1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
|
"""
Helpers to make elasticsearch queries.
"""
import sys
import json
from gettext import gettext
import datetime
import elasticsearch
from pydantic import BaseModel
from dynaconf import settings
from dataclasses import dataclass
from elasticsearch_dsl import Search, Q
from typing import List, Dict, Tuple, Optional, Any, Sequence
# i18n note: the use of gettext below doesn't actually do the translation here,
# it just ensures that the strings are caught by babel for translation later
class FulltextQuery(BaseModel):
q: Optional[str] = None
limit: Optional[int] = None
offset: Optional[int] = None
filter_time: Optional[str] = None
filter_type: Optional[str] = None
filter_availability: Optional[str] = None
sort_order: Optional[str] = None
time_options: Any = {
"label": gettext("Release Date"),
"slug": "filter_time",
"default": "all_time",
"list": [
{"label": gettext("All Time"), "slug": "all_time"},
{"label": gettext("Past Week"), "slug": "past_week"},
{"label": gettext("Past Year"), "slug": "past_year"},
{"label": gettext("Since 2000"), "slug": "since_2000"},
{"label": gettext("Before 1925"), "slug": "before_1925"},
],
}
type_options: Any = {
"label": gettext("Resource Type"),
"slug": "filter_type",
"default": "papers",
"list": [
{"label": gettext("Papers"), "slug": "papers"},
{"label": gettext("Reports"), "slug": "reports"},
{"label": gettext("Datasets"), "slug": "datasets"},
{"label": gettext("Everything"), "slug": "everything"},
],
}
availability_options: Any = {
"label": gettext("Availability"),
"slug": "filter_availability",
"default": "everything",
"list": [
{"label": gettext("Everything"), "slug": "everything"},
{"label": gettext("Fulltext"), "slug": "fulltext"},
{"label": gettext("Open Access"), "slug": "oa"},
],
}
sort_options: Any = {
"label": gettext("Sort Order"),
"slug": "sort_order",
"default": "relevancy",
"list": [
{"label": gettext("All Time"), "slug": "relevancy"},
{"label": gettext("Recent First"), "slug": "time_desc"},
{"label": gettext("Oldest First"), "slug": "time_asc"},
],
}
class FulltextHits(BaseModel):
count_returned: int
count_found: int
offset: int
limit: int
deep_page_limit: int
query_time_ms: int
results: List[Any]
def do_fulltext_search(query: FulltextQuery, deep_page_limit: int = 2000) -> FulltextHits:
es_client = elasticsearch.Elasticsearch(settings.ELASTICSEARCH_BACKEND)
search = Search(using=es_client, index=settings.ELASTICSEARCH_FULLTEXT_INDEX)
# Convert raw DOIs to DOI queries
if len(query.q.split()) == 1 and query.q.startswith("10.") and query.q.count("/") >= 1:
search = search.filter("terms", doi=query.q)
query.q = "*"
# type filters
if query.filter_type == "papers":
search = search.filter("terms", type=[ "article-journal", "paper-conference", "chapter", ])
elif query.filter_type == "reports":
search = search.filter("terms", type=[ "report", "standard", ])
elif query.filter_type == "datasets":
search = search.filter("terms", type=[ "dataset", "software", ])
elif query.filter_type == "everything" or query.filter_type == None:
pass
else:
raise ValueError(f"Unknown 'filter_type' parameter value: '{query.filter_type}'")
# time filters
if query.filter_time == "past_week":
week_ago_date = str(datetime.date.today() - datetime.timedelta(days=7))
search = search.filter("range", date=dict(gte=week_ago_date))
elif query.filter_time == "past_year":
# (date in the past year) or (year is this year)
# the later to catch papers which don't have release_date defined
year_ago_date = str(datetime.date.today() - datetime.timedelta(days=365))
this_year = datetime.date.today().year
search = search.filter(Q("range", date=dict(gte=year_ago_date)) | Q("term", year=this_year))
elif query.filter_time == "since_2000":
search = search.filter("range", year=dict(gte=2000))
elif query.filter_time == "before_1925":
search = search.filter("range", year=dict(lt=1925))
elif query.filter_time == "all_time" or query.filter_time == None:
pass
else:
raise ValueError(f"Unknown 'filter_time' parameter value: '{query.filter_time}'")
# we combined several queries to improve scoring.
# this query use the fancy built-in query string parser
basic_fulltext = Q(
'query_string',
query=query.q,
default_operator="AND",
analyze_wildcard=True,
allow_leading_wildcard=False,
lenient=True,
fields=[
"title^5",
"biblio_all^3",
"abstracts_all^2",
"everything",
],
)
has_fulltext = Q(
'terms',
access_type=["ia_sim", "ia_file", "wayback"],
)
poor_metadata = Q(
'bool',
should=[
# if these fields aren't set, metadata is poor. The more that do
# not exist, the stronger the signal.
Q("bool", must_not=Q("exists", field="title")),
Q("bool", must_not=Q("exists", field="year")),
Q("bool", must_not=Q("exists", field="type")),
Q("bool", must_not=Q("exists", field="stage")),
],
)
search = search.query(
"boosting",
positive=Q(
"bool",
must=basic_fulltext,
should=[has_fulltext],
),
negative=poor_metadata,
negative_boost=0.5,
)
search = search.highlight(
"abstracts_all",
"fulltext.body",
"fulltext.annex",
number_of_fragments=2,
fragment_size=300,
)
# sort order
if query.sort_order == "time_asc":
search = search.sort("year", "date")
elif query.sort_order == "time_desc":
search = search.sort("-year", "-date")
elif query.sort_order == "relevancy" or query.sort_order == None:
pass
else:
raise ValueError(f"Unknown 'sort_order' parameter value: '{query.sort_order}'")
# Sanity checks
limit = min((int(query.limit or 25), 100))
offset = max((int(query.offset or 0), 0))
if offset > deep_page_limit:
# Avoid deep paging problem.
offset = deep_page_limit
search = search[offset:offset+limit]
try:
resp = search.execute()
except elasticsearch.exceptions.RequestError as e:
# this is a "user" error
print("elasticsearch 400: " + str(e.info), file=sys.stderr)
if e.info.get('error', {}).get('root_cause', {}):
raise ValueError(str(e.info['error']['root_cause'][0].get('reason')))
else:
raise ValueError(str(e.info))
except elasticsearch.exceptions.TransportError as e:
# all other errors
print("elasticsearch non-200 status code: {}".format(e.info), file=sys.stderr)
raise IOError(str(e.info))
# convert from objects to python dicts
results = []
for h in resp:
r = h._d_
#print(json.dumps(h.meta._d_, indent=2))
r['_highlights'] = []
if 'highlight' in dir(h.meta):
highlights = h.meta.highlight._d_
for k in highlights:
r['_highlights'] += highlights[k]
results.append(r)
for h in results:
# Handle surrogate strings that elasticsearch returns sometimes,
# probably due to mangled data processing in some pipeline.
# "Crimes against Unicode"; production workaround
for key in h:
if type(h[key]) is str:
h[key] = h[key].encode('utf8', 'ignore').decode('utf8')
return FulltextHits(
count_returned=len(results),
count_found=int(resp.hits.total),
offset=offset,
limit=limit,
deep_page_limit=deep_page_limit,
query_time_ms=int(resp.took),
results=results,
)
|
