""
GIVEN:
- The "en" pattern normalizer
WHEN:
- It processes a single character
THEN:
- Exactly one, one-character form is returned. A bracket
class body is normalized one character at a time and the
answer is used only when it is a single one-character
form, so a stemmer that changed a lone character would
silently disable folding inside classes
"""
forms = _forms(_make_pattern_normalizer("en"), char)
assert len(forms) == 1
assert len(forms[0]) == 1
+224
View File
@@ -0,0 +1,224 @@
from collections.abc import Sequence
import pytest
from whoosh_compat import FieldKind
from whoosh_compat import FieldRegistry
from whoosh_compat.fields import ResolvedField
from documents.search._fields import PUBLIC_FIELDS
from documents.search._registry import get_field_registry
@pytest.fixture
def registry() -> FieldRegistry:
return get_field_registry(None)
def _resolve(registry: FieldRegistry, name: str) -> ResolvedField:
ref = registry.make_ref(name)
assert ref is not None, f"{name} is not a valid field ref"
resolved = registry.resolve(ref)
assert resolved is not None, f"{name} did not resolve"
return resolved
def _distinct_forms(result: str | Sequence[str]) -> tuple[str, ...]:
"""The forms a term may match, in order, the way whoosh-compat's emitter
reads a pattern_normalizer's answer: a bare str is one form, a sequence is
several, deduplicated."""
if isinstance(result, str):
return (result,)
return tuple(dict.fromkeys(result))
class TestFieldRegistry:
def test_no_queryable_field_name_ends_in_id(self) -> None:
"""
GIVEN:
- PUBLIC_FIELDS, the canonical query-syntax field table
WHEN:
- Every declared field name is inspected
THEN:
- None of them end in "_id" (internal id columns, written for
permission filtering and joins, must never reach the query
surface; checked against PUBLIC_FIELDS rather than the
registry so a leak is caught where it is declared)
"""
leaked = [f.name for f in PUBLIC_FIELDS if f.name.endswith("_id")]
assert not leaked, f"internal id fields reached the query surface: {leaked}"
def test_type_alias_resolves_to_document_type(
self,
registry: FieldRegistry,
) -> None:
"""
GIVEN:
- The field registry
WHEN:
- The alias "type" is resolved
THEN:
- It resolves to the canonical "document_type" field
"""
assert _resolve(registry, "type").spec.name == "document_type"
def test_path_alias_resolves_to_storage_path(self, registry: FieldRegistry) -> None:
"""
GIVEN:
- The field registry
WHEN:
- The alias "path" is resolved
THEN:
- It resolves to the canonical "storage_path" field
"""
assert _resolve(registry, "path").spec.name == "storage_path"
def test_notes_json_subpaths_resolve(self, registry: FieldRegistry) -> None:
"""
GIVEN:
- The field registry
WHEN:
- "notes.user" is resolved
THEN:
- It resolves to the "notes" field with json_path "user"
"""
resolved = _resolve(registry, "notes.user")
assert resolved.spec.name == "notes"
assert resolved.json_path == "user"
assert resolved.is_subpath is True
def test_custom_fields_json_subpaths_resolve(self, registry: FieldRegistry) -> None:
"""
GIVEN:
- The field registry
WHEN:
- "custom_fields.name" and "custom_fields.value" are resolved
THEN:
- Both resolve without error
"""
for raw in ("custom_fields.name", "custom_fields.value"):
_resolve(registry, raw)
def test_tag_is_comma_values(self, registry: FieldRegistry) -> None:
"""
GIVEN:
- The field registry
WHEN:
- The "tag" field is resolved
THEN:
- It is marked comma_values=True
"""
assert _resolve(registry, "tag").spec.comma_values is True
def test_correspondent_is_not_comma_values(self, registry: FieldRegistry) -> None:
"""
GIVEN:
- The field registry
WHEN:
- The "correspondent" field is resolved
THEN:
- It is not marked comma_values ("tag" is the only field that
opts in; end to end the two readings of
"correspondent:foo,bar" agree anyway, since the analyzer
splits the literal value on the comma regardless, so this is
only observable at the registry level)
"""
assert _resolve(registry, "correspondent").spec.comma_values is False
def test_created_is_date_kind(self, registry: FieldRegistry) -> None:
"""
GIVEN:
- The field registry
WHEN:
- The "created" field is resolved
THEN:
- Its kind is DATE and date_only is True
"""
resolved = _resolve(registry, "created")
assert resolved.spec.kind is FieldKind.DATE
assert resolved.spec.date_only is True
def test_analyzer_lowercases_and_ascii_folds(self, registry: FieldRegistry) -> None:
"""
GIVEN:
- The field registry with no language configured (no stemmer
in the analyzer chain)
WHEN:
- The "title" field's analyzer processes "Café"
THEN:
- It is lowercased and ASCII-folded to the single token "cafe"
"""
resolved = _resolve(registry, "title")
assert resolved.spec.analyzer is not None
assert resolved.spec.analyzer("Café") == ["cafe"]
def test_checksum_analyzer_is_identity_single_token(
self,
registry: FieldRegistry,
) -> None:
"""
GIVEN:
- The field registry
WHEN:
- The "checksum" field's analyzer (raw tokenizer, no
splitting) processes "ABC-123"
THEN:
- It is returned unchanged as a single token
"""
resolved = _resolve(registry, "checksum")
assert resolved.spec.analyzer is not None
assert resolved.spec.analyzer("ABC-123") == ["ABC-123"]
def test_pattern_normalizer_follows_the_registry_language(
self,
registry: FieldRegistry,
) -> None:
"""
GIVEN:
- A registry with no language, and a registry built for "en"
WHEN:
- The "title" field's pattern normalizer processes "Running"
THEN:
- With no language, only the folded run is offered
("running"), since the index holds surface forms
- With "en", the stem is offered too ("run"), since indexed
terms are stemmed and the pattern has to reach them
"""
resolved = _resolve(registry, "title")
assert resolved.spec.pattern_normalizer is not None
assert _distinct_forms(resolved.spec.pattern_normalizer("Running")) == (
"running",
)
resolved_en = _resolve(get_field_registry("en"), "title")
assert resolved_en.spec.pattern_normalizer is not None
assert _distinct_forms(resolved_en.spec.pattern_normalizer("Running")) == (
"running",
"run",
)
def test_registry_is_cached_per_language(self) -> None:
"""
GIVEN:
- Two calls to get_field_registry("en")
WHEN:
- Both calls are made
THEN:
- They return the same registry instance
"""
a = get_field_registry("en")
b = get_field_registry("en")
assert a is b
def test_registry_rebuilds_on_language_change(self) -> None:
"""
GIVEN:
- A call to get_field_registry("en") and a call to
get_field_registry("de")
WHEN:
- Both calls are made
THEN:
- They return different registry instances
"""
a = get_field_registry("en")
b = get_field_registry("de")
assert a is not b
+70 -1
View File
@@ -5,12 +5,17 @@ from typing import TYPE_CHECKING
import pytest
from documents.search._fields import PUBLIC_FIELDS
from documents.search._schema import SCHEMA_VERSION
from documents.search._schema import build_schema
from documents.search._schema import field_descriptors
from documents.search._schema import needs_rebuild
from documents.search._schema import schema_fingerprint
if TYPE_CHECKING:
from pathlib import Path
import tantivy
from pytest_django.fixtures import Settings
@@ -30,7 +35,13 @@ class TestNeedsRebuild:
) -> None:
settings.SEARCH_LANGUAGE = "en"
(index_dir / ".index_settings.json").write_text(
json.dumps({"schema_version": SCHEMA_VERSION, "language": "en"}),
json.dumps(
{
"schema_version": SCHEMA_VERSION,
"language": "en",
"schema_fingerprint": schema_fingerprint(),
},
),
)
assert needs_rebuild(index_dir) is False
@@ -77,3 +88,61 @@ class TestNeedsRebuild:
json.dumps({"schema_version": SCHEMA_VERSION, "language": "en"}),
)
assert needs_rebuild(index_dir) is True
def _schema_fields(schema: tantivy.Schema) -> dict[str, dict]:
"""{name: field-state} for every field declared on a tantivy Schema.
tantivy-py 0.26 exposes no public introspection API on Schema (no
__iter__, get_field, to_json, etc.) -- __reduce__() (used internally for
pickling) is the only way to recover the field list, so we lean on it
here for test assertions only.
"""
state = schema.__reduce__()[1][0]
return {field["name"]: field for field in state["inner"]}
class TestSchemaMatchesPublicFields:
def test_every_public_field_is_in_the_schema(self) -> None:
"""
GIVEN:
- PUBLIC_FIELDS and the tantivy schema built by build_schema()
WHEN:
- Every field declared in PUBLIC_FIELDS is checked against the
schema
THEN:
- Each one is present as a field in the built schema
"""
schema = build_schema()
schema_field_names = set(_schema_fields(schema))
for field in PUBLIC_FIELDS:
assert field.name in schema_field_names, (
f"{field.name} is in PUBLIC_FIELDS but missing from build_schema()"
)
class TestFastFlagAgreement:
def test_every_public_field_fast_flag_matches_the_built_schema(self) -> None:
"""
GIVEN:
- PUBLIC_FIELDS and field_descriptors() (the latter is exactly
the input build_schema()'s SchemaBuilder consumes for the
`fast` kwarg on every field kind, so it pins the agreement
without depending on a private tantivy-py pickled
representation)
WHEN:
- Every PUBLIC_FIELDS entry's fast flag is compared against
field_descriptors()' fast flag for the same field
THEN:
- They agree for every field, catching a fast=True
PUBLIC_FIELDS entry the builder silently ignores here
instead of at a user's field:* existence query, which
whoosh-compat's registry trusts PUBLIC_FIELDS' fast flag to
resolve
"""
descriptor_fast = {d.name: d.fast for d in field_descriptors()}
for public_field in PUBLIC_FIELDS:
assert descriptor_fast[public_field.name] == public_field.fast, (
f"{public_field.name}: PUBLIC_FIELDS says fast={public_field.fast} but"
f" field_descriptors() says fast={descriptor_fast[public_field.name]}"
)
@@ -0,0 +1,587 @@
"""The schema fingerprint stamped into .index_settings.json.
tantivy compares schemas by *ordered* field list, and `tantivy.Index(schema,
path=...)` (what every write path does) raises on any difference. SCHEMA_VERSION
is the manual guard against that, but build_schema() is edited for *parser*
reasons - adding an alias, flipping fast=True, adding a subpath - by people not
thinking about the on-disk index, and forgetting the bump is exactly how this
branch's bug happened.
The fingerprint is the automatic guard: it hashes the field descriptor list that
build_schema() itself iterates, so any change to a field's name, kind, options
or *position* forces a rebuild on its own.
"""
from __future__ import annotations
import hashlib
import json
from typing import TYPE_CHECKING
import pytest
import tantivy
from documents.search import _schema
from documents.search._schema import SCHEMA_VERSION
from documents.search._schema import FieldDescriptor
from documents.search._schema import _write_sentinels
from documents.search._schema import build_schema
from documents.search._schema import field_descriptors
from documents.search._schema import needs_rebuild
from documents.search._schema import schema_fingerprint
if TYPE_CHECKING:
from pathlib import Path
from pytest_django.fixtures import SettingsWrapper
pytestmark = pytest.mark.search
# The on-disk field layout of a v2 index, pinned as data. Any edit here is an
# index-format change: it must come with a rebuild, which the fingerprint now
# forces automatically. Reproduced from build_schema()'s output as it stood
# before the descriptor refactor, so it also pins that the refactor changed
# nothing.
PINNED_DESCRIPTORS: tuple[FieldDescriptor, ...] = (
FieldDescriptor("id", "u64", stored=True, indexed=True, fast=True, tokenizer=None),
FieldDescriptor(
"title",
"text",
stored=True,
indexed=True,
fast=False,
tokenizer="paperless_text",
),
FieldDescriptor(
"content",
"text",
stored=True,
indexed=True,
fast=False,
tokenizer="paperless_text",
),
FieldDescriptor(
"correspondent",
"text",
stored=True,
indexed=True,
fast=False,
tokenizer="paperless_text",
),
FieldDescriptor(
"document_type",
"text",
stored=True,
indexed=True,
fast=False,
tokenizer="paperless_text",
),
FieldDescriptor(
"storage_path",
"text",
stored=True,
indexed=True,
fast=False,
tokenizer="paperless_text",
),
FieldDescriptor(
"original_filename",
"text",
stored=True,
indexed=True,
fast=False,
tokenizer="paperless_text",
),
FieldDescriptor(
"tag",
"text",
stored=True,
indexed=True,
fast=False,
tokenizer="paperless_text",
),
FieldDescriptor(
"checksum",
"text",
stored=True,
indexed=True,
fast=False,
tokenizer="raw",
),
FieldDescriptor("asn", "u64", stored=True, indexed=True, fast=True, tokenizer=None),
FieldDescriptor(
"page_count",
"u64",
stored=True,
indexed=True,
fast=True,
tokenizer=None,
),
FieldDescriptor(
"num_notes",
"u64",
stored=True,
indexed=True,
fast=True,
tokenizer=None,
),
FieldDescriptor(
"created",
"date",
stored=True,
indexed=True,
fast=True,
tokenizer=None,
),
FieldDescriptor(
"modified",
"date",
stored=True,
indexed=True,
fast=True,
tokenizer=None,
),
FieldDescriptor(
"added",
"date",
stored=True,
indexed=True,
fast=True,
tokenizer=None,
),
FieldDescriptor(
"notes",
"json",
stored=True,
indexed=True,
fast=False,
tokenizer="paperless_text",
),
FieldDescriptor(
"notes_text",
"text",
stored=True,
indexed=True,
fast=False,
tokenizer="paperless_text",
),
FieldDescriptor(
"custom_fields",
"json",
stored=True,
indexed=True,
fast=False,
tokenizer="paperless_text",
),
FieldDescriptor(
"title_sort",
"text",
stored=False,
indexed=True,
fast=True,
tokenizer="simple_analyzer",
),
FieldDescriptor(
"correspondent_sort",
"text",
stored=False,
indexed=True,
fast=True,
tokenizer="simple_analyzer",
),
FieldDescriptor(
"type_sort",
"text",
stored=False,
indexed=True,
fast=True,
tokenizer="simple_analyzer",
),
FieldDescriptor(
"bigram_content",
"text",
stored=False,
indexed=True,
fast=False,
tokenizer="bigram_analyzer",
),
FieldDescriptor(
"bigram_title",
"text",
stored=False,
indexed=True,
fast=False,
tokenizer="bigram_analyzer",
),
FieldDescriptor(
"bigram_correspondent",
"text",
stored=False,
indexed=True,
fast=False,
tokenizer="bigram_analyzer",
),
FieldDescriptor(
"bigram_document_type",
"text",
stored=False,
indexed=True,
fast=False,
tokenizer="bigram_analyzer",
),
FieldDescriptor(
"bigram_tag",
"text",
stored=False,
indexed=True,
fast=False,
tokenizer="bigram_analyzer",
),
FieldDescriptor(
"simple_title",
"text",
stored=False,
indexed=True,
fast=False,
tokenizer="simple_search_analyzer",
),
FieldDescriptor(
"simple_content",
"text",
stored=False,
indexed=True,
fast=False,
tokenizer="simple_search_analyzer",
),
FieldDescriptor(
"autocomplete_word",
"text",
stored=False,
indexed=True,
fast=False,
tokenizer="raw",
),
FieldDescriptor(
"owner_id",
"u64",
stored=False,
indexed=True,
fast=True,
tokenizer=None,
),
FieldDescriptor(
"viewer_id",
"u64",
stored=False,
indexed=True,
fast=True,
tokenizer=None,
),
FieldDescriptor(
"viewer_group_id",
"u64",
stored=False,
indexed=True,
fast=True,
tokenizer=None,
),
)
def _schema_fields(schema: tantivy.Schema) -> list[dict]:
"""The tantivy-level field list, in declaration order.
tantivy-py 0.26 exposes no public introspection API on Schema, so
__reduce__() (its pickling hook) is the only way to recover the field list.
It is used here, in a test, precisely because it is the representation the
persisted fingerprint must NOT depend on.
"""
return schema.__reduce__()[1][0]["inner"]
def _sentinels(index_dir: Path, **overrides: object) -> None:
data = {
"schema_version": SCHEMA_VERSION,
"language": None,
"schema_fingerprint": schema_fingerprint(),
}
data.update(overrides)
(index_dir / ".index_settings.json").write_text(json.dumps(data))
class TestDescriptorsDescribeTheBuiltSchema:
def test_descriptors_match_the_pinned_field_layout(self) -> None:
"""
GIVEN:
- PINNED_DESCRIPTORS, a frozen snapshot of the v2 on-disk field
layout, reproduced from build_schema()'s output as it stood
before the descriptor refactor
WHEN:
- field_descriptors() is called
THEN:
- It matches the pinned layout exactly, in the same order,
pinning that the refactor changed nothing
"""
assert tuple(field_descriptors()) == PINNED_DESCRIPTORS
def test_built_schema_matches_the_descriptors(self) -> None:
"""
GIVEN:
- The schema built by build_schema()
WHEN:
- Its fields are read back via __reduce__() (schema.__reduce__(),
tantivy-py's pickling hook)
THEN:
- Every field's name, kind, stored/fast flags and tokenizer
match what field_descriptors() declared as input; the
descriptors are not a parallel description, they are the
input, so a descriptor edit cannot claim a shape the
SchemaBuilder did not actually build
"""
kinds = {"text": "text", "json": "json_object", "u64": "u64", "date": "date"}
built = [
(
field["name"],
field["type"],
field["options"]["stored"],
bool(field["options"].get("fast")),
(field["options"].get("indexing") or {}).get("tokenizer"),
)
for field in _schema_fields(build_schema())
]
expected = [
(
descriptor.name,
kinds[descriptor.kind],
descriptor.stored,
descriptor.fast,
descriptor.tokenizer,
)
for descriptor in field_descriptors()
]
assert built == expected
class TestFingerprintSensitivity:
def test_a_field_option_change_moves_the_fingerprint(
self,
monkeypatch: pytest.MonkeyPatch,
) -> None:
"""
GIVEN:
- The current schema fingerprint
WHEN:
- A single field descriptor's "fast" option is changed, with
no other change
THEN:
- The fingerprint changes
"""
before = schema_fingerprint()
changed = field_descriptors()
changed[1] = changed[1]._replace(fast=True)
monkeypatch.setattr(_schema, "field_descriptors", lambda: changed)
assert schema_fingerprint() != before
def test_reordering_alone_moves_the_fingerprint(
self,
monkeypatch: pytest.MonkeyPatch,
) -> None:
"""
GIVEN:
- The current schema fingerprint
WHEN:
- Two field descriptors are swapped, with no other change (the
original bug: same fields, different declaration order)
THEN:
- The fingerprint changes; a set- or dict-based fingerprint
would be blind to this, and tantivy would reject every write
against the existing index
"""
before = schema_fingerprint()
swapped = field_descriptors()
swapped[1], swapped[2] = swapped[2], swapped[1]
monkeypatch.setattr(_schema, "field_descriptors", lambda: swapped)
assert schema_fingerprint() != before
class TestFingerprintIsIndependentOfTantivy:
def test_a_tantivy_option_key_addition_would_not_move_it(self) -> None:
"""
GIVEN:
- The built schema's raw field list, and the same list with a
new tantivy-internal option key added (simulating a
tantivy-py upgrade)
WHEN:
- Both raw lists are hashed directly, and schema_fingerprint()
is compared against a hash of field_descriptors()
THEN:
- The raw hashes differ (hashing schema.__reduce__() would
force a global reindex on every tantivy-py upgrade), but
schema_fingerprint() is unaffected, since it hashes
field_descriptors(), never tantivy's own representation
"""
fields = _schema_fields(build_schema())
upgraded = [
{**field, "options": {**field["options"], "coerce": True}}
for field in fields
]
assert _hash(upgraded) != _hash(fields)
assert schema_fingerprint() == _fingerprint_of(field_descriptors())
def test_fingerprint_never_touches_the_schema_builder(
self,
monkeypatch: pytest.MonkeyPatch,
) -> None:
"""
GIVEN:
- tantivy.SchemaBuilder replaced with a stand-in that raises if
constructed
WHEN:
- build_schema() is called (and raises), then
schema_fingerprint() is called again
THEN:
- schema_fingerprint() still matches its earlier value,
proving it never consults SchemaBuilder
"""
before = schema_fingerprint()
class _RemovedSchemaBuilder:
def __init__(self) -> None:
raise AssertionError("tantivy.SchemaBuilder was consulted")
monkeypatch.setattr(tantivy, "SchemaBuilder", _RemovedSchemaBuilder)
with pytest.raises(AssertionError):
build_schema()
assert schema_fingerprint() == before
def _hash(payload: object) -> str:
return hashlib.blake2b(json.dumps(payload).encode()).hexdigest()
def _fingerprint_of(descriptors: list[FieldDescriptor]) -> str:
return _hash([list(descriptor) for descriptor in descriptors])
class TestNeedsRebuildOnFingerprint:
def test_matching_fingerprint_does_not_rebuild(
self,
index_dir: Path,
settings: SettingsWrapper,
) -> None:
"""
GIVEN:
- An index directory whose sentinel file records the current
schema_fingerprint()
WHEN:
- needs_rebuild() is called
THEN:
- It returns False
"""
settings.SEARCH_LANGUAGE = None
_sentinels(index_dir)
assert needs_rebuild(index_dir) is False
def test_stale_fingerprint_rebuilds_despite_a_matching_version(
self,
index_dir: Path,
settings: SettingsWrapper,
monkeypatch: pytest.MonkeyPatch,
) -> None:
"""
GIVEN:
- An index directory whose sentinel matches SCHEMA_VERSION,
but field_descriptors() is patched to add a field the
fingerprint never saw (schema edited, version not bumped)
WHEN:
- needs_rebuild() is called
THEN:
- It returns True; without the fingerprint check,
`reindex --if-needed` would report the index up to date and
every subsequent write would raise
"""
settings.SEARCH_LANGUAGE = None
_sentinels(index_dir)
extended = [
*field_descriptors(),
FieldDescriptor(
"new_field",
"u64",
stored=False,
indexed=True,
fast=True,
tokenizer=None,
),
]
monkeypatch.setattr(_schema, "field_descriptors", lambda: extended)
assert needs_rebuild(index_dir) is True
def test_reordered_schema_rebuilds(
self,
index_dir: Path,
settings: SettingsWrapper,
monkeypatch: pytest.MonkeyPatch,
) -> None:
"""
GIVEN:
- An index directory whose sentinel matches the current
fingerprint, but field_descriptors() is patched to swap two
fields' order
WHEN:
- needs_rebuild() is called
THEN:
- It returns True
"""
settings.SEARCH_LANGUAGE = None
_sentinels(index_dir)
reordered = field_descriptors()
reordered[1], reordered[2] = reordered[2], reordered[1]
monkeypatch.setattr(_schema, "field_descriptors", lambda: reordered)
assert needs_rebuild(index_dir) is True
def test_missing_fingerprint_rebuilds(
self,
index_dir: Path,
settings: SettingsWrapper,
) -> None:
"""
GIVEN:
- An index directory whose sentinel has no "schema_fingerprint"
key at all
WHEN:
- needs_rebuild() is called
THEN:
- It returns True; an index whose schema shape nobody recorded
is rebuilt rather than trusted
"""
settings.SEARCH_LANGUAGE = None
(index_dir / ".index_settings.json").write_text(
json.dumps({"schema_version": SCHEMA_VERSION, "language": None}),
)
assert needs_rebuild(index_dir) is True
def test_written_sentinels_satisfy_the_check(
self,
index_dir: Path,
settings: SettingsWrapper,
) -> None:
"""
GIVEN:
- An index directory whose sentinels are written by
_write_sentinels() itself
WHEN:
- needs_rebuild() is called
THEN:
- It returns False
"""
settings.SEARCH_LANGUAGE = "en"
_write_sentinels(index_dir)
assert needs_rebuild(index_dir) is False
@@ -0,0 +1,178 @@
"""SCHEMA_VERSION must change whenever build_schema()'s field list or order does.
tantivy compares schemas by *ordered* field list. ``Index.open()`` loads the
schema from the index's own ``meta.json``, so reads against an index built by an
older release keep working after a field reorder. Writes do not:
``WriteBatch.__enter__`` calls ``tantivy.Index(build_schema(), path=...)``, an
open-or-create that raises ``ValueError`` on any schema difference. Nothing
catches that ValueError, so consumption, index_document and bulk edit all
hard-fail while ``/api/status/`` still reports the index healthy.
The only thing that saves such an install is ``needs_rebuild()`` noticing the
version stamped in ``.index_settings.json`` is stale.
"""
from __future__ import annotations
import json
from typing import TYPE_CHECKING
import pytest
import tantivy
from django.conf import settings as django_settings
from documents.search._schema import build_schema
from documents.search._schema import needs_rebuild
from documents.search._schema import open_or_rebuild_index
if TYPE_CHECKING:
from pathlib import Path
pytestmark = [pytest.mark.search]
RELEASED_V1_SCHEMA_VERSION = 1
def _build_released_v1_schema() -> tantivy.Schema:
"""Frozen copy of build_schema() as shipped in v3.0.x (schema version 1).
Deliberately duplicated rather than imported: it must keep describing the
on-disk layout of already-deployed indexes even as build_schema() evolves.
"""
sb = tantivy.SchemaBuilder()
sb.add_unsigned_field("id", stored=True, indexed=True, fast=True)
sb.add_text_field("checksum", stored=True, tokenizer_name="raw")
for field in (
"title",
"correspondent",
"document_type",
"storage_path",
"original_filename",
"content",
):
sb.add_text_field(field, stored=True, tokenizer_name="paperless_text")
for field in ("title_sort", "correspondent_sort", "type_sort"):
sb.add_text_field(
field,
stored=False,
tokenizer_name="simple_analyzer",
fast=True,
)
for field in (
"bigram_content",
"bigram_title",
"bigram_correspondent",
"bigram_document_type",
"bigram_tag",
):
sb.add_text_field(field, stored=False, tokenizer_name="bigram_analyzer")
for field in ("simple_title", "simple_content"):
sb.add_text_field(field, stored=False, tokenizer_name="simple_search_analyzer")
sb.add_text_field("autocomplete_word", stored=False, tokenizer_name="raw")
sb.add_text_field("tag", stored=True, tokenizer_name="paperless_text")
sb.add_json_field("notes", stored=True, tokenizer_name="paperless_text")
sb.add_text_field("notes_text", stored=True, tokenizer_name="paperless_text")
sb.add_json_field("custom_fields", stored=True, tokenizer_name="paperless_text")
for field in (
"correspondent_id",
"document_type_id",
"storage_path_id",
"tag_id",
"owner_id",
"viewer_id",
"viewer_group_id",
):
sb.add_unsigned_field(field, stored=False, indexed=True, fast=True)
for field in ("created", "modified", "added"):
sb.add_date_field(field, stored=True, indexed=True, fast=True)
for field in ("asn", "page_count", "num_notes"):
sb.add_unsigned_field(field, stored=True, indexed=True, fast=True)
return sb.build()
@pytest.fixture
def released_v1_index(tmp_path: Path) -> Path:
"""An index directory as a v3.0.x install would leave it on disk."""
index_dir = tmp_path / "index"
index_dir.mkdir()
tantivy.Index(_build_released_v1_schema(), path=str(index_dir))
(index_dir / ".index_settings.json").write_text(
json.dumps(
{
"schema_version": RELEASED_V1_SCHEMA_VERSION,
"language": django_settings.SEARCH_LANGUAGE,
},
),
)
return index_dir
class TestUpgradeFromReleasedV1Index:
def test_released_v1_index_is_flagged_for_rebuild(
self,
released_v1_index: Path,
) -> None:
"""
GIVEN:
- An index directory laid out exactly as a v3.0.x (schema
version 1) install would leave it
WHEN:
- needs_rebuild() is called
THEN:
- It returns True; if this fails,
`document_index reindex --if-needed` prints "Search index is
up to date" and skips, leaving the mismatched index in place
"""
assert needs_rebuild(released_v1_index) is True
def test_opening_a_v1_index_leaves_it_writable(
self,
released_v1_index: Path,
) -> None:
"""
GIVEN:
- A v1 index directory
WHEN:
- open_or_rebuild_index() is called against it
THEN:
- The directory can be reopened with the current schema
without raising; end to end, open_or_rebuild_index must
hand back an index the write path can reopen. Before the
version bump, needs_rebuild() returned False here, and the
stale directory survived untouched, so every subsequent
write against it raised tantivy's own schema-mismatch
ValueError
"""
open_or_rebuild_index(released_v1_index)
tantivy.Index(build_schema(), path=str(released_v1_index))
def test_rebuilt_index_is_not_rebuilt_again(
self,
released_v1_index: Path,
) -> None:
"""
GIVEN:
- A v1 index directory that has just been rebuilt by
open_or_rebuild_index()
WHEN:
- needs_rebuild() is called again
THEN:
- It returns False; the rebuild must stamp the version it
actually wrote, otherwise every startup wipes and reindexes
the whole corpus
"""
open_or_rebuild_index(released_v1_index)
assert needs_rebuild(released_v1_index) is False
@@ -0,0 +1,37 @@
from __future__ import annotations
import pytest
from documents.search._tokenizer import stem_pattern_text
pytestmark = pytest.mark.search
class TestStemPatternText:
def test_unsupported_language_returns_text_unchanged(self) -> None:
"""
GIVEN:
- A language code with no Snowball stemmer mapping
WHEN:
- A pattern run is stemmed for that language
THEN:
- The run is returned unchanged, since the stemming gate that
disables stemming for an unsupported language also disables
the pattern-side stemmer
"""
assert stem_pattern_text("running", "klingon") == "running"
def test_run_past_remove_long_limit_returns_text_unchanged(self) -> None:
"""
GIVEN:
- A supported language and a run longer than the remove_long
filter's limit (129 characters, matching Document.title's
max_length)
WHEN:
- The over-long run is stemmed
THEN:
- The remove_long filter drops the token entirely, leaving no
stem to substitute, so the run is returned unchanged
"""
long_run = "a" * 130
assert stem_pattern_text(long_run, "en") == long_run
+2 -2
View File
@@ -7,8 +7,8 @@ import pytest
import tantivy
from documents.search._tokenizer import _bigram_analyzer
from documents.search._tokenizer import _paperless_text
from documents.search._tokenizer import _simple_search_analyzer
from documents.search._tokenizer import paperless_text_analyzer
from documents.search._tokenizer import register_tokenizers
if TYPE_CHECKING:
@@ -25,7 +25,7 @@ class TestTokenizers:
sb.add_text_field("content", stored=True, tokenizer_name="paperless_text")
schema = sb.build()
idx = tantivy.Index(schema, path=None)
idx.register_tokenizer("paperless_text", _paperless_text(""))
idx.register_tokenizer("paperless_text", paperless_text_analyzer(""))
return idx
@pytest.fixture
-7
View File
@@ -1,6 +1,5 @@
import pytest
import regex
from django.conf import settings
from pytest_mock import MockerFixture
from documents.regex import safe_regex_finditer
@@ -10,12 +9,6 @@ from documents.regex import safe_regex_sub
from documents.regex import validate_regex_pattern
def test_regex_timeout_uses_configured_setting() -> None:
from documents.regex import REGEX_TIMEOUT_SECONDS
assert REGEX_TIMEOUT_SECONDS == settings.MATCH_REGEX_TIMEOUT_SECONDS
class TestValidateRegexPattern:
def test_valid_pattern(self) -> None:
validate_regex_pattern(r"\d+")
-4
View File
@@ -102,10 +102,6 @@ CLASSIFIER_MATCH_THRESHOLD: Final[float] = get_float_from_env(
"PAPERLESS_CLASSIFIER_MATCH_THRESHOLD",
0.6,
)
MATCH_REGEX_TIMEOUT_SECONDS: Final[float] = get_float_from_env(
"PAPERLESS_MATCH_REGEX_TIMEOUT_SECONDS",
0.1,
)
LLM_INDEX_DIR = DATA_DIR / "llm_index"
LLM_INDEX_LOCK = LLM_INDEX_DIR / "index.lock"
# Cross-process read/write lock guarding the LLM index compaction/migration
Generated
+19
View File
@@ -2932,6 +2932,7 @@ dependencies = [
{ name = "torch", version = "2.13.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'linux'" },
{ name = "watchfiles" },
{ name = "whitenoise" },
{ name = "whoosh-compat", extra = ["tantivy"] },
{ name = "zxing-cpp" },
]
@@ -3090,6 +3091,7 @@ requires-dist = [
{ name = "torch", specifier = "~=2.13.0", index = "https://download.pytorch.org/whl/cpu" },
{ name = "watchfiles", specifier = ">=1.2" },
{ name = "whitenoise", specifier = "~=6.11" },
{ name = "whoosh-compat", extras = ["tantivy"], specifier = "==0.1.0" },
{ name = "zxing-cpp", specifier = "~=3.1.0" },
]
provides-extras = ["mariadb", "postgres", "webserver"]
@@ -5638,6 +5640,23 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/db/eb/d5583a11486211f3ebd4b385545ae787f32363d453c19fffd81106c9c138/whitenoise-6.12.0-py3-none-any.whl", hash = "sha256:fc5e8c572e33ebf24795b47b6a7da8da3c00cff2349f5b04c02f28d0cc5a3cc2", size = 20302, upload-time = "2026-02-27T00:05:40.086Z" },
]
[[package]]
name = "whoosh-compat"
version = "0.1.0"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "python-dateutil" },
]
sdist = { url = "https://files.pythonhosted.org/packages/5d/f7/3e45f4a484afa174cd42e424ce2b8c514ae54f564830122656ad17b765e2/whoosh_compat-0.1.0.tar.gz", hash = "sha256:86935bdc159ed9b0a06a4661d17f1251d8280340b84e218cc73d915a2edaddf7", size = 577543, upload-time = "2026-08-25T15:22:42.316Z" }
wheels = [
{ url = "https://files.pythonhosted.org/packages/3f/51/9a8399d0f472814e136a2884bf910c6c59843a5a2c66afc4b5133eb531ea/whoosh_compat-0.1.0-py3-none-any.whl", hash = "sha256:3e7c5f519b4d397dbf4f8d7bbe4ca1eb6004da24c8892bc701844ed393bc97e7", size = 153875, upload-time = "2026-08-25T15:22:40.787Z" },
]
[package.optional-dependencies]
tantivy = [
{ name = "tantivy" },
]
[[package]]
name = "wrapt"
version = "2.0.1"