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Metadata Filtering with LanceDB

LanceDB supports filtering features of query results based on metadata fields. While joint vector and metadata search at scale presents a significant challenge, LanceDB achieves sub-100ms latency at thousands of QPS, enabling efficient vector search with filtering capabilities even on datasets containing billions of records.

Pre-filtering is applied to top-k results by default before executing the vector search. This narrow down the search space within large datasets, thereby reducing query latency. You can also use post-filtering to refine results after the vector search completes.

Example: Metadata Filtering

To illustrate filtering capabilities, let's try four data points with combinations of vectors and metadata:

data = [
    {"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
    {"vector": [5.9, 26.5], "item": "bar", "price": 20.0},
    {"vector": [10.2, 100.8], "item": "baz", "price": 30.0},
    {"vector": [1.4, 9.5], "item": "fred", "price": 40.0},
]
table = db.create_table("metadata_filter_example", data=data, mode="overwrite")

const data = [
{ vector: [3.1, 4.1], item: "foo", price: 10.0 },
{ vector: [5.9, 26.5], item: "bar", price: 20.0 },
{ vector: [10.2, 100.8], item: "baz", price: 30.0 },
{ vector: [1.4, 9.5], item: "fred", price: 40.0 },
];

const tableName = "metadata_filter_example";
const table = await db.createTable(tableName, data, {
mode: "overwrite",
});

You can always filter your data without search. This is useful when you need to query based on metadata:

filtered_no_search_result = (
    table.search()
    .where("(item IN ('foo', 'bar', 'baz')) AND (price > 15.0)")
    .limit(3)
    .to_arrow()
)

filtered_no_search_result = await (
    async_table.query()
    .where("(item IN ('foo', 'bar', 'baz')) AND (price > 15.0)")
    .limit(3)
    .to_arrow()
)

const filteredResult = await table
  .query()
  .where("(item IN ('foo', 'bar', 'baz')) AND (price > 15.0)")
  .limit(3)
  .toArray();

Limit Output

If your table is large, this could potentially return a very large amount of data. Please be sure to use a limit clause unless you're sure you want to return the whole result set.

filtered_result = (
    table.search([100, 102])
    .where("(item IN ('foo', 'bar')) AND (price > 15.0)")
    .limit(3)
    .to_arrow()
)

filtered_result = await (
    async_table.query()
    .where("(item IN ('foo', 'bar')) AND (price > 15.0)")
    .nearest_to([100, 102])
    .limit(3)
    .to_arrow()
)

const results = await table
  .search([100, 102])
  .where("(item IN ('foo', 'bar')) AND (price > 15.0)")
  .toArray();

Post-Filtering with Vector Search

post_filtered_result = (
    table.search([100, 102])
    .where("(item IN ('foo', 'bar')) AND (price > 15.0)", prefilter=False)
    .limit(3)
    .to_arrow()
)

post_filtered_result = await (
    async_table.query()
    .where("(item IN ('foo', 'bar')) AND (price > 15.0)", prefilter=False)
    .nearest_to([100, 102])
    .limit(3)
    .to_arrow()
)

const postFilteredResult = await (table.search([100, 102]) as VectorQuery)
  .where("(item IN ('foo', 'bar')) AND (price > 15.0)")
  .postfilter()
  .limit(3)
  .toArray();

Resource Usage Warning

When querying large tables, omitting a limit clause may overwhelm resources and return excessive data. It can also increase costs as query pricing scales with data scanned and data returned (LanceDB Cloud pricing).

Filtering with SQL

Because it's built on top of DataFusion, LanceDB embraces the utilization of standard SQL expressions as predicates for filtering operations. SQL can be used during vector search, update, and deletion operations.

LanceDB supports a growing list of SQL expressions:

SQL Expression Description
>, >=, <, <=, = Comparison operators
AND, OR, NOT Logical operators
IS NULL, IS NOT NULL Null checks
IS TRUE, IS NOT TRUE, IS FALSE, IS NOT FALSE Boolean checks
IN Value matching from a set
LIKE, NOT LIKE Pattern matching
CAST Type conversion
regexp_match(column, pattern) Regular expression matching
DataFusion Functions Additional SQL functions

Simple SQL Filters

For example, the following filter string is acceptable:

tbl.search([100, 102]).where(
    "(item IN ('foo', 'baz')) AND (price > 20.0)"
).to_arrow()

await (
    async_tbl.query()
    .where("(item IN ('foo', 'baz')) AND (price > 20.0)")
    .nearest_to([100, 102])
    .to_arrow()
)

await table
  .search([100, 102])
  .where("(item IN ('foo', 'baz')) AND (price > 20.0)")
  .toArray();

Advanced SQL Filters

If your column name contains special characters, upper-case characters, or is a SQL Keyword, you can use backtick (`) to escape it. For nested fields, each segment of the path must be wrapped in backticks.

`CUBE` = 10 AND `UpperCaseName` = '3' AND `column name with space` IS NOT NULL
AND `nested with space`.`inner with space` < 2

Field Name Limitation

Field names containing periods (.) are NOT supported.

Dates, Timestamps, Decimals

Literals for dates, timestamps, and decimals can be written by writing the string value after the type name. For example:

date_col = date '2021-01-01'
and timestamp_col = timestamp '2021-01-01 00:00:00'
and decimal_col = decimal(8,3) '1.000'

For timestamp columns, the precision can be specified as a number in the type parameter. Microsecond precision (6) is the default.

SQL Time unit
timestamp(0) Seconds
timestamp(3) Milliseconds
timestamp(6) Microseconds
timestamp(9) Nanoseconds

Apache Arrow Mapping

LanceDB internally stores data in Apache Arrow format. The mapping from SQL types to Arrow types is:

SQL type Arrow type
boolean Boolean
tinyint / tinyint unsigned Int8 / UInt8
smallint / smallint unsigned Int16 / UInt16
int or integer / int unsigned or integer unsigned Int32 / UInt32
bigint / bigint unsigned Int64 / UInt64
float Float32
double Float64
decimal(precision, scale) Decimal128
date Date32
timestamp Timestamp 1
string Utf8
binary Binary

Best Practices

Scalar Indexes: We strongly recommend creating scalar indices on columns used for filtering, whether combined with a search operation or applied independently (e.g., for updates or deletions).

For best performance with large tables or high query volumes:

  • Build a scalar index on frequently filtered columns
  • Use exact column names in filters (e.g., user_id instead of USER_ID)
  • Avoid complex transformations in filter expressions (keep them simple)
  • When running concurrent queries, use connection pooling for better throughput

For a column of type LIST(T), you can use LABEL_LIST to create a scalar index. Then you should leverage DataFusion's array functions like array_has_any or array_has_all for optimized filtering.

Limitations

Both pre-filtering and post-filtering can yield false positives. For pre-filtering, if the filter is too selective, it might eliminate relevant items that the vector search would have otherwise identified as a good match. In this case, increasing nprobes parameter will help reduce such false positives. It is recommended to call bypass_vector_index() if you know that the filter is highly selective.

Similarly, a highly selective post-filter can lead to false positives. Increasing both nprobes and refine_factor can mitigate this issue. When deciding between pre-filtering and post-filtering, pre-filtering is generally the safer choice if you're uncertain.

  1. See precision mapping in previous table. ↩

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