138 lines
2.4 KiB
Python
138 lines
2.4 KiB
Python
from contextlib import suppress
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import frappe
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from frappe.search.sqlite_search import SQLiteSearch, SQLiteSearchIndexMissingError
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from frappe.utils import nowdate
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class LearningSearch(SQLiteSearch):
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INDEX_NAME = "learning.db"
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INDEX_SCHEMA = {
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"metadata_fields": [
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"owner",
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"published",
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"published_on",
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"start_date",
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"status",
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"company_name",
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"creation",
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],
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"tokenizer": "unicode61 remove_diacritics 2 tokenchars '-_'",
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}
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INDEXABLE_DOCTYPES = {
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"LMS Course": {
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"fields": [
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"name",
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"title",
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{"content": "description"},
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"short_introduction",
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"published",
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"category",
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"owner",
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{"modified": "published_on"},
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],
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},
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"LMS Batch": {
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"fields": [
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"name",
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"title",
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"description",
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{"content": "batch_details"},
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"published",
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"category",
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"owner",
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{"modified": "start_date"},
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],
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},
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"Job Opportunity": {
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"fields": [
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"name",
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{"title": "job_title"},
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{"content": "description"},
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"owner",
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"location",
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"country",
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"company_name",
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"status",
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"creation",
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{"modified": "creation"},
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],
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},
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}
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DOCTYPE_FIELDS = {
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"LMS Course": [
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"name",
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"title",
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"description",
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"short_introduction",
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"category",
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"creation",
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"modified",
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"owner",
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],
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"LMS Batch": [
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"name",
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"title",
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"description",
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"batch_details",
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"category",
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"creation",
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"modified",
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"owner",
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],
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"Job Opportunity": [
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"name",
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"job_title",
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"company_name",
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"description",
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"creation",
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"modified",
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"owner",
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],
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}
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def build_index(self):
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try:
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super().build_index()
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except Exception as e:
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frappe.throw(e)
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def get_search_filters(self):
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return {}
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@SQLiteSearch.scoring_function
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def get_doctype_boost(self, row, query, query_words):
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doctype = row["doctype"]
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if doctype == "LMS Course":
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if row["published"]:
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return 1.3
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elif doctype == "LMS Batch":
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if row["published"] and row["start_date"] >= nowdate():
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return 1.3
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elif row["published"]:
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return 1.2
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return 1.0
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class LearningSearchIndexMissingError(SQLiteSearchIndexMissingError):
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pass
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def build_index():
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search = LearningSearch()
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search.build_index()
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def build_index_in_background():
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if not frappe.cache().get_value("learning_search_indexing_in_progress"):
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frappe.enqueue(build_index, queue="long")
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def build_index_if_not_exists():
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search = LearningSearch()
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if not search.index_exists():
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build_index()
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