58 lines
2.4 KiB
Python
58 lines
2.4 KiB
Python
import time
|
|
|
|
import Levenshtein
|
|
from folkugat_web.config import search as config
|
|
from folkugat_web.dal.sql import get_connection
|
|
from folkugat_web.dal.sql.temes import query as temes_q
|
|
from folkugat_web.log import logger
|
|
from folkugat_web.model import search as search_model
|
|
from folkugat_web.model import temes as model
|
|
|
|
|
|
def get_query_word_similarity(query_word: str, text_ngrams: model.NGrams) -> search_model.SearchMatch:
|
|
n = len(query_word)
|
|
if n < config.MIN_NGRAM_LENGTH:
|
|
return search_model.SearchMatch(distance=0.0, ngram='')
|
|
ns = filter(lambda i: i >= config.MIN_NGRAM_LENGTH, range(
|
|
n - config.QUERY_NGRAM_RANGE, n + config.QUERY_NGRAM_RANGE + 1))
|
|
candidate_ngrams = ((m, ngram)
|
|
for m, ngrams in map(lambda i: (i, text_ngrams.get(i, [])), ns)
|
|
for ngram in ngrams)
|
|
return min(search_model.SearchMatch(distance=Levenshtein.distance(query_word, ngram)/m,
|
|
ngram=ngram)
|
|
for m, ngram in candidate_ngrams)
|
|
|
|
|
|
def get_query_similarity(query: str, ngrams: model.NGrams) -> search_model.SearchMatch:
|
|
query_words = query.lower().split()
|
|
word_matches = map(lambda query_word: get_query_word_similarity(query_word, ngrams), query_words)
|
|
return search_model.SearchMatch.combine_matches(word_matches)
|
|
|
|
|
|
def build_result(query: str, entry: tuple[int, model.NGrams]) -> search_model.QueryResult:
|
|
if len(query) == 0:
|
|
return search_model.QueryResult(
|
|
id=entry[0],
|
|
distance=0,
|
|
ngram="",
|
|
)
|
|
match = get_query_similarity(query, entry[1])
|
|
return search_model.QueryResult(
|
|
id=entry[0],
|
|
distance=match.distance,
|
|
ngram=match.ngram,
|
|
)
|
|
|
|
|
|
def busca_temes(query: str) -> list[model.Tema]:
|
|
t0 = time.time()
|
|
with get_connection() as con:
|
|
tema_id_to_ngrams = temes_q.get_tema_id_to_ngrams(con)
|
|
search_results = (build_result(query, entry) for entry in tema_id_to_ngrams.items())
|
|
filtered_results = filter(lambda qr: qr.distance <= config.SEARCH_DISTANCE_THRESHOLD, search_results)
|
|
# filtered_results = filter(lambda qr: True, search_results)
|
|
sorted_results = sorted(filtered_results, key=lambda qr: qr.distance)
|
|
sorted_temes = list(filter(None, map(lambda qr: temes_q.get_tema_by_id(qr.id, con), sorted_results)))
|
|
logger.info(f"Search time: { int((time.time() - t0) * 1000) } ms")
|
|
return sorted_temes
|