Tokenization Models

Pretrained models (llmlingua2, bert_base_uncased, wiki_tocken, gemma2b), custom corpus-trained models, HuggingFace tokenizers, and Lindera models.

In pg_tokenizer, a Model maps string tokens (or raw text) to integer token IDs (INT[]). You can choose from four built-in pretrained models, train a custom vocabulary model directly on your PostgreSQL table, import any HuggingFace tokenizer.json, or use a Lindera morphological model.

1. Built-in Pretrained Models

Model Name	Source / Architecture	Requires `text_analyzer`?	Memory Footprint	Best For
`llmlingua2`	`microsoft/llmlingua-2-xlm-roberta-large-meetingbank` (XLM-RoBERTa multilingual subword)	No (forbidden)	~200 MB (default preloaded)	Multilingual general text out of the box without training a custom vocabulary.
`bert_base_uncased`	`google-bert/bert-base-uncased` (WordPiece, 30,522 vocab)	No (forbidden)	~15 MB	General English text; fast startup and compact vocabulary.
`wiki_tocken`	`Snowolf/wiki-tocken` (Built-in English Wikipedia token dictionary)	Yes (required)	~30 MB	English full-text search combined with custom stemmers, stopwords, and synonyms.
`gemma2b`	`google/gemma-2b` (SentencePiece BPE, 256k vocab)	No (forbidden)	~100 MB	Code, technical identifiers, and multilingual LLM workloads.

Example: Using `bert_base_uncased` or `llmlingua2`

SELECT create_tokenizer('bert_tok', $$
model = "bert_base_uncased"
$$);

SELECT tokenize('VectorChord-BM25 brings Block-WeakAnd to PostgreSQL.', 'bert_tok');

2. Custom Corpus Models (`create_custom_model`)

For maximum BM25 accuracy and minimal memory footprint, you can build a custom vocabulary model directly from your own table column. Unlike subword LLM tokenizers (which split rare domain words into multiple sub-tokens), a custom model assigns a dedicated 1:1 integer ID to every unique normalized token produced by your text_analyzer—and weighs only a few megabytes in memory.

Option A: One-Step Helper (`create_custom_model_tokenizer_and_trigger`)

This convenience function creates the custom model, creates the tokenizer, populates the target bm25vector column for all existing rows, and installs a BEFORE INSERT OR UPDATE trigger that automatically learns new vocabulary tokens and updates the bm25vector column when rows change:

-- 1. Define how text should be analyzed
SELECT create_text_analyzer('my_analyzer', $$
character_filters = ["to_lowercase"]
pre_tokenizer = "unicode_segmentation"
token_filters = [
    "skip_non_alphanumeric",
    { stopwords = "nltk_english" },
    { stemmer = "english_porter2" },
]
$$);

-- 2. Create model, tokenizer, backfill embeddings, and attach trigger
SELECT create_custom_model_tokenizer_and_trigger(
    tokenizer_name     => 'my_tokenizer',
    model_name         => 'my_model',
    text_analyzer_name => 'my_analyzer',
    table_name         => 'documents',
    source_column      => 'passage',
    target_column      => 'embedding'
);

Option B: Step-by-Step (`create_custom_model` Without Trigger)

If you prefer not to attach an automatic trigger (or want to control when embedding is populated):

-- 1. Build the custom model from the existing table column
SELECT create_custom_model('my_model', $$
table = "documents"
column = "passage"
text_analyzer = "my_analyzer"
$$);

-- 2. Create the tokenizer referencing the analyzer and custom model
SELECT create_tokenizer('my_tokenizer', $$
text_analyzer = "my_analyzer"
model = "my_model"
$$);

-- 3. Populate or refresh embeddings manually
UPDATE documents SET embedding = tokenize(passage, 'my_tokenizer');

3. Importing HuggingFace Tokenizers (`create_huggingface_model`)

You can import any HuggingFace tokenizers JSON specification (tokenizer.json) into PostgreSQL using create_huggingface_model.

In psql, you can fetch tokenizer.json directly from HuggingFace Hub into a variable using \set and pass it to create_huggingface_model:

\set content `wget -q -O - https://huggingface.co/google-bert/bert-base-uncased/resolve/main/tokenizer.json`
SELECT create_huggingface_model('bert_import', :'content');

SELECT create_tokenizer('bert_import_tok', $$
model = "bert_import"
$$);

Or pass the contents of a tokenizer.json file directly as a dollar-quoted string:

SELECT create_huggingface_model('bge_m3_model', $$
{
  "version": "1.0",
  ...
}
$$);

SELECT create_tokenizer('bge_m3_tok', $$
model = "bge_m3_model"
$$);

4. Lindera Morphological Models (`create_lindera_model`)

For East Asian languages such as Japanese, Korean, and Chinese that benefit from morphological dictionary analysis, pg_tokenizer integrates lindera.

Note

If you build pg_tokenizer from source, enable the desired Lindera dictionary features at compile time:

cargo pgrx install --release --features "lindera-ipadic lindera-ipadic-neologd lindera-unidic lindera-ko-dic lindera-cc-cedict"

Example configuration using ipadic with iteration mark normalization, NFKC Unicode normalization, character mapping, compound word merging, number normalization, Japanese part-of-speech stop-tags, Katakana stemming, and diacritical mark removal:

SELECT create_lindera_model('japanese_ipadic', $$
[segmenter]
mode = "normal"
  [segmenter.dictionary]
  kind = "ipadic"

[[character_filters]]
kind = "japanese_iteration_mark"
  [character_filters.args]
  normalize_kanji = true
  normalize_kana = true

[[character_filters]]
kind = "unicode_normalize"
  [character_filters.args]
  kind = "nfkc"

[[character_filters]]
kind = "mapping"
  [character_filters.args.mapping]
  "リンデラ" = "Lindera"

[[token_filters]]
kind = "japanese_compound_word"
  [token_filters.args]
  kind = "ipadic"
  tags = [
    "名詞,数",
    "名詞,接尾,助数詞",
  ]
  new_tag = "名詞,数"

[[token_filters]]
kind = "japanese_number"
  [token_filters.args]
  tags = ["名詞,数"]

[[token_filters]]
kind = "japanese_stop_tags"
  [token_filters.args]
  tags = [
    "接続詞",
    "助詞",
    "助詞,格助詞",
    "助詞,格助詞,一般",
    "助詞,格助詞,引用",
    "助詞,格助詞,連語",
    "助詞,係助詞",
    "助詞,副助詞",
    "助詞,間投助詞",
    "助詞,並立助詞",
    "助詞,終助詞",
    "助詞,副助詞／並立助詞／終助詞",
    "助詞,連体化",
    "助詞,副詞化",
    "助詞,特殊",
    "助動詞",
    "記号",
    "記号,一般",
    "記号,読点",
    "記号,句点",
    "記号,空白",
    "記号,括弧閉",
    "その他,間投",
    "フィラー",
    "非言語音",
  ]

[[token_filters]]
kind = "japanese_katakana_stem"
  [token_filters.args]
  min = 3

[[token_filters]]
kind = "remove_diacritical_mark"
  [token_filters.args]
  japanese = false
$$);

SELECT create_tokenizer('jp_tok', $$
model = "japanese_ipadic"
$$);

5. Model Preloading (`add_preload_model`)

Large models (llmlingua2 at ~200 MB, gemma2b at ~100 MB, japanese_ipadic) take several seconds and significant RAM to load on first query in each new PostgreSQL backend process. By preloading models during shared_preload_libraries startup, PostgreSQL loads the model once in the postmaster process and shares it across all backend connections via copy-on-write memory (llmlingua2 is preloaded by default):

-- Add a model to the preload list (persisted in $PGDATA/pg_tokenizer/preload_models)
SELECT add_preload_model('my_model');

-- List currently configured preload models (default: {llmlingua2})
SELECT list_preload_models();

-- Remove a model from the preload list (e.g., to save ~200 MB if llmlingua2 is unused)
SELECT remove_preload_model('llmlingua2');

Restart PostgreSQL after calling add_preload_model(...) (with shared_preload_libraries = 'pg_tokenizer.so' set in postgresql.conf) for preloading to take effect across all backends.

Tokenization Models

1. Built-in Pretrained Models

Example: Using bert_base_uncased or llmlingua2

2. Custom Corpus Models (create_custom_model)

Option A: One-Step Helper (create_custom_model_tokenizer_and_trigger)

Option B: Step-by-Step (create_custom_model Without Trigger)

3. Importing HuggingFace Tokenizers (create_huggingface_model)

4. Lindera Morphological Models (create_lindera_model)

5. Model Preloading (add_preload_model)