我在 python 代码中有一个如下所示的场景。在此，我试图明确地将 new york 和 ny 定义为同义词。但不幸的是它不起作用。你能指导我吗，因为我是弹性搜索的新手。我也在使用自定义分析器。我也有包含文本的文件同义词.txt：ny,newyork,nyorkfrom datetime import datetimefrom elasticsearch import Elasticsearches = Elasticsearch()keywords = ['thousand eyes', 'facebook', 'superdoc', 'quora', 'your story', 'Surgery', 'lending club', 'ad roll', 'the honest company', 'Draft kings', 'newyork']count = 1doc_setting = { "settings": { "analysis": { "analyzer": { "my_analyzer_keyword": { "type": "custom", "tokenizer": "keyword", "filter": [ "asciifolding", "lowercase", "synonym" ] }, "my_analyzer_shingle": { "type": "custom", "tokenizer": "standard", "filter": [ "asciifolding", "lowercase", "synonym" ] } }, "filter": { "synonym": { "type": "synonym", "synonyms_path": "synonyms.txt", "ignore_case": "true" } } } }, "mappings": { "your_type": { "properties": { "keyword": { "type": "string", "index_analyzer": "my_analyzer_keyword", "search_analyzer": "my_analyzer_shingle" } } } }}
1 回答
呼唤远方
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PUT /test_index
{
"settings": {
"analysis": {
"analyzer": {
"my_analyzer_keyword": {
"type": "custom",
"tokenizer": "keyword",
"filter": [
"asciifolding",
"lowercase",
"synonym"
]
},
"my_analyzer_shingle": {
"type": "custom",
"tokenizer": "standard",
"filter": [
"asciifolding",
"lowercase",
"synonym"
]
}
},
"filter": {
"synonym" : {
"type" : "synonym",
"lenient": true,
"synonyms" : ["ny,newyork,nyork"]
}
}
}
}, "mappings": {
"your_type": {
"properties": {
"keyword": {
"type": "text",
"analyzer": "my_analyzer_keyword",
"search_analyzer": "my_analyzer_shingle"
}
}
}
}
}
然后分析使用
POST /test_index/_analyze
{
"analyzer" : "my_analyzer_shingle",
"text" : "I saw news on ny news channel of lending club on facebook, your story and quorat"
}
我得到的令牌是
{
"tokens": [
{
"token": "i",
"start_offset": 0,
"end_offset": 1,
"type": "<ALPHANUM>",
"position": 0
},
{
"token": "saw",
"start_offset": 2,
"end_offset": 5,
"type": "<ALPHANUM>",
"position": 1
},
{
"token": "news",
"start_offset": 6,
"end_offset": 10,
"type": "<ALPHANUM>",
"position": 2
},
{
"token": "on",
"start_offset": 11,
"end_offset": 13,
"type": "<ALPHANUM>",
"position": 3
},
{
"token": "ny",
"start_offset": 14,
"end_offset": 16,
"type": "<ALPHANUM>",
"position": 4
},
{
"token": "newyork",
"start_offset": 14,
"end_offset": 16,
"type": "SYNONYM",
"position": 4
},
{
"token": "nyork",
"start_offset": 14,
"end_offset": 16,
"type": "SYNONYM",
"position": 4
},
{
"token": "news",
"start_offset": 17,
"end_offset": 21,
"type": "<ALPHANUM>",
"position": 5
},
{
"token": "channel",
"start_offset": 22,
"end_offset": 29,
"type": "<ALPHANUM>",
"position": 6
},
{
"token": "of",
"start_offset": 30,
"end_offset": 32,
"type": "<ALPHANUM>",
"position": 7
},
{
"token": "lending",
"start_offset": 33,
"end_offset": 40,
"type": "<ALPHANUM>",
"position": 8
},
{
"token": "club",
"start_offset": 41,
"end_offset": 45,
"type": "<ALPHANUM>",
"position": 9
},
{
"token": "on",
"start_offset": 46,
"end_offset": 48,
"type": "<ALPHANUM>",
"position": 10
},
{
"token": "facebook",
"start_offset": 49,
"end_offset": 57,
"type": "<ALPHANUM>",
"position": 11
},
{
"token": "your",
"start_offset": 59,
"end_offset": 63,
"type": "<ALPHANUM>",
"position": 12
},
{
"token": "story",
"start_offset": 64,
"end_offset": 69,
"type": "<ALPHANUM>",
"position": 13
},
{
"token": "and",
"start_offset": 70,
"end_offset": 73,
"type": "<ALPHANUM>",
"position": 14
},
{
"token": "quorat",
"start_offset": 74,
"end_offset": 80,
"type": "<ALPHANUM>",
"position": 15
}
]
}
并且搜索产生
POST /test_index/_search
{
"query" : {
"match" : { "keyword" : "I saw news on ny news channel of lending club on facebook, your story and quora" }
}
}
{
"took": 36,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 3,
"max_score": 1.6858001,
"hits": [
{
"_index": "test_index",
"_type": "your_type",
"_id": "4",
"_score": 1.6858001,
"_source": {
"keyword": "newyork"
}
},
{
"_index": "test_index",
"_type": "your_type",
"_id": "2",
"_score": 1.1727304,
"_source": {
"keyword": "facebook"
}
},
{
"_index": "test_index",
"_type": "your_type",
"_id": "5",
"_score": 0.6931472,
"_source": {
"keyword": "quora"
}
}
]
}
}
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