加权词条查询
编辑加权词条查询
编辑在 8.15.0 中已弃用。
此查询已被 稀疏向量 取代，并将在未来的版本中移除。
此功能为技术预览版，可能会在未来版本中更改或删除。Elastic 将努力修复任何问题，但技术预览版中的功能不受官方 GA 功能的支持 SLA 的约束。
加权词条查询需要一个词条-权重对的列表，该列表与查询一起发送，而不是使用自然语言处理模型计算得出。然后，这些词条对将用于对 稀疏向量 或 排名特征 字段的查询。
当您想使用外部查询扩展模型，或者快速原型化更改而无需重新索引新模型时，加权词条查询非常有用。
请求示例
编辑resp = client.search(
query={
"weighted_tokens": {
"query_expansion_field": {
"tokens": {
"2161": 0.4679,
"2621": 0.307,
"2782": 0.1299,
"2851": 0.1056,
"3088": 0.3041,
"3376": 0.1038,
"3467": 0.4873,
"3684": 0.8958,
"4380": 0.334,
"4542": 0.4636,
"4633": 2.2805,
"4785": 1.2628,
"4860": 1.0655,
"5133": 1.0709,
"7139": 1.0016,
"7224": 0.2486,
"7387": 0.0985,
"7394": 0.0542,
"8915": 0.369,
"9156": 2.8947,
"10505": 0.2771,
"11464": 0.3996,
"13525": 0.0088,
"14178": 0.8161,
"16893": 0.1376,
"17851": 1.5348,
"19939": 0.6012
},
"pruning_config": {
"tokens_freq_ratio_threshold": 5,
"tokens_weight_threshold": 0.4,
"only_score_pruned_tokens": False
}
}
}
},
)
print(resp)
response = client.search(
body: {
query: {
weighted_tokens: {
query_expansion_field: {
tokens: {
"2161": 0.4679,
"2621": 0.307,
"2782": 0.1299,
"2851": 0.1056,
"3088": 0.3041,
"3376": 0.1038,
"3467": 0.4873,
"3684": 0.8958,
"4380": 0.334,
"4542": 0.4636,
"4633": 2.2805,
"4785": 1.2628,
"4860": 1.0655,
"5133": 1.0709,
"7139": 1.0016,
"7224": 0.2486,
"7387": 0.0985,
"7394": 0.0542,
"8915": 0.369,
"9156": 2.8947,
"10505": 0.2771,
"11464": 0.3996,
"13525": 0.0088,
"14178": 0.8161,
"16893": 0.1376,
"17851": 1.5348,
"19939": 0.6012
},
pruning_config: {
tokens_freq_ratio_threshold: 5,
tokens_weight_threshold: 0.4,
only_score_pruned_tokens: false
}
}
}
}
}
)
puts response
const response = await client.search({
query: {
weighted_tokens: {
query_expansion_field: {
tokens: {
"2161": 0.4679,
"2621": 0.307,
"2782": 0.1299,
"2851": 0.1056,
"3088": 0.3041,
"3376": 0.1038,
"3467": 0.4873,
"3684": 0.8958,
"4380": 0.334,
"4542": 0.4636,
"4633": 2.2805,
"4785": 1.2628,
"4860": 1.0655,
"5133": 1.0709,
"7139": 1.0016,
"7224": 0.2486,
"7387": 0.0985,
"7394": 0.0542,
"8915": 0.369,
"9156": 2.8947,
"10505": 0.2771,
"11464": 0.3996,
"13525": 0.0088,
"14178": 0.8161,
"16893": 0.1376,
"17851": 1.5348,
"19939": 0.6012,
},
pruning_config: {
tokens_freq_ratio_threshold: 5,
tokens_weight_threshold: 0.4,
only_score_pruned_tokens: false,
},
},
},
},
});
console.log(response);
POST _search
{
"query": {
"weighted_tokens": {
"query_expansion_field": {
"tokens": {"2161": 0.4679, "2621": 0.307, "2782": 0.1299, "2851": 0.1056, "3088": 0.3041, "3376": 0.1038, "3467": 0.4873, "3684": 0.8958, "4380": 0.334, "4542": 0.4636, "4633": 2.2805, "4785": 1.2628, "4860": 1.0655, "5133": 1.0709, "7139": 1.0016, "7224": 0.2486, "7387": 0.0985, "7394": 0.0542, "8915": 0.369, "9156": 2.8947, "10505": 0.2771, "11464": 0.3996, "13525": 0.0088, "14178": 0.8161, "16893": 0.1376, "17851": 1.5348, "19939": 0.6012},
"pruning_config": {
"tokens_freq_ratio_threshold": 5,
"tokens_weight_threshold": 0.4,
"only_score_pruned_tokens": false
}
}
}
}
}
weighted_token 的顶层参数
编辑-
<tokens> -
(必需，字典) 一个词条-权重对的字典。
-
pruning_config -
(可选，对象) 可选的修剪配置。如果启用，这将从查询中省略不重要的词条，以提高查询性能。默认值：禁用。
<pruning_config>的参数为-
tokens_freq_ratio_threshold - (可选，整数) 频率高于指定字段中所有词条的平均频率的
tokens_freq_ratio_threshold倍的词条被视为异常值并被修剪。此值必须介于 1 和 100 之间。默认值：5。 -
tokens_weight_threshold - (可选，浮点数) 权重小于
tokens_weight_threshold的词条被认为是不重要的并被修剪。此值必须介于 0 和 1 之间。默认值：0.4。 -
only_score_pruned_tokens - (可选，布尔值) 如果为
true，我们只将修剪后的词条输入到评分中，并丢弃未修剪的词条。强烈建议将主查询设置为false，但是可以将其设置为true以便对重新评分查询获得更相关的结果。默认值：false。
tokens_freq_ratio_threshold和tokens_weight_threshold的默认值是根据使用 ELSER 的测试选择的，这些测试提供了最佳的结果。 -
-
带有修剪配置和重新评分的加权词条查询示例
编辑以下示例向 text_expansion 查询添加了修剪配置。修剪配置标识要从查询中修剪的不重要词条，以提高查询性能。
词条修剪发生在分片级别。虽然这应该导致相同的词条在分片之间被标记为不重要，但这并不能根据每个分片的组成来保证。因此，如果您在多分片索引上使用 pruning_config 运行 text_expansion，我们强烈建议添加一个 对过滤的搜索结果重新评分 函数，其中包含最初从查询中修剪的词条。这将有助于缓解修剪词条的任何分片级别不一致性，并提供更好的整体相关性。
resp = client.search(
index="my-index",
query={
"weighted_tokens": {
"query_expansion_field": {
"tokens": {
"2161": 0.4679,
"2621": 0.307,
"2782": 0.1299,
"2851": 0.1056,
"3088": 0.3041,
"3376": 0.1038,
"3467": 0.4873,
"3684": 0.8958,
"4380": 0.334,
"4542": 0.4636,
"4633": 2.2805,
"4785": 1.2628,
"4860": 1.0655,
"5133": 1.0709,
"7139": 1.0016,
"7224": 0.2486,
"7387": 0.0985,
"7394": 0.0542,
"8915": 0.369,
"9156": 2.8947,
"10505": 0.2771,
"11464": 0.3996,
"13525": 0.0088,
"14178": 0.8161,
"16893": 0.1376,
"17851": 1.5348,
"19939": 0.6012
},
"pruning_config": {
"tokens_freq_ratio_threshold": 5,
"tokens_weight_threshold": 0.4,
"only_score_pruned_tokens": False
}
}
}
},
rescore={
"window_size": 100,
"query": {
"rescore_query": {
"weighted_tokens": {
"query_expansion_field": {
"tokens": {
"2161": 0.4679,
"2621": 0.307,
"2782": 0.1299,
"2851": 0.1056,
"3088": 0.3041,
"3376": 0.1038,
"3467": 0.4873,
"3684": 0.8958,
"4380": 0.334,
"4542": 0.4636,
"4633": 2.2805,
"4785": 1.2628,
"4860": 1.0655,
"5133": 1.0709,
"7139": 1.0016,
"7224": 0.2486,
"7387": 0.0985,
"7394": 0.0542,
"8915": 0.369,
"9156": 2.8947,
"10505": 0.2771,
"11464": 0.3996,
"13525": 0.0088,
"14178": 0.8161,
"16893": 0.1376,
"17851": 1.5348,
"19939": 0.6012
},
"pruning_config": {
"tokens_freq_ratio_threshold": 5,
"tokens_weight_threshold": 0.4,
"only_score_pruned_tokens": True
}
}
}
}
}
},
)
print(resp)
response = client.search(
index: 'my-index',
body: {
query: {
weighted_tokens: {
query_expansion_field: {
tokens: {
"2161": 0.4679,
"2621": 0.307,
"2782": 0.1299,
"2851": 0.1056,
"3088": 0.3041,
"3376": 0.1038,
"3467": 0.4873,
"3684": 0.8958,
"4380": 0.334,
"4542": 0.4636,
"4633": 2.2805,
"4785": 1.2628,
"4860": 1.0655,
"5133": 1.0709,
"7139": 1.0016,
"7224": 0.2486,
"7387": 0.0985,
"7394": 0.0542,
"8915": 0.369,
"9156": 2.8947,
"10505": 0.2771,
"11464": 0.3996,
"13525": 0.0088,
"14178": 0.8161,
"16893": 0.1376,
"17851": 1.5348,
"19939": 0.6012
},
pruning_config: {
tokens_freq_ratio_threshold: 5,
tokens_weight_threshold: 0.4,
only_score_pruned_tokens: false
}
}
}
},
rescore: {
window_size: 100,
query: {
rescore_query: {
weighted_tokens: {
query_expansion_field: {
tokens: {
"2161": 0.4679,
"2621": 0.307,
"2782": 0.1299,
"2851": 0.1056,
"3088": 0.3041,
"3376": 0.1038,
"3467": 0.4873,
"3684": 0.8958,
"4380": 0.334,
"4542": 0.4636,
"4633": 2.2805,
"4785": 1.2628,
"4860": 1.0655,
"5133": 1.0709,
"7139": 1.0016,
"7224": 0.2486,
"7387": 0.0985,
"7394": 0.0542,
"8915": 0.369,
"9156": 2.8947,
"10505": 0.2771,
"11464": 0.3996,
"13525": 0.0088,
"14178": 0.8161,
"16893": 0.1376,
"17851": 1.5348,
"19939": 0.6012
},
pruning_config: {
tokens_freq_ratio_threshold: 5,
tokens_weight_threshold: 0.4,
only_score_pruned_tokens: true
}
}
}
}
}
}
}
)
puts response
const response = await client.search({
index: "my-index",
query: {
weighted_tokens: {
query_expansion_field: {
tokens: {
"2161": 0.4679,
"2621": 0.307,
"2782": 0.1299,
"2851": 0.1056,
"3088": 0.3041,
"3376": 0.1038,
"3467": 0.4873,
"3684": 0.8958,
"4380": 0.334,
"4542": 0.4636,
"4633": 2.2805,
"4785": 1.2628,
"4860": 1.0655,
"5133": 1.0709,
"7139": 1.0016,
"7224": 0.2486,
"7387": 0.0985,
"7394": 0.0542,
"8915": 0.369,
"9156": 2.8947,
"10505": 0.2771,
"11464": 0.3996,
"13525": 0.0088,
"14178": 0.8161,
"16893": 0.1376,
"17851": 1.5348,
"19939": 0.6012,
},
pruning_config: {
tokens_freq_ratio_threshold: 5,
tokens_weight_threshold: 0.4,
only_score_pruned_tokens: false,
},
},
},
},
rescore: {
window_size: 100,
query: {
rescore_query: {
weighted_tokens: {
query_expansion_field: {
tokens: {
"2161": 0.4679,
"2621": 0.307,
"2782": 0.1299,
"2851": 0.1056,
"3088": 0.3041,
"3376": 0.1038,
"3467": 0.4873,
"3684": 0.8958,
"4380": 0.334,
"4542": 0.4636,
"4633": 2.2805,
"4785": 1.2628,
"4860": 1.0655,
"5133": 1.0709,
"7139": 1.0016,
"7224": 0.2486,
"7387": 0.0985,
"7394": 0.0542,
"8915": 0.369,
"9156": 2.8947,
"10505": 0.2771,
"11464": 0.3996,
"13525": 0.0088,
"14178": 0.8161,
"16893": 0.1376,
"17851": 1.5348,
"19939": 0.6012,
},
pruning_config: {
tokens_freq_ratio_threshold: 5,
tokens_weight_threshold: 0.4,
only_score_pruned_tokens: true,
},
},
},
},
},
},
});
console.log(response);
GET my-index/_search
{
"query":{
"weighted_tokens": {
"query_expansion_field": {
"tokens": {"2161": 0.4679, "2621": 0.307, "2782": 0.1299, "2851": 0.1056, "3088": 0.3041, "3376": 0.1038, "3467": 0.4873, "3684": 0.8958, "4380": 0.334, "4542": 0.4636, "4633": 2.2805, "4785": 1.2628, "4860": 1.0655, "5133": 1.0709, "7139": 1.0016, "7224": 0.2486, "7387": 0.0985, "7394": 0.0542, "8915": 0.369, "9156": 2.8947, "10505": 0.2771, "11464": 0.3996, "13525": 0.0088, "14178": 0.8161, "16893": 0.1376, "17851": 1.5348, "19939": 0.6012},
"pruning_config": {
"tokens_freq_ratio_threshold": 5,
"tokens_weight_threshold": 0.4,
"only_score_pruned_tokens": false
}
}
}
},
"rescore": {
"window_size": 100,
"query": {
"rescore_query": {
"weighted_tokens": {
"query_expansion_field": {
"tokens": {"2161": 0.4679, "2621": 0.307, "2782": 0.1299, "2851": 0.1056, "3088": 0.3041, "3376": 0.1038, "3467": 0.4873, "3684": 0.8958, "4380": 0.334, "4542": 0.4636, "4633": 2.2805, "4785": 1.2628, "4860": 1.0655, "5133": 1.0709, "7139": 1.0016, "7224": 0.2486, "7387": 0.0985, "7394": 0.0542, "8915": 0.369, "9156": 2.8947, "10505": 0.2771, "11464": 0.3996, "13525": 0.0088, "14178": 0.8161, "16893": 0.1376, "17851": 1.5348, "19939": 0.6012},
"pruning_config": {
"tokens_freq_ratio_threshold": 5,
"tokens_weight_threshold": 0.4,
"only_score_pruned_tokens": true
}
}
}
}
}
}
}