funkwhale/api/funkwhale_api/common/search.py

248 wiersze
8.1 KiB
Python

import re
from django.contrib.postgres.search import SearchQuery
from django.db.models import Q
from . import utils
QUERY_REGEX = re.compile(r'(((?P<key>\w+):)?(?P<value>"[^"]+"|[\S]+))')
def parse_query(query):
"""
Given a search query such as "hello is:issue status:opened",
returns a list of dictionnaries discribing each query token
"""
matches = [m.groupdict() for m in QUERY_REGEX.finditer(query.lower())]
for m in matches:
if m["value"].startswith('"') and m["value"].endswith('"'):
m["value"] = m["value"][1:-1]
return matches
def normalize_query(
query_string,
findterms=re.compile(r'"([^"]+)"|(\S+)').findall,
normspace=re.compile(r"\s{2,}").sub,
):
""" Splits the query string in invidual keywords, getting rid of unecessary spaces
and grouping quoted words together.
Example:
>>> normalize_query(' some random words "with quotes " and spaces')
['some', 'random', 'words', 'with quotes', 'and', 'spaces']
"""
return [normspace(" ", (t[0] or t[1]).strip()) for t in findterms(query_string)]
def get_query(query_string, search_fields):
""" Returns a query, that is a combination of Q objects. That combination
aims to search keywords within a model by testing the given search fields.
"""
query = None # Query to search for every search term
terms = normalize_query(query_string)
for term in terms:
or_query = None # Query to search for a given term in each field
for field_name in search_fields:
q = Q(**{"%s__icontains" % field_name: term})
if or_query is None:
or_query = q
else:
or_query = or_query | q
if query is None:
query = or_query
else:
query = query & or_query
return query
def remove_chars(string, chars):
for char in chars:
string = string.replace(char, "")
return string
def get_fts_query(query_string, fts_fields=["body_text"], model=None):
search_type = "raw"
if query_string.startswith('"') and query_string.endswith('"'):
# we pass the query directly to the FTS engine
query_string = query_string[1:-1]
else:
query_string = remove_chars(query_string, ['"', "&", "(", ")", "!", "'"])
parts = query_string.replace(":", "").split(" ")
parts = ["{}:*".format(p) for p in parts if p]
if not parts:
return Q(pk=None)
query_string = "&".join(parts)
if not fts_fields or not query_string.strip():
return Q(pk=None)
query = None
for field in fts_fields:
if "__" in field and model:
# When we have a nested lookup, we switch to a subquery for enhanced performance
fk_field_name, lookup = (
field.split("__")[0],
"__".join(field.split("__")[1:]),
)
fk_field = model._meta.get_field(fk_field_name)
related_model = fk_field.related_model
subquery = related_model.objects.filter(
**{
lookup: SearchQuery(
query_string, search_type=search_type, config="english_nostop"
)
}
).values_list("pk", flat=True)
new_query = Q(**{"{}__in".format(fk_field_name): list(subquery)})
else:
new_query = Q(
**{
field: SearchQuery(
query_string, search_type=search_type, config="english_nostop"
)
}
)
query = utils.join_queries_or(query, new_query)
return query
def filter_tokens(tokens, valid):
return [t for t in tokens if t["key"] in valid]
def apply(qs, config_data):
for k in ["filter_query", "search_query"]:
q = config_data.get(k)
if q:
qs = qs.filter(q)
distinct = config_data.get("distinct", False)
if distinct:
qs = qs.distinct()
return qs
class SearchConfig:
def __init__(self, search_fields={}, filter_fields={}, types=[]):
self.filter_fields = filter_fields
self.search_fields = search_fields
self.types = types
def clean(self, query):
tokens = parse_query(query)
cleaned_data = {}
cleaned_data["types"] = self.clean_types(filter_tokens(tokens, ["is"]))
cleaned_data["search_query"] = self.clean_search_query(
filter_tokens(tokens, [None, "in"] + list(self.search_fields.keys()))
)
unhandled_tokens = [
t
for t in tokens
if t["key"] not in [None, "is", "in"] + list(self.search_fields.keys())
]
cleaned_data["filter_query"], matching_filters = self.clean_filter_query(
unhandled_tokens
)
if matching_filters:
cleaned_data["distinct"] = any(
[
self.filter_fields[k].get("distinct", False)
for k in matching_filters
if k in self.filter_fields
]
)
else:
cleaned_data["distinct"] = False
return cleaned_data
def clean_search_query(self, tokens):
if not self.search_fields or not tokens:
return
fields_subset = {
f for t in filter_tokens(tokens, ["in"]) for f in t["value"].split(",")
} or set(self.search_fields.keys())
fields_subset = set(self.search_fields.keys()) & fields_subset
to_fields = [self.search_fields[k]["to"] for k in fields_subset]
specific_field_query = None
for token in tokens:
if token["key"] not in self.search_fields:
continue
to = self.search_fields[token["key"]]["to"]
try:
field = token["field"]
value = field.clean(token["value"])
except KeyError:
# no cleaning to apply
value = token["value"]
q = Q(**{"{}__icontains".format(to): value})
if not specific_field_query:
specific_field_query = q
else:
specific_field_query &= q
query_string = " ".join([t["value"] for t in filter_tokens(tokens, [None])])
unhandled_tokens_query = get_query(query_string, sorted(to_fields))
if specific_field_query and unhandled_tokens_query:
return unhandled_tokens_query & specific_field_query
elif specific_field_query:
return specific_field_query
elif unhandled_tokens_query:
return unhandled_tokens_query
return None
def clean_filter_query(self, tokens):
if not self.filter_fields or not tokens:
return None, []
matching = [t for t in tokens if t["key"] in self.filter_fields]
queries = [self.get_filter_query(token) for token in matching]
query = None
for q in queries:
if not query:
query = q
else:
query = query & q
return query, [m["key"] for m in matching]
def get_filter_query(self, token):
raw_value = token["value"]
try:
field = self.filter_fields[token["key"]]["field"]
value = field.clean(raw_value)
except KeyError:
# no cleaning to apply
value = raw_value
try:
query_field = self.filter_fields[token["key"]]["to"]
return Q(**{query_field: value})
except KeyError:
pass
# we don't have a basic filter -> field mapping, this likely means we
# have a dynamic handler in the config
handler = self.filter_fields[token["key"]]["handler"]
value = handler(value)
return value
def clean_types(self, tokens):
if not self.types:
return []
if not tokens:
# no filtering on type, we return all types
return [t for key, t in self.types]
types = []
for token in tokens:
for key, t in self.types:
if key.lower() == token["value"]:
types.append(t)
return types