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* adding min_hash token filter docs #8155 Signed-off-by: Anton Rubin <[email protected]> * updating parameter table Signed-off-by: Anton Rubin <[email protected]> * Doc review Signed-off-by: Fanit Kolchina <[email protected]> * Apply suggestions from code review Co-authored-by: Nathan Bower <[email protected]> Signed-off-by: kolchfa-aws <[email protected]> --------- Signed-off-by: Anton Rubin <[email protected]> Signed-off-by: Fanit Kolchina <[email protected]> Signed-off-by: kolchfa-aws <[email protected]> Co-authored-by: Fanit Kolchina <[email protected]> Co-authored-by: kolchfa-aws <[email protected]> Co-authored-by: Nathan Bower <[email protected]> (cherry picked from commit e69a64d) Signed-off-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,138 @@ | ||
| --- | ||
| layout: default | ||
| title: Min hash | ||
| parent: Token filters | ||
| nav_order: 270 | ||
| --- | ||
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| # Min hash token filter | ||
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| The `min_hash` token filter is used to generate hashes for tokens based on a [MinHash](https://en.wikipedia.org/wiki/MinHash) approximation algorithm, which is useful for detecting similarity between documents. The `min_hash` token filter generates hashes for a set of tokens (typically from an analyzed field). | ||
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| ## Parameters | ||
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| The `min_hash` token filter can be configured with the following parameters. | ||
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| Parameter | Required/Optional | Data type | Description | ||
| :--- | :--- | :--- | :--- | ||
| `hash_count` | Optional | Integer | The number of hash values to generate for each token. Increasing this value generally improves the accuracy of similarity estimation but increases the computational cost. Default is `1`. | ||
| `bucket_count` | Optional | Integer | The number of hash buckets to use. This affects the granularity of the hashing. A larger number of buckets provides finer granularity and reduces hash collisions but requires more memory. Default is `512`. | ||
| `hash_set_size` | Optional | Integer | The number of hashes to retain in each bucket. This can influence the hashing quality. Larger set sizes may lead to better similarity detection but consume more memory. Default is `1`. | ||
| `with_rotation` | Optional | Boolean | When set to `true`, the filter populates empty buckets with the value from the first non-empty bucket found to its circular right, provided that the `hash_set_size` is `1`. If the `bucket_count` argument exceeds `1`, this setting automatically defaults to `true`; otherwise, it defaults to `false`. | ||
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| ## Example | ||
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| The following example request creates a new index named `minhash_index` and configures an analyzer with a `min_hash` filter: | ||
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| ```json | ||
| PUT /minhash_index | ||
| { | ||
| "settings": { | ||
| "analysis": { | ||
| "filter": { | ||
| "minhash_filter": { | ||
| "type": "min_hash", | ||
| "hash_count": 3, | ||
| "bucket_count": 512, | ||
| "hash_set_size": 1, | ||
| "with_rotation": false | ||
| } | ||
| }, | ||
| "analyzer": { | ||
| "minhash_analyzer": { | ||
| "type": "custom", | ||
| "tokenizer": "standard", | ||
| "filter": [ | ||
| "minhash_filter" | ||
| ] | ||
| } | ||
| } | ||
| } | ||
| } | ||
| } | ||
| ``` | ||
| {% include copy-curl.html %} | ||
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| ## Generated tokens | ||
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| Use the following request to examine the tokens generated using the analyzer: | ||
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| ```json | ||
| POST /minhash_index/_analyze | ||
| { | ||
| "analyzer": "minhash_analyzer", | ||
| "text": "OpenSearch is very powerful." | ||
| } | ||
| ``` | ||
| {% include copy-curl.html %} | ||
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| The response contains the generated tokens (the tokens are not human readable because they represent hashes): | ||
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| ```json | ||
| { | ||
| "tokens" : [ | ||
| { | ||
| "token" : "\u0000\u0000㳠锯ੲ걌䐩䉵", | ||
| "start_offset" : 0, | ||
| "end_offset" : 27, | ||
| "type" : "MIN_HASH", | ||
| "position" : 0 | ||
| }, | ||
| { | ||
| "token" : "\u0000\u0000㳠锯ੲ걌䐩䉵", | ||
| "start_offset" : 0, | ||
| "end_offset" : 27, | ||
| "type" : "MIN_HASH", | ||
| "position" : 0 | ||
| }, | ||
| ... | ||
| ``` | ||
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| In order to demonstrate the usefulness of the `min_hash` token filter, you can use the following Python script to compare the two strings using the previously created analyzer: | ||
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| ```python | ||
| from opensearchpy import OpenSearch | ||
| from requests.auth import HTTPBasicAuth | ||
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| # Initialize the OpenSearch client with authentication | ||
| host = 'https://localhost:9200' # Update if using a different host/port | ||
| auth = ('admin', 'admin') # Username and password | ||
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| # Create the OpenSearch client with SSL verification turned off | ||
| client = OpenSearch( | ||
| hosts=[host], | ||
| http_auth=auth, | ||
| use_ssl=True, | ||
| verify_certs=False, # Disable SSL certificate validation | ||
| ssl_show_warn=False # Suppress SSL warnings in the output | ||
| ) | ||
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| # Analyzes text and returns the minhash tokens | ||
| def analyze_text(index, text): | ||
| response = client.indices.analyze( | ||
| index=index, | ||
| body={ | ||
| "analyzer": "minhash_analyzer", | ||
| "text": text | ||
| } | ||
| ) | ||
| return [token['token'] for token in response['tokens']] | ||
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| # Analyze two similar texts | ||
| tokens_1 = analyze_text('minhash_index', 'OpenSearch is a powerful search engine.') | ||
| tokens_2 = analyze_text('minhash_index', 'OpenSearch is a very powerful search engine.') | ||
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| # Calculate Jaccard similarity | ||
| set_1 = set(tokens_1) | ||
| set_2 = set(tokens_2) | ||
| shared_tokens = set_1.intersection(set_2) | ||
| jaccard_similarity = len(shared_tokens) / len(set_1.union(set_2)) | ||
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| print(f"Jaccard Similarity: {jaccard_similarity}") | ||
| ``` | ||
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| The response should contain the Jaccard similarity score: | ||
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Check failure on line 134 in _analyzers/token-filters/min-hash.md
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| ```yaml | ||
| Jaccard Similarity: 0.8571428571428571 | ||
| ``` | ||
