Uses the MinHash technique to produce a
signature for a token stream. You can use MinHash signatures to estimate the
similarity of documents. See Using the min_hash
token filter for similarity search.
The min_hash
filter performs the following operations on a token stream in
order:
 Hashes each token in the stream.
 Assigns the hashes to buckets, keeping only the smallest hashes of each bucket.
 Outputs the smallest hash from each bucket as a token stream.
This filter uses Lucene’s MinHashFilter.

bucket_count

(Optional, integer)
Number of buckets to which hashes are assigned. Defaults to
512
. 
hash_count

(Optional, integer)
Number of ways to hash each token in the stream. Defaults to
1
. 
hash_set_size

(Optional, integer) Number of hashes to keep from each bucket. Defaults to
1
.Hashes are retained by ascending size, starting with the bucket’s smallest hash first.

with_rotation

(Optional, boolean)
If
true
, the filter fills empty buckets with the value of the first nonempty bucket to its circular right if thehash_set_size
is1
. If thebucket_count
argument is greater than1
, this parameter defaults totrue
. Otherwise, this parameter defaults tofalse
.

min_hash
filter input tokens should typically be kwords shingles produced from shingle token filter. You should choosek
large enough so that the probability of any given shingle occurring in a document is low. At the same time, as internally each shingle is hashed into to 128bit hash, you should choosek
small enough so that all possible different kwords shingles can be hashed to 128bit hash with minimal collision. 
We recommend you test different arguments for the
hash_count
,bucket_count
andhash_set_size
parameters:
To improve precision, increase the
bucket_count
orhash_set_size
arguments. Higherbucket_count
andhash_set_size
values increase the likelihood that different tokens are indexed to different buckets. 
To improve the recall, increase the value of the
hash_count
argument. For example, settinghash_count
to2
hashes each token in two different ways, increasing the number of potential candidates for search.

To improve precision, increase the

By default, the
min_hash
filter produces 512 tokens for each document. Each token is 16 bytes in size. This means each document’s size will be increased by around 8Kb. 
The
min_hash
filter is used for Jaccard similarity. This means that it doesn’t matter how many times a document contains a certain token, only that if it contains it or not.
The min_hash
token filter allows you to hash documents for similarity search.
Similarity search, or nearest neighbor search is a complex problem.
A naive solution requires an exhaustive pairwise comparison between a query
document and every document in an index. This is a prohibitive operation
if the index is large. A number of approximate nearest neighbor search
solutions have been developed to make similarity search more practical and
computationally feasible. One of these solutions involves hashing of documents.
Documents are hashed in a way that similar documents are more likely to produce the same hash code and are put into the same hash bucket, while dissimilar documents are more likely to be hashed into different hash buckets. This type of hashing is known as locality sensitive hashing (LSH).
Depending on what constitutes the similarity between documents, various LSH functions have been proposed. For Jaccard similarity, a popular LSH function is MinHash. A general idea of the way MinHash produces a signature for a document is by applying a random permutation over the whole index vocabulary (random numbering for the vocabulary), and recording the minimum value for this permutation for the document (the minimum number for a vocabulary word that is present in the document). The permutations are run several times; combining the minimum values for all of them will constitute a signature for the document.
In practice, instead of random permutations, a number of hash functions are chosen. A hash function calculates a hash code for each of a document’s tokens and chooses the minimum hash code among them. The minimum hash codes from all hash functions are combined to form a signature for the document.
To customize the min_hash
filter, duplicate it to create the basis for a new
custom token filter. You can modify the filter using its configurable
parameters.
For example, the following create index API request uses the following custom token filters to configure a new custom analyzer:

my_shingle_filter
, a customshingle
filter.my_shingle_filter
only outputs fiveword shingles. 
my_minhash_filter
, a custommin_hash
filter.my_minhash_filter
hashes each fiveword shingle once. It then assigns the hashes into 512 buckets, keeping only the smallest hash from each bucket.
The request also assigns the custom analyzer to the fingerprint
field mapping.
PUT /my_index { "settings": { "analysis": { "filter": { "my_shingle_filter": { "type": "shingle", "min_shingle_size": 5, "max_shingle_size": 5, "output_unigrams": false }, "my_minhash_filter": { "type": "min_hash", "hash_count": 1, "bucket_count": 512, "hash_set_size": 1, "with_rotation": true } }, "analyzer": { "my_analyzer": { "tokenizer": "standard", "filter": [ "my_shingle_filter", "my_minhash_filter" ] } } } }, "mappings": { "properties": { "fingerprint": { "type": "text", "analyzer": "my_analyzer" } } } }