ElasticSearch "H & R Block" with partial word search - elasticsearch

Question

The requirements are to be able to search the following terms :
"H & R" to find "H & R Block".
I have managed to implement this requirement alone using word_delimiter, as mentionned in this answer elasticsearch tokenize "H&R Blocks" as "H", "R", "H&R", "Blocks"
Using ruby code :
{
char_filter: {
strip_punctuation: { type: "mapping", mappings: [".=>", ",=>", "!=>", "?=>"] },
},
filter: {
my_splitter: {
type: "word_delimiter",
preserve_original: true
}
},
analyzer: {
my_analyzer {
char_filter: %w[strip_punctuation],
type: "custom",
tokenizer: "whitespace",
filter: %w[lowercase asciifolding my_splitter]
}
}
}
But also, in the same query, we want autocomplete functionality or partial word matching, so
"Ser", "Serv", "Servi", "Servic" and "Service" all find "Service" and "Services".
I have managed to implement this requirement alone, using ngram.
{
char_filter: {
strip_punctuation: { type: "mapping", mappings: [".=>", ",=>", "!=>", "?=>"] }
},
analyzer: {
my_analyzer: {
char_filter: %w[strip_punctuation],
tokenizer: "my_ngram",
filter: %w[lowercase asciifolding]
}
},
tokenizer: {
my_ngram: {
type: "nGram",
min_gram: "3",
max_gram: "10",
token_chars: %w[letter digit]
}
}
}
I just can't manage to implement them together. When I use ngram, short words are ignored so "H & R" is left out. When I use word_delimiter, partial word searches stop working. Below, my latest attempt at merging both requirements, it results in supporting partial word searches but not "H & R".
{
char_filter: {
strip_punctuation: { type: "mapping", mappings: [".=>", ",=>", "!=>", "?=>"] }
},
filter: {
my_splitter: {
type: "word_delimiter",
preserve_original: true
}
},
analyzer: {
my_analyzer: {
char_filter: %w[strip_punctuation],
type: "custom",
tokenizer: "my_tokenizer",
filter: %w[lowercase asciifolding my_splitter]
}
},
tokenizer: {
my_tokenizer: {
type: "nGram",
min_gram: "3",
max_gram: "10",
token_chars: %w[letter digit]
}
}
}

Answer 1

You can use multi_field from your mapping to index the same field in multiple ways. You can use your full text search with custom tokenizer on the default field, and create a special indexing for your autocompletion needs.
"title": {
"type": "string",
"fields": {
"raw": { "type": "string", "index": "not_analyzed" }
}
}
Your query will need to be slightly different when performing the autocomplete as the field will be title.raw instead of just title.
Once the field is indexed in all the ways that make sense for your query, you can query the index using a boolean "should" query, matching the tokenized version and the word start query. It is likely that a larger boost should be provided to the first query matching complete words to get the direct hits on top.