* Add search phase results processor Signed-off-by: Fanit Kolchina <[email protected]> * Add hybrid query Signed-off-by: Fanit Kolchina <[email protected]> * Normalization processor additions Signed-off-by: Fanit Kolchina <[email protected]> * Add more details Signed-off-by: Fanit Kolchina <[email protected]> * Continue writing Signed-off-by: Fanit Kolchina <[email protected]> * Add more query then fetch details and diagram Signed-off-by: Fanit Kolchina <[email protected]> * Small rewording Signed-off-by: Fanit Kolchina <[email protected]> * Leaner left nav headers Signed-off-by: Fanit Kolchina <[email protected]> * Tech review feedback Signed-off-by: Fanit Kolchina <[email protected]> * Add semantic search tutorial Signed-off-by: Fanit Kolchina <[email protected]> * Reworded prerequisites Signed-off-by: Fanit Kolchina <[email protected]> * Removed comma Signed-off-by: Fanit Kolchina <[email protected]> * Rewording advanced prerequisites Signed-off-by: Fanit Kolchina <[email protected]> * Changed searching for ML model to shorter request Signed-off-by: Fanit Kolchina <[email protected]> * Update task type in register model response Signed-off-by: Fanit Kolchina <[email protected]> * Changing example Signed-off-by: Fanit Kolchina <[email protected]> * Added huggingface prefix to model names Signed-off-by: Fanit Kolchina <[email protected]> * Change example responses Signed-off-by: Fanit Kolchina <[email protected]> * Added note about huggingface prefix Signed-off-by: Fanit Kolchina <[email protected]> * Update _ml-commons-plugin/semantic-search.md Co-authored-by: Naarcha-AWS <[email protected]> Signed-off-by: kolchfa-aws <[email protected]> * Implemented doc review comments Signed-off-by: Fanit Kolchina <[email protected]> * List weights under parameters Signed-off-by: Fanit Kolchina <[email protected]> * Remove one-shard warning for normalization processor Signed-off-by: Fanit Kolchina <[email protected]> * Apply suggestions from code review Co-authored-by: Nathan Bower <[email protected]> Signed-off-by: kolchfa-aws <[email protected]> * Implemented editorial comments Signed-off-by: Fanit Kolchina <[email protected]> * Change links Signed-off-by: Fanit Kolchina <[email protected]> * More editorial feedback Signed-off-by: Fanit Kolchina <[email protected]> * Change model-serving framework to ML framework Signed-off-by: Fanit Kolchina <[email protected]> * Use get model API to check model status Signed-off-by: Fanit Kolchina <[email protected]> * Implemented tech review comments Signed-off-by: Fanit Kolchina <[email protected]> * Added neural search description and diagram Signed-off-by: Fanit Kolchina <[email protected]> * More editorial comments Signed-off-by: Fanit Kolchina <[email protected]> * Add link to profile API Signed-off-by: Fanit Kolchina <[email protected]> * Addressed more tech review comments Signed-off-by: Fanit Kolchina <[email protected]> * Implemented editorial comments on changes Signed-off-by: Fanit Kolchina <[email protected]> --------- Signed-off-by: Fanit Kolchina <[email protected]> Signed-off-by: kolchfa-aws <[email protected]> Co-authored-by: Naarcha-AWS <[email protected]> Co-authored-by: Nathan Bower <[email protected]>
3.9 KiB
layout, title, parent, grand_parent, nav_order, redirect_from
| layout | title | parent | grand_parent | nav_order | redirect_from | |
|---|---|---|---|---|---|---|
| default | Boosting | Compound queries | Query DSL | 30 |
|
Boosting queries
If you're searching for the word "pitcher", your results may relate to either baseball players or containers for liquids. For a search in the context of baseball, you might want to completely exclude results that contain the words "glass" or "water" by using the must_not clause. However, if you want to keep those results but downgrade them in relevance, you can do so with boosting queries.
A boosting query returns documents that match a positive query. Among those documents, the ones that also match the negative query are scored lower in relevance (their relevance score is multiplied by the negative boosting factor).
Example
Consider an index with two documents that you index as follows:
PUT testindex/_doc/1
{
"article_name": "The greatest pitcher in baseball history"
}
PUT testindex/_doc/2
{
"article_name": "The making of a glass pitcher"
}
Use the following match query to search for documents containing the word "pitcher":
GET testindex/_search
{
"query": {
"match": {
"article_name": "pitcher"
}
}
}
Both returned documents have the same relevance score:
{
"took": 5,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 2,
"relation": "eq"
},
"max_score": 0.18232156,
"hits": [
{
"_index": "testindex",
"_id": "1",
"_score": 0.18232156,
"_source": {
"article_name": "The greatest pitcher in baseball history"
}
},
{
"_index": "testindex",
"_id": "2",
"_score": 0.18232156,
"_source": {
"article_name": "The making of a glass pitcher"
}
}
]
}
}
Now use the following boosting query to search for documents containing the word "pitcher" but downgrade the documents that contain the words "glass", "crystal", or "water":
GET testindex/_search
{
"query": {
"boosting": {
"positive": {
"match": {
"article_name": "pitcher"
}
},
"negative": {
"match": {
"article_name": "glass crystal water"
}
},
"negative_boost": 0.1
}
}
}
{% include copy-curl.html %}
Both documents are still returned, but the document with the word "glass" has a relevance score that is 10 times lower than in the previous case:
{
"took": 13,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 2,
"relation": "eq"
},
"max_score": 0.18232156,
"hits": [
{
"_index": "testindex",
"_id": "1",
"_score": 0.18232156,
"_source": {
"article_name": "The greatest pitcher in baseball history"
}
},
{
"_index": "testindex",
"_id": "2",
"_score": 0.018232157,
"_source": {
"article_name": "The making of a glass pitcher"
}
}
]
}
}
Parameters
The following table lists all top-level parameters supported by boosting queries.
| Parameter | Description |
|---|---|
positive |
The query that a document must match to be returned in the results. Required. |
negative |
If a document in the results matches this query, its relevance score is reduced by multiplying its original relevance score (produced by the positive query) by the negative_boost parameter. Required. |
negative_boost |
A floating-point factor between 0 and 1.0 that the original relevance score is multiplied by in order to reduce the relevance of documents that match the negative query. Required. |