56 lines
1.8 KiB
Python
56 lines
1.8 KiB
Python
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"""Full text search module.
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Implements full text search by using Postgre's capabilities and
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creating temporary tables containing keywords as ts_vectors.
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"""
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from enum import Enum
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from typing import Tuple
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from ereuse_devicehub.db import db
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class Weight(Enum):
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"""TS Rank weight as an Enum."""
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A = 'A'
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B = 'B'
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C = 'C'
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D = 'D'
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class Search:
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"""Methods for building queries with Postgre's Full text search.
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Based on `Rachid Belaid's post <http://rachbelaid.com/
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postgres-full-text-search-is-good-enough/>`_ and
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`Code for America's post <https://www.codeforamerica.org/blog/2015/07/02/
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multi-table-full-text-search-with-postgres-flask-and-sqlalchemy/>`.
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"""
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LANG = 'english'
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@staticmethod
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def match(column: db.Column, search: str, lang=LANG):
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"""Query that matches a TSVECTOR column with search words."""
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return column.op('@@')(db.func.plainto_tsquery(lang, search))
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@staticmethod
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def rank(column: db.Column, search: str, lang=LANG):
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"""Query that ranks a TSVECTOR column with search words."""
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return db.func.ts_rank(column, db.func.plainto_tsquery(lang, search))
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@staticmethod
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def _vectorize(col: db.Column, weight: Weight = Weight.D, lang=LANG):
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return db.func.setweight(db.func.to_tsvector(lang, db.func.coalesce(col, '')), weight.name)
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@classmethod
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def vectorize(cls, *cols_with_weights: Tuple[db.Column, Weight], lang=LANG):
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"""Produces a query that takes one ore more columns and their
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respective weights, and generates one big TSVECTOR.
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This method takes care of `null` column values.
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"""
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first, rest = cols_with_weights[0], cols_with_weights[1:]
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tokens = cls._vectorize(*first, lang=lang)
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for unit in rest:
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tokens = tokens.concat(cls._vectorize(*unit, lang=lang))
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return tokens
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