Summary: The lakebase_tokenizer extension adds configurable whole-word tokenization to Postgres full-text search, including lowercase conversion, Unicode normalization, accent removal, English possessive removal, custom stop words, one-to-one synonyms, and English stemming.

Configurable tokenization for Postgres full-text search

The lakebase_tokenizer extension adds configurable whole-word tokenization to Postgres full-text search. Text-search configurations built with the extension work with to_tsvector, the @@ operator, ranking functions, and GIN indexes. You can also use generated tsvector values with lakebase_text for BM25 ranking.

The extension provides the tokenizer_wholeword template through Postgres's standard text-search dictionary interface. The template supports lowercase conversion, Unicode normalization, accent removal, English possessive removal, custom stop words, one-to-one synonyms, and English stemming.

Install the extension in the Neon SQL Editor or from a client such as psql. The examples on this page use a dedicated schema so the extension objects are easy to identify:

CREATE SCHEMA IF NOT EXISTS tokenizer_ext;
CREATE EXTENSION IF NOT EXISTS lakebase_tokenizer WITH SCHEMA tokenizer_ext;

lakebase_tokenizer requires Postgres 16 or later. The extension is relocatable. You can replace tokenizer_ext with another schema when you install it.

A new Lakebase Search release can add features, fixes, and performance improvements. Although Lakebase Search is released as part of Neon updates, it does not upgrade everything automatically.

Upgrading is not urgent. The extension is compatible with SQL objects from older versions, but staying current keeps you on the supported, best-performing path and avoids a larger migration later, so upgrade when convenient rather than deferring indefinitely.

ALTER EXTENSION does not regenerate stored tsvector values or rebuild dependent GIN or lakebase_bm25 indexes. If an update changes tokenization output, regenerate stored tsvector values and follow the release notes for any required index maintenance.

The following example creates a dictionary from the tokenizer_wholeword template, then maps common Postgres token types to it in a text-search configuration:

CREATE TEXT SEARCH DICTIONARY documents_dict (
  TEMPLATE          = tokenizer_ext.tokenizer_wholeword,
  Lowercase         = 'true',
  StripAccents      = 'true',
  Stemmer           = 'english'
);

CREATE TEXT SEARCH CONFIGURATION documents_cfg (COPY = pg_catalog.simple);

ALTER TEXT SEARCH CONFIGURATION documents_cfg
  ALTER MAPPING FOR asciiword, word, numword, hword_numpart, hword_part, hword_asciipart
  WITH documents_dict;

Use the configuration to produce a tsvector, create a GIN index, and run full-text queries:

CREATE TABLE documents (
  id            BIGSERIAL PRIMARY KEY,
  body          TEXT NOT NULL,
  search_vector TSVECTOR GENERATED ALWAYS AS (
    to_tsvector('documents_cfg', body)
  ) STORED
);

INSERT INTO documents (body) VALUES
  ('Cats are running near the café.'),
  ('A dog is sleeping in the house.');

CREATE INDEX documents_search_idx ON documents USING gin (search_vector);

SELECT id, body
FROM documents
WHERE search_vector @@ plainto_tsquery('documents_cfg', 'running café');

Use the same text-search configuration for documents and queries so both sides apply the same tokenization strategy and options.

For each token passed to the dictionary by Postgres's text-search parser, tokenizer_wholeword applies these operations:

1. Lowercase: Convert the token to lowercase.
2. Normalize: Apply Unicode normalization.
3. StripAccents: Remove accents.
4. EnglishPossessive: Remove an English possessive suffix when at least one character remains.
5. Stopwords: Emit no lexeme and stop processing if the token matches a configured stop word. The token is omitted from the generated tsvector.
6. Synonyms: Emit the configured replacement and stop processing if the token matches a synonym.
7. Stemmer: If no synonym matched and stemming is enabled, apply the English stemmer.

The tokenizer_wholeword template can load custom stop words and synonyms from the extension-managed lakebase_tokenizer_stopwords and lakebase_tokenizer_synonyms SQL tables. The name column groups rows into a set that you select with the Stopwords or Synonyms dictionary option.

INSERT INTO tokenizer_ext.lakebase_tokenizer_stopwords (name, word) VALUES
  ('app_stopwords', 'the'),
  ('app_stopwords', 'and'),
  ('app_stopwords', 'or');

INSERT INTO tokenizer_ext.lakebase_tokenizer_synonyms (name, word, synonym) VALUES
  ('app_synonyms', 'usa', 'united_states'),
  ('app_synonyms', 'uk', 'united_kingdom');

Reference the sets when you create or alter a tokenizer_wholeword dictionary:

ALTER TEXT SEARCH DICTIONARY documents_dict (
  Stopwords = 'app_stopwords',
  Synonyms  = 'app_synonyms'
);

The extension compares stop words and synonym source words with each token after applying Lowercase, Normalize, StripAccents, and EnglishPossessive, but before applying Stemmer. Catalog entries are not transformed automatically, so store them in the exact form produced by those enabled options:

• With Lowercase = 'true', use lowercase entries. With Lowercase = 'false', capitalization must match the token.
• With Normalize enabled, store entries in the selected Unicode normalization form.
• With StripAccents = 'true', store the accent-stripped form. For example, store cafe to match café.
• Store the form before stemming. For example, with Stemmer = 'english', a run entry does not match running. Add running to filter or replace that token.

Set names can contain up to 256 bytes. Words and synonyms can contain up to 1024 bytes. Each named stop-word or synonym set can contain up to 100,000 rows.

A synonym replacement is emitted exactly as stored and is not processed by the stemmer. Synonyms support one replacement for each source word. To represent a multiword replacement as one lexeme, use a separator such as an underscore, as in united_states.

After changing a set, the owner of each dictionary that references the set must run a no-op ALTER TEXT SEARCH DICTIONARY to force a reload:

ALTER TEXT SEARCH DICTIONARY documents_dict (dummy);

The dummy option does not exist on tokenizer_wholeword. Omitting a value asks Postgres to remove this nonexistent option, which invalidates the cache without changing any of the dictionary's configured options.

After reloading the dictionary, regenerate the stored tsvector values by rewriting the source rows:

UPDATE documents SET body = body;

Use two roles to separate application access from tokenizer administration. The examples assume these roles already exist:

• app_role uses existing dictionaries. It needs USAGE on the relevant schemas and SELECT on the extension catalog tables, but it does not need to own the dictionaries.
• tokenizer_admin manages stop-word and synonym sets, creates and owns dictionaries and text-search configurations, and runs the reload command after changing a set.

Grant access to the extension schema and catalog tables:

GRANT USAGE ON SCHEMA tokenizer_ext TO app_role, tokenizer_admin;

GRANT SELECT ON
  tokenizer_ext.lakebase_tokenizer_stopwords,
  tokenizer_ext.lakebase_tokenizer_synonyms
TO app_role;

GRANT SELECT, INSERT, UPDATE, DELETE ON
  tokenizer_ext.lakebase_tokenizer_stopwords,
  tokenizer_ext.lakebase_tokenizer_synonyms
TO tokenizer_admin;

tokenizer_admin also needs CREATE on the schema where dictionaries and text-search configurations are stored. Create these objects as tokenizer_admin, or transfer their ownership to it. Write access to the catalog tables does not grant ownership of existing dictionaries.

Specify tokenizer_wholeword options in CREATE TEXT SEARCH DICTIONARY or ALTER TEXT SEARCH DICTIONARY. Option names are case-insensitive.

Text-search configurations built with lakebase_tokenizer produce standard Postgres tsvector values that are compatible with lakebase_text. To use BM25 relevance ranking and top-K retrieval, create a lakebase_bm25 index on the same tsvector column. For installation, index creation, and query syntax, see The lakebase_text extension.

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