lancedb/docs/web/search/full-text-search.mdx

---
title: Full-Text Search (FTS)
sidebarTitle: Full-Text Search (FTS)
description: Learn how to implement full-text search in LanceDB using BM25 for keyword-based retrieval.
icon: "book"
availability:
  oss: available
  enterprise: available
  summary: >-
    Full-text search runs in the embedded library and against a deployment. On a
    deployment, indexes are built in the background and kept up to date by the
    deployment rather than by optimize().
---

import {
    PyFtsPrefiltering,
    PyFtsPostfiltering,
    PyFtsIncrementalIndex,
} from '/snippets/search.mdx'
import { PyFtsBooleanQueries } from '/snippets/doc_function_examples.mdx'

LanceDB provides support for Full-Text Search via Lance, allowing you to incorporate keyword-based search (based on BM25) in your retrieval solutions.

<Note>
In SQL, full-text search is the [`fts()`](/sql/functions#fts) table function, or the [`contains_tokens`](/sql/scalar-functions#contains-tokens) predicate when you do not need a score. [SQL](/basics/sql) runs on a deployment.
</Note>

## Basic Usage {#basic-usage}

Consider that we have a LanceDB table named `my_table`, whose string column `text` we want to index and query via keyword search, the FTS index must be created before you can search via keywords.

### Table Setup {#table-setup}

First, open or create the table you want to search:

<CodeGroup>
```python Python icon="python"
import lancedb
from lancedb.index import FTS

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

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

```ts TypeScript icon="square-js"
import * as lancedb from "@lancedb/lancedb";
const uri = "data/sample-lancedb"
const db = await lancedb.connect(uri);

const data = [
    { vector: [3.1, 4.1], text: "Frodo was a happy puppy" },
    { vector: [5.9, 26.5], text: "There are several kittens playing" },
];
const tbl = await db.createTable("my_table", data, { mode: "overwrite" });
```

```rust Rust icon="rust"
let uri = "data/sample-lancedb";
let db = connect(uri).execute().await?;
let initial_data: Box<dyn RecordBatchReader + Send> = create_some_records()?;
let tbl = db
    .create_table("my_table", initial_data)
    .execute()
    .await?;
```
</CodeGroup>

### Construct FTS Index {#construct-fts-index}

Create a full-text search index on your text column:

<Note>
In Python, this page shows the synchronous `create_fts_index(...)` form. For the
asynchronous equivalent (`await table.create_index("text", config=FTS(...))`), see
[FTS index](/indexing/fts-index).
</Note>

<CodeGroup>
```python Python icon="python"
table.create_fts_index("text")
```

```typescript TypeScript icon="square-js"
await tbl.createIndex("text", {
    config: lancedb.Index.fts(),
});
```

```rust Rust icon="rust"
tbl
    .create_index(&["text"], Index::FTS(FtsIndexBuilder::default()))
    .execute()
    .await?;
```
</CodeGroup>

Locally the index is built before the call returns. On a deployment the call
starts a background build and returns at once; pass `wait_timeout` or call
`wait_for_index` before relying on the index.

### Full-text Search {#full-text-search}

Perform full-text search and retrieve results:

<CodeGroup>
```python Python icon="python"
results = (
    table.search("puppy")
    .limit(10)
    .select(["text"])
    .to_list()
)
# [{'text': 'Frodo was a happy puppy', '_score': 0.6931471824645996}]
```

```typescript TypeScript icon="square-js"
const results = await tbl
    .search("puppy", "fts")
    .select(["text"])
    .limit(10)
    .toArray();
```

```rust Rust icon="Rust"
let results = tbl
    .query()
    .full_text_search(FullTextSearchQuery::new("puppy".to_owned()))
    .select(lancedb::query::Select::Columns(vec!["text".to_owned()]))
    .limit(10)
    .execute()
    .await?;
```
</CodeGroup>

The search is conducted on all indexed columns by default, so it's useful when there are multiple indexed columns.

If you want to specify which columns to search use `fts_columns="text"`

<Note>
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.
</Note>

If a table has more than one FTS index, specify the indexed text column in the query. In Python you can use `fts_columns` or the query builder's `nearest_to_text(..., columns=...)`; in TypeScript, use `query().nearestToText(..., columns)`. The newer Lance-native FTS does not accept legacy Tantivy-only index parameters.

A deployment that distributes search across its workers scores each worker's
rows with that worker's statistics unless it is configured for global scoring,
so scores and close rankings can differ slightly from the embedded library's.

### Keeping the index up to date {#keeping-the-index-up-to-date}

Rows you add after building an FTS index aren't part of the index until you optimize the table. Until then, queries fall back to a flat scan over the unindexed fragments to keep results complete, which slows them down as the unindexed tail grows. Call `table.optimize()` to fold new rows into the existing index — it's the same operation used for vector indexes:

<CodeGroup>
<CodeBlock filename="Python" language="python" icon="python">
{PyFtsIncrementalIndex}
</CodeBlock>

```typescript TypeScript icon="square-js"
await tbl.add([{ vector: [3.1, 4.1], text: "Frodo was a happy puppy" }]);
await tbl.optimize();
```

```rust Rust icon="rust"
tbl.add(new_data).execute().await?;
tbl.optimize(OptimizeAction::All).await?;
```
</CodeGroup>

A useful rule of thumb is to call `optimize()` after roughly 100,000 row changes or 20 data-modification operations, whichever comes first. For tables with continuous ingest, schedule it on a cadence that keeps `num_unindexed_rows` (from `table.index_stats(...)`) close to zero. If you want to skip the flat scan over unindexed rows entirely — for example, on a hot read path where stale results are acceptable — call `.fast_search()` on the query so the search returns only indexed results.

On a deployment, `optimize()` is not available: the deployment adds new rows to the index itself, and until it has, queries scan the unindexed rows.

## Advanced Usage {#advanced-usage}

### Tokenize Table Data {#tokenize-table-data}

By default, the text is tokenized by splitting on punctuation and whitespaces, and would filter out words that are longer than 40 characters. All words are converted to lowercase.

Stemming is useful for improving search results by reducing words to their root form, e.g. "running" to "run". LanceDB supports stemming for Arabic, Danish, Dutch, English, Finnish, French, German, Greek, Hungarian, Italian, Norwegian, Portuguese, Romanian, Russian, Spanish, Swedish, Tamil, and Turkish. You should set the `base_tokenizer` parameter rather than `tokenizer_name` because you cannot customize the tokenizer if `tokenizer_name` is specified.

Tokenization and language filters are separate settings. `base_tokenizer` controls how text is split into searchable tokens. `language` controls stemming and stop-word removal when `stem=True` or `remove_stop_words=True`; choose the tokenizer for CJK or mixed-language segmentation.

For example, to enable stemming for English:

<CodeGroup>
```python Python icon="python"
table.create_fts_index("text", language="English", replace=True)
```
</CodeGroup>

The tokenizer is customizable, you can specify how the tokenizer splits the text, and how it filters out words, etc.

**Default index parameters:**
- `base_tokenizer`: `"simple"`
- `language`: English
- `with_position`: false
- `max_token_length`: 40
- `lower_case`: true
- `stem`: true
- `remove_stop_words`: true
- `ascii_folding`: true
- `custom_stop_words`: `None` — pass a `list[str]` to drop additional words beyond the language defaults. Requires `remove_stop_words=True`.

For multilingual use cases, use `base_tokenizer="icu"` for unicode-aware word segmentation on mixed-language text. ICU stands for [International Components for Unicode](https://icu.unicode.org/). The ICU tokenizer uses bundled ICU4X segmenter data, so it does not require external tokenizer model files. It is a good default when documents mix languages or include scripts where the simple tokenizer would keep an unspaced span as one large token.

The Python API also supports tokenizer implementations that load language model files. Use `base_tokenizer="jieba/default"` for Jieba tokenization, which segments Chinese text into searchable word tokens when the text is written without spaces between words. Use Lindera-backed tokenizers for dictionary-based East Asian morphological segmentation, such as `base_tokenizer="lindera/ipadic"` for Japanese or `base_tokenizer="lindera/ko-dic"` for Korean when you have installed and compiled that Lindera model. These are language-specific tokenizers; ICU is the broader mixed-language option.

<CodeGroup>
```python Python icon="python"
table.create_fts_index(
    "text",
    base_tokenizer="jieba/default",
    stem=False,
    remove_stop_words=False,
    ascii_folding=False,
    replace=True,
)
```
</CodeGroup>

Model-backed tokenizers 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 a different model root:

```bash
export LANCE_LANGUAGE_MODEL_HOME=/path/to/lance/language_models
```

For example, `jieba/default` is resolved under `<model-home>/jieba/default/...`, `lindera/ipadic` under `<model-home>/lindera/ipadic/...`, and `lindera/ko-dic` under `<model-home>/lindera/ko-dic/...`.

The model files are read where the index is built and queried. For a table on a
deployment that is the deployment, so ask its administrator whether they are
installed there.

<Note>
Built-in stop-word removal supports Danish, Dutch, English, Finnish, French, German, Hungarian, Italian, Norwegian, Portuguese, Russian, Spanish, and Swedish. If you use another stemming language, such as Arabic, Greek, Romanian, Tamil, or Turkish, set `remove_stop_words=False` or pass `custom_stop_words`.
</Note>

For example, for language with accents, you can specify the tokenizer to use `ascii_folding` to remove accents, e.g. 'é' to 'e':

<CodeGroup>
```python Python icon="python"
table.create_fts_index(
        "text",
        language="French",
        stem=True,
        ascii_folding=True,
        replace=True,
    )
```
</CodeGroup>

### Filtering Options {#filtering-options}

LanceDB full text search supports to filter the search results by a condition, both pre-filtering and post-filtering are supported.

This can be invoked via the familiar `where` syntax.
 
With pre-filtering:

<CodeGroup>

<CodeBlock filename="Python" language="python" icon="python">
{PyFtsPrefiltering}
</CodeBlock>

```typescript TypeScript icon="square-js"
await tbl
.search("puppy")
.select(["id", "doc"])
.limit(10)
.where("meta='foo'")
.prefilter(true)
.toArray();
```

```rust Rust icon="Rust"
table
    .query()
    .full_text_search(FullTextSearchQuery::new("puppy".to_owned()))
    .select(lancedb::query::Select::Columns(vec!["doc".to_owned()]))
    .limit(10)
    .only_if("meta='foo'")
    .execute()
    .await?;
```
</CodeGroup>

With post-filtering:

<CodeGroup>
<CodeBlock filename="Python" language="python" icon="python">
{PyFtsPostfiltering}
</CodeBlock>

```typescript TypeScript icon="square-js"
await tbl
.search("apple")
.select(["id", "doc"])
.limit(10)
.where("meta='foo'")
.prefilter(false)
.toArray();
```

```rust Rust icon="Rust"
table
    .query()
    .full_text_search(FullTextSearchQuery::new(words[0].to_owned()))
    .select(lancedb::query::Select::Columns(vec!["doc".to_owned()]))
    .postfilter()
    .limit(10)
    .only_if("meta='foo'")
    .execute()
    .await?;
```
</CodeGroup>

### Phrase vs. Terms Queries {#phrase-vs-terms-queries}

<Warning>
Lance-based FTS doesn't support queries using boolean operators `OR`, `AND` in the search string.
</Warning>

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 man sea`. 

To search for a phrase, the index must be created with `with_position=True` and `remove_stop_words=False`:

<CodeGroup>
```python Python icon="python"
table.create_fts_index("text", with_position=True, replace=True)
```
</CodeGroup>

This will allow you to search for phrases, but it will also significantly increase the index size and indexing time.

### Fuzzy Search {#fuzzy-search}

Fuzzy search allows you to find matches even when the search terms contain typos or slight variations. 
LanceDB uses the classic [Levenshtein distance](https://en.wikipedia.org/wiki/Levenshtein_distance) 
to find similar terms within a specified edit distance.

| Parameter      | Type | Default | Description                                                                                                                                                 |
| -------------- | ---- | ------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------- |
| fuzziness      | int  | 0       | Maximum edit distance allowed for each term. If not specified, automatically set based on term length: 0 for length ≤ 2, 1 for length ≤ 5, 2 for length > 5 |
| max_expansions | int  | 50      | Maximum number of terms to consider for fuzzy matching. Higher values may improve recall but increase search time                                           |

For a complete walkthrough that creates a sample table and demonstrates fuzzy search and relevance boosting, see [the worked example below](#fuzzy-search-and-boosting-example).

### Search for Substring {#search-for-substring}

LanceDB supports searching for substrings in the text column, you can set the `base_tokenizer` parameter to `"ngram"` to enable this feature, and use the parameters `ngram_min_length` and `ngram_max_length` to control the length of the substrings:

| Parameter        | Type | Default | Description                                        |
| ---------------- | ---- | ------- | -------------------------------------------------- |
| ngram_min_length | int  | 3       | Minimum length of the n-grams to search for        |
| ngram_max_length | int  | 3       | Maximum length of the n-grams to search for        |
| prefix_only      | bool | false   | Whether to only search for prefixes of the n-grams |


## Fuzzy Search and Boosting Example {#fuzzy-search-and-boosting-example}

### Generate Data {#generate-data}

First, let's create a table with sample text data for testing fuzzy search:

<CodeGroup>
```python Python icon="python"
import lancedb
import numpy as np
import pandas as pd
import random

# Connect to LanceDB
db = lancedb.connect(
    uri="db://your-project-slug",
    api_key="your-api-key",
    region="us-east-1"
)

# Generate 100 rows of random "<noun> <verb> <adverb> <adjective>" text
table_name = "fts-fuzzy-boosting-test"
vectors = [np.random.randn(128) for _ in range(100)]
verbs = ("runs", "hits", "jumps", "drives", "barfs")
adv = ("crazily.", "dutifully.", "foolishly.", "merrily.", "occasionally.")
adj = ("adorable", "clueless", "dirty", "odd", "stupid")

def sentence(nouns):
    return " ".join(random.choice(words) for words in (nouns, verbs, adv, adj))

text = [sentence(("puppy", "car")) for _ in range(100)]
text2 = [sentence(("rabbit", "girl", "monkey")) for _ in range(100)]
count = [random.randint(1, 10000) for _ in range(100)]
```

```typescript TypeScript icon="square-js"
import * as lancedb from "@lancedb/lancedb"

const db = await lancedb.connect({
    uri: "db://your-project-slug",
    apiKey: "your-api-key",
    region: "us-east-1"
});

// Generate 100 rows of random "<noun> <verb> <adverb> <adjective>" text
const tableName = "fts-fuzzy-boosting-test-ts";
const n = 100;
const verbs = ["runs", "hits", "jumps", "drives", "barfs"];
const adverbs = ["crazily", "dutifully", "foolishly", "merrily", "occasionally"];
const adjectives = ["adorable", "clueless", "dirty", "odd", "stupid"];

const pick = (words: string[]) => words[Math.floor(Math.random() * words.length)];
const sentence = (nouns: string[]) =>
    [nouns, verbs, adverbs, adjectives].map(pick).join(" ");

const vectors = Array.from({ length: n }, () =>
    Array.from({ length: 128 }, () => Math.random() * 2 - 1)
);
const text = Array.from({ length: n }, () => sentence(["puppy", "car"]));
const text2 = Array.from({ length: n }, () => sentence(["rabbit", "girl", "monkey"]));
const count = Array.from({ length: n }, () => Math.floor(Math.random() * 10000) + 1);
```
</CodeGroup>

### Create Table {#create-table}

<CodeGroup>
```python Python icon="python"
# Create table with sample data
table = db.create_table(
    table_name,
    data=pd.DataFrame({
        "vector": vectors,
        "id": [i % 2 for i in range(100)],
        "text": text,
        "text2": text2,
        "count": count,
    }),
    mode="overwrite"
)
```

```typescript TypeScript icon="square-js"
// Create table with sample data
const data = makeArrowTable(
    vectors.map((vector, i) => ({
        vector,
        id: i % 2,
        text: text[i],
        text2: text2[i],
        count: count[i],
    }))
);

const table = await db.createTable(tableName, data, { mode: "overwrite" });
```
</CodeGroup>

### Construct the index for these examples {#example-construct-fts-index}

Create a full-text search index on the first text column:

<CodeGroup>
```python Python icon="python"
# Create FTS index on first text column
table.create_fts_index("text")
wait_for_index(table, "text_idx")
```

```typescript TypeScript icon="square-js"
// Create FTS index on first text column
await table.createIndex("text", { config: Index.fts() });
await waitForIndex(table, "text_idx");
```
</CodeGroup>

Then, create an index on the second text column:

<CodeGroup>
```python Python icon="python"
# Create FTS index on second text column
table.create_fts_index("text2")
wait_for_index(table, "text2_idx")
```

```typescript TypeScript icon="square-js"
// Create FTS index on second text column
await table.createIndex("text2", { config: Index.fts() });
await waitForIndex(table, "text2_idx");
```
</CodeGroup>

### Basic and Fuzzy Search {#basic-and-fuzzy-search}

Now we can perform basic, fuzzy, and prefix match searches:

#### Basic Exact Search {#basic-exact-search}

<CodeGroup>
```python Python icon="python"
from lancedb.query import MatchQuery

# Basic match (exact search)
basic_match_results = (
    table.search(MatchQuery("crazily", "text"))
    .select(["id", "text"])
    .limit(100)
    .to_pandas()
)
```

```typescript TypeScript icon="square-js"
import { MatchQuery } from "@lancedb/lancedb";

// Basic match (exact search)
const basicMatchResults = await table.query()
    .fullTextSearch(new MatchQuery("crazily", "text"))
    .select(["id", "text"])
    .limit(100)
    .toArray();
```
</CodeGroup>

#### Fuzzy Search with Typos {#fuzzy-search-with-typos}

<CodeGroup>
```python Python icon="python"
# Fuzzy match (allows typos)
fuzzy_results = (
    table.search(MatchQuery("craziou", "text", fuzziness=2))
    .select(["id", "text"])
    .limit(100)
    .to_pandas()
)
```

```typescript TypeScript icon="square-js"
// Fuzzy match (allows typos)
const fuzzyResults = await table.query()
    .fullTextSearch(new MatchQuery("craziou", "text", {
        fuzziness: 2,
    }))
    .select(["id", "text"])
    .limit(100)
    .toArray();
```
</CodeGroup>

#### Prefix based Match {#prefix-based-match}

Prefix-based match allows you to search for documents containing words that start with a specific prefix. 

<CodeGroup>
```python Python icon="python"
# Fuzzy match (allows typos)
fuzzy_results = (
    table.search(MatchQuery("cra", "text", prefix_length=3))
    .select(["id", "text"])
    .limit(100)
    .to_pandas()
)
```

```typescript TypeScript icon="square-js"
// Fuzzy match (allows typos)
const fuzzyResults = await table.query()
    .fullTextSearch(new MatchQuery("cra", "text", {
        prefixLength: 3,
    }))
    .select(["id", "text"])
    .limit(100)
    .toArray();
```
</CodeGroup>

### Phrase Match {#phrase-match}

Phrase matching enables you to search for exact sequences of words. Unlike regular text search 
which matches individual terms independently, phrase matching requires words to appear in the 
specified order with no intervening terms. 

<Note>
Phrase queries are supported but only for a single column; providing multiple columns with a quoted phrase raises an error.
</Note>

Phrase matching is particularly useful for:

- Searching for specific multi-word expressions
- Matching exact titles or quotes  
- Finding precise word combinations in a specific order

<CodeGroup>
```python Python icon="python"
# Exact phrase match
from lancedb.query import PhraseQuery

phrase_results = (
    table.search(PhraseQuery("puppy runs", "text"))
    .select(["id", "text"])
    .limit(100)
    .to_pandas()
)
```

```typescript TypeScript icon="square-js"
import { PhraseQuery } from "@lancedb/lancedb";

// Exact phrase match
const phraseResults = await table.query()
  .fullTextSearch(new PhraseQuery("puppy runs", "text"))
  .select(["id", "text"])
  .limit(100)
  .toArray();
```
</CodeGroup>

#### Flexible Phrase Match {#flexible-phrase-match}
To provide more flexible phrase matching, LanceDB supports the `slop` parameter. This allows you to match phrases where the terms appear close to each other, even if they are not directly adjacent or in the exact order, as long as they are within the specified `slop` value.

For example, the phrase query "puppy merrily" would not return any results by default. However, if you set `slop=1`, it will match phrases like "puppy jumps merrily", "puppy runs merrily", and similar variations where one word appears between "puppy" and "merrily".

<CodeGroup>
```python Python icon="python"
# Flexible phrase match with slop=1 for 'puppy merrily'
from lancedb.query import PhraseQuery

phrase_results = (
    table.search(PhraseQuery("puppy merrily", "text", slop=1))
    .select(["id", "text"])
    .limit(100)
    .to_pandas()
)
```

```typescript TypeScript icon="square-js"
import { PhraseQuery } from "@lancedb/lancedb";

// Flexible phrase match with slop=1 for 'puppy runs'
const phraseResults = await table.query()
  .fullTextSearch(new PhraseQuery("puppy runs", "text", { slop: 1 }))
  .select(["id", "text"])
  .limit(100)
  .toArray();
```
</CodeGroup>

### Search with Boosting {#search-with-boosting}

Boosting allows you to control the relative importance of different search terms or fields 
in your queries. This feature is particularly useful when you need to:

* Prioritize matches in certain columns
* Promote specific terms while demoting others 
* Fine-tune relevance scoring for better search results

| Parameter      | Type  | Default  | Description                                                        |
| -------------- | ----- | -------- | ------------------------------------------------------------------ |
| positive       | Query | required | The primary query terms to match and promote in results            |
| negative       | Query | required | Terms to demote in the search results                              |
| negative_boost | float | 0.5      | Multiplier for negative matches (lower values = stronger demotion) |

<CodeGroup>
```python Python icon="python"
from lancedb.query import MatchQuery, BoostQuery, MultiMatchQuery

# Boost data with 'runs' in text more than 'puppy' in text
boosting_results = (
  table.search(
      BoostQuery(
          MatchQuery("runs", "text"),
          MatchQuery("puppy", "text"),
          negative_boost=0.2,
      ),
  )
  .select(["id", "text"])
  .limit(100)
  .to_pandas()
)

# Search across both text and text2
multi_match_results = (
    table.search(MultiMatchQuery("crazily", ["text", "text2"]))
    .select(["id", "text", "text2"])
    .limit(100)
    .to_pandas()
)

# Search with field boosting
multi_match_boosting_results = (
    table.search(
        MultiMatchQuery("crazily", ["text", "text2"], boosts=[1.0, 2.0]),
    )
    .select(["id", "text", "text2"])
    .limit(100)
    .to_pandas()
)
```

```typescript TypeScript icon="square-js"
import { MatchQuery, BoostQuery, MultiMatchQuery } from "@lancedb/lancedb";

// Boosting Example
const boostingResults = await table.query()
  .fullTextSearch(new BoostQuery(new MatchQuery("runs", "text"), new MatchQuery("puppy", "text"), {
    negativeBoost: 0.2,
  }))
  .select(["id", "text"])
  .limit(100)
  .toArray();

// Search across both text fields
const multiMatchResults = await table.query()
  .fullTextSearch(new MultiMatchQuery("crazily", ["text", "text2"]))
  .select(["id", "text", "text2"])
  .limit(100)
  .toArray();

// Search with field boosting
const multiMatchBoostingResults = await table.query()
  .fullTextSearch(new MultiMatchQuery("crazily", ["text", "text2"], {
    boosts: [1.0, 2.0],
  }))
  .select(["id", "text", "text2"])
  .limit(100)
  .toArray();
```
</CodeGroup>

<Card title="Best practices" icon="flag">
- Use fuzzy search when handling user input that may contain typos or variations
- Apply field boosting to prioritize matches in more important columns
- Combine fuzzy search with boosting for robust and precise search results

**Recommendations for optimal FTS performance:**

- Create full-text search indices on text columns that will be frequently searched
- For hybrid search combining text and vectors, see our [hybrid search guide](/search/hybrid-search/)
- For performance benchmarks, check our [benchmark results](/enterprise/benchmarks/)
- For complex queries on a deployment, use SQL to combine FTS with other filter conditions
</Card>

### Boolean Queries {#boolean-queries}
LanceDB supports boolean logic in full-text search, allowing you to combine multiple queries using `and` and `or` operators. This is useful when you want to match documents that satisfy multiple conditions (intersection) or at least one of several conditions (union).

#### Combining Two Match Queries {#combining-two-match-queries}

In Python, combine two `MatchQuery` objects with `&` for intersection or `|` for union. Python's `and` and `or` operators return one operand and do not construct a boolean query.

<CodeBlock filename="Python" language="Python" icon="python">
{PyFtsBooleanQueries}
</CodeBlock>

In TypeScript, boolean queries are constructed using the `BooleanQuery` class with a list of [Occur, subquery] pairs. For example, to perform an AND query:

```sql SQL icon="code"
BooleanQuery([
[Occur.Must, new MatchQuery("puppy", "text")],
[Occur.Must, new MatchQuery("merrily", "text")],
])
```

This approach allows you to specify complex boolean logic by combining multiple subqueries with different Occur values (such as `Must`, `Should`, or `MustNot`).

<Info>
**Which queries are allowed?**

A boolean query must include at least one `SHOULD` or `MUST` clause. Queries that contain only a `MUST_NOT` clause are not allowed.
</Info>

<CodeGroup>
```python Python icon="python"
from lancedb.query import MatchQuery

# Example: Find documents containing both "puppy" and "merrily"
and_query = MatchQuery("puppy", "text") & MatchQuery("merrily", "text")
and_results = (
    table.search(and_query)
    .select(["id", "text"])
    .limit(100)
    .to_pandas()
)

# Example: Find documents containing either "puppy" or "merrily"
or_query = MatchQuery("puppy", "text") | MatchQuery("merrily", "text")
or_results = (
    table.search(or_query)
    .select(["id", "text"])
    .limit(100)
    .to_pandas()
)
```

```typescript TypeScript icon="square-js" expandable=true
import { MatchQuery, BooleanQuery, Occur } from "@lancedb/lancedb";

// Flexible boolean queries with MatchQuery

// Find documents containing both "puppy" and "merrily"
const mustResults = await table
    .search(
      new BooleanQuery([
        [Occur.Must, new MatchQuery("puppy", "text")],
        [Occur.Must, new MatchQuery("merrily", "text")],
      ]),
    )
    .select(["id", "text"])
    .limit(100)
    .toArray();

// Find documents containing either "puppy" or "merrily"
const shouldResults = await table
    .search(
      new BooleanQuery([
        [Occur.Should, new MatchQuery("puppy", "text")],
        [Occur.Should, new MatchQuery("merrily", "text")],
      ]),
    )
    .select(["id", "text"])
    .limit(100)
    .toArray();
```
</CodeGroup>

<Info>
**How to use booleans?**

- Use `and`/`&`(Python), `Occur.Must`(Typescript) for intersection (documents must match all queries).
- Use `or`/`|`(Python), `Occur.Should`(Typescript) for union (documents must match at least one query).
</Info>

## Substring Search Example {#substring-search-example}

LanceDB supports searching for substrings in text columns using n-gram tokenization. This is useful for finding partial matches within text content.

### Setting Up the Table {#setting-up-the-table}

First, create a table with sample text data and configure n-gram tokenization:

<CodeGroup>
```python Python icon="python"
import pyarrow as pa
import lancedb

db = lancedb.connect(":memory:")

data = pa.table({"text": ["hello world", "lance database", "lance is cool"]})
table = db.create_table("test", data=data)
table.create_fts_index("text", base_tokenizer="ngram")
```
</CodeGroup>

### Basic Substring Search {#basic-substring-search}

With the default n-gram settings (minimum length of 3), you can search for substrings of length 3 or more:

<CodeGroup>
```python Python icon="python"
results = table.search("lan", query_type="fts").limit(10).to_list()
assert len(results) == 2
assert set(r["text"] for r in results) == {"lance database", "lance is cool"}

results = (
    table.search("nce", query_type="fts").limit(10).to_list()
)  # spellchecker:disable-line
assert len(results) == 2
assert set(r["text"] for r in results) == {"lance database", "lance is cool"}
```
</CodeGroup>

### Handling Short Substrings {#handling-short-substrings}

By default, the minimum n-gram length is 3, so shorter substrings like "la" won't match:

<CodeGroup>
```python Python icon="python"
results = table.search("la", query_type="fts").limit(10).to_list()
assert len(results) == 0
```
</CodeGroup>

### Customizing N-gram Parameters {#customizing-n-gram-parameters}

You can customize the n-gram behavior by adjusting the minimum length and using prefix-only matching:

<CodeGroup>
```python Python icon="python"
table.create_fts_index(
    "text",
    base_tokenizer="ngram",
    replace=True,
    ngram_min_length=2,
    prefix_only=True,
)
```
</CodeGroup>

### Testing Custom N-gram Settings {#testing-custom-n-gram-settings}

With the new settings, you can now search for shorter substrings and use prefix-only matching:

<CodeGroup>
```python Python icon="python"
results = table.search("lan", query_type="fts").limit(10).to_list()
assert len(results) == 2
assert set(r["text"] for r in results) == {"lance database", "lance is cool"}

results = (
    table.search("nce", query_type="fts").limit(10).to_list()
)  # spellchecker:disable-line
assert len(results) == 0

results = table.search("la", query_type="fts").limit(10).to_list()
assert len(results) == 2
assert set(r["text"] for r in results) == {"lance database", "lance is cool"}
```
</CodeGroup>

## Full-Text Search on Array Fields {#full-text-search-on-array-fields}

LanceDB supports full-text search on string array columns, enabling efficient keyword-based search across multiple values within a single field (e.g., tags, keywords).

### Setting Up the Connection {#setting-up-the-connection}

Connect to your LanceDB instance:

<CodeGroup>
```python Python icon="python"
import lancedb

# Connect to LanceDB
db = lancedb.connect(
  uri="db://your-project-slug",
  api_key="your-api-key",
  region="us-east-1"
)
```

```typescript TypeScript icon="square-js" expandable=true
import * as lancedb from "@lancedb/lancedb"

const db = await lancedb.connect({
  uri: "db://your-project-slug",
  apiKey: "your-api-key",
  region: "us-east-1"
});
```
</CodeGroup>

### Defining the Schema {#defining-the-schema}

Create a schema that includes an array field for tags:

<CodeGroup>
```python Python icon="python"
table_name = "fts-array-field-test"
schema = pa.schema([
    pa.field("id", pa.string()),
    pa.field("tags", pa.list_(pa.string())),
    pa.field("description", pa.string())
])
```

```typescript TypeScript icon="square-js" expandable=true
const tableName = "fts-array-field-test-ts";

// Create schema
const schema = new Schema([
  new Field("id", new Utf8(), false),
  new Field("tags", new List(new Field("item", new Utf8()))),
  new Field("description", new Utf8(), false)
]);
```
</CodeGroup>

### Creating Sample Data {#creating-sample-data}

Generate sample data with array fields containing tags:

<CodeGroup>
```python Python icon="python" expandable=true
# Generate sample data
data = {
    "id": [f"doc_{i}" for i in range(10)],
    "tags": [
        ["python", "machine learning", "data science"],
        ["deep learning", "neural networks", "AI"],
        ["database", "indexing", "search"],
        ["vector search", "embeddings", "AI"],
        ["full text search", "indexing", "database"],
        ["python", "web development", "flask"],
        ["machine learning", "deep learning", "pytorch"],
        ["database", "SQL", "postgresql"],
        ["search engine", "elasticsearch", "indexing"],
        ["AI", "transformers", "NLP"]
    ],
    "description": [
        "Python for data science projects",
        "Deep learning fundamentals",
        "Database indexing techniques",
        "Vector search implementations",
        "Full-text search guide",
        "Web development with Python",
        "Machine learning with PyTorch",
        "Database management systems",
        "Search engine optimization",
        "AI and NLP applications"
    ]
}
```

```typescript TypeScript icon="square-js" expandable=true
// Generate sample data
const data = makeArrowTable(
  Array(10).fill(0).map((_, i) => ({
    id: `doc_${i}`,
    tags: [
      ["python", "machine learning", "data science"],
      ["deep learning", "neural networks", "AI"],
      ["database", "indexing", "search"],
      ["vector search", "embeddings", "AI"],
      ["full text search", "indexing", "database"],
      ["python", "web development", "flask"],
      ["machine learning", "deep learning", "pytorch"],
      ["database", "SQL", "postgresql"],
      ["search engine", "elasticsearch", "indexing"],
      ["AI", "transformers", "NLP"]
    ][i],
    description: [
      "Python for data science projects",
      "Deep learning fundamentals",
      "Database indexing techniques",
      "Vector search implementations",
      "Full-text search guide",
      "Web development with Python",
      "Machine learning with PyTorch",
      "Database management systems",
      "Search engine optimization",
      "AI and NLP applications"
    ][i]
  })),
  { schema }
);
```
</CodeGroup>

### Creating the Table and Adding Data {#creating-the-table-and-adding-data}

Create the table and populate it with the sample data:

<CodeGroup>
```python Python icon="python"
# Create table and add data
table = db.create_table(table_name, schema=schema, mode="overwrite")
table_data = pa.Table.from_pydict(data, schema=schema)
table.add(table_data)
```

```typescript TypeScript icon="square-js"
// Create table
const table = await db.createTable(tableName, data, { mode: "overwrite" });
console.log(`Created table: ${tableName}`);
```
</CodeGroup>

### Building the Full-Text Search Index {#building-the-full-text-search-index}

Create an FTS index on the tags column to enable efficient text search:

<CodeGroup>
```python Python icon="python"
# Create FTS index
table.create_fts_index("tags")
wait_for_index(table, "tags_idx")
```

```typescript TypeScript icon="square-js"
// Create FTS index
console.log("Creating FTS index on 'tags' column...");
await table.createIndex("tags", {
  config: Index.fts()
});

// Wait for index
const ftsIndexName = "tags_idx";
await waitForIndex(table, ftsIndexName);
```
</CodeGroup>

### Performing Fuzzy Search {#performing-fuzzy-search}

Search for terms with typos using fuzzy matching:

<CodeGroup>
```python Python icon="python"
# Search examples
print("\nSearching for 'learning' in tags with a typo:")
result = (
    table.search(MatchQuery("learnin", column="tags", fuzziness=1))
    .select(['id', 'tags', 'description'])
    .to_arrow()
)
```

```typescript TypeScript icon="square-js">
// Search examples
console.log("\nSearching for 'learning' in tags with a typo:");
const fuzzyResults = await table.query()
  .fullTextSearch(new MatchQuery("learnin", "tags", {
    fuzziness: 2,
  }))
  .select(["id", "tags", "description"])
  .toArray();
console.log(fuzzyResults);
```
</CodeGroup>

### Performing Phrase Search {#performing-phrase-search}

Search for exact phrases within the array fields:

<CodeGroup>
```python Python icon="python"
print("\nSearching for 'machine learning' in tags:")
result = (
    table.search(PhraseQuery("machine learning", column="tags"))
    .select(['id', 'tags', 'description'])
    .to_arrow()
)
```

```typescript TypeScript icon="square-js"
console.log("\nSearching for 'machine learning' in tags:");
const phraseResults = await table.query()
  .fullTextSearch(new PhraseQuery("machine learning", "tags"))
  .select(["id", "tags", "description"])
  .toArray();
console.log(phraseResults);
```
</CodeGroup>