@lancedb/lancedb • Docs @lancedb/lancedb / QueryBase Class: QueryBase<NativeQueryType> Common methods supported by all query types See Query VectorQuery Extended by Query VectorQuery Type Parameters • NativeQueryType extends NativeQuery | NativeVectorQuery Implements AsyncIterable<RecordBatch> Properties inner protected inner: NativeQueryType | Promise<NativeQueryType>; Methods analyzePlan() analyzePlan(): Promise<string> Executes the query and returns the physical query plan annotated with runtime metrics. This is useful for debugging and performance analysis, as it shows how the query was executed and includes metrics such as elapsed time, rows processed, and I/O statistics. Returns Promise<string> A query execution plan with runtime metrics for each step. Example import * as lancedb from "@lancedb/lancedb" const db = await lancedb.connect("./.lancedb"); const table = await db.createTable("my_table", [ { vector: [1.1, 0.9], id: "1" }, ]); const plan = await table.query().nearestTo([0.5, 0.2]).analyzePlan(); Example output (with runtime metrics inlined): AnalyzeExec verbose=true, metrics=[] ProjectionExec: expr=[id@3 as id, vector@0 as vector, _distance@2 as _distance], metrics=[output_rows=1, elapsed_compute=3.292µs] Take: columns="vector, _rowid, _distance, (id)", metrics=[output_rows=1, elapsed_compute=66.001µs, batches_processed=1, bytes_read=8, iops=1, requests=1] CoalesceBatchesExec: target_batch_size=1024, metrics=[output_rows=1, elapsed_compute=3.333µs] GlobalLimitExec: skip=0, fetch=10, metrics=[output_rows=1, elapsed_compute=167ns] FilterExec: _distance@2 IS NOT NULL, metrics=[output_rows=1, elapsed_compute=8.542µs] SortExec: TopK(fetch=10), expr=[_distance@2 ASC NULLS LAST], metrics=[output_rows=1, elapsed_compute=63.25µs, row_replacements=1] KNNVectorDistance: metric=l2, metrics=[output_rows=1, elapsed_compute=114.333µs, output_batches=1] LanceScan: uri=/path/to/data, projection=[vector], row_id=true, row_addr=false, ordered=false, metrics=[output_rows=1, elapsed_compute=103.626µs, bytes_read=549, iops=2, requests=2] execute() protected execute(options?): RecordBatchIterator Execute the query and return the results as an Parameters options?: Partial<QueryExecutionOptions> Returns RecordBatchIterator See AsyncIterator of RecordBatch. By default, LanceDb will use many threads to calculate results and, when the result set is large, multiple batches will be processed at one time. This readahead is limited however and backpressure will be applied if this stream is consumed slowly (this constrains the maximum memory used by a single query) explainPlan() explainPlan(verbose): Promise<string> Generates an explanation of the query execution plan. Parameters verbose: boolean = false If true, provides a more detailed explanation. Defaults to false. Returns Promise<string> A Promise that resolves to a string containing the query execution plan explanation. Example import * as lancedb from "@lancedb/lancedb" const db = await lancedb.connect("./.lancedb"); const table = await db.createTable("my_table", [ { vector: [1.1, 0.9], id: "1" }, ]); const plan = await table.query().nearestTo([0.5, 0.2]).explainPlan(); fastSearch() fastSearch(): this Skip searching un-indexed data. This can make search faster, but will miss any data that is not yet indexed. Use Table#optimize to index all un-indexed data. Returns this filter() filter(predicate): this A filter statement to be applied to this query. Parameters predicate: string Returns this See where Deprecated Use where instead fullTextSearch() fullTextSearch(query, options?): this Parameters query: string | FullTextQuery options?: Partial<FullTextSearchOptions> Returns this limit() limit(limit): this Set the maximum number of results to return. By default, a plain search has no limit. If this method is not called then every valid row from the table will be returned. Parameters limit: number Returns this offset() offset(offset): this Parameters offset: number Returns this select() select(columns): this Return only the specified columns. By default a query will return all columns from the table. However, this can have a very significant impact on latency. LanceDb stores data in a columnar fashion. This means we can finely tune our I/O to select exactly the columns we need. As a best practice you should always limit queries to the columns that you need. If you pass in an array of column names then only those columns will be returned. You can also use this method to create new "dynamic" columns based on your existing columns. For example, you may not care about "a" or "b" but instead simply want "a + b". This is often seen in the SELECT clause of an SQL query (e.g. SELECT a+b FROM my_table). To create dynamic columns you can pass in a Map. A column will be returned for each entry in the map. The key provides the name of the column. The value is an SQL string used to specify how the column is calculated. For example, an SQL query might state SELECT a + b AS combined, c. The equivalent input to this method would be: Parameters columns: string | string[] | Record<string, string> | Map<string, string> Returns this Example new Map([["combined", "a + b"], ["c", "c"]]) Columns will always be returned in the order given, even if that order is different than the order used when adding the data. Note that you can pass in a `Record<string, string>` (e.g. an object literal). This method uses `Object.entries` which should preserve the insertion order of the object. However, object insertion order is easy to get wrong and `Map` is more foolproof. toArray() toArray(options?): Promise<any[]> Collect the results as an array of objects. Parameters options?: Partial<QueryExecutionOptions> Returns Promise<any[]> toArrow() toArrow(options?): Promise<Table<any>> Collect the results as an Arrow Parameters options?: Partial<QueryExecutionOptions> Returns Promise<Table<any>> See ArrowTable. where() where(predicate): this A filter statement to be applied to this query. The filter should be supplied as an SQL query string. For example: Parameters predicate: string Returns this Example x > 10 y > 0 AND y < 100 x > 5 OR y = 'test' Filtering performance can often be improved by creating a scalar index on the filter column(s). withRowId() withRowId(): this Whether to return the row id in the results. This column can be used to match results between different queries. For example, to match results from a full text search and a vector search in order to perform hybrid search. Returns this