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LanceDB provides performant full-text search based on BM25, allowing you to incorporate keyword-based search in your retrieval solutions. This page shows examples on how to create and configure FTS indexes in LanceDB OSS and Enterprise, using the synchronous and asynchronous APIs.
In LanceDB Enterprise, create_fts_index API returns immediately, but index building happens asynchronously.

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Creating FTS Indexes

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Synchronous API

Use create_fts_index with synchronous LanceDB connections: Check FTS index status using the API: wait_for_index(...) waits until the named FTS index exists and index_stats(...) reports num_unindexed_rows == 0. It can time out if writes keep adding rows faster than the index catches up. If a table has multiple FTS indexes, specify the target text column when querying instead of relying on implicit selection.

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Asynchronous API

When using async connections (connect_async), use create_index with the FTS configuration:
The create_fts_index method is not available on AsyncTable. Use create_index with FTS config instead.

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Nested field paths

FTS indexes can target text leaves inside struct columns by passing a dotted path (for example, payload.text). The same path works for MatchQuery and PhraseQuery, and for the columns argument on async nearest_to_text queries. You can point an index at any string leaf nested in a struct, regardless of depth. The struct container itself isn’t indexable: you have to name a specific text field. LanceDB rejects paths that don’t resolve to a text leaf:
  • A struct container (for example, payload): raises ValueError: FTS index cannot be created ....
  • A non-text leaf such as an integer or float (for example, payload.count): raises the same error.
  • A path that doesn’t exist in the schema (for example, payload.missing): raises ValueError: Field path ... not found.
The async API accepts the same dotted paths through create_index:
Python

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Configuration Options

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FTS Parameters

  • max_token_length can filter out base64 blobs or long URLs.
  • Disabling with_position reduces index size but disables phrase queries.
  • ascii_folding helps with international text (e.g., “café” → “cafe”).
Model-backed tokenizers such as jieba/default and lindera/ipadic require tokenizer model files in Lance’s language model home. Lance looks under the default platform data directory for lance/language_models, or you can set LANCE_LANGUAGE_MODEL_HOME to point to another model root. For example, jieba/default is resolved under <model-home>/jieba/default/....

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Phrase Query Configuration

Enable phrase queries by setting:

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Indexing nested string fields

You can build an FTS index on a string field inside a struct by passing its full dotted path, like nested.text. The same path is used when you query the index through fts_columns, and the indexed column is reported back as the full path from list_indices().
Use the canonical Lance path: dot-separate each struct field from root to leaf (for example, metadata.author.name). The same convention applies to scalar and vector indexes.