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Author SHA1 Message Date
stumpylog 03e05e0804 test(search): move schema fingerprint/version tests from PR2
Both files exercise documents.search._schema exclusively (build_schema,
schema_fingerprint, needs_rebuild, open_or_rebuild_index) with no
dependency on whoosh-compat query routing, so they belong with the
schema/field-registry work rather than PR2's query rewrite.
2026-08-28 09:46:57 -07:00
stumpylog 2f79549f92 style(search): remove em dashes from PR1 comments/docstrings 2026-08-28 09:35:24 -07:00
stumpylog 9e9f3f4e09 refactor(search): dispatch table for _public_field_descriptors
Collapse the TEXT/KEYWORD/U64/DATE/DATETIME if/elif chain into a
FieldKind -> (schema kind, tokenizer) lookup table. JSON stays an
explicit branch since it can emit a second, synthetic notes_text
descriptor.
2026-08-28 09:30:06 -07:00
stumpylog d0869bec01 feat(search): add whoosh-compat, the shared field table and the field registry 2026-08-28 08:42:14 -07:00
14 changed files with 1474 additions and 92 deletions
+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 (
+10
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@@ -0,0 +1,10 @@
from whoosh_compat import FieldKind
from documents.search._fields import PUBLIC_FIELDS
class TestPublicFields:
def test_json_fields_have_subpaths(self) -> None:
for field in PUBLIC_FIELDS:
if field.kind is FieldKind.JSON:
assert field.subpaths, f"{field.name} is JSON but has no subpaths"
@@ -0,0 +1,83 @@
"""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:
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 +1,60 @@
"""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:
"""One form, the run as typed: a KEYWORD pattern must never be 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
def test_text_runs_still_offer_their_stem(self) -> None:
"""A TEXT field offers the stem alongside the typed run, so a term
matching either one is reachable."""
normalize = _normalizer(get_field_registry("en"), "title")
assert tuple(normalize("Running")) == ("running", "run")
+149
View File
@@ -0,0 +1,149 @@
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_internal_id_fields_are_not_registered(
self,
registry: FieldRegistry,
) -> None:
for name in (
"tag_id",
"owner_id",
"viewer_id",
"correspondent_id",
"document_type_id",
"storage_path_id",
"viewer_group_id",
):
assert name not in registry
def test_no_queryable_field_name_ends_in_id(self) -> None:
# The list above names the seven that were dropped; this catches the
# eighth. Internal *_id columns are written for permission filtering
# and joins, and whoosh only exposed them as query fields by accident,
# so a new one reaching the query surface is a leak rather than a
# feature. Checked against PUBLIC_FIELDS rather than the registry so
# an internal field 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:
assert _resolve(registry, "type").spec.name == "document_type"
def test_path_alias_resolves_to_storage_path(self, registry: FieldRegistry) -> None:
assert _resolve(registry, "path").spec.name == "storage_path"
def test_notes_json_subpaths_resolve(self, registry: FieldRegistry) -> None:
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:
for raw in ("custom_fields.name", "custom_fields.value"):
_resolve(registry, raw)
def test_unregistered_json_subpath_does_not_resolve(
self,
registry: FieldRegistry,
) -> None:
# An unregistered subpath is not even a valid FieldRef: make_ref
# returns None for a dotted name whose subpath isn't registered
# (it doesn't produce a ref for resolve() to then reject).
assert registry.make_ref("notes.bogus") is None
def test_tag_is_comma_values(self, registry: FieldRegistry) -> None:
assert _resolve(registry, "tag").spec.comma_values is True
def test_correspondent_is_not_comma_values(self, registry: FieldRegistry) -> None:
# "tag" is the only field that opts in. This is only observable here:
# end to end the two readings of "correspondent:foo,bar" agree,
# because the analyzer splits the literal value on the comma anyway,
# so a result-level test cannot tell a value list from literal text.
assert _resolve(registry, "correspondent").spec.comma_values is False
def test_created_is_date_kind(self, registry: FieldRegistry) -> None:
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:
# title uses the paperless_text analyzer: simple -> remove_long ->
# lowercase -> ascii_fold [-> stemmer]. With no language configured
# (None), no stemmer runs, so "Café" folds 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:
# checksum uses the raw tokenizer at index time (no splitting).
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:
# Index terms are stemmed, so patterns offer their stem too, using the
# registry's own language: "Running" has to reach the indexed "run".
# Without a language the index holds surface forms, so there is no
# second form and the run is only case/accent-folded.
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:
a = get_field_registry("en")
b = get_field_registry("en")
assert a is b
def test_registry_rebuilds_on_language_change(self) -> None:
a = get_field_registry("en")
b = get_field_registry("de")
assert a is not b
+84 -1
View File
@@ -1,12 +1,20 @@
from __future__ import annotations
import json
from datetime import UTC
from datetime import datetime
from typing import TYPE_CHECKING
import pytest
import tantivy
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
from documents.search._tokenizer import register_tokenizers
if TYPE_CHECKING:
from pathlib import Path
@@ -29,7 +37,13 @@ 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
@@ -76,3 +90,72 @@ 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:
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()"
)
def test_asn_page_count_num_notes_are_fast_unsigned_fields(self) -> None:
# Spot-check kind-derived construction for the U64 fields.
schema = build_schema()
doc = tantivy.Document()
doc.add_unsigned("id", 1)
doc.add_text("checksum", "x")
doc.add_unsigned("asn", 42)
doc.add_unsigned("page_count", 3)
doc.add_unsigned("num_notes", 0)
doc.add_date("created", datetime(2020, 1, 1, tzinfo=UTC))
doc.add_date("modified", datetime(2020, 1, 1, tzinfo=UTC))
doc.add_date("added", datetime(2020, 1, 1, tzinfo=UTC))
index = tantivy.Index(schema)
register_tokenizers(index, None)
writer = index.writer()
writer.add_document(doc)
writer.commit()
index.reload()
searcher = index.searcher()
results = searcher.search(tantivy.Query.term_query(schema, "asn", 42), limit=1)
assert len(results.hits) == 1
class TestFastFlagAgreement:
def test_every_public_field_fast_flag_matches_the_built_schema(self) -> None:
# whoosh-compat's registry trusts PUBLIC_FIELDS' fast flag when resolving
# field:* existence checks (its FAST_FIELD strategy); a fast=True
# entry whose actual tantivy column is not fast would make those
# searches silently match nothing at search time. Only the U64 and
# DATE descriptors can carry the flag today, so this
# pins the agreement for EVERY kind: a future fast=True
# TEXT/KEYWORD/JSON entry the builder silently ignores fails here
# instead of at a user's query.
#
# field_descriptors() (not tantivy-py's __reduce__() pickling
# internals) is used as the probe here: it is exactly the input
# build_schema()'s SchemaBuilder consumes for the `fast` kwarg on
# every field kind, so it pins the same agreement without depending
# on a private pickled representation surviving a tantivy-py
# upgrade.
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 +1,492 @@
"""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:
assert tuple(field_descriptors()) == PINNED_DESCRIPTORS
def test_built_schema_matches_the_descriptors(self) -> None:
"""The descriptors are not a parallel description - they are the input.
Reading the built schema back proves the loop honours every option, so
a descriptor edit cannot claim a shape the SchemaBuilder did not 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:
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:
"""The original bug: same fields, different declaration order.
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
def test_repeated_calls_agree(self) -> None:
assert schema_fingerprint() == schema_fingerprint()
class TestFingerprintIsIndependentOfTantivy:
def test_a_tantivy_option_key_addition_would_not_move_it(self) -> None:
"""A tantivy-py upgrade must not force a global reindex.
Hashing schema.__reduce__() would do exactly that: the simulated new
option key below changes that payload for every user with no schema
change at all.
"""
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:
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:
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:
"""The failure this task exists to prevent: schema edited, version not
bumped. Without the fingerprint check, `reindex --if-needed` reports the
index up to date and every write then raises."""
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:
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:
"""No seeding: 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:
settings.SEARCH_LANGUAGE = "en"
_write_sentinels(index_dir)
assert needs_rebuild(index_dir) is False
@@ -0,0 +1,164 @@
"""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:
"""The current schema differs from v1's, so the sentinel must be stale.
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_v1_index_rejects_writes_against_the_current_schema(
self,
released_v1_index: Path,
) -> None:
"""The failure mode the version bump exists to prevent.
This is exactly what WriteBatch.__enter__ does on every index write.
"""
with pytest.raises(ValueError, match="schema does not match"):
tantivy.Index(build_schema(), path=str(released_v1_index))
def test_opening_a_v1_index_leaves_it_writable(
self,
released_v1_index: Path,
) -> None:
"""End to end: open_or_rebuild_index must hand back an index that the
write path can reopen. Before the version bump, needs_rebuild() returned
False here, the stale directory survived untouched, and every subsequent
write raised the ValueError above."""
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:
"""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
+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
Generated
+23 -4
View File
@@ -4,11 +4,11 @@ requires-python = ">=3.11"
resolution-markers = [
"python_full_version >= '3.15' and sys_platform == 'darwin'",
"python_full_version >= '3.15' and sys_platform == 'linux'",
"python_full_version == '3.14.*' and platform_machine == 'x86_64' and sys_platform == 'linux'",
"python_full_version == '3.14.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
"python_full_version >= '3.12' and python_full_version < '3.15' and sys_platform == 'darwin'",
"python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform == 'linux'",
"python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
"python_full_version == '3.14.*' and platform_machine == 'x86_64' and sys_platform == 'linux'",
"python_full_version == '3.14.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
"(python_full_version >= '3.12' and python_full_version < '3.15' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'linux') or (python_full_version == '3.13.*' and platform_machine == 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.13.*' and platform_machine == 'x86_64' and sys_platform == 'linux')",
"python_full_version < '3.12' and sys_platform == 'darwin'",
"python_full_version < '3.12' and sys_platform == 'linux'",
@@ -2933,6 +2933,7 @@ 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" },
]
@@ -3091,6 +3092,7 @@ 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"]
@@ -5014,10 +5016,10 @@ version = "2.13.0+cpu"
source = { registry = "https://download.pytorch.org/whl/cpu" }
resolution-markers = [
"python_full_version >= '3.15' and sys_platform == 'linux'",
"python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform == 'linux'",
"python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
"python_full_version == '3.14.*' and platform_machine == 'x86_64' and sys_platform == 'linux'",
"python_full_version == '3.14.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
"python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform == 'linux'",
"python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
"(python_full_version >= '3.12' and python_full_version < '3.15' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'linux') or (python_full_version == '3.13.*' and platform_machine == 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.13.*' and platform_machine == 'x86_64' and sys_platform == 'linux')",
"python_full_version < '3.12' and sys_platform == 'linux'",
]
@@ -5652,6 +5654,23 @@ 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"