def whoosh_search(user_id, query_terms):
ret = {}
user_artists_profile = get_user_artists_profile(user_id)
# q = qp.parse(
# "(" + query + " ANDMAYBE ((" + ") OR (".join([
# (" artist_id:" + str(artist_id) + "^" + str(1 + artist_score)) for (
# artist_id, artist_score) in user_artists_profile]) + ")))")
# see https://pythonhosted.org/Whoosh/api/query.html#whoosh.query.AndMaybe
q = AndMaybe(
And([Term('title', qt) for qt in query_terms.split(' ')]),
Or([Term('artist_id', artist_id, boost=artist_score) for (
artist_id, artist_score) in user_artists_profile]))
with ix.searcher() as searcher:
results = searcher.search(q, limit=10)
ret = {
'items': [hit.fields() for hit in results],
'runtime': results.runtime}
return ret
def whoosh_search(user_id, query_terms):
ret = {}
user_artists_profile = get_user_artists_profile(user_id)
# q = qp.parse(
# "(" + query + " ANDMAYBE ((" + ") OR (".join([
# (" artist_id:" + str(artist_id) + "^" + str(1 + artist_score)) for (
# artist_id, artist_score) in user_artists_profile]) + ")))")
# see https://pythonhosted.org/Whoosh/api/query.html#whoosh.query.AndMaybe
q = AndMaybe(
And([Term('title', qt) for qt in query_terms.split(' ')]),
Or([
Term('artist_id', artist_id, boost=artist_score)
for (artist_id, artist_score) in user_artists_profile
]))
with ix.searcher() as searcher:
results = searcher.search(q, limit=10)
ret = {
'items': [hit.fields() for hit in results],
'runtime': results.runtime
}
return ret
def elasticsearch_search(user_id, query_terms, ix_name="songs"):
ret = {}
es = Elasticsearch()
user_artists_profile = get_user_artists_profile(user_id)
# https://www.elastic.co/guide/en/elasticsearch/guide/current/bool-query.html
# The bool query calculates the relevance _score for each document
# by adding together the _score from all of the matching must and should clauses,
# and then dividing by the total number of must and should clauses.
# All the must clauses must match, and all the must_not clauses must not match,
# but how many should clauses should match? By default,
# none of the should clauses are required to match, with one exception:
# if there are no must clauses, then at least one should clause must match.
query = {
"query": {
"bool": {
"must": {
"match": {
"title": {
"query": query_terms,
"operator": "and"
}
}
# slop could be interesting as order of term
# seems important in this context
# "span_near": {
# "title": {
# "query": query_terms,
# "slop": 50
# }
},
"should": [{
"match": {
"artist_id": {
"query": str(artist_id),
"boost": artist_score
}
}
} for artist_id, artist_score in user_artists_profile]
}
},
# this should be slightly more performing once es cache management is right (
# though incorrect unless window size is large enough)
# https://www.elastic.co/guide/en/elasticsearch/reference/2.1/search-request-rescore.html
# https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-function-score-query.html#score-functions
# "rescore": {
# "window_size": 100,
# "query": {
# "rescore_query": {
# "function_score": {
# "functions": [
# {
# "filter": {
# "term": {
# "artist_id": str(artist_id)
# },
# },
# "script_score": {
# "script": "_score + " + str(artist_score)
# }
# }
# for artist_id, artist_score in user_artists_profile
# ],
# "score_mode": "first",
# "boost_mode": "replace"
# }
# }
# }
# }
}
ret = es.search(index=ix_name, body=query)
return ret
def elasticsearch_search(user_id, query_terms, ix_name="songs"):
ret = {}
es = Elasticsearch()
user_artists_profile = get_user_artists_profile(user_id)
# https://www.elastic.co/guide/en/elasticsearch/guide/current/bool-query.html
# The bool query calculates the relevance _score for each document
# by adding together the _score from all of the matching must and should clauses,
# and then dividing by the total number of must and should clauses.
# All the must clauses must match, and all the must_not clauses must not match,
# but how many should clauses should match? By default,
# none of the should clauses are required to match, with one exception:
# if there are no must clauses, then at least one should clause must match.
query = {
"query": {
"bool": {
"must": {
"match": {
"title": {
"query": query_terms,
"operator": "and"
}
}
# slop could be interesting as order of term
# seems important in this context
# "span_near": {
# "title": {
# "query": query_terms,
# "slop": 50
# }
},
"should": [
{"match": {
"artist_id": {
"query": str(artist_id),
"boost": artist_score
}
}} for artist_id, artist_score in user_artists_profile
]
}
},
# this should be slightly more performing once es cache management is right (
# though incorrect unless window size is large enough)
# https://www.elastic.co/guide/en/elasticsearch/reference/2.1/search-request-rescore.html
# https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-function-score-query.html#score-functions
# "rescore": {
# "window_size": 100,
# "query": {
# "rescore_query": {
# "function_score": {
# "functions": [
# {
# "filter": {
# "term": {
# "artist_id": str(artist_id)
# },
# },
# "script_score": {
# "script": "_score + " + str(artist_score)
# }
# }
# for artist_id, artist_score in user_artists_profile
# ],
# "score_mode": "first",
# "boost_mode": "replace"
# }
# }
# }
# }
}
ret = es.search(index=ix_name, body=query)
return ret
