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Full-text search

LanceDB provides support for full-text search via Tantivy (currently Python only), allowing you to incorporate keyword-based search (based on BM25) in your retrieval solutions. Our goal is to push the FTS integration down to the Rust level in the future, so that it's available for JavaScript users as well.

A hybrid search solution combining vector and full-text search is also on the way.


To use full-text search, install the dependency tantivy-py:

# Say you want to use tantivy==0.20.1
pip install tantivy==0.20.1


Consider that we have a LanceDB table named my_table, whose string column text we want to index and query via keyword search.

import lancedb

uri = "data/sample-lancedb"
db = lancedb.connect(uri)

table = db.create_table(
        {"vector": [3.1, 4.1], "text": "Frodo was a happy puppy"},
        {"vector": [5.9, 26.5], "text": "There are several kittens playing"},

Create FTS index on single column

The FTS index must be created before you can search via keywords.


To search an FTS index via keywords, LanceDB's accepts a string as input:"puppy").limit(10).select(["text"]).to_list()

This returns the result as a list of dictionaries as follows.

[{'text': 'Frodo was a happy puppy', 'score': 0.6931471824645996}]


LanceDB automatically searches on the existing FTS index if the input to the search is of type str. If you provide a vector as input, LanceDB will search the ANN index instead.

Index multiple columns

If you have multiple string columns to index, there's no need to combine them manually -- simply pass them all as a list to create_fts_index:

table.create_fts_index(["text1", "text2"])

Note that the search API call does not change - you can search over all indexed columns at once.


Currently the LanceDB full text search feature supports post-filtering, meaning filters are applied on top of the full text search results. This can be invoked via the familiar where syntax:"puppy").limit(10).where("meta='foo'").to_list()


For full-text search you can perform either a phrase query like "the old man and the sea", or a structured search query like "(Old AND Man) AND Sea". Double quotes are used to disambiguate.

For example:

If you intended "they could have been dogs OR cats" as a phrase query, this actually raises a syntax error since OR is a recognized operator. If you make or lower case, this avoids the syntax error. However, it is cumbersome to have to remember what will conflict with the query syntax. Instead, if you search using'"they could have been dogs OR cats"'), then the syntax checker avoids checking inside the quotes.


By default, LanceDB configures a 1GB heap size limit for creating the index. You can reduce this if running on a smaller node, or increase this for faster performance while indexing a larger corpus.

# configure a 512MB heap size
heap = 1024 * 1024 * 512
table.create_fts_index(["text1", "text2"], writer_heap_size=heap, replace=True)

Current limitations

  1. Currently we do not yet support incremental writes. If you add data after FTS index creation, it won't be reflected in search results until you do a full reindex.

  2. We currently only support local filesystem paths for the FTS index. This is a tantivy limitation. We've implemented an object store plugin but there's no way in tantivy-py to specify to use it.