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The search treated the whole query as one substring match, so "bambu petg-cf" had to appear verbatim in a single field and returned nothing, even though "bambu" and "petg-cf" each matched on their own. Vendor-name matching also ran as a separate query, so a term could never be satisfied by the vendor while another was satisfied by the filament. Split the query on whitespace and require every term to match some searchable field, letting different terms match different fields: - Filaments search vendor.name in the same pass as their own fields via an outerjoin, replacing the separate vendor query. This is what makes "bambu petg-cf" work. - Extra-field matching moves from a follow-up query to a correlated EXISTS inside each term's OR-clause, so extras participate in the AND too. - Ranking generalizes to a per-field-kind base rank (native 0, vendor 2, extra 4) with prefix hits beating substrings. A row ranks by its worst term, so rows matching everything strongly sort first; the reported match_field is the single best hit. Single-term queries behave as before. Standard SQL only (ilike, correlated EXISTS), so this holds on all four supported databases. Claude-Session: https://claude.ai/code/session_014Cwk5Yyze9yPvkYkUtMAP4
408 lines
14 KiB
Python
408 lines
14 KiB
Python
"""Cross-entity free-text / id / color search backing the /search endpoint.
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Searches spools, filaments and vendors in one call and reports, per result, which
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field matched. The query is split on whitespace into terms and a row must match
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*every* term (each term may match a different field), so "bambu petg-cf" finds the
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PETG-CF filaments of the Bambu Lab vendor. Text matching uses case-insensitive
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``ilike`` so it works on all four supported databases. A purely numeric query also
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matches a spool by its id, and a query that is a hex code or a CSS color name runs a
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color-similarity search over filaments (reusing
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:func:`spoolman.database.filament.find_by_color`).
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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from typing import TYPE_CHECKING
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from sqlalchemy import ColumnElement, and_, or_, select
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from sqlalchemy.orm import InstrumentedAttribute, joinedload
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from spoolman.colors import resolve_color
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from spoolman.database import filament as filament_db
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from spoolman.database import models
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from spoolman.extra_field_registry import EntityType, ExtraFieldType, get_extra_fields
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from spoolman.math import delta_e, hex_to_rgb, rgb_to_lab
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if TYPE_CHECKING:
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from sqlalchemy.ext.asyncio import AsyncSession
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# How many candidate rows to pull per entity before ranking and trimming to `limit`.
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# The search box only needs the top handful; this bound keeps ranking cheap while
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# still letting prefix matches win over substring ones within a generous window.
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_CANDIDATE_CAP = 200
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# Upper bound on how many whitespace-separated terms we honor, so a pathological
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# query can't turn into an arbitrarily large AND-of-ORs.
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_MAX_TERMS = 8
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# Extra-field types whose stored value is human-readable text worth searching.
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_TEXT_EXTRA_TYPES = (ExtraFieldType.text, ExtraFieldType.choice)
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# Rank buckets (lower sorts first). Each base is the rank of a prefix/exact hit on a
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# field of that kind; a mere substring hit ranks one worse.
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_RANK_ID_OR_COLOR = -1 # exact id / color-similarity match
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_BASE_NATIVE = 0 # the entity's own text fields
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_BASE_VENDOR = 2 # related vendor name (filaments only)
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_BASE_EXTRA = 4 # extra-field text
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_WEAK_PENALTY = 1
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@dataclass
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class SpoolMatch:
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spool: models.Spool
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match_field: str
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@dataclass
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class FilamentMatch:
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filament: models.Filament
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match_field: str
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@dataclass
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class VendorMatch:
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vendor: models.Vendor
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match_field: str
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@dataclass
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class SearchResult:
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spools: list[SpoolMatch]
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filaments: list[FilamentMatch]
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vendors: list[VendorMatch]
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is_color_query: bool
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def _split_terms(q: str) -> list[str]:
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"""Lower-cased, de-duplicated search terms, in query order."""
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terms: list[str] = []
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for raw in q.lower().split():
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if raw not in terms:
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terms.append(raw)
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return terms[:_MAX_TERMS]
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def _classify_match(terms: list[str], fields: list[tuple[str, str | None, int]]) -> tuple[str, int] | None:
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"""Return ``(match_field, rank)`` for a row, or ``None`` if some term doesn't match.
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``fields`` are ordered ``(label, value, base_rank)`` triples. Every term must hit at
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least one field; the reported field is the single best hit across all terms, while
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the rank is the *worst* term's rank, so rows where everything matched strongly sort
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above rows that only just scraped by.
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"""
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best: tuple[str, int] | None = None
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worst_rank = _RANK_ID_OR_COLOR
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for term in terms:
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term_best: tuple[str, int] | None = None
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for label, value, base in fields:
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if not value:
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continue
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v = value.lower()
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if term not in v:
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continue
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rank = base if v.startswith(term) else base + _WEAK_PENALTY
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if term_best is None or rank < term_best[1]:
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term_best = (label, rank)
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if term_best is None:
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return None
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worst_rank = max(worst_rank, term_best[1])
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if best is None or term_best[1] < best[1]:
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best = term_best
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if best is None:
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return None
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return best[0], worst_rank
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def _term_clause(
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term: str,
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columns: list[InstrumentedAttribute],
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extra_exists: ColumnElement | None,
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) -> ColumnElement:
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"""Build the SQL predicate for a single term: it must appear in any searchable column."""
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clauses = [col.ilike(f"%{term}%") for col in columns]
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if extra_exists is not None:
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clauses.append(extra_exists)
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return or_(*clauses)
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async def _text_extra_keys(db: AsyncSession, entity_type: EntityType) -> list[str]:
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"""Keys of the entity's text/choice extra fields (the ones worth text-searching)."""
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fields = await get_extra_fields(db, entity_type)
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return [f.key for f in fields if f.field_type in _TEXT_EXTRA_TYPES]
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def _extra_exists(
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field_model: type,
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owner_col: InstrumentedAttribute,
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owner_id_col: InstrumentedAttribute,
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keys: list[str],
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term: str,
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) -> ColumnElement | None:
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"""Build a correlated EXISTS matching ``term`` against the row's text/choice extra fields."""
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if not keys:
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return None
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return (
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select(1)
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.where(
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owner_col == owner_id_col,
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field_model.key.in_(keys),
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field_model.value.ilike(f"%{term}%"),
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)
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.exists()
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)
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async def _extra_values(
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db: AsyncSession,
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field_model: type,
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owner_col: InstrumentedAttribute,
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keys: list[str],
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ids: list[int],
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) -> dict[int, list[tuple[str, str | None, int]]]:
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"""Fetch the searchable extra-field values of the given rows, as classifier fields."""
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if not keys or not ids:
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return {}
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stmt = select(owner_col, field_model.key, field_model.value).where(
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owner_col.in_(ids),
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field_model.key.in_(keys),
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)
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out: dict[int, list[tuple[str, str | None, int]]] = {}
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for owner_id, key, value in (await db.execute(stmt)).all():
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out.setdefault(owner_id, []).append((f"extra.{key}", value, _BASE_EXTRA))
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return out
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async def _search_spools(db: AsyncSession, terms: list[str], limit: int) -> list[SpoolMatch]:
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native_fields = (
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("comment", models.Spool.comment),
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("location", models.Spool.location),
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("lot_nr", models.Spool.lot_nr),
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)
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columns = [col for _, col in native_fields]
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keys = await _text_extra_keys(db, EntityType.spool)
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load = (joinedload(models.Spool.filament).joinedload(models.Filament.vendor),)
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stmt = (
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select(models.Spool)
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.where(
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and_(
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*(
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_term_clause(
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term,
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columns,
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_extra_exists(models.SpoolField, models.SpoolField.spool_id, models.Spool.id, keys, term),
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)
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for term in terms
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),
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),
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)
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.options(*load)
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.order_by(models.Spool.id)
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.limit(_CANDIDATE_CAP)
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)
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rows = (await db.execute(stmt)).unique().scalars().all()
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extras = await _extra_values(db, models.SpoolField, models.SpoolField.spool_id, keys, [s.id for s in rows])
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hits: dict[int, tuple[str, int]] = {}
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spools: dict[int, models.Spool] = {}
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for spool in rows:
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fields = [(label, getattr(spool, attr.key), _BASE_NATIVE) for label, attr in native_fields]
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fields.extend(extras.get(spool.id, []))
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classified = _classify_match(terms, fields)
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if classified is None:
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continue
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spools[spool.id] = spool
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hits[spool.id] = classified
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ordered = sorted(hits.items(), key=lambda kv: (kv[1][1], kv[0]))[:limit]
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return [SpoolMatch(spool=spools[sid], match_field=field) for sid, (field, _rank) in ordered]
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async def _search_filaments(
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db: AsyncSession,
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terms: list[str],
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limit: int,
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color_hits: list[FilamentMatch],
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) -> list[FilamentMatch]:
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hits: dict[int, tuple[str, int]] = {}
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filaments: dict[int, models.Filament] = {}
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# Color matches (already ordered by closeness) claim the top rank; their relative
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# order is preserved below via `color_order`.
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for match in color_hits:
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fid = match.filament.id
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filaments[fid] = match.filament
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hits[fid] = (match.match_field, _RANK_ID_OR_COLOR)
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native_fields = (
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("name", models.Filament.name),
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("material", models.Filament.material),
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("article_number", models.Filament.article_number),
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("comment", models.Filament.comment),
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)
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# The vendor name is searched alongside the filament's own fields, so a query mixing
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# a manufacturer and a material ("bambu petg-cf") can satisfy one term from each.
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columns = [col for _, col in native_fields] + [models.Vendor.name]
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keys = await _text_extra_keys(db, EntityType.filament)
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load = (joinedload(models.Filament.vendor),)
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stmt = (
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select(models.Filament)
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.outerjoin(models.Filament.vendor)
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.where(
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and_(
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*(
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_term_clause(
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term,
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columns,
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_extra_exists(
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models.FilamentField,
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models.FilamentField.filament_id,
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models.Filament.id,
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keys,
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term,
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),
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)
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for term in terms
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),
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),
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)
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.options(*load)
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.order_by(models.Filament.id)
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.limit(_CANDIDATE_CAP)
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)
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rows = (await db.execute(stmt)).unique().scalars().all()
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extras = await _extra_values(
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db,
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models.FilamentField,
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models.FilamentField.filament_id,
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keys,
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[f.id for f in rows],
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)
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for filament in rows:
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if filament.id in hits:
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continue # color match already annotated this one
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fields = [(label, getattr(filament, attr.key), _BASE_NATIVE) for label, attr in native_fields]
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fields.append(("vendor.name", filament.vendor.name if filament.vendor else None, _BASE_VENDOR))
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fields.extend(extras.get(filament.id, []))
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classified = _classify_match(terms, fields)
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if classified is None:
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continue
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filaments[filament.id] = filament
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hits[filament.id] = classified
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# Color hits keep their supplied order; everyone else sorts by (rank, id).
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color_order = {m.filament.id: i for i, m in enumerate(color_hits)}
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ordered = sorted(
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hits.items(),
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key=lambda kv: (kv[1][1], color_order.get(kv[0], 0), kv[0]),
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)[:limit]
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return [FilamentMatch(filament=filaments[fid], match_field=field) for fid, (field, _rank) in ordered]
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async def _search_vendors(db: AsyncSession, terms: list[str], limit: int) -> list[VendorMatch]:
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native_fields = (
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("name", models.Vendor.name),
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("comment", models.Vendor.comment),
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)
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columns = [col for _, col in native_fields]
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keys = await _text_extra_keys(db, EntityType.vendor)
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stmt = (
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select(models.Vendor)
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.where(
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and_(
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*(
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_term_clause(
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term,
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columns,
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_extra_exists(models.VendorField, models.VendorField.vendor_id, models.Vendor.id, keys, term),
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)
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for term in terms
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),
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),
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)
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.order_by(models.Vendor.id)
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.limit(_CANDIDATE_CAP)
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)
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rows = (await db.execute(stmt)).unique().scalars().all()
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extras = await _extra_values(db, models.VendorField, models.VendorField.vendor_id, keys, [v.id for v in rows])
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hits: dict[int, tuple[str, int]] = {}
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vendors: dict[int, models.Vendor] = {}
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for vendor in rows:
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fields = [(label, getattr(vendor, attr.key), _BASE_NATIVE) for label, attr in native_fields]
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fields.extend(extras.get(vendor.id, []))
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classified = _classify_match(terms, fields)
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if classified is None:
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continue
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vendors[vendor.id] = vendor
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hits[vendor.id] = classified
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ordered = sorted(hits.items(), key=lambda kv: (kv[1][1], kv[0]))[:limit]
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return [VendorMatch(vendor=vendors[vid], match_field=field) for vid, (field, _rank) in ordered]
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def _filament_color_distance(filament: models.Filament, query_lab: list[float]) -> float:
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"""Smallest CIELAB delta-E between the query color and any of the filament's colors."""
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if filament.color_hex is not None:
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colors = [filament.color_hex]
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elif filament.multi_color_hexes is not None:
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colors = filament.multi_color_hexes.split(",")
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else:
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return float("inf")
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return min(delta_e(query_lab, rgb_to_lab(hex_to_rgb(color))) for color in colors)
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async def _color_matches(db: AsyncSession, color_hex: str, threshold: float, limit: int) -> list[FilamentMatch]:
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"""Color-similar filaments, closest first."""
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matched = await filament_db.find_by_color(
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db=db,
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color_query_hex=color_hex,
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similarity_threshold=threshold,
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)
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query_lab = rgb_to_lab(hex_to_rgb(color_hex))
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matched.sort(key=lambda f: _filament_color_distance(f, query_lab))
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return [FilamentMatch(filament=f, match_field="color") for f in matched[:limit]]
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async def search(
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*,
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db: AsyncSession,
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query: str,
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color_similarity_threshold: float = 20.0,
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limit: int = 20,
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) -> SearchResult:
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"""Run a cross-entity search for ``query`` and return categorized, annotated results."""
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q = query.strip()
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terms = _split_terms(q)
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if not terms:
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return SearchResult(spools=[], filaments=[], vendors=[], is_color_query=False)
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color_hex = resolve_color(q)
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is_color_query = color_hex is not None
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color_hits = await _color_matches(db, color_hex, color_similarity_threshold, limit) if color_hex else []
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spools = await _search_spools(db, terms, limit)
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filaments = await _search_filaments(db, terms, limit, color_hits)
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vendors = await _search_vendors(db, terms, limit)
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# Numeric query: surface the spool with that exact id at the very top.
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if q.isdigit():
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spool_id = int(q)
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spool = await db.get(
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models.Spool, spool_id, options=[joinedload(models.Spool.filament).joinedload(models.Filament.vendor)]
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)
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if spool is not None:
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spools = [m for m in spools if m.spool.id != spool_id] # drop weaker text match
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spools.insert(0, SpoolMatch(spool=spool, match_field="id"))
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spools = spools[:limit]
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return SearchResult(spools=spools, filaments=filaments, vendors=vendors, is_color_query=is_color_query)
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