I have a question on how to query the code below using Elastica?:
curl 'localhost:9200/hotels/_search/' -d '{
"query": {
"function_score": {
"functions": [
{
"DECAY_FUNCTION": {
"price": {
"origin": "0",
"scale": "20"
}
}
},
{
"DECAY_FUNCTION": {
"location": {
"origin": "11, 12",
"scale": "2km"
}
}
}
],
"query": {
"match": {
"properties": "balcony"
}
},
`` "score_mode": "multiply"
}
}
}'
Hope someone can lend me an arm here.
Thanks,
Related
I'm new to ES, but already have a basic query that I need to extend.
The query is currently doing a search based on keywords and also on geo-distance.
Now, I have added a custom tag into my index, and I wish to take it into account too.
I wish to filter and score my results based on the imageTags (tag + score)!
PS: I have seen other similar posts, but I can't figure out how to adapt my query (none of my attempts work).
Here is my query:
GET /posts_index/_search
{
"track_total_hits": true,
"from":0,
"size":10,
"_source": [
"id",
"tags",
"taxonomies",
"storesNames",
"geoLocations",
"storesIds",
"imageUri"
],
"query": {
"function_score": {
"query": {
"bool": {
"should": [
{
"distance_feature": {
"field": "geoLocations",
"pivot": "10km",
"origin": [
-71.3,
41.15
]
}
},
{
"distance_feature": {
"field": "creationTime",
"pivot": "14d",
"origin": "now"
}
},
{
"query_string": {
"fields": [
"tags^3",
"taxonomies^5"
],
"query": "",
"fuzziness": "auto"
}
},
]
}
},
"functions": [
{
"script_score": {
"script": {
"source": "Math.sqrt(doc['commentsCount'].value)"
}
}
},
{
"script_score": {
"script": {
"source": "Math.log(2 + doc['likesCount'].value)"
}
}
},
{
"script_score": {
"script": {
"source": "Math.log(2 + doc['viewsCount'].value)"
}
}
}
],
"score_mode": "avg"
}
}
}
Here is one of my attempts to modify it:
{
"query": {
"nested": {
"path": "imageTags",
"score_mode": "sum",
"query": {
"function_score": {
"query": {
"match": {
"imageTags.tag.keyword": "tripod"
}
},
"field_value_factor": {
"field": "imageTags.score",
"factor": 1,
"missing": 0
}
}
}
}
}
}
By example, here is and example of the index:
{
"id": "4a9afd93-62bc-e8b2-29b4-39f5b073336d",
"tags": [
"fashion",
"mode",
"summer"
],
"imageTags": [
{
"score": 0.95150965,
"tag": "four-poster"
},
{
"score": 0.014835004,
"tag": "window"
},
{
"score": 0.014835004,
"tag": "shade"
},
{
"score": 0.009375425,
"tag": "sliding"
},
{
"score": 0.009375425,
"tag": "door"
}
],
"taxonomies": [],
"categories": [],
"qualityScore": 0.0,
"geoLocations": [
{
"lat": 50.4651156,
"lon": 4.865208
}
],
"storesIds": [
"ba9b3f59-50aa-8774-11a7-39f5ad58ae1a"
],
"storesNames": [
"Zara Namur"
],
"creationTime": "2020-06-10T12:48:30.5710000Z",
"updateTime": "2020-06-10T12:48:30.5710000Z",
"imageUri": "https://localhost:44359/cdn/e_76372856-f7a0-49cc-d3d9-39f5ad58ad6d/653d147084637b2af68b39f5b0733359.jpg",
"description": "",
"likesCount": 0,
"viewsCount": 0,
"commentsCount": 0,
"ImageTags": [
{
"score": 0.95150965,
"tag": "four-poster"
},
{
"score": 0.014835004,
"tag": "window"
},
{
"score": 0.014835004,
"tag": "shade"
},
{
"score": 0.009375425,
"tag": "sliding"
},
{
"score": 0.009375425,
"tag": "door"
}
]
}
Here is my sample dataset,
{
"parent":[
{
"name":"John Doe 1",
"age":"100 year",
"sex":"male",
"child":[
{
"name":"Jane Doe 1",
"height":100.00,
"width":100.00
},
{
"name":"Jane Doe 2",
"height":100.00,
"width":100.00
}
]
},
{
"name":"John Doe 2",
"age":"100 year",
"sex":"male",
"child":[
{
"name":"Jane Doe 3",
"height":100.00,
"width":100.00
},
{
"name":"Jane Doe 4",
"height":100.00,
"width":100.00
}
]
}
]
}
And my definition:
{
"settings": {
"index": {
"analysis": {
"analyzer": {
"default": {
"type": "simple"
}
}
}
}
},
"mappings": {
"_doc": {
"properties": {
"parent": {
"type": "nested",
"properties": {
"name": {
"type": "keyword"
},
"age": {
"type": "text"
},
"sex": {
"type": "text"
},
"child": {
"type": "nested",
"properties": {
"name": {
"type": "text"
},
"height": {
"type": "float"
},
"width": {
"type": "float"
}
}
}
}
}
}
}
}
}
I'm using the following query to look for matches in the parent.name property and can get highlights.
{
"query": {
"bool": {
"should": [
{
"nested": {
"inner_hits": {
"highlight": {
"fields": {
"parent.name": {}
},
"number_of_fragments": 0,
"pre_tags": [
"<span>"
],
"post_tags": [
"</span>"
]
}
},
"path": "parent",
"query": {
"bool": {
"must": [
{
"match": {
"parent.name": {
"query": "John",
"fuzziness": "AUTO:3,6",
"prefix_length": "0"
}
}
}
]
}
}
}
}
],
}
},
"_source": ["parent"],
"sort": [
{
"_score": {
"order": "desc"
}
},
{
"createdOn": {
"order": "desc"
}
}
]
}
Is there a way to get inline highlights for the matches in the child.name properties also so that it would be easy to find exactly which element of that corresponding array got matched?
For example, for the given sample data, if I search by "Doe", I'm expecting to get 6 hits, whereas if I search by "Jane", I would get only 4.
You can simply add another nested query clause inside you top level should.
Here's how your query should look:
{
"query": {
"bool": {
"should": [
{
"nested": {
"inner_hits": {
"highlight": {
"fields": {
"parent.name": {}
},
"number_of_fragments": 0,
"pre_tags": [
"<span>"
],
"post_tags": [
"</span>"
]
}
},
"path": "parent",
"query": {
"bool": {
"must": [
{
"match": {
"parent.name": {
"query": "John Doe 1"
}
}
}
]
}
}
}
},
{
"nested": {
"inner_hits": {
"highlight": {
"fields": {
"parent.child.name": {}
},
"number_of_fragments": 0,
"pre_tags": [
"<span>"
],
"post_tags": [
"</span>"
]
}
},
"path": "parent.child",
"query": {
"bool": {
"must": [
{
"match": {
"parent.child.name": {
"query": "Jane Doe 1"
}
}
}
]
}
}
}
}
],
"minimum_should_match": 1
}
},
"_source": ["parent"],
"sort": [
{
"_score": {
"order": "desc"
}
},
{
"createdOn": {
"order": "desc"
}
}
]
}
I want to use the example on official documents combined with normal match and boolean queries. How to do that?
GET /_search
{
"query": {
"function_score": {
"field_value_factor": {
"field": "likes",
"factor": 1.2,
"modifier": "sqrt",
"missing": 1
}
}
}
}
https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-function-score-query.html#function-field-value-factor
Match query:
"query": {
"match" : {
"name" : "star wars"
}
}
Boolean query:
{
"query": {
"bool": {
"must": [
{
"match": {
"name": "Star Wars"
}
}
],
"should": [
{
"term": {
"name.keyword": {
"value": "Star Wars"
}
}
}
]
}
}
}
Yes, this should be doable. If you read further down in the documentation that you linked to, there is an example:
GET /_search
{
"query": {
"function_score": {
"functions": [
{
"gauss": {
"price": {
"origin": "0",
"scale": "20"
}
}
},
{
"gauss": {
"location": {
"origin": "11, 12",
"scale": "2km"
}
}
}
],
"query": {
"match": {
"properties": "balcony"
}
},
"score_mode": "multiply"
}
}
}
Modifying that slightly for your use case should look something like this:
GET /_search
{
"query": {
"function_score": {
"functions": [
{
"field_value_factor": {
"field": "likes",
"factor": 1.2,
"modifier": "sqrt",
"missing": 1
}
}
],
"query": {
"match": {
"name": "Star Wars"
}
},
"score_mode": "multiply"
}
}
}
Disclaimer: I haven't tested this, just going off of the documentation.
I am pretty new to elasticsearch and have not really got the hold of it. So I have a search, the results of which will be weighed according to the weight of their tags, which works absolutely fine, but later when I introduced a filter, the search always gives me empty results. Here is what I have tried:
{
"nested": {
"path": "tags",
"score_mode": "sum",
"query": {
"function_score": {
"query": {
"bool": {
"must": [
{
"match_phrase_prefix": {
"tags.tag": "big"
}
}
],
"filter": {
"term": {
"type.primary": "audio"
}
}
}
},
"field_value_factor": {
"field": "tags.weight"
},
"boost_mode": "multiply",
"boost": 10
}
}
}
}
The example result with the filter should be something like this:
{
"_index": "assets",
"_type": "Asset",
"_id": "5a1dc3c0848662ee49e36f43s",
"_score": 886.8744,
"_source": {
"name": "And Action Breakbeat",
"meta_data": {
"type": "audio/mp3",
"file_name": "music_zapsplat_and_action_breakbeat.mp3"
},
"file_key": "music_zapsplat_and_action_breakbeat.mp3",
"src": {
"url": "https://exapmle.com/music_zapsplat_and_action_breakbeat.mp3"
},
"type": {
"primary": "AUDIO",
"secondary": "mp3"
},
"thumbnail_url": "https://example.com/thumbnail/audio.jpg",
"tags": [
{
"tag": "big",
"weight": 10
},
{
"tag": "beat",
"weight": 5
},
{
"tag": "music",
"weight": 3.3333333333333335
}
],
"isDeleted": false,
}
}
Thank you!
You cannot match type.primary inside a nested query for tags. Try this query instead:
{
"query": {
"bool": {
"filter": {
"term": {
"type.primary": "audio"
}
},
"must": [
{
"nested": {
"path": "tags",
"query": {
"function_score": {
"query": {
"match_phrase_prefix": {
"tags.tag": "big"
}
},
"field_value_factor": {
"field": "tags.weight"
},
"score_mode": "sum",
"boost_mode": "multiply",
"boost": 10
}
}
}
}
]
}
}
}
On my ElasticSearch (2.x) I have documents like this:
{
"title": "A good title",
"formats": [{
"name": "pdf",
"prices": [{
"price": 11.99,
"currency": "EUR"
}, {
"price": 18.99,
"currency": "AUD"
}]
}]
}
I'd like to sort documents by formats.prices.price but only where the formats.prices.currency === 'EUR'
I tried to do a nested field on formats.prices and then run this query:
{
"query": {
"filtered": {
"query": {
"and": [
{
"match_all": {}
}
]
}
}
},
"sort": {
"formats.prices.price": {
"order": "desc",
"nested_path": "formats.prices",
"nested_filter": {
"term": {
"currency": "EUR"
}
}
}
}
}
But unfortunately I cannot get the right order.
UPDATE:
Relevant part of mapping:
"formats": {
"properties": {
"name": {
"type": "string"
},
"prices": {
"type": "nested",
"include_in_parent": true,
"properties": {
"currency": {
"type": "string"
},
"price": {
"type": "double"
}
}
}
}
},
i hope this will solve your problem
{
"query": {
"bool": {
"must": [
{
"nested": {
"path": "formats.prices",
"filter": {
"match": {
"formats.prices.currency": "EUR"
}
}
}
}
]
}
},
"from": 0,
"size": 50,
"sort": [
{
"formats.prices.price": {
"order": "asc",
"nested_path": "formats.prices",
"nested_filter": {
"match": {
"formats.prices.currency": "EUR"
}
}
}
}
]
}