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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 Rust and JavaScript users as well. Follow along at this Github issue

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()


You can pre-sort the documents by specifying ordering_field_names when creating the full-text search index. Once pre-sorted, you can then specify ordering_field_name while searching to return results sorted by the given field. For example,

table.create_fts_index(["text_field"], ordering_field_names=["sort_by_field"])

("terms", ordering_field_name="sort_by_field")


If you wish to specify an ordering field at query time, you must also have specified it during indexing time. Otherwise at query time, an error will be raised that looks like ValueError: The field does not exist: xxx


The fields to sort on must be of typed unsigned integer, or else you will see an error during indexing that looks like TypeError: argument 'value': 'float' object cannot be interpreted as an integer.


You can specify multiple fields for ordering at indexing time. But at query time only one ordering field is supported.

Phrase queries vs. terms queries

For full-text search you can specify either a phrase query like "the old man and the sea", or a terms search query like "(Old AND Man) AND Sea". For more details on the terms query syntax, see Tantivy's query parser rules.


The query parser will raise an exception on queries that are ambiguous. For example, in the query they could have been dogs OR cats, OR is capitalized so it's considered a keyword query operator. But it's ambiguous how the left part should be treated. So if you submit this search query as is, you'll get Syntax Error: they could have been dogs OR cats.

# This raises a syntax error"they could have been dogs OR cats")

On the other hand, lowercasing OR to or will work, because there are no capitalized logical operators and the query is treated as a phrase query.

# This works!"they could have been dogs or cats")

It can be cumbersome to have to remember what will cause a syntax error depending on the type of query you want to perform. To make this simpler, when you want to perform a phrase query, you can enforce it in one of two ways:

  1. Place the double-quoted query inside single quotes. For example,'"they could have been dogs OR cats"') is treated as a phrase query.
  2. Explicitly declare the phrase_query() method. This is useful when you have a phrase query that itself contains double quotes. For example,'the cats OR dogs were not really "pets" at all').phrase_query() is treated as a phrase query.

In general, a query that's declared as a phrase query will be wrapped in double quotes during parsing, with nested double quotes replaced by single 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.