* Add search phase results processor Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Add hybrid query Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Normalization processor additions Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Add more details Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Continue writing Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Add more query then fetch details and diagram Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Small rewording Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Leaner left nav headers Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Tech review feedback Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Add semantic search tutorial Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Reworded prerequisites Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Removed comma Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Rewording advanced prerequisites Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Changed searching for ML model to shorter request Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Update task type in register model response Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Changing example Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Added huggingface prefix to model names Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Change example responses Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Added note about huggingface prefix Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Update _ml-commons-plugin/semantic-search.md Co-authored-by: Naarcha-AWS <97990722+Naarcha-AWS@users.noreply.github.com> Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> * Implemented doc review comments Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * List weights under parameters Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Remove one-shard warning for normalization processor Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Apply suggestions from code review Co-authored-by: Nathan Bower <nbower@amazon.com> Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> * Implemented editorial comments Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Change links Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * More editorial feedback Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Change model-serving framework to ML framework Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Use get model API to check model status Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Implemented tech review comments Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Added neural search description and diagram Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * More editorial comments Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Add link to profile API Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Addressed more tech review comments Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Implemented editorial comments on changes Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> --------- Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> Co-authored-by: Naarcha-AWS <97990722+Naarcha-AWS@users.noreply.github.com> Co-authored-by: Nathan Bower <nbower@amazon.com>
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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. |