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47 changed files with 7049 additions and 2569 deletions
+1
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@@ -77,6 +77,7 @@ dependencies = [
"torch~=2.13.0", "torch~=2.13.0",
"watchfiles>=1.2", "watchfiles>=1.2",
"whitenoise~=6.11", "whitenoise~=6.11",
"whoosh-compat[tantivy]==0.2",
"zxing-cpp~=3.1.0", "zxing-cpp~=3.1.0",
] ]
[project.optional-dependencies] [project.optional-dependencies]
+10 -2
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@@ -6,13 +6,20 @@ from documents.search._backend import TantivyRelevanceList
from documents.search._backend import WriteBatch from documents.search._backend import WriteBatch
from documents.search._backend import get_backend from documents.search._backend import get_backend
from documents.search._backend import reset_backend from documents.search._backend import reset_backend
from documents.search._errors import InvalidDateQuery
from documents.search._errors import InvalidNumberQuery
from documents.search._errors import MultipleSearchQueryErrors
from documents.search._errors import QueryTooLongError
from documents.search._errors import SearchQueryError
from documents.search._errors import search_query_error_messages
from documents.search._schema import needs_rebuild from documents.search._schema import needs_rebuild
from documents.search._schema import wipe_index from documents.search._schema import wipe_index
from documents.search._translate import InvalidDateQuery
from documents.search._translate import SearchQueryError
__all__ = [ __all__ = [
"InvalidDateQuery", "InvalidDateQuery",
"InvalidNumberQuery",
"MultipleSearchQueryErrors",
"QueryTooLongError",
"SearchHit", "SearchHit",
"SearchIndexLockError", "SearchIndexLockError",
"SearchMode", "SearchMode",
@@ -23,5 +30,6 @@ __all__ = [
"get_backend", "get_backend",
"needs_rebuild", "needs_rebuild",
"reset_backend", "reset_backend",
"search_query_error_messages",
"wipe_index", "wipe_index",
] ]
+59 -9
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@@ -22,7 +22,6 @@ import tantivy
from django.conf import settings from django.conf import settings
from django.utils.timezone import get_current_timezone from django.utils.timezone import get_current_timezone
from documents.search._query import build_permission_filter
from documents.search._query import extract_cjk_text from documents.search._query import extract_cjk_text
from documents.search._query import parse_simple_text_highlight_query from documents.search._query import parse_simple_text_highlight_query
from documents.search._query import parse_simple_text_query from documents.search._query import parse_simple_text_query
@@ -40,6 +39,7 @@ from documents.utils import QuerySetStream
from documents.utils import identity from documents.utils import identity
if TYPE_CHECKING: if TYPE_CHECKING:
from collections.abc import Iterable
from collections.abc import Iterator from collections.abc import Iterator
from collections.abc import Sequence from collections.abc import Sequence
from pathlib import Path from pathlib import Path
@@ -325,6 +325,47 @@ class WriteBatch:
self._writer.add_document(doc) self._writer.add_document(doc)
def build_permission_filter(
schema: tantivy.Schema,
user: AbstractUser,
viewer_group_ids: Iterable[int] = (),
) -> tantivy.Query:
"""
Build a query filter for user document permissions.
Creates a query that matches only documents visible to the specified user
according to paperless-ngx permission rules:
- Public documents (no owner) are visible to all users
- Private documents are visible to their owner
- Documents explicitly shared with the user are visible
- Documents shared with one of the user's current groups are visible
Args:
schema: Tantivy schema for field validation
user: User to check permissions for
viewer_group_ids: Current group memberships for the user
Returns:
Tantivy query that filters results to visible documents
"""
owner_any = tantivy.Query.exists_query("owner_id")
no_owner = tantivy.Query.boolean_query(
[
(tantivy.Occur.Must, tantivy.Query.all_query()),
(tantivy.Occur.MustNot, owner_any),
],
)
owned = tantivy.Query.term_query(schema, "owner_id", user.pk)
shared = tantivy.Query.term_query(schema, "viewer_id", user.pk)
group_shared = [
tantivy.Query.term_query(schema, "viewer_group_id", group_id)
for group_id in viewer_group_ids
]
return tantivy.Query.disjunction_max_query(
[no_owner, owned, shared, *group_shared],
)
class TantivyBackend: class TantivyBackend:
""" """
Tantivy search backend with explicit lifecycle management. Tantivy search backend with explicit lifecycle management.
@@ -498,7 +539,6 @@ class TantivyBackend:
doc.add_text("correspondent_sort", document.correspondent.name) doc.add_text("correspondent_sort", document.correspondent.name)
if cjk_corr := extract_cjk_text(document.correspondent.name): if cjk_corr := extract_cjk_text(document.correspondent.name):
doc.add_text("bigram_correspondent", cjk_corr) doc.add_text("bigram_correspondent", cjk_corr)
doc.add_unsigned("correspondent_id", document.correspondent_id)
# Document type # Document type
if document.document_type: if document.document_type:
@@ -506,12 +546,10 @@ class TantivyBackend:
doc.add_text("type_sort", document.document_type.name) doc.add_text("type_sort", document.document_type.name)
if cjk_type := extract_cjk_text(document.document_type.name): if cjk_type := extract_cjk_text(document.document_type.name):
doc.add_text("bigram_document_type", cjk_type) doc.add_text("bigram_document_type", cjk_type)
doc.add_unsigned("document_type_id", document.document_type_id)
# Storage path # Storage path
if document.storage_path: if document.storage_path:
doc.add_text("storage_path", document.storage_path.name) doc.add_text("storage_path", document.storage_path.name)
doc.add_unsigned("storage_path_id", document.storage_path_id)
# Tags — collect names for autocomplete in the same pass # Tags — collect names for autocomplete in the same pass
tag_names: list[str] = [] tag_names: list[str] = []
@@ -519,12 +557,13 @@ class TantivyBackend:
doc.add_text("tag", tag.name) doc.add_text("tag", tag.name)
if cjk_tag := extract_cjk_text(tag.name): if cjk_tag := extract_cjk_text(tag.name):
doc.add_text("bigram_tag", cjk_tag) doc.add_text("bigram_tag", cjk_tag)
doc.add_unsigned("tag_id", tag.pk)
tag_names.append(tag.name) tag_names.append(tag.name)
# Notes — JSON for structured queries (notes.user:alice, notes.note:text). # Notes — JSON for structured queries (notes.user:alice, notes.note:text).
# notes_text is a plain-text companion for snippet/highlight generation; # notes_text is a plain-text companion for snippet/highlight generation;
# tantivy's SnippetGenerator does not support JSON fields. # tantivy's SnippetGenerator does not support JSON fields. It is not in
# _DEFAULT_SEARCH_FIELDS, so an unqualified query never searches it: a
# note matches through the JSON field or not at all.
num_notes = 0 num_notes = 0
note_texts: list[str] = [] note_texts: list[str] = []
for note in document.notes.all(): for note in document.notes.all():
@@ -540,8 +579,9 @@ class TantivyBackend:
if note_texts: if note_texts:
doc.add_text("notes_text", " ".join(note_texts)) doc.add_text("notes_text", " ".join(note_texts))
# Custom fields JSON for structured queries (custom_fields.name:x, custom_fields.value:y), # Custom fields: JSON for structured queries (custom_fields.name:x,
# companion text field for default full-text search. # custom_fields.value:y). There is no companion text field here, unlike
# notes: custom field values are reachable only through the JSON field.
for cfi in document.custom_fields.all(): for cfi in document.custom_fields.all():
search_value = cfi.value_for_search search_value = cfi.value_for_search
# Skip fields where there is no value yet # Skip fields where there is no value yet
@@ -708,7 +748,17 @@ class TantivyBackend:
user_query = self._parse_query(query, search_mode) user_query = self._parse_query(query, search_mode)
highlight_query = user_query highlight_query = user_query
if search_mode is SearchMode.TEXT: if search_mode is SearchMode.TEXT:
highlight_query = parse_simple_text_highlight_query(self._index, query) try:
highlight_query = parse_simple_text_highlight_query(
self._index,
query,
)
except ValueError:
logger.debug(
"Skipping simple text highlight query: token string is not "
"valid tantivy query syntax: %r",
query,
)
# For notes_text snippet generation, we need a query that targets the # For notes_text snippet generation, we need a query that targets the
# notes_text field directly. user_query may contain JSON-field terms # notes_text field directly. user_query may contain JSON-field terms
-171
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@@ -1,171 +0,0 @@
from __future__ import annotations
from datetime import UTC
from datetime import date
from datetime import datetime
from datetime import timedelta
from typing import TYPE_CHECKING
from typing import Final
from dateutil.relativedelta import relativedelta
if TYPE_CHECKING:
from datetime import tzinfo
_DATE_ONLY_FIELDS = frozenset({"created"})
_TODAY: Final[str] = "today"
_YESTERDAY: Final[str] = "yesterday"
_PREVIOUS_WEEK: Final[str] = "previous week"
_THIS_MONTH: Final[str] = "this month"
_PREVIOUS_MONTH: Final[str] = "previous month"
_THIS_YEAR: Final[str] = "this year"
_PREVIOUS_YEAR: Final[str] = "previous year"
_PREVIOUS_QUARTER: Final[str] = "previous quarter"
_DATE_KEYWORDS = frozenset(
{
_TODAY,
_YESTERDAY,
_PREVIOUS_WEEK,
_THIS_MONTH,
_PREVIOUS_MONTH,
_THIS_YEAR,
_PREVIOUS_YEAR,
_PREVIOUS_QUARTER,
},
)
def _fmt(dt: datetime) -> str:
"""Format a datetime as an ISO 8601 UTC string for use in Tantivy range queries."""
return dt.astimezone(UTC).strftime("%Y-%m-%dT%H:%M:%SZ")
def _iso_range(lo: datetime, hi: datetime) -> str:
"""
Format a half-open ``[lo TO hi)`` range in ISO 8601 for Tantivy query syntax.
``hi`` is always the exclusive ceiling of a computed period (the start of
the *next* day/week/month/quarter/year), so the closing bracket must be
the Tantivy exclusive-range brace ``}`` rather than ``]`` — otherwise the
first instant of the following period (e.g. the 1st of next month) is
incorrectly included in the match.
"""
return f"[{_fmt(lo)} TO {_fmt(hi)}}}"
def _quarter_start(d: date) -> date:
"""Return the first day of the calendar quarter containing ``d``."""
return date(d.year, ((d.month - 1) // 3) * 3 + 1, 1)
def _midnight(d: date, tz: tzinfo) -> datetime:
"""Convert a calendar date at local-timezone midnight to a UTC datetime."""
return datetime(d.year, d.month, d.day, tzinfo=tz).astimezone(UTC)
def _keyword_bounds(keyword: str, tz: tzinfo) -> tuple[date, date]:
"""
Map a relative date keyword to ``(start, exclusive_end)`` calendar dates.
``tz`` only determines what "today" is; the caller decides how the returned
dates become UTC datetime boundaries (date-only vs. local-midnight offset).
"""
today = datetime.now(tz).date()
if keyword == _TODAY:
return today, today + timedelta(days=1)
if keyword == _YESTERDAY:
return today - timedelta(days=1), today
if keyword == _PREVIOUS_WEEK:
this_monday = today - timedelta(days=today.weekday())
return this_monday - timedelta(weeks=1), this_monday
if keyword == _THIS_MONTH:
first = today.replace(day=1)
return first, first + relativedelta(months=1)
if keyword == _PREVIOUS_MONTH:
this_first = today.replace(day=1)
return this_first - relativedelta(months=1), this_first
if keyword == _THIS_YEAR:
return date(today.year, 1, 1), date(today.year + 1, 1, 1)
if keyword == _PREVIOUS_YEAR:
return date(today.year - 1, 1, 1), date(today.year, 1, 1)
if keyword == _PREVIOUS_QUARTER:
this_quarter = _quarter_start(today)
return this_quarter - relativedelta(months=3), this_quarter
raise ValueError(f"Unknown keyword: {keyword}")
def _date_only_range(keyword: str, tz: tzinfo) -> str:
"""
For `created` (DateField): use the local calendar date, converted to
midnight UTC boundaries. No offset arithmetic — date only.
"""
start, end = _keyword_bounds(keyword, tz)
lo = datetime(start.year, start.month, start.day, tzinfo=UTC)
hi = datetime(end.year, end.month, end.day, tzinfo=UTC)
return _iso_range(lo, hi)
def _datetime_range(keyword: str, tz: tzinfo) -> str:
"""
For `added` / `modified` (DateTimeField, stored as UTC): convert local day
boundaries to UTC — full offset arithmetic required.
"""
start, end = _keyword_bounds(keyword, tz)
return _iso_range(_midnight(start, tz), _midnight(end, tz))
def _precision_bounds(digits: str) -> tuple[date, date] | None:
"""
Map a 4/6/8-digit date token to (start, exclusive_end) calendar dates.
YYYY -> whole year, YYYYMM -> whole month, YYYYMMDD -> single day.
Returns None for any unparsable or out-of-range value (e.g. month 23),
so callers can emit a no-match clause instead of erroring (Whoosh parity).
"""
try:
if len(digits) == 4:
year = int(digits)
return date(year, 1, 1), date(year + 1, 1, 1)
if len(digits) == 6:
year, month = int(digits[:4]), int(digits[4:6])
start = date(year, month, 1)
end = date(year + 1, 1, 1) if month == 12 else date(year, month + 1, 1)
return start, end
if len(digits) == 8:
start = date(int(digits[:4]), int(digits[4:6]), int(digits[6:8]))
return start, start + timedelta(days=1)
except ValueError:
return None
return None
def _utc_bounds_for_field(
field: str,
start: date,
end: date,
tz: tzinfo,
) -> tuple[datetime, datetime]:
"""
Convert calendar-date bounds to UTC datetimes per the field's storage type.
For DateField (``created``) the bounds are UTC midnight (no offset). For
DateTimeField (``added``/``modified``) the bounds are local-tz midnight
converted to UTC, matching how each field is indexed.
"""
if field in _DATE_ONLY_FIELDS:
return (
datetime(start.year, start.month, start.day, tzinfo=UTC),
datetime(end.year, end.month, end.day, tzinfo=UTC),
)
return (
datetime(start.year, start.month, start.day, tzinfo=tz).astimezone(UTC),
datetime(end.year, end.month, end.day, tzinfo=tz).astimezone(UTC),
)
def _field_range_from_dates(field: str, start: date, end: date, tz: tzinfo) -> str:
"""Build a Tantivy ``field:[lo TO hi]`` ISO range from calendar-date bounds."""
lo, hi = _utc_bounds_for_field(field, start, end, tz)
return f"{field}:{_iso_range(lo, hi)}"
+71
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@@ -0,0 +1,71 @@
from __future__ import annotations
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from collections.abc import Sequence
class SearchQueryError(ValueError):
"""
Base for user-fixable search query errors.
Carries a message safe to surface to the user (no internal details). The
view layer catches this and returns an HTTP 400, so any future subclass
gets the same treatment.
"""
class InvalidDateQuery(SearchQueryError):
"""Raised when a date field value or range bound cannot be parsed."""
def __init__(self, field: str | None, value: str | None) -> None:
self.field = field
self.value = value
super().__init__(f"Invalid date value {value!r} for field {field!r}.")
class InvalidNumberQuery(SearchQueryError):
"""Raised when a numeric field value or range bound cannot be parsed."""
def __init__(self, field: str | None, value: str | None) -> None:
self.field = field
self.value = value
super().__init__(f"Invalid numeric value {value!r} for field {field!r}.")
class QueryTooLongError(SearchQueryError):
"""Raised when a query string exceeds the maximum allowed length.
whoosh-compat's fieldname tagger is O(n^2) in plain word characters, so an
unbounded query is a CPU-exhaustion vector against a single request
handler. This is a hard boundary, not a validation nicety.
"""
def __init__(self, length: int, limit: int) -> None:
self.length = length
self.limit = limit
super().__init__(
f"The search query is too long ({length} characters). "
f"The maximum allowed length is {limit} characters.",
)
class MultipleSearchQueryErrors(SearchQueryError):
"""Aggregates every user-fixable error from one parse, not just the first."""
def __init__(self, errors: Sequence[SearchQueryError]) -> None:
self.errors = tuple(errors)
super().__init__("; ".join(str(e) for e in self.errors))
def search_query_error_messages(e: SearchQueryError) -> list[str]:
"""The user-facing message list for a SearchQueryError.
Every offending value's message, not just the first, so the user can
fix them all in one round-trip. Shared by every view that maps
SearchQueryError to an HTTP 400.
"""
if isinstance(e, MultipleSearchQueryErrors):
return [str(sub) for sub in e.errors]
return [str(e)]
+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)},
),
)
+474 -145
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@@ -6,22 +6,30 @@ from typing import Final
import regex import regex
import tantivy import tantivy
import whoosh_compat as wc
from django.conf import settings from django.conf import settings
from whoosh_compat.emitters.tantivy_ import emit as tantivy_emit
from whoosh_compat.errors import Cause
from whoosh_compat.errors import Diagnostic
from whoosh_compat.errors import DiagnosticKind
from whoosh_compat.errors import QueryError
from documents.search._errors import InvalidDateQuery
from documents.search._errors import InvalidNumberQuery
from documents.search._errors import MultipleSearchQueryErrors
from documents.search._errors import SearchQueryError
from documents.search._registry import get_field_registry
from documents.search._tokenizer import simple_search_tokens from documents.search._tokenizer import simple_search_tokens
from documents.search._translate import SearchQueryError
from documents.search._translate import translate_query
if TYPE_CHECKING: if TYPE_CHECKING:
from collections.abc import Iterable
from datetime import tzinfo from datetime import tzinfo
from django.contrib.auth.base_user import AbstractBaseUser
logger = logging.getLogger("paperless.search") logger = logging.getLogger("paperless.search")
# Maximum seconds any single regex substitution may run. # Maximum seconds any single regex substitution over user-supplied query text
# Prevents ReDoS on adversarial user-supplied query strings. # may run. The one remaining use is a character class, which cannot backtrack,
# so the bound is an upper limit on that substitution's cost, not the ReDoS
# guard it was originally written as.
_REGEX_TIMEOUT: Final[float] = 1.0 _REGEX_TIMEOUT: Final[float] = 1.0
# Matches CJK/Hangul characters so queries can be routed to bigram fields. # Matches CJK/Hangul characters so queries can be routed to bigram fields.
@@ -29,6 +37,73 @@ _REGEX_TIMEOUT: Final[float] = 1.0
_CJK_RE: Final = regex.compile(r"[\p{Han}\p{Hiragana}\p{Katakana}\p{Hangul}]+") _CJK_RE: Final = regex.compile(r"[\p{Han}\p{Hiragana}\p{Katakana}\p{Hangul}]+")
def _user_facing_emit_message(d: Diagnostic) -> str:
"""A user-safe message for an emit-time QueryError's Diagnostic.
Built from the Diagnostic's structured fields (kind, field), never from
d.message: whoosh-compat documents that as developer/log output with no
stability guarantee, and PATTERN_TOO_COMPLEX embeds the raw backend
error text in it. SCHEMA_FIELD_MISSING never reaches here: _map_emit_error
re-raises it before calling this function, the same as INTERNAL.
"""
field = str(d.field) if d.field is not None else None
if d.kind is DiagnosticKind.EXISTS_REQUIRES_FAST:
return f"Existence searches (field:*) are not supported for field {field!r}."
if d.kind is DiagnosticKind.TEXT_RANGE:
return f"Range searches are not supported for field {field!r}."
if d.kind is DiagnosticKind.PATTERN_TOO_COMPLEX:
return f"The wildcard pattern for field {field!r} is too complex."
logger.warning(
"Unmapped emit diagnostic %s: %s",
d.kind,
d.message,
) # pragma: no cover
return "The search query could not be executed." # pragma: no cover
def _map_emit_error(e: QueryError) -> SearchQueryError:
"""Route an emit-time QueryError by its Diagnostic's Cause.
INVALID_INPUT/UNSUPPORTED are user-input errors, exactly like a parse
diagnostic, and map to a 400. INTERNAL means a defect in whoosh-compat
or in our own AST handling, never the user's query, so the QueryError is
re-raised rather than converted, reaching the generic 500 handler instead
of blaming the query. MISCONFIGURED other than EXISTS_REQUIRES_FAST is
treated the same way as INTERNAL: the registry and the index schema
disagree, which only an operator can fix, and the exact same query would
succeed on its own once the index is rebuilt. That makes it a transient
server-side condition, not a permanently bad request, so it is logged as
an error and re-raised rather than converted to a 400: telling the
client their query is invalid would be wrong, it would work once the
index catches up, and a 400 also hides the condition from monitoring
that only watches 5xx rates.
EXISTS_REQUIRES_FAST is the one MISCONFIGURED kind that is not a
disagreement. whoosh-compat derives it from the registry's own FieldSpec
(kind plus fast) without ever consulting the index schema, so it fires
whenever a non-fast field of a kind that cannot answer "exists" is asked
to: for us that is only the JSON fields, which field_descriptors() builds
non-fast on purpose. "notes:*" and the five other spellings of it are
ordinary user error that no operator action can clear, so they get the
400 without the alert.
"""
d = e.diagnostic
if d.cause is Cause.INTERNAL:
raise e
if (
d.cause is Cause.MISCONFIGURED
and d.kind is not DiagnosticKind.EXISTS_REQUIRES_FAST
):
logger.error(
"Search index misconfiguration for field %s (%s): %s",
d.field,
d.kind.name,
d.message,
)
raise e
return SearchQueryError(_user_facing_emit_message(d))
def _has_cjk(text: str) -> bool: def _has_cjk(text: str) -> bool:
"""Return True if text contains any CJK characters.""" """Return True if text contains any CJK characters."""
return bool(_CJK_RE.search(text)) return bool(_CJK_RE.search(text))
@@ -37,14 +112,36 @@ def _has_cjk(text: str) -> bool:
def extract_cjk_text(text: str) -> str: def extract_cjk_text(text: str) -> str:
"""Join the CJK runs in ``text`` for indexing into bigram (char-ngram) fields. """Join the CJK runs in ``text`` for indexing into bigram (char-ngram) fields.
Mirrors the query side (``_build_cjk_query``): only CJK runs are ever searched Mirrors the query side, which extracts the CJK runs of whatever it is
against the bigram fields, so only CJK runs are worth indexing there. Latin about to search for (the raw string in simple modes, the parsed query's
text fed to a character-bigram field is never matched and only bloats the free-text tokens in query mode): only CJK runs are ever searched against
the bigram fields, so only CJK runs are worth indexing there. Latin text
fed to a character-bigram field is never matched and only bloats the
index and slows indexing/merge. Returns "" when there is no CJK text. index and slows indexing/merge. Returns "" when there is no CJK text.
""" """
return " ".join(_CJK_RE.findall(text)) return " ".join(_CJK_RE.findall(text))
def _parse_cjk_text(
index: tantivy.Index,
cjk_text: str,
fields: list[str],
) -> tantivy.Query | None:
"""Parse a plain CJK run string against ``fields``, or None if it won't parse."""
try:
return index.parse_query(cjk_text, fields)
except Exception:
# Broad on purpose, unlike _try_parse_fuzzy_query's narrower
# ValueError: cjk_text isn't filtered to a guaranteed-safe token
# set the way the fuzzy blend's word string is, so the exact
# failure mode tantivy could raise here isn't pinned down.
logger.debug(
"Skipping CJK search clause: could not parse CJK text: %r",
cjk_text,
)
return None
def _build_cjk_query( def _build_cjk_query(
index: tantivy.Index, index: tantivy.Index,
raw_query: str, raw_query: str,
@@ -52,91 +149,268 @@ def _build_cjk_query(
) -> tantivy.Query | None: ) -> tantivy.Query | None:
"""Build a bigram-field query from the CJK runs in ``raw_query``. """Build a bigram-field query from the CJK runs in ``raw_query``.
Only the CJK character runs are extracted and parsed; ASCII field prefixes, For the simple (TEXT/TITLE) modes, whose input is plain text and carries
boolean operators and date keywords are discarded. This keeps the CJK clause no query grammar to respect. Only the CJK character runs are extracted, so
plain-text and consistent across query/simple modes (no leaked ``field:`` a stray ``field:`` prefix or ``-``/``+`` in the input can neither leak
semantics, no parse failures from spaced ``-``/``+``), and avoids feeding field semantics nor fail the parse, and no Latin token reaches the
Latin tokens into the character-bigram matcher (which would produce spurious character-bigram matcher (where it would produce spurious matches against
matches against unrelated Latin text). Returns None when there is no CJK unrelated Latin text). Returns None when there is no CJK text or the parse
text or the parse fails. fails.
""" """
cjk_text = " ".join(_CJK_RE.findall(raw_query)) cjk_text = extract_cjk_text(raw_query)
if not cjk_text: if not cjk_text:
return None return None
return _parse_cjk_text(index, cjk_text, fields)
def _build_ast_cjk_query(
index: tantivy.Index,
ast: wc.ast.Node,
registry: wc.FieldRegistry,
) -> tantivy.Query | None:
"""Build the bigram clause of a QUERY-mode search from the parsed AST.
Same discipline as the fuzzy clause (see _try_parse_fuzzy_query): the CJK
runs come from whoosh_compat's ``free_text_tokens`` over the parsed tree,
never from the raw query string, so a term the user negated or restricted
to a field outside the default search fields contributes nothing, instead
of resurfacing as a top-level clause matching every bigram field.
``free_text_tokens`` reports no field of its own, so the tokens are
collected one default field at a time: a bare term, which the parser has
already copied onto every default field, is therefore searched across
every bigram field, while ``title:東京`` reaches ``bigram_title`` alone.
Fields whose CJK text is identical (the bare-term case) share a single
parse over all of their bigram fields at once.
Raw (``analyzed=False``) tokens are used because the bigram fields have
their own character-ngram analyzer: the default fields' word analyzers
have no useful say over a CJK run, and running them first would only
risk dropping it (remove_long) before the run is ever extracted.
Returns None when the query has no CJK free text.
"""
fields_by_text: dict[str, list[str]] = {}
for field, bigram_field in _CJK_BIGRAM_FIELDS.items():
tokens = wc.free_text_tokens(
ast,
registry=registry,
fields=[field],
analyzed=False,
)
cjk_text = extract_cjk_text(" ".join(tokens))
if cjk_text:
fields_by_text.setdefault(cjk_text, []).append(bigram_field)
clauses: list[tuple[tantivy.Occur, tantivy.Query]] = [
(tantivy.Occur.Should, query)
for cjk_text, bigram_fields in fields_by_text.items()
if (query := _parse_cjk_text(index, cjk_text, bigram_fields)) is not None
]
return _any_of(clauses) if clauses else None
# A joined fuzzy word string must stay plain words: it goes back through
# tantivy's own query parser, and the raw query text the clause collects
# routinely carries characters that parser reads as grammar (a colon, a
# bracket, a quote, a leading -). Each token is cut into its word runs and
# only those are kept, so no field syntax, pattern, range or grouping can
# reach the parser. Cutting rather than dropping the whole token is what
# keeps ordinary hyphenated, dotted and quoted input ("COVID-19",
# "hello@example.com", "tax reports") contributing to the clause at all.
_WORD_RUN_RE = regex.compile(r"\w+")
# The one piece of tantivy grammar that survives the cut: its boolean
# keywords are themselves word runs. Only these exact spellings are
# grammar there ("And"/"and" are ordinary terms), so lowercasing exactly
# these turns them back into the ordinary terms the field analyzer used to
# make of them, before the clause switched to raw text. Left alone, a
# quoted phrase would silently restructure the clause ("tax AND reports"
# becoming a conjunction) or fail to parse and drop it entirely
# ("tax AND", or "IN" anywhere).
#
# Only these words are touched: tantivy lowercases query terms with the
# field's own analyzer, and doing it ourselves first is not always the
# same operation (Python folds a final sigma to a different letter than
# tantivy does, and turns Turkish 'İ' into a sequence tantivy then splits
# in two), which would search for terms the index does not contain.
_TANTIVY_KEYWORDS: Final[frozenset[str]] = frozenset({"AND", "OR", "NOT", "IN"})
def _try_parse_fuzzy_query(
index: tantivy.Index,
ast: wc.ast.Node,
registry: wc.FieldRegistry,
) -> tantivy.Query | None:
"""Build the fuzzy blend clause from the parsed query's free-text
words, or None if it has none.
The clause is built by handing tantivy's own query parser a plain
word string (there's no clean AST-level fuzzy equivalent to
whoosh-compat's parse tree, and fuzzy matching was always an
approximate, secondary, 0.1-boosted clause). The words come from
whoosh_compat's ``free_text_tokens`` over the already-parsed AST,
never from the raw query string: raw whoosh grammar (date keywords,
``[2005 to 2009]`` ranges, bracket-class wildcards) is not tantivy
syntax, and feeding it here used to knock the fuzzy clause out for
the whole query the moment any such construct appeared alongside a
typo'd word. The helper also keeps excluded terms out: a ``NOT``'d
word must not resurface through the fuzzy clause.
Chosen trade-off: a term explicitly fielded on one of the default
search fields (``correspondent:acme``) contributes its text to the
word string UNFIELDED, so the fuzzy clause searches it across all
default fields rather than just the one the user named. That is
recall-only widening on a secondary 0.1-boosted clause the score
threshold already disciplines, accepted in exchange for never feeding
field syntax to tantivy's parser. What the word string guarantees is
exactly that: no field prefix, pattern, range, grouping or quoting
survives, and the boolean keywords that do survive (they are word
runs) are lowercased into ordinary terms; see _TANTIVY_KEYWORDS.
The words are the query's RAW text, not the analyzer's output
(``analyzed=False``), because ``index.parse_query`` analyzes whatever
it is given and analysis is not idempotent: ``universities`` stems to
``univers``, and handing that back stems it again to ``univ``, a term
the index does not contain. ``prefix=True`` hid this as over-broad
matching (``univ`` also prefixes ``unicycle``) rather than as no
matches at all. Raw text is untokenized, which is why it is cut into
word runs above rather than taken whole.
The ValueError guard stays as insurance (the word string is plain
tokens, so tantivy accepting it is expected, not assumed): on a parse
failure the fuzzy clause is skipped and the exact/CJK clauses stand,
rather than the whole query failing.
"""
tokens = wc.free_text_tokens(
ast,
registry=registry,
fields=_DEFAULT_SEARCH_FIELDS,
analyzed=False,
)
words = list(
dict.fromkeys(
word.lower() if word in _TANTIVY_KEYWORDS else word
for token in tokens
for word in _WORD_RUN_RE.findall(token)
),
)
if not words:
return None
fuzzy_text = " ".join(words)
try: try:
return index.parse_query(cjk_text, fields) return index.parse_query(
except Exception: fuzzy_text,
_DEFAULT_SEARCH_FIELDS,
field_boosts=_FIELD_BOOSTS,
fuzzy_fields={f: (True, 1, True) for f in _DEFAULT_SEARCH_FIELDS},
)
except ValueError:
logger.debug(
"Skipping fuzzy search clause: token string is not valid "
"tantivy query syntax: %r",
fuzzy_text,
)
return None return None
def build_permission_filter( _DEFAULT_SEARCH_FIELDS: Final[list[str]] = [
schema: tantivy.Schema,
user: AbstractBaseUser,
viewer_group_ids: Iterable[int] = (),
) -> tantivy.Query:
"""
Build a query filter for user document permissions.
Creates a query that matches only documents visible to the specified user
according to paperless-ngx permission rules:
- Public documents (no owner) are visible to all users
- Private documents are visible to their owner
- Documents explicitly shared with the user are visible
- Documents shared with one of the user's current groups are visible
Args:
schema: Tantivy schema for field validation
user: User to check permissions for
viewer_group_ids: Current group memberships for the user
Returns:
Tantivy query that filters results to visible documents
"""
owner_any = tantivy.Query.exists_query("owner_id")
no_owner = tantivy.Query.boolean_query(
[
(tantivy.Occur.Must, tantivy.Query.all_query()),
(tantivy.Occur.MustNot, owner_any),
],
)
owned = tantivy.Query.term_query(schema, "owner_id", user.pk)
shared = tantivy.Query.term_query(schema, "viewer_id", user.pk)
group_shared = [
tantivy.Query.term_query(schema, "viewer_group_id", group_id)
for group_id in viewer_group_ids
]
return tantivy.Query.disjunction_max_query(
[no_owner, owned, shared, *group_shared],
)
DEFAULT_SEARCH_FIELDS = [
"title", "title",
"content", "content",
"correspondent", "correspondent",
"document_type", "document_type",
"tag", "tag",
] ]
SIMPLE_SEARCH_FIELDS = ["simple_title", "simple_content"] _SIMPLE_SEARCH_FIELDS: Final[list[str]] = ["simple_title", "simple_content"]
TITLE_SEARCH_FIELDS = ["simple_title"] _TITLE_SEARCH_FIELDS: Final[list[str]] = ["simple_title"]
_CJK_ALL_FIELDS: Final[list[str]] = [ # The bigram (character-ngram) companion of each default search field.
"bigram_content", _CJK_BIGRAM_FIELDS: Final[dict[str, str]] = {
"bigram_title", field: f"bigram_{field}" for field in _DEFAULT_SEARCH_FIELDS
"bigram_correspondent", }
"bigram_document_type",
"bigram_tag",
]
_CJK_CONTENT_FIELDS: Final[list[str]] = ["bigram_content"] _CJK_CONTENT_FIELDS: Final[list[str]] = ["bigram_content"]
_CJK_TITLE_FIELDS: Final[list[str]] = ["bigram_title"] _CJK_TITLE_FIELDS: Final[list[str]] = ["bigram_title"]
_FIELD_BOOSTS = {"title": 2.0} _FIELD_BOOSTS = {"title": 2.0}
_SIMPLE_FIELD_BOOSTS = {"simple_title": 2.0} _SIMPLE_FIELD_BOOSTS = {"simple_title": 2.0}
def _simple_query_tokens(raw_query: str) -> list[str]: class _ConjunctiveNegations(wc.ast.Visitor[tuple["wc.ast.Node", ...]]):
# Tokenize and fold via the same analyzer used to index simple_title / """Collect the subtrees an AST excludes from every document it matches.
# simple_content, so query terms fold identically to the indexed terms
# (single source of truth for ASCII folding). A negation reached through ``And``/``AndNot``/``Require`` (and through
return simple_search_tokens(raw_query) the required half of an ``AndMaybe``) constrains the whole query, so it
can be re-stated above the blend. ``Or`` is deliberately not descended
into: in ``invoice OR NOT secret`` the negation is one branch's own
condition, and hoisting it would throw away documents the other branch
matches. Nor is a collected subtree descended into, since a negation
inside a negation is not an exclusion.
Node types with no negation to contribute (every leaf, ``Or``) fall
through to ``generic_visit``.
"""
def generic_visit(self, node: wc.ast.Node) -> tuple[wc.ast.Node, ...]:
return ()
def visit_not(self, node: wc.ast.Not) -> tuple[wc.ast.Node, ...]:
return (node.child,)
def visit_andnot(self, node: wc.ast.AndNot) -> tuple[wc.ast.Node, ...]:
return (*self.visit(node.positive), node.negative)
def visit_and(self, node: wc.ast.And) -> tuple[wc.ast.Node, ...]:
return tuple(
negation for child in node.children for negation in self.visit(child)
)
def visit_boosted(self, node: wc.ast.Boosted) -> tuple[wc.ast.Node, ...]:
return self.visit(node.child)
def visit_andmaybe(self, node: wc.ast.AndMaybe) -> tuple[wc.ast.Node, ...]:
return self.visit(node.required)
def visit_require(self, node: wc.ast.Require) -> tuple[wc.ast.Node, ...]:
return (*self.visit(node.scored), *self.visit(node.filter_only))
def _negation_clauses(
index: tantivy.Index,
ast: wc.ast.Node,
registry: wc.FieldRegistry,
) -> list[tuple[tantivy.Occur, tantivy.Query]]:
"""MustNot clauses for everything ``ast`` excludes conjunctively.
Each excluded subtree is emitted as its own positive query and attached
with ``MustNot``, rather than emitting a negative query and hoping
tantivy accepts a bare one.
The except branch has no reachable trigger under the current control
flow: this only runs after ``exact = tantivy_emit(result.ast, ...)``
(parse_user_query) has already emitted the *whole* AST successfully,
and every subtree ``_ConjunctiveNegations`` collects here is a piece
of that same tree. Kept as insurance, not dead weight: re-emitting a
subtree in isolation is not proven identical to emitting it in
context, just believed to be, and this is the seam that finds out if
that belief is ever wrong.
"""
try:
return [
(
tantivy.Occur.MustNot,
tantivy_emit(negation, index=index, registry=registry),
)
for negation in _ConjunctiveNegations().visit(ast)
]
except QueryError as e: # pragma: no cover
raise _map_emit_error(e) from e
def _any_of(clauses: list[tuple[tantivy.Occur, tantivy.Query]]) -> tantivy.Query:
"""Collapse a clause list: none -> empty, one -> itself (no wasted
single-clause boolean_query wrapping), many -> boolean_query(clauses)."""
if not clauses:
return tantivy.Query.empty_query()
if len(clauses) == 1:
return clauses[0][1]
return tantivy.Query.boolean_query(clauses)
def _build_simple_token_query( def _build_simple_token_query(
@@ -168,9 +442,7 @@ def _build_simple_token_query(
query = tantivy.Query.boost_query(query, boost) query = tantivy.Query.boost_query(query, boost)
field_queries.append((tantivy.Occur.Should, query)) field_queries.append((tantivy.Occur.Should, query))
if len(field_queries) == 1: return _any_of(field_queries)
return field_queries[0][1]
return tantivy.Query.boolean_query(field_queries)
def parse_user_query( def parse_user_query(
@@ -179,52 +451,53 @@ def parse_user_query(
tz: tzinfo, tz: tzinfo,
) -> tantivy.Query: ) -> tantivy.Query:
""" """
Parse user query through the complete preprocessing pipeline. Parse user query through whoosh-compat, then blend in fuzzy/CJK clauses.
Transforms the raw user query through multiple stages: 1. wc.parse() against the shared FieldRegistry (whoosh grammar -> AST).
1. Date keyword rewriting (today → ISO 8601 ranges) Bare notes:/custom_fields: prefixes resolve to their default subpath
2. Query normalization (comma expansion, whitespace cleanup) (notes.note:/custom_fields.value:) directly in the registry, via
3. Tantivy parsing with field boosts each JSON field's SubpathSpec(default=True).
4. Optional fuzzy query blending (if ADVANCED_FUZZY_SEARCH_THRESHOLD set) 2. Any diagnostics (bad dates/numbers) map to SearchQueryError subclasses
and raise, the view returns HTTP 400 with every offending field
Args: listed, not just the first.
index: Tantivy index with registered tokenizers 3. emit() turns the AST into a tantivy.Query directly (no string
raw_query: Original user query string round-trip). A QueryError is routed by its Diagnostic's Cause
tz: Timezone for date boundary calculations (_map_emit_error): a construct that parses but can't execute against
tantivy (e.g. a text-field range) is a 400, a registry/schema
Returns: mismatch is logged and a 400, and an INTERNAL defect is re-raised.
Parsed Tantivy query ready for execution 4. Optional fuzzy blend (ADVANCED_FUZZY_SEARCH_THRESHOLD) builds a
plain word string from the parsed AST's free-text tokens
Note: (whoosh_compat.free_text_tokens) and feeds THAT to
When ADVANCED_FUZZY_SEARCH_THRESHOLD is configured, adds a low-priority index.parse_query, never raw_query, whose whoosh grammar (date
fuzzy query as a Should clause (0.1 boost) to catch approximate matches keywords, bracket-class wildcards, etc.) tantivy's parser rejects,
while keeping exact matches ranked higher. The threshold value is applied which used to silently knock the fuzzy clause out of any mixed
as a post-search score filter, not during query construction. query (see _try_parse_fuzzy_query).
5. Optional CJK bigram clause, built from the same parsed AST for the
same reason (see _build_ast_cjk_query): a CJK term the query negated
or fielded must not resurface through it.
6. When any optional clause was added, the query's conjunctive
exclusions are restated as MustNot above the blend
(_negation_clauses): a clause built from positive terms cannot
express them, and as a bare Should it would undo them.
""" """
registry = get_field_registry(settings.SEARCH_LANGUAGE)
result = wc.parse(
raw_query,
registry=registry,
default_fields=_DEFAULT_SEARCH_FIELDS,
field_boosts=_FIELD_BOOSTS,
tz=tz,
)
if result.diagnostics:
raise _diagnostics_to_error(result.diagnostics)
try: try:
query_str = translate_query(raw_query, tz) exact = tantivy_emit(result.ast, index=index, registry=registry)
except SearchQueryError: except QueryError as e:
# Intentional, user-fixable error (e.g. an unparsable date). Propagate so raise _map_emit_error(e) from e
# the view can return a 400 with a helpful message rather than falling
# back to the raw (still-invalid) query.
raise
except Exception: # pragma: no cover - defensive
logger.warning("Query translation failed; using raw query", exc_info=True)
query_str = raw_query
exact = index.parse_query(
query_str,
DEFAULT_SEARCH_FIELDS,
field_boosts=_FIELD_BOOSTS,
)
# The standard analyzer keeps a whitespace-free CJK run as a single token,
# so substring queries can't match content/title (and long runs are dropped
# by remove_long). Route CJK queries to the bigram fields, whose ngram
# tokenizer indexes overlapping 2-grams for substring matching.
cjk_query = ( cjk_query = (
_build_cjk_query(index, raw_query, _CJK_ALL_FIELDS) _build_ast_cjk_query(index, result.ast, registry)
if _has_cjk(raw_query) if _has_cjk(raw_query)
else None else None
) )
@@ -235,22 +508,79 @@ def parse_user_query(
threshold = settings.ADVANCED_FUZZY_SEARCH_THRESHOLD threshold = settings.ADVANCED_FUZZY_SEARCH_THRESHOLD
if threshold is not None: if threshold is not None:
fuzzy = index.parse_query( fuzzy = _try_parse_fuzzy_query(index, result.ast, registry)
query_str, if fuzzy is not None:
DEFAULT_SEARCH_FIELDS, clauses.append(
field_boosts=_FIELD_BOOSTS, (tantivy.Occur.Should, tantivy.Query.boost_query(fuzzy, 0.1)),
# (prefix=True, distance=1, transposition_cost_one=True) — edit-distance fuzziness )
fuzzy_fields={f: (True, 1, True) for f in DEFAULT_SEARCH_FIELDS},
)
# 0.1 boost keeps fuzzy hits ranked below exact matches (intentional)
clauses.append((tantivy.Occur.Should, tantivy.Query.boost_query(fuzzy, 0.1)))
if cjk_query is not None: if cjk_query is not None:
clauses.append((tantivy.Occur.Should, cjk_query)) clauses.append((tantivy.Occur.Should, cjk_query))
if len(clauses) == 1: if len(clauses) == 1:
return exact return exact
return tantivy.Query.boolean_query(clauses) # The fuzzy and CJK clauses are built from positive terms only, so as
# plain Shoulds beside the exact clause they re-admit exactly the
# documents the query excluded. Restate the exclusions once, above the
# whole blend. Redundant against the exact clause, which already
# carries them, but idempotently so.
negations = _negation_clauses(index, result.ast, registry)
if not negations:
return _any_of(clauses)
return tantivy.Query.boolean_query(
[(tantivy.Occur.Must, _any_of(clauses)), *negations],
)
# The three whoosh-compat kinds for a wildcard on a field that cannot
# carry one. d.field_kind supplies the discriminator, so naming the field's
# type needs no second trip through the registry.
_PATTERN_ON_KINDS: Final = frozenset(
{
DiagnosticKind.PATTERN_ON_NUMERIC,
DiagnosticKind.PATTERN_ON_BOOLEAN_EXISTS,
DiagnosticKind.PATTERN_ON_SUBPATH,
},
)
def _diagnostics_to_error(diagnostics: tuple[Diagnostic, ...]) -> SearchQueryError:
errors = [_single_diagnostic_to_error(d) for d in diagnostics]
return errors[0] if len(errors) == 1 else MultipleSearchQueryErrors(errors)
def _single_diagnostic_to_error(d: Diagnostic) -> SearchQueryError:
# d.field is a FieldRef, not a str: str(d.field) gives the canonical
# dotted name (an aliased query, e.g. type:, reports document_type).
field_name = str(d.field) if d.field is not None else None
if d.kind is DiagnosticKind.BAD_DATE:
return InvalidDateQuery(field_name, d.raw_value)
if d.kind is DiagnosticKind.BAD_NUMBER:
return InvalidNumberQuery(field_name, d.raw_value)
if d.kind is DiagnosticKind.TOO_DEEP:
return SearchQueryError("The search query is nested too deeply.")
if d.kind in _PATTERN_ON_KINDS:
kind_label = f" ({d.field_kind.name.lower()})" if d.field_kind else ""
return SearchQueryError(
f"Wildcard patterns are not supported for field "
f"{field_name!r}{kind_label}.",
)
if d.kind is DiagnosticKind.SINGLE_CHAR_BRACKET_RANGE:
field_label = f" for field {field_name!r}" if field_name else ""
return SearchQueryError(
f"{d.raw_value!r} looks like a bracket range{field_label}, but "
"'[' is not a wildcard character on its own. Combine it with a "
"wildcard, e.g. a trailing '*', or double-quote the value to "
"search it as literal text.",
)
logger.warning(
"Unmapped parse diagnostic %s: %s",
d.kind,
d.message,
) # pragma: no cover
return SearchQueryError(
"The search query could not be executed.",
) # pragma: no cover
def parse_simple_query( def parse_simple_query(
@@ -268,7 +598,7 @@ def parse_simple_query(
CJK substrings the simple analyzer can't (long whitespace-free runs are CJK substrings the simple analyzer can't (long whitespace-free runs are
dropped by remove_long). dropped by remove_long).
""" """
tokens = _simple_query_tokens(raw_query) tokens = simple_search_tokens(raw_query)
clauses: list[tuple[tantivy.Occur, tantivy.Query]] = [] clauses: list[tuple[tantivy.Occur, tantivy.Query]] = []
if tokens: if tokens:
@@ -291,23 +621,14 @@ def parse_simple_query(
) )
for token in tokens for token in tokens
] ]
simple_query = ( clauses.append((tantivy.Occur.Should, _any_of(token_queries)))
token_queries[0][1]
if len(token_queries) == 1
else tantivy.Query.boolean_query(token_queries)
)
clauses.append((tantivy.Occur.Should, simple_query))
if cjk_fields and _has_cjk(raw_query): if cjk_fields and _has_cjk(raw_query):
cjk_q = _build_cjk_query(index, raw_query, cjk_fields) cjk_q = _build_cjk_query(index, raw_query, cjk_fields)
if cjk_q is not None: if cjk_q is not None:
clauses.append((tantivy.Occur.Should, cjk_q)) clauses.append((tantivy.Occur.Should, cjk_q))
if not clauses: return _any_of(clauses)
return tantivy.Query.empty_query()
if len(clauses) == 1:
return clauses[0][1]
return tantivy.Query.boolean_query(clauses)
def parse_simple_text_highlight_query( def parse_simple_text_highlight_query(
@@ -322,13 +643,21 @@ def parse_simple_text_highlight_query(
# Strip Tantivy operator chars before tokenizing: this is a plain-text # Strip Tantivy operator chars before tokenizing: this is a plain-text
# highlight query, not a structured boolean query, so +/- are separators. # highlight query, not a structured boolean query, so +/- are separators.
tokens = _simple_query_tokens( tokens = simple_search_tokens(
regex.sub(r"[-+]", " ", raw_query, timeout=_REGEX_TIMEOUT), regex.sub(r"[-+]", " ", raw_query, timeout=_REGEX_TIMEOUT),
) )
if not tokens: if not tokens:
return tantivy.Query.empty_query() return tantivy.Query.empty_query()
return index.parse_query(" ".join(tokens), ["content"]) # Quote each token as its own phrase, escaping backslashes and embedded
# quotes. simple search tokens can carry arbitrary Tantivy syntax
# characters (`"`, `:`, `(`, `[`, `/`, ...) that the query-string parser
# would otherwise interpret as query grammar rather than literal text.
quoted_tokens = [
'"' + token.replace("\\", "\\\\").replace('"', '\\"') + '"' for token in tokens
]
return index.parse_query(" ".join(quoted_tokens), ["content"])
def parse_simple_text_query( def parse_simple_text_query(
@@ -342,7 +671,7 @@ def parse_simple_text_query(
return parse_simple_query( return parse_simple_query(
index, index,
raw_query, raw_query,
SIMPLE_SEARCH_FIELDS, _SIMPLE_SEARCH_FIELDS,
cjk_fields=_CJK_CONTENT_FIELDS, cjk_fields=_CJK_CONTENT_FIELDS,
) )
@@ -358,6 +687,6 @@ def parse_simple_title_query(
return parse_simple_query( return parse_simple_query(
index, index,
raw_query, raw_query,
TITLE_SEARCH_FIELDS, _TITLE_SEARCH_FIELDS,
cjk_fields=_CJK_TITLE_FIELDS, cjk_fields=_CJK_TITLE_FIELDS,
) )
+91
View File
@@ -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
View File
@@ -1,14 +1,19 @@
from __future__ import annotations from __future__ import annotations
import hashlib
import json import json
import logging import logging
import shutil import shutil
from typing import TYPE_CHECKING from typing import TYPE_CHECKING
from typing import Final from typing import Final
from typing import NamedTuple
from typing import cast from typing import cast
import tantivy import tantivy
from django.conf import settings from django.conf import settings
from whoosh_compat import FieldKind
from documents.search._fields import PUBLIC_FIELDS
if TYPE_CHECKING: if TYPE_CHECKING:
from pathlib import Path from pathlib import Path
@@ -16,7 +21,185 @@ if TYPE_CHECKING:
logger = logging.getLogger("paperless.search") logger = logging.getLogger("paperless.search")
# v1 - Initial tantivy schema format # 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: def build_schema() -> tantivy.Schema:
@@ -32,85 +215,37 @@ def build_schema() -> tantivy.Schema:
""" """
sb = tantivy.SchemaBuilder() sb = tantivy.SchemaBuilder()
sb.add_unsigned_field("id", stored=True, indexed=True, fast=True) for descriptor in field_descriptors():
sb.add_text_field("checksum", stored=True, tokenizer_name="raw") if descriptor.kind == "text":
sb.add_text_field(
for field in ( descriptor.name,
"title", stored=descriptor.stored,
"correspondent", fast=descriptor.fast,
"document_type", tokenizer_name=cast("str", descriptor.tokenizer),
"storage_path", )
"original_filename", elif descriptor.kind == "json":
"content", sb.add_json_field(
): descriptor.name,
sb.add_text_field(field, stored=True, tokenizer_name="paperless_text") stored=descriptor.stored,
fast=descriptor.fast,
# Shadow sort fields - fast, not stored/indexed tokenizer_name=cast("str", descriptor.tokenizer),
for field in ("title_sort", "correspondent_sort", "type_sort"): )
sb.add_text_field( elif descriptor.kind == "u64":
field, sb.add_unsigned_field(
stored=False, descriptor.name,
tokenizer_name="simple_analyzer", stored=descriptor.stored,
fast=True, indexed=descriptor.indexed,
) fast=descriptor.fast,
)
# CJK support - not stored, indexed only elif descriptor.kind == "date":
sb.add_text_field("bigram_content", stored=False, tokenizer_name="bigram_analyzer") sb.add_date_field(
sb.add_text_field("bigram_title", stored=False, tokenizer_name="bigram_analyzer") descriptor.name,
sb.add_text_field( stored=descriptor.stored,
"bigram_correspondent", indexed=descriptor.indexed,
stored=False, fast=descriptor.fast,
tokenizer_name="bigram_analyzer", )
) else:
sb.add_text_field( raise ValueError(f"Unknown schema field kind: {descriptor.kind}")
"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)
return sb.build() return sb.build()
@@ -119,9 +254,9 @@ def needs_rebuild(index_dir: Path) -> bool:
""" """
Check if the search index needs rebuilding. Check if the search index needs rebuilding.
Reads .index_settings.json to compare the stored schema version and Reads .index_settings.json to compare the stored schema version, search
search language against the current configuration. Returns True if the language and schema fingerprint against the current configuration. Returns
file is missing, unparsable, or either value mismatches. True if the file is missing, unparsable, or any value mismatches.
Args: Args:
index_dir: Path to the search index directory 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: if "language" not in data or data["language"] != settings.SEARCH_LANGUAGE:
logger.info("Search index language changed - rebuilding.") logger.info("Search index language changed - rebuilding.")
return True return True
if data.get("schema_fingerprint") != schema_fingerprint():
logger.info("Search index schema fingerprint mismatch - rebuilding.")
return True
except ValueError: except ValueError:
return True return True
return False return False
@@ -170,6 +308,7 @@ def _write_sentinels(index_dir: Path) -> None:
{ {
"schema_version": SCHEMA_VERSION, "schema_version": SCHEMA_VERSION,
"language": settings.SEARCH_LANGUAGE, "language": settings.SEARCH_LANGUAGE,
"schema_fingerprint": schema_fingerprint(),
}, },
), ),
) )
+51 -2
View File
@@ -1,6 +1,7 @@
from __future__ import annotations from __future__ import annotations
import logging import logging
from functools import cache
from typing import Final from typing import Final
import tantivy 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 use fast=True and Tantivy requires fast-field tokenizers to exist
even for documents that omit those fields. 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("simple_analyzer", _simple_analyzer())
index.register_tokenizer("bigram_analyzer", _bigram_analyzer()) index.register_tokenizer("bigram_analyzer", _bigram_analyzer())
index.register_tokenizer("simple_search_analyzer", _simple_search_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()) 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]""" """Main full-text tokenizer for content, title, etc: simple -> remove_long(129) -> lowercase -> ascii_fold [-> stemmer]"""
builder = ( builder = (
tantivy.TextAnalyzerBuilder(tantivy.Tokenizer.simple()) tantivy.TextAnalyzerBuilder(tantivy.Tokenizer.simple())
@@ -100,6 +101,54 @@ def _paperless_text(language: str | None) -> tantivy.TextAnalyzer:
return builder.build() 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: def _simple_analyzer() -> tantivy.TextAnalyzer:
"""Tokenizer for shadow sort fields (title_sort, correspondent_sort, type_sort): simple -> lowercase -> ascii_fold.""" """Tokenizer for shadow sort fields (title_sort, correspondent_sort, type_sort): simple -> lowercase -> ascii_fold."""
return ( return (
-610
View File
@@ -1,610 +0,0 @@
from __future__ import annotations
from dataclasses import dataclass
from datetime import UTC
from datetime import datetime
from datetime import timedelta
from typing import TYPE_CHECKING
from typing import TypeAlias
import regex
from dateutil.relativedelta import relativedelta
from documents.search._dates import _DATE_KEYWORDS
from documents.search._dates import _DATE_ONLY_FIELDS
from documents.search._dates import _date_only_range
from documents.search._dates import _datetime_range
from documents.search._dates import _field_range_from_dates
from documents.search._dates import _fmt
from documents.search._dates import _precision_bounds
from documents.search._dates import _utc_bounds_for_field
# Compiled regex that matches any known multi-word (or single-word) date keyword
# at the start of a match position, longest alternatives first so "previous week"
# wins over a hypothetical shorter "previous".
_KEYWORD_VALUE_RE = regex.compile(
"|".join(sorted((regex.escape(k) for k in _DATE_KEYWORDS), key=len, reverse=True)),
regex.IGNORECASE,
)
if TYPE_CHECKING:
from datetime import tzinfo
# TODO: this module translates date queries into Tantivy *string* syntax, which
# forces a workaround for something Tantivy's string parser cannot express on
# date fields: open-ended ranges use far-past/far-future string sentinels
# (OPEN_LO/OPEN_HI). These can be replaced with a real tantivy.Query object
# (Query.range_query(..., None) for open bounds) once tantivy-py accepts Python
# datetimes in range_query/term_query on Date fields. That support exists on
# tantivy-py master (PRs #655 + #666) but postdates the pinned 0.26.0 wheel, so
# it is blocked only on a published release > 0.26.0 and a dependency bump.
# (Unparsable dates now raise InvalidDateQuery -> HTTP 400 rather than using a
# no-match string sentinel.)
# Fields that store exact, non-analyzed comma-joined tokens in the index and so
# need explicit comma->AND expansion (Whoosh KEYWORD(commas=True) set).
MULTI_VALUE_FIELDS = frozenset({"tag", "tag_id", "viewer_id"})
# Date fields whose values/ranges get rewritten to RFC3339 Tantivy ranges.
DATE_FIELDS = frozenset({"created", "modified", "added"})
# Field aliases: Whoosh (v2) field names that were renamed in the Tantivy schema.
# Preserved here so v2 queries using the old names continue to work without 400
# errors instead of silently failing. Applied by _render to non-date field tokens.
FIELD_ALIASES: dict[str, str] = {
"type": "document_type",
"type_id": "document_type_id",
"path": "storage_path",
"path_id": "storage_path_id",
}
# Known schema fields: a comma immediately followed by ``<known>:`` is a clause
# separator. Restricting to known fields prevents URL-like ``http:`` misfires.
KNOWN_FIELDS = frozenset(
{
"title",
"content",
"correspondent",
"document_type",
"type", # v2 alias -> document_type
"storage_path",
"path", # v2 alias -> storage_path
"tag",
"tag_id",
"correspondent_id",
"document_type_id",
"type_id", # v2 alias -> document_type_id
"storage_path_id",
"path_id", # v2 alias -> storage_path_id
"owner_id",
"viewer_id",
"asn",
"page_count",
"num_notes",
"created",
"modified",
"added",
"original_filename",
"checksum",
"notes",
"custom_fields",
},
)
_FIELD_RE = regex.compile(r"(?P<field>\w+):")
# Matches the TO separator inside a range bracket. Handles three forms:
# middle: "lo TO hi" (either lo or hi may be empty)
# trailing: "lo TO" (open upper bound)
# leading: "TO hi" (open lower bound)
# Bounds MAY contain internal spaces (e.g. "-7 days"), so we use .*? / .+?
# and split on the whitespace-delimited " TO " / " to " separator.
_RANGE_RE = regex.compile(
r"^\s*(?P<lo>.*?)\s+[Tt][Oo]\s+(?P<hi>.+?)\s*$"
r"|"
r"^\s*(?P<lo2>.+?)\s+[Tt][Oo]\s*$"
r"|"
r"^\s*[Tt][Oo]\s+(?P<hi2>.+?)\s*$",
)
@dataclass(frozen=True, slots=True)
class FieldValue:
field: str
value: str
# Produced by the comma-resolution pass (not by scan()).
@dataclass(frozen=True, slots=True)
class FieldValueList:
field: str
values: tuple[str, ...]
@dataclass(frozen=True, slots=True)
class FieldRange:
field: str
open: str
lo: str
hi: str
close: str
# Produced by the comma-resolution pass (not by scan()).
@dataclass(frozen=True, slots=True)
class Comma:
pass
@dataclass(frozen=True, slots=True)
class Passthrough:
raw: str
Token: TypeAlias = FieldValue | FieldValueList | FieldRange | Comma | Passthrough
_CLOSE: dict[str, str] = {"[": "]", "{": "}"}
def scan(query: str) -> list[Token]:
"""
Tokenize a raw query into date/comma-aware tokens, leaving everything else
as verbatim ``Passthrough`` runs. Non-recursive: finds the first matching
close bracket/quote. Nested brackets are not valid Tantivy range syntax and
pass through verbatim on mismatch.
"""
tokens: list[Token] = []
buf: list[str] = [] # accumulates passthrough chars
i, n = 0, len(query)
while i < n:
matched = _match_field_token(query, i)
if matched is None:
buf.append(query[i])
i += 1
continue
token, i = matched
if buf and buf[-1] == ",":
buf.pop()
_flush(buf, tokens)
tokens.append(Comma())
else:
_flush(buf, tokens)
tokens.append(token)
i = _maybe_comma(query, i, tokens)
_flush(buf, tokens)
return tokens
def _flush(buf: list[str], tokens: list[Token]) -> None:
"""Emit any accumulated passthrough characters as a single token."""
if buf:
tokens.append(Passthrough("".join(buf)))
buf.clear()
def _at_word_boundary(query: str, i: int) -> bool:
"""A field token may begin only at the start or after a non-word character."""
return i == 0 or not (query[i - 1].isalnum() or query[i - 1] == "_")
def _match_field_token(query: str, i: int) -> tuple[Token, int] | None:
"""
If a known ``field:`` token starts at ``i``, consume it and return
``(token, end_index)``; otherwise return None so the caller treats the
character as passthrough. Handles both ``field:[range]`` and ``field:value``,
and returns None when the range/value cannot be consumed.
"""
m = _FIELD_RE.match(query, i)
if m is None or m.group("field") not in KNOWN_FIELDS:
return None
if not _at_word_boundary(query, i):
return None
field = m.group("field")
j = m.end()
if j < len(query) and query[j] in "[{":
return _consume_range(query, j, field)
consumed = _consume_field_value(query, field, j)
if consumed is None:
return None
value, end = consumed
return FieldValue(field, value), end
def _consume_field_value(query: str, field: str, start: int) -> tuple[str, int] | None:
"""
Consume a field value starting at ``start``: a multi-word date keyword phrase
(date fields only), or a bare/quoted value, then absorb any comma-joined
continuation that is not a clause separator. ``resolve_commas`` later splits a
multi-value field's joined value into a ``FieldValueList``; for other fields
the comma stays literal.
"""
n = len(query)
consumed = None
if field in DATE_FIELDS:
km = _KEYWORD_VALUE_RE.match(query, start)
if km is not None and (km.end() >= n or query[km.end()] in " \t),"):
consumed = (km.group(0), km.end())
if consumed is None:
consumed = _consume_value(query, start)
if consumed is None:
return None
value, k = consumed
while k < n and query[k] == ",":
if _looks_like_known_field(query, k + 1):
break # clause separator: left for _maybe_comma to emit a Comma()
more = _consume_value(query, k + 1)
if more is None:
break
value = f"{value},{more[0]}"
k = more[1]
return value, k
def _consume_range(
query: str,
start: int,
field: str,
) -> tuple[FieldRange, int] | None:
"""Consume ``[lo TO hi]`` / ``{lo TO hi}`` from ``start`` (the bracket)."""
open_br = query[start]
close_br = _CLOSE[open_br]
end = query.find(close_br, start + 1)
if end == -1:
return None
inner = query[start + 1 : end]
m = _RANGE_RE.match(inner)
if m is not None:
if m.group("lo") is not None or m.group("hi") is not None:
# Middle form: "lo TO hi" (either may be empty string)
lo = (m.group("lo") or "").strip()
hi = (m.group("hi") or "").strip()
elif m.group("lo2") is not None:
# Trailing form: "lo TO"
lo = m.group("lo2").strip()
hi = ""
else:
# Leading form: "TO hi"
lo = ""
hi = (m.group("hi2") or "").strip()
else:
lo, hi = inner.strip(), ""
return FieldRange(field, open_br, lo, hi, close_br), end + 1
def _consume_value(query: str, start: int) -> tuple[str, int] | None:
"""Consume a bare or quoted field value from ``start``, stopping at comma."""
n = len(query)
if start >= n or query[start] in " \t":
return None
if query[start] in "\"'":
quote = query[start]
end = query.find(quote, start + 1)
if end == -1:
return None
return query[start : end + 1], end + 1
j = start
while j < n and query[j] not in " \t),":
j += 1
return query[start:j], j
def _looks_like_known_field(query: str, pos: int) -> bool:
"""True if a known ``field:`` token starts at ``pos``."""
m = _FIELD_RE.match(query, pos)
return bool(m and m.group("field") in KNOWN_FIELDS)
def _maybe_comma(query: str, i: int, tokens: list) -> int:
"""If a clause-separator comma follows at ``i``, emit ``Comma()`` and advance."""
if i < len(query) and query[i] == "," and _looks_like_known_field(query, i + 1):
tokens.append(Comma())
return i + 1
return i
def resolve_commas(tokens: list) -> list:
"""
Collapse value-list commas into ``FieldValueList`` and keep clause-separator
commas as ``Comma``. (Clause-sep commas are already emitted by ``scan`` via
the value-stop logic; this pass folds value-lists.)
"""
out: list = []
for tok in tokens:
if (
isinstance(tok, FieldValue)
and tok.field in MULTI_VALUE_FIELDS
and "," in tok.value
):
values = tuple(v for v in tok.value.split(",") if v)
out.append(FieldValueList(tok.field, values))
else:
out.append(tok)
return out
class SearchQueryError(ValueError):
"""
Base for user-fixable search query errors.
Carries a message safe to surface to the user (no internal details). The view
layer catches this and returns an HTTP 400, so any future subclass (unknown
field, malformed range, wrapped parser errors) gets the same treatment.
"""
class InvalidDateQuery(SearchQueryError):
"""Raised when a date field value or range bound cannot be parsed."""
def __init__(self, field: str, value: str) -> None:
self.field = field
self.value = value
super().__init__(f"Invalid date value {value!r} for field {field!r}.")
_DIGITS_RE = regex.compile(r"^\d{4}(?:\d{2}){0,2}$")
_ISO_RE = regex.compile(r"^\d{4}(?:-\d{2}(?:-\d{2})?)?$")
def translate_scalar(field: str, value: str, tz: tzinfo) -> str:
"""Translate a bare date-field value to a Tantivy range string."""
bare = value.strip("\"'").lower()
if bare in _DATE_KEYWORDS:
if field in _DATE_ONLY_FIELDS:
return f"{field}:{_date_only_range(bare, tz)}"
return f"{field}:{_datetime_range(bare, tz)}"
digits = value.replace("-", "")
if _DIGITS_RE.match(value) or _ISO_RE.match(value):
bounds = _precision_bounds(digits)
if bounds is None:
raise InvalidDateQuery(field, value)
return _field_range_from_dates(field, bounds[0], bounds[1], tz)
if regex.fullmatch(r"\d{14}", value):
try:
dt = datetime(
int(value[0:4]),
int(value[4:6]),
int(value[6:8]),
int(value[8:10]),
int(value[10:12]),
int(value[12:14]),
tzinfo=UTC,
)
except ValueError:
raise InvalidDateQuery(field, value) from None
iso = _fmt(dt)
return f"{field}:[{iso} TO {iso}]"
# Unrecognized shape -> tell the user their date is malformed rather than
# silently matching nothing or emitting invalid Tantivy syntax.
raise InvalidDateQuery(field, value)
# Open-bound sentinels for date ranges. These far-past/far-future strings allow
# open-ended ranges to be expressed as Tantivy string queries until tantivy-py
# exposes Query.range_query(..., None) on Date fields (see module TODO).
OPEN_LO = "0001-01-01T00:00:00Z"
OPEN_HI = "9999-12-31T23:59:59Z"
# Matches compact now-offset tokens like now-7d, now+1h, now-30m.
_NOW_COMPACT_RE = regex.compile(
r"^now(?P<sign>[+-])(?P<n>\d+)(?P<unit>[dhm])$",
regex.IGNORECASE,
)
# Matches "±N <unit>" Whoosh-style offsets (e.g. -7 days, -1 week, +3 hours).
# Whoosh's own date parser (qparser.dateparse.PlusMinus) additionally accepted
# abbreviated unit spellings (e.g. "yrs", "yr", "y", "mos", "wks", "hrs", "mins",
# "secs"); saved views/searches created under the old Whoosh backend can still
# contain those tokens (e.g. "-999yrs"), so they are accepted here too and
# normalized to a canonical unit via _UNIT_ALIASES below.
_NOW_SPACED_RE = regex.compile(
r"^(?P<sign>[+-])(?P<n>\d+)\s*"
r"(?P<unit>years|year|yrs|yr|ys|y"
r"|months|month|mons|mon|mos|mo"
r"|weeks|week|wks|wk|ws|w"
r"|days|day|dys|dy|ds|d"
r"|hours|hour|hrs|hr|hs|h"
r"|minutes|minute|mins|min|ms|m"
r"|seconds|second|secs|sec|s)$",
regex.IGNORECASE,
)
# Maps every accepted unit spelling (including Whoosh-era abbreviations) to the
# canonical unit name used as a key into the delta map in _resolve_relative_bound.
_UNIT_ALIASES: dict[str, str] = {
alias: canonical
for canonical, aliases in {
"year": ("years", "year", "yrs", "yr", "ys", "y"),
"month": ("months", "month", "mons", "mon", "mos", "mo"),
"week": ("weeks", "week", "wks", "wk", "ws", "w"),
"day": ("days", "day", "dys", "dy", "ds", "d"),
"hour": ("hours", "hour", "hrs", "hr", "hs", "h"),
"minute": ("minutes", "minute", "mins", "min", "ms", "m"),
"second": ("seconds", "second", "secs", "sec", "s"),
}.items()
for alias in aliases
}
def _resolve_relative_bound(token: str) -> datetime | None:
"""
Resolve a relative bound token to an exact UTC instant, or return None.
Supported forms:
- ``now`` -> current UTC instant
- ``now+/-<n>d/h/m`` -> now +/- timedelta (d=days, h=hours, m=minutes)
- ``±N <unit>`` -> now +/- delta; month/year use relativedelta;
unit also accepts Whoosh-era abbreviations
(e.g. "yrs", "mos", "wks", "hrs", "mins", "secs")
"""
stripped = token.strip()
low = stripped.lower()
now = datetime.now(UTC)
if low == "now":
return now
m = _NOW_COMPACT_RE.match(stripped)
if m:
sign = 1 if m.group("sign") == "+" else -1
n = int(m.group("n"))
unit = m.group("unit").lower()
delta = (
sign
* {
"d": timedelta(days=n),
"h": timedelta(hours=n),
"m": timedelta(minutes=n),
}[unit]
)
return now + delta
m = _NOW_SPACED_RE.match(stripped)
if m:
sign = 1 if m.group("sign") == "+" else -1
n = int(m.group("n"))
unit = _UNIT_ALIASES[m.group("unit").lower()]
delta_map: dict[str, timedelta | relativedelta] = {
"second": timedelta(seconds=n),
"minute": timedelta(minutes=n),
"hour": timedelta(hours=n),
"day": timedelta(days=n),
"week": timedelta(weeks=n),
"month": relativedelta(months=n),
"year": relativedelta(years=n),
}
return now - delta_map[unit] if sign == -1 else now + delta_map[unit]
return None
def _bound_datetimes(
field: str,
token: str,
tz: tzinfo,
) -> tuple[datetime, datetime] | None:
"""
Return (floor_dt, ceil_dt) UTC datetimes for a single range bound token, or
None if the token is unparsable. ``now`` and relative offsets resolve to the
current instant (floor == ceil == that instant; no day-flooring).
"""
token = token.strip()
# Try relative/now forms first (before stripping hyphens which would mangle them).
rel = _resolve_relative_bound(token)
if rel is not None:
return rel, rel
# Full ISO datetime token (contains "T"): parse directly and return an exact
# instant (floor == ceil). Python 3.11+ datetime.fromisoformat accepts trailing Z.
if "T" in token:
try:
dt = datetime.fromisoformat(token)
# Ensure timezone-aware UTC result.
dt = dt.replace(tzinfo=UTC) if dt.tzinfo is None else dt.astimezone(UTC)
return dt, dt
except ValueError:
return None
digits = token.replace("-", "")
bounds = _precision_bounds(digits)
if bounds is None:
return None
start, end = bounds
return _utc_bounds_for_field(field, start, end, tz)
def _render(tok: Token, tz: tzinfo) -> str:
"""Render a single token back to a Tantivy query string fragment."""
if isinstance(tok, Passthrough):
return tok.raw
if isinstance(tok, Comma):
return " AND "
if isinstance(tok, FieldValueList):
field = FIELD_ALIASES.get(tok.field, tok.field)
return " AND ".join(f"{field}:{v}" for v in tok.values)
if isinstance(tok, FieldValue):
field = FIELD_ALIASES.get(tok.field, tok.field)
if field in DATE_FIELDS:
return translate_scalar(field, tok.value, tz)
return f"{field}:{tok.value}"
if isinstance(tok, FieldRange):
field = FIELD_ALIASES.get(tok.field, tok.field)
if field in DATE_FIELDS:
return translate_range(field, tok.lo, tok.hi, tz)
return f"{field}:{tok.open}{tok.lo} TO {tok.hi}{tok.close}"
return "" # pragma: no cover
# Post-render operator normalization patterns: collapse repeated whitespace and
# strip spaced/trailing Tantivy boolean operators that would otherwise be invalid.
_MULTI_SPACE_RE = regex.compile(r" {2,}")
_TRAILING_OP_RE = regex.compile(r"\s+[-+]+\s*$")
_SPACED_OP_RE = regex.compile(r"\s+[-+]\s+")
def _normalize_operators(text: str) -> str:
"""
Collapse multiple spaces, strip trailing dangling operators, and replace
spaced operators (`` - `` / `` + ``) with a single space.
Applied only to Passthrough fragments (the rendered output is scanned for
operator artifacts outside bracketed ranges) via a post-render pass on the
full rendered string. This preserves date ranges (``[... TO ...]``) verbatim
while cleaning natural-language separators in the surrounding text.
"""
text = _MULTI_SPACE_RE.sub(" ", text)
text = _TRAILING_OP_RE.sub("", text).strip()
text = _SPACED_OP_RE.sub(" ", text).strip()
return text
def translate_query(raw: str, tz: tzinfo) -> str:
"""Translate a raw Whoosh-style query into Tantivy-compatible syntax."""
tokens = resolve_commas(scan(raw))
rendered = "".join(_render(t, tz) for t in tokens)
return _normalize_operators(rendered)
def translate_range(field: str, lo: str, hi: str, tz: tzinfo) -> str:
"""Translate a date-field ``[lo TO hi]`` range to a Tantivy ISO range string.
Handles partial-date bounds (YYYY, YYYYMM, YYYYMMDD, ISO dash variants),
open bounds (empty string -> OPEN_LO/OPEN_HI), ``now``, and reversed ranges
(swaps tokens before computing floor/ceil so the span is always correct).
"""
lo_s = lo.strip()
hi_s = hi.strip()
# Parse both bounds to (floor, ceil) pairs when present.
lo_pair: tuple[datetime, datetime] | None = None
hi_pair: tuple[datetime, datetime] | None = None
if lo_s:
lo_pair = _bound_datetimes(field, lo_s, tz)
if lo_pair is None:
raise InvalidDateQuery(field, lo_s)
if hi_s:
hi_pair = _bound_datetimes(field, hi_s, tz)
if hi_pair is None:
raise InvalidDateQuery(field, hi_s)
# Detect a reversed range: only swap when BOTH bounds are present.
if lo_pair is not None and hi_pair is not None and lo_pair[0] > hi_pair[0]:
lo_pair, hi_pair = hi_pair, lo_pair
lo_iso = _fmt(lo_pair[0]) if lo_pair is not None else OPEN_LO
# A bound resolves to (floor, ceil) where floor == ceil for an exact instant
# (a full ISO datetime, "now", or a "+/-N unit" offset) and floor != ceil for
# a coarser period token (year/month/day precision). Only the latter needs a
# half-open close: its ceil is the start of the *next* period and must be
# excluded, or that instant (e.g. the 1st of next month) wrongly matches.
if hi_pair is not None:
hi_iso = _fmt(hi_pair[1])
hi_close = "]" if hi_pair[0] == hi_pair[1] else "}"
else:
hi_iso = OPEN_HI
hi_close = "]"
return f"{field}:[{lo_iso} TO {hi_iso}{hi_close}"
-12
View File
@@ -1,15 +1,11 @@
from __future__ import annotations from __future__ import annotations
import tempfile
from typing import TYPE_CHECKING from typing import TYPE_CHECKING
import pytest import pytest
import tantivy
from documents.search._backend import TantivyBackend from documents.search._backend import TantivyBackend
from documents.search._backend import reset_backend from documents.search._backend import reset_backend
from documents.search._schema import build_schema
from documents.search._tokenizer import register_tokenizers
if TYPE_CHECKING: if TYPE_CHECKING:
from collections.abc import Generator from collections.abc import Generator
@@ -35,11 +31,3 @@ def backend() -> Generator[TantivyBackend, None, None]:
finally: finally:
b.close() b.close()
reset_backend() reset_backend()
@pytest.fixture(scope="module")
def index() -> tantivy.Index:
"""A real Tantivy index for parse-acceptance tests (module scope for speed)."""
idx = tantivy.Index(build_schema(), path=tempfile.mkdtemp())
register_tokenizers(idx, "english")
return idx
@@ -0,0 +1,541 @@
"""Result-level acceptance corpus: real documents indexed via build_schema(),
real queries run through parse_user_query(), matched-document-ID sets
asserted, not intermediate ASTs or query strings. This is paperless-ngx's
analogue of whoosh-compat's own tests/emitter/test_acceptance_e2e.py.
Supersedes test_query.py's TestParseUserQuery result-level cases.
"""
from __future__ import annotations
from datetime import UTC
from datetime import datetime
from typing import TYPE_CHECKING
import pytest
import time_machine
from django.contrib.auth.models import User
from documents.models import CustomField
from documents.models import CustomFieldInstance
from documents.models import Document
from documents.models import DocumentType
from documents.models import Note
from documents.models import StoragePath
from documents.search._query import parse_user_query
if TYPE_CHECKING:
from documents.search._backend import TantivyBackend
pytestmark = [pytest.mark.search, pytest.mark.django_db]
FROZEN_NOW = datetime(2026, 6, 15, 12, 0, tzinfo=UTC)
def _matched_ids(backend: TantivyBackend, query: str) -> set[int]:
return set(backend.search_ids(query, user=None))
def _index(backend: TantivyBackend, **kwargs: object) -> Document:
"""Create a Document and index it in one step, for the common case
where nothing needs to happen between the two (no related Note/
CustomFieldInstance to attach first)."""
doc = Document.objects.create(**kwargs)
backend.add_or_update(doc)
return doc
@pytest.fixture
def indexed_documents(backend: TantivyBackend) -> dict[str, int]:
"""Index a small fixture set, return {label: doc_id} for corpus queries."""
docs = {
"invoice_2020": _index(
backend,
title="Invoice 2020",
content="invoice total due",
checksum="acc-invoice-2020",
archive_serial_number=100,
),
"invoice_2021": _index(
backend,
title="Invoice 2021",
content="invoice total due",
checksum="acc-invoice-2021",
archive_serial_number=101,
),
"invoice_2023": _index(
backend,
title="Invoice 2023",
content="invoice total due",
checksum="acc-invoice-2023",
archive_serial_number=102,
),
"receipt_2022": _index(
backend,
title="Receipt 2022",
content="receipt total due",
checksum="acc-receipt-2022",
archive_serial_number=103,
),
}
return {label: doc.pk for label, doc in docs.items()}
class TestIssue13568BracketWildcard:
"""paperless-ngx#13568: title:202[0-3]* must keep its character class,
not fold to a prefix query that silently drops it."""
def test_bracket_class_wildcard_matches_only_in_range_years(
self,
backend: TantivyBackend,
indexed_documents: dict[str, int],
) -> None:
"""
GIVEN:
- Four indexed documents titled Invoice 2020/2021/2023 and
Receipt 2022
WHEN:
- "title:202[0-1]*" is searched ([0-1], not [0-3], is
deliberate: the fixture's trailing digits are 0/1/2/3, so a
[0-3] class would match all four and pass even if the
character class were silently dropped and folded to an
unconstrained "202*" prefix; [0-1] partitions the fixture
into a genuine in-range/out-of-range split)
THEN:
- Only the 2020 and 2021 documents match, proving the bracket
character class survived (issue #13568's original bug)
"""
matched = _matched_ids(backend, "title:202[0-1]*")
expected = {
indexed_documents["invoice_2020"],
indexed_documents["invoice_2021"],
}
assert matched == expected, (
"title:202[0-1]* must match 2020/2021 titles and exclude 2022/2023 "
"- if this matches everything, the wildcard's character class was "
"silently dropped (issue #13568's original bug)"
)
class TestFieldBoosts:
def test_title_boost_ranks_title_match_above_content_only_match(
self,
backend: TantivyBackend,
) -> None:
"""
GIVEN:
- One document whose title contains the query word and another
whose content (not title) contains it
WHEN:
- The query word is searched unfielded
THEN:
- The title match ranks first, proving our title field boost
actually affects ranking
"""
title_match = _index(
backend,
title="urgent",
content="nothing else relevant",
checksum="acc-boost-title",
)
_index(
backend,
title="nothing",
content="urgent matter here",
checksum="acc-boost-content",
)
query = parse_user_query(backend._index, "urgent", UTC)
searcher = backend._index.searcher()
results = searcher.search(query, limit=10)
ranked_ids = [
searcher.doc(addr).to_dict()["id"][0] for _score, addr in results.hits
]
assert ranked_ids[0] == title_match.pk
class TestJsonSubpaths:
def test_notes_user_matches_document_with_that_note_author(
self,
backend: TantivyBackend,
) -> None:
"""
GIVEN:
- A document with a Note authored by "alice" and a second,
unrelated document with no note
WHEN:
- "notes.user:alice" is searched
THEN:
- Only the document with alice's note matches
"""
alice = User.objects.create_user(username="alice")
doc_with_note = Document.objects.create(
title="Has note",
content="x",
checksum="acc-note-with",
)
Note.objects.create(document=doc_with_note, user=alice, note="reminder")
backend.add_or_update(doc_with_note)
_index(backend, title="No note", content="x", checksum="acc-note-without")
matched = _matched_ids(backend, "notes.user:alice")
assert matched == {doc_with_note.pk}
def test_custom_fields_name_and_value_combine(
self,
backend: TantivyBackend,
) -> None:
"""
GIVEN:
- A document with a "Contract Number" custom field valued
"policy", and a second document with a differently-named
custom field also valued "policy"
WHEN:
- 'custom_fields.name:"Contract Number" custom_fields.value:policy'
is searched
THEN:
- Only the document whose field name AND value both match is
returned
"""
field = CustomField.objects.create(
name="Contract Number",
data_type=CustomField.FieldDataType.STRING,
)
other_field = CustomField.objects.create(
name="Other Field",
data_type=CustomField.FieldDataType.STRING,
)
matching = Document.objects.create(
title="Matching",
content="x",
checksum="acc-cf-matching",
)
CustomFieldInstance.objects.create(
document=matching,
field=field,
value_text="policy",
)
backend.add_or_update(matching)
non_matching = Document.objects.create(
title="Non-matching",
content="x",
checksum="acc-cf-nonmatching",
)
CustomFieldInstance.objects.create(
document=non_matching,
field=other_field,
value_text="policy",
)
backend.add_or_update(non_matching)
matched = _matched_ids(
backend,
'custom_fields.name:"Contract Number" custom_fields.value:policy',
)
assert matched == {matching.pk}
class TestUnregisteredIdFieldFoldsToLiteralText:
"""tag_id, owner_id, etc. are intentionally excluded from the
FieldRegistry - always internal index columns, never meant to be
query-addressable. Prove an unregistered field folds to a literal
text search that matches nothing, rather than erroring."""
def test_tag_id_query_matches_nothing(
self,
backend: TantivyBackend,
indexed_documents: dict[str, int],
) -> None:
"""
GIVEN:
- A real indexed corpus and "tag_id", a field intentionally
excluded from the FieldRegistry (an internal index column,
never meant to be query-addressable)
WHEN:
- "tag_id:5" is searched
THEN:
- It folds to a literal text search and matches nothing,
rather than erroring
"""
matched = _matched_ids(backend, "tag_id:5")
assert matched == set()
class TestFuzzyBlendSurvivesWhooshGrammar:
"""A query mixing whoosh-only grammar (a date keyword) with a typo'd
free-text word must still fuzzy-match the intended document when
ADVANCED_FUZZY_SEARCH_THRESHOLD is enabled. The fuzzy clause is built
from the parsed query's free-text tokens (whoosh_compat's
free_text_tokens), never from the raw query string, so whoosh grammar
that tantivy's own parser rejects cannot knock the fuzzy clause out."""
def test_typo_fuzzy_matches_alongside_date_keyword(
self,
backend: TantivyBackend,
settings,
) -> None:
"""
GIVEN:
- ADVANCED_FUZZY_SEARCH_THRESHOLD enabled, and a document
indexed with content "receipt total due"
WHEN:
- The query blends whoosh-only grammar tantivy's own parser
rejects ("added:today") with a one-transposition misspelling
of a word in the indexed content
THEN:
- The document still matches, because the fuzzy clause is
built from the parsed query's free-text tokens
(whoosh_compat's free_text_tokens), never from the raw
query string, so grammar tantivy's parser cannot handle
cannot knock the fuzzy clause out
"""
settings.ADVANCED_FUZZY_SEARCH_THRESHOLD = 0.5
with time_machine.travel(FROZEN_NOW, tick=False):
doc = _index(
backend,
title="Receipt March",
content="receipt total due",
checksum="fuzzy-blend-1",
archive_serial_number=900,
)
# Sanity: the exact spelling matches through the exact clause.
assert doc.pk in _matched_ids(backend, "added:today receipt")
# The regression: the misspelling (one transposition) only
# matches via the fuzzy clause, and "added:today" is
# whoosh-only grammar tantivy's parser rejects, so raw-string
# fuzzy parsing skips the clause entirely and this returns
# nothing. The typo is deliberate; keep codespell away from it.
typo_query = "added:today reciept" # codespell:ignore reciept
assert doc.pk in _matched_ids(backend, typo_query)
def test_negated_words_do_not_fuzzy_match(
self,
backend: TantivyBackend,
settings,
) -> None:
"""
GIVEN:
- ADVANCED_FUZZY_SEARCH_THRESHOLD enabled, and a document
containing the NOT'd word ("receipt") but not the positive
word ("total"), so nothing matches the exact clause -- the
shape a naive fuzzy string built from ALL words (including
the NOT'd one) would make this document the sole hit,
normalize its score to 1.0, and survive any threshold (a
shape with an exact-matching sibling document would NOT
discriminate: normalization would rank the resurfaced
document far below the exact match and the threshold would
cut it even for a naive implementation)
WHEN:
- "added:today total NOT receipt" is searched
THEN:
- The document does not match; a term the user excluded must
not resurface through the fuzzy clause
"""
settings.ADVANCED_FUZZY_SEARCH_THRESHOLD = 0.5
with time_machine.travel(FROZEN_NOW, tick=False):
_index(
backend,
title="Receipt Archive",
content="receipt archived stack",
checksum="fuzzy-blend-2",
archive_serial_number=901,
)
assert _matched_ids(backend, "added:today total NOT receipt") == set()
class TestUnquotedDateKeywordPhrases:
"""The unquoted spelling (added:previous month) is honored natively by
whoosh-compat's own grammar for this closed phrase vocabulary, no
app-level rewrite is involved. Pins that the historically supported
spelling keeps working now that paperless no longer pre-quotes it."""
@pytest.fixture
def period_documents(self, backend: TantivyBackend) -> dict[str, int]:
with time_machine.travel(FROZEN_NOW, tick=False):
in_may = _index(
backend,
title="May Doc",
content="statement",
checksum="kw-may",
archive_serial_number=910,
added=datetime(2026, 5, 20, 12, 0, tzinfo=UTC),
)
in_june = _index(
backend,
title="June Doc",
content="statement",
checksum="kw-june",
archive_serial_number=911,
added=datetime(2026, 6, 10, 12, 0, tzinfo=UTC),
)
return {"in_may": in_may.pk, "in_june": in_june.pk}
@pytest.mark.parametrize(
"query",
[
pytest.param("added:previous month", id="unquoted"),
pytest.param('added:"previous month"', id="quoted"),
pytest.param("added:Previous Month", id="unquoted-mixed-case"),
],
)
def test_unquoted_matches_the_same_documents_as_quoted(
self,
backend: TantivyBackend,
period_documents: dict[str, int],
query: str,
) -> None:
"""
GIVEN:
- Two documents added in different months, time frozen so
only one falls in "previous month"
WHEN:
- The same date-keyword phrase is spelled unquoted, quoted,
and unquoted with mixed case
THEN:
- All three spellings match the same document; paperless no
longer pre-quotes this phrase before parsing, relying on
whoosh-compat's own grammar to accept it unquoted natively
"""
with time_machine.travel(FROZEN_NOW, tick=False):
assert _matched_ids(backend, query) == {period_documents["in_may"]}
@pytest.mark.parametrize(
"query",
[
pytest.param("added:this month", id="this-month"),
pytest.param("added:this year", id="this-year"),
pytest.param("added:previous week", id="previous-week"),
pytest.param("added:previous quarter", id="previous-quarter"),
pytest.param("added:previous year", id="previous-year"),
pytest.param("created:previous month", id="created-field"),
pytest.param("modified:previous month", id="modified-field"),
],
)
def test_every_phrase_and_date_field_parses_without_error(
self,
backend: TantivyBackend,
period_documents: dict[str, int],
query: str,
) -> None:
"""
GIVEN:
- Our real schema and every date-keyword phrase in the
vocabulary, against every date field we expose (added,
created, modified)
WHEN:
- Each combination is searched
THEN:
- It parses and searches cleanly against our schema (no
SearchQueryError, so no HTTP 400); exact window semantics
are whoosh-compat's own and are pinned in its own suite
"""
with time_machine.travel(FROZEN_NOW, tick=False):
_matched_ids(backend, query)
def test_text_field_keyword_words_are_ordinary_text(
self,
backend: TantivyBackend,
period_documents: dict[str, int],
) -> None:
"""
GIVEN:
- period_documents (indexed by added-date) and a third
document whose title literally contains the words
"previous month"
WHEN:
- "title:previous month" is searched
THEN:
- Only the document whose title contains those words matches;
"previous month" after a TEXT field (or unfielded) is
ordinary text, not a date phrase, so the date-window
documents do not match
"""
with time_machine.travel(FROZEN_NOW, tick=False):
wordy = _index(
backend,
title="Notes from the previous month",
content="meeting notes",
checksum="kw-text",
archive_serial_number=912,
)
assert _matched_ids(backend, "title:previous month") == {wordy.pk}
class TestFieldAliases:
"""type:/path: are registry aliases for document_type:/storage_path:.
The only other alias coverage is parse-shape; these prove resolution
end-to-end against a real index."""
def test_type_alias_and_canonical_name_match_the_same_document(
self,
backend: TantivyBackend,
) -> None:
"""
GIVEN:
- A document with document_type "invoice", and a decoy
document with no type whose content merely mentions
"invoice" (document_type is itself a default search field,
so if alias resolution ever broke and "type:invoice"
demoted to unfielded text, the token would STILL match the
typed document through the field value; the decoy carrying
the query word in content is what makes a demoted search
distinguishable, since it would then match both documents
and fail the exact-set assertion -- the title avoids
stemming to "type": English stems Typed -> type)
WHEN:
- "type:invoice" and "document_type:invoice" are each
searched
THEN:
- Both resolve to the same document, proving the "type" alias
and its canonical field name agree end-to-end against a
real index
"""
invoice_type = DocumentType.objects.create(name="invoice")
typed = _index(
backend,
title="First",
content="quarterly statement",
checksum="alias-type-1",
document_type=invoice_type,
)
_index(
backend,
title="Second",
content="invoice mentioned in body",
checksum="alias-type-2",
)
assert _matched_ids(backend, "type:invoice") == {typed.pk}
assert _matched_ids(backend, "document_type:invoice") == {typed.pk}
def test_path_alias_and_canonical_name_match_the_same_document(
self,
backend: TantivyBackend,
) -> None:
"""
GIVEN:
- A document stored under storage_path "archive", and a decoy
document with no storage_path whose content merely mentions
"archive" (storage_path is NOT a default search field
today, so a demoted "path:archive" already matches nothing;
the content decoy keeps this test discriminating even if
storage_path ever joins the defaults)
WHEN:
- "path:archive" and "storage_path:archive" are each searched
THEN:
- Both resolve to the same document, proving the "path" alias
and its canonical field name agree end-to-end against a
real index
"""
archive = StoragePath.objects.create(name="archive", path="archive/{title}")
stored = _index(
backend,
title="Stored",
content="quarterly statement",
checksum="alias-path-1",
storage_path=archive,
)
_index(
backend,
title="Loose",
content="archive mentioned in body",
checksum="alias-path-2",
)
assert _matched_ids(backend, "path:archive") == {stored.pk}
assert _matched_ids(backend, "storage_path:archive") == {stored.pk}
@@ -0,0 +1,82 @@
"""``checksum`` wildcard patterns stay literal end to end, once user queries
route through whoosh-compat.
The registry-level fact (the pattern normalizer folds a KEYWORD pattern
rather than stemming it) is pinned on its own in
``test_keyword_pattern_literal.py``. This proves it actually reaches a real
query: ``checksum:ceded*`` must match only the document whose checksum
starts with "ceded", not the one whose checksum stems to the same run.
"""
from __future__ import annotations
from typing import TYPE_CHECKING
import pytest
from documents.models import Document
if TYPE_CHECKING:
from documents.search._backend import TantivyBackend
pytestmark = [pytest.mark.search, pytest.mark.django_db]
CEDEF00D = "cedef00ddeadbeef0123456789abcdef01234567"
CEDEDEAD = "cededeadbeef567801234567" + "89abcdef01234567"
class TestChecksumPrefixQueries:
@pytest.fixture
def indexed(self, backend: TantivyBackend) -> None:
for i, checksum in enumerate((CEDEF00D, CEDEDEAD)):
doc = Document.objects.create(
title=f"Checksum doc {i}",
content="invoices for the quarter",
checksum=checksum,
archive_serial_number=940 + i,
)
backend.add_or_update(doc)
def _ids(self, backend: TantivyBackend, query: str) -> set[int]:
return set(backend.search_ids(query, user=None))
def test_prefix_matches_only_the_document_that_starts_with_it(
self,
backend: TantivyBackend,
indexed: None,
) -> None:
"""
GIVEN:
- Two documents indexed with checksums that share a stem when
run through the English stemmer ("cedef00d..." and
"cededead...") but only one literally starts with "ceded"
WHEN:
- "checksum:ceded*" is searched
THEN:
- Only the document whose checksum literally starts with
"ceded" matches; the pattern normalizer folds a KEYWORD
pattern rather than stemming it, so this reaches a real
query end to end
"""
matched = self._ids(backend, "checksum:ceded*")
expected = Document.objects.get(checksum=CEDEDEAD).pk
assert matched == {expected}
def test_text_prefix_still_reaches_the_stemmed_index(
self,
backend: TantivyBackend,
indexed: None,
) -> None:
"""
GIVEN:
- Two documents indexed with content "invoices for the
quarter"
WHEN:
- "invoice*" is searched against the TEXT content field
THEN:
- Both documents match, confirming the checksum field's
literal-pattern behavior is specific to KEYWORD fields and
does not affect TEXT field wildcard matching against
stemmed terms
"""
assert len(self._ids(backend, "invoice*")) == 2
@@ -0,0 +1,212 @@
"""The CJK bigram clause blended into QUERY-mode searches.
The clause exists so CJK runs are matchable at all (the default analyzers
keep a whitespace-free CJK run as one indivisible token), but it must not
widen the query beyond what the user asked for: a CJK term the query
excludes, or restricts to one field, must not come back through it.
"""
from __future__ import annotations
from typing import TYPE_CHECKING
import pytest
from documents.models import Document
if TYPE_CHECKING:
from pytest_django.fixtures import SettingsWrapper
from documents.search._backend import TantivyBackend
pytestmark = [pytest.mark.search, pytest.mark.django_db]
def _matched_ids(backend: TantivyBackend, query: str) -> set[int]:
return set(backend.search_ids(query, user=None))
def _index(backend: TantivyBackend, **kwargs: object) -> Document:
doc = Document.objects.create(**kwargs)
backend.add_or_update(doc)
return doc
class TestCjkParseFailureDegradesGracefully:
def test_a_cjk_run_tantivy_cannot_parse_drops_the_clause_only(self) -> None:
"""
GIVEN:
- A CJK run and an index-like object whose parse_query is
forced to raise
WHEN:
- _parse_cjk_text is called
THEN:
- It returns None instead of propagating, so a CJK run tantivy
cannot parse only drops the bigram clause rather than
failing the whole query. Broad on purpose (bare except
Exception), unlike the fuzzy blend's narrower ValueError
guard: a CJK run is not filtered to a guaranteed-safe token
set the way the fuzzy blend's word string is, so the exact
failure mode tantivy could raise here is not pinned down
"""
from documents.search._query import _parse_cjk_text
class _RaisingIndex:
def parse_query(self, *args: object, **kwargs: object) -> object:
raise RuntimeError("synthetic parse failure")
assert _parse_cjk_text(_RaisingIndex(), "東京", ["bigram_content"]) is None
def test_no_cjk_text_at_all_returns_none_without_parsing(self) -> None:
"""
GIVEN:
- A raw query string with no CJK characters at all
WHEN:
- _build_cjk_query (the simple TEXT/TITLE-mode builder) is
called directly
THEN:
- It returns None without ever attempting to parse anything.
The only real caller already guards this with _has_cjk(),
so this is defensive: it keeps the function safe to call on
its own, not a path a real search currently reaches
"""
from documents.search._query import _build_cjk_query
assert _build_cjk_query(None, "invoice total due", ["bigram_content"]) is None
class TestCjkClauseFollowsTheParsedQuery:
def test_negated_cjk_term_is_excluded(self, backend: TantivyBackend) -> None:
"""
GIVEN:
- Two documents both matching "invoice", one whose content
also contains 漢字
WHEN:
- "invoice NOT 漢字" is searched
THEN:
- Only the document without 漢字 matches; 'invoice NOT 漢字'
must not return the document containing 漢字
"""
with_cjk = _index(
backend,
title="Invoice A",
content="invoice total 漢字",
checksum="cjk-neg-1",
)
without_cjk = _index(
backend,
title="Invoice B",
content="invoice total only",
checksum="cjk-neg-2",
)
assert _matched_ids(backend, "invoice") == {with_cjk.pk, without_cjk.pk}
assert _matched_ids(backend, "invoice NOT 漢字") == {without_cjk.pk}
@pytest.mark.parametrize(
("threshold", "expected"),
[
pytest.param(None, {"titled"}, id="fuzzy_off"),
pytest.param(0.0, {"titled", "content_only"}, id="fuzzy_on"),
],
)
def test_fielded_cjk_term_searches_only_that_field(
self,
backend: TantivyBackend,
settings: SettingsWrapper,
threshold: float | None,
expected: set[str],
) -> None:
"""
GIVEN:
- One document with 東京 in its title, another with 東京 only
in its content, and ADVANCED_FUZZY_SEARCH_THRESHOLD either
off or on
WHEN:
- "title:東京" is searched
THEN:
- With fuzzy off, only the titled document matches: the CJK
clause honours the field, so 'title:東京' must not match a
document whose 東京 is only in the content. With fuzzy on,
the content-only document is also readmitted, because the
fuzzy clause contributes every free-text term UNFIELDED by
design (see _try_parse_fuzzy_query) on its own
0.1-boosted terms -- a documented trade-off, pinned here so
it stays deliberate
"""
settings.ADVANCED_FUZZY_SEARCH_THRESHOLD = threshold
content_only = _index(
backend,
title="Tokyo report",
content="東京都の人口は約1400万人です",
checksum="cjk-field-1",
)
titled = _index(
backend,
title="東京都の報告書",
content="an english summary",
checksum="cjk-field-2",
)
pks = {"titled": titled.pk, "content_only": content_only.pk}
assert _matched_ids(backend, "東京") == set(pks.values())
assert _matched_ids(backend, "title:東京") == {pks[label] for label in expected}
def test_cjk_on_a_non_default_field_builds_no_clause(
self,
backend: TantivyBackend,
) -> None:
"""
GIVEN:
- A document with 東京 in its content
WHEN:
- "notes:東京" is searched (a field outside the default
search fields)
THEN:
- Nothing matches; a CJK term restricted to a field outside
the default search fields has nothing to contribute to the
bigram clause, so it must not fall back to matching 東京 in
the content
"""
_index(
backend,
title="Tokyo report",
content="東京都の人口は約1400万人です",
checksum="cjk-notes-1",
)
assert _matched_ids(backend, "notes:東京") == set()
def test_bare_cjk_term_still_matches_every_default_field(
self,
backend: TantivyBackend,
) -> None:
"""
GIVEN:
- One document with 重要 in its content, another with 重要 in
its title
WHEN:
- "重要" and "重要 OR report" are each searched unfielded
THEN:
- Both documents match either way; the clause's reason for
existing is that an unfielded CJK run matches wherever it
is indexed, and does so alongside a latin term
"""
in_content = _index(
backend,
title="report",
content="本文に重要な情報",
checksum="cjk-bare-1",
)
in_title = _index(
backend,
title="重要な報告書",
content="english only",
checksum="cjk-bare-2",
)
assert _matched_ids(backend, "重要") == {in_content.pk, in_title.pk}
assert _matched_ids(backend, "重要 OR report") == {
in_content.pk,
in_title.pk,
}
@@ -0,0 +1,86 @@
"""Whoosh's compact, separator-free date spelling, resolved end to end.
whoosh-compat owns both widths of this spelling and asserts both forms'
bounds directly in its own test suite: the 8-digit form as a whole calendar
day (lower bound, upper bound and exclusivity), and the 14-digit form as a
single instant. The 14-digit form is kept here as the single representative
because it is the one that exercises paperless's ``added`` DATETIME fast
field at full precision: the corpus separates a document at the named
instant from one on the same calendar day at another hour and one on the
next day at the same hour, so a query that degrades into a whole-day
window, or drops the time of day, matches the wrong set rather than passing
on a corpus that could not tell the difference.
"""
from __future__ import annotations
from datetime import UTC
from datetime import datetime
from typing import TYPE_CHECKING
import pytest
from documents.models import Document
if TYPE_CHECKING:
from documents.search._backend import TantivyBackend
pytestmark = [pytest.mark.search, pytest.mark.django_db]
def _matched_ids(backend: TantivyBackend, query: str) -> set[int]:
return set(backend.search_ids(query, user=None))
def _index(backend: TantivyBackend, **kwargs: object) -> Document:
doc = Document.objects.create(**kwargs)
backend.add_or_update(doc)
return doc
@pytest.fixture
def docs(backend: TantivyBackend) -> dict[str, int]:
return {
"instant": _index(
backend,
title="On the instant",
content="x",
checksum="compact-date-instant",
added=datetime(2005, 3, 4, 15, 30, tzinfo=UTC),
).pk,
"same_day": _index(
backend,
title="Same day, other hour",
content="x",
checksum="compact-date-same-day",
added=datetime(2005, 3, 4, 9, 0, tzinfo=UTC),
).pk,
"next_day": _index(
backend,
title="Next day, same hour",
content="x",
checksum="compact-date-next-day",
added=datetime(2005, 3, 5, 15, 30, tzinfo=UTC),
).pk,
}
def test_fourteen_digits_is_a_single_instant(
backend: TantivyBackend,
docs: dict[str, int],
) -> None:
"""
GIVEN:
- Three documents indexed on the ``added`` DATETIME fast field:
one at 2005-03-04T15:30:00, one on the same calendar day at a
different hour, and one on the next day at the same hour
WHEN:
- Searching with the 14-digit compact date form
``added:20050304153000``
THEN:
- Only the document at that exact instant matches; the same-day
document is what tells this apart from the 8-digit day-window
form, and the next-day document from a form that ignored the
time of day altogether
"""
assert _matched_ids(backend, "added:20050304153000") == {docs["instant"]}
@@ -0,0 +1,149 @@
"""_ConjunctiveNegations, the AST visitor that collects the subtrees a
query excludes from every document it matches, and _any_of, the clause-list
collapsing helper it feeds into.
Result-level proof that a negation reached through NOT/AND survives the
fuzzy/CJK blend lives in test_query_negation.py. These are direct unit
tests of the visitor's dispatch for the rarer grammar shapes
(AndNot/Boosted/AndMaybe/Require) that file's real-corpus queries don't
happen to exercise, plus the empty-clause-list case of _any_of.
"""
from __future__ import annotations
from typing import TYPE_CHECKING
import pytest
import whoosh_compat.ast as wc_ast
from documents.models import Document
from documents.search._query import _any_of
from documents.search._query import _ConjunctiveNegations
if TYPE_CHECKING:
from documents.search._backend import TantivyBackend
pytestmark = [pytest.mark.search, pytest.mark.django_db]
def _term(text: str) -> wc_ast.Term:
return wc_ast.Term(field=None, text=text)
class TestConjunctiveNegationsVisitor:
def test_visit_andnot_hoists_the_negative_branch(self) -> None:
"""
GIVEN:
- An AndNot(positive=a, negative=b) node
WHEN:
- _ConjunctiveNegations visits it
THEN:
- The negative branch is collected as an exclusion, since
AndNot requires positive and excludes negative
"""
negative = _term("b")
node = wc_ast.AndNot(positive=_term("a"), negative=negative)
assert _ConjunctiveNegations().visit(node) == (negative,)
def test_visit_andnot_also_collects_negations_already_in_the_positive_branch(
self,
) -> None:
"""
GIVEN:
- An AndNot node whose positive branch already contains a NOT
WHEN:
- _ConjunctiveNegations visits it
THEN:
- Both the positive branch's own negation and the AndNot's
negative branch are collected
"""
excluded_in_positive = _term("excluded")
negative = _term("negative")
node = wc_ast.AndNot(
positive=wc_ast.Not(child=excluded_in_positive),
negative=negative,
)
assert _ConjunctiveNegations().visit(node) == (excluded_in_positive, negative)
def test_visit_boosted_passes_through_to_the_child(self) -> None:
"""
GIVEN:
- A Boosted node (e.g. "(invoice NOT secret)^2") wrapping a
NOT
WHEN:
- _ConjunctiveNegations visits it
THEN:
- The negation inside the boosted child is still collected: a
boost must not shield an exclusion from being hoisted
"""
excluded = _term("secret")
node = wc_ast.Boosted(child=wc_ast.Not(child=excluded), boost=2.0)
assert _ConjunctiveNegations().visit(node) == (excluded,)
def test_visit_andmaybe_only_descends_into_required(self) -> None:
"""
GIVEN:
- An AndMaybe(required=a, optional=b) node where both required
and optional contain their own NOT
WHEN:
- _ConjunctiveNegations visits it
THEN:
- Only the negation in the required branch is collected. The
optional branch is not a conjunctive constraint on the whole
query (documents that fail it still match), so hoisting a
negation from it would exclude documents the query does not
actually exclude
"""
excluded_in_required = _term("excluded_in_required")
excluded_in_optional = _term("excluded_in_optional")
node = wc_ast.AndMaybe(
required=wc_ast.Not(child=excluded_in_required),
optional=wc_ast.Not(child=excluded_in_optional),
)
assert _ConjunctiveNegations().visit(node) == (excluded_in_required,)
def test_visit_require_descends_into_both_branches(self) -> None:
"""
GIVEN:
- A Require(scored=a, filter_only=b) node where both scored
and filter_only contain their own NOT
WHEN:
- _ConjunctiveNegations visits it
THEN:
- Both negations are collected: Require constrains the whole
query with both branches, one merely scored and the other
filter-only, so both are conjunctive
"""
excluded_in_scored = _term("excluded_in_scored")
excluded_in_filter = _term("excluded_in_filter")
node = wc_ast.Require(
scored=wc_ast.Not(child=excluded_in_scored),
filter_only=wc_ast.Not(child=excluded_in_filter),
)
assert _ConjunctiveNegations().visit(node) == (
excluded_in_scored,
excluded_in_filter,
)
class TestAnyOfEmptyClauseList:
def test_no_clauses_returns_a_query_that_matches_nothing(
self,
backend: TantivyBackend,
) -> None:
"""
GIVEN:
- No clauses at all
WHEN:
- _any_of is called with an empty list
THEN:
- It returns tantivy's empty_query() rather than raising or
wrapping zero clauses in a boolean_query, and running it
against a real index matches no documents
"""
doc = Document.objects.create(title="x", content="x", checksum="any-of-empty")
backend.add_or_update(doc)
query = _any_of([])
results = backend._index.searcher().search(query, limit=10)
assert len(results.hits) == 0
@@ -0,0 +1,91 @@
"""Pins the correctness gained by deleting the pre-parse
_quote_date_keyword_phrases rewrite.
That rewrite matched date-keyword phrases (e.g. "previous month" after a
date field) anywhere in the raw query string, including inside an
unrelated quoted string, and inserted quotes mid-phrase there too. Its
own docstring gave ``title:"see added:previous month notes"`` as the
example of what it corrupted. whoosh-compat's grammar accepts the same
phrase vocabulary unquoted natively (see TestUnquotedDateKeywordPhrases
in test_acceptance.py), so the rewrite was redundant everywhere it was
safe and actively wrong everywhere it was not. This is the one case that
tells the two apart: a literal title phrase that happens to contain
"added:previous month" as running text.
"""
from __future__ import annotations
from typing import TYPE_CHECKING
import pytest
from documents.models import Document
if TYPE_CHECKING:
from documents.search._backend import TantivyBackend
pytestmark = [pytest.mark.search, pytest.mark.django_db]
def _matched_ids(backend: TantivyBackend, query: str) -> set[int]:
return set(backend.search_ids(query, user=None))
def _index(backend: TantivyBackend, **kwargs: object) -> Document:
doc = Document.objects.create(**kwargs)
backend.add_or_update(doc)
return doc
class TestQuotedStringContainingDateKeywordText:
"""A quoted title phrase containing the literal text
"added:previous month" as running words must match on that literal
text alone, never spill into an unfielded search for "previous" and
"month" across the default search fields the way the deleted rewrite
would have decomposed it into."""
def test_matches_only_the_literal_phrase(
self,
backend: TantivyBackend,
) -> None:
"""
GIVEN:
- A document whose title literally contains "see
added:previous month notes", and a decoy document whose
title/content carry the individual fragments the deleted
_quote_date_keyword_phrases rewrite would have decomposed
the phrase into (the decoy would incorrectly match under
the deleted rewrite: its title contains the "see added:"
and " notes" fragments the corrupted parse required as
title phrases, and its content supplies "previous" and
"month" as the decomposed word-match clauses the rewrite
turned the middle of the phrase into)
WHEN:
- 'title:"see added:previous month notes"' is searched
THEN:
- Only the document with the literal phrase matches; it must
never spill into an unfielded search for "previous" and
"month" across the default search fields
"""
literal = _index(
backend,
title="see added:previous month notes",
content="quarterly filing",
checksum="dkp-literal",
archive_serial_number=920,
)
# Under the deleted rewrite, this decoy would incorrectly match:
# its title contains the "see added:" and " notes" fragments the
# corrupted parse required as title phrases, and its content
# supplies "previous" and "month" as the decomposed word-match
# clauses the rewrite turned the middle of the phrase into.
decoy = _index(
backend,
title="see added: quarterly report notes",
content="we reviewed the previous statement about month end",
checksum="dkp-decoy",
archive_serial_number=921,
)
query = 'title:"see added:previous month notes"'
assert _matched_ids(backend, query) == {literal.pk}
assert decoy.pk not in _matched_ids(backend, query)
@@ -0,0 +1,100 @@
"""Date keyword phrases (``today``, etc.) resolved in a non-UTC timezone,
end to end.
paperless's own ``tz=get_current_timezone()`` plumbing
(``TantivyBackend._parse_query``) is exercised elsewhere only for
relative *ranges* (``added:[-1 week to now]``, in
documents/tests/test_api_search.py). This covers a date *keyword*
(``today``), whose day boundary depends on the active timezone the same
way but goes through whoosh-compat's DateParserPlugin resolution instead
of an explicit range.
Discriminating shape: frozen at 2026-06-15T02:00 UTC, which is
2026-06-14T22:00 in America/New_York -- still "today" (06-14) there, but
already "today" (06-15) in UTC. Two documents pin both directions of the
mistake a hardcoded-UTC bug would make:
- ``in_ny_today`` (added 2026-06-14T20:00 UTC = 2026-06-14T16:00 NY) is
inside New York's "today" window and outside a naive UTC-calendar-day
window. A ``tz``-ignoring bug would miss it.
- ``in_utc_calendar_day_only`` (added 2026-06-15T10:00 UTC =
2026-06-15T06:00 NY) is inside a naive UTC-calendar-day window but
outside New York's actual "today" window. A ``tz``-ignoring bug would
wrongly match it.
"""
from __future__ import annotations
from datetime import UTC
from datetime import datetime
from typing import TYPE_CHECKING
import pytest
import time_machine
from documents.models import Document
if TYPE_CHECKING:
from pytest_django.fixtures import SettingsWrapper
from documents.search._backend import TantivyBackend
pytestmark = [pytest.mark.search, pytest.mark.django_db]
FROZEN_NOW = datetime(2026, 6, 15, 2, 0, tzinfo=UTC)
def _matched_ids(backend: TantivyBackend, query: str) -> set[int]:
return set(backend.search_ids(query, user=None))
def _index(backend: TantivyBackend, **kwargs: object) -> Document:
doc = Document.objects.create(**kwargs)
backend.add_or_update(doc)
return doc
class TestDateKeywordUsesTheActiveTimezone:
def test_today_matches_the_new_york_calendar_day_not_the_utc_one(
self,
backend: TantivyBackend,
settings: SettingsWrapper,
) -> None:
"""
GIVEN:
- TIME_ZONE set to America/New_York, time frozen at
2026-06-15T02:00 UTC (2026-06-14T22:00 NY -- still "today"
there, but already "today" in UTC), and two documents: one
added inside New York's "today" window but outside a naive
UTC-calendar-day window, the other the reverse (inside a
naive UTC-calendar-day window but outside New York's actual
"today")
WHEN:
- "added:today" is searched
THEN:
- Only the document inside New York's actual "today" window
matches, proving our tz=get_current_timezone() plumbing
resolves the date keyword in the active timezone rather
than a hardcoded UTC calendar day
"""
settings.TIME_ZONE = "America/New_York"
with time_machine.travel(FROZEN_NOW, tick=False):
in_ny_today = _index(
backend,
title="NY today",
content="x",
checksum="tz-keyword-ny-today",
added=datetime(2026, 6, 14, 20, 0, tzinfo=UTC),
)
# Not captured: the exact-set assertion below already proves
# this document (inside a naive UTC-calendar-day window, but
# outside New York's actual "today") does not match.
_index(
backend,
title="UTC calendar day only",
content="x",
checksum="tz-keyword-utc-calendar-day-only",
added=datetime(2026, 6, 15, 10, 0, tzinfo=UTC),
)
assert _matched_ids(backend, "added:today") == {in_ny_today.pk}
@@ -0,0 +1,32 @@
"""``_DEFAULT_SEARCH_FIELDS`` must stay a subset of the registered public
field names.
Nothing enforced this before: a rename in PUBLIC_FIELDS not mirrored in
``_DEFAULT_SEARCH_FIELDS`` (documents/search/_query.py) would 400 every
unfielded search at request time, since ``index.parse_query`` and the
fuzzy/CJK clause builders are handed a field name the schema no longer
has.
"""
from __future__ import annotations
from documents.search._fields import PUBLIC_FIELDS
from documents.search._query import _DEFAULT_SEARCH_FIELDS
class TestDefaultSearchFieldsAreRegistered:
def test_every_default_search_field_is_a_public_field(self) -> None:
"""
GIVEN:
- PUBLIC_FIELDS and _DEFAULT_SEARCH_FIELDS, our own field
tables
WHEN:
- Every name in _DEFAULT_SEARCH_FIELDS is checked against the
registered public field names
THEN:
- Every one is present; a rename in PUBLIC_FIELDS not
mirrored here would 400 every unfielded search at request
time
"""
public_field_names = {f.name for f in PUBLIC_FIELDS}
assert set(_DEFAULT_SEARCH_FIELDS) <= public_field_names
@@ -0,0 +1,474 @@
"""Pins the search syntax that ``docs/usage.md`` promises users.
Every query here appears verbatim, or as a direct paraphrase, in the
"Document searches" section of ``docs/usage.md``. Each case indexes real
documents and asserts on matched document IDs rather than on the parsed
query, because a query that parses cleanly is not necessarily a query that
means what the documentation says it means: ``added:now`` parses without a
single diagnostic and then matches nothing, because it resolves to an
instant rather than to a span.
The negative cases matter as much as the positive ones. They pin the
behaviours the docs explicitly warn about, so that if any of them ever
starts working the warning can be removed deliberately rather than being
left standing as a lie.
"""
from __future__ import annotations
from datetime import UTC
from datetime import datetime
from typing import TYPE_CHECKING
import pytest
import time_machine
from documents.models import Document
from documents.models import Note
from documents.models import Tag
from documents.search._errors import InvalidDateQuery
if TYPE_CHECKING:
from collections.abc import Generator
from django.contrib.auth.models import User
from documents.search._backend import TantivyBackend
pytestmark = [pytest.mark.search, pytest.mark.django_db]
# A Monday, so that "next monday"/"last monday" land a clean week either side.
FROZEN_NOW = datetime(2026, 6, 15, 12, 0, tzinfo=UTC)
# The checksum used in the docs' `checksum:` example.
DOC_CHECKSUM = "9f86d081884c7d659a2feaa0c55ad015a3bf4f1b2b0b822cd15d6c15b0f00a08"
def _matched_ids(backend: TantivyBackend, query: str) -> set[int]:
return set(backend.search_ids(query, user=None))
def _index(backend: TantivyBackend, **kwargs: object) -> Document:
doc = Document.objects.create(**kwargs)
backend.add_or_update(doc)
return doc
class TestLogicalExpressions:
@pytest.fixture
def docs(self, backend: TantivyBackend) -> dict[str, int]:
return {
"secret": _index(
backend,
title="Invoice one",
content="invoice secret contents",
checksum="doc-syntax-secret",
).pk,
"plain": _index(
backend,
title="Invoice two",
content="invoice ordinary contents",
checksum="doc-syntax-plain",
).pk,
}
def test_not_excludes_a_term(
self,
backend: TantivyBackend,
docs: dict[str, int],
) -> None:
"""
GIVEN:
- Two indexed documents, one containing "secret" and one not
WHEN:
- "invoice NOT secret" is searched, as docs/usage.md documents
THEN:
- Only the document without "secret" matches
"""
assert _matched_ids(backend, "invoice NOT secret") == {docs["plain"]}
def test_leading_hyphen_requires_the_term_instead_of_excluding_it(
self,
backend: TantivyBackend,
docs: dict[str, int],
) -> None:
"""
GIVEN:
- Two indexed documents, one containing "secret" and one not
WHEN:
- "invoice -secret" is searched (a leading hyphen, not "NOT")
THEN:
- Only the document containing "secret" matches, because
separators are stripped at index time, so "-secret" is
indexed as the plain term "secret" and the query becomes an
AND rather than an exclusion, exactly as the docs warn
"""
assert _matched_ids(backend, "invoice -secret") == {docs["secret"]}
def test_or_inside_parentheses_matches_either_branch(
self,
backend: TantivyBackend,
docs: dict[str, int],
) -> None:
"""
GIVEN:
- Two indexed documents, one containing "secret" and one
containing "ordinary"
WHEN:
- "invoice AND (secret OR ordinary)" is searched
THEN:
- Both documents match
"""
matched = _matched_ids(backend, "invoice AND (secret OR ordinary)")
assert matched == {docs["secret"], docs["plain"]}
class TestPhraseSearch:
def test_quoted_phrase_requires_the_words_in_order(
self,
backend: TantivyBackend,
) -> None:
"""
GIVEN:
- A document whose content contains "the quick brown fox jumps"
WHEN:
- A quoted phrase is searched, in order and out of order
THEN:
- The in-order phrase matches, and the same words reordered do
not
"""
doc = _index(
backend,
title="Phrase",
content="the quick brown fox jumps",
checksum="doc-syntax-phrase",
)
assert _matched_ids(backend, '"quick brown fox"') == {doc.pk}
assert _matched_ids(backend, '"brown quick fox"') == set()
class TestTagCommaList:
"""``tag:bills,unpaid`` is published syntax (docs/usage.md), so this checks
that the documented spelling still returns what the docs promise: only the
document carrying every listed tag.
It is deliberately not proof of paperless's field configuration, and must
not be read as such. Removing ``comma_values`` from the ``tag`` FieldSpec
leaves this test passing, because paperless's analyzer splits the literal
value "bills,unpaid" into the same two tokens the value-list reading
produces, so the two readings select the same documents. The registry fact
-- that ``tag`` opts in and no other field does -- is observable only at
the registry, and is owned by test_registry.py's
``test_tag_is_comma_values``/``test_correspondent_is_not_comma_values``.
"""
def test_comma_list_requires_every_listed_tag(
self,
backend: TantivyBackend,
) -> None:
"""
GIVEN:
- A document carrying both "bills" and "unpaid" tags, and a
second document carrying only "bills" (plus "archived")
WHEN:
- "tag:bills,unpaid" is searched
THEN:
- Only the document carrying every listed tag matches, and a
single-tag "tag:bills" search still matches both documents
"""
bills = Tag.objects.create(name="bills")
unpaid = Tag.objects.create(name="unpaid")
archived = Tag.objects.create(name="archived")
both = Document.objects.create(
title="Both tags",
content="body",
checksum="doc-syntax-tag-both",
)
both.tags.add(bills, unpaid)
backend.add_or_update(both)
one = Document.objects.create(
title="One tag",
content="body",
checksum="doc-syntax-tag-one",
)
one.tags.add(bills, archived)
backend.add_or_update(one)
assert _matched_ids(backend, "tag:bills,unpaid") == {both.pk}
assert _matched_ids(backend, "tag:bills") == {both.pk, one.pk}
class TestArchiveMetadataFields:
@pytest.fixture
def doc(self, backend: TantivyBackend, admin_user: User) -> Document:
doc = Document.objects.create(
title="Metadata",
content="body",
checksum=DOC_CHECKSUM,
archive_serial_number=100,
page_count=12,
original_filename="invoice.pdf",
)
Note.objects.create(document=doc, user=admin_user, note="a note")
backend.add_or_update(doc)
return doc
@pytest.mark.parametrize(
"query",
[
"asn:100",
"asn:[50 to 150]",
"page_count:12",
"page_count:[10 to 20]",
"num_notes:1",
"num_notes:[1 to 5]",
"original_filename:invoice.pdf",
f"checksum:{DOC_CHECKSUM}",
"checksum:9f86d081*",
# A checksum term is stored verbatim, but a checksum *pattern* is
# lowercased before it is matched, which the docs now say outright
# next to the "only a complete, lowercase checksum matches" rule
# that the uppercase term in the negative list below pins.
"checksum:9F86D081*",
],
)
def test_documented_metadata_query_matches(
self,
backend: TantivyBackend,
doc: Document,
query: str,
) -> None:
"""
GIVEN:
- A document with an ASN, page count, a note, an original
filename and a known checksum
WHEN:
- Every documented metadata-field spelling (exact value,
range, and, for checksum, a lowercase prefix pattern
regardless of the case the pattern itself is typed in) is
searched
THEN:
- Each one matches the document
"""
assert _matched_ids(backend, query) == {doc.pk}
@pytest.mark.parametrize(
"query",
[
# The docs say only a complete, lowercase checksum matches.
"checksum:9f86d081",
f"checksum:{DOC_CHECKSUM.upper()}",
],
)
def test_partial_or_uppercase_checksum_matches_nothing(
self,
backend: TantivyBackend,
doc: Document,
query: str,
) -> None:
"""
GIVEN:
- A document with a known, complete, lowercase checksum
WHEN:
- An exact-value search is run with a partial or uppercase
spelling of that checksum
THEN:
- Nothing matches, as the docs say only a complete, lowercase
checksum matches as an exact value
"""
assert _matched_ids(backend, query) == set()
class TestDocumentedDateForms:
@pytest.fixture(autouse=True)
def frozen_now(self) -> Generator[None, None, None]:
with time_machine.travel(FROZEN_NOW, tick=False):
yield
@pytest.fixture
def dated(self, backend: TantivyBackend) -> dict[str, int]:
stamps = {
"today": datetime(2026, 6, 15, 9, 0, tzinfo=UTC),
"yesterday": datetime(2026, 6, 14, 9, 0, tzinfo=UTC),
"tomorrow": datetime(2026, 6, 16, 9, 0, tzinfo=UTC),
"next_monday": datetime(2026, 6, 22, 10, 0, tzinfo=UTC),
"last_monday": datetime(2026, 6, 8, 10, 0, tzinfo=UTC),
"january": datetime(2026, 1, 10, 10, 0, tzinfo=UTC),
"old": datetime(2005, 3, 4, 15, 30, tzinfo=UTC),
}
return {
label: _index(
backend,
title=label,
content="dated body",
checksum=f"doc-syntax-date-{label}",
added=stamp,
).pk
for label, stamp in stamps.items()
}
@pytest.mark.parametrize(
("query", "label"),
[
("added:today", "today"),
("added:yesterday", "yesterday"),
("added:tomorrow", "tomorrow"),
('added:"next monday"', "next_monday"),
('added:"last monday"', "last_monday"),
("added:january", "january"),
("added:2005-03-04", "old"),
("added:2005-03", "old"),
("added:[2005-01-01 to 2005-12-31]", "old"),
("added:[2005 to 2009]", "old"),
# A full timestamp works, but only quoted when it stands alone,
# and only unquoted when it is a range bound. The bare standalone
# spelling is pinned as a non-match below.
('added:"2005-03-04T15:30:00Z"', "old"),
("added:[2005-03-04T09:00:00Z to 2005-03-04T17:00:00Z]", "old"),
# A quoted range bound works when the quotes are single ones; the
# double-quoted spelling is pinned as an error below.
("added:['2005-03-04' to 2005-03-05]", "old"),
],
)
def test_documented_date_form_matches_its_day_or_month(
self,
backend: TantivyBackend,
dated: dict[str, int],
query: str,
label: str,
) -> None:
"""
GIVEN:
- Documents dated today, yesterday, tomorrow, next/last
Monday, in January, and on an old fixed date, indexed
against a frozen "now" (a Monday)
WHEN:
- Every documented date-form spelling is searched: relative
keywords, quoted multi-word phrases, a bare year-month, an
explicit range, a quoted full timestamp standing alone, an
unquoted full timestamp as a range bound, and a
single-quoted range bound
THEN:
- Each form matches exactly the document dated on its day or
within its month
"""
assert _matched_ids(backend, query) == {dated[label]}
@pytest.mark.parametrize(
"query",
[
# Zero-width: these resolve to a single instant, not a span, so
# nothing in a realistic corpus lands on them. The docs warn
# about them rather than presenting them as usable.
"added:now",
"added:noon",
"added:midnight",
# Quoting is what rescues the other multi-word date expressions,
# so pin that it does not rescue these: the problem is the width
# of the resulting range, not the way the value is delimited.
# One quoted spelling is enough for that; which keyword sits
# inside the quotes is grammar whoosh-compat owns.
'added:"now"',
# A relative offset, which the warning in the docs names by this
# exact spelling. Standing alone it is an instant like the rest of
# this list; the same offset used as a range bound is a real
# window, pinned by the test below.
'added:"-1 week"',
],
)
def test_forms_the_docs_warn_about_match_nothing(
self,
backend: TantivyBackend,
dated: dict[str, int],
query: str,
) -> None:
"""
GIVEN:
- A realistic dated corpus (see the `dated` fixture)
WHEN:
- A zero-width date form ("now", "noon", "midnight", a quoted
"now") or a standalone relative offset ("-1 week") is
searched: each resolves to a single instant rather than a
span, and quoting does not rescue them the way it rescues
other multi-word date expressions, since the problem is the
width of the resulting range, not how the value is
delimited
THEN:
- Nothing matches, exactly as the docs warn, rather than
presenting these as usable spellings
"""
assert _matched_ids(backend, query) == set()
def test_bare_timestamp_is_rejected_rather_than_matching_nothing(
self,
backend: TantivyBackend,
dated: dict[str, int],
) -> None:
"""
GIVEN:
- A realistic dated corpus, including a document dated at a
known full timestamp
WHEN:
- The bare, unquoted spelling of that full timestamp is
searched (the quoted and range-bound spellings pinned above
do work and match this fixture's document)
THEN:
- `InvalidDateQuery` is raised rather than the query silently
matching nothing, since this is a user-fixable error the
docs tell the user to quote, and the reported value is the
whole contiguous fragment the user typed, not just the
prefix the date grammar's tokenizer first split on
"""
with pytest.raises(InvalidDateQuery) as exc_info:
_matched_ids(backend, "added:2005-03-04T15:30:00Z")
assert exc_info.value.field == "added"
assert exc_info.value.value == "2005-03-04T15:30:00Z"
def test_relative_offset_as_a_range_bound_is_a_real_window(
self,
backend: TantivyBackend,
dated: dict[str, int],
) -> None:
"""
GIVEN:
- A realistic dated corpus, including a document dated two
hours before a "last Monday to now" window opens, and
documents dated today and yesterday, inside that window
WHEN:
- "added:['-1 week' to now]" is searched: the same offset
that matches nothing standing alone (see the test above),
used here as a range bound instead
THEN:
- The window matches today and yesterday but excludes the
document two hours before it opens, showing the bound is
the offset itself and not a whole-day rounding of it, as
the docs say next to the warning about the standalone form
"""
assert _matched_ids(backend, "added:['-1 week' to now]") == {
dated["today"],
dated["yesterday"],
}
def test_double_quoted_range_bound_is_rejected(
self,
backend: TantivyBackend,
dated: dict[str, int],
) -> None:
"""
GIVEN:
- A realistic dated corpus
WHEN:
- A range bound is double-quoted rather than single-quoted
("added:[\"2005-03-04\" to 2005-03-05]")
THEN:
- `InvalidDateQuery` is raised, pinning which of the two
quote characters fails: quoting a range bound is allowed,
but only with single quotes, since the double-quoted
spelling reaches the date grammar with its quotes still
attached and is not a recognizable date
"""
with pytest.raises(InvalidDateQuery) as exc_info:
_matched_ids(backend, 'added:["2005-03-04" to 2005-03-05]')
assert exc_info.value.value == '"2005-03-04"'
@@ -0,0 +1,364 @@
"""Diagnostics route by Cause, and user-facing messages are host-owned.
whoosh-compat documents ``Diagnostic.message`` as developer output with no
stability guarantee, so it must never reach an HTTP response body.
"""
from __future__ import annotations
import logging
from datetime import UTC
import pytest
import tantivy
from whoosh_compat.errors import Diagnostic
from whoosh_compat.errors import DiagnosticKind
from whoosh_compat.errors import QueryError
from whoosh_compat.errors import cause_for
from whoosh_compat.fields import FieldKind
from whoosh_compat.fields import FieldRef
from documents.search._errors import SearchQueryError
from documents.search._query import _map_emit_error
from documents.search._query import _single_diagnostic_to_error
from documents.search._query import parse_user_query
from documents.search._schema import build_schema
from documents.search._tokenizer import register_tokenizers
pytestmark = pytest.mark.search
_LIBRARY_PROSE = "INTERNAL LIBRARY WORDING WITH raw tantivy detail"
@pytest.fixture(scope="module")
def query_index() -> tantivy.Index:
"""An in-memory, unstemmed index; these tests only parse, never index."""
idx = tantivy.Index(build_schema(), path=None)
register_tokenizers(idx, "")
return idx
def _diagnostic(
kind: DiagnosticKind,
*,
field: FieldRef | None = FieldRef("title"),
field_kind: FieldKind | None = FieldKind.TEXT,
) -> Diagnostic:
"""A Diagnostic shaped like the emitter's, with the library's own
kind -> cause mapping rather than a hand-picked cause."""
return Diagnostic(
kind=kind,
cause=cause_for(kind),
message=_LIBRARY_PROSE,
field=field,
field_kind=field_kind,
)
class TestEmitErrorRouting:
"""Every Cause gets a distinguishable treatment, not just "a 400"."""
@pytest.mark.parametrize(
"kind",
[
DiagnosticKind.BACKEND_REJECTED,
DiagnosticKind.AST_INVALID_SHAPE,
DiagnosticKind.AST_UNKNOWN_FIELD,
],
)
def test_internal_cause_is_not_converted(self, kind: DiagnosticKind) -> None:
"""
GIVEN:
- A QueryError wrapping a Diagnostic whose Cause is INTERNAL
(BACKEND_REJECTED/AST_INVALID_SHAPE/AST_UNKNOWN_FIELD)
WHEN:
- _map_emit_error processes it
THEN:
- The original QueryError propagates unchanged, so it surfaces
as a 500 monitoring can see, never a 400 blaming the user
"""
error = QueryError(_diagnostic(kind))
with pytest.raises(QueryError) as excinfo:
_map_emit_error(error)
assert excinfo.value is error
def test_misconfigured_cause_is_logged_and_reraised(
self,
caplog: pytest.LogCaptureFixture,
) -> None:
"""
GIVEN:
- A QueryError for SCHEMA_FIELD_MISSING naming field "asn"
WHEN:
- _map_emit_error processes it
THEN:
- Exactly one ERROR log record is emitted naming the field and
the diagnostic kind, and the original QueryError propagates
unchanged: a registry/schema disagreement is transient (the
exact same query succeeds once the index is rebuilt), so it
surfaces as a 500 an operator can see rather than a 400
telling the client their query is permanently invalid
"""
kind = DiagnosticKind.SCHEMA_FIELD_MISSING
error = QueryError(_diagnostic(kind, field=FieldRef("asn")))
with (
caplog.at_level(logging.ERROR, logger="paperless.search"),
pytest.raises(QueryError) as excinfo,
):
_map_emit_error(error)
assert excinfo.value is error
errors = [r for r in caplog.records if r.levelno == logging.ERROR]
assert len(errors) == 1
assert "asn" in errors[0].getMessage()
assert kind.name in errors[0].getMessage()
@pytest.mark.parametrize(
"kind",
[
DiagnosticKind.TEXT_RANGE,
DiagnosticKind.PATTERN_TOO_COMPLEX,
DiagnosticKind.EXISTS_REQUIRES_FAST,
],
)
def test_unsupported_cause_is_a_400_with_no_operator_log(
self,
kind: DiagnosticKind,
caplog: pytest.LogCaptureFixture,
) -> None:
"""
GIVEN:
- A QueryError for a query tantivy cannot run
(TEXT_RANGE/PATTERN_TOO_COMPLEX/EXISTS_REQUIRES_FAST)
WHEN:
- _map_emit_error processes it
THEN:
- It becomes a SearchQueryError with no log record at WARNING
or above; a query tantivy cannot run is the user's to fix,
not an operator alert. EXISTS_REQUIRES_FAST is nominally
MISCONFIGURED but belongs here: it is decided from the
registry's own FieldSpec, so it never reports a disagreement
anyone could resolve
"""
with caplog.at_level(logging.WARNING, logger="paperless.search"):
error = _map_emit_error(QueryError(_diagnostic(kind)))
assert isinstance(error, SearchQueryError)
assert caplog.records == []
@pytest.mark.parametrize(
"kind",
[
DiagnosticKind.TEXT_RANGE,
DiagnosticKind.PATTERN_TOO_COMPLEX,
DiagnosticKind.EXISTS_REQUIRES_FAST,
],
)
def test_user_facing_message_never_echoes_library_prose(
self,
kind: DiagnosticKind,
) -> None:
"""
GIVEN:
- A QueryError carrying whoosh-compat's own developer-facing
message text (SCHEMA_FIELD_MISSING excluded: it is now
re-raised rather than converted, so it never produces a
user-facing message at all, see
test_misconfigured_cause_is_logged_and_reraised)
WHEN:
- _map_emit_error processes it
THEN:
- The resulting error's string never contains that library
prose
"""
error = _map_emit_error(QueryError(_diagnostic(kind)))
assert _LIBRARY_PROSE not in str(error)
@pytest.mark.parametrize(
"kind",
[
DiagnosticKind.TEXT_RANGE,
DiagnosticKind.PATTERN_TOO_COMPLEX,
DiagnosticKind.EXISTS_REQUIRES_FAST,
],
)
def test_user_facing_message_names_the_field(
self,
kind: DiagnosticKind,
) -> None:
"""
GIVEN:
- A QueryError for a JSON subpath field (custom_fields.value)
WHEN:
- _map_emit_error processes it
THEN:
- The resulting error names the field using its canonical
dotted form, including the subpath (FieldRef.__str__ yields
this dotted name, so every user-reachable emit kind can name
it)
"""
diagnostic = _diagnostic(
kind,
field=FieldRef("custom_fields", "value"),
field_kind=FieldKind.JSON,
)
error = _map_emit_error(QueryError(diagnostic))
assert "custom_fields.value" in str(error)
class TestParseDiagnosticMessages:
"""Parse-time diagnostics are host-worded too, off field_kind."""
def test_too_deep_is_a_400_without_library_prose(self) -> None:
"""
GIVEN:
- A parse-time Diagnostic for TOO_DEEP with no field
WHEN:
- _single_diagnostic_to_error processes it
THEN:
- It becomes a SearchQueryError with no library prose in its
message
"""
error = _single_diagnostic_to_error(
_diagnostic(DiagnosticKind.TOO_DEEP, field=None, field_kind=None),
)
assert isinstance(error, SearchQueryError)
assert _LIBRARY_PROSE not in str(error)
@pytest.mark.parametrize(
("kind", "field_kind"),
[
(DiagnosticKind.PATTERN_ON_NUMERIC, FieldKind.U64),
(DiagnosticKind.PATTERN_ON_BOOLEAN_EXISTS, FieldKind.BOOLEAN_EXISTS),
(DiagnosticKind.PATTERN_ON_SUBPATH, FieldKind.JSON),
],
)
def test_pattern_on_kinds_name_the_field_and_its_kind(
self,
kind: DiagnosticKind,
field_kind: FieldKind,
) -> None:
"""
GIVEN:
- A parse-time Diagnostic for a pattern used against a kind
that cannot take one
(PATTERN_ON_NUMERIC/PATTERN_ON_BOOLEAN_EXISTS/PATTERN_ON_SUBPATH)
WHEN:
- _single_diagnostic_to_error processes it
THEN:
- The message names both the field and its kind, with no
library prose
"""
error = _single_diagnostic_to_error(
_diagnostic(kind, field=FieldRef("asn"), field_kind=field_kind),
)
message = str(error)
assert _LIBRARY_PROSE not in message
assert "asn" in message
assert field_kind.name.lower() in message
def test_single_char_bracket_range_names_the_field_and_the_value(self) -> None:
"""
GIVEN:
- A SINGLE_CHAR_BRACKET_RANGE diagnostic for "title" with
raw_value "200[1-9]"
WHEN:
- _single_diagnostic_to_error processes it
THEN:
- The resulting SearchQueryError names both the field and the
offending value, with no library prose
"""
diagnostic = Diagnostic(
kind=DiagnosticKind.SINGLE_CHAR_BRACKET_RANGE,
cause=cause_for(DiagnosticKind.SINGLE_CHAR_BRACKET_RANGE),
message=_LIBRARY_PROSE,
field=FieldRef("title"),
field_kind=FieldKind.TEXT,
raw_value="200[1-9]",
)
error = _single_diagnostic_to_error(diagnostic)
message = str(error)
assert isinstance(error, SearchQueryError)
assert _LIBRARY_PROSE not in message
assert "title" in message
assert "200[1-9]" in message
class TestRealQueriesRouteCorrectly:
"""The routing table against diagnostics emit() really produces."""
def test_text_range_is_a_400_naming_the_field(
self,
query_index: tantivy.Index,
) -> None:
"""
GIVEN:
- A real query index
WHEN:
- parse_user_query is called with a text-range query
("title:[a to b]")
THEN:
- It raises SearchQueryError naming "title"
"""
with pytest.raises(SearchQueryError) as excinfo:
parse_user_query(query_index, "title:[a to b]", UTC)
assert "title" in str(excinfo.value)
def test_wildcard_on_a_numeric_field_is_a_400_naming_the_field(
self,
query_index: tantivy.Index,
) -> None:
"""
GIVEN:
- A real query index
WHEN:
- parse_user_query is called with a wildcard on a numeric
field ("asn:12*")
THEN:
- It raises SearchQueryError naming "asn"
"""
with pytest.raises(SearchQueryError) as excinfo:
parse_user_query(query_index, "asn:12*", UTC)
assert "asn" in str(excinfo.value)
def test_single_char_bracket_range_is_a_400_naming_field_and_value(
self,
query_index: tantivy.Index,
) -> None:
"""
GIVEN:
- A real query index
WHEN:
- parse_user_query is called with "title:200[1-9]"
THEN:
- It raises SearchQueryError naming both "title" and
"200[1-9]"
"""
with pytest.raises(SearchQueryError) as excinfo:
parse_user_query(query_index, "title:200[1-9]", UTC)
message = str(excinfo.value)
assert "title" in message
assert "200[1-9]" in message
def test_internal_diagnostic_escapes_as_a_query_error(
self,
query_index: tantivy.Index,
monkeypatch: pytest.MonkeyPatch,
) -> None:
"""
GIVEN:
- tantivy_emit monkeypatched to raise a QueryError with an
INTERNAL-cause diagnostic (BACKEND_REJECTED), the one case
with no query text of its own involved
WHEN:
- parse_user_query runs a normal query ("invoice")
THEN:
- The QueryError propagates unconverted; emit() reporting a
defect in itself must not become a user-facing 400
"""
import documents.search._query as query_mod
def raise_internal(*args: object, **kwargs: object) -> None:
raise QueryError(_diagnostic(DiagnosticKind.BACKEND_REJECTED))
monkeypatch.setattr(query_mod, "tantivy_emit", raise_internal)
with pytest.raises(QueryError):
parse_user_query(query_index, "invoice", UTC)
@@ -0,0 +1,124 @@
"""``field:*`` on a JSON field is user error, not an operator alert.
whoosh-compat classifies EXISTS_REQUIRES_FAST as MISCONFIGURED, and
_map_emit_error used to route every MISCONFIGURED diagnostic to an ERROR log.
But the kind is decided from the registry's own FieldSpec (kind plus fast)
without consulting the index schema, and field_descriptors() builds the JSON
fields non-fast deliberately, so nothing is misconfigured and no operator
action can clear the condition. Any authenticated user could otherwise emit
ERROR lines in a loop by repeating ``notes:*``.
SCHEMA_FIELD_MISSING, the other MISCONFIGURED kind, does compare the registry
against the live schema, so it stays an ERROR log. But it is not a 400
either: the exact same query would succeed once the index is rebuilt, so it
is a transient server-side condition, not a permanently bad request, and is
re-raised the same way an INTERNAL cause is.
"""
from __future__ import annotations
import logging
from datetime import UTC
import pytest
import tantivy
from whoosh_compat.errors import Diagnostic
from whoosh_compat.errors import DiagnosticKind
from whoosh_compat.errors import QueryError
from whoosh_compat.errors import cause_for
from whoosh_compat.fields import FieldKind
from whoosh_compat.fields import FieldRef
from documents.search._errors import SearchQueryError
from documents.search._query import _map_emit_error
from documents.search._query import parse_user_query
from documents.search._schema import build_schema
from documents.search._tokenizer import register_tokenizers
pytestmark = pytest.mark.search
# Every spelling of "does this JSON field have a value" a user can type.
EXISTS_QUERIES = [
"notes:*",
"notes.note:*",
"notes.user:*",
"custom_fields:*",
"custom_fields.name:*",
"custom_fields.value:*",
]
@pytest.fixture(scope="module")
def query_index() -> tantivy.Index:
idx = tantivy.Index(build_schema(), path=None)
register_tokenizers(idx, "")
return idx
class TestJsonExistsIsUserError:
@pytest.mark.parametrize("query", EXISTS_QUERIES)
def test_query_is_a_400_that_emits_no_error_log(
self,
query_index: tantivy.Index,
caplog: pytest.LogCaptureFixture,
query: str,
) -> None:
"""
GIVEN:
- A real query index, and every spelling of "does this JSON
field have a value" (notes:*, notes.note:*, custom_fields:*,
etc.)
WHEN:
- parse_user_query runs the query
THEN:
- It raises SearchQueryError naming the field, and no
ERROR-level log record is emitted; EXISTS_REQUIRES_FAST on a
JSON field is by design, not a misconfiguration an operator
could act on
"""
with caplog.at_level(logging.WARNING, logger="paperless.search"):
with pytest.raises(SearchQueryError) as excinfo:
parse_user_query(query_index, query, UTC)
assert query.split(":", maxsplit=1)[0] in str(excinfo.value)
assert [r for r in caplog.records if r.levelno >= logging.ERROR] == []
class TestGenuineMisconfigurationStillLogs:
def test_schema_field_missing_is_an_error_log_and_reraised(
self,
caplog: pytest.LogCaptureFixture,
) -> None:
"""
GIVEN:
- A QueryError for SCHEMA_FIELD_MISSING: the registry naming a
field the index schema does not have
WHEN:
- _map_emit_error processes it
THEN:
- It logs exactly one ERROR record naming the diagnostic kind
(a real mismatch an operator can fix, so it keeps the
alert), and the original QueryError propagates unchanged
rather than becoming a SearchQueryError: the exact same
query would succeed once the index is rebuilt, so this is a
transient server-side condition, not a permanently bad
request, and surfaces as a 500 rather than a 400
"""
kind = DiagnosticKind.SCHEMA_FIELD_MISSING
error = QueryError(
Diagnostic(
kind=kind,
cause=cause_for(kind),
message="field 'asn' is not defined in the index schema",
field=FieldRef("asn"),
field_kind=FieldKind.U64,
),
)
with (
caplog.at_level(logging.ERROR, logger="paperless.search"),
pytest.raises(QueryError) as excinfo,
):
_map_emit_error(error)
assert excinfo.value is error
records = [r for r in caplog.records if r.levelno == logging.ERROR]
assert len(records) == 1
assert kind.name in records[0].getMessage()
@@ -0,0 +1,261 @@
"""The words the fuzzy blend clause hands back to tantivy's parser.
The clause re-parses a word string through tantivy, which analyzes it
again, so the words must be the query's raw text rather than the analyzed
text (analysis is not idempotent), and must still be split into plain
words so that hyphenated, dotted and quoted terms keep contributing.
"""
from __future__ import annotations
from typing import TYPE_CHECKING
import pytest
from documents.models import Document
if TYPE_CHECKING:
from pytest_django.fixtures import SettingsWrapper
from documents.search._backend import TantivyBackend
pytestmark = [pytest.mark.search, pytest.mark.django_db]
def _matched_ids(backend: TantivyBackend, query: str) -> set[int]:
return set(backend.search_ids(query, user=None))
def _index(backend: TantivyBackend, **kwargs: object) -> Document:
doc = Document.objects.create(**kwargs)
backend.add_or_update(doc)
return doc
@pytest.fixture(autouse=True)
def fuzzy_enabled(settings: SettingsWrapper) -> None:
"""Enable the fuzzy blend clause. The threshold doubles as a minimum
score filter, so it is set to 0.0: every hit passes and the test sees
the clause's matching behaviour, not the filter's."""
settings.ADVANCED_FUZZY_SEARCH_THRESHOLD = 0.0
class TestFuzzyClauseParseFailureDegradesGracefully:
def test_a_word_string_tantivy_rejects_drops_the_clause_only(self) -> None:
"""
GIVEN:
- A parsed query with free-text words, and an index-like
object whose parse_query is forced to raise ValueError
WHEN:
- _try_parse_fuzzy_query is called
THEN:
- It returns None instead of propagating, so a fuzzy word
string tantivy's own parser rejects only drops the fuzzy
clause: the exact/CJK clauses still stand rather than the
whole query failing. The ValueError guard is insurance (the
word string is plain tokens, so tantivy accepting it is
expected, not assumed)
"""
import whoosh_compat as wc
from documents.search._query import _DEFAULT_SEARCH_FIELDS
from documents.search._query import _try_parse_fuzzy_query
from documents.search._registry import get_field_registry
registry = get_field_registry(None)
result = wc.parse(
"invoice",
registry=registry,
default_fields=_DEFAULT_SEARCH_FIELDS,
)
class _RaisingIndex:
def parse_query(self, *args: object, **kwargs: object) -> object:
raise ValueError("synthetic parse failure")
assert _try_parse_fuzzy_query(_RaisingIndex(), result.ast, registry) is None
class TestFuzzyClauseWords:
def test_a_stemmed_word_is_not_stemmed_a_second_time(
self,
backend: TantivyBackend,
) -> None:
"""
GIVEN:
- Documents whose content contains "universities", a
one-transposition typo of it ("universties"), and two
unrelated words that share its stem prefix ("univalent",
"unicycle")
WHEN:
- Searching for "universities" with the fuzzy blend enabled
THEN:
- Only the correctly-spelled document and its typo match; the
clause does not widen far enough to reach the unrelated
words. 'universities' stems to 'univers'; feeding that back
to tantivy would stem it again to 'univ', whose fuzzy prefix
reaches unrelated words - the clause must stay wide enough
for a typo and no wider
"""
wanted = _index(
backend,
title="A",
content="universities of europe",
checksum="fuzz-stem-1",
)
typo = _index(
backend,
title="B",
content="universties of europe",
checksum="fuzz-stem-2",
)
_index(
backend,
title="C",
content="univalent chemical bonds",
checksum="fuzz-stem-3",
)
_index(
backend,
title="D",
content="unicycle repair manual",
checksum="fuzz-stem-4",
)
assert _matched_ids(backend, "universities") == {wanted.pk, typo.pk}
def test_a_hyphenated_term_still_reaches_the_clause(
self,
backend: TantivyBackend,
) -> None:
"""
GIVEN:
- A document whose content contains a near-miss of "COVID-19"
("covidx")
WHEN:
- Searching for "COVID-19" with the fuzzy blend enabled
THEN:
- The document matches; 'COVID-19' is one raw token, so unless
it is split into words, it carries characters the re-parse
would read as grammar, is dropped, and the whole query loses
its fuzzy clause
"""
misspelled = _index(
backend,
title="A",
content="covidx testing results",
checksum="fuzz-hyphen-1",
)
assert _matched_ids(backend, "COVID-19") == {misspelled.pk}
def test_a_phrase_still_reaches_the_clause(
self,
backend: TantivyBackend,
) -> None:
"""
GIVEN:
- A document whose content near-misses a quoted phrase
WHEN:
- Searching for the quoted phrase '"tax reports"' with the
fuzzy blend enabled
THEN:
- The document matches; a phrase is one raw token carrying a
space, and is the whole query's only free text here, so it
must still reach the clause
"""
near_miss = _index(
backend,
title="A",
content="taxation reportage weekly",
checksum="fuzz-phrase-1",
)
assert _matched_ids(backend, '"tax reports"') == {near_miss.pk}
class TestBooleanKeywordsInRawText:
"""Tantivy's boolean keywords are word runs, so they survive the cut
into words and its own parser reads them as grammar. Raw query text
reaches that parser with its case intact, so a quoted phrase can carry
them in."""
@pytest.fixture
def corpus(self, backend: TantivyBackend) -> dict[str, int]:
both = _index(
backend,
title="A",
content="taxation reportage weekly",
checksum="fuzz-kw-1",
)
tax_only = _index(
backend,
title="B",
content="taxation only here",
checksum="fuzz-kw-2",
)
report_only = _index(
backend,
title="C",
content="reportage only here",
checksum="fuzz-kw-3",
)
return {
"both": both.pk,
"tax_only": tax_only.pk,
"report_only": report_only.pk,
}
@pytest.mark.parametrize(
"query",
[
pytest.param('"tax AND reports"', id="and"),
pytest.param('"tax OR reports"', id="or"),
pytest.param('"tax NOT reports"', id="not"),
pytest.param('"tax IN reports"', id="in"),
],
)
def test_a_keyword_inside_a_phrase_stays_an_ordinary_word(
self,
backend: TantivyBackend,
corpus: dict[str, int],
query: str,
) -> None:
"""
GIVEN:
- Three documents: one with both "taxation" and "reportage",
one with only "taxation", one with only "reportage"
WHEN:
- Searching for a quoted phrase carrying a tantivy boolean
keyword as one of its words (e.g. '"tax AND reports"')
THEN:
- The keyword stays an ordinary word inside the phrase, and
the fuzzy clause matches all three documents, the same
disjunction as the plain '"tax reports"' phrase: AND must
not turn it into a conjunction, NOT must not give it its own
exclusion, IN must not fail the parse
"""
assert _matched_ids(backend, '"tax reports"') == set(corpus.values())
assert _matched_ids(backend, query) == set(corpus.values())
def test_a_trailing_keyword_does_not_drop_the_clause(
self,
backend: TantivyBackend,
corpus: dict[str, int],
) -> None:
"""
GIVEN:
- Three documents: one with both "taxation" and "reportage",
one with only "taxation", one with only "reportage"
WHEN:
- Searching for '"tax AND"', a phrase ending in a tantivy
syntax error
THEN:
- The fuzzy clause still matches on "tax"; 'tax AND' alone is
a syntax error to tantivy's parser, which would otherwise
cost the whole query its fuzzy clause
"""
assert _matched_ids(backend, '"tax AND"') == {
corpus["both"],
corpus["tax_only"],
}
@@ -0,0 +1,249 @@
"""Regression coverage for the unguarded TEXT-mode highlight query.
parse_simple_text_highlight_query re-parses simple-search tokens through
Tantivy's query-string parser to build a SnippetGenerator-compatible query.
Simple-search tokens keep arbitrary punctuation (quotes, colons, brackets,
slashes), so any token carrying Tantivy query grammar raised an unguarded
ValueError. The search itself had already succeeded by the time this ran:
only the highlight step failed, and with the DocumentViewSet.list
exception handler narrowed elsewhere on this branch, that ValueError now
reaches the client as a bare 500 rather than a 400.
Covers three angles:
- the query builder itself: quoting each token as its own escaped phrase
should let it parse instead of raising, for every failure mode a plain-
text query can trigger (syntax error, unknown field, unsupported regex).
- highlight_hits: even when a token still can't be expressed as a
highlight query, the guard must fall back to a query that still
produces usable highlight HTML, not silently empty ones.
- the real API endpoint: pinning the previously-500 status to 200.
"""
from __future__ import annotations
from typing import TYPE_CHECKING
import pytest
import tantivy
from rest_framework import status
from documents.search._backend import SearchMode
from documents.search._query import parse_simple_text_highlight_query
from documents.search._schema import build_schema
from documents.search._tokenizer import register_tokenizers
from documents.tests.factories import DocumentFactory
if TYPE_CHECKING:
from rest_framework.test import APIClient
from documents.search._backend import TantivyBackend
pytestmark = [pytest.mark.search, pytest.mark.django_db]
# Each spelling below trips a different Tantivy parser failure mode:
# 'a"b' -> Syntax Error (unterminated quote)
# foo:bar -> unknown field
# (a -> Syntax Error (unbalanced group)
# [a -> Syntax Error (unbalanced range)
# /a/ -> Unsupported query (regex queries disallowed)
_MALFORMED_QUERIES = [
pytest.param('a"b', id="unterminated_quote"),
pytest.param("foo:bar", id="unknown_field"),
pytest.param("(a", id="unbalanced_group"),
pytest.param("[a", id="unbalanced_range"),
pytest.param("/a/", id="unsupported_regex"),
]
@pytest.fixture(scope="module")
def query_index() -> tantivy.Index:
"""An in-memory, unstemmed index for parse-only tests."""
schema = build_schema()
idx = tantivy.Index(schema, path=None)
register_tokenizers(idx, "")
return idx
class TestParseSimpleTextHighlightQueryDoesNotRaise:
"""The query builder itself must tolerate Tantivy syntax in its tokens."""
@pytest.mark.parametrize("raw_query", _MALFORMED_QUERIES)
def test_malformed_token_does_not_raise(
self,
query_index: tantivy.Index,
raw_query: str,
) -> None:
"""
GIVEN:
- A simple-search query token carrying Tantivy query grammar
(unterminated quote, unknown field, unbalanced group/range,
or unsupported regex)
WHEN:
- parse_simple_text_highlight_query builds a highlight query
from it
THEN:
- It returns a tantivy.Query instead of raising, since each
token is quoted as its own escaped phrase rather than fed
to the parser raw
"""
assert isinstance(
parse_simple_text_highlight_query(query_index, raw_query),
tantivy.Query,
)
class TestHighlightHitsProducesUsableHighlights:
"""highlight_hits must keep producing real <b>-wrapped snippet HTML for
these queries, not merely avoid raising."""
@pytest.mark.parametrize(
"raw_query",
[*_MALFORMED_QUERIES, pytest.param("plain text", id="plain_text_sanity")],
)
def test_highlight_still_contains_matched_text(
self,
backend: TantivyBackend,
raw_query: str,
) -> None:
"""
GIVEN:
- A document whose content contains the raw query text
verbatim
WHEN:
- backend.highlight_hits builds highlights for a TEXT-mode
search using that same (possibly Tantivy-grammar-carrying)
query text
THEN:
- The hit still carries a content highlight with real
<b>-wrapped matched-term markup, not an empty fallback
"""
doc = DocumentFactory.create(
title="probe",
content=f"needle content containing {raw_query} literally here",
)
backend.add_or_update(doc)
hits = backend.highlight_hits(
raw_query,
[doc.pk],
search_mode=SearchMode.TEXT,
)
assert len(hits) == 1
highlights = hits[0]["highlights"]
assert "content" in highlights, (
f"Expected a content highlight for {raw_query!r}, got: {highlights!r}"
)
assert "<b>" in highlights["content"], (
f"Highlight for {raw_query!r} carries no matched-term markup: "
f"{highlights['content']!r}"
)
class TestHighlightGuardDiscriminatesOnValueError:
"""The guard added to highlight_hits must catch exactly ValueError, the
same shape as the sibling notes_text guard, and let anything else
through -- so a real library defect is never mistaken for a harmless
syntax error."""
def test_non_value_error_is_not_swallowed(
self,
backend: TantivyBackend,
monkeypatch: pytest.MonkeyPatch,
) -> None:
"""
GIVEN:
- parse_simple_text_highlight_query patched to raise
RuntimeError instead of a syntax-related ValueError
WHEN:
- backend.highlight_hits is called
THEN:
- The RuntimeError propagates unguarded; the highlight guard
must catch exactly ValueError, the same shape as the
sibling notes_text guard, never mistaking a real library
defect for a harmless syntax error
"""
import documents.search._backend as backend_mod
def raise_runtime_error(*args: object, **kwargs: object) -> object:
raise RuntimeError("synthetic bug, unrelated to query syntax")
monkeypatch.setattr(
backend_mod,
"parse_simple_text_highlight_query",
raise_runtime_error,
)
doc = DocumentFactory.create(title="probe", content="anything here")
backend.add_or_update(doc)
with pytest.raises(RuntimeError):
backend.highlight_hits(
"anything",
[doc.pk],
search_mode=SearchMode.TEXT,
)
@pytest.mark.usefixtures("_search_index")
class TestApiNoLongerReturns500:
"""Pins the actual regression: a matching TEXT-mode search whose query
string carries Tantivy syntax must return results, not a server error."""
@pytest.mark.parametrize("raw_query", _MALFORMED_QUERIES)
def test_malformed_text_query_returns_200(
self,
admin_client: APIClient,
raw_query: str,
) -> None:
"""
GIVEN:
- A matching document whose content contains the raw query
text, indexed via the real search index fixture
WHEN:
- A TEXT-mode search is issued through the real API with a
query string carrying Tantivy syntax
THEN:
- The response is 200 with the expected result count, not a
500 (the regression this file exists to pin)
"""
from documents.search import get_backend
doc = DocumentFactory.create(
title="probe",
content=f"needle content containing {raw_query} literally here",
)
get_backend().add_or_update(doc)
response = admin_client.get(f"/api/documents/?text={raw_query}")
assert response.status_code == status.HTTP_200_OK
assert response.data["count"] == 1
def test_plain_text_query_still_returns_200(
self,
admin_client: APIClient,
) -> None:
"""
GIVEN:
- A matching document indexed via the real search index
fixture
WHEN:
- An ordinary TEXT-mode search (no Tantivy syntax) is issued
THEN:
- The response is 200 with the expected result count; sanity
check that the guard does not mask a total failure of the
ordinary highlight path
"""
from documents.search import get_backend
doc = DocumentFactory.create(
title="probe",
content="needle content containing plain text literally here",
)
get_backend().add_or_update(doc)
response = admin_client.get("/api/documents/?text=plain text")
assert response.status_code == status.HTTP_200_OK
assert response.data["count"] == 1
@@ -0,0 +1,206 @@
"""Bare notes:/custom_fields: prefix resolution.
"notes:foo"/"custom_fields:foo" were valid fielded searches before the
whoosh-compat migration. The registry only exposes them as JSON subpaths, so
each JSON FieldSpec declares a default subpath (SubpathSpec(default=True)):
notes: resolves to notes.note:, custom_fields: resolves to
custom_fields.value:. This replaced an earlier regex-based rewrite
(_rewrite_bare_json_field_prefixes) that ran on the raw query string before
parsing and was blind to quoting, so a phrase like
content:"payment notes: none" was silently corrupted into a notes-field
search and matched nothing. Resolving the default subpath inside the parser
instead means quoting is already understood by the time it happens.
"""
from __future__ import annotations
from typing import TYPE_CHECKING
import pytest
from django.contrib.auth.models import User
from documents.models import CustomField
from documents.models import CustomFieldInstance
from documents.models import Document
from documents.models import Note
if TYPE_CHECKING:
from documents.search._backend import TantivyBackend
pytestmark = [pytest.mark.search, pytest.mark.django_db]
def _matched_ids(backend: TantivyBackend, query: str) -> set[int]:
return set(backend.search_ids(query, user=None))
def _index(backend: TantivyBackend, **kwargs: object) -> Document:
doc = Document.objects.create(**kwargs)
backend.add_or_update(doc)
return doc
class TestBareJsonFieldPrefixes:
def test_bare_notes_prefix_searches_note_text(
self,
backend: TantivyBackend,
) -> None:
"""
GIVEN:
- A document with a note whose text contains a word, and a
decoy document whose content (not notes) contains the same
word
WHEN:
- A bare "notes:" prefix query is run (notes declares "note"
as its default subpath)
THEN:
- Only the document whose note matches is returned; the
decoy's content match does not resurface through a demoted
text search
"""
alice = User.objects.create_user(username="alice")
with_note = Document.objects.create(
title="Has note",
content="x",
checksum="bare-notes-with",
)
Note.objects.create(document=with_note, user=alice, note="crocodile")
backend.add_or_update(with_note)
# This document's CONTENT contains the words a demoted text search
# would match; it must NOT match once the prefix addresses notes.
_index(
backend,
title="Notes about things",
content="notes crocodile mention",
checksum="bare-notes-decoy",
)
assert _matched_ids(backend, "notes:crocodile") == {with_note.pk}
def test_bare_custom_fields_prefix_searches_values(
self,
backend: TantivyBackend,
) -> None:
"""
GIVEN:
- A document with a custom field instance whose value
contains a word, and a decoy document whose content (not a
custom field value) contains the same word
WHEN:
- A bare "custom_fields:" prefix query is run (custom_fields
declares "value" as its default subpath)
THEN:
- Only the document whose custom field value matches is
returned
"""
field = CustomField.objects.create(
name="Policy Number",
data_type=CustomField.FieldDataType.STRING,
)
with_value = Document.objects.create(
title="Has field",
content="x",
checksum="bare-cf-with",
)
CustomFieldInstance.objects.create(
document=with_value,
field=field,
value_text="crocodile",
)
backend.add_or_update(with_value)
_index(
backend,
title="Custom things",
content="custom fields crocodile",
checksum="bare-cf-decoy",
)
assert _matched_ids(backend, "custom_fields:crocodile") == {with_value.pk}
def test_subpath_spellings_are_untouched(
self,
backend: TantivyBackend,
) -> None:
"""
GIVEN:
- A document with a note carrying both an author and note text
WHEN:
- The explicit subpath spellings "notes.user:" and
"notes.note:" are queried
THEN:
- Both resolve to their intended subpath and match the
document; the default-subpath resolution for the bare
prefix does not interfere with explicit subpath addressing
"""
bob = User.objects.create_user(username="bob")
doc = Document.objects.create(
title="Bob note",
content="x",
checksum="bare-subpath",
)
Note.objects.create(document=doc, user=bob, note="remark")
backend.add_or_update(doc)
assert _matched_ids(backend, "notes.user:bob") == {doc.pk}
assert _matched_ids(backend, "notes.note:remark") == {doc.pk}
class TestQuotedPhraseContainingNotesColonIsNotCorrupted:
"""The regex rewrite this migration removes was blind to quoting: it
matched "notes:" anywhere in the raw query string, including inside an
already-quoted phrase on an unrelated field, silently turning
content:"payment notes: none" into a notes-field search that matched
nothing. Resolving the default subpath during parsing (which is
quote-aware) fixes this."""
def test_quoted_phrase_with_notes_colon_matches_by_content(
self,
backend: TantivyBackend,
) -> None:
"""
GIVEN:
- A document whose content literally contains the text
"payment notes: none" inside a quoted phrase
WHEN:
- A query quoting that exact phrase against the content
field is run
THEN:
- It matches by content, rather than the "notes:" substring
inside the quotes being corrupted into a notes-field search
that matches nothing (the bug the deleted regex rewrite
caused, since it was blind to quoting)
"""
target = _index(
backend,
title="Statement",
content="payment notes: none",
checksum="quoted-phrase-notes-colon",
)
assert _matched_ids(
backend,
'content:"payment notes: none"',
) == {target.pk}
def test_quoted_phrase_matches_the_same_document_unquoted(
self,
backend: TantivyBackend,
) -> None:
"""
GIVEN:
- A document whose content contains the same words as the
previous test's phrase, but without the colon
WHEN:
- A query quoting that phrase against the content field is
run
THEN:
- It matches by content, proving the earlier fix is about
quote-awareness specifically, not about the words
themselves being unsearchable
"""
target = _index(
backend,
title="Statement",
content="payment notes none",
checksum="quoted-phrase-no-colon",
)
assert _matched_ids(
backend,
'content:"payment notes none"',
) == {target.pk}
@@ -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
@@ -0,0 +1,220 @@
"""Wildcard patterns must match a stemmed index, end to end.
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 end-to-end tests against a real indexed document and a real
query, proving the pattern normalizer's stem-alternates contract actually
reaches a stemmed index term. The pure unit tests against the normalizer
function itself live in ``test_pattern_normalizer.py``.
"""
from __future__ import annotations
from typing import TYPE_CHECKING
import pytest
from documents.models import Document
if TYPE_CHECKING:
from documents.search._backend import TantivyBackend
pytestmark = [pytest.mark.search, pytest.mark.django_db]
CONTENT = (
"invoice total due for electricity from both companies, "
"payments made to the university library, copies attached"
)
def _matched_ids(backend: TantivyBackend, query: str) -> set[int]:
return set(backend.search_ids(query, user=None))
@pytest.fixture
def indexed_doc(backend: TantivyBackend) -> Document:
doc = Document.objects.create(
title="Invoice 2020 productname",
content=CONTENT,
checksum="pattern-stemming-1",
archive_serial_number=900,
)
backend.add_or_update(doc)
return doc
class TestPrefixStemming:
@pytest.mark.parametrize(
"query",
[
"invoice*",
"electricity*",
"companies*",
"payments*",
"library*",
"title:Invoice*",
],
)
def test_full_word_prefix_matches_its_stem(
self,
backend: TantivyBackend,
indexed_doc: Document,
query: str,
) -> None:
"""
GIVEN:
- A document indexed with content containing "invoice",
"electricity", "companies", "payments", "library" and title
"Invoice 2020 productname"
WHEN:
- A prefix wildcard on the full, unstemmed word is queried
(e.g. "invoice*", "title:Invoice*")
THEN:
- The document matches, since the pattern normalizer offers
the word's stem as an alternative alongside the typed run,
reaching the stemmed index term
"""
assert _matched_ids(backend, query) == {indexed_doc.id}
@pytest.mark.parametrize("query", ["invoic*", "electr*", "payment*"])
def test_already_stemmed_prefix_still_matches(
self,
backend: TantivyBackend,
indexed_doc: Document,
query: str,
) -> None:
"""
GIVEN:
- The same indexed document
WHEN:
- A prefix wildcard is typed already in its stemmed spelling
(e.g. "invoic*")
THEN:
- The document still matches, since the typed-run alternative
is itself a prefix of the stored stemmed term
"""
assert _matched_ids(backend, query) == {indexed_doc.id}
@pytest.mark.parametrize("query", ["univers*", "librar*"])
def test_partial_prefix_reaches_the_stemmed_term(
self,
backend: TantivyBackend,
indexed_doc: Document,
query: str,
) -> None:
"""
GIVEN:
- The same indexed document
WHEN:
- A prefix shorter than a whole word is queried ("univers*",
"librar*")
THEN:
- It still matches, and neither case needs the two-alternative
path to do it: measured under "en", the stemmer leaves
"librar" alone, so it has one form, and that form is a
prefix of the "librari" the index holds for "library";
"univers" stems to the *shorter* "univ", and the run as
typed and its stem are both prefixes of the "univers" the
index holds for "university". The case where the two forms
genuinely diverge, and only one of them matches, is
test_stem_substitution_reaches_both_the_inflection_and_the_compound
"""
assert _matched_ids(backend, query) == {indexed_doc.id}
def test_full_word_reaches_the_stem_but_a_fragment_of_it_does_not(
self,
backend: TantivyBackend,
indexed_doc: Document,
) -> None:
"""
GIVEN:
- The same indexed document, storing "university" as "univers"
WHEN:
- "universities*" and "universit*" are each queried
THEN:
- "universities*" matches, since the stem of "universities" is
that same "univers"; "universit*" matches nothing, since
"universit" is a prefix of neither its own stem nor the
stored term. The alternatives widen recall without turning
a wildcard into a prefix search over the original text, and
usage.md names this exact pair so a reader told that
`universit*` fails is also told which spelling works
"""
assert _matched_ids(backend, "universities*") == {indexed_doc.id}
assert _matched_ids(backend, "universit*") == set()
def test_pattern_past_the_stem_boundary_is_documented_not_fixed(
self,
backend: TantivyBackend,
indexed_doc: Document,
) -> None:
"""
GIVEN:
- The same indexed document, with "productname" indexed as
"productnam"
WHEN:
- "produ*name" (a pattern straddling the stem boundary) is
queried
THEN:
- It matches nothing; produ*name cannot match a stemmed
index, and usage.md must not advertise it. Pinned so the
limitation is deliberate, not accidental
"""
assert _matched_ids(backend, "produ*name") == set()
def test_stem_substitution_reaches_both_the_inflection_and_the_compound(
self,
backend: TantivyBackend,
indexed_doc: Document,
) -> None:
"""
GIVEN:
- The indexed document (containing "copies") plus a second
document titled "Copyright notice" with content "copyright
notice for the work"
WHEN:
- "copy*" and "copyright*" are each queried
THEN:
- "copy*" matches both documents, and "copyright*" matches
only the compound one. English stemming substitutes as well
as truncates: "copy" and "copies" both index as "copi",
while "copyright" keeps its literal "y". Neither form is a
prefix of the other, so no single normalized string reaches
both; the run is therefore emitted as a disjunction of the
folded and stemmed forms, and "copy*" reaches the base
word, its inflections and the compound alike
"""
compound = Document.objects.create(
title="Copyright notice",
content="copyright notice for the work",
checksum="pattern-stemming-2",
archive_serial_number=901,
)
backend.add_or_update(compound)
assert _matched_ids(backend, "copy*") == {indexed_doc.id, compound.id}
assert _matched_ids(backend, "copyright*") == {compound.id}
class TestBracketClassStillFolds:
def test_class_body_matches_case_insensitively(
self,
backend: TantivyBackend,
indexed_doc: Document,
) -> None:
"""
GIVEN:
- The indexed document, titled "Invoice 2020 productname"
WHEN:
- A bracket-class pattern mixing case is queried
("title:[IP]nvoice*")
THEN:
- It matches: the class body is folded per character, which
the alternatives contract preserves only because a lone
character stems to itself
"""
assert _matched_ids(backend, "title:[IP]nvoice*") == {indexed_doc.id}
@@ -0,0 +1,198 @@
"""Permission filtering must hold against the real indexed document shape.
Only three of the index's unsigned ``*_id`` columns are load-bearing:
``owner_id``, ``viewer_id`` and ``viewer_group_id``, all read by
build_permission_filter. The rest (correspondent/document_type/storage_path/tag
ids) were written on every document and read by nothing, and were dropped.
These tests index real Documents through the backend's own document builder and
assert result-level visibility per user, so a mistake about which columns are
load-bearing shows up as documents leaking across users rather than as a passing
unit test over a hand-built index.
"""
from __future__ import annotations
from typing import TYPE_CHECKING
import pytest
from django.contrib.auth.models import Group
from django.contrib.auth.models import User
from guardian.shortcuts import assign_perm
from documents.models import Correspondent
from documents.models import Document
from documents.models import DocumentType
from documents.models import StoragePath
from documents.models import Tag
if TYPE_CHECKING:
from documents.search._backend import TantivyBackend
pytestmark = [pytest.mark.search, pytest.mark.django_db]
@pytest.fixture
def owner() -> User:
return User.objects.create_user(username="owner")
@pytest.fixture
def stranger() -> User:
return User.objects.create_user(username="stranger")
@pytest.fixture
def viewer() -> User:
return User.objects.create_user(username="viewer")
@pytest.fixture
def group_member() -> User:
user = User.objects.create_user(username="group_member")
user.groups.add(Group.objects.create(name="accounting"))
return user
class TestPermissionFilteringOnIndexedDocuments:
def test_unowned_document_is_visible_to_everyone(
self,
backend: TantivyBackend,
stranger: User,
) -> None:
"""
GIVEN:
- A document with no owner, indexed via the backend's real
document builder
WHEN:
- A stranger (no relation to the document) searches
THEN:
- The document is visible to them
"""
doc = Document.objects.create(
title="Public Invoice",
content="invoice total due",
checksum="perm-unowned",
)
backend.add_or_update(doc)
assert backend.search_ids("invoice", user=stranger) == [doc.pk]
def test_owned_document_is_visible_only_to_its_owner(
self,
backend: TantivyBackend,
owner: User,
stranger: User,
) -> None:
"""
GIVEN:
- A document owned by one user, indexed via the backend's
real document builder
WHEN:
- The owner and an unrelated stranger each search
THEN:
- The owner sees the document; the stranger does not
"""
doc = Document.objects.create(
title="Private Invoice",
content="invoice total due",
checksum="perm-owned",
owner=owner,
)
backend.add_or_update(doc)
assert backend.search_ids("invoice", user=owner) == [doc.pk]
assert backend.search_ids("invoice", user=stranger) == []
def test_explicitly_shared_document_is_visible_to_the_viewer(
self,
backend: TantivyBackend,
owner: User,
viewer: User,
stranger: User,
) -> None:
"""
GIVEN:
- A document owned by one user and explicitly shared with a
second user via guardian's view_document permission,
indexed via the backend's real document builder
WHEN:
- The shared viewer and an unrelated stranger each search
THEN:
- The viewer sees the document; the stranger does not
"""
doc = Document.objects.create(
title="Shared Invoice",
content="invoice total due",
checksum="perm-shared-user",
owner=owner,
)
assign_perm("view_document", viewer, doc)
backend.add_or_update(doc)
assert backend.search_ids("invoice", user=viewer) == [doc.pk]
assert backend.search_ids("invoice", user=stranger) == []
def test_group_shared_document_is_visible_to_group_members(
self,
backend: TantivyBackend,
owner: User,
group_member: User,
stranger: User,
) -> None:
"""
GIVEN:
- A document owned by one user and shared with a group via
guardian's view_document permission, indexed via the
backend's real document builder
WHEN:
- A member of that group and an unrelated stranger each
search
THEN:
- The group member sees the document; the stranger does not
"""
doc = Document.objects.create(
title="Group Invoice",
content="invoice total due",
checksum="perm-shared-group",
owner=owner,
)
assign_perm("view_document", group_member.groups.first(), doc)
backend.add_or_update(doc)
assert backend.search_ids("invoice", user=group_member) == [doc.pk]
assert backend.search_ids("invoice", user=stranger) == []
def test_metadata_does_not_widen_visibility(
self,
backend: TantivyBackend,
owner: User,
stranger: User,
) -> None:
"""
GIVEN:
- A document owned by one user and carrying
correspondent/document_type/storage_path/tag metadata,
indexed via the backend's real document builder
WHEN:
- The owner and an unrelated stranger each search
THEN:
- The owner sees the document; the stranger does not, since
the dropped, non-load-bearing metadata *_id columns must
not widen visibility beyond the owner_id/viewer_id/
viewer_group_id filter
"""
doc = Document.objects.create(
title="Tagged Invoice",
content="invoice total due",
checksum="perm-metadata",
owner=owner,
correspondent=Correspondent.objects.create(name="ACME"),
document_type=DocumentType.objects.create(name="Bill"),
storage_path=StoragePath.objects.create(name="Archive", path="archive/"),
)
doc.tags.add(Tag.objects.create(name="paid"))
backend.add_or_update(doc)
assert backend.search_ids("invoice", user=owner) == [doc.pk]
assert backend.search_ids("invoice", user=stranger) == []
File diff suppressed because it is too large Load Diff
@@ -0,0 +1,198 @@
"""Negation must survive the blended query.
parse_user_query ORs an exact clause with optional fuzzy and CJK clauses.
Each of those is built from positive terms only, so unless the query's
exclusions are applied to the blend as a whole, a document the exact
clause excluded is re-admitted by whichever other clause is enabled.
"""
from __future__ import annotations
from typing import TYPE_CHECKING
import pytest
from documents.models import Document
if TYPE_CHECKING:
from pytest_django.fixtures import SettingsWrapper
from documents.search._backend import TantivyBackend
pytestmark = [pytest.mark.search, pytest.mark.django_db]
def _matched_ids(backend: TantivyBackend, query: str) -> set[int]:
return set(backend.search_ids(query, user=None))
def _index(backend: TantivyBackend, **kwargs: object) -> Document:
doc = Document.objects.create(**kwargs)
backend.add_or_update(doc)
return doc
@pytest.fixture
def fuzzy_enabled(settings: SettingsWrapper) -> None:
"""Enable the fuzzy blend clause. The threshold doubles as a minimum
score filter, so it is set to 0.0: every hit passes and the test sees
the clause's matching behaviour, not the filter's."""
settings.ADVANCED_FUZZY_SEARCH_THRESHOLD = 0.0
class TestNegationConstrainsEveryClause:
@pytest.mark.usefixtures("fuzzy_enabled")
def test_fuzzy_clause_does_not_readmit_an_excluded_document(
self,
backend: TantivyBackend,
) -> None:
"""
GIVEN:
- Two documents both matching a positive term, one of which
also contains a word the query excludes, with the fuzzy
blend clause enabled
WHEN:
- A query combining the positive term with a NOT exclusion is
run
THEN:
- Only the document without the excluded word is returned;
the fuzzy clause (built from positive terms only) does not
readmit the document the exact clause excluded
"""
secret = _index(
backend,
title="Invoice A",
content="invoice total secret",
checksum="neg-fuzzy-1",
)
public = _index(
backend,
title="Invoice B",
content="invoice total public",
checksum="neg-fuzzy-2",
)
assert _matched_ids(backend, "invoice") == {secret.pk, public.pk}
assert _matched_ids(backend, "invoice NOT secret") == {public.pk}
def test_cjk_clause_does_not_readmit_an_excluded_document(
self,
backend: TantivyBackend,
) -> None:
"""
GIVEN:
- Two documents both containing a CJK run, one of which also
contains a word the query excludes
WHEN:
- A query combining the CJK term with a NOT exclusion is run
THEN:
- Only the document without the excluded word is returned;
the CJK clause legitimately carries the CJK run, so
rebuilding it from the AST cannot help here, only applying
the exclusion above the blend keeps the excluded document
out
"""
secret = _index(
backend,
title="Tokyo A",
content="東京都の秘密です secret",
checksum="neg-cjk-1",
)
public = _index(
backend,
title="Tokyo B",
content="東京都の報告書です public",
checksum="neg-cjk-2",
)
assert _matched_ids(backend, "東京") == {secret.pk, public.pk}
assert _matched_ids(backend, "東京 NOT secret") == {public.pk}
@pytest.mark.usefixtures("fuzzy_enabled")
def test_disjunctive_negation_still_admits_the_other_branch(
self,
backend: TantivyBackend,
) -> None:
"""
GIVEN:
- A document matching a positive term and also containing a
word a disjunctive NOT branch excludes, plus an unrelated
document
WHEN:
- A query of the shape "term OR NOT excluded_word" is run
THEN:
- Both documents are returned; "invoice OR NOT secret"
excludes nothing on its own, so a document matching the
left branch stays in even though it contains the excluded
word
"""
secret_invoice = _index(
backend,
title="Invoice A",
content="invoice total secret",
checksum="neg-or-1",
)
unrelated = _index(
backend,
title="Recipe",
content="flour and water",
checksum="neg-or-2",
)
assert _matched_ids(backend, "invoice OR NOT secret") == {
secret_invoice.pk,
unrelated.pk,
}
def test_a_negation_under_or_does_not_constrain_the_cjk_clause(
self,
backend: TantivyBackend,
) -> None:
"""
GIVEN:
- Two CJK documents, one of which also contains a word an OR
branch's own NOT excludes, plus an unrelated latin document
WHEN:
- The exclusion is under a disjunctive OR branch, versus in
conjunctive position
THEN:
- Under OR, the excluded document still matches through the
CJK clause (an exclusion that is one branch's own condition
cannot be restated above the blend without dropping
documents the other branch matches, so it is left where it
is and the CJK clause stays unconstrained by it -- this
shows through here in a way it does not for latin text,
since the exact clause cannot match a CJK run at all, so
the CJK clause is the only thing matching the CJK
documents, and the excluded one comes with it)
- Under conjunctive "AND NOT", the same exclusion is hoisted
and does constrain the CJK clause, pinning the deliberate
limit of the hoist
"""
secret = _index(
backend,
title="Tokyo A",
content="東京都の秘密です secret",
checksum="neg-or-cjk-1",
)
public = _index(
backend,
title="Tokyo B",
content="東京都の報告書です public",
checksum="neg-or-cjk-2",
)
bill = _index(
backend,
title="Bill",
content="bill payment received",
checksum="neg-or-cjk-3",
)
assert _matched_ids(backend, "(東京 AND NOT secret) OR bill") == {
bill.pk,
public.pk,
secret.pk,
}
# The same exclusion in conjunctive position is hoisted, and does
# constrain the CJK clause.
assert _matched_ids(backend, "東京 AND NOT secret") == {public.pk}
+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 import pytest
from documents.search._fields import PUBLIC_FIELDS
from documents.search._schema import SCHEMA_VERSION 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 needs_rebuild
from documents.search._schema import schema_fingerprint
if TYPE_CHECKING: if TYPE_CHECKING:
from pathlib import Path from pathlib import Path
import tantivy
from pytest_django.fixtures import Settings from pytest_django.fixtures import Settings
@@ -30,7 +35,13 @@ class TestNeedsRebuild:
) -> None: ) -> None:
settings.SEARCH_LANGUAGE = "en" settings.SEARCH_LANGUAGE = "en"
(index_dir / ".index_settings.json").write_text( (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 assert needs_rebuild(index_dir) is False
@@ -77,3 +88,61 @@ class TestNeedsRebuild:
json.dumps({"schema_version": SCHEMA_VERSION, "language": "en"}), json.dumps({"schema_version": SCHEMA_VERSION, "language": "en"}),
) )
assert needs_rebuild(index_dir) is True 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 import tantivy
from documents.search._tokenizer import _bigram_analyzer 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 _simple_search_analyzer
from documents.search._tokenizer import paperless_text_analyzer
from documents.search._tokenizer import register_tokenizers from documents.search._tokenizer import register_tokenizers
if TYPE_CHECKING: if TYPE_CHECKING:
@@ -25,7 +25,7 @@ class TestTokenizers:
sb.add_text_field("content", stored=True, tokenizer_name="paperless_text") sb.add_text_field("content", stored=True, tokenizer_name="paperless_text")
schema = sb.build() schema = sb.build()
idx = tantivy.Index(schema, path=None) idx = tantivy.Index(schema, path=None)
idx.register_tokenizer("paperless_text", _paperless_text("")) idx.register_tokenizer("paperless_text", paperless_text_analyzer(""))
return idx return idx
@pytest.fixture @pytest.fixture
@@ -1,810 +0,0 @@
from __future__ import annotations
from datetime import UTC
from datetime import datetime
from typing import TYPE_CHECKING
from zoneinfo import ZoneInfo
import pytest
import time_machine
from documents.search._dates import _precision_bounds
if TYPE_CHECKING:
import tantivy
from documents.search._query import _FIELD_BOOSTS
from documents.search._query import DEFAULT_SEARCH_FIELDS
from documents.search._translate import OPEN_HI
from documents.search._translate import OPEN_LO
from documents.search._translate import Comma
from documents.search._translate import FieldRange
from documents.search._translate import FieldValue
from documents.search._translate import FieldValueList
from documents.search._translate import InvalidDateQuery
from documents.search._translate import Passthrough
from documents.search._translate import resolve_commas
from documents.search._translate import scan
from documents.search._translate import translate_query
from documents.search._translate import translate_range
from documents.search._translate import translate_scalar
@pytest.mark.search
class TestPrecisionBounds:
@pytest.mark.parametrize(
("digits", "expected"),
[
("2020", ((2020, 1, 1), (2021, 1, 1))),
("202003", ((2020, 3, 1), (2020, 4, 1))),
("202012", ((2020, 12, 1), (2021, 1, 1))),
("20200115", ((2020, 1, 15), (2020, 1, 16))),
("20201231", ((2020, 12, 31), (2021, 1, 1))),
],
)
def test_valid(self, digits, expected):
lo, hi = _precision_bounds(digits)
assert (lo.year, lo.month, lo.day) == expected[0]
assert (hi.year, hi.month, hi.day) == expected[1]
@pytest.mark.parametrize("digits", ["202023", "20200230", "20201301", "20", "abcd"])
def test_invalid_returns_none(self, digits):
assert _precision_bounds(digits) is None
@pytest.mark.search
class TestScan:
def test_plain_words_are_passthrough(self):
assert scan("bank statement") == [Passthrough("bank statement")]
def test_field_value(self):
assert scan("created:2020") == [FieldValue("created", "2020")]
def test_field_value_in_boolean(self):
toks = scan("created:2020 OR foo")
assert toks == [
FieldValue("created", "2020"),
Passthrough(" OR foo"),
]
def test_field_value_in_parens(self):
toks = scan("(created:2020 OR foo)")
assert toks == [
Passthrough("("),
FieldValue("created", "2020"),
Passthrough(" OR foo)"),
]
def test_quoted_value(self):
assert scan('correspondent:"A B"') == [FieldValue("correspondent", '"A B"')]
def test_field_range(self):
assert scan("created:[2020 TO 2021]") == [
FieldRange("created", "[", "2020", "2021", "]"),
]
@pytest.mark.parametrize(
("query", "expected"),
[
pytest.param(
"created:[2020 to]",
FieldRange("created", "[", "2020", "", "]"),
id="open_upper",
),
pytest.param(
"created:[to 2020]",
FieldRange("created", "[", "", "2020", "]"),
id="open_lower",
),
],
)
def test_open_range(self, query, expected):
assert scan(query) == [expected]
def test_comma_inside_range_not_split(self):
# No depth-0 comma here; the whole thing is one range token.
toks = scan("created:[2020 TO 2021]")
assert len(toks) == 1
# --- Edge-case / regression tests (scan must never raise) ---
def test_url_is_passthrough(self):
# "http" is not a known field; the whole URL must pass through verbatim.
assert scan("http://example.com") == [Passthrough("http://example.com")]
def test_unterminated_quote_is_passthrough(self):
# title is a known field but the quoted value has no closing quote;
# _consume_value returns None so the whole string falls into passthrough.
assert scan('title:"abc') == [Passthrough('title:"abc')]
def test_unterminated_bracket_is_passthrough(self):
# created is a known field but the range bracket is never closed;
# _consume_range returns None so the whole string falls into passthrough.
assert scan("created:[2020") == [Passthrough("created:[2020")]
def test_empty_value_at_end_is_passthrough(self):
# created is a known field but there is no value after the colon
# (_consume_value returns None for start >= n), so passthrough.
assert scan("created:") == [Passthrough("created:")]
def test_value_containing_colon(self):
# The bare-word value reader stops at whitespace/paren, not at colon,
# so "2020:30" is consumed as a single value token.
assert scan("created:2020:30") == [FieldValue("created", "2020:30")]
def test_comma_followed_by_unconsumable_value_stops(self):
# A comma followed by whitespace is neither a value-list continuation nor a
# clause separator: the value stops and the comma stays as passthrough.
assert scan("tag:foo, bar") == [
FieldValue("tag", "foo"),
Passthrough(", bar"),
]
def test_bracket_without_to_is_open_upper_bound(self):
# A bracketed value with no TO falls back to (value, "") -> open upper bound.
assert scan("created:[2020]") == [
FieldRange("created", "[", "2020", "", "]"),
]
def test_known_field_name_midword_is_passthrough(self):
# A known field name embedded mid-word is not a field token (the
# word-boundary guard); the whole run stays passthrough.
assert scan("xtag:foo") == [Passthrough("xtag:foo")]
@pytest.mark.search
class TestCommaResolution:
def test_value_list_multi_value_field(self):
toks = resolve_commas(scan("tag:foo,bar"))
assert toks == [FieldValueList("tag", ("foo", "bar"))]
def test_value_list_three(self):
toks = resolve_commas(scan("tag_id:1,2,3"))
assert toks == [FieldValueList("tag_id", ("1", "2", "3"))]
def test_text_field_comma_is_literal(self):
# correspondent is not multi-value: comma stays inside the value.
toks = resolve_commas(scan("correspondent:foo,bar"))
assert toks == [FieldValue("correspondent", "foo,bar")]
def test_clause_separator_before_known_field(self):
toks = resolve_commas(scan("tag:foo,type:bar"))
assert toks == [FieldValue("tag", "foo"), Comma(), FieldValue("type", "bar")]
def test_clause_separator_after_range(self):
toks = resolve_commas(scan("created:[2020 TO 2021],added:[2022 TO 2023]"))
assert toks == [
FieldRange("created", "[", "2020", "2021", "]"),
Comma(),
FieldRange("added", "[", "2022", "2023", "]"),
]
def test_clause_separator_after_quote(self):
toks = resolve_commas(scan('correspondent:"A B",created:[2020 TO 2021]'))
assert toks == [
FieldValue("correspondent", '"A B"'),
Comma(),
FieldRange("created", "[", "2020", "2021", "]"),
]
def test_url_comma_is_literal_passthrough(self):
toks = resolve_commas(scan("http://example.com/a,b"))
assert toks == [Passthrough("http://example.com/a,b")]
def test_non_multi_value_comma_is_literal(self):
# title is not in MULTI_VALUE_FIELDS: comma stays inside the value.
toks = resolve_commas(scan("title:10,20"))
assert toks == [FieldValue("title", "10,20")]
def test_clause_separator_before_known_date_field(self):
# The comma between a bare value and a known date field acts as a
# clause separator; both sides survive as distinct tokens.
toks = resolve_commas(scan("correspondent:foo,created:[2020 TO 2021]"))
assert toks == [
FieldValue("correspondent", "foo"),
Comma(),
FieldRange("created", "[", "2020", "2021", "]"),
]
@pytest.mark.search
class TestTranslateScalar:
@pytest.mark.parametrize(
("field", "value", "expected"),
[
(
"created",
"2020",
"created:[2020-01-01T00:00:00Z TO 2021-01-01T00:00:00Z}",
),
(
"created",
"202003",
"created:[2020-03-01T00:00:00Z TO 2020-04-01T00:00:00Z}",
),
(
"created",
"20200115",
"created:[2020-01-15T00:00:00Z TO 2020-01-16T00:00:00Z}",
),
(
"created",
"2020-01-15",
"created:[2020-01-15T00:00:00Z TO 2020-01-16T00:00:00Z}",
),
(
"created",
"2020-03",
"created:[2020-03-01T00:00:00Z TO 2020-04-01T00:00:00Z}",
),
],
)
def test_partial_and_iso_dates(self, field: str, value: str, expected: str) -> None:
assert translate_scalar(field, value, UTC) == expected
def test_invalid_date_raises(self) -> None:
with pytest.raises(InvalidDateQuery) as exc_info:
translate_scalar("created", "202023", UTC)
assert exc_info.value.field == "created"
assert exc_info.value.value == "202023"
def test_keyword_delegates(self) -> None:
# keyword path produces a half-open range; just assert it is a created range
out = translate_scalar("created", "today", UTC)
assert out.startswith("created:[") and out.endswith("}")
def test_14digit_compact_datetime(self) -> None:
out = translate_scalar("created", "20240115120000", UTC)
assert "20240115120000" not in out
assert out.startswith("created:")
assert out == "created:[2024-01-15T12:00:00Z TO 2024-01-15T12:00:00Z]"
def test_14digit_invalid_month_raises(self) -> None:
with pytest.raises(InvalidDateQuery) as exc_info:
translate_scalar("created", "20231300120000", UTC)
assert exc_info.value.field == "created"
assert exc_info.value.value == "20231300120000"
def test_unrecognized_value_raises(self) -> None:
# A value that is not a keyword, digits, ISO date, or compact timestamp
# raises rather than producing invalid Tantivy syntax or silently matching
# nothing.
with pytest.raises(InvalidDateQuery) as exc_info:
translate_scalar("created", "garbage", UTC)
assert exc_info.value.field == "created"
assert exc_info.value.value == "garbage"
@pytest.mark.search
class TestTranslateRange:
@pytest.mark.parametrize(
("lo", "hi", "expected"),
[
("2005", "2009", "created:[2005-01-01T00:00:00Z TO 2010-01-01T00:00:00Z}"),
(
"202001",
"202006",
"created:[2020-01-01T00:00:00Z TO 2020-07-01T00:00:00Z}",
),
(
"20200101",
"20201231",
"created:[2020-01-01T00:00:00Z TO 2021-01-01T00:00:00Z}",
),
(
"2020-01-01",
"2020-12-31",
"created:[2020-01-01T00:00:00Z TO 2021-01-01T00:00:00Z}",
),
],
)
def test_absolute_ranges(self, lo, hi, expected):
assert translate_range("created", lo, hi, UTC) == expected
def test_reversed_swaps(self):
assert translate_range("created", "2009", "2005", UTC) == (
"created:[2005-01-01T00:00:00Z TO 2010-01-01T00:00:00Z}"
)
def test_open_upper(self):
out = translate_range("created", "2020", "", UTC)
assert out == f"created:[2020-01-01T00:00:00Z TO {OPEN_HI}]"
def test_open_lower(self):
out = translate_range("created", "", "2020", UTC)
assert out == f"created:[{OPEN_LO} TO 2021-01-01T00:00:00Z}}"
def test_invalid_bound_raises(self):
with pytest.raises(InvalidDateQuery) as exc_info:
translate_range("created", "202023", "2025", UTC)
assert exc_info.value.field == "created"
assert exc_info.value.value == "202023"
def test_invalid_high_bound_raises(self):
# Low bound parses, high bound does not -> raise on the high bound.
with pytest.raises(InvalidDateQuery) as exc_info:
translate_range("created", "2020", "garbage", UTC)
assert exc_info.value.field == "created"
assert exc_info.value.value == "garbage"
@pytest.mark.search
class TestTranslateQuery:
@pytest.mark.parametrize(
("raw", "expected"),
[
(
"created:2020",
"created:[2020-01-01T00:00:00Z TO 2021-01-01T00:00:00Z}",
),
("tag:foo,bar", "tag:foo AND tag:bar"),
# 'type' is a user-facing alias rewritten to 'document_type' (the real schema field)
("tag:foo,type:bar", "tag:foo AND document_type:bar"),
(
"created:[2020 TO 2021],added:[2022 TO 2023]",
(
"created:[2020-01-01T00:00:00Z TO 2022-01-01T00:00:00Z}"
" AND "
"added:[2022-01-01T00:00:00Z TO 2024-01-01T00:00:00Z}"
),
),
# correspondent is not multi-value: comma stays literal inside the value
("correspondent:foo,bar", "correspondent:foo,bar"),
],
)
def test_golden(self, raw: str, expected: str) -> None:
assert translate_query(raw, UTC) == expected
@pytest.mark.parametrize(
"raw",
[
"created:2020",
"created:202003",
"created:[20200101 TO 20201231]",
"created:[2020-01-01 TO 2020-12-31]",
"created:[2020 to]",
"created:[to 2020]",
"title:x,created:[2020 TO 2021]",
"created:2020 OR foo",
"(created:2020 OR invoice)",
"tag:foo,type:bar",
"bank statement",
],
)
def test_parse_acceptance(self, index: tantivy.Index, raw: str) -> None:
translated = translate_query(raw, UTC)
# Must not raise:
index.parse_query(translated, DEFAULT_SEARCH_FIELDS, field_boosts=_FIELD_BOOSTS)
@pytest.mark.search
class TestFieldAliasing:
"""Whoosh->Tantivy field-name aliasing (type/path -> document_type/storage_path)."""
def test_type_alias(self) -> None:
assert translate_query("type:invoice", UTC) == "document_type:invoice"
def test_path_alias(self) -> None:
assert translate_query("path:/foo/bar", UTC) == "storage_path:/foo/bar"
def test_type_id_alias(self) -> None:
assert translate_query("type_id:5", UTC) == "document_type_id:5"
def test_path_id_alias(self) -> None:
assert translate_query("path_id:7", UTC) == "storage_path_id:7"
def test_clause_separator_plus_alias(self) -> None:
# Comma between known fields acts as AND separator; alias still applied.
assert (
translate_query("tag:foo,type:bar", UTC) == "tag:foo AND document_type:bar"
)
def test_type_range_alias(self) -> None:
# type is not a date field; range passes through verbatim with alias applied.
assert (
translate_query("type:[2020 TO 2021]", UTC)
== "document_type:[2020 TO 2021]"
)
def test_parse_acceptance_type(self, index: tantivy.Index) -> None:
# Translated output must be accepted by the real Tantivy parser.
translated = translate_query("type:invoice", UTC)
index.parse_query(translated, DEFAULT_SEARCH_FIELDS, field_boosts=_FIELD_BOOSTS)
def test_parse_acceptance_path(self, index: tantivy.Index) -> None:
translated = translate_query("path:foo", UTC)
index.parse_query(translated, DEFAULT_SEARCH_FIELDS, field_boosts=_FIELD_BOOSTS)
# Freeze time so relative-date tests are deterministic.
_FROZEN_NOW = datetime(2026, 3, 28, 12, 0, 0, tzinfo=UTC)
@pytest.mark.search
class TestRelativeRanges:
"""Relative date-range tokens resolved against a frozen clock."""
@time_machine.travel(_FROZEN_NOW, tick=False)
def test_minus_7_days_to_now(self) -> None:
assert translate_query("added:[-7 days to now]", UTC) == (
"added:[2026-03-21T12:00:00Z TO 2026-03-28T12:00:00Z]"
)
@time_machine.travel(_FROZEN_NOW, tick=False)
def test_minus_1_week_to_now(self) -> None:
assert translate_query("added:[-1 week to now]", UTC) == (
"added:[2026-03-21T12:00:00Z TO 2026-03-28T12:00:00Z]"
)
@time_machine.travel(_FROZEN_NOW, tick=False)
def test_minus_1_month_to_now(self) -> None:
assert translate_query("created:[-1 month to now]", UTC) == (
"created:[2026-02-28T12:00:00Z TO 2026-03-28T12:00:00Z]"
)
@time_machine.travel(_FROZEN_NOW, tick=False)
def test_minus_1_year_to_now(self) -> None:
assert translate_query("modified:[-1 year to now]", UTC) == (
"modified:[2025-03-28T12:00:00Z TO 2026-03-28T12:00:00Z]"
)
@time_machine.travel(_FROZEN_NOW, tick=False)
def test_minus_3_hours_to_now(self) -> None:
assert translate_query("added:[-3 hours to now]", UTC) == (
"added:[2026-03-28T09:00:00Z TO 2026-03-28T12:00:00Z]"
)
@time_machine.travel(_FROZEN_NOW, tick=False)
def test_uppercase_units(self) -> None:
assert translate_query("added:[-1 WEEK TO NOW]", UTC) == (
"added:[2026-03-21T12:00:00Z TO 2026-03-28T12:00:00Z]"
)
@time_machine.travel(_FROZEN_NOW, tick=False)
def test_now_minus_7d_compact(self) -> None:
assert translate_query("added:[now-7d TO now]", UTC) == (
"added:[2026-03-21T12:00:00Z TO 2026-03-28T12:00:00Z]"
)
@time_machine.travel(_FROZEN_NOW, tick=False)
def test_reversed_range_swapped(self) -> None:
# now+1h TO now-1h is reversed; translate_range swaps -> lo=now-1h, hi=now+1h
assert translate_query("added:[now+1h TO now-1h]", UTC) == (
"added:[2026-03-28T11:00:00Z TO 2026-03-28T13:00:00Z]"
)
@pytest.mark.parametrize(
"raw",
[
"added:[-7 days to now]",
"added:[-1 week to now]",
"created:[-1 month to now]",
"modified:[-1 year to now]",
"added:[-3 hours to now]",
"added:[now-7d TO now]",
"added:[now+1h TO now-1h]",
],
)
@time_machine.travel(_FROZEN_NOW, tick=False)
def test_parse_acceptance(self, index: tantivy.Index, raw: str) -> None:
translated = translate_query(raw, UTC)
index.parse_query(translated, DEFAULT_SEARCH_FIELDS, field_boosts=_FIELD_BOOSTS)
@pytest.mark.search
class TestWhooshUnitAbbreviations:
"""
Whoosh's PlusMinus date grammar accepted abbreviated unit spellings
(e.g. "yrs", "mos", "wks", "hrs", "mins", "secs"); saved views/searches
created under the old Whoosh backend can contain those tokens (see
https://github.com/paperless-ngx/paperless-ngx/issues/13482), so the
Tantivy translator must still accept them.
"""
@time_machine.travel(_FROZEN_NOW, tick=False)
def test_minus_999_yrs(self) -> None:
assert translate_query("created:[-999yrs to now]", UTC) == (
"created:[1027-03-28T12:00:00Z TO 2026-03-28T12:00:00Z]"
)
@pytest.mark.parametrize(
("token", "expected_lo"),
[
("-1y", "2025-03-28T12:00:00Z"),
("-1yr", "2025-03-28T12:00:00Z"),
("-3mos", "2025-12-28T12:00:00Z"),
("-3mo", "2025-12-28T12:00:00Z"),
("-2wks", "2026-03-14T12:00:00Z"),
("-2wk", "2026-03-14T12:00:00Z"),
("-5dys", "2026-03-23T12:00:00Z"),
("-5dy", "2026-03-23T12:00:00Z"),
("-1hrs", "2026-03-28T11:00:00Z"),
("-1hr", "2026-03-28T11:00:00Z"),
("-10mins", "2026-03-28T11:50:00Z"),
("-10min", "2026-03-28T11:50:00Z"),
("-30secs", "2026-03-28T11:59:30Z"),
("-30sec", "2026-03-28T11:59:30Z"),
],
)
@time_machine.travel(_FROZEN_NOW, tick=False)
def test_abbreviated_units(self, token: str, expected_lo: str) -> None:
assert translate_query(f"added:[{token} to now]", UTC) == (
f"added:[{expected_lo} TO 2026-03-28T12:00:00Z]"
)
@pytest.mark.parametrize(
"raw",
[
"created:[-999yrs to now]",
"added:[-1y to now]",
"created:[-3mos to now]",
"added:[-2wks to now]",
"added:[-5dys to now]",
"added:[-1hrs to now]",
"added:[-10mins to now]",
"added:[-30secs to now]",
],
)
@time_machine.travel(_FROZEN_NOW, tick=False)
def test_parse_acceptance(self, index: tantivy.Index, raw: str) -> None:
translated = translate_query(raw, UTC)
index.parse_query(translated, DEFAULT_SEARCH_FIELDS, field_boosts=_FIELD_BOOSTS)
@pytest.mark.search
class TestOperatorNormalization:
"""Post-render operator normalization in translate_query."""
def test_spaced_dash_removed(self) -> None:
assert (
translate_query("H52.1 - Kurzsichtigkeit", UTC) == "H52.1 Kurzsichtigkeit"
)
def test_spaced_dash_simple(self) -> None:
assert translate_query("bar - baz", UTC) == "bar baz"
def test_trailing_operator_stripped(self) -> None:
assert translate_query("foo -", UTC) == "foo"
def test_date_range_preserved(self) -> None:
out = translate_query("created:[2020 TO 2021]", UTC)
# Must not corrupt the ISO range
assert out == "created:[2020-01-01T00:00:00Z TO 2022-01-01T00:00:00Z}"
def test_date_scalar_with_or(self) -> None:
out = translate_query("created:2020 OR foo", UTC)
# The created scalar becomes a range; " OR foo" passes through verbatim.
assert out.startswith("created:[")
assert "OR foo" in out
def test_parse_acceptance_spaced_dash(self, index: tantivy.Index) -> None:
translated = translate_query("H52.1 - Kurzsichtigkeit", UTC)
index.parse_query(translated, DEFAULT_SEARCH_FIELDS, field_boosts=_FIELD_BOOSTS)
def test_parse_acceptance_trailing_op(self, index: tantivy.Index) -> None:
translated = translate_query("foo -", UTC)
index.parse_query(translated, DEFAULT_SEARCH_FIELDS, field_boosts=_FIELD_BOOSTS)
@pytest.mark.search
class TestMultiWordDateKeywords:
"""scan() must consume multi-word date keywords as a single value."""
def test_scan_previous_week_as_single_token(self) -> None:
# "created:previous week" must produce one FieldValue with value "previous week",
# not FieldValue("created","previous") + Passthrough(" week").
toks = scan("created:previous week")
assert toks == [FieldValue("created", "previous week")]
def test_scan_this_month_as_single_token(self) -> None:
toks = scan("added:this month")
assert toks == [FieldValue("added", "this month")]
def test_scan_previous_month_as_single_token(self) -> None:
toks = scan("created:previous month")
assert toks == [FieldValue("created", "previous month")]
def test_scan_this_year_as_single_token(self) -> None:
toks = scan("added:this year")
assert toks == [FieldValue("added", "this year")]
def test_scan_previous_year_as_single_token(self) -> None:
toks = scan("created:previous year")
assert toks == [FieldValue("created", "previous year")]
def test_scan_previous_quarter_as_single_token(self) -> None:
toks = scan("created:previous quarter")
assert toks == [FieldValue("created", "previous quarter")]
def test_quoted_multi_word_keyword_still_works(self) -> None:
# The quoted form must continue to work as before.
toks = scan('created:"previous week"')
assert toks == [FieldValue("created", '"previous week"')]
def test_non_date_field_not_affected(self) -> None:
# "previous" stops at the space for non-date fields; " week" passes through.
toks = scan("correspondent:previous week")
assert toks == [
FieldValue("correspondent", "previous"),
Passthrough(" week"),
]
@pytest.mark.search
class TestKeywordDateResolution:
"""Relative date keywords resolve to exact ISO ranges against a frozen clock.
Frozen at 2026-03-28 12:00 UTC (a Saturday in Q1) so the week, month,
quarter and year rollovers are all exercised by a single anchor.
"""
# created is a DateField: bounds are UTC midnight, no timezone offset.
@pytest.mark.parametrize(
("keyword", "expected"),
[
pytest.param(
"today",
"created:[2026-03-28T00:00:00Z TO 2026-03-29T00:00:00Z}",
id="today",
),
pytest.param(
"yesterday",
"created:[2026-03-27T00:00:00Z TO 2026-03-28T00:00:00Z}",
id="yesterday",
),
pytest.param(
"previous week",
"created:[2026-03-16T00:00:00Z TO 2026-03-23T00:00:00Z}",
id="previous-week",
),
pytest.param(
"this month",
"created:[2026-03-01T00:00:00Z TO 2026-04-01T00:00:00Z}",
id="this-month",
),
pytest.param(
"previous month",
"created:[2026-02-01T00:00:00Z TO 2026-03-01T00:00:00Z}",
id="previous-month",
),
pytest.param(
"this year",
"created:[2026-01-01T00:00:00Z TO 2027-01-01T00:00:00Z}",
id="this-year",
),
pytest.param(
"previous year",
"created:[2025-01-01T00:00:00Z TO 2026-01-01T00:00:00Z}",
id="previous-year",
),
pytest.param(
"previous quarter",
"created:[2025-10-01T00:00:00Z TO 2026-01-01T00:00:00Z}",
id="previous-quarter",
),
],
)
@time_machine.travel(_FROZEN_NOW, tick=False)
def test_date_only_field_keyword_ranges(
self,
keyword: str,
expected: str,
) -> None:
assert translate_query(f"created:{keyword}", UTC) == expected
# added is a DateTimeField: local-tz midnight converted to UTC. Tokyo
# (+09:00, no DST) shifts each midnight boundary back to 15:00Z the day
# before, so this also exercises the local-midnight offset path.
@pytest.mark.parametrize(
("keyword", "expected"),
[
pytest.param(
"today",
"added:[2026-03-27T15:00:00Z TO 2026-03-28T15:00:00Z}",
id="today",
),
pytest.param(
"yesterday",
"added:[2026-03-26T15:00:00Z TO 2026-03-27T15:00:00Z}",
id="yesterday",
),
pytest.param(
"previous week",
"added:[2026-03-15T15:00:00Z TO 2026-03-22T15:00:00Z}",
id="previous-week",
),
pytest.param(
"this month",
"added:[2026-02-28T15:00:00Z TO 2026-03-31T15:00:00Z}",
id="this-month",
),
pytest.param(
"previous month",
"added:[2026-01-31T15:00:00Z TO 2026-02-28T15:00:00Z}",
id="previous-month",
),
pytest.param(
"this year",
"added:[2025-12-31T15:00:00Z TO 2026-12-31T15:00:00Z}",
id="this-year",
),
pytest.param(
"previous year",
"added:[2024-12-31T15:00:00Z TO 2025-12-31T15:00:00Z}",
id="previous-year",
),
pytest.param(
"previous quarter",
"added:[2025-09-30T15:00:00Z TO 2025-12-31T15:00:00Z}",
id="previous-quarter",
),
],
)
@time_machine.travel(_FROZEN_NOW, tick=False)
def test_datetime_field_keyword_ranges_local_tz(
self,
keyword: str,
expected: str,
) -> None:
assert translate_query(f"added:{keyword}", ZoneInfo("Asia/Tokyo")) == expected
@pytest.mark.search
class TestISODatetimeBounds:
"""Full ISO datetime tokens in range bounds must be parsed directly."""
def test_translate_range_iso_bounds_passthrough(self) -> None:
# Already-ISO datetime bounds must pass through as-is (exact instant).
result = translate_range(
"created",
"2020-01-01T00:00:00Z",
"2021-01-01T00:00:00Z",
UTC,
)
assert result == "created:[2020-01-01T00:00:00Z TO 2021-01-01T00:00:00Z]"
def test_translate_query_iso_range_preserved(self) -> None:
q = "created:[2026-01-01T00:00:00Z TO 2026-06-01T00:00:00Z]"
assert translate_query(q, UTC) == q
def test_translate_query_comma_separated_iso_ranges(self) -> None:
q = (
"created:[2026-01-01T00:00:00Z TO 2026-06-01T00:00:00Z],"
"added:[2026-05-01T00:00:00Z TO 2026-06-01T00:00:00Z]"
)
result = translate_query(q, UTC)
assert result == (
"created:[2026-01-01T00:00:00Z TO 2026-06-01T00:00:00Z]"
" AND "
"added:[2026-05-01T00:00:00Z TO 2026-06-01T00:00:00Z]"
)
def test_translate_query_text_before_comma_separated_date_clause(self) -> None:
result = translate_query("schäfersee,created:previous year", UTC)
assert result == (
"schäfersee AND created:[2025-01-01T00:00:00Z TO 2026-01-01T00:00:00Z}"
)
def test_invalid_iso_datetime_raises(self) -> None:
# A token with "T" that is not valid ISO datetime -> raise.
with pytest.raises(InvalidDateQuery) as exc_info:
translate_range(
"created",
"2020-01-01T99:00:00Z",
"2021-01-01T00:00:00Z",
UTC,
)
assert exc_info.value.field == "created"
assert exc_info.value.value == "2020-01-01T99:00:00Z"
def test_parse_acceptance_iso_bounds(self, index: tantivy.Index) -> None:
q = "created:[2026-01-01T00:00:00Z TO 2026-06-01T00:00:00Z]"
translated = translate_query(q, UTC)
index.parse_query(translated, DEFAULT_SEARCH_FIELDS, field_boosts=_FIELD_BOOSTS)
def test_parse_acceptance_comma_iso_ranges(self, index: tantivy.Index) -> None:
q = (
"created:[2026-01-01T00:00:00Z TO 2026-06-01T00:00:00Z],"
"added:[2026-05-01T00:00:00Z TO 2026-06-01T00:00:00Z]"
)
translated = translate_query(q, UTC)
index.parse_query(translated, DEFAULT_SEARCH_FIELDS, field_boosts=_FIELD_BOOSTS)
@@ -339,3 +339,29 @@ class TestBulkDownload(DirectoriesMixin, SampleDirMixin, APITestCase):
self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN) self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)
self.assertEqual(response.content, b"Insufficient permissions") self.assertEqual(response.content, b"Insufficient permissions")
def test_bad_search_query_returns_400(self) -> None:
"""
GIVEN:
- Bulk download request selects documents via a saved-search
query filter
WHEN:
- The query contains a malformed field value (an invalid date)
THEN:
- The response is a 400 naming the bad value, exactly like the
search list endpoint, never a 500
"""
response = self.client.post(
self.ENDPOINT,
json.dumps(
{
"all": True,
"filters": {"query": "added:notadate"},
"content": "originals",
},
),
content_type="application/json",
)
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
self.assertIn(b"notadate", response.content)
+27
View File
@@ -2059,3 +2059,30 @@ class TestBulkEditAPI(DirectoriesMixin, APITestCase):
self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(LogEntry.objects.filter(object_pk=self.doc1.id).count(), 2) self.assertEqual(LogEntry.objects.filter(object_pk=self.doc1.id).count(), 2)
def test_api_bulk_edit_with_bad_search_query_returns_400(self) -> None:
"""
GIVEN:
- Bulk edit request selects documents via a saved-search query
filter
WHEN:
- The query contains a malformed field value (an invalid date)
THEN:
- The response is a 400 naming the bad value, exactly like the
search list endpoint, never a 500
"""
response = self.client.post(
"/api/documents/bulk_edit/",
json.dumps(
{
"all": True,
"filters": {"query": "added:notadate"},
"method": "set_storage_path",
"parameters": {"storage_path": self.sp1.id},
},
),
content_type="application/json",
)
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
self.assertIn(b"notadate", response.content)
+121
View File
@@ -786,6 +786,10 @@ class TestDocumentSearchApi(DirectoriesMixin, APITestCase):
tick=False, tick=False,
): ):
response = self.client.get("/api/documents/?query=added:previous month") response = self.client.get("/api/documents/?query=added:previous month")
assert response.status_code == 200, (
f"expected a successful search response, got {response.status_code}: "
f"{response.data!r}"
)
results = response.data["results"] results = response.data["results"]
self.assertEqual(len(results), 1) self.assertEqual(len(results), 1)
@@ -818,6 +822,26 @@ class TestDocumentSearchApi(DirectoriesMixin, APITestCase):
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
self.assertIn("invalid-date", str(response.data["query"])) self.assertIn("invalid-date", str(response.data["query"]))
def test_search_multiple_bad_fields_returns_all_messages(self) -> None:
"""
GIVEN:
- One document added
WHEN:
- Query with multiple bad fields (e.g. invalid date and invalid number)
THEN:
- 400 Bad Request with error messages for every bad field,
so the user can fix them all in one round-trip
"""
response = self.client.get(
"/api/documents/",
{"query": "created:notadate AND asn:notanumber"},
)
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
messages = response.data["query"]
self.assertEqual(len(messages), 2)
self.assertTrue(any("created" in m for m in messages))
self.assertTrue(any("asn" in m for m in messages))
@override_settings( @override_settings(
TIME_ZONE="UTC", TIME_ZONE="UTC",
) )
@@ -861,6 +885,29 @@ class TestDocumentSearchApi(DirectoriesMixin, APITestCase):
results = response.data["results"] results = response.data["results"]
self.assertEqual({r["id"] for r in results}, {1, 2}) self.assertEqual({r["id"] for r in results}, {1, 2})
@mock.patch("documents.search._backend.parse_user_query")
def test_search_parser_bug_surfaces_as_500_not_400(self, m) -> None:
"""
GIVEN:
- The query parser itself fails (a whoosh-compat bug, per
QueryParserError's own contract: not user-fixable input)
WHEN:
- Any search request runs
THEN:
- The error surfaces as a 500 monitoring can see, never a 400
blaming the user for a library defect
"""
from whoosh_compat.errors import QueryParserError
m.side_effect = QueryParserError("synthetic parser bug")
self.client.raise_request_exception = False
response = self.client.get("/api/documents/?query=anything")
self.assertEqual(
response.status_code,
status.HTTP_500_INTERNAL_SERVER_ERROR,
)
@mock.patch("documents.search._backend.TantivyBackend.autocomplete") @mock.patch("documents.search._backend.TantivyBackend.autocomplete")
def test_search_autocomplete_limits(self, m) -> None: def test_search_autocomplete_limits(self, m) -> None:
""" """
@@ -2058,3 +2105,77 @@ class TestDocumentSearchApi(DirectoriesMixin, APITestCase):
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
response = self.client.get("/api/search/?query=no") response = self.client.get("/api/search/?query=no")
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
def _assert_query_finds(self, doc: Document, query: str) -> None:
get_backend().add_or_update(doc)
response = self.client.get("/api/documents/", {"query": query})
self.assertEqual(response.status_code, status.HTTP_200_OK)
ids = [r["id"] for r in response.data["results"]]
self.assertIn(doc.id, ids)
def test_search_by_asn(self) -> None:
"""
GIVEN:
- A document with an archive serial number, indexed
WHEN:
- A query filters by "asn:<value>"
THEN:
- The document is found
"""
doc = Document.objects.create(
title="Has ASN",
content="content",
checksum="asn-checksum",
archive_serial_number=555,
)
self._assert_query_finds(doc, "asn:555")
def test_search_by_page_count(self) -> None:
"""
GIVEN:
- A document with a page count, indexed
WHEN:
- A query filters by "page_count:<value>"
THEN:
- The document is found
"""
doc = Document.objects.create(
title="Multi-page",
content="content",
checksum="page-count-checksum",
page_count=42,
)
self._assert_query_finds(doc, "page_count:42")
def test_search_by_original_filename(self) -> None:
"""
GIVEN:
- A document with an original filename, indexed
WHEN:
- A query filters by "original_filename:<value>"
THEN:
- The document is found
"""
doc = Document.objects.create(
title="Named file",
content="content",
checksum="filename-checksum",
original_filename="quarterly-report.pdf",
)
self._assert_query_finds(doc, "original_filename:quarterly-report.pdf")
def test_search_by_checksum(self) -> None:
"""
GIVEN:
- A document with a checksum, indexed
WHEN:
- A query filters by "checksum:<value>"
THEN:
- The document is found
"""
doc = Document.objects.create(
title="Checksum doc",
content="content",
checksum="deadbeef1234",
)
self._assert_query_finds(doc, "checksum:deadbeef1234")
@@ -0,0 +1,344 @@
"""The search list endpoint's exception handling: what becomes a 400 and
what a library defect surfaces as instead.
Companion to documents/tests/search/test_error_routing.py, which pins the
Cause -> SearchQueryError/QueryError routing inside documents/search/_query.py.
These tests pin the layer above it: DocumentViewSet.list's own except clauses,
which decide what an already-routed error becomes on the wire.
"""
from __future__ import annotations
from typing import TYPE_CHECKING
import pytest
from rest_framework import status
from whoosh_compat.errors import Cause
from whoosh_compat.errors import Diagnostic
from whoosh_compat.errors import DiagnosticKind
from whoosh_compat.errors import QueryError
from documents.search import SearchQueryError
from documents.tests.factories import DocumentFactory
if TYPE_CHECKING:
from rest_framework.test import APIClient
from documents.models import Document
pytestmark = [pytest.mark.django_db, pytest.mark.usefixtures("_search_index")]
@pytest.fixture
def indexed_document() -> Document:
from documents.search import get_backend
doc = DocumentFactory.create(title="quarterly invoice", content="acme corp")
get_backend().add_or_update(doc)
return doc
class TestSearchQueryErrorStillBecomesA400:
def test_search_query_error_becomes_a_400_naming_the_field(
self,
admin_client: APIClient,
monkeypatch: pytest.MonkeyPatch,
indexed_document: Document,
) -> None:
"""
GIVEN:
- parse_user_query() raising a SearchQueryError naming a field
WHEN:
- The document list endpoint is queried
THEN:
- The response is a 400 whose body names the field
"""
import documents.search._backend as backend_mod
def raise_search_query_error(*args: object, **kwargs: object) -> object:
raise SearchQueryError("bad value for field 'added'")
monkeypatch.setattr(
backend_mod,
"parse_user_query",
raise_search_query_error,
)
response = admin_client.get("/api/documents/?query=anything")
assert response.status_code == status.HTTP_400_BAD_REQUEST
assert "added" in str(response.data["query"])
class TestLibraryDefectsPropagate:
"""The exact regression this task exists to fix: an unexpected or
INTERNAL-cause library error must not be relabeled a 400."""
def test_unexpected_exception_is_not_converted_to_a_400(
self,
admin_client: APIClient,
monkeypatch: pytest.MonkeyPatch,
indexed_document: Document,
) -> None:
"""
GIVEN:
- parse_user_query() raising an unrelated exception
(ZeroDivisionError), not a SearchQueryError
WHEN:
- The document list endpoint is queried
THEN:
- The exception propagates unconverted, rather than being
relabeled a 400
"""
import documents.search._backend as backend_mod
def raise_zero_division(*args: object, **kwargs: object) -> object:
raise ZeroDivisionError("synthetic bug, unrelated to search grammar")
monkeypatch.setattr(
backend_mod,
"parse_user_query",
raise_zero_division,
)
with pytest.raises(ZeroDivisionError):
admin_client.get("/api/documents/?query=anything")
def test_internal_cause_query_error_is_not_converted_to_a_400(
self,
admin_client: APIClient,
monkeypatch: pytest.MonkeyPatch,
indexed_document: Document,
) -> None:
"""
GIVEN:
- A real query string running through the real parse and
routing pipeline (pre-parse rewrites, wc.parse(), and
_map_emit_error's own Cause routing all run for real), except
the final emit call (tantivy_emit) is forced to report a
library-internal defect (Cause.INTERNAL) - the one
library-internal failure mode reachable from a real query
WHEN:
- The document list endpoint is queried
THEN:
- The QueryError propagates unconverted, rather than being
relabeled a 400
"""
import documents.search._query as query_mod
def raise_internal(*args: object, **kwargs: object) -> object:
raise QueryError(
Diagnostic(
kind=DiagnosticKind.BACKEND_REJECTED,
cause=Cause.INTERNAL,
message="synthetic whoosh-compat emitter defect",
),
)
monkeypatch.setattr(query_mod, "tantivy_emit", raise_internal)
with pytest.raises(QueryError):
admin_client.get("/api/documents/?query=invoice")
class TestSelectionPathsAgreeWithSearch:
"""DocumentSelectionMixin backs bulk edit, bulk download, and a
more_like_id selection filter. It catches only SearchQueryError -- the
same contract the search list endpoint enforces above -- so all three
must map SearchQueryError to a 400 and let anything else surface."""
def test_bulk_edit_maps_search_query_error_to_a_400(
self,
admin_client: APIClient,
monkeypatch: pytest.MonkeyPatch,
indexed_document: Document,
) -> None:
"""
GIVEN:
- parse_user_query() raising a SearchQueryError naming a field
WHEN:
- The bulk_edit endpoint is called with a query filter
THEN:
- The response is a 400 whose body names the field
"""
import documents.search._backend as backend_mod
def raise_search_query_error(*args: object, **kwargs: object) -> object:
raise SearchQueryError("bad value for field 'added'")
monkeypatch.setattr(
backend_mod,
"parse_user_query",
raise_search_query_error,
)
response = admin_client.post(
"/api/documents/bulk_edit/",
{
"documents": [],
"all": True,
"filters": {"query": "anything"},
"method": "set_document_type",
"parameters": {"document_type": None},
},
format="json",
)
assert response.status_code == status.HTTP_400_BAD_REQUEST
assert "added" in str(response.data["query"])
def test_bulk_edit_lets_an_unexpected_exception_surface(
self,
admin_client: APIClient,
monkeypatch: pytest.MonkeyPatch,
indexed_document: Document,
) -> None:
"""
GIVEN:
- parse_user_query() raising an unrelated exception
(ZeroDivisionError), not a SearchQueryError
WHEN:
- The bulk_edit endpoint is called with a query filter
THEN:
- The exception propagates unconverted, rather than being
relabeled a 400
"""
import documents.search._backend as backend_mod
def raise_zero_division(*args: object, **kwargs: object) -> object:
raise ZeroDivisionError("synthetic bug, unrelated to search grammar")
monkeypatch.setattr(
backend_mod,
"parse_user_query",
raise_zero_division,
)
with pytest.raises(ZeroDivisionError):
admin_client.post(
"/api/documents/bulk_edit/",
{
"documents": [],
"all": True,
"filters": {"query": "anything"},
"method": "set_document_type",
"parameters": {"document_type": None},
},
format="json",
)
def test_bulk_download_maps_search_query_error_to_a_400(
self,
admin_client: APIClient,
monkeypatch: pytest.MonkeyPatch,
indexed_document: Document,
) -> None:
"""
GIVEN:
- parse_user_query() raising a SearchQueryError naming a field
WHEN:
- The bulk_download endpoint is called with a query filter
THEN:
- The response is a 400 whose body names the field
"""
import documents.search._backend as backend_mod
def raise_search_query_error(*args: object, **kwargs: object) -> object:
raise SearchQueryError("bad value for field 'added'")
monkeypatch.setattr(
backend_mod,
"parse_user_query",
raise_search_query_error,
)
response = admin_client.post(
"/api/documents/bulk_download/",
{
"documents": [],
"all": True,
"filters": {"query": "anything"},
},
format="json",
)
assert response.status_code == status.HTTP_400_BAD_REQUEST
assert "added" in str(response.data["query"])
def test_more_like_id_selection_filter_maps_search_query_error_to_a_400(
self,
admin_client: APIClient,
monkeypatch: pytest.MonkeyPatch,
indexed_document: Document,
) -> None:
"""
GIVEN:
- TantivyBackend.more_like_this_ids() raising a
SearchQueryError
WHEN:
- The bulk_download endpoint is called with a more_like_id
filter
THEN:
- The response is a 400
"""
import documents.search._backend as backend_mod
def raise_search_query_error(*args: object, **kwargs: object) -> object:
raise SearchQueryError("similar-document lookup is unavailable")
monkeypatch.setattr(
backend_mod.TantivyBackend,
"more_like_this_ids",
raise_search_query_error,
)
response = admin_client.post(
"/api/documents/bulk_download/",
{
"documents": [],
"all": True,
"filters": {"more_like_id": indexed_document.pk},
},
format="json",
)
assert response.status_code == status.HTTP_400_BAD_REQUEST
def test_more_like_id_selection_filter_lets_an_unexpected_exception_surface(
self,
admin_client: APIClient,
monkeypatch: pytest.MonkeyPatch,
indexed_document: Document,
) -> None:
"""
GIVEN:
- TantivyBackend.more_like_this_ids() raising an unrelated
exception (ZeroDivisionError), not a SearchQueryError
WHEN:
- The bulk_download endpoint is called with a more_like_id
filter
THEN:
- The exception propagates unconverted, rather than being
relabeled a 400
"""
import documents.search._backend as backend_mod
def raise_zero_division(*args: object, **kwargs: object) -> object:
raise ZeroDivisionError("synthetic bug, unrelated to similarity lookup")
monkeypatch.setattr(
backend_mod.TantivyBackend,
"more_like_this_ids",
raise_zero_division,
)
with pytest.raises(ZeroDivisionError):
admin_client.post(
"/api/documents/bulk_download/",
{
"documents": [],
"all": True,
"filters": {"more_like_id": indexed_document.pk},
},
format="json",
)
@@ -0,0 +1,88 @@
"""An unterminated ``[`` date range bracket at the API level.
``created:[2020`` (with or without a dangling ``to <value>``) now raises
BAD_DATE and the search endpoint returns HTTP 400, where it used to parse
past the missing ``]`` and silently pass the malformed range through.
A 400 is correct: malformed input should fail loudly rather than silently
matching an unintended query. Pinned at the API level -- the layer a user
or client actually sees -- rather than only against the parser directly.
The properly closed decoy proves the bracket is what matters, not
whoosh-compat's date grammar generally: ``created:[2020 to 2021]`` parses
and searches cleanly.
"""
from __future__ import annotations
from typing import TYPE_CHECKING
import pytest
from rest_framework import status
from documents.tests.factories import DocumentFactory
if TYPE_CHECKING:
from rest_framework.test import APIClient
from documents.models import Document
pytestmark = [pytest.mark.django_db, pytest.mark.usefixtures("_search_index")]
@pytest.fixture
def indexed_document() -> Document:
from documents.search import get_backend
doc = DocumentFactory.create(title="quarterly invoice", content="acme corp")
get_backend().add_or_update(doc)
return doc
class TestUnterminatedBracketReturnsA400:
@pytest.mark.parametrize(
"query",
[
pytest.param("created:[2020", id="missing_upper_bound_and_bracket"),
pytest.param("created:[2020 to 2021", id="missing_closing_bracket"),
],
)
def test_unterminated_bracket_is_a_400(
self,
admin_client: APIClient,
indexed_document: Document,
query: str,
) -> None:
"""
GIVEN:
- The search endpoint
WHEN:
- A date-range query with a missing closing `]` (with or
without a dangling upper bound) is submitted
THEN:
- The response is a 400 naming the field, rather than parsing
past the missing bracket and silently passing the malformed
range through
"""
response = admin_client.get(f"/api/documents/?query={query}")
assert response.status_code == status.HTTP_400_BAD_REQUEST
assert "created" in str(response.data["query"])
def test_properly_closed_bracket_still_searches_cleanly(
self,
admin_client: APIClient,
indexed_document: Document,
) -> None:
"""
GIVEN:
- The search endpoint
WHEN:
- A properly closed date-range query is submitted
THEN:
- The response is a 200 (the decoy proving the missing
bracket, not whoosh-compat's date grammar generally, is
what the 400 above is about)
"""
response = admin_client.get(
"/api/documents/?query=created:[2020 to 2021]",
)
assert response.status_code == status.HTTP_200_OK
+60 -25
View File
@@ -16,6 +16,7 @@ from time import mktime
from time import sleep from time import sleep
from typing import TYPE_CHECKING from typing import TYPE_CHECKING
from typing import Any from typing import Any
from typing import Final
from typing import Literal from typing import Literal
from typing import NamedTuple from typing import NamedTuple
from unicodedata import normalize from unicodedata import normalize
@@ -288,17 +289,40 @@ logger = logging.getLogger("paperless.api")
_TANTIVY_INTERSECT_THRESHOLD = 5_000 _TANTIVY_INTERSECT_THRESHOLD = 5_000
_TANTIVY_SEARCH_PARAM_NAMES = ("text", "title_search", "query", "more_like_id") _TANTIVY_SEARCH_PARAM_NAMES = ("text", "title_search", "query", "more_like_id")
# whoosh-compat's fieldname tagger (used only for SearchMode.QUERY, via the
# whoosh grammar in parse_user_query) is O(n^2) in plain word characters:
# measured at ~0.96s/10k chars, ~3.67s/20k, ~14.4s/40k against the real field
# registry. Django's DATA_UPLOAD_MAX_MEMORY_SIZE default (2.5 MB) does not
# bound this on the POST-body selection-filter path, so an unbounded query
# is a single-request CPU exhaustion vector. 4096 chars caps the worst case
# at roughly 0.16s (quadratic extrapolation from the measurements above),
# far beyond any plausible hand-typed advanced query, while still being fast
# enough to absorb inside a request handler. Applied to all three modes at
# this shared choke point: TEXT and TITLE route through simple_search_tokens
# instead and measure linear even at 20k chars, so the cap is hygiene for
# them, not a fix, but a single limit here is simpler than one exemption.
# Not exposed as a PAPERLESS_* setting: this is a hard security boundary,
# not a tunable, and a raisable ceiling would let a misconfiguration
# reintroduce the exact hazard this exists to close.
_MAX_QUERY_LENGTH: Final[int] = 4096
def _get_tantivy_query_and_mode(params): def _get_tantivy_query_and_mode(params):
from documents.search import QueryTooLongError
from documents.search import SearchMode from documents.search import SearchMode
if "text" in params: if "text" in params:
return str(params["text"]), SearchMode.TEXT raw, mode = str(params["text"]), SearchMode.TEXT
if "title_search" in params: elif "title_search" in params:
return str(params["title_search"]), SearchMode.TITLE raw, mode = str(params["title_search"]), SearchMode.TITLE
if "query" in params: elif "query" in params:
return str(params["query"]), SearchMode.QUERY raw, mode = str(params["query"]), SearchMode.QUERY
return None # pragma: no cover else:
return None # pragma: no cover
if len(raw) > _MAX_QUERY_LENGTH:
raise QueryTooLongError(len(raw), _MAX_QUERY_LENGTH)
return raw, mode
def _get_more_like_id(query_params: dict[str, Any], user: User | None) -> int: def _get_more_like_id(query_params: dict[str, Any], user: User | None) -> int:
@@ -2504,6 +2528,7 @@ class UnifiedSearchViewSet(DocumentViewSet):
from documents.search import TantivyBackend from documents.search import TantivyBackend
from documents.search import TantivyRelevanceList from documents.search import TantivyRelevanceList
from documents.search import get_backend from documents.search import get_backend
from documents.search import search_query_error_messages
def parse_search_params() -> SearchParams: def parse_search_params() -> SearchParams:
"""Extract query string, search mode, and ordering from request.""" """Extract query string, search mode, and ordering from request."""
@@ -2694,15 +2719,10 @@ class UnifiedSearchViewSet(DocumentViewSet):
except ValidationError: except ValidationError:
raise raise
except SearchQueryError as e: except SearchQueryError as e:
# User-fixable query error (e.g. an unparsable date): surface the # User-fixable query error(s) (e.g. unparsable dates/numbers):
# specific message so the user can correct it, rather than a generic # surface every offending field's message, not just the first,
# 400 or silently empty results. # so the user can fix them all in one round-trip.
raise ValidationError({"query": [str(e)]}) from e raise ValidationError({"query": search_query_error_messages(e)}) from e
except Exception as e:
logger.warning(f"An error occurred listing search results: {e!s}")
return HttpResponseBadRequest(
"Error listing search results, check logs for more detail.",
)
@action(detail=False, methods=["GET"], name="Get Next ASN") @action(detail=False, methods=["GET"], name="Get Next ASN")
def next_asn(self, request, *args, **kwargs): def next_asn(self, request, *args, **kwargs):
@@ -2838,23 +2858,34 @@ class DocumentSelectionMixin:
}, },
) )
from documents.search import SearchQueryError
from documents.search import get_backend from documents.search import get_backend
from documents.search import search_query_error_messages
filter_name = search_filters[0] filter_name = search_filters[0]
backend = get_backend() backend = get_backend()
search_user = None if user.is_superuser else user search_user = None if user.is_superuser else user
if filter_name == "more_like_id": try:
more_like_doc_id = _get_more_like_id(filters, user) if filter_name == "more_like_id":
more_like_doc_id = _get_more_like_id(filters, user)
search_ids = backend.more_like_this_ids(more_like_doc_id, user=search_user) search_ids = backend.more_like_this_ids(
else: more_like_doc_id,
query_str, search_mode = _get_tantivy_query_and_mode(filters) user=search_user,
search_ids = backend.search_ids( )
query_str, else:
user=search_user, query_str, search_mode = _get_tantivy_query_and_mode(filters)
search_mode=search_mode, search_ids = backend.search_ids(
) query_str,
user=search_user,
search_mode=search_mode,
)
except SearchQueryError as e:
# Same user-fixable-query mapping as the search list endpoint:
# a bad date/number in a bulk selection filter is a 400 naming
# the value, never a 500.
raise ValidationError({"query": search_query_error_messages(e)}) from e
return search_ids return search_ids
@@ -3695,6 +3726,10 @@ class GlobalSearchView(PassUserMixin):
return HttpResponseBadRequest("Query required") return HttpResponseBadRequest("Query required")
if len(query) < 3: if len(query) < 3:
return HttpResponseBadRequest("Query must be at least 3 characters") return HttpResponseBadRequest("Query must be at least 3 characters")
if len(query) > _MAX_QUERY_LENGTH:
return HttpResponseBadRequest(
f"Query must be at most {_MAX_QUERY_LENGTH} characters",
)
db_only = request.query_params.get("db_only", False) db_only = request.query_params.get("db_only", False)
Generated
+23 -4
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 = "torch", version = "2.13.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'linux'" },
{ name = "watchfiles" }, { name = "watchfiles" },
{ name = "whitenoise" }, { name = "whitenoise" },
{ name = "whoosh-compat", extra = ["tantivy"] },
{ name = "zxing-cpp" }, { name = "zxing-cpp" },
] ]
@@ -3036,14 +3037,14 @@ requires-dist = [
{ name = "django-cors-headers", specifier = "~=4.9.0" }, { name = "django-cors-headers", specifier = "~=4.9.0" },
{ name = "django-extensions", specifier = "~=4.1" }, { name = "django-extensions", specifier = "~=4.1" },
{ name = "django-filter", specifier = "~=25.1" }, { name = "django-filter", specifier = "~=25.1" },
{ name = "django-guardian", specifier = ">=3.3.3,<3.5.0" }, { name = "django-guardian", specifier = ">=3.3.3,<3.5" },
{ name = "django-multiselectfield", specifier = "~=1.0.1" }, { name = "django-multiselectfield", specifier = "~=1.0.1" },
{ name = "django-rich", specifier = "~=2.2.0" }, { name = "django-rich", specifier = "~=2.2.0" },
{ name = "django-soft-delete", specifier = "~=1.0.18" }, { name = "django-soft-delete", specifier = "~=1.0.18" },
{ name = "django-treenode", specifier = ">=0.24" }, { name = "django-treenode", specifier = ">=0.24" },
{ name = "djangorestframework", specifier = "~=3.16" }, { name = "djangorestframework", specifier = "~=3.16" },
{ name = "drf-spectacular", specifier = "~=0.30" }, { name = "drf-spectacular", specifier = "~=0.30" },
{ name = "drf-spectacular-sidecar", specifier = ">=2026.7.1,<2026.9.0" }, { name = "drf-spectacular-sidecar", specifier = ">=2026.7.1,<2026.9" },
{ name = "drf-writable-nested", specifier = "~=0.7.1" }, { name = "drf-writable-nested", specifier = "~=0.7.1" },
{ name = "filelock", specifier = "~=3.32.0" }, { name = "filelock", specifier = "~=3.32.0" },
{ name = "flower", specifier = ">=2.0.1,<2.2" }, { name = "flower", specifier = ">=2.0.1,<2.2" },
@@ -3090,6 +3091,7 @@ requires-dist = [
{ name = "torch", specifier = "~=2.13.0", index = "https://download.pytorch.org/whl/cpu" }, { name = "torch", specifier = "~=2.13.0", index = "https://download.pytorch.org/whl/cpu" },
{ name = "watchfiles", specifier = ">=1.2" }, { name = "watchfiles", specifier = ">=1.2" },
{ name = "whitenoise", specifier = "~=6.11" }, { name = "whitenoise", specifier = "~=6.11" },
{ name = "whoosh-compat", extras = ["tantivy"], specifier = "==0.2" },
{ name = "zxing-cpp", specifier = "~=3.1.0" }, { name = "zxing-cpp", specifier = "~=3.1.0" },
] ]
provides-extras = ["mariadb", "postgres", "webserver"] provides-extras = ["mariadb", "postgres", "webserver"]
@@ -3100,7 +3102,7 @@ dev = [
{ name = "factory-boy", specifier = "~=3.3.1" }, { name = "factory-boy", specifier = "~=3.3.1" },
{ name = "faker", specifier = ">=40.36,<40.38" }, { name = "faker", specifier = ">=40.36,<40.38" },
{ name = "imagehash" }, { name = "imagehash" },
{ name = "prek", specifier = ">=0.4.11,<0.6.0" }, { name = "prek", specifier = ">=0.4.11,<0.6" },
{ name = "pytest", specifier = "~=9.1.1" }, { name = "pytest", specifier = "~=9.1.1" },
{ name = "pytest-cov", specifier = "~=7.1.0" }, { name = "pytest-cov", specifier = "~=7.1.0" },
{ name = "pytest-django", specifier = ">=4.12,<4.15" }, { name = "pytest-django", specifier = ">=4.12,<4.15" },
@@ -3116,7 +3118,7 @@ dev = [
] ]
docs = [{ name = "zensical", specifier = ">=0.0.51" }] docs = [{ name = "zensical", specifier = ">=0.0.51" }]
lint = [ lint = [
{ name = "prek", specifier = ">=0.4.11,<0.6.0" }, { name = "prek", specifier = ">=0.4.11,<0.6" },
{ name = "ruff", specifier = "~=0.16.1" }, { name = "ruff", specifier = "~=0.16.1" },
] ]
testing = [ testing = [
@@ -5638,6 +5640,23 @@ wheels = [
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] ]
[[package]]
name = "whoosh-compat"
version = "0.2.0"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "python-dateutil" },
]
sdist = { url = "https://files.pythonhosted.org/packages/d7/b2/ef410aa5297d61e9e98448f88ea9385c92840d811e0878de2f9ed2710620/whoosh_compat-0.2.0.tar.gz", hash = "sha256:f5d1b8bf2956a304c9b9c147ec840f2487d976cb7fa872bea767dcdeed7c3e45", size = 605669, upload-time = "2026-08-27T20:30:08.154Z" }
wheels = [
{ url = "https://files.pythonhosted.org/packages/e2/3f/78e37cd794ae26ee9b94d81d608906a8aefa31a08897ef3aaeafcbb3a55a/whoosh_compat-0.2.0-py3-none-any.whl", hash = "sha256:891e98508042673862516d3811e010a62205998a31ae6e09b80a221eb8d544d3", size = 158514, upload-time = "2026-08-27T20:30:06.764Z" },
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[package.optional-dependencies]
tantivy = [
{ name = "tantivy" },
]
[[package]] [[package]]
name = "wrapt" name = "wrapt"
version = "2.0.1" version = "2.0.1"