Bump version: 0.17.1-beta.2 → 0.17.1-beta.3

User reported on Discord, when using
`table.vector_search([np.float16(1.0), np.float16(2.0), ...])`, it
yields `TypeError: 'numpy.float16' object is not iterable`

.bumpversion.toml

@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.14.0-beta.2"
+current_version = "0.14.1-beta.2"
 parse = """(?x)
     (?P<major>0|[1-9]\\d*)\\.
     (?P<minor>0|[1-9]\\d*)\\.

Cargo.toml

@@ -21,16 +21,16 @@ categories = ["database-implementations"]
 rust-version = "1.80.0"                                                     # TODO: lower this once we upgrade Lance again.
 
 [workspace.dependencies]
-lance = { "version" = "=0.20.0", "features" = [
+lance = { "version" = "=0.21.0", "features" = [
     "dynamodb",
-] }
-lance-io = "0.20.0"
-lance-index = "0.20.0"
-lance-linalg = "0.20.0"
-lance-table = "0.20.0"
-lance-testing = "0.20.0"
-lance-datafusion = "0.20.0"
-lance-encoding = "0.20.0"
+], git = "https://github.com/lancedb/lance.git", tag = "v0.21.0-beta.2" }
+lance-io = { version = "=0.21.0", git = "https://github.com/lancedb/lance.git", tag = "v0.21.0-beta.2" }
+lance-index = { version = "=0.21.0", git = "https://github.com/lancedb/lance.git", tag = "v0.21.0-beta.2" }
+lance-linalg = { version = "=0.21.0", git = "https://github.com/lancedb/lance.git", tag = "v0.21.0-beta.2" }
+lance-table = { version = "=0.21.0", git = "https://github.com/lancedb/lance.git", tag = "v0.21.0-beta.2" }
+lance-testing = { version = "=0.21.0", git = "https://github.com/lancedb/lance.git", tag = "v0.21.0-beta.2" }
+lance-datafusion = { version = "=0.21.0", git = "https://github.com/lancedb/lance.git", tag = "v0.21.0-beta.2" }
+lance-encoding = { version = "=0.21.0", git = "https://github.com/lancedb/lance.git", tag = "v0.21.0-beta.2" }
 # Note that this one does not include pyarrow
 arrow = { version = "53.2", optional = false }
 arrow-array = "53.2"

docs/src/ann_indexes.md

@@ -83,6 +83,7 @@ The following IVF_PQ paramters can be specified:
 - **num_sub_vectors**: The number of sub-vectors (M) that will be created during Product Quantization (PQ).
   For D dimensional vector, it will be divided into `M` subvectors with dimension `D/M`, each of which is replaced by
   a single PQ code. The default is the dimension of the vector divided by 16.
+- **num_bits**: The number of bits used to encode each sub-vector. Only 4 and 8 are supported. The higher the number of bits, the higher the accuracy of the index, also the slower search. The default is 8.
 
 !!! note
 
@@ -142,11 +143,11 @@ There are a couple of parameters that can be used to fine-tune the search:
 - **nprobes** (default: 20): The number of probes used. A higher number makes search more accurate but also slower.<br/>
   Most of the time, setting nprobes to cover 5-15% of the dataset should achieve high recall with low latency.<br/>
     - _For example_, For a dataset of 1 million vectors divided into 256 partitions, `nprobes` should be set to ~20-40. This value can be adjusted to achieve the optimal balance between search latency and search quality. <br/>
-  
+
 - **refine_factor** (default: None): Refine the results by reading extra elements and re-ranking them in memory.<br/>
   A higher number makes search more accurate but also slower. If you find the recall is less than ideal, try refine_factor=10 to start.<br/>
     - _For example_, For a dataset of 1 million vectors divided into 256 partitions, setting the `refine_factor` to 200 will initially retrieve the top 4,000 candidates (top k * refine_factor) from all searched partitions. These candidates are then reranked to determine the final top 20 results.<br/>
-!!! note 
+!!! note
     Both `nprobes` and `refine_factor` are only applicable if an ANN index is present. If specified on a table without an ANN index, those parameters are ignored.
 
 
@@ -288,4 +289,4 @@ less space distortion, and thus yields better accuracy. However, a higher `num_s
 
 `m` determines the number of connections a new node establishes with its closest neighbors upon entering the graph. Typically, `m` falls within the range of 5 to 48. Lower `m` values are suitable for low-dimensional data or scenarios where recall is less critical. Conversely, higher `m` values are beneficial for high-dimensional data or when high recall is required. In essence, a larger `m` results in a denser graph with increased connectivity, but at the expense of higher memory consumption.
 
-`ef_construction` balances build speed and accuracy. Higher values increase accuracy but slow down the build process. A typical range is 150 to 300. For good search results, a minimum value of 100 is recommended. In most cases, setting this value above 500 offers no additional benefit. Ensure that `ef_construction` is always set to a value equal to or greater than `ef` in the search phase
+`ef_construction` balances build speed and accuracy. Higher values increase accuracy but slow down the build process. A typical range is 150 to 300. For good search results, a minimum value of 100 is recommended. In most cases, setting this value above 500 offers no additional benefit. Ensure that `ef_construction` is always set to a value equal to or greater than `ef` in the search phase

docs/src/js/.nojekyll

@@ -1 +0,0 @@
-TypeDoc added this file to prevent GitHub Pages from using Jekyll. You can turn off this behavior by setting the `githubPages` option to false.

docs/src/js/classes/Connection.md

@@ -27,7 +27,9 @@ the underlying connection has been closed.
 
 ### new Connection()
 
-> **new Connection**(): [`Connection`](Connection.md)
+```ts
+new Connection(): Connection
+```
 
 #### Returns
 
@@ -37,7 +39,9 @@ the underlying connection has been closed.
 
 ### close()
 
-> `abstract` **close**(): `void`
+```ts
+abstract close(): void
+```
 
 Close the connection, releasing any underlying resources.
 
@@ -53,21 +57,24 @@ Any attempt to use the connection after it is closed will result in an error.
 
 ### createEmptyTable()
 
-> `abstract` **createEmptyTable**(`name`, `schema`, `options`?): `Promise`<[`Table`](Table.md)>
+```ts
+abstract createEmptyTable(
+   name,
+   schema,
+   options?): Promise<Table>
+```
 
 Creates a new empty Table
 
 #### Parameters
 
-• **name**: `string`
+* **name**: `string`
+    The name of the table.
 
-The name of the table.
+* **schema**: `SchemaLike`
+    The schema of the table
 
-• **schema**: `SchemaLike`
-
-The schema of the table
-
-• **options?**: `Partial`&lt;[`CreateTableOptions`](../interfaces/CreateTableOptions.md)&gt;
+* **options?**: `Partial`&lt;[`CreateTableOptions`](../interfaces/CreateTableOptions.md)&gt;
 
 #### Returns
 
@@ -79,15 +86,16 @@ The schema of the table
 
 #### createTable(options)
 
-> `abstract` **createTable**(`options`): `Promise`&lt;[`Table`](Table.md)&gt;
+```ts
+abstract createTable(options): Promise<Table>
+```
 
 Creates a new Table and initialize it with new data.
 
 ##### Parameters
 
-• **options**: `object` & `Partial`&lt;[`CreateTableOptions`](../interfaces/CreateTableOptions.md)&gt;
-
-The options object.
+* **options**: `object` & `Partial`&lt;[`CreateTableOptions`](../interfaces/CreateTableOptions.md)&gt;
+    The options object.
 
 ##### Returns
 
@@ -95,22 +103,25 @@ The options object.
 
 #### createTable(name, data, options)
 
-> `abstract` **createTable**(`name`, `data`, `options`?): `Promise`&lt;[`Table`](Table.md)&gt;
+```ts
+abstract createTable(
+   name,
+   data,
+   options?): Promise<Table>
+```
 
 Creates a new Table and initialize it with new data.
 
 ##### Parameters
 
-• **name**: `string`
+* **name**: `string`
+    The name of the table.
 
-The name of the table.
+* **data**: `TableLike` \| `Record`&lt;`string`, `unknown`&gt;[]
+    Non-empty Array of Records
+    to be inserted into the table
 
-• **data**: `TableLike` \| `Record`&lt;`string`, `unknown`&gt;[]
-
-Non-empty Array of Records
-to be inserted into the table
-
-• **options?**: `Partial`&lt;[`CreateTableOptions`](../interfaces/CreateTableOptions.md)&gt;
+* **options?**: `Partial`&lt;[`CreateTableOptions`](../interfaces/CreateTableOptions.md)&gt;
 
 ##### Returns
 
@@ -120,7 +131,9 @@ to be inserted into the table
 
 ### display()
 
-> `abstract` **display**(): `string`
+```ts
+abstract display(): string
+```
 
 Return a brief description of the connection
 
@@ -132,15 +145,16 @@ Return a brief description of the connection
 
 ### dropTable()
 
-> `abstract` **dropTable**(`name`): `Promise`&lt;`void`&gt;
+```ts
+abstract dropTable(name): Promise<void>
+```
 
 Drop an existing table.
 
 #### Parameters
 
-• **name**: `string`
-
-The name of the table to drop.
+* **name**: `string`
+    The name of the table to drop.
 
 #### Returns
 
@@ -150,7 +164,9 @@ The name of the table to drop.
 
 ### isOpen()
 
-> `abstract` **isOpen**(): `boolean`
+```ts
+abstract isOpen(): boolean
+```
 
 Return true if the connection has not been closed
 
@@ -162,17 +178,18 @@ Return true if the connection has not been closed
 
 ### openTable()
 
-> `abstract` **openTable**(`name`, `options`?): `Promise`&lt;[`Table`](Table.md)&gt;
+```ts
+abstract openTable(name, options?): Promise<Table>
+```
 
 Open a table in the database.
 
 #### Parameters
 
-• **name**: `string`
+* **name**: `string`
+    The name of the table
 
-The name of the table
-
-• **options?**: `Partial`&lt;`OpenTableOptions`&gt;
+* **options?**: `Partial`&lt;`OpenTableOptions`&gt;
 
 #### Returns
 
@@ -182,7 +199,9 @@ The name of the table
 
 ### tableNames()
 
-> `abstract` **tableNames**(`options`?): `Promise`&lt;`string`[]&gt;
+```ts
+abstract tableNames(options?): Promise<string[]>
+```
 
 List all the table names in this database.
 
@@ -190,10 +209,9 @@ Tables will be returned in lexicographical order.
 
 #### Parameters
 
-• **options?**: `Partial`&lt;[`TableNamesOptions`](../interfaces/TableNamesOptions.md)&gt;
-
-options to control the
-paging / start point
+* **options?**: `Partial`&lt;[`TableNamesOptions`](../interfaces/TableNamesOptions.md)&gt;
+    options to control the
+    paging / start point
 
 #### Returns
 

docs/src/js/classes/Index.md

@@ -8,9 +8,30 @@
 
 ## Methods
 
+### bitmap()
+
+```ts
+static bitmap(): Index
+```
+
+Create a bitmap index.
+
+A `Bitmap` index stores a bitmap for each distinct value in the column for every row.
+
+This index works best for low-cardinality columns, where the number of unique values
+is small (i.e., less than a few hundreds).
+
+#### Returns
+
+[`Index`](Index.md)
+
+***
+
 ### btree()
 
-> `static` **btree**(): [`Index`](Index.md)
+```ts
+static btree(): Index
+```
 
 Create a btree index
 
@@ -36,9 +57,82 @@ block size may be added in the future.
 
 ***
 
+### fts()
+
+```ts
+static fts(options?): Index
+```
+
+Create a full text search index
+
+A full text search index is an index on a string column, so that you can conduct full
+text searches on the column.
+
+The results of a full text search are ordered by relevance measured by BM25.
+
+You can combine filters with full text search.
+
+For now, the full text search index only supports English, and doesn't support phrase search.
+
+#### Parameters
+
+* **options?**: `Partial`<`FtsOptions`>
+
+#### Returns
+
+[`Index`](Index.md)
+
+***
+
+### hnswPq()
+
+```ts
+static hnswPq(options?): Index
+```
+
+Create a hnswPq index
+
+HNSW-PQ stands for Hierarchical Navigable Small World - Product Quantization.
+It is a variant of the HNSW algorithm that uses product quantization to compress
+the vectors.
+
+#### Parameters
+
+* **options?**: `Partial`<`HnswPqOptions`>
+
+#### Returns
+
+[`Index`](Index.md)
+
+***
+
+### hnswSq()
+
+```ts
+static hnswSq(options?): Index
+```
+
+Create a hnswSq index
+
+HNSW-SQ stands for Hierarchical Navigable Small World - Scalar Quantization.
+It is a variant of the HNSW algorithm that uses scalar quantization to compress
+the vectors.
+
+#### Parameters
+
+* **options?**: `Partial`<`HnswSqOptions`>
+
+#### Returns
+
+[`Index`](Index.md)
+
+***
+
 ### ivfPq()
 
-> `static` **ivfPq**(`options`?): [`Index`](Index.md)
+```ts
+static ivfPq(options?): Index
+```
 
 Create an IvfPq index
 
@@ -63,29 +157,25 @@ currently is also a memory intensive operation.
 
 #### Parameters
 
-• **options?**: `Partial`<[`IvfPqOptions`](../interfaces/IvfPqOptions.md)>
+* **options?**: `Partial`<[`IvfPqOptions`](../interfaces/IvfPqOptions.md)>
 
 #### Returns
 
 [`Index`](Index.md)
 
-### fts()
+***
 
-> `static` **fts**(`options`?): [`Index`](Index.md)
+### labelList()
 
-Create a full text search index
+```ts
+static labelList(): Index
+```
 
-This index is used to search for text data.  The index is created by tokenizing the text
-into words and then storing occurrences of these words in a data structure called inverted index
-that allows for fast search.
+Create a label list index.
 
-During a search the query is tokenized and the inverted index is used to find the rows that
-contain the query words.  The rows are then scored based on BM25 and the top scoring rows are
-sorted and returned.
-
-#### Parameters
-
-• **options?**: `Partial`<[`FtsOptions`](../interfaces/FtsOptions.md)>
+LabelList index is a scalar index that can be used on `List<T>` columns to
+support queries with `array_contains_all` and `array_contains_any`
+using an underlying bitmap index.
 
 #### Returns
 

docs/src/js/classes/MakeArrowTableOptions.md

@@ -12,11 +12,13 @@ Options to control the makeArrowTable call.
 
 ### new MakeArrowTableOptions()
 
-> **new MakeArrowTableOptions**(`values`?): [`MakeArrowTableOptions`](MakeArrowTableOptions.md)
+```ts
+new MakeArrowTableOptions(values?): MakeArrowTableOptions
+```
 
 #### Parameters
 
-• **values?**: `Partial`<[`MakeArrowTableOptions`](MakeArrowTableOptions.md)>
+* **values?**: `Partial`<[`MakeArrowTableOptions`](MakeArrowTableOptions.md)>
 
 #### Returns
 
@@ -26,7 +28,9 @@ Options to control the makeArrowTable call.
 
 ### dictionaryEncodeStrings
 
-> **dictionaryEncodeStrings**: `boolean` = `false`
+```ts
+dictionaryEncodeStrings: boolean = false;
+```
 
 If true then string columns will be encoded with dictionary encoding
 
@@ -40,22 +44,30 @@ If `schema` is provided then this property is ignored.
 
 ### embeddingFunction?
 
-> `optional` **embeddingFunction**: [`EmbeddingFunctionConfig`](../namespaces/embedding/interfaces/EmbeddingFunctionConfig.md)
+```ts
+optional embeddingFunction: EmbeddingFunctionConfig;
+```
 
 ***
 
 ### embeddings?
 
-> `optional` **embeddings**: [`EmbeddingFunction`](../namespaces/embedding/classes/EmbeddingFunction.md)<`unknown`, `FunctionOptions`>
+```ts
+optional embeddings: EmbeddingFunction<unknown, FunctionOptions>;
+```
 
 ***
 
 ### schema?
 
-> `optional` **schema**: `SchemaLike`
+```ts
+optional schema: SchemaLike;
+```
 
 ***
 
 ### vectorColumns
 
-> **vectorColumns**: `Record`&lt;`string`, [`VectorColumnOptions`](VectorColumnOptions.md)&gt;
+```ts
+vectorColumns: Record<string, VectorColumnOptions>;
+```

docs/src/js/classes/Query.md

@@ -16,11 +16,13 @@ A builder for LanceDB queries.
 
 ### new Query()
 
-> **new Query**(`tbl`): [`Query`](Query.md)
+```ts
+new Query(tbl): Query
+```
 
 #### Parameters
 
-• **tbl**: `Table`
+* **tbl**: `Table`
 
 #### Returns
 
@@ -34,7 +36,9 @@ A builder for LanceDB queries.
 
 ### inner
 
-> `protected` **inner**: `Query` \| `Promise`<`Query`>
+```ts
+protected inner: Query | Promise<Query>;
+```
 
 #### Inherited from
 
@@ -44,7 +48,9 @@ A builder for LanceDB queries.
 
 ### \[asyncIterator\]()
 
-> **\[asyncIterator\]**(): `AsyncIterator`&lt;`RecordBatch`&lt;`any`&gt;, `any`, `undefined`&gt;
+```ts
+asyncIterator: AsyncIterator<RecordBatch<any>, any, undefined>
+```
 
 #### Returns
 
@@ -58,11 +64,13 @@ A builder for LanceDB queries.
 
 ### doCall()
 
-> `protected` **doCall**(`fn`): `void`
+```ts
+protected doCall(fn): void
+```
 
 #### Parameters
 
-• **fn**
+* **fn**
 
 #### Returns
 
@@ -76,13 +84,15 @@ A builder for LanceDB queries.
 
 ### execute()
 
-> `protected` **execute**(`options`?): [`RecordBatchIterator`](RecordBatchIterator.md)
+```ts
+protected execute(options?): RecordBatchIterator
+```
 
 Execute the query and return the results as an
 
 #### Parameters
 
-• **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;
+* **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;
 
 #### Returns
 
@@ -108,15 +118,16 @@ single query)
 
 ### explainPlan()
 
-> **explainPlan**(`verbose`): `Promise`&lt;`string`&gt;
+```ts
+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.
+* **verbose**: `boolean` = `false`
+    If true, provides a more detailed explanation. Defaults to false.
 
 #### Returns
 
@@ -141,15 +152,38 @@ const plan = await table.query().nearestTo([0.5, 0.2]).explainPlan();
 
 ***
 
+### fastSearch()
+
+```ts
+fastSearch(): this
+```
+
+Skip searching un-indexed data. This can make search faster, but will miss
+any data that is not yet indexed.
+
+Use lancedb.Table#optimize to index all un-indexed data.
+
+#### Returns
+
+`this`
+
+#### Inherited from
+
+[`QueryBase`](QueryBase.md).[`fastSearch`](QueryBase.md#fastsearch)
+
+***
+
 ### ~~filter()~~
 
-> **filter**(`predicate`): `this`
+```ts
+filter(predicate): this
+```
 
 A filter statement to be applied to this query.
 
 #### Parameters
 
-• **predicate**: `string`
+* **predicate**: `string`
 
 #### Returns
 
@@ -169,9 +203,33 @@ Use `where` instead
 
 ***
 
+### fullTextSearch()
+
+```ts
+fullTextSearch(query, options?): this
+```
+
+#### Parameters
+
+* **query**: `string`
+
+* **options?**: `Partial`&lt;`FullTextSearchOptions`&gt;
+
+#### Returns
+
+`this`
+
+#### Inherited from
+
+[`QueryBase`](QueryBase.md).[`fullTextSearch`](QueryBase.md#fulltextsearch)
+
+***
+
 ### limit()
 
-> **limit**(`limit`): `this`
+```ts
+limit(limit): this
+```
 
 Set the maximum number of results to return.
 
@@ -180,7 +238,7 @@ called then every valid row from the table will be returned.
 
 #### Parameters
 
-• **limit**: `number`
+* **limit**: `number`
 
 #### Returns
 
@@ -194,11 +252,13 @@ called then every valid row from the table will be returned.
 
 ### nativeExecute()
 
-> `protected` **nativeExecute**(`options`?): `Promise`&lt;`RecordBatchIterator`&gt;
+```ts
+protected nativeExecute(options?): Promise<RecordBatchIterator>
+```
 
 #### Parameters
 
-• **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;
+* **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;
 
 #### Returns
 
@@ -212,7 +272,9 @@ called then every valid row from the table will be returned.
 
 ### nearestTo()
 
-> **nearestTo**(`vector`): [`VectorQuery`](VectorQuery.md)
+```ts
+nearestTo(vector): VectorQuery
+```
 
 Find the nearest vectors to the given query vector.
 
@@ -232,7 +294,7 @@ If there is more than one vector column you must use
 
 #### Parameters
 
-• **vector**: `IntoVector`
+* **vector**: `IntoVector`
 
 #### Returns
 
@@ -264,9 +326,49 @@ a default `limit` of 10 will be used.
 
 ***
 
+### nearestToText()
+
+```ts
+nearestToText(query, columns?): Query
+```
+
+#### Parameters
+
+* **query**: `string`
+
+* **columns?**: `string`[]
+
+#### Returns
+
+[`Query`](Query.md)
+
+***
+
+### offset()
+
+```ts
+offset(offset): this
+```
+
+#### Parameters
+
+* **offset**: `number`
+
+#### Returns
+
+`this`
+
+#### Inherited from
+
+[`QueryBase`](QueryBase.md).[`offset`](QueryBase.md#offset)
+
+***
+
 ### select()
 
-> **select**(`columns`): `this`
+```ts
+select(columns): this
+```
 
 Return only the specified columns.
 
@@ -290,7 +392,7 @@ input to this method would be:
 
 #### Parameters
 
-• **columns**: `string` \| `string`[] \| `Record`&lt;`string`, `string`&gt; \| `Map`&lt;`string`, `string`&gt;
+* **columns**: `string` \| `string`[] \| `Record`&lt;`string`, `string`&gt; \| `Map`&lt;`string`, `string`&gt;
 
 #### Returns
 
@@ -317,13 +419,15 @@ object insertion order is easy to get wrong and `Map` is more foolproof.
 
 ### toArray()
 
-> **toArray**(`options`?): `Promise`&lt;`any`[]&gt;
+```ts
+toArray(options?): Promise<any[]>
+```
 
 Collect the results as an array of objects.
 
 #### Parameters
 
-• **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;
+* **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;
 
 #### Returns
 
@@ -337,13 +441,15 @@ Collect the results as an array of objects.
 
 ### toArrow()
 
-> **toArrow**(`options`?): `Promise`&lt;`Table`&lt;`any`&gt;&gt;
+```ts
+toArrow(options?): Promise<Table<any>>
+```
 
 Collect the results as an Arrow
 
 #### Parameters
 
-• **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;
+* **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;
 
 #### Returns
 
@@ -361,7 +467,9 @@ ArrowTable.
 
 ### where()
 
-> **where**(`predicate`): `this`
+```ts
+where(predicate): this
+```
 
 A filter statement to be applied to this query.
 
@@ -369,7 +477,7 @@ The filter should be supplied as an SQL query string.  For example:
 
 #### Parameters
 
-• **predicate**: `string`
+* **predicate**: `string`
 
 #### Returns
 
@@ -389,3 +497,25 @@ on the filter column(s).
 #### Inherited from
 
 [`QueryBase`](QueryBase.md).[`where`](QueryBase.md#where)
+
+***
+
+### withRowId()
+
+```ts
+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`
+
+#### Inherited from
+
+[`QueryBase`](QueryBase.md).[`withRowId`](QueryBase.md#withrowid)

docs/src/js/classes/QueryBase.md

@@ -25,11 +25,13 @@ Common methods supported by all query types
 
 ### new QueryBase()
 
-> `protected` **new QueryBase**<`NativeQueryType`>(`inner`): [`QueryBase`](QueryBase.md)<`NativeQueryType`>
+```ts
+protected new QueryBase<NativeQueryType>(inner): QueryBase<NativeQueryType>
+```
 
 #### Parameters
 
-• **inner**: `NativeQueryType` \| `Promise`&lt;`NativeQueryType`&gt;
+* **inner**: `NativeQueryType` \| `Promise`&lt;`NativeQueryType`&gt;
 
 #### Returns
 
@@ -39,13 +41,17 @@ Common methods supported by all query types
 
 ### inner
 
-> `protected` **inner**: `NativeQueryType` \| `Promise`&lt;`NativeQueryType`&gt;
+```ts
+protected inner: NativeQueryType | Promise<NativeQueryType>;
+```
 
 ## Methods
 
 ### \[asyncIterator\]()
 
-> **\[asyncIterator\]**(): `AsyncIterator`&lt;`RecordBatch`&lt;`any`&gt;, `any`, `undefined`&gt;
+```ts
+asyncIterator: AsyncIterator<RecordBatch<any>, any, undefined>
+```
 
 #### Returns
 
@@ -59,11 +65,13 @@ Common methods supported by all query types
 
 ### doCall()
 
-> `protected` **doCall**(`fn`): `void`
+```ts
+protected doCall(fn): void
+```
 
 #### Parameters
 
-• **fn**
+* **fn**
 
 #### Returns
 
@@ -73,13 +81,15 @@ Common methods supported by all query types
 
 ### execute()
 
-> `protected` **execute**(`options`?): [`RecordBatchIterator`](RecordBatchIterator.md)
+```ts
+protected execute(options?): RecordBatchIterator
+```
 
 Execute the query and return the results as an
 
 #### Parameters
 
-• **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;
+* **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;
 
 #### Returns
 
@@ -101,15 +111,16 @@ single query)
 
 ### explainPlan()
 
-> **explainPlan**(`verbose`): `Promise`&lt;`string`&gt;
+```ts
+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.
+* **verbose**: `boolean` = `false`
+    If true, provides a more detailed explanation. Defaults to false.
 
 #### Returns
 
@@ -130,15 +141,34 @@ const plan = await table.query().nearestTo([0.5, 0.2]).explainPlan();
 
 ***
 
+### fastSearch()
+
+```ts
+fastSearch(): this
+```
+
+Skip searching un-indexed data. This can make search faster, but will miss
+any data that is not yet indexed.
+
+Use lancedb.Table#optimize to index all un-indexed data.
+
+#### Returns
+
+`this`
+
+***
+
 ### ~~filter()~~
 
-> **filter**(`predicate`): `this`
+```ts
+filter(predicate): this
+```
 
 A filter statement to be applied to this query.
 
 #### Parameters
 
-• **predicate**: `string`
+* **predicate**: `string`
 
 #### Returns
 
@@ -154,9 +184,29 @@ Use `where` instead
 
 ***
 
+### fullTextSearch()
+
+```ts
+fullTextSearch(query, options?): this
+```
+
+#### Parameters
+
+* **query**: `string`
+
+* **options?**: `Partial`&lt;`FullTextSearchOptions`&gt;
+
+#### Returns
+
+`this`
+
+***
+
 ### limit()
 
-> **limit**(`limit`): `this`
+```ts
+limit(limit): this
+```
 
 Set the maximum number of results to return.
 
@@ -165,7 +215,7 @@ called then every valid row from the table will be returned.
 
 #### Parameters
 
-• **limit**: `number`
+* **limit**: `number`
 
 #### Returns
 
@@ -175,11 +225,13 @@ called then every valid row from the table will be returned.
 
 ### nativeExecute()
 
-> `protected` **nativeExecute**(`options`?): `Promise`&lt;`RecordBatchIterator`&gt;
+```ts
+protected nativeExecute(options?): Promise<RecordBatchIterator>
+```
 
 #### Parameters
 
-• **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;
+* **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;
 
 #### Returns
 
@@ -187,9 +239,27 @@ called then every valid row from the table will be returned.
 
 ***
 
+### offset()
+
+```ts
+offset(offset): this
+```
+
+#### Parameters
+
+* **offset**: `number`
+
+#### Returns
+
+`this`
+
+***
+
 ### select()
 
-> **select**(`columns`): `this`
+```ts
+select(columns): this
+```
 
 Return only the specified columns.
 
@@ -213,7 +283,7 @@ input to this method would be:
 
 #### Parameters
 
-• **columns**: `string` \| `string`[] \| `Record`&lt;`string`, `string`&gt; \| `Map`&lt;`string`, `string`&gt;
+* **columns**: `string` \| `string`[] \| `Record`&lt;`string`, `string`&gt; \| `Map`&lt;`string`, `string`&gt;
 
 #### Returns
 
@@ -236,13 +306,15 @@ object insertion order is easy to get wrong and `Map` is more foolproof.
 
 ### toArray()
 
-> **toArray**(`options`?): `Promise`&lt;`any`[]&gt;
+```ts
+toArray(options?): Promise<any[]>
+```
 
 Collect the results as an array of objects.
 
 #### Parameters
 
-• **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;
+* **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;
 
 #### Returns
 
@@ -252,13 +324,15 @@ Collect the results as an array of objects.
 
 ### toArrow()
 
-> **toArrow**(`options`?): `Promise`&lt;`Table`&lt;`any`&gt;&gt;
+```ts
+toArrow(options?): Promise<Table<any>>
+```
 
 Collect the results as an Arrow
 
 #### Parameters
 
-• **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;
+* **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;
 
 #### Returns
 
@@ -272,7 +346,9 @@ ArrowTable.
 
 ### where()
 
-> **where**(`predicate`): `this`
+```ts
+where(predicate): this
+```
 
 A filter statement to be applied to this query.
 
@@ -280,7 +356,7 @@ The filter should be supplied as an SQL query string.  For example:
 
 #### Parameters
 
-• **predicate**: `string`
+* **predicate**: `string`
 
 #### Returns
 
@@ -296,3 +372,21 @@ x > 5 OR y = 'test'
 Filtering performance can often be improved by creating a scalar index
 on the filter column(s).
 ```
+
+***
+
+### withRowId()
+
+```ts
+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`

docs/src/js/classes/RecordBatchIterator.md

@@ -14,11 +14,13 @@
 
 ### new RecordBatchIterator()
 
-> **new RecordBatchIterator**(`promise`?): [`RecordBatchIterator`](RecordBatchIterator.md)
+```ts
+new RecordBatchIterator(promise?): RecordBatchIterator
+```
 
 #### Parameters
 
-• **promise?**: `Promise`<`RecordBatchIterator`>
+* **promise?**: `Promise`<`RecordBatchIterator`>
 
 #### Returns
 
@@ -28,7 +30,9 @@
 
 ### next()
 
-> **next**(): `Promise`<`IteratorResult`<`RecordBatch`<`any`>, `any`>>
+```ts
+next(): Promise<IteratorResult<RecordBatch<any>, any>>
+```
 
 #### Returns
 

docs/src/js/classes/Table.md

@@ -21,7 +21,9 @@ collected.
 
 ### new Table()
 
-> **new Table**(): [`Table`](Table.md)
+```ts
+new Table(): Table
+```
 
 #### Returns
 
@@ -31,7 +33,9 @@ collected.
 
 ### name
 
-> `get` `abstract` **name**(): `string`
+```ts
+get abstract name(): string
+```
 
 Returns the name of the table
 
@@ -43,17 +47,18 @@ Returns the name of the table
 
 ### add()
 
-> `abstract` **add**(`data`, `options`?): `Promise`<`void`>
+```ts
+abstract add(data, options?): Promise<void>
+```
 
 Insert records into this Table.
 
 #### Parameters
 
-• **data**: [`Data`](../type-aliases/Data.md)
+* **data**: [`Data`](../type-aliases/Data.md)
+    Records to be inserted into the Table
 
-Records to be inserted into the Table
-
-• **options?**: `Partial`&lt;[`AddDataOptions`](../interfaces/AddDataOptions.md)&gt;
+* **options?**: `Partial`&lt;[`AddDataOptions`](../interfaces/AddDataOptions.md)&gt;
 
 #### Returns
 
@@ -63,18 +68,19 @@ Records to be inserted into the Table
 
 ### addColumns()
 
-> `abstract` **addColumns**(`newColumnTransforms`): `Promise`&lt;`void`&gt;
+```ts
+abstract addColumns(newColumnTransforms): Promise<void>
+```
 
 Add new columns with defined values.
 
 #### Parameters
 
-• **newColumnTransforms**: [`AddColumnsSql`](../interfaces/AddColumnsSql.md)[]
-
-pairs of column names and
-the SQL expression to use to calculate the value of the new column. These
-expressions will be evaluated for each row in the table, and can
-reference existing columns in the table.
+* **newColumnTransforms**: [`AddColumnsSql`](../interfaces/AddColumnsSql.md)[]
+    pairs of column names and
+    the SQL expression to use to calculate the value of the new column. These
+    expressions will be evaluated for each row in the table, and can
+    reference existing columns in the table.
 
 #### Returns
 
@@ -84,16 +90,17 @@ reference existing columns in the table.
 
 ### alterColumns()
 
-> `abstract` **alterColumns**(`columnAlterations`): `Promise`&lt;`void`&gt;
+```ts
+abstract alterColumns(columnAlterations): Promise<void>
+```
 
 Alter the name or nullability of columns.
 
 #### Parameters
 
-• **columnAlterations**: [`ColumnAlteration`](../interfaces/ColumnAlteration.md)[]
-
-One or more alterations to
-apply to columns.
+* **columnAlterations**: [`ColumnAlteration`](../interfaces/ColumnAlteration.md)[]
+    One or more alterations to
+    apply to columns.
 
 #### Returns
 
@@ -103,7 +110,9 @@ apply to columns.
 
 ### checkout()
 
-> `abstract` **checkout**(`version`): `Promise`&lt;`void`&gt;
+```ts
+abstract checkout(version): Promise<void>
+```
 
 Checks out a specific version of the table _This is an in-place operation._
 
@@ -116,9 +125,8 @@ wish to return to standard mode, call `checkoutLatest`.
 
 #### Parameters
 
-• **version**: `number`
-
-The version to checkout
+* **version**: `number`
+    The version to checkout
 
 #### Returns
 
@@ -144,7 +152,9 @@ console.log(await table.version()); // 2
 
 ### checkoutLatest()
 
-> `abstract` **checkoutLatest**(): `Promise`&lt;`void`&gt;
+```ts
+abstract checkoutLatest(): Promise<void>
+```
 
 Checkout the latest version of the table. _This is an in-place operation._
 
@@ -159,7 +169,9 @@ version of the table.
 
 ### close()
 
-> `abstract` **close**(): `void`
+```ts
+abstract close(): void
+```
 
 Close the table, releasing any underlying resources.
 
@@ -175,13 +187,15 @@ Any attempt to use the table after it is closed will result in an error.
 
 ### countRows()
 
-> `abstract` **countRows**(`filter`?): `Promise`&lt;`number`&gt;
+```ts
+abstract countRows(filter?): Promise<number>
+```
 
 Count the total number of rows in the dataset.
 
 #### Parameters
 
-• **filter?**: `string`
+* **filter?**: `string`
 
 #### Returns
 
@@ -191,7 +205,9 @@ Count the total number of rows in the dataset.
 
 ### createIndex()
 
-> `abstract` **createIndex**(`column`, `options`?): `Promise`&lt;`void`&gt;
+```ts
+abstract createIndex(column, options?): Promise<void>
+```
 
 Create an index to speed up queries.
 
@@ -202,9 +218,9 @@ vector and non-vector searches)
 
 #### Parameters
 
-• **column**: `string`
+* **column**: `string`
 
-• **options?**: `Partial`&lt;[`IndexOptions`](../interfaces/IndexOptions.md)&gt;
+* **options?**: `Partial`&lt;[`IndexOptions`](../interfaces/IndexOptions.md)&gt;
 
 #### Returns
 
@@ -245,13 +261,15 @@ await table.createIndex("my_float_col");
 
 ### delete()
 
-> `abstract` **delete**(`predicate`): `Promise`&lt;`void`&gt;
+```ts
+abstract delete(predicate): Promise<void>
+```
 
 Delete the rows that satisfy the predicate.
 
 #### Parameters
 
-• **predicate**: `string`
+* **predicate**: `string`
 
 #### Returns
 
@@ -261,7 +279,9 @@ Delete the rows that satisfy the predicate.
 
 ### display()
 
-> `abstract` **display**(): `string`
+```ts
+abstract display(): string
+```
 
 Return a brief description of the table
 
@@ -273,7 +293,9 @@ Return a brief description of the table
 
 ### dropColumns()
 
-> `abstract` **dropColumns**(`columnNames`): `Promise`&lt;`void`&gt;
+```ts
+abstract dropColumns(columnNames): Promise<void>
+```
 
 Drop one or more columns from the dataset
 
@@ -284,11 +306,10 @@ then call ``cleanup_files`` to remove the old files.
 
 #### Parameters
 
-• **columnNames**: `string`[]
-
-The names of the columns to drop. These can
-be nested column references (e.g. "a.b.c") or top-level column names
-(e.g. "a").
+* **columnNames**: `string`[]
+    The names of the columns to drop. These can
+    be nested column references (e.g. "a.b.c") or top-level column names
+    (e.g. "a").
 
 #### Returns
 
@@ -298,15 +319,16 @@ be nested column references (e.g. "a.b.c") or top-level column names
 
 ### indexStats()
 
-> `abstract` **indexStats**(`name`): `Promise`&lt;`undefined` \| [`IndexStatistics`](../interfaces/IndexStatistics.md)&gt;
+```ts
+abstract indexStats(name): Promise<undefined | IndexStatistics>
+```
 
 List all the stats of a specified index
 
 #### Parameters
 
-• **name**: `string`
-
-The name of the index.
+* **name**: `string`
+    The name of the index.
 
 #### Returns
 
@@ -318,7 +340,9 @@ The stats of the index. If the index does not exist, it will return undefined
 
 ### isOpen()
 
-> `abstract` **isOpen**(): `boolean`
+```ts
+abstract isOpen(): boolean
+```
 
 Return true if the table has not been closed
 
@@ -330,7 +354,9 @@ Return true if the table has not been closed
 
 ### listIndices()
 
-> `abstract` **listIndices**(): `Promise`&lt;[`IndexConfig`](../interfaces/IndexConfig.md)[]&gt;
+```ts
+abstract listIndices(): Promise<IndexConfig[]>
+```
 
 List all indices that have been created with [Table.createIndex](Table.md#createindex)
 
@@ -340,13 +366,29 @@ List all indices that have been created with [Table.createIndex](Table.md#create
 
 ***
 
+### listVersions()
+
+```ts
+abstract listVersions(): Promise<Version[]>
+```
+
+List all the versions of the table
+
+#### Returns
+
+`Promise`&lt;`Version`[]&gt;
+
+***
+
 ### mergeInsert()
 
-> `abstract` **mergeInsert**(`on`): `MergeInsertBuilder`
+```ts
+abstract mergeInsert(on): MergeInsertBuilder
+```
 
 #### Parameters
 
-• **on**: `string` \| `string`[]
+* **on**: `string` \| `string`[]
 
 #### Returns
 
@@ -356,7 +398,9 @@ List all indices that have been created with [Table.createIndex](Table.md#create
 
 ### optimize()
 
-> `abstract` **optimize**(`options`?): `Promise`&lt;`OptimizeStats`&gt;
+```ts
+abstract optimize(options?): Promise<OptimizeStats>
+```
 
 Optimize the on-disk data and indices for better performance.
 
@@ -388,7 +432,7 @@ Modeled after ``VACUUM`` in PostgreSQL.
 
 #### Parameters
 
-• **options?**: `Partial`&lt;`OptimizeOptions`&gt;
+* **options?**: `Partial`&lt;[`OptimizeOptions`](../interfaces/OptimizeOptions.md)&gt;
 
 #### Returns
 
@@ -398,7 +442,9 @@ Modeled after ``VACUUM`` in PostgreSQL.
 
 ### query()
 
-> `abstract` **query**(): [`Query`](Query.md)
+```ts
+abstract query(): Query
+```
 
 Create a [Query](Query.md) Builder.
 
@@ -466,7 +512,9 @@ for await (const batch of table.query()) {
 
 ### restore()
 
-> `abstract` **restore**(): `Promise`&lt;`void`&gt;
+```ts
+abstract restore(): Promise<void>
+```
 
 Restore the table to the currently checked out version
 
@@ -487,7 +535,9 @@ out state and the read_consistency_interval, if any, will apply.
 
 ### schema()
 
-> `abstract` **schema**(): `Promise`&lt;`Schema`&lt;`any`&gt;&gt;
+```ts
+abstract schema(): Promise<Schema<any>>
+```
 
 Get the schema of the table.
 
@@ -499,61 +549,41 @@ Get the schema of the table.
 
 ### search()
 
-#### search(query)
-
-> `abstract` **search**(`query`, `queryType`, `ftsColumns`): [`VectorQuery`](VectorQuery.md)
+```ts
+abstract search(
+   query,
+   queryType?,
+   ftsColumns?): VectorQuery | Query
+```
 
 Create a search query to find the nearest neighbors
-of the given query vector, or the documents
-with the highest relevance to the query string.
+of the given query
 
-##### Parameters
+#### Parameters
 
-• **query**: `string`
+* **query**: `string` \| `IntoVector`
+    the query, a vector or string
 
-the query. This will be converted to a vector using the table's provided embedding function,
-or the query string for full-text search if `queryType` is "fts".
+* **queryType?**: `string`
+    the type of the query, "vector", "fts", or "auto"
 
-• **queryType**: `string` =  `"auto"` \| `"fts"`
+* **ftsColumns?**: `string` \| `string`[]
+    the columns to search in for full text search
+    for now, only one column can be searched at a time.
+    when "auto" is used, if the query is a string and an embedding function is defined, it will be treated as a vector query
+    if the query is a string and no embedding function is defined, it will be treated as a full text search query
 
-the type of query to run. If "auto", the query type will be determined based on the query.
+#### Returns
 
-• **ftsColumns**: `string[] | str` =  undefined
-
-the columns to search in. If not provided, all indexed columns will be searched.
-
-For now, this can support to search only one column.
-
-##### Returns
-
-[`VectorQuery`](VectorQuery.md)
-
-##### Note
-
-If no embedding functions are defined in the table, this will error when collecting the results.
-
-#### search(query)
-
-> `abstract` **search**(`query`): [`VectorQuery`](VectorQuery.md)
-
-Create a search query to find the nearest neighbors
-of the given query vector
-
-##### Parameters
-
-• **query**: `IntoVector`
-
-the query vector
-
-##### Returns
-
-[`VectorQuery`](VectorQuery.md)
+[`VectorQuery`](VectorQuery.md) \| [`Query`](Query.md)
 
 ***
 
 ### toArrow()
 
-> `abstract` **toArrow**(): `Promise`&lt;`Table`&lt;`any`&gt;&gt;
+```ts
+abstract toArrow(): Promise<Table<any>>
+```
 
 Return the table as an arrow table
 
@@ -567,13 +597,15 @@ Return the table as an arrow table
 
 #### update(opts)
 
-> `abstract` **update**(`opts`): `Promise`&lt;`void`&gt;
+```ts
+abstract update(opts): Promise<void>
+```
 
 Update existing records in the Table
 
 ##### Parameters
 
-• **opts**: `object` & `Partial`&lt;[`UpdateOptions`](../interfaces/UpdateOptions.md)&gt;
+* **opts**: `object` & `Partial`&lt;[`UpdateOptions`](../interfaces/UpdateOptions.md)&gt;
 
 ##### Returns
 
@@ -587,13 +619,15 @@ table.update({where:"x = 2", values:{"vector": [10, 10]}})
 
 #### update(opts)
 
-> `abstract` **update**(`opts`): `Promise`&lt;`void`&gt;
+```ts
+abstract update(opts): Promise<void>
+```
 
 Update existing records in the Table
 
 ##### Parameters
 
-• **opts**: `object` & `Partial`&lt;[`UpdateOptions`](../interfaces/UpdateOptions.md)&gt;
+* **opts**: `object` & `Partial`&lt;[`UpdateOptions`](../interfaces/UpdateOptions.md)&gt;
 
 ##### Returns
 
@@ -607,7 +641,9 @@ table.update({where:"x = 2", valuesSql:{"x": "x + 1"}})
 
 #### update(updates, options)
 
-> `abstract` **update**(`updates`, `options`?): `Promise`&lt;`void`&gt;
+```ts
+abstract update(updates, options?): Promise<void>
+```
 
 Update existing records in the Table
 
@@ -626,20 +662,17 @@ repeatedly calilng this method.
 
 ##### Parameters
 
-• **updates**: `Record`&lt;`string`, `string`&gt; \| `Map`&lt;`string`, `string`&gt;
+* **updates**: `Record`&lt;`string`, `string`&gt; \| `Map`&lt;`string`, `string`&gt;
+    the
+    columns to update
+    Keys in the map should specify the name of the column to update.
+    Values in the map provide the new value of the column.  These can
+    be SQL literal strings (e.g. "7" or "'foo'") or they can be expressions
+    based on the row being updated (e.g. "my_col + 1")
 
-the
-columns to update
-
-Keys in the map should specify the name of the column to update.
-Values in the map provide the new value of the column.  These can
-be SQL literal strings (e.g. "7" or "'foo'") or they can be expressions
-based on the row being updated (e.g. "my_col + 1")
-
-• **options?**: `Partial`&lt;[`UpdateOptions`](../interfaces/UpdateOptions.md)&gt;
-
-additional options to control
-the update behavior
+* **options?**: `Partial`&lt;[`UpdateOptions`](../interfaces/UpdateOptions.md)&gt;
+    additional options to control
+    the update behavior
 
 ##### Returns
 
@@ -649,7 +682,9 @@ the update behavior
 
 ### vectorSearch()
 
-> `abstract` **vectorSearch**(`vector`): [`VectorQuery`](VectorQuery.md)
+```ts
+abstract vectorSearch(vector): VectorQuery
+```
 
 Search the table with a given query vector.
 
@@ -659,7 +694,7 @@ by `query`.
 
 #### Parameters
 
-• **vector**: `IntoVector`
+* **vector**: `IntoVector`
 
 #### Returns
 
@@ -673,7 +708,9 @@ by `query`.
 
 ### version()
 
-> `abstract` **version**(): `Promise`&lt;`number`&gt;
+```ts
+abstract version(): Promise<number>
+```
 
 Retrieve the version of the table
 
@@ -685,15 +722,20 @@ Retrieve the version of the table
 
 ### parseTableData()
 
-> `static` **parseTableData**(`data`, `options`?, `streaming`?): `Promise`&lt;`object`&gt;
+```ts
+static parseTableData(
+   data,
+   options?,
+   streaming?): Promise<object>
+```
 
 #### Parameters
 
-• **data**: `TableLike` \| `Record`&lt;`string`, `unknown`&gt;[]
+* **data**: `TableLike` \| `Record`&lt;`string`, `unknown`&gt;[]
 
-• **options?**: `Partial`&lt;[`CreateTableOptions`](../interfaces/CreateTableOptions.md)&gt;
+* **options?**: `Partial`&lt;[`CreateTableOptions`](../interfaces/CreateTableOptions.md)&gt;
 
-• **streaming?**: `boolean` = `false`
+* **streaming?**: `boolean` = `false`
 
 #### Returns
 
@@ -701,8 +743,12 @@ Retrieve the version of the table
 
 ##### buf
 
-> **buf**: `Buffer`
+```ts
+buf: Buffer;
+```
 
 ##### mode
 
-> **mode**: `string`
+```ts
+mode: string;
+```

docs/src/js/classes/VectorColumnOptions.md

@@ -10,11 +10,13 @@
 
 ### new VectorColumnOptions()
 
-> **new VectorColumnOptions**(`values`?): [`VectorColumnOptions`](VectorColumnOptions.md)
+```ts
+new VectorColumnOptions(values?): VectorColumnOptions
+```
 
 #### Parameters
 
-• **values?**: `Partial`<[`VectorColumnOptions`](VectorColumnOptions.md)>
+* **values?**: `Partial`<[`VectorColumnOptions`](VectorColumnOptions.md)>
 
 #### Returns
 
@@ -24,6 +26,8 @@
 
 ### type
 
-> **type**: `Float`<`Floats`>
+```ts
+type: Float<Floats>;
+```
 
 Vector column type.

docs/src/js/classes/VectorQuery.md

@@ -18,11 +18,13 @@ This builder can be reused to execute the query many times.
 
 ### new VectorQuery()
 
-> **new VectorQuery**(`inner`): [`VectorQuery`](VectorQuery.md)
+```ts
+new VectorQuery(inner): VectorQuery
+```
 
 #### Parameters
 
-• **inner**: `VectorQuery` \| `Promise`<`VectorQuery`>
+* **inner**: `VectorQuery` \| `Promise`<`VectorQuery`>
 
 #### Returns
 
@@ -36,7 +38,9 @@ This builder can be reused to execute the query many times.
 
 ### inner
 
-> `protected` **inner**: `VectorQuery` \| `Promise`<`VectorQuery`>
+```ts
+protected inner: VectorQuery | Promise<VectorQuery>;
+```
 
 #### Inherited from
 
@@ -46,7 +50,9 @@ This builder can be reused to execute the query many times.
 
 ### \[asyncIterator\]()
 
-> **\[asyncIterator\]**(): `AsyncIterator`&lt;`RecordBatch`&lt;`any`&gt;, `any`, `undefined`&gt;
+```ts
+asyncIterator: AsyncIterator<RecordBatch<any>, any, undefined>
+```
 
 #### Returns
 
@@ -58,9 +64,27 @@ This builder can be reused to execute the query many times.
 
 ***
 
+### addQueryVector()
+
+```ts
+addQueryVector(vector): VectorQuery
+```
+
+#### Parameters
+
+* **vector**: `IntoVector`
+
+#### Returns
+
+[`VectorQuery`](VectorQuery.md)
+
+***
+
 ### bypassVectorIndex()
 
-> **bypassVectorIndex**(): [`VectorQuery`](VectorQuery.md)
+```ts
+bypassVectorIndex(): VectorQuery
+```
 
 If this is called then any vector index is skipped
 
@@ -78,7 +102,9 @@ calculate your recall to select an appropriate value for nprobes.
 
 ### column()
 
-> **column**(`column`): [`VectorQuery`](VectorQuery.md)
+```ts
+column(column): VectorQuery
+```
 
 Set the vector column to query
 
@@ -87,7 +113,7 @@ the call to
 
 #### Parameters
 
-• **column**: `string`
+* **column**: `string`
 
 #### Returns
 
@@ -104,7 +130,9 @@ whose data type is a fixed-size-list of floats.
 
 ### distanceType()
 
-> **distanceType**(`distanceType`): [`VectorQuery`](VectorQuery.md)
+```ts
+distanceType(distanceType): VectorQuery
+```
 
 Set the distance metric to use
 
@@ -114,7 +142,7 @@ use.  See
 
 #### Parameters
 
-• **distanceType**: `"l2"` \| `"cosine"` \| `"dot"`
+* **distanceType**: `"l2"` \| `"cosine"` \| `"dot"`
 
 #### Returns
 
@@ -135,11 +163,13 @@ By default "l2" is used.
 
 ### doCall()
 
-> `protected` **doCall**(`fn`): `void`
+```ts
+protected doCall(fn): void
+```
 
 #### Parameters
 
-• **fn**
+* **fn**
 
 #### Returns
 
@@ -151,15 +181,41 @@ By default "l2" is used.
 
 ***
 
+### ef()
+
+```ts
+ef(ef): VectorQuery
+```
+
+Set the number of candidates to consider during the search
+
+This argument is only used when the vector column has an HNSW index.
+If there is no index then this value is ignored.
+
+Increasing this value will increase the recall of your query but will
+also increase the latency of your query. The default value is 1.5*limit.
+
+#### Parameters
+
+* **ef**: `number`
+
+#### Returns
+
+[`VectorQuery`](VectorQuery.md)
+
+***
+
 ### execute()
 
-> `protected` **execute**(`options`?): [`RecordBatchIterator`](RecordBatchIterator.md)
+```ts
+protected execute(options?): RecordBatchIterator
+```
 
 Execute the query and return the results as an
 
 #### Parameters
 
-• **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;
+* **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;
 
 #### Returns
 
@@ -185,15 +241,16 @@ single query)
 
 ### explainPlan()
 
-> **explainPlan**(`verbose`): `Promise`&lt;`string`&gt;
+```ts
+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.
+* **verbose**: `boolean` = `false`
+    If true, provides a more detailed explanation. Defaults to false.
 
 #### Returns
 
@@ -218,15 +275,38 @@ const plan = await table.query().nearestTo([0.5, 0.2]).explainPlan();
 
 ***
 
+### fastSearch()
+
+```ts
+fastSearch(): this
+```
+
+Skip searching un-indexed data. This can make search faster, but will miss
+any data that is not yet indexed.
+
+Use lancedb.Table#optimize to index all un-indexed data.
+
+#### Returns
+
+`this`
+
+#### Inherited from
+
+[`QueryBase`](QueryBase.md).[`fastSearch`](QueryBase.md#fastsearch)
+
+***
+
 ### ~~filter()~~
 
-> **filter**(`predicate`): `this`
+```ts
+filter(predicate): this
+```
 
 A filter statement to be applied to this query.
 
 #### Parameters
 
-• **predicate**: `string`
+* **predicate**: `string`
 
 #### Returns
 
@@ -246,9 +326,33 @@ Use `where` instead
 
 ***
 
+### fullTextSearch()
+
+```ts
+fullTextSearch(query, options?): this
+```
+
+#### Parameters
+
+* **query**: `string`
+
+* **options?**: `Partial`&lt;`FullTextSearchOptions`&gt;
+
+#### Returns
+
+`this`
+
+#### Inherited from
+
+[`QueryBase`](QueryBase.md).[`fullTextSearch`](QueryBase.md#fulltextsearch)
+
+***
+
 ### limit()
 
-> **limit**(`limit`): `this`
+```ts
+limit(limit): this
+```
 
 Set the maximum number of results to return.
 
@@ -257,7 +361,7 @@ called then every valid row from the table will be returned.
 
 #### Parameters
 
-• **limit**: `number`
+* **limit**: `number`
 
 #### Returns
 
@@ -271,11 +375,13 @@ called then every valid row from the table will be returned.
 
 ### nativeExecute()
 
-> `protected` **nativeExecute**(`options`?): `Promise`&lt;`RecordBatchIterator`&gt;
+```ts
+protected nativeExecute(options?): Promise<RecordBatchIterator>
+```
 
 #### Parameters
 
-• **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;
+* **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;
 
 #### Returns
 
@@ -289,7 +395,9 @@ called then every valid row from the table will be returned.
 
 ### nprobes()
 
-> **nprobes**(`nprobes`): [`VectorQuery`](VectorQuery.md)
+```ts
+nprobes(nprobes): VectorQuery
+```
 
 Set the number of partitions to search (probe)
 
@@ -314,7 +422,7 @@ you the desired recall.
 
 #### Parameters
 
-• **nprobes**: `number`
+* **nprobes**: `number`
 
 #### Returns
 
@@ -322,9 +430,31 @@ you the desired recall.
 
 ***
 
+### offset()
+
+```ts
+offset(offset): this
+```
+
+#### Parameters
+
+* **offset**: `number`
+
+#### Returns
+
+`this`
+
+#### Inherited from
+
+[`QueryBase`](QueryBase.md).[`offset`](QueryBase.md#offset)
+
+***
+
 ### postfilter()
 
-> **postfilter**(): [`VectorQuery`](VectorQuery.md)
+```ts
+postfilter(): VectorQuery
+```
 
 If this is called then filtering will happen after the vector search instead of
 before.
@@ -356,7 +486,9 @@ factor can often help restore some of the results lost by post filtering.
 
 ### refineFactor()
 
-> **refineFactor**(`refineFactor`): [`VectorQuery`](VectorQuery.md)
+```ts
+refineFactor(refineFactor): VectorQuery
+```
 
 A multiplier to control how many additional rows are taken during the refine step
 
@@ -388,7 +520,7 @@ distance between the query vector and the actual uncompressed vector.
 
 #### Parameters
 
-• **refineFactor**: `number`
+* **refineFactor**: `number`
 
 #### Returns
 
@@ -398,7 +530,9 @@ distance between the query vector and the actual uncompressed vector.
 
 ### select()
 
-> **select**(`columns`): `this`
+```ts
+select(columns): this
+```
 
 Return only the specified columns.
 
@@ -422,7 +556,7 @@ input to this method would be:
 
 #### Parameters
 
-• **columns**: `string` \| `string`[] \| `Record`&lt;`string`, `string`&gt; \| `Map`&lt;`string`, `string`&gt;
+* **columns**: `string` \| `string`[] \| `Record`&lt;`string`, `string`&gt; \| `Map`&lt;`string`, `string`&gt;
 
 #### Returns
 
@@ -449,13 +583,15 @@ object insertion order is easy to get wrong and `Map` is more foolproof.
 
 ### toArray()
 
-> **toArray**(`options`?): `Promise`&lt;`any`[]&gt;
+```ts
+toArray(options?): Promise<any[]>
+```
 
 Collect the results as an array of objects.
 
 #### Parameters
 
-• **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;
+* **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;
 
 #### Returns
 
@@ -469,13 +605,15 @@ Collect the results as an array of objects.
 
 ### toArrow()
 
-> **toArrow**(`options`?): `Promise`&lt;`Table`&lt;`any`&gt;&gt;
+```ts
+toArrow(options?): Promise<Table<any>>
+```
 
 Collect the results as an Arrow
 
 #### Parameters
 
-• **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;
+* **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;
 
 #### Returns
 
@@ -493,7 +631,9 @@ ArrowTable.
 
 ### where()
 
-> **where**(`predicate`): `this`
+```ts
+where(predicate): this
+```
 
 A filter statement to be applied to this query.
 
@@ -501,7 +641,7 @@ The filter should be supplied as an SQL query string.  For example:
 
 #### Parameters
 
-• **predicate**: `string`
+* **predicate**: `string`
 
 #### Returns
 
@@ -521,3 +661,25 @@ on the filter column(s).
 #### Inherited from
 
 [`QueryBase`](QueryBase.md).[`where`](QueryBase.md#where)
+
+***
+
+### withRowId()
+
+```ts
+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`
+
+#### Inherited from
+
+[`QueryBase`](QueryBase.md).[`withRowId`](QueryBase.md#withrowid)

docs/src/js/enumerations/WriteMode.md

@@ -12,16 +12,22 @@ Write mode for writing a table.
 
 ### Append
 
-> **Append**: `"Append"`
+```ts
+Append: "Append";
+```
 
 ***
 
 ### Create
 
-> **Create**: `"Create"`
+```ts
+Create: "Create";
+```
 
 ***
 
 ### Overwrite
 
-> **Overwrite**: `"Overwrite"`
+```ts
+Overwrite: "Overwrite";
+```

docs/src/js/functions/connect.md

@@ -8,7 +8,9 @@
 
 ## connect(uri, opts)
 
-> **connect**(`uri`, `opts`?): `Promise`<[`Connection`](../classes/Connection.md)>
+```ts
+function connect(uri, opts?): Promise<Connection>
+```
 
 Connect to a LanceDB instance at the given URI.
 
@@ -20,12 +22,11 @@ Accepted formats:
 
 ### Parameters
 
-• **uri**: `string`
+* **uri**: `string`
+    The uri of the database. If the database uri starts
+    with `db://` then it connects to a remote database.
 
-The uri of the database. If the database uri starts
-with `db://` then it connects to a remote database.
-
-• **opts?**: `Partial`&lt;[`ConnectionOptions`](../interfaces/ConnectionOptions.md) \| `RemoteConnectionOptions`&gt;
+* **opts?**: `Partial`&lt;[`ConnectionOptions`](../interfaces/ConnectionOptions.md)&gt;
 
 ### Returns
 
@@ -50,7 +51,9 @@ const conn = await connect(
 
 ## connect(opts)
 
-> **connect**(`opts`): `Promise`&lt;[`Connection`](../classes/Connection.md)&gt;
+```ts
+function connect(opts): Promise<Connection>
+```
 
 Connect to a LanceDB instance at the given URI.
 
@@ -62,7 +65,7 @@ Accepted formats:
 
 ### Parameters
 
-• **opts**: `Partial`&lt;[`ConnectionOptions`](../interfaces/ConnectionOptions.md) \| `RemoteConnectionOptions`&gt; & `object`
+* **opts**: `Partial`&lt;[`ConnectionOptions`](../interfaces/ConnectionOptions.md)&gt; & `object`
 
 ### Returns
 

docs/src/js/functions/makeArrowTable.md

@@ -6,7 +6,12 @@
 
 # Function: makeArrowTable()
 
-> **makeArrowTable**(`data`, `options`?, `metadata`?): `ArrowTable`
+```ts
+function makeArrowTable(
+   data,
+   options?,
+   metadata?): ArrowTable
+```
 
 An enhanced version of the makeTable function from Apache Arrow
 that supports nested fields and embeddings columns.
@@ -40,11 +45,11 @@ rules are as follows:
 
 ## Parameters
 
-• **data**: `Record`<`string`, `unknown`>[]
+* **data**: `Record`<`string`, `unknown`>[]
 
-• **options?**: `Partial`<[`MakeArrowTableOptions`](../classes/MakeArrowTableOptions.md)>
+* **options?**: `Partial`<[`MakeArrowTableOptions`](../classes/MakeArrowTableOptions.md)>
 
-• **metadata?**: `Map`<`string`, `string`>
+* **metadata?**: `Map`<`string`, `string`>
 
 ## Returns
 

docs/src/js/globals.md

@@ -28,17 +28,19 @@
 
 - [AddColumnsSql](interfaces/AddColumnsSql.md)
 - [AddDataOptions](interfaces/AddDataOptions.md)
+- [ClientConfig](interfaces/ClientConfig.md)
 - [ColumnAlteration](interfaces/ColumnAlteration.md)
 - [ConnectionOptions](interfaces/ConnectionOptions.md)
 - [CreateTableOptions](interfaces/CreateTableOptions.md)
 - [ExecutableQuery](interfaces/ExecutableQuery.md)
 - [IndexConfig](interfaces/IndexConfig.md)
-- [IndexMetadata](interfaces/IndexMetadata.md)
 - [IndexOptions](interfaces/IndexOptions.md)
 - [IndexStatistics](interfaces/IndexStatistics.md)
 - [IvfPqOptions](interfaces/IvfPqOptions.md)
-- [FtsOptions](interfaces/FtsOptions.md)
+- [OptimizeOptions](interfaces/OptimizeOptions.md)
+- [RetryConfig](interfaces/RetryConfig.md)
 - [TableNamesOptions](interfaces/TableNamesOptions.md)
+- [TimeoutConfig](interfaces/TimeoutConfig.md)
 - [UpdateOptions](interfaces/UpdateOptions.md)
 - [WriteOptions](interfaces/WriteOptions.md)
 

docs/src/js/interfaces/AddColumnsSql.md

@@ -12,7 +12,9 @@ A definition of a new column to add to a table.
 
 ### name
 
-> **name**: `string`
+```ts
+name: string;
+```
 
 The name of the new column.
 
@@ -20,7 +22,9 @@ The name of the new column.
 
 ### valueSql
 
-> **valueSql**: `string`
+```ts
+valueSql: string;
+```
 
 The values to populate the new column with, as a SQL expression.
 The expression can reference other columns in the table.

docs/src/js/interfaces/AddDataOptions.md

@@ -12,7 +12,9 @@ Options for adding data to a table.
 
 ### mode
 
-> **mode**: `"append"` \| `"overwrite"`
+```ts
+mode: "append" | "overwrite";
+```
 
 If "append" (the default) then the new data will be added to the table
 

docs/src/js/interfaces/ClientConfig.md

@@ -0,0 +1,31 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / ClientConfig
+
+# Interface: ClientConfig
+
+## Properties
+
+### retryConfig?
+
+```ts
+optional retryConfig: RetryConfig;
+```
+
+***
+
+### timeoutConfig?
+
+```ts
+optional timeoutConfig: TimeoutConfig;
+```
+
+***
+
+### userAgent?
+
+```ts
+optional userAgent: string;
+```

docs/src/js/interfaces/ColumnAlteration.md

@@ -13,9 +13,29 @@ must be provided.
 
 ## Properties
 
+### dataType?
+
+```ts
+optional dataType: string;
+```
+
+A new data type for the column. If not provided then the data type will not be changed.
+Changing data types is limited to casting to the same general type. For example, these
+changes are valid:
+* `int32` -> `int64` (integers)
+* `double` -> `float` (floats)
+* `string` -> `large_string` (strings)
+But these changes are not:
+* `int32` -> `double` (mix integers and floats)
+* `string` -> `int32` (mix strings and integers)
+
+***
+
 ### nullable?
 
-> `optional` **nullable**: `boolean`
+```ts
+optional nullable: boolean;
+```
 
 Set the new nullability. Note that a nullable column cannot be made non-nullable.
 
@@ -23,7 +43,9 @@ Set the new nullability. Note that a nullable column cannot be made non-nullable
 
 ### path
 
-> **path**: `string`
+```ts
+path: string;
+```
 
 The path to the column to alter. This is a dot-separated path to the column.
 If it is a top-level column then it is just the name of the column. If it is
@@ -34,7 +56,9 @@ a nested column then it is the path to the column, e.g. "a.b.c" for a column
 
 ### rename?
 
-> `optional` **rename**: `string`
+```ts
+optional rename: string;
+```
 
 The new name of the column. If not provided then the name will not be changed.
 This must be distinct from the names of all other columns in the table.

docs/src/js/interfaces/ConnectionOptions.md

@@ -8,9 +8,44 @@
 
 ## Properties
 
+### apiKey?
+
+```ts
+optional apiKey: string;
+```
+
+(For LanceDB cloud only): the API key to use with LanceDB Cloud.
+
+Can also be set via the environment variable `LANCEDB_API_KEY`.
+
+***
+
+### clientConfig?
+
+```ts
+optional clientConfig: ClientConfig;
+```
+
+(For LanceDB cloud only): configuration for the remote HTTP client.
+
+***
+
+### hostOverride?
+
+```ts
+optional hostOverride: string;
+```
+
+(For LanceDB cloud only): the host to use for LanceDB cloud. Used
+for testing purposes.
+
+***
+
 ### readConsistencyInterval?
 
-> `optional` **readConsistencyInterval**: `number`
+```ts
+optional readConsistencyInterval: number;
+```
 
 (For LanceDB OSS only): The interval, in seconds, at which to check for
 updates to the table from other processes. If None, then consistency is not
@@ -24,9 +59,22 @@ always consistent.
 
 ***
 
+### region?
+
+```ts
+optional region: string;
+```
+
+(For LanceDB cloud only): the region to use for LanceDB cloud.
+Defaults to 'us-east-1'.
+
+***
+
 ### storageOptions?
 
-> `optional` **storageOptions**: `Record`<`string`, `string`>
+```ts
+optional storageOptions: Record<string, string>;
+```
 
 (For LanceDB OSS only): configuration for object storage.
 

docs/src/js/interfaces/CreateTableOptions.md

@@ -8,15 +8,46 @@
 
 ## Properties
 
+### dataStorageVersion?
+
+```ts
+optional dataStorageVersion: string;
+```
+
+The version of the data storage format to use.
+
+The default is `stable`.
+Set to "legacy" to use the old format.
+
+***
+
 ### embeddingFunction?
 
-> `optional` **embeddingFunction**: [`EmbeddingFunctionConfig`](../namespaces/embedding/interfaces/EmbeddingFunctionConfig.md)
+```ts
+optional embeddingFunction: EmbeddingFunctionConfig;
+```
+
+***
+
+### enableV2ManifestPaths?
+
+```ts
+optional enableV2ManifestPaths: boolean;
+```
+
+Use the new V2 manifest paths. These paths provide more efficient
+opening of datasets with many versions on object stores.  WARNING:
+turning this on will make the dataset unreadable for older versions
+of LanceDB (prior to 0.10.0). To migrate an existing dataset, instead
+use the LocalTable#migrateManifestPathsV2 method.
 
 ***
 
 ### existOk
 
-> **existOk**: `boolean`
+```ts
+existOk: boolean;
+```
 
 If this is true and the table already exists and the mode is "create"
 then no error will be raised.
@@ -25,7 +56,9 @@ then no error will be raised.
 
 ### mode
 
-> **mode**: `"overwrite"` \| `"create"`
+```ts
+mode: "overwrite" | "create";
+```
 
 The mode to use when creating the table.
 
@@ -39,13 +72,17 @@ If this is set to "overwrite" then any existing table will be replaced.
 
 ### schema?
 
-> `optional` **schema**: `SchemaLike`
+```ts
+optional schema: SchemaLike;
+```
 
 ***
 
 ### storageOptions?
 
-> `optional` **storageOptions**: `Record`<`string`, `string`>
+```ts
+optional storageOptions: Record<string, string>;
+```
 
 Configuration for object storage.
 
@@ -58,8 +95,12 @@ The available options are described at https://lancedb.github.io/lancedb/guides/
 
 ### useLegacyFormat?
 
-> `optional` **useLegacyFormat**: `boolean`
+```ts
+optional useLegacyFormat: boolean;
+```
 
 If true then data files will be written with the legacy format
 
-The default is true while the new format is in beta
+The default is false.
+
+Deprecated. Use data storage version instead.

docs/src/js/interfaces/FtsOptions.md

@@ -1,25 +0,0 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / FtsOptions
-
-# Interface: FtsOptions
-
-Options to create an `FTS` index
-
-## Properties
-
-### withPosition?
-
-> `optional` **withPosition**: `boolean`
-
-Whether to store the positions of the term in the document.
-
-If this is true then the index will store the positions of the term in the document.
-This allows phrase queries to be run. But it also increases the size of the index,
-and the time to build the index.
-
-The default value is true.
-
-***

docs/src/js/interfaces/IndexConfig.md

@@ -12,7 +12,9 @@ A description of an index currently configured on a column
 
 ### columns
 
-> **columns**: `string`[]
+```ts
+columns: string[];
+```
 
 The columns in the index
 
@@ -23,7 +25,9 @@ be more columns to represent composite indices.
 
 ### indexType
 
-> **indexType**: `string`
+```ts
+indexType: string;
+```
 
 The type of the index
 
@@ -31,6 +35,8 @@ The type of the index
 
 ### name
 
-> **name**: `string`
+```ts
+name: string;
+```
 
 The name of the index

docs/src/js/interfaces/IndexMetadata.md

@@ -1,19 +0,0 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / IndexMetadata
-
-# Interface: IndexMetadata
-
-## Properties
-
-### indexType?
-
-> `optional` **indexType**: `string`
-
-***
-
-### metricType?
-
-> `optional` **metricType**: `string`

docs/src/js/interfaces/IndexOptions.md

@@ -10,7 +10,9 @@
 
 ### config?
 
-> `optional` **config**: [`Index`](../classes/Index.md)
+```ts
+optional config: Index;
+```
 
 Advanced index configuration
 
@@ -26,7 +28,9 @@ will be used to determine the most useful kind of index to create.
 
 ### replace?
 
-> `optional` **replace**: `boolean`
+```ts
+optional replace: boolean;
+```
 
 Whether to replace the existing index
 

docs/src/js/interfaces/IndexStatistics.md

@@ -8,32 +8,52 @@
 
 ## Properties
 
-### indexType?
+### distanceType?
 
-> `optional` **indexType**: `string`
+```ts
+optional distanceType: string;
+```
+
+The type of the distance function used by the index. This is only
+present for vector indices. Scalar and full text search indices do
+not have a distance function.
+
+***
+
+### indexType
+
+```ts
+indexType: string;
+```
 
 The type of the index
 
 ***
 
-### indices
-
-> **indices**: [`IndexMetadata`](IndexMetadata.md)[]
-
-The metadata for each index
-
-***
-
 ### numIndexedRows
 
-> **numIndexedRows**: `number`
+```ts
+numIndexedRows: number;
+```
 
 The number of rows indexed by the index
 
 ***
 
+### numIndices?
+
+```ts
+optional numIndices: number;
+```
+
+The number of parts this index is split into.
+
+***
+
 ### numUnindexedRows
 
-> **numUnindexedRows**: `number`
+```ts
+numUnindexedRows: number;
+```
 
 The number of rows not indexed

docs/src/js/interfaces/IvfPqOptions.md

@@ -12,7 +12,9 @@ Options to create an `IVF_PQ` index
 
 ### distanceType?
 
-> `optional` **distanceType**: `"l2"` \| `"cosine"` \| `"dot"`
+```ts
+optional distanceType: "l2" | "cosine" | "dot";
+```
 
 Distance type to use to build the index.
 
@@ -50,7 +52,9 @@ L2 norm is 1), then dot distance is equivalent to the cosine distance.
 
 ### maxIterations?
 
-> `optional` **maxIterations**: `number`
+```ts
+optional maxIterations: number;
+```
 
 Max iteration to train IVF kmeans.
 
@@ -66,7 +70,9 @@ The default value is 50.
 
 ### numPartitions?
 
-> `optional` **numPartitions**: `number`
+```ts
+optional numPartitions: number;
+```
 
 The number of IVF partitions to create.
 
@@ -82,7 +88,9 @@ part of the search (searching within a partition) will be slow.
 
 ### numSubVectors?
 
-> `optional` **numSubVectors**: `number`
+```ts
+optional numSubVectors: number;
+```
 
 Number of sub-vectors of PQ.
 
@@ -101,7 +109,9 @@ will likely result in poor performance.
 
 ### sampleRate?
 
-> `optional` **sampleRate**: `number`
+```ts
+optional sampleRate: number;
+```
 
 The number of vectors, per partition, to sample when training IVF kmeans.
 

docs/src/js/interfaces/OptimizeOptions.md

@@ -0,0 +1,39 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / OptimizeOptions
+
+# Interface: OptimizeOptions
+
+## Properties
+
+### cleanupOlderThan
+
+```ts
+cleanupOlderThan: Date;
+```
+
+If set then all versions older than the given date
+be removed.  The current version will never be removed.
+The default is 7 days
+
+#### Example
+
+```ts
+// Delete all versions older than 1 day
+const olderThan = new Date();
+olderThan.setDate(olderThan.getDate() - 1));
+tbl.cleanupOlderVersions(olderThan);
+
+// Delete all versions except the current version
+tbl.cleanupOlderVersions(new Date());
+```
+
+***
+
+### deleteUnverified
+
+```ts
+deleteUnverified: boolean;
+```

docs/src/js/interfaces/RetryConfig.md

@@ -0,0 +1,90 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / RetryConfig
+
+# Interface: RetryConfig
+
+Retry configuration for the remote HTTP client.
+
+## Properties
+
+### backoffFactor?
+
+```ts
+optional backoffFactor: number;
+```
+
+The backoff factor to apply between retries. Default is 0.25. Between each retry
+the client will wait for the amount of seconds:
+`{backoff factor} * (2 ** ({number of previous retries}))`. So for the default
+of 0.25, the first retry will wait 0.25 seconds, the second retry will wait 0.5
+seconds, the third retry will wait 1 second, etc.
+
+You can also set this via the environment variable
+`LANCE_CLIENT_RETRY_BACKOFF_FACTOR`.
+
+***
+
+### backoffJitter?
+
+```ts
+optional backoffJitter: number;
+```
+
+The jitter to apply to the backoff factor, in seconds. Default is 0.25.
+
+A random value between 0 and `backoff_jitter` will be added to the backoff
+factor in seconds. So for the default of 0.25 seconds, between 0 and 250
+milliseconds will be added to the sleep between each retry.
+
+You can also set this via the environment variable
+`LANCE_CLIENT_RETRY_BACKOFF_JITTER`.
+
+***
+
+### connectRetries?
+
+```ts
+optional connectRetries: number;
+```
+
+The maximum number of retries for connection errors. Default is 3. You
+can also set this via the environment variable `LANCE_CLIENT_CONNECT_RETRIES`.
+
+***
+
+### readRetries?
+
+```ts
+optional readRetries: number;
+```
+
+The maximum number of retries for read errors. Default is 3. You can also
+set this via the environment variable `LANCE_CLIENT_READ_RETRIES`.
+
+***
+
+### retries?
+
+```ts
+optional retries: number;
+```
+
+The maximum number of retries for a request. Default is 3. You can also
+set this via the environment variable `LANCE_CLIENT_MAX_RETRIES`.
+
+***
+
+### statuses?
+
+```ts
+optional statuses: number[];
+```
+
+The HTTP status codes for which to retry the request. Default is
+[429, 500, 502, 503].
+
+You can also set this via the environment variable
+`LANCE_CLIENT_RETRY_STATUSES`. Use a comma-separated list of integers.

docs/src/js/interfaces/TableNamesOptions.md

@@ -10,7 +10,9 @@
 
 ### limit?
 
-> `optional` **limit**: `number`
+```ts
+optional limit: number;
+```
 
 An optional limit to the number of results to return.
 
@@ -18,7 +20,9 @@ An optional limit to the number of results to return.
 
 ### startAfter?
 
-> `optional` **startAfter**: `string`
+```ts
+optional startAfter: string;
+```
 
 If present, only return names that come lexicographically after the
 supplied value.

docs/src/js/interfaces/TimeoutConfig.md

@@ -0,0 +1,46 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / TimeoutConfig
+
+# Interface: TimeoutConfig
+
+Timeout configuration for remote HTTP client.
+
+## Properties
+
+### connectTimeout?
+
+```ts
+optional connectTimeout: number;
+```
+
+The timeout for establishing a connection in seconds. Default is 120
+seconds (2 minutes). This can also be set via the environment variable
+`LANCE_CLIENT_CONNECT_TIMEOUT`, as an integer number of seconds.
+
+***
+
+### poolIdleTimeout?
+
+```ts
+optional poolIdleTimeout: number;
+```
+
+The timeout for keeping idle connections in the connection pool in seconds.
+Default is 300 seconds (5 minutes). This can also be set via the
+environment variable `LANCE_CLIENT_CONNECTION_TIMEOUT`, as an integer
+number of seconds.
+
+***
+
+### readTimeout?
+
+```ts
+optional readTimeout: number;
+```
+
+The timeout for reading data from the server in seconds. Default is 300
+seconds (5 minutes). This can also be set via the environment variable
+`LANCE_CLIENT_READ_TIMEOUT`, as an integer number of seconds.

docs/src/js/interfaces/UpdateOptions.md

@@ -10,7 +10,9 @@
 
 ### where
 
-> **where**: `string`
+```ts
+where: string;
+```
 
 A filter that limits the scope of the update.
 

docs/src/js/interfaces/WriteOptions.md

@@ -12,6 +12,8 @@ Write options when creating a Table.
 
 ### mode?
 
-> `optional` **mode**: [`WriteMode`](../enumerations/WriteMode.md)
+```ts
+optional mode: WriteMode;
+```
 
 Write mode for writing to a table.

docs/src/js/namespaces/embedding/README.md

@@ -12,16 +12,12 @@
 
 - [EmbeddingFunction](classes/EmbeddingFunction.md)
 - [EmbeddingFunctionRegistry](classes/EmbeddingFunctionRegistry.md)
-- [OpenAIEmbeddingFunction](classes/OpenAIEmbeddingFunction.md)
+- [TextEmbeddingFunction](classes/TextEmbeddingFunction.md)
 
 ### Interfaces
 
 - [EmbeddingFunctionConfig](interfaces/EmbeddingFunctionConfig.md)
 
-### Type Aliases
-
-- [OpenAIOptions](type-aliases/OpenAIOptions.md)
-
 ### Functions
 
 - [LanceSchema](functions/LanceSchema.md)

docs/src/js/namespaces/embedding/classes/EmbeddingFunction.md

@@ -10,7 +10,7 @@ An embedding function that automatically creates vector representation for a giv
 
 ## Extended by
 
-- [`OpenAIEmbeddingFunction`](OpenAIEmbeddingFunction.md)
+- [`TextEmbeddingFunction`](TextEmbeddingFunction.md)
 
 ## Type Parameters
 
@@ -22,7 +22,9 @@ An embedding function that automatically creates vector representation for a giv
 
 ### new EmbeddingFunction()
 
-> **new EmbeddingFunction**<`T`, `M`>(): [`EmbeddingFunction`](EmbeddingFunction.md)<`T`, `M`>
+```ts
+new EmbeddingFunction<T, M>(): EmbeddingFunction<T, M>
+```
 
 #### Returns
 
@@ -32,13 +34,15 @@ An embedding function that automatically creates vector representation for a giv
 
 ### computeQueryEmbeddings()
 
-> **computeQueryEmbeddings**(`data`): `Promise`&lt;`number`[] \| `Float32Array` \| `Float64Array`&gt;
+```ts
+computeQueryEmbeddings(data): Promise<number[] | Float32Array | Float64Array>
+```
 
 Compute the embeddings for a single query
 
 #### Parameters
 
-• **data**: `T`
+* **data**: `T`
 
 #### Returns
 
@@ -48,13 +52,15 @@ Compute the embeddings for a single query
 
 ### computeSourceEmbeddings()
 
-> `abstract` **computeSourceEmbeddings**(`data`): `Promise`&lt;`number`[][] \| `Float32Array`[] \| `Float64Array`[]&gt;
+```ts
+abstract computeSourceEmbeddings(data): Promise<number[][] | Float32Array[] | Float64Array[]>
+```
 
 Creates a vector representation for the given values.
 
 #### Parameters
 
-• **data**: `T`[]
+* **data**: `T`[]
 
 #### Returns
 
@@ -64,7 +70,9 @@ Creates a vector representation for the given values.
 
 ### embeddingDataType()
 
-> `abstract` **embeddingDataType**(): `Float`&lt;`Floats`&gt;
+```ts
+abstract embeddingDataType(): Float<Floats>
+```
 
 The datatype of the embeddings
 
@@ -74,9 +82,23 @@ The datatype of the embeddings
 
 ***
 
+### init()?
+
+```ts
+optional init(): Promise<void>
+```
+
+#### Returns
+
+`Promise`&lt;`void`&gt;
+
+***
+
 ### ndims()
 
-> **ndims**(): `undefined` \| `number`
+```ts
+ndims(): undefined | number
+```
 
 The number of dimensions of the embeddings
 
@@ -88,15 +110,16 @@ The number of dimensions of the embeddings
 
 ### sourceField()
 
-> **sourceField**(`optionsOrDatatype`): [`DataType`&lt;`Type`, `any`&gt;, `Map`&lt;`string`, [`EmbeddingFunction`](EmbeddingFunction.md)&lt;`any`, `FunctionOptions`&gt;&gt;]
+```ts
+sourceField(optionsOrDatatype): [DataType<Type, any>, Map<string, EmbeddingFunction<any, FunctionOptions>>]
+```
 
 sourceField is used in combination with `LanceSchema` to provide a declarative data model
 
 #### Parameters
 
-• **optionsOrDatatype**: `DataType`&lt;`Type`, `any`&gt; \| `Partial`&lt;`FieldOptions`&lt;`DataType`&lt;`Type`, `any`&gt;&gt;&gt;
-
-The options for the field or the datatype
+* **optionsOrDatatype**: `DataType`&lt;`Type`, `any`&gt; \| `Partial`&lt;`FieldOptions`&lt;`DataType`&lt;`Type`, `any`&gt;&gt;&gt;
+    The options for the field or the datatype
 
 #### Returns
 
@@ -110,7 +133,9 @@ lancedb.LanceSchema
 
 ### toJSON()
 
-> `abstract` **toJSON**(): `Partial`&lt;`M`&gt;
+```ts
+abstract toJSON(): Partial<M>
+```
 
 Convert the embedding function to a JSON object
 It is used to serialize the embedding function to the schema
@@ -145,13 +170,15 @@ class MyEmbeddingFunction extends EmbeddingFunction {
 
 ### vectorField()
 
-> **vectorField**(`optionsOrDatatype`?): [`DataType`&lt;`Type`, `any`&gt;, `Map`&lt;`string`, [`EmbeddingFunction`](EmbeddingFunction.md)&lt;`any`, `FunctionOptions`&gt;&gt;]
+```ts
+vectorField(optionsOrDatatype?): [DataType<Type, any>, Map<string, EmbeddingFunction<any, FunctionOptions>>]
+```
 
 vectorField is used in combination with `LanceSchema` to provide a declarative data model
 
 #### Parameters
 
-• **optionsOrDatatype?**: `DataType`&lt;`Type`, `any`&gt; \| `Partial`&lt;`FieldOptions`&lt;`DataType`&lt;`Type`, `any`&gt;&gt;&gt;
+* **optionsOrDatatype?**: `DataType`&lt;`Type`, `any`&gt; \| `Partial`&lt;`FieldOptions`&lt;`DataType`&lt;`Type`, `any`&gt;&gt;&gt;
 
 #### Returns
 

docs/src/js/namespaces/embedding/classes/EmbeddingFunctionRegistry.md

@@ -15,7 +15,9 @@ or TextEmbeddingFunction and registering it with the registry
 
 ### new EmbeddingFunctionRegistry()
 
-> **new EmbeddingFunctionRegistry**(): [`EmbeddingFunctionRegistry`](EmbeddingFunctionRegistry.md)
+```ts
+new EmbeddingFunctionRegistry(): EmbeddingFunctionRegistry
+```
 
 #### Returns
 
@@ -25,11 +27,13 @@ or TextEmbeddingFunction and registering it with the registry
 
 ### functionToMetadata()
 
-> **functionToMetadata**(`conf`): `Record`<`string`, `any`>
+```ts
+functionToMetadata(conf): Record<string, any>
+```
 
 #### Parameters
 
-• **conf**: [`EmbeddingFunctionConfig`](../interfaces/EmbeddingFunctionConfig.md)
+* **conf**: [`EmbeddingFunctionConfig`](../interfaces/EmbeddingFunctionConfig.md)
 
 #### Returns
 
@@ -39,7 +43,9 @@ or TextEmbeddingFunction and registering it with the registry
 
 ### get()
 
-> **get**&lt;`T`, `Name`&gt;(`name`): `Name` *extends* `"openai"` ? `EmbeddingFunctionCreate`&lt;[`OpenAIEmbeddingFunction`](OpenAIEmbeddingFunction.md)&gt; : `undefined` \| `EmbeddingFunctionCreate`&lt;`T`&gt;
+```ts
+get<T>(name): undefined | EmbeddingFunctionCreate<T>
+```
 
 Fetch an embedding function by name
 
@@ -47,27 +53,26 @@ Fetch an embedding function by name
 
 • **T** *extends* [`EmbeddingFunction`](EmbeddingFunction.md)&lt;`unknown`, `FunctionOptions`&gt;
 
-• **Name** *extends* `string` = `""`
-
 #### Parameters
 
-• **name**: `Name` *extends* `"openai"` ? `"openai"` : `string`
-
-The name of the function
+* **name**: `string`
+    The name of the function
 
 #### Returns
 
-`Name` *extends* `"openai"` ? `EmbeddingFunctionCreate`&lt;[`OpenAIEmbeddingFunction`](OpenAIEmbeddingFunction.md)&gt; : `undefined` \| `EmbeddingFunctionCreate`&lt;`T`&gt;
+`undefined` \| `EmbeddingFunctionCreate`&lt;`T`&gt;
 
 ***
 
 ### getTableMetadata()
 
-> **getTableMetadata**(`functions`): `Map`&lt;`string`, `string`&gt;
+```ts
+getTableMetadata(functions): Map<string, string>
+```
 
 #### Parameters
 
-• **functions**: [`EmbeddingFunctionConfig`](../interfaces/EmbeddingFunctionConfig.md)[]
+* **functions**: [`EmbeddingFunctionConfig`](../interfaces/EmbeddingFunctionConfig.md)[]
 
 #### Returns
 
@@ -75,9 +80,25 @@ The name of the function
 
 ***
 
+### length()
+
+```ts
+length(): number
+```
+
+Get the number of registered functions
+
+#### Returns
+
+`number`
+
+***
+
 ### register()
 
-> **register**&lt;`T`&gt;(`this`, `alias`?): (`ctor`) => `any`
+```ts
+register<T>(this, alias?): (ctor) => any
+```
 
 Register an embedding function
 
@@ -87,9 +108,9 @@ Register an embedding function
 
 #### Parameters
 
-• **this**: [`EmbeddingFunctionRegistry`](EmbeddingFunctionRegistry.md)
+* **this**: [`EmbeddingFunctionRegistry`](EmbeddingFunctionRegistry.md)
 
-• **alias?**: `string`
+* **alias?**: `string`
 
 #### Returns
 
@@ -97,7 +118,7 @@ Register an embedding function
 
 ##### Parameters
 
-• **ctor**: `T`
+* **ctor**: `T`
 
 ##### Returns
 
@@ -111,13 +132,15 @@ Error if the function is already registered
 
 ### reset()
 
-> **reset**(`this`): `void`
+```ts
+reset(this): void
+```
 
 reset the registry to the initial state
 
 #### Parameters
 
-• **this**: [`EmbeddingFunctionRegistry`](EmbeddingFunctionRegistry.md)
+* **this**: [`EmbeddingFunctionRegistry`](EmbeddingFunctionRegistry.md)
 
 #### Returns
 

docs/src/js/namespaces/embedding/classes/TextEmbeddingFunction.md

@@ -2,31 +2,33 @@
 
 ***
 
-[@lancedb/lancedb](../../../globals.md) / [embedding](../README.md) / OpenAIEmbeddingFunction
+[@lancedb/lancedb](../../../globals.md) / [embedding](../README.md) / TextEmbeddingFunction
 
-# Class: OpenAIEmbeddingFunction
+# Class: `abstract` TextEmbeddingFunction<M>
 
-An embedding function that automatically creates vector representation for a given column.
+an abstract class for implementing embedding functions that take text as input
 
 ## Extends
 
-- [`EmbeddingFunction`](EmbeddingFunction.md)<`string`, `Partial`<[`OpenAIOptions`](../type-aliases/OpenAIOptions.md)>>
+- [`EmbeddingFunction`](EmbeddingFunction.md)<`string`, `M`>
+
+## Type Parameters
+
+• **M** *extends* `FunctionOptions` = `FunctionOptions`
 
 ## Constructors
 
-### new OpenAIEmbeddingFunction()
+### new TextEmbeddingFunction()
 
-> **new OpenAIEmbeddingFunction**(`options`): [`OpenAIEmbeddingFunction`](OpenAIEmbeddingFunction.md)
-
-#### Parameters
-
-• **options**: `Partial`<[`OpenAIOptions`](../type-aliases/OpenAIOptions.md)> = `...`
+```ts
+new TextEmbeddingFunction<M>(): TextEmbeddingFunction<M>
+```
 
 #### Returns
 
-[`OpenAIEmbeddingFunction`](OpenAIEmbeddingFunction.md)
+[`TextEmbeddingFunction`](TextEmbeddingFunction.md)&lt;`M`&gt;
 
-#### Overrides
+#### Inherited from
 
 [`EmbeddingFunction`](EmbeddingFunction.md).[`constructor`](EmbeddingFunction.md#constructors)
 
@@ -34,17 +36,19 @@ An embedding function that automatically creates vector representation for a giv
 
 ### computeQueryEmbeddings()
 
-> **computeQueryEmbeddings**(`data`): `Promise`&lt;`number`[]&gt;
+```ts
+computeQueryEmbeddings(data): Promise<number[] | Float32Array | Float64Array>
+```
 
 Compute the embeddings for a single query
 
 #### Parameters
 
-• **data**: `string`
+* **data**: `string`
 
 #### Returns
 
-`Promise`&lt;`number`[]&gt;
+`Promise`&lt;`number`[] \| `Float32Array` \| `Float64Array`&gt;
 
 #### Overrides
 
@@ -54,17 +58,19 @@ Compute the embeddings for a single query
 
 ### computeSourceEmbeddings()
 
-> **computeSourceEmbeddings**(`data`): `Promise`&lt;`number`[][]&gt;
+```ts
+computeSourceEmbeddings(data): Promise<number[][] | Float32Array[] | Float64Array[]>
+```
 
 Creates a vector representation for the given values.
 
 #### Parameters
 
-• **data**: `string`[]
+* **data**: `string`[]
 
 #### Returns
 
-`Promise`&lt;`number`[][]&gt;
+`Promise`&lt;`number`[][] \| `Float32Array`[] \| `Float64Array`[]&gt;
 
 #### Overrides
 
@@ -74,7 +80,9 @@ Creates a vector representation for the given values.
 
 ### embeddingDataType()
 
-> **embeddingDataType**(): `Float`&lt;`Floats`&gt;
+```ts
+embeddingDataType(): Float<Floats>
+```
 
 The datatype of the embeddings
 
@@ -88,17 +96,53 @@ The datatype of the embeddings
 
 ***
 
+### generateEmbeddings()
+
+```ts
+abstract generateEmbeddings(texts, ...args): Promise<number[][] | Float32Array[] | Float64Array[]>
+```
+
+#### Parameters
+
+* **texts**: `string`[]
+
+* ...**args**: `any`[]
+
+#### Returns
+
+`Promise`&lt;`number`[][] \| `Float32Array`[] \| `Float64Array`[]&gt;
+
+***
+
+### init()?
+
+```ts
+optional init(): Promise<void>
+```
+
+#### Returns
+
+`Promise`&lt;`void`&gt;
+
+#### Inherited from
+
+[`EmbeddingFunction`](EmbeddingFunction.md).[`init`](EmbeddingFunction.md#init)
+
+***
+
 ### ndims()
 
-> **ndims**(): `number`
+```ts
+ndims(): undefined | number
+```
 
 The number of dimensions of the embeddings
 
 #### Returns
 
-`number`
+`undefined` \| `number`
 
-#### Overrides
+#### Inherited from
 
 [`EmbeddingFunction`](EmbeddingFunction.md).[`ndims`](EmbeddingFunction.md#ndims)
 
@@ -106,16 +150,12 @@ The number of dimensions of the embeddings
 
 ### sourceField()
 
-> **sourceField**(`optionsOrDatatype`): [`DataType`&lt;`Type`, `any`&gt;, `Map`&lt;`string`, [`EmbeddingFunction`](EmbeddingFunction.md)&lt;`any`, `FunctionOptions`&gt;&gt;]
+```ts
+sourceField(): [DataType<Type, any>, Map<string, EmbeddingFunction<any, FunctionOptions>>]
+```
 
 sourceField is used in combination with `LanceSchema` to provide a declarative data model
 
-#### Parameters
-
-• **optionsOrDatatype**: `DataType`&lt;`Type`, `any`&gt; \| `Partial`&lt;`FieldOptions`&lt;`DataType`&lt;`Type`, `any`&gt;&gt;&gt;
-
-The options for the field or the datatype
-
 #### Returns
 
 [`DataType`&lt;`Type`, `any`&gt;, `Map`&lt;`string`, [`EmbeddingFunction`](EmbeddingFunction.md)&lt;`any`, `FunctionOptions`&gt;&gt;]
@@ -124,7 +164,7 @@ The options for the field or the datatype
 
 lancedb.LanceSchema
 
-#### Inherited from
+#### Overrides
 
 [`EmbeddingFunction`](EmbeddingFunction.md).[`sourceField`](EmbeddingFunction.md#sourcefield)
 
@@ -132,7 +172,9 @@ lancedb.LanceSchema
 
 ### toJSON()
 
-> **toJSON**(): `object`
+```ts
+abstract toJSON(): Partial<M>
+```
 
 Convert the embedding function to a JSON object
 It is used to serialize the embedding function to the schema
@@ -144,11 +186,7 @@ If it does not, the embedding function will not be able to be recreated, or coul
 
 #### Returns
 
-`object`
-
-##### model
-
-> **model**: `string` & `object` \| `"text-embedding-ada-002"` \| `"text-embedding-3-small"` \| `"text-embedding-3-large"`
+`Partial`&lt;`M`&gt;
 
 #### Example
 
@@ -167,7 +205,7 @@ class MyEmbeddingFunction extends EmbeddingFunction {
 }
 ```
 
-#### Overrides
+#### Inherited from
 
 [`EmbeddingFunction`](EmbeddingFunction.md).[`toJSON`](EmbeddingFunction.md#tojson)
 
@@ -175,13 +213,15 @@ class MyEmbeddingFunction extends EmbeddingFunction {
 
 ### vectorField()
 
-> **vectorField**(`optionsOrDatatype`?): [`DataType`&lt;`Type`, `any`&gt;, `Map`&lt;`string`, [`EmbeddingFunction`](EmbeddingFunction.md)&lt;`any`, `FunctionOptions`&gt;&gt;]
+```ts
+vectorField(optionsOrDatatype?): [DataType<Type, any>, Map<string, EmbeddingFunction<any, FunctionOptions>>]
+```
 
 vectorField is used in combination with `LanceSchema` to provide a declarative data model
 
 #### Parameters
 
-• **optionsOrDatatype?**: `DataType`&lt;`Type`, `any`&gt; \| `Partial`&lt;`FieldOptions`&lt;`DataType`&lt;`Type`, `any`&gt;&gt;&gt;
+* **optionsOrDatatype?**: `DataType`&lt;`Type`, `any`&gt; \| `Partial`&lt;`FieldOptions`&lt;`DataType`&lt;`Type`, `any`&gt;&gt;&gt;
 
 #### Returns
 

docs/src/js/namespaces/embedding/functions/LanceSchema.md

@@ -6,13 +6,15 @@
 
 # Function: LanceSchema()
 
-> **LanceSchema**(`fields`): `Schema`
+```ts
+function LanceSchema(fields): Schema
+```
 
 Create a schema with embedding functions.
 
 ## Parameters
 
-• **fields**: `Record`<`string`, `object` \| [`object`, `Map`<`string`, [`EmbeddingFunction`](../classes/EmbeddingFunction.md)<`any`, `FunctionOptions`>>]>
+* **fields**: `Record`<`string`, `object` \| [`object`, `Map`<`string`, [`EmbeddingFunction`](../classes/EmbeddingFunction.md)<`any`, `FunctionOptions`>>]>
 
 ## Returns
 

docs/src/js/namespaces/embedding/functions/getRegistry.md

@@ -6,7 +6,9 @@
 
 # Function: getRegistry()
 
-> **getRegistry**(): [`EmbeddingFunctionRegistry`](../classes/EmbeddingFunctionRegistry.md)
+```ts
+function getRegistry(): EmbeddingFunctionRegistry
+```
 
 Utility function to get the global instance of the registry
 

docs/src/js/namespaces/embedding/functions/register.md

@@ -6,11 +6,13 @@
 
 # Function: register()
 
-> **register**(`name`?): (`ctor`) => `any`
+```ts
+function register(name?): (ctor) => any
+```
 
 ## Parameters
 
-• **name?**: `string`
+* **name?**: `string`
 
 ## Returns
 
@@ -18,7 +20,7 @@
 
 ### Parameters
 
-• **ctor**: `EmbeddingFunctionConstructor`<[`EmbeddingFunction`](../classes/EmbeddingFunction.md)<`any`, `FunctionOptions`>>
+* **ctor**: `EmbeddingFunctionConstructor`<[`EmbeddingFunction`](../classes/EmbeddingFunction.md)<`any`, `FunctionOptions`>>
 
 ### Returns
 

docs/src/js/namespaces/embedding/interfaces/EmbeddingFunctionConfig.md

@@ -10,16 +10,22 @@
 
 ### function
 
-> **function**: [`EmbeddingFunction`](../classes/EmbeddingFunction.md)<`any`, `FunctionOptions`>
+```ts
+function: EmbeddingFunction<any, FunctionOptions>;
+```
 
 ***
 
 ### sourceColumn
 
-> **sourceColumn**: `string`
+```ts
+sourceColumn: string;
+```
 
 ***
 
 ### vectorColumn?
 
-> `optional` **vectorColumn**: `string`
+```ts
+optional vectorColumn: string;
+```

docs/src/js/namespaces/embedding/type-aliases/OpenAIOptions.md

@@ -1,19 +0,0 @@
-[**@lancedb/lancedb**](../../../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../../../globals.md) / [embedding](../README.md) / OpenAIOptions
-
-# Type Alias: OpenAIOptions
-
-> **OpenAIOptions**: `object`
-
-## Type declaration
-
-### apiKey
-
-> **apiKey**: `string`
-
-### model
-
-> **model**: `EmbeddingCreateParams`\[`"model"`\]

docs/src/js/type-aliases/Data.md

@@ -6,6 +6,8 @@
 
 # Type Alias: Data
 
-> **Data**: `Record`<`string`, `unknown`>[] \| `TableLike`
+```ts
+type Data: Record<string, unknown>[] | TableLike;
+```
 
 Data type accepted by NodeJS SDK

java/core/pom.xml

@@ -8,7 +8,7 @@
     <parent>
         <groupId>com.lancedb</groupId>
         <artifactId>lancedb-parent</artifactId>
-        <version>0.14.0-beta.2</version>
+        <version>0.14.1-beta.2</version>
         <relativePath>../pom.xml</relativePath>
     </parent>
 

java/pom.xml

@@ -6,7 +6,7 @@
 
     <groupId>com.lancedb</groupId>
     <artifactId>lancedb-parent</artifactId>
-    <version>0.14.0-beta.2</version>
+    <version>0.14.1-beta.2</version>
     <packaging>pom</packaging>
 
     <name>LanceDB Parent</name>

node/package-lock.json

@@ -1,12 +1,12 @@
 {
   "name": "vectordb",
-  "version": "0.14.0-beta.2",
+  "version": "0.14.1-beta.2",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
     "": {
       "name": "vectordb",
-      "version": "0.14.0-beta.2",
+      "version": "0.14.1-beta.2",
       "cpu": [
         "x64",
         "arm64"
@@ -52,14 +52,14 @@
         "uuid": "^9.0.0"
       },
       "optionalDependencies": {
-        "@lancedb/vectordb-darwin-arm64": "0.14.0-beta.2",
-        "@lancedb/vectordb-darwin-x64": "0.14.0-beta.2",
-        "@lancedb/vectordb-linux-arm64-gnu": "0.14.0-beta.2",
-        "@lancedb/vectordb-linux-arm64-musl": "0.14.0-beta.2",
-        "@lancedb/vectordb-linux-x64-gnu": "0.14.0-beta.2",
-        "@lancedb/vectordb-linux-x64-musl": "0.14.0-beta.2",
-        "@lancedb/vectordb-win32-arm64-msvc": "0.14.0-beta.2",
-        "@lancedb/vectordb-win32-x64-msvc": "0.14.0-beta.2"
+        "@lancedb/vectordb-darwin-arm64": "0.14.1-beta.2",
+        "@lancedb/vectordb-darwin-x64": "0.14.1-beta.2",
+        "@lancedb/vectordb-linux-arm64-gnu": "0.14.1-beta.2",
+        "@lancedb/vectordb-linux-arm64-musl": "0.14.1-beta.2",
+        "@lancedb/vectordb-linux-x64-gnu": "0.14.1-beta.2",
+        "@lancedb/vectordb-linux-x64-musl": "0.14.1-beta.2",
+        "@lancedb/vectordb-win32-arm64-msvc": "0.14.1-beta.2",
+        "@lancedb/vectordb-win32-x64-msvc": "0.14.1-beta.2"
       },
       "peerDependencies": {
         "@apache-arrow/ts": "^14.0.2",

node/package.json

@@ -1,7 +1,8 @@
 {
   "name": "vectordb",
-  "version": "0.14.0-beta.2",
+  "version": "0.14.1-beta.2",
   "description": " Serverless, low-latency vector database for AI applications",
+  "private": false,
   "main": "dist/index.js",
   "types": "dist/index.d.ts",
   "scripts": {
@@ -91,13 +92,13 @@
     }
   },
   "optionalDependencies": {
-    "@lancedb/vectordb-darwin-x64": "0.14.0-beta.2",
-    "@lancedb/vectordb-darwin-arm64": "0.14.0-beta.2",
-    "@lancedb/vectordb-linux-x64-gnu": "0.14.0-beta.2",
-    "@lancedb/vectordb-linux-arm64-gnu": "0.14.0-beta.2",
-    "@lancedb/vectordb-linux-x64-musl": "0.14.0-beta.2",
-    "@lancedb/vectordb-linux-arm64-musl": "0.14.0-beta.2",
-    "@lancedb/vectordb-win32-x64-msvc": "0.14.0-beta.2",
-    "@lancedb/vectordb-win32-arm64-msvc": "0.14.0-beta.2"
+    "@lancedb/vectordb-darwin-x64": "0.14.1-beta.2",
+    "@lancedb/vectordb-darwin-arm64": "0.14.1-beta.2",
+    "@lancedb/vectordb-linux-x64-gnu": "0.14.1-beta.2",
+    "@lancedb/vectordb-linux-arm64-gnu": "0.14.1-beta.2",
+    "@lancedb/vectordb-linux-x64-musl": "0.14.1-beta.2",
+    "@lancedb/vectordb-linux-arm64-musl": "0.14.1-beta.2",
+    "@lancedb/vectordb-win32-x64-msvc": "0.14.1-beta.2",
+    "@lancedb/vectordb-win32-arm64-msvc": "0.14.1-beta.2"
   }
 }

nodejs/Cargo.toml

@@ -1,7 +1,7 @@
 [package]
 name = "lancedb-nodejs"
 edition.workspace = true
-version = "0.14.0-beta.2"
+version = "0.14.1-beta.2"
 license.workspace = true
 description.workspace = true
 repository.workspace = true

nodejs/__test__/arrow.test.ts

@@ -13,11 +13,10 @@ import { Schema } from "apache-arrow";
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-import * as arrow13 from "apache-arrow-13";
-import * as arrow14 from "apache-arrow-14";
 import * as arrow15 from "apache-arrow-15";
 import * as arrow16 from "apache-arrow-16";
 import * as arrow17 from "apache-arrow-17";
+import * as arrow18 from "apache-arrow-18";
 
 import {
   convertToTable,
@@ -45,22 +44,16 @@ function sampleRecords(): Array<Record<string, any>> {
     },
   ];
 }
-describe.each([arrow13, arrow14, arrow15, arrow16, arrow17])(
+describe.each([arrow15, arrow16, arrow17, arrow18])(
   "Arrow",
   (
-    arrow:
-      | typeof arrow13
-      | typeof arrow14
-      | typeof arrow15
-      | typeof arrow16
-      | typeof arrow17,
+    arrow: typeof arrow15 | typeof arrow16 | typeof arrow17 | typeof arrow18,
   ) => {
     type ApacheArrow =
-      | typeof arrow13
-      | typeof arrow14
       | typeof arrow15
       | typeof arrow16
-      | typeof arrow17;
+      | typeof arrow17
+      | typeof arrow18;
     const {
       Schema,
       Field,
@@ -498,40 +491,40 @@ describe.each([arrow13, arrow14, arrow15, arrow16, arrow17])(
 
     describe("when using two versions of arrow", function () {
       it("can still import data", async function () {
-        const schema = new arrow13.Schema([
-          new arrow13.Field("id", new arrow13.Int32()),
-          new arrow13.Field(
+        const schema = new arrow15.Schema([
+          new arrow15.Field("id", new arrow15.Int32()),
+          new arrow15.Field(
             "vector",
-            new arrow13.FixedSizeList(
+            new arrow15.FixedSizeList(
               1024,
-              new arrow13.Field("item", new arrow13.Float32(), true),
+              new arrow15.Field("item", new arrow15.Float32(), true),
             ),
           ),
-          new arrow13.Field(
+          new arrow15.Field(
             "struct",
-            new arrow13.Struct([
-              new arrow13.Field(
+            new arrow15.Struct([
+              new arrow15.Field(
                 "nested",
-                new arrow13.Dictionary(
-                  new arrow13.Utf8(),
-                  new arrow13.Int32(),
+                new arrow15.Dictionary(
+                  new arrow15.Utf8(),
+                  new arrow15.Int32(),
                   1,
                   true,
                 ),
               ),
-              new arrow13.Field(
+              new arrow15.Field(
                 "ts_with_tz",
-                new arrow13.TimestampNanosecond("some_tz"),
+                new arrow15.TimestampNanosecond("some_tz"),
               ),
-              new arrow13.Field(
+              new arrow15.Field(
                 "ts_no_tz",
-                new arrow13.TimestampNanosecond(null),
+                new arrow15.TimestampNanosecond(null),
               ),
             ]),
           ),
           // biome-ignore lint/suspicious/noExplicitAny: skip
         ]) as any;
-        schema.metadataVersion = arrow13.MetadataVersion.V5;
+        schema.metadataVersion = arrow15.MetadataVersion.V5;
         const table = makeArrowTable([], { schema });
 
         const buf = await fromTableToBuffer(table);
@@ -543,13 +536,13 @@ describe.each([arrow13, arrow14, arrow15, arrow16, arrow17])(
         // Deep equality gets hung up on some very minor unimportant differences
         // between arrow version 13 and 15 which isn't really what we're testing for
         // and so we do our own comparison that just checks name/type/nullability
-        function compareFields(lhs: arrow13.Field, rhs: arrow13.Field) {
+        function compareFields(lhs: arrow15.Field, rhs: arrow15.Field) {
           expect(lhs.name).toEqual(rhs.name);
           expect(lhs.nullable).toEqual(rhs.nullable);
           expect(lhs.typeId).toEqual(rhs.typeId);
           if ("children" in lhs.type && lhs.type.children !== null) {
-            const lhsChildren = lhs.type.children as arrow13.Field[];
-            lhsChildren.forEach((child: arrow13.Field, idx) => {
+            const lhsChildren = lhs.type.children as arrow15.Field[];
+            lhsChildren.forEach((child: arrow15.Field, idx) => {
               compareFields(child, rhs.type.children[idx]);
             });
           }

nodejs/__test__/registry.test.ts

@@ -12,11 +12,10 @@ import * as apiArrow from "apache-arrow";
 // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 // See the License for the specific language governing permissions and
 // limitations under the License.
-import * as arrow13 from "apache-arrow-13";
-import * as arrow14 from "apache-arrow-14";
 import * as arrow15 from "apache-arrow-15";
 import * as arrow16 from "apache-arrow-16";
 import * as arrow17 from "apache-arrow-17";
+import * as arrow18 from "apache-arrow-18";
 
 import * as tmp from "tmp";
 
@@ -24,154 +23,144 @@ import { connect } from "../lancedb";
 import { EmbeddingFunction, LanceSchema } from "../lancedb/embedding";
 import { getRegistry, register } from "../lancedb/embedding/registry";
 
-describe.each([arrow13, arrow14, arrow15, arrow16, arrow17])(
-  "LanceSchema",
-  (arrow) => {
-    test("should preserve input order", async () => {
-      const schema = LanceSchema({
-        id: new arrow.Int32(),
-        text: new arrow.Utf8(),
-        vector: new arrow.Float32(),
-      });
-      expect(schema.fields.map((x) => x.name)).toEqual([
-        "id",
-        "text",
-        "vector",
-      ]);
+describe.each([arrow15, arrow16, arrow17, arrow18])("LanceSchema", (arrow) => {
+  test("should preserve input order", async () => {
+    const schema = LanceSchema({
+      id: new arrow.Int32(),
+      text: new arrow.Utf8(),
+      vector: new arrow.Float32(),
     });
-  },
-);
+    expect(schema.fields.map((x) => x.name)).toEqual(["id", "text", "vector"]);
+  });
+});
 
-describe.each([arrow13, arrow14, arrow15, arrow16, arrow17])(
-  "Registry",
-  (arrow) => {
-    let tmpDir: tmp.DirResult;
-    beforeEach(() => {
-      tmpDir = tmp.dirSync({ unsafeCleanup: true });
-    });
+describe.each([arrow15, arrow16, arrow17, arrow18])("Registry", (arrow) => {
+  let tmpDir: tmp.DirResult;
+  beforeEach(() => {
+    tmpDir = tmp.dirSync({ unsafeCleanup: true });
+  });
 
-    afterEach(() => {
-      tmpDir.removeCallback();
-      getRegistry().reset();
-    });
+  afterEach(() => {
+    tmpDir.removeCallback();
+    getRegistry().reset();
+  });
 
-    it("should register a new item to the registry", async () => {
-      @register("mock-embedding")
-      class MockEmbeddingFunction extends EmbeddingFunction<string> {
-        toJSON(): object {
-          return {
-            someText: "hello",
-          };
-        }
-        constructor() {
-          super();
-        }
-        ndims() {
-          return 3;
-        }
-        embeddingDataType() {
-          return new arrow.Float32() as apiArrow.Float;
-        }
-        async computeSourceEmbeddings(data: string[]) {
-          return data.map(() => [1, 2, 3]);
-        }
+  it("should register a new item to the registry", async () => {
+    @register("mock-embedding")
+    class MockEmbeddingFunction extends EmbeddingFunction<string> {
+      toJSON(): object {
+        return {
+          someText: "hello",
+        };
       }
-
-      const func = getRegistry()
-        .get<MockEmbeddingFunction>("mock-embedding")!
-        .create();
-
-      const schema = LanceSchema({
-        id: new arrow.Int32(),
-        text: func.sourceField(new arrow.Utf8() as apiArrow.DataType),
-        vector: func.vectorField(),
-      });
-
-      const db = await connect(tmpDir.name);
-      const table = await db.createTable(
-        "test",
-        [
-          { id: 1, text: "hello" },
-          { id: 2, text: "world" },
-        ],
-        { schema },
-      );
-      const expected = [
-        [1, 2, 3],
-        [1, 2, 3],
-      ];
-      const actual = await table.query().toArrow();
-      const vectors = actual.getChild("vector")!.toArray();
-      expect(JSON.parse(JSON.stringify(vectors))).toEqual(
-        JSON.parse(JSON.stringify(expected)),
-      );
-    });
-    test("should error if registering with the same name", async () => {
-      class MockEmbeddingFunction extends EmbeddingFunction<string> {
-        toJSON(): object {
-          return {
-            someText: "hello",
-          };
-        }
-        constructor() {
-          super();
-        }
-        ndims() {
-          return 3;
-        }
-        embeddingDataType() {
-          return new arrow.Float32() as apiArrow.Float;
-        }
-        async computeSourceEmbeddings(data: string[]) {
-          return data.map(() => [1, 2, 3]);
-        }
+      constructor() {
+        super();
       }
-      register("mock-embedding")(MockEmbeddingFunction);
-      expect(() => register("mock-embedding")(MockEmbeddingFunction)).toThrow(
-        'Embedding function with alias "mock-embedding" already exists',
-      );
-    });
-    test("schema should contain correct metadata", async () => {
-      class MockEmbeddingFunction extends EmbeddingFunction<string> {
-        toJSON(): object {
-          return {
-            someText: "hello",
-          };
-        }
-        constructor() {
-          super();
-        }
-        ndims() {
-          return 3;
-        }
-        embeddingDataType() {
-          return new arrow.Float32() as apiArrow.Float;
-        }
-        async computeSourceEmbeddings(data: string[]) {
-          return data.map(() => [1, 2, 3]);
-        }
+      ndims() {
+        return 3;
       }
-      const func = new MockEmbeddingFunction();
+      embeddingDataType() {
+        return new arrow.Float32() as apiArrow.Float;
+      }
+      async computeSourceEmbeddings(data: string[]) {
+        return data.map(() => [1, 2, 3]);
+      }
+    }
 
-      const schema = LanceSchema({
-        id: new arrow.Int32(),
-        text: func.sourceField(new arrow.Utf8() as apiArrow.DataType),
-        vector: func.vectorField(),
-      });
-      const expectedMetadata = new Map<string, string>([
-        [
-          "embedding_functions",
-          JSON.stringify([
-            {
-              sourceColumn: "text",
-              vectorColumn: "vector",
-              name: "MockEmbeddingFunction",
-              model: { someText: "hello" },
-            },
-          ]),
-        ],
-      ]);
-      expect(schema.metadata).toEqual(expectedMetadata);
+    const func = getRegistry()
+      .get<MockEmbeddingFunction>("mock-embedding")!
+      .create();
+
+    const schema = LanceSchema({
+      id: new arrow.Int32(),
+      text: func.sourceField(new arrow.Utf8() as apiArrow.DataType),
+      vector: func.vectorField(),
     });
-  },
-);
+
+    const db = await connect(tmpDir.name);
+    const table = await db.createTable(
+      "test",
+      [
+        { id: 1, text: "hello" },
+        { id: 2, text: "world" },
+      ],
+      { schema },
+    );
+    const expected = [
+      [1, 2, 3],
+      [1, 2, 3],
+    ];
+    const actual = await table.query().toArrow();
+    const vectors = actual.getChild("vector")!.toArray();
+    expect(JSON.parse(JSON.stringify(vectors))).toEqual(
+      JSON.parse(JSON.stringify(expected)),
+    );
+  });
+  test("should error if registering with the same name", async () => {
+    class MockEmbeddingFunction extends EmbeddingFunction<string> {
+      toJSON(): object {
+        return {
+          someText: "hello",
+        };
+      }
+      constructor() {
+        super();
+      }
+      ndims() {
+        return 3;
+      }
+      embeddingDataType() {
+        return new arrow.Float32() as apiArrow.Float;
+      }
+      async computeSourceEmbeddings(data: string[]) {
+        return data.map(() => [1, 2, 3]);
+      }
+    }
+    register("mock-embedding")(MockEmbeddingFunction);
+    expect(() => register("mock-embedding")(MockEmbeddingFunction)).toThrow(
+      'Embedding function with alias "mock-embedding" already exists',
+    );
+  });
+  test("schema should contain correct metadata", async () => {
+    class MockEmbeddingFunction extends EmbeddingFunction<string> {
+      toJSON(): object {
+        return {
+          someText: "hello",
+        };
+      }
+      constructor() {
+        super();
+      }
+      ndims() {
+        return 3;
+      }
+      embeddingDataType() {
+        return new arrow.Float32() as apiArrow.Float;
+      }
+      async computeSourceEmbeddings(data: string[]) {
+        return data.map(() => [1, 2, 3]);
+      }
+    }
+    const func = new MockEmbeddingFunction();
+
+    const schema = LanceSchema({
+      id: new arrow.Int32(),
+      text: func.sourceField(new arrow.Utf8() as apiArrow.DataType),
+      vector: func.vectorField(),
+    });
+    const expectedMetadata = new Map<string, string>([
+      [
+        "embedding_functions",
+        JSON.stringify([
+          {
+            sourceColumn: "text",
+            vectorColumn: "vector",
+            name: "MockEmbeddingFunction",
+            model: { someText: "hello" },
+          },
+        ]),
+      ],
+    ]);
+    expect(schema.metadata).toEqual(expectedMetadata);
+  });
+});

nodejs/__test__/table.test.ts

@@ -16,11 +16,10 @@ import * as fs from "fs";
 import * as path from "path";
 import * as tmp from "tmp";
 
-import * as arrow13 from "apache-arrow-13";
-import * as arrow14 from "apache-arrow-14";
 import * as arrow15 from "apache-arrow-15";
 import * as arrow16 from "apache-arrow-16";
 import * as arrow17 from "apache-arrow-17";
+import * as arrow18 from "apache-arrow-18";
 
 import { Table, connect } from "../lancedb";
 import {
@@ -44,7 +43,7 @@ import {
 } from "../lancedb/embedding";
 import { Index } from "../lancedb/indices";
 
-describe.each([arrow13, arrow14, arrow15, arrow16, arrow17])(
+describe.each([arrow15, arrow16, arrow17, arrow18])(
   "Given a table",
   // biome-ignore lint/suspicious/noExplicitAny: <explanation>
   (arrow: any) => {
@@ -52,11 +51,10 @@ describe.each([arrow13, arrow14, arrow15, arrow16, arrow17])(
     let table: Table;
 
     const schema:
-      | import("apache-arrow-13").Schema
-      | import("apache-arrow-14").Schema
       | import("apache-arrow-15").Schema
       | import("apache-arrow-16").Schema
-      | import("apache-arrow-17").Schema = new arrow.Schema([
+      | import("apache-arrow-17").Schema
+      | import("apache-arrow-18").Schema = new arrow.Schema([
       new arrow.Field("id", new arrow.Float64(), true),
     ]);
 
@@ -569,6 +567,15 @@ describe("When creating an index", () => {
     // TODO: Verify parameters when we can load index config as part of list indices
   });
 
+  it("should be able to create 4bit IVF_PQ", async () => {
+    await tbl.createIndex("vec", {
+      config: Index.ivfPq({
+        numPartitions: 10,
+        numBits: 4,
+      }),
+    });
+  });
+
   it("should allow me to replace (or not) an existing index", async () => {
     await tbl.createIndex("id");
     // Default is replace=true
@@ -939,7 +946,7 @@ describe("when optimizing a dataset", () => {
   });
 });
 
-describe.each([arrow13, arrow14, arrow15, arrow16, arrow17])(
+describe.each([arrow15, arrow16, arrow17, arrow18])(
   "when optimizing a dataset",
   // biome-ignore lint/suspicious/noExplicitAny: <explanation>
   (arrow: any) => {

nodejs/lancedb/indices.ts

@@ -47,6 +47,16 @@ export interface IvfPqOptions {
    */
   numSubVectors?: number;
 
+  /**
+   * Number of bits per sub-vector.
+   *
+   * This value controls how much each subvector is compressed.  The more bits the more
+   * accurate the index will be but the slower search.  The default is 8 bits.
+   *
+   * The number of bits must be 4 or 8.
+   */
+  numBits?: number;
+
   /**
    * Distance type to use to build the index.
    *

nodejs/npm/darwin-arm64/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-darwin-arm64",
-	"version": "0.14.0-beta.2",
+	"version": "0.14.1-beta.2",
 	"os": ["darwin"],
 	"cpu": ["arm64"],
 	"main": "lancedb.darwin-arm64.node",

nodejs/npm/darwin-x64/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-darwin-x64",
-	"version": "0.14.0-beta.2",
+	"version": "0.14.1-beta.2",
 	"os": ["darwin"],
 	"cpu": ["x64"],
 	"main": "lancedb.darwin-x64.node",

nodejs/npm/linux-arm64-gnu/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-arm64-gnu",
-	"version": "0.14.0-beta.2",
+	"version": "0.14.1-beta.2",
 	"os": ["linux"],
 	"cpu": ["arm64"],
 	"main": "lancedb.linux-arm64-gnu.node",

nodejs/npm/linux-arm64-musl/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-arm64-musl",
-	"version": "0.14.0-beta.2",
+	"version": "0.14.1-beta.2",
 	"os": ["linux"],
 	"cpu": ["arm64"],
 	"main": "lancedb.linux-arm64-musl.node",

nodejs/npm/linux-x64-gnu/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-x64-gnu",
-	"version": "0.14.0-beta.2",
+	"version": "0.14.1-beta.2",
 	"os": ["linux"],
 	"cpu": ["x64"],
 	"main": "lancedb.linux-x64-gnu.node",

nodejs/npm/linux-x64-musl/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-x64-musl",
-	"version": "0.14.0-beta.2",
+	"version": "0.14.1-beta.2",
 	"os": ["linux"],
 	"cpu": ["x64"],
 	"main": "lancedb.linux-x64-musl.node",

nodejs/npm/win32-arm64-msvc/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@lancedb/lancedb-win32-arm64-msvc",
-  "version": "0.14.0-beta.2",
+  "version": "0.14.1-beta.2",
   "os": [
     "win32"
   ],

nodejs/npm/win32-x64-msvc/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-win32-x64-msvc",
-	"version": "0.14.0-beta.2",
+	"version": "0.14.1-beta.2",
 	"os": ["win32"],
 	"cpu": ["x64"],
 	"main": "lancedb.win32-x64-msvc.node",

nodejs/package-lock.json

@@ -1,12 +1,12 @@
 {
   "name": "@lancedb/lancedb",
-  "version": "0.13.0",
+  "version": "0.14.0",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
     "": {
       "name": "@lancedb/lancedb",
-      "version": "0.13.0",
+      "version": "0.14.0",
       "cpu": [
         "x64",
         "arm64"
@@ -31,11 +31,10 @@
         "@types/jest": "^29.1.2",
         "@types/node": "^22.7.4",
         "@types/tmp": "^0.2.6",
-        "apache-arrow-13": "npm:apache-arrow@13.0.0",
-        "apache-arrow-14": "npm:apache-arrow@14.0.0",
         "apache-arrow-15": "npm:apache-arrow@15.0.0",
         "apache-arrow-16": "npm:apache-arrow@16.0.0",
         "apache-arrow-17": "npm:apache-arrow@17.0.0",
+        "apache-arrow-18": "npm:apache-arrow@18.0.0",
         "eslint": "^8.57.0",
         "jest": "^29.7.0",
         "shx": "^0.3.4",
@@ -54,7 +53,7 @@
         "openai": "^4.29.2"
       },
       "peerDependencies": {
-        "apache-arrow": ">=13.0.0 <=17.0.0"
+        "apache-arrow": ">=15.0.0 <=18.1.0"
       }
     },
     "node_modules/@75lb/deep-merge": {
@@ -5146,12 +5145,6 @@
       "integrity": "sha512-ve2KP6f/JnbPBFyobGHuerC9g1FYGn/F8n1LWTwNxCEzd6IfqTwUQcNXgEtmmQ6DlRrC1hrSrBnCZPokRrDHjw==",
       "devOptional": true
     },
-    "node_modules/@types/pad-left": {
-      "version": "2.1.1",
-      "resolved": "https://registry.npmjs.org/@types/pad-left/-/pad-left-2.1.1.tgz",
-      "integrity": "sha512-Xd22WCRBydkGSApl5Bw0PhAOHKSVjNL3E3AwzKaps96IMraPqy5BvZIsBVK6JLwdybUzjHnuWVwpDd0JjTfHXA==",
-      "dev": true
-    },
     "node_modules/@types/semver": {
       "version": "7.5.6",
       "resolved": "https://registry.npmjs.org/@types/semver/-/semver-7.5.6.tgz",
@@ -5341,74 +5334,6 @@
         "arrow2csv": "bin/arrow2csv.cjs"
       }
     },
-    "node_modules/apache-arrow-13": {
-      "name": "apache-arrow",
-      "version": "13.0.0",
-      "resolved": "https://registry.npmjs.org/apache-arrow/-/apache-arrow-13.0.0.tgz",
-      "integrity": "sha512-3gvCX0GDawWz6KFNC28p65U+zGh/LZ6ZNKWNu74N6CQlKzxeoWHpi4CgEQsgRSEMuyrIIXi1Ea2syja7dwcHvw==",
-      "dev": true,
-      "dependencies": {
-        "@types/command-line-args": "5.2.0",
-        "@types/command-line-usage": "5.0.2",
-        "@types/node": "20.3.0",
-        "@types/pad-left": "2.1.1",
-        "command-line-args": "5.2.1",
-        "command-line-usage": "7.0.1",
-        "flatbuffers": "23.5.26",
-        "json-bignum": "^0.0.3",
-        "pad-left": "^2.1.0",
-        "tslib": "^2.5.3"
-      },
-      "bin": {
-        "arrow2csv": "bin/arrow2csv.js"
-      }
-    },
-    "node_modules/apache-arrow-13/node_modules/@types/command-line-args": {
-      "version": "5.2.0",
-      "resolved": "https://registry.npmjs.org/@types/command-line-args/-/command-line-args-5.2.0.tgz",
-      "integrity": "sha512-UuKzKpJJ/Ief6ufIaIzr3A/0XnluX7RvFgwkV89Yzvm77wCh1kFaFmqN8XEnGcN62EuHdedQjEMb8mYxFLGPyA==",
-      "dev": true
-    },
-    "node_modules/apache-arrow-13/node_modules/@types/node": {
-      "version": "20.3.0",
-      "resolved": "https://registry.npmjs.org/@types/node/-/node-20.3.0.tgz",
-      "integrity": "sha512-cumHmIAf6On83X7yP+LrsEyUOf/YlociZelmpRYaGFydoaPdxdt80MAbu6vWerQT2COCp2nPvHdsbD7tHn/YlQ==",
-      "dev": true
-    },
-    "node_modules/apache-arrow-14": {
-      "name": "apache-arrow",
-      "version": "14.0.0",
-      "resolved": "https://registry.npmjs.org/apache-arrow/-/apache-arrow-14.0.0.tgz",
-      "integrity": "sha512-9cKE24YxkaqAZWJddrVnjUJMLwq6CokOjK+AHpm145rMJNsBZXQkzqouemQyEX0+/iHYRnGym6X6ZgNcHHrcWA==",
-      "dev": true,
-      "dependencies": {
-        "@types/command-line-args": "5.2.0",
-        "@types/command-line-usage": "5.0.2",
-        "@types/node": "20.3.0",
-        "@types/pad-left": "2.1.1",
-        "command-line-args": "5.2.1",
-        "command-line-usage": "7.0.1",
-        "flatbuffers": "23.5.26",
-        "json-bignum": "^0.0.3",
-        "pad-left": "^2.1.0",
-        "tslib": "^2.5.3"
-      },
-      "bin": {
-        "arrow2csv": "bin/arrow2csv.js"
-      }
-    },
-    "node_modules/apache-arrow-14/node_modules/@types/command-line-args": {
-      "version": "5.2.0",
-      "resolved": "https://registry.npmjs.org/@types/command-line-args/-/command-line-args-5.2.0.tgz",
-      "integrity": "sha512-UuKzKpJJ/Ief6ufIaIzr3A/0XnluX7RvFgwkV89Yzvm77wCh1kFaFmqN8XEnGcN62EuHdedQjEMb8mYxFLGPyA==",
-      "dev": true
-    },
-    "node_modules/apache-arrow-14/node_modules/@types/node": {
-      "version": "20.3.0",
-      "resolved": "https://registry.npmjs.org/@types/node/-/node-20.3.0.tgz",
-      "integrity": "sha512-cumHmIAf6On83X7yP+LrsEyUOf/YlociZelmpRYaGFydoaPdxdt80MAbu6vWerQT2COCp2nPvHdsbD7tHn/YlQ==",
-      "dev": true
-    },
     "node_modules/apache-arrow-15": {
       "name": "apache-arrow",
       "version": "15.0.0",
@@ -5529,6 +5454,54 @@
       "integrity": "sha512-ve2KP6f/JnbPBFyobGHuerC9g1FYGn/F8n1LWTwNxCEzd6IfqTwUQcNXgEtmmQ6DlRrC1hrSrBnCZPokRrDHjw==",
       "dev": true
     },
+    "node_modules/apache-arrow-18": {
+      "name": "apache-arrow",
+      "version": "18.0.0",
+      "resolved": "https://registry.npmjs.org/apache-arrow/-/apache-arrow-18.0.0.tgz",
+      "integrity": "sha512-gFlPaqN9osetbB83zC29AbbZqGiCuFH1vyyPseJ+B7SIbfBtESV62mMT/CkiIt77W6ykC/nTWFzTXFs0Uldg4g==",
+      "dev": true,
+      "dependencies": {
+        "@swc/helpers": "^0.5.11",
+        "@types/command-line-args": "^5.2.3",
+        "@types/command-line-usage": "^5.0.4",
+        "@types/node": "^20.13.0",
+        "command-line-args": "^5.2.1",
+        "command-line-usage": "^7.0.1",
+        "flatbuffers": "^24.3.25",
+        "json-bignum": "^0.0.3",
+        "tslib": "^2.6.2"
+      },
+      "bin": {
+        "arrow2csv": "bin/arrow2csv.js"
+      }
+    },
+    "node_modules/apache-arrow-18/node_modules/@types/command-line-usage": {
+      "version": "5.0.4",
+      "resolved": "https://registry.npmjs.org/@types/command-line-usage/-/command-line-usage-5.0.4.tgz",
+      "integrity": "sha512-BwR5KP3Es/CSht0xqBcUXS3qCAUVXwpRKsV2+arxeb65atasuXG9LykC9Ab10Cw3s2raH92ZqOeILaQbsB2ACg==",
+      "dev": true
+    },
+    "node_modules/apache-arrow-18/node_modules/@types/node": {
+      "version": "20.17.9",
+      "resolved": "https://registry.npmjs.org/@types/node/-/node-20.17.9.tgz",
+      "integrity": "sha512-0JOXkRyLanfGPE2QRCwgxhzlBAvaRdCNMcvbd7jFfpmD4eEXll7LRwy5ymJmyeZqk7Nh7eD2LeUyQ68BbndmXw==",
+      "dev": true,
+      "dependencies": {
+        "undici-types": "~6.19.2"
+      }
+    },
+    "node_modules/apache-arrow-18/node_modules/flatbuffers": {
+      "version": "24.3.25",
+      "resolved": "https://registry.npmjs.org/flatbuffers/-/flatbuffers-24.3.25.tgz",
+      "integrity": "sha512-3HDgPbgiwWMI9zVB7VYBHaMrbOO7Gm0v+yD2FV/sCKj+9NDeVL7BOBYUuhWAQGKWOzBo8S9WdMvV0eixO233XQ==",
+      "dev": true
+    },
+    "node_modules/apache-arrow-18/node_modules/undici-types": {
+      "version": "6.19.8",
+      "resolved": "https://registry.npmjs.org/undici-types/-/undici-types-6.19.8.tgz",
+      "integrity": "sha512-ve2KP6f/JnbPBFyobGHuerC9g1FYGn/F8n1LWTwNxCEzd6IfqTwUQcNXgEtmmQ6DlRrC1hrSrBnCZPokRrDHjw==",
+      "dev": true
+    },
     "node_modules/apache-arrow/node_modules/@types/node": {
       "version": "20.16.10",
       "resolved": "https://registry.npmjs.org/@types/node/-/node-20.16.10.tgz",
@@ -8533,18 +8506,6 @@
       "integrity": "sha512-UEZIS3/by4OC8vL3P2dTXRETpebLI2NiI5vIrjaD/5UtrkFX/tNbwjTSRAGC/+7CAo2pIcBaRgWmcBBHcsaCIw==",
       "optional": true
     },
-    "node_modules/pad-left": {
-      "version": "2.1.0",
-      "resolved": "https://registry.npmjs.org/pad-left/-/pad-left-2.1.0.tgz",
-      "integrity": "sha512-HJxs9K9AztdIQIAIa/OIazRAUW/L6B9hbQDxO4X07roW3eo9XqZc2ur9bn1StH9CnbbI9EgvejHQX7CBpCF1QA==",
-      "dev": true,
-      "dependencies": {
-        "repeat-string": "^1.5.4"
-      },
-      "engines": {
-        "node": ">=0.10.0"
-      }
-    },
     "node_modules/parent-module": {
       "version": "1.0.1",
       "resolved": "https://registry.npmjs.org/parent-module/-/parent-module-1.0.1.tgz",
@@ -8885,15 +8846,6 @@
       "resolved": "https://registry.npmjs.org/reflect-metadata/-/reflect-metadata-0.2.2.tgz",
       "integrity": "sha512-urBwgfrvVP/eAyXx4hluJivBKzuEbSQs9rKWCrCkbSxNv8mxPcUZKeuoF3Uy4mJl3Lwprp6yy5/39VWigZ4K6Q=="
     },
-    "node_modules/repeat-string": {
-      "version": "1.6.1",
-      "resolved": "https://registry.npmjs.org/repeat-string/-/repeat-string-1.6.1.tgz",
-      "integrity": "sha512-PV0dzCYDNfRi1jCDbJzpW7jNNDRuCOG/jI5ctQcGKt/clZD+YcPS3yIlWuTJMmESC8aevCFmWJy5wjAFgNqN6w==",
-      "dev": true,
-      "engines": {
-        "node": ">=0.10"
-      }
-    },
     "node_modules/require-directory": {
       "version": "2.1.1",
       "resolved": "https://registry.npmjs.org/require-directory/-/require-directory-2.1.1.tgz",

nodejs/package.json

@@ -10,7 +10,8 @@
     "vector database",
     "ann"
   ],
-  "version": "0.14.0-beta.2",
+  "private": false,
+  "version": "0.14.1-beta.2",
   "main": "dist/index.js",
   "exports": {
     ".": "./dist/index.js",
@@ -30,7 +31,8 @@
         "aarch64-unknown-linux-gnu",
         "x86_64-unknown-linux-musl",
         "aarch64-unknown-linux-musl",
-        "x86_64-pc-windows-msvc"
+        "x86_64-pc-windows-msvc",
+        "aarch64-pc-windows-msvc"
       ]
     }
   },
@@ -46,11 +48,10 @@
     "@types/jest": "^29.1.2",
     "@types/node": "^22.7.4",
     "@types/tmp": "^0.2.6",
-    "apache-arrow-13": "npm:apache-arrow@13.0.0",
-    "apache-arrow-14": "npm:apache-arrow@14.0.0",
     "apache-arrow-15": "npm:apache-arrow@15.0.0",
     "apache-arrow-16": "npm:apache-arrow@16.0.0",
     "apache-arrow-17": "npm:apache-arrow@17.0.0",
+    "apache-arrow-18": "npm:apache-arrow@18.0.0",
     "eslint": "^8.57.0",
     "jest": "^29.7.0",
     "shx": "^0.3.4",
@@ -77,6 +78,7 @@
     "build-release": "npm run build:release && tsc -b && shx cp lancedb/native.d.ts dist/native.d.ts",
     "lint-ci": "biome ci .",
     "docs": "typedoc --plugin typedoc-plugin-markdown --out ../docs/src/js lancedb/index.ts",
+    "postdocs": "node typedoc_post_process.js",
     "lint": "biome check . && biome format .",
     "lint-fix": "biome check --write . && biome format --write .",
     "prepublishOnly": "napi prepublish -t npm",
@@ -93,6 +95,6 @@
     "openai": "^4.29.2"
   },
   "peerDependencies": {
-    "apache-arrow": ">=13.0.0 <=17.0.0"
+    "apache-arrow": ">=15.0.0 <=18.1.0"
   }
 }

nodejs/src/index.rs

@@ -45,6 +45,7 @@ impl Index {
         distance_type: Option<String>,
         num_partitions: Option<u32>,
         num_sub_vectors: Option<u32>,
+        num_bits: Option<u32>,
         max_iterations: Option<u32>,
         sample_rate: Option<u32>,
     ) -> napi::Result<Self> {
@@ -59,6 +60,9 @@ impl Index {
         if let Some(num_sub_vectors) = num_sub_vectors {
             ivf_pq_builder = ivf_pq_builder.num_sub_vectors(num_sub_vectors);
         }
+        if let Some(num_bits) = num_bits {
+            ivf_pq_builder = ivf_pq_builder.num_bits(num_bits);
+        }
         if let Some(max_iterations) = max_iterations {
             ivf_pq_builder = ivf_pq_builder.max_iterations(max_iterations);
         }

nodejs/typedoc.json

@@ -8,5 +8,6 @@
     "lancedb/native.d.ts:Table"
   ],
   "useHTMLEncodedBrackets": true,
+  "useCodeBlocks": true,
   "disableSources": true
 }

nodejs/typedoc_post_process.js

@@ -0,0 +1,63 @@
+const fs = require("fs");
+const path = require("path");
+
+// Read all files in the directory
+function processDirectory(directoryPath) {
+  fs.readdir(directoryPath, { withFileTypes: true }, (err, files) => {
+    if (err) {
+      return console.error("Unable to scan directory: " + err);
+    }
+
+    files.forEach((file) => {
+      const filePath = path.join(directoryPath, file.name);
+
+      if (file.isDirectory()) {
+        // Recursively process subdirectory
+        processDirectory(filePath);
+      } else if (file.isFile()) {
+        // Read each file
+        fs.readFile(filePath, "utf8", (err, data) => {
+          if (err) {
+            return console.error("Unable to read file: " + err);
+          }
+
+          // Process the file content
+          const processedData = processContents(data);
+
+          // Write the processed content back to the file
+          fs.writeFile(filePath, processedData, "utf8", (err) => {
+            if (err) {
+              return console.error("Unable to write file: " + err);
+            }
+            console.log(`Processed file: ${filePath}`);
+          });
+        });
+      }
+    });
+  });
+}
+
+function processContents(contents) {
+  // This changes the parameters section to put the parameter description on
+  // the same line as the bullet with the parameter name and type.
+  return contents.replace(/(## Parameters[\s\S]*?)(?=##|$)/g, (match) => {
+    let lines = match
+      .split("\n")
+      .map((line) => line.trim())
+
+      .filter((line) => line !== "")
+      .map((line) => {
+        if (line.startsWith("##")) {
+          return line;
+        } else if (line.startsWith("•")) {
+          return "\n*" + line.substring(1);
+        } else {
+          return "    " + line;
+        }
+      });
+    return lines.join("\n") + "\n\n";
+  });
+}
+
+// Start processing from the root directory
+processDirectory("../docs/src/js");

python/.bumpversion.toml

@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.17.0"
+current_version = "0.17.1-beta.3"
 parse = """(?x)
     (?P<major>0|[1-9]\\d*)\\.
     (?P<minor>0|[1-9]\\d*)\\.

python/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "lancedb-python"
-version = "0.17.0"
+version = "0.17.1-beta.3"
 edition.workspace = true
 description = "Python bindings for LanceDB"
 license.workspace = true

python/pyproject.toml

@@ -3,7 +3,7 @@ name = "lancedb"
 # version in Cargo.toml
 dependencies = [
     "deprecation",
-    "pylance==0.20.0",
+    "pylance==0.21.0b2",
     "tqdm>=4.27.0",
     "pydantic>=1.10",
     "packaging",

python/python/lancedb/__init__.py

@@ -110,6 +110,7 @@ def connect(
             # TODO: remove this (deprecation warning downstream)
             request_thread_pool=request_thread_pool,
             client_config=client_config,
+            storage_options=storage_options,
             **kwargs,
         )
 

python/python/lancedb/_lancedb.pyi

@@ -79,9 +79,21 @@ class Query:
     def limit(self, limit: int): ...
     def offset(self, offset: int): ...
     def nearest_to(self, query_vec: pa.Array) -> VectorQuery: ...
-    def nearest_to_text(self, query: dict) -> Query: ...
+    def nearest_to_text(self, query: dict) -> FTSQuery: ...
     async def execute(self, max_batch_legnth: Optional[int]) -> RecordBatchStream: ...
 
+class FTSQuery:
+    def where(self, filter: str): ...
+    def select(self, columns: List[str]): ...
+    def limit(self, limit: int): ...
+    def offset(self, offset: int): ...
+    def fast_search(self): ...
+    def with_row_id(self): ...
+    def postfilter(self): ...
+    def nearest_to(self, query_vec: pa.Array) -> HybridQuery: ...
+    async def execute(self, max_batch_length: Optional[int]) -> RecordBatchStream: ...
+    async def explain_plan(self) -> str: ...
+
 class VectorQuery:
     async def execute(self) -> RecordBatchStream: ...
     def where(self, filter: str): ...
@@ -95,6 +107,24 @@ class VectorQuery:
     def refine_factor(self, refine_factor: int): ...
     def nprobes(self, nprobes: int): ...
     def bypass_vector_index(self): ...
+    def nearest_to_text(self, query: dict) -> HybridQuery: ...
+
+class HybridQuery:
+    def where(self, filter: str): ...
+    def select(self, columns: List[str]): ...
+    def limit(self, limit: int): ...
+    def offset(self, offset: int): ...
+    def fast_search(self): ...
+    def with_row_id(self): ...
+    def postfilter(self): ...
+    def distance_type(self, distance_type: str): ...
+    def refine_factor(self, refine_factor: int): ...
+    def nprobes(self, nprobes: int): ...
+    def bypass_vector_index(self): ...
+    def to_vector_query(self) -> VectorQuery: ...
+    def to_fts_query(self) -> FTSQuery: ...
+    def get_limit(self) -> int: ...
+    def get_with_row_id(self) -> bool: ...
 
 class CompactionStats:
     fragments_removed: int

python/python/lancedb/index.py

@@ -178,6 +178,12 @@ class HnswPq:
         If the dimension is not visible by 8 then we use 1 subvector.  This is not
         ideal and will likely result in poor performance.
 
+     num_bits: int, default 8
+        Number of bits to encode each sub-vector.
+
+        This value controls how much the sub-vectors are compressed.  The more bits
+        the more accurate the index but the slower search. Only 4 and 8 are supported.
+
     max_iterations, default 50
 
         Max iterations to train kmeans.
@@ -232,6 +238,7 @@ class HnswPq:
         distance_type: Optional[str] = None,
         num_partitions: Optional[int] = None,
         num_sub_vectors: Optional[int] = None,
+        num_bits: Optional[int] = None,
         max_iterations: Optional[int] = None,
         sample_rate: Optional[int] = None,
         m: Optional[int] = None,
@@ -241,6 +248,7 @@ class HnswPq:
             distance_type=distance_type,
             num_partitions=num_partitions,
             num_sub_vectors=num_sub_vectors,
+            num_bits=num_bits,
             max_iterations=max_iterations,
             sample_rate=sample_rate,
             m=m,
@@ -387,6 +395,7 @@ class IvfPq:
         distance_type: Optional[str] = None,
         num_partitions: Optional[int] = None,
         num_sub_vectors: Optional[int] = None,
+        num_bits: Optional[int] = None,
         max_iterations: Optional[int] = None,
         sample_rate: Optional[int] = None,
     ):
@@ -449,6 +458,12 @@ class IvfPq:
 
             If the dimension is not visible by 8 then we use 1 subvector.  This is not
             ideal and will likely result in poor performance.
+        num_bits: int, default 8
+            Number of bits to encode each sub-vector.
+
+            This value controls how much the sub-vectors are compressed.  The more bits
+            the more accurate the index but the slower search.  The default is 8
+            bits.  Only 4 and 8 are supported.
         max_iterations: int, default 50
             Max iteration to train kmeans.
 
@@ -482,6 +497,7 @@ class IvfPq:
             distance_type=distance_type,
             num_partitions=num_partitions,
             num_sub_vectors=num_sub_vectors,
+            num_bits=num_bits,
             max_iterations=max_iterations,
             sample_rate=sample_rate,
         )

python/python/lancedb/query.py

@@ -1,15 +1,5 @@
-#  Copyright 2023 LanceDB Developers
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
 from __future__ import annotations
 
@@ -26,6 +16,7 @@ from typing import (
     Union,
 )
 
+import asyncio
 import deprecation
 import numpy as np
 import pyarrow as pa
@@ -44,6 +35,8 @@ if TYPE_CHECKING:
     import polars as pl
 
     from ._lancedb import Query as LanceQuery
+    from ._lancedb import FTSQuery as LanceFTSQuery
+    from ._lancedb import HybridQuery as LanceHybridQuery
     from ._lancedb import VectorQuery as LanceVectorQuery
     from .common import VEC
     from .pydantic import LanceModel
@@ -1124,35 +1117,55 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
             fts_results = fts_future.result()
             vector_results = vector_future.result()
 
-        # convert to ranks first if needed
-        if self._norm == "rank":
-            vector_results = self._rank(vector_results, "_distance")
-            fts_results = self._rank(fts_results, "_score")
+        return self._combine_hybrid_results(
+            fts_results=fts_results,
+            vector_results=vector_results,
+            norm=self._norm,
+            fts_query=self._fts_query._query,
+            reranker=self._reranker,
+            limit=self._limit,
+            with_row_ids=self._with_row_id,
+        )
+
+    @staticmethod
+    def _combine_hybrid_results(
+        fts_results: pa.Table,
+        vector_results: pa.Table,
+        norm: str,
+        fts_query: str,
+        reranker,
+        limit: int,
+        with_row_ids: bool,
+    ) -> pa.Table:
+        if norm == "rank":
+            vector_results = LanceHybridQueryBuilder._rank(vector_results, "_distance")
+            fts_results = LanceHybridQueryBuilder._rank(fts_results, "_score")
 
         # normalize the scores to be between 0 and 1, 0 being most relevant
-        vector_results = self._normalize_scores(vector_results, "_distance")
+        vector_results = LanceHybridQueryBuilder._normalize_scores(
+            vector_results, "_distance"
+        )
 
         # In fts higher scores represent relevance. Not inverting them here as
         # rerankers might need to preserve this score to support `return_score="all"`
-        fts_results = self._normalize_scores(fts_results, "_score")
+        fts_results = LanceHybridQueryBuilder._normalize_scores(fts_results, "_score")
 
-        results = self._reranker.rerank_hybrid(
-            self._fts_query._query, vector_results, fts_results
-        )
+        results = reranker.rerank_hybrid(fts_query, vector_results, fts_results)
 
         check_reranker_result(results)
 
-        # apply limit after reranking
-        results = results.slice(length=self._limit)
+        results = results.slice(length=limit)
 
-        if not self._with_row_id:
+        if not with_row_ids:
             results = results.drop(["_rowid"])
+
         return results
 
     def to_batches(self):
         raise NotImplementedError("to_batches not yet supported on a hybrid query")
 
-    def _rank(self, results: pa.Table, column: str, ascending: bool = True):
+    @staticmethod
+    def _rank(results: pa.Table, column: str, ascending: bool = True):
         if len(results) == 0:
             return results
         # Get the _score column from results
@@ -1169,7 +1182,8 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
         )
         return results
 
-    def _normalize_scores(self, results: pa.Table, column: str, invert=False):
+    @staticmethod
+    def _normalize_scores(results: pa.Table, column: str, invert=False):
         if len(results) == 0:
             return results
         # Get the _score column from results
@@ -1620,7 +1634,7 @@ class AsyncQuery(AsyncQueryBase):
         if (
             isinstance(query_vector, list)
             and len(query_vector) > 0
-            and not isinstance(query_vector[0], (float, int))
+            and isinstance(query_vector[0], (list, np.ndarray, pa.Array))
         ):
             # multiple have been passed
             query_vectors = [AsyncQuery._query_vec_to_array(v) for v in query_vector]
@@ -1635,7 +1649,7 @@ class AsyncQuery(AsyncQueryBase):
 
     def nearest_to_text(
         self, query: str, columns: Union[str, List[str]] = []
-    ) -> AsyncQuery:
+    ) -> AsyncFTSQuery:
         """
         Find the documents that are most relevant to the given text query.
 
@@ -1658,8 +1672,90 @@ class AsyncQuery(AsyncQueryBase):
         """
         if isinstance(columns, str):
             columns = [columns]
-        self._inner.nearest_to_text({"query": query, "columns": columns})
-        return self
+        return AsyncFTSQuery(
+            self._inner.nearest_to_text({"query": query, "columns": columns})
+        )
+
+
+class AsyncFTSQuery(AsyncQueryBase):
+    """A query for full text search for LanceDB."""
+
+    def __init__(self, inner: LanceFTSQuery):
+        super().__init__(inner)
+        self._inner = inner
+
+    def get_query(self):
+        self._inner.get_query()
+
+    def nearest_to(
+        self,
+        query_vector: Union[VEC, Tuple, List[VEC]],
+    ) -> AsyncHybridQuery:
+        """
+        In addition doing text search on the LanceDB Table, also
+        find the nearest vectors to the given query vector.
+
+        This converts the query from a FTS Query to a Hybrid query. Results
+        from the vector search will be combined with results from the FTS query.
+
+        This method will attempt to convert the input to the query vector
+        expected by the embedding model.  If the input cannot be converted
+        then an error will be thrown.
+
+        By default, there is no embedding model, and the input should be
+        something that can be converted to a pyarrow array of floats.  This
+        includes lists, numpy arrays, and tuples.
+
+        If there is only one vector column (a column whose data type is a
+        fixed size list of floats) then the column does not need to be specified.
+        If there is more than one vector column you must use
+        [AsyncVectorQuery.column][lancedb.query.AsyncVectorQuery.column] to specify
+        which column you would like to compare with.
+
+        If no index has been created on the vector column then a vector query
+        will perform a distance comparison between the query vector and every
+        vector in the database and then sort the results.  This is sometimes
+        called a "flat search"
+
+        For small databases, with tens of thousands of vectors or less, this can
+        be reasonably fast.  In larger databases you should create a vector index
+        on the column.  If there is a vector index then an "approximate" nearest
+        neighbor search (frequently called an ANN search) will be performed.  This
+        search is much faster, but the results will be approximate.
+
+        The query can be further parameterized using the returned builder.  There
+        are various ANN search parameters that will let you fine tune your recall
+        accuracy vs search latency.
+
+        Hybrid searches always have a [limit][].  If `limit` has not been called then
+        a default `limit` of 10 will be used.
+
+        Typically, a single vector is passed in as the query. However, you can also
+        pass in multiple vectors.  This can be useful if you want to find the nearest
+        vectors to multiple query vectors. This is not expected to be faster than
+        making multiple queries concurrently; it is just a convenience method.
+        If multiple vectors are passed in then an additional column `query_index`
+        will be added to the results.  This column will contain the index of the
+        query vector that the result is nearest to.
+        """
+        if query_vector is None:
+            raise ValueError("query_vector can not be None")
+
+        if (
+            isinstance(query_vector, list)
+            and len(query_vector) > 0
+            and not isinstance(query_vector[0], (float, int))
+        ):
+            # multiple have been passed
+            query_vectors = [AsyncQuery._query_vec_to_array(v) for v in query_vector]
+            new_self = self._inner.nearest_to(query_vectors[0])
+            for v in query_vectors[1:]:
+                new_self.add_query_vector(v)
+            return AsyncHybridQuery(new_self)
+        else:
+            return AsyncHybridQuery(
+                self._inner.nearest_to(AsyncQuery._query_vec_to_array(query_vector))
+            )
 
 
 class AsyncVectorQuery(AsyncQueryBase):
@@ -1796,3 +1892,160 @@ class AsyncVectorQuery(AsyncQueryBase):
         """
         self._inner.bypass_vector_index()
         return self
+
+    def nearest_to_text(
+        self, query: str, columns: Union[str, List[str]] = []
+    ) -> AsyncHybridQuery:
+        """
+        Find the documents that are most relevant to the given text query,
+        in addition to vector search.
+
+        This converts the vector query into a hybrid query.
+
+        This search will perform a full text search on the table and return
+        the most relevant documents, combined with the vector query results.
+        The text relevance is determined by BM25.
+
+        The columns to search must be with native FTS index
+        (Tantivy-based can't work with this method).
+
+        By default, all indexed columns are searched,
+        now only one column can be searched at a time.
+
+        Parameters
+        ----------
+        query: str
+            The text query to search for.
+        columns: str or list of str, default None
+            The columns to search in. If None, all indexed columns are searched.
+            For now only one column can be searched at a time.
+        """
+        if isinstance(columns, str):
+            columns = [columns]
+        return AsyncHybridQuery(
+            self._inner.nearest_to_text({"query": query, "columns": columns})
+        )
+
+
+class AsyncHybridQuery(AsyncQueryBase):
+    """
+    A query builder that performs hybrid vector and full text search.
+    Results are combined and reranked based on the specified reranker.
+    By default, the results are reranked using the RRFReranker, which
+    uses reciprocal rank fusion score for reranking.
+
+    To make the vector and fts results comparable, the scores are normalized.
+    Instead of normalizing scores, the `normalize` parameter can be set to "rank"
+    in the `rerank` method to convert the scores to ranks and then normalize them.
+    """
+
+    def __init__(self, inner: LanceHybridQuery):
+        super().__init__(inner)
+        self._inner = inner
+        self._norm = "score"
+        self._reranker = RRFReranker()
+
+    def rerank(
+        self, reranker: Reranker = RRFReranker(), normalize: str = "score"
+    ) -> AsyncHybridQuery:
+        """
+        Rerank the hybrid search results using the specified reranker. The reranker
+        must be an instance of Reranker class.
+
+        Parameters
+        ----------
+        reranker: Reranker, default RRFReranker()
+            The reranker to use. Must be an instance of Reranker class.
+        normalize: str, default "score"
+            The method to normalize the scores. Can be "rank" or "score". If "rank",
+            the scores are converted to ranks and then normalized. If "score", the
+            scores are normalized directly.
+        Returns
+        -------
+        AsyncHybridQuery
+            The AsyncHybridQuery object.
+        """
+        if normalize not in ["rank", "score"]:
+            raise ValueError("normalize must be 'rank' or 'score'.")
+        if reranker and not isinstance(reranker, Reranker):
+            raise ValueError("reranker must be an instance of Reranker class.")
+
+        self._norm = normalize
+        self._reranker = reranker
+
+        return self
+
+    async def to_batches(self):
+        raise NotImplementedError("to_batches not yet supported on a hybrid query")
+
+    async def to_arrow(self) -> pa.Table:
+        fts_query = AsyncFTSQuery(self._inner.to_fts_query())
+        vec_query = AsyncVectorQuery(self._inner.to_vector_query())
+
+        # save the row ID choice that was made on the query builder and force it
+        # to actually fetch the row ids because we need this for reranking
+        with_row_ids = self._inner.get_with_row_id()
+        fts_query.with_row_id()
+        vec_query.with_row_id()
+
+        fts_results, vector_results = await asyncio.gather(
+            fts_query.to_arrow(),
+            vec_query.to_arrow(),
+        )
+
+        return LanceHybridQueryBuilder._combine_hybrid_results(
+            fts_results=fts_results,
+            vector_results=vector_results,
+            norm=self._norm,
+            fts_query=fts_query.get_query(),
+            reranker=self._reranker,
+            limit=self._inner.get_limit(),
+            with_row_ids=with_row_ids,
+        )
+
+    async def explain_plan(self, verbose: Optional[bool] = False):
+        """Return the execution plan for this query.
+
+        The output includes both the vector and FTS search plans.
+
+        Examples
+        --------
+        >>> import asyncio
+        >>> from lancedb import connect_async
+        >>> from lancedb.index import FTS
+        >>> async def doctest_example():
+        ...     conn = await connect_async("./.lancedb")
+        ...     table = await conn.create_table("my_table", [{"vector": [99, 99], "text": "hello world"}])
+        ...     await table.create_index("text", config=FTS(with_position=False))
+        ...     query = [100, 100]
+        ...     plan = await table.query().nearest_to([1, 2]).nearest_to_text("hello").explain_plan(True)
+        ...     print(plan)
+        >>> asyncio.run(doctest_example()) # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
+        Vector Search Plan:
+        ProjectionExec: expr=[vector@0 as vector, text@3 as text, _distance@2 as _distance]
+            Take: columns="vector, _rowid, _distance, (text)"
+                CoalesceBatchesExec: target_batch_size=1024
+                GlobalLimitExec: skip=0, fetch=10
+                    FilterExec: _distance@2 IS NOT NULL
+                    SortExec: TopK(fetch=10), expr=[_distance@2 ASC NULLS LAST], preserve_partitioning=[false]
+                        KNNVectorDistance: metric=l2
+                        LanceScan: uri=..., projection=[vector], row_id=true, row_addr=false, ordered=false
+        FTS Search Plan:
+        LanceScan: uri=..., projection=[vector, text], row_id=false, row_addr=false, ordered=true
+
+        Parameters
+        ----------
+        verbose : bool, default False
+            Use a verbose output format.
+
+        Returns
+        -------
+        plan
+        """  # noqa: E501
+
+        results = ["Vector Search Plan:"]
+        results.append(await self._inner.to_vector_query().explain_plan(verbose))
+        results.append("FTS Search Plan:")
+        results.append(await self._inner.to_fts_query().explain_plan(verbose))
+
+        return "\n".join(results)

python/python/lancedb/remote/db.py

@@ -44,9 +44,9 @@ class RemoteDBConnection(DBConnection):
         client_config: Union[ClientConfig, Dict[str, Any], None] = None,
         connection_timeout: Optional[float] = None,
         read_timeout: Optional[float] = None,
+        storage_options: Optional[Dict[str, str]] = None,
     ):
         """Connect to a remote LanceDB database."""
-
         if isinstance(client_config, dict):
             client_config = ClientConfig(**client_config)
         elif client_config is None:
@@ -94,6 +94,7 @@ class RemoteDBConnection(DBConnection):
                 region=region,
                 host_override=host_override,
                 client_config=client_config,
+                storage_options=storage_options,
             )
         )
 

python/python/lancedb/remote/table.py

@@ -78,7 +78,7 @@ class RemoteTable(Table):
 
     def list_versions(self):
         """List all versions of the table"""
-        return self._loop.run_until_complete(self._table.list_versions())
+        return LOOP.run(self._table.list_versions())
 
     def to_arrow(self) -> pa.Table:
         """to_arrow() is not yet supported on LanceDB cloud."""
@@ -89,10 +89,10 @@ class RemoteTable(Table):
         return NotImplementedError("to_pandas() is not yet supported on LanceDB cloud.")
 
     def checkout(self, version):
-        return self._loop.run_until_complete(self._table.checkout(version))
+        return LOOP.run(self._table.checkout(version))
 
     def checkout_latest(self):
-        return self._loop.run_until_complete(self._table.checkout_latest())
+        return LOOP.run(self._table.checkout_latest())
 
     def list_indices(self):
         """List all the indices on the table"""
@@ -157,9 +157,7 @@ class RemoteTable(Table):
             remove_stop_words=remove_stop_words,
             ascii_folding=ascii_folding,
         )
-        self._loop.run_until_complete(
-            self._table.create_index(column, config=config, replace=replace)
-        )
+        LOOP.run(self._table.create_index(column, config=config, replace=replace))
 
     def create_index(
         self,

python/python/lancedb/table.py

@@ -413,6 +413,8 @@ class Table(ABC):
         replace: bool = True,
         accelerator: Optional[str] = None,
         index_cache_size: Optional[int] = None,
+        *,
+        num_bits: int = 8,
     ):
         """Create an index on the table.
 
@@ -439,6 +441,9 @@ class Table(ABC):
             Only support "cuda" for now.
         index_cache_size : int, optional
             The size of the index cache in number of entries. Default value is 256.
+        num_bits: int
+            The number of bits to encode sub-vectors. Only used with the IVF_PQ index.
+            Only 4 and 8 are supported.
         """
         raise NotImplementedError
 
@@ -1430,6 +1435,8 @@ class LanceTable(Table):
         accelerator: Optional[str] = None,
         index_cache_size: Optional[int] = None,
         index_type="IVF_PQ",
+        *,
+        num_bits: int = 8,
     ):
         """Create an index on the table."""
         self._dataset_mut.create_index(
@@ -1441,6 +1448,7 @@ class LanceTable(Table):
             replace=replace,
             accelerator=accelerator,
             index_cache_size=index_cache_size,
+            num_bits=num_bits,
         )
 
     def create_scalar_index(

python/python/tests/test_hybrid_query.py

@@ -0,0 +1,111 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+import lancedb
+
+import pyarrow as pa
+import pytest
+import pytest_asyncio
+
+from lancedb.index import FTS
+from lancedb.table import AsyncTable
+
+
+@pytest_asyncio.fixture
+async def table(tmpdir_factory) -> AsyncTable:
+    tmp_path = str(tmpdir_factory.mktemp("data"))
+    db = await lancedb.connect_async(tmp_path)
+    data = pa.table(
+        {
+            "text": pa.array(["a", "b", "cat", "dog"]),
+            "vector": pa.array(
+                [[0.1, 0.1], [2, 2], [-0.1, -0.1], [0.5, -0.5]],
+                type=pa.list_(pa.float32(), list_size=2),
+            ),
+        }
+    )
+    table = await db.create_table("test", data)
+    await table.create_index("text", config=FTS(with_position=False))
+    return table
+
+
+@pytest.mark.asyncio
+async def test_async_hybrid_query(table: AsyncTable):
+    result = await (
+        table.query().nearest_to([0.0, 0.4]).nearest_to_text("dog").limit(2).to_arrow()
+    )
+    assert len(result) == 2
+    # ensure we get results that would match well for text and vector
+    assert result["text"].to_pylist() == ["a", "dog"]
+
+    # ensure there is no rowid by default
+    assert "_rowid" not in result
+
+
+@pytest.mark.asyncio
+async def test_async_hybrid_query_with_row_ids(table: AsyncTable):
+    result = await (
+        table.query()
+        .nearest_to([0.0, 0.4])
+        .nearest_to_text("dog")
+        .limit(2)
+        .with_row_id()
+        .to_arrow()
+    )
+    assert len(result) == 2
+    # ensure we get results that would match well for text and vector
+    assert result["text"].to_pylist() == ["a", "dog"]
+    assert result["_rowid"].to_pylist() == [0, 3]
+
+
+@pytest.mark.asyncio
+async def test_async_hybrid_query_filters(table: AsyncTable):
+    # test that query params are passed down from the regular builder to
+    # child vector/fts builders
+    result = await (
+        table.query()
+        .where("text not in ('a', 'dog')")
+        .nearest_to([0.3, 0.3])
+        .nearest_to_text("*a*")
+        .limit(2)
+        .to_arrow()
+    )
+    assert len(result) == 2
+    # ensure we get results that would match well for text and vector
+    assert result["text"].to_pylist() == ["cat", "b"]
+
+
+@pytest.mark.asyncio
+async def test_async_hybrid_query_default_limit(table: AsyncTable):
+    # add 10 new rows
+    new_rows = []
+    for i in range(100):
+        if i < 2:
+            new_rows.append({"text": "close_vec", "vector": [0.1, 0.1]})
+        else:
+            new_rows.append({"text": "far_vec", "vector": [5 * i, 5 * i]})
+    await table.add(new_rows)
+    result = await (
+        table.query().nearest_to_text("dog").nearest_to([0.1, 0.1]).to_arrow()
+    )
+
+    # assert we got the default limit of 10
+    assert len(result) == 10
+
+    # assert we got the closest vectors and the text searched for
+    texts = result["text"].to_pylist()
+    assert texts.count("close_vec") == 2
+    assert texts.count("dog") == 1
+    assert texts.count("a") == 1
+
+
+@pytest.mark.asyncio
+async def test_explain_plan(table: AsyncTable):
+    plan = await (
+        table.query().nearest_to_text("dog").nearest_to([0.1, 0.1]).explain_plan(True)
+    )
+
+    assert "Vector Search Plan" in plan
+    assert "KNNVectorDistance" in plan
+    assert "FTS Search Plan" in plan
+    assert "LanceScan" in plan

python/python/tests/test_index.py

@@ -108,6 +108,29 @@ async def test_create_vector_index(some_table: AsyncTable):
     assert stats.num_indices == 1
 
 
+@pytest.mark.asyncio
+async def test_create_4bit_ivfpq_index(some_table: AsyncTable):
+    # Can create
+    await some_table.create_index("vector", config=IvfPq(num_bits=4))
+    # Can recreate if replace=True
+    await some_table.create_index("vector", config=IvfPq(num_bits=4), replace=True)
+    # Can't recreate if replace=False
+    with pytest.raises(RuntimeError, match="already exists"):
+        await some_table.create_index("vector", replace=False)
+    indices = await some_table.list_indices()
+    assert len(indices) == 1
+    assert indices[0].index_type == "IvfPq"
+    assert indices[0].columns == ["vector"]
+    assert indices[0].name == "vector_idx"
+
+    stats = await some_table.index_stats("vector_idx")
+    assert stats.index_type == "IVF_PQ"
+    assert stats.distance_type == "l2"
+    assert stats.num_indexed_rows == await some_table.count_rows()
+    assert stats.num_unindexed_rows == 0
+    assert stats.num_indices == 1
+
+
 @pytest.mark.asyncio
 async def test_create_hnswpq_index(some_table: AsyncTable):
     await some_table.create_index("vector", config=HnswPq(num_partitions=10))

python/python/tests/test_query.py

@@ -3,6 +3,7 @@
 
 import unittest.mock as mock
 from datetime import timedelta
+from pathlib import Path
 
 import lancedb
 from lancedb.index import IvfPq
@@ -384,3 +385,19 @@ async def test_query_to_list_async(table_async: AsyncTable):
     assert len(list) == 2
     assert list[0]["vector"] == [1, 2]
     assert list[1]["vector"] == [3, 4]
+
+
+@pytest.mark.asyncio
+async def test_query_with_f16(tmp_path: Path):
+    db = await lancedb.connect_async(tmp_path)
+    f16_arr = np.array([1.0, 2.0, 3.0, 4.0], dtype=np.float16)
+
+    df = pa.table(
+        {
+            "vector": pa.FixedSizeListArray.from_arrays(f16_arr, 2),
+            "id": pa.array([1, 2]),
+        }
+    )
+    tbl = await db.create_table("test", df)
+    results = await tbl.vector_search([np.float16(1), np.float16(2)]).to_pandas()
+    assert len(results) == 2

python/python/tests/test_remote_db.py

@@ -229,6 +229,44 @@ def test_table_add_in_threadpool():
                 future.result()
 
 
+def test_table_create_indices():
+    def handler(request):
+        if request.path == "/v1/table/test/create_index/":
+            request.send_response(200)
+            request.end_headers()
+        elif request.path == "/v1/table/test/create/?mode=create":
+            request.send_response(200)
+            request.send_header("Content-Type", "application/json")
+            request.end_headers()
+            request.wfile.write(b"{}")
+        elif request.path == "/v1/table/test/describe/":
+            request.send_response(200)
+            request.send_header("Content-Type", "application/json")
+            request.end_headers()
+            payload = json.dumps(
+                dict(
+                    version=1,
+                    schema=dict(
+                        fields=[
+                            dict(name="id", type={"type": "int64"}, nullable=False),
+                        ]
+                    ),
+                )
+            )
+            request.wfile.write(payload.encode())
+        else:
+            request.send_response(404)
+            request.end_headers()
+
+    with mock_lancedb_connection(handler) as db:
+        # Parameters are well-tested through local and async tests.
+        # This is a smoke-test.
+        table = db.create_table("test", [{"id": 1}])
+        table.create_scalar_index("id")
+        table.create_fts_index("text")
+        table.create_scalar_index("vector")
+
+
 @contextlib.contextmanager
 def query_test_table(query_handler):
     def handler(request):
@@ -305,6 +343,7 @@ def test_query_sync_maximal():
         assert body == {
             "distance_type": "cosine",
             "k": 42,
+            "offset": 10,
             "prefilter": True,
             "refine_factor": 10,
             "vector": [1.0, 2.0, 3.0],
@@ -325,6 +364,7 @@ def test_query_sync_maximal():
             table.search([1, 2, 3], vector_column_name="vector2", fast_search=True)
             .metric("cosine")
             .limit(42)
+            .offset(10)
             .refine_factor(10)
             .nprobes(5)
             .where("id > 0", prefilter=True)

python/python/tests/test_table.py

@@ -530,6 +530,7 @@ def test_create_index_method():
             replace=True,
             accelerator=None,
             index_cache_size=256,
+            num_bits=8,
         )
 
 

python/src/index.rs

@@ -47,12 +47,13 @@ impl Index {
 
 #[pymethods]
 impl Index {
-    #[pyo3(signature = (distance_type=None, num_partitions=None, num_sub_vectors=None, max_iterations=None, sample_rate=None))]
+    #[pyo3(signature = (distance_type=None, num_partitions=None, num_sub_vectors=None,num_bits=None, max_iterations=None, sample_rate=None))]
     #[staticmethod]
     pub fn ivf_pq(
         distance_type: Option<String>,
         num_partitions: Option<u32>,
         num_sub_vectors: Option<u32>,
+        num_bits: Option<u32>,
         max_iterations: Option<u32>,
         sample_rate: Option<u32>,
     ) -> PyResult<Self> {
@@ -75,6 +76,9 @@ impl Index {
         if let Some(num_sub_vectors) = num_sub_vectors {
             ivf_pq_builder = ivf_pq_builder.num_sub_vectors(num_sub_vectors);
         }
+        if let Some(num_bits) = num_bits {
+            ivf_pq_builder = ivf_pq_builder.num_bits(num_bits);
+        }
         if let Some(max_iterations) = max_iterations {
             ivf_pq_builder = ivf_pq_builder.max_iterations(max_iterations);
         }
@@ -148,12 +152,14 @@ impl Index {
         }
     }
 
-    #[pyo3(signature = (distance_type=None, num_partitions=None, num_sub_vectors=None, max_iterations=None, sample_rate=None, m=None, ef_construction=None))]
+    #[pyo3(signature = (distance_type=None, num_partitions=None, num_sub_vectors=None,num_bits=None, max_iterations=None, sample_rate=None, m=None, ef_construction=None))]
     #[staticmethod]
+    #[allow(clippy::too_many_arguments)]
     pub fn hnsw_pq(
         distance_type: Option<String>,
         num_partitions: Option<u32>,
         num_sub_vectors: Option<u32>,
+        num_bits: Option<u32>,
         max_iterations: Option<u32>,
         sample_rate: Option<u32>,
         m: Option<u32>,
@@ -170,6 +176,9 @@ impl Index {
         if let Some(num_sub_vectors) = num_sub_vectors {
             hnsw_pq_builder = hnsw_pq_builder.num_sub_vectors(num_sub_vectors);
         }
+        if let Some(num_bits) = num_bits {
+            hnsw_pq_builder = hnsw_pq_builder.num_bits(num_bits);
+        }
         if let Some(max_iterations) = max_iterations {
             hnsw_pq_builder = hnsw_pq_builder.max_iterations(max_iterations);
         }

python/src/query.rs

@@ -18,7 +18,8 @@ use arrow::pyarrow::FromPyArrow;
 use lancedb::index::scalar::FullTextSearchQuery;
 use lancedb::query::QueryExecutionOptions;
 use lancedb::query::{
-    ExecutableQuery, Query as LanceDbQuery, QueryBase, Select, VectorQuery as LanceDbVectorQuery,
+    ExecutableQuery, HasQuery, Query as LanceDbQuery, QueryBase, Select,
+    VectorQuery as LanceDbVectorQuery,
 };
 use pyo3::exceptions::PyRuntimeError;
 use pyo3::prelude::{PyAnyMethods, PyDictMethods};
@@ -87,7 +88,7 @@ impl Query {
         Ok(VectorQuery { inner })
     }
 
-    pub fn nearest_to_text(&mut self, query: Bound<'_, PyDict>) -> PyResult<()> {
+    pub fn nearest_to_text(&mut self, query: Bound<'_, PyDict>) -> PyResult<FTSQuery> {
         let query_text = query
             .get_item("query")?
             .ok_or(PyErr::new::<PyRuntimeError, _>(
@@ -100,9 +101,11 @@ impl Query {
             .transpose()?;
 
         let fts_query = FullTextSearchQuery::new(query_text).columns(columns);
-        self.inner = self.inner.clone().full_text_search(fts_query);
 
-        Ok(())
+        Ok(FTSQuery {
+            fts_query,
+            inner: self.inner.clone(),
+        })
     }
 
     #[pyo3(signature = (max_batch_length=None))]
@@ -133,6 +136,87 @@ impl Query {
 }
 
 #[pyclass]
+#[derive(Clone)]
+pub struct FTSQuery {
+    inner: LanceDbQuery,
+    fts_query: FullTextSearchQuery,
+}
+
+#[pymethods]
+impl FTSQuery {
+    pub fn r#where(&mut self, predicate: String) {
+        self.inner = self.inner.clone().only_if(predicate);
+    }
+
+    pub fn select(&mut self, columns: Vec<(String, String)>) {
+        self.inner = self.inner.clone().select(Select::dynamic(&columns));
+    }
+
+    pub fn limit(&mut self, limit: u32) {
+        self.inner = self.inner.clone().limit(limit as usize);
+    }
+
+    pub fn offset(&mut self, offset: u32) {
+        self.inner = self.inner.clone().offset(offset as usize);
+    }
+
+    pub fn fast_search(&mut self) {
+        self.inner = self.inner.clone().fast_search();
+    }
+
+    pub fn with_row_id(&mut self) {
+        self.inner = self.inner.clone().with_row_id();
+    }
+
+    pub fn postfilter(&mut self) {
+        self.inner = self.inner.clone().postfilter();
+    }
+
+    #[pyo3(signature = (max_batch_length=None))]
+    pub fn execute(
+        self_: PyRef<'_, Self>,
+        max_batch_length: Option<u32>,
+    ) -> PyResult<Bound<'_, PyAny>> {
+        let inner = self_
+            .inner
+            .clone()
+            .full_text_search(self_.fts_query.clone());
+
+        future_into_py(self_.py(), async move {
+            let mut opts = QueryExecutionOptions::default();
+            if let Some(max_batch_length) = max_batch_length {
+                opts.max_batch_length = max_batch_length;
+            }
+            let inner_stream = inner.execute_with_options(opts).await.infer_error()?;
+            Ok(RecordBatchStream::new(inner_stream))
+        })
+    }
+
+    pub fn nearest_to(&mut self, vector: Bound<'_, PyAny>) -> PyResult<HybridQuery> {
+        let vector_query = Query::new(self.inner.clone()).nearest_to(vector)?;
+        Ok(HybridQuery {
+            inner_fts: self.clone(),
+            inner_vec: vector_query,
+        })
+    }
+
+    pub fn explain_plan(self_: PyRef<'_, Self>, verbose: bool) -> PyResult<Bound<'_, PyAny>> {
+        let inner = self_.inner.clone();
+        future_into_py(self_.py(), async move {
+            inner
+                .explain_plan(verbose)
+                .await
+                .map_err(|e| PyRuntimeError::new_err(e.to_string()))
+        })
+    }
+
+    pub fn get_query(&self) -> String {
+        self.fts_query.query.clone()
+    }
+}
+
+#[pyclass]
+#[derive(Clone)]
 pub struct VectorQuery {
     inner: LanceDbVectorQuery,
 }
@@ -229,4 +313,105 @@ impl VectorQuery {
                 .map_err(|e| PyRuntimeError::new_err(e.to_string()))
         })
     }
+
+    pub fn nearest_to_text(&mut self, query: Bound<'_, PyDict>) -> PyResult<HybridQuery> {
+        let fts_query = Query::new(self.inner.mut_query().clone()).nearest_to_text(query)?;
+        Ok(HybridQuery {
+            inner_vec: self.clone(),
+            inner_fts: fts_query,
+        })
+    }
+}
+
+#[pyclass]
+pub struct HybridQuery {
+    inner_vec: VectorQuery,
+    inner_fts: FTSQuery,
+}
+
+#[pymethods]
+impl HybridQuery {
+    pub fn r#where(&mut self, predicate: String) {
+        self.inner_vec.r#where(predicate.clone());
+        self.inner_fts.r#where(predicate);
+    }
+
+    pub fn select(&mut self, columns: Vec<(String, String)>) {
+        self.inner_vec.select(columns.clone());
+        self.inner_fts.select(columns);
+    }
+
+    pub fn limit(&mut self, limit: u32) {
+        self.inner_vec.limit(limit);
+        self.inner_fts.limit(limit);
+    }
+
+    pub fn offset(&mut self, offset: u32) {
+        self.inner_vec.offset(offset);
+        self.inner_fts.offset(offset);
+    }
+
+    pub fn fast_search(&mut self) {
+        self.inner_vec.fast_search();
+        self.inner_fts.fast_search();
+    }
+
+    pub fn with_row_id(&mut self) {
+        self.inner_fts.with_row_id();
+        self.inner_vec.with_row_id();
+    }
+
+    pub fn postfilter(&mut self) {
+        self.inner_vec.postfilter();
+        self.inner_fts.postfilter();
+    }
+
+    pub fn add_query_vector(&mut self, vector: Bound<'_, PyAny>) -> PyResult<()> {
+        self.inner_vec.add_query_vector(vector)
+    }
+
+    pub fn column(&mut self, column: String) {
+        self.inner_vec.column(column);
+    }
+
+    pub fn distance_type(&mut self, distance_type: String) -> PyResult<()> {
+        self.inner_vec.distance_type(distance_type)
+    }
+
+    pub fn refine_factor(&mut self, refine_factor: u32) {
+        self.inner_vec.refine_factor(refine_factor);
+    }
+
+    pub fn nprobes(&mut self, nprobe: u32) {
+        self.inner_vec.nprobes(nprobe);
+    }
+
+    pub fn ef(&mut self, ef: u32) {
+        self.inner_vec.ef(ef);
+    }
+
+    pub fn bypass_vector_index(&mut self) {
+        self.inner_vec.bypass_vector_index();
+    }
+
+    pub fn to_vector_query(&mut self) -> PyResult<VectorQuery> {
+        Ok(VectorQuery {
+            inner: self.inner_vec.inner.clone(),
+        })
+    }
+
+    pub fn to_fts_query(&mut self) -> PyResult<FTSQuery> {
+        Ok(FTSQuery {
+            inner: self.inner_fts.inner.clone(),
+            fts_query: self.inner_fts.fts_query.clone(),
+        })
+    }
+
+    pub fn get_limit(&mut self) -> Option<u32> {
+        self.inner_fts.inner.limit.map(|i| i as u32)
+    }
+
+    pub fn get_with_row_id(&mut self) -> bool {
+        self.inner_fts.inner.with_row_id
+    }
 }

rust/ffi/node/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "lancedb-node"
-version = "0.14.0-beta.2"
+version = "0.14.1-beta.2"
 description = "Serverless, low-latency vector database for AI applications"
 license.workspace = true
 edition.workspace = true

rust/lancedb/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "lancedb"
-version = "0.14.0-beta.2"
+version = "0.14.1-beta.2"
 edition.workspace = true
 description = "LanceDB: A serverless, low-latency vector database for AI applications"
 license.workspace = true

rust/lancedb/src/index/scalar.rs

@@ -53,7 +53,10 @@ pub struct LabelListIndexBuilder {}
 /// A full text search index is an index on a string column that allows for full text search
 #[derive(Debug, Clone)]
 pub struct FtsIndexBuilder {
-    pub(crate) with_position: bool,
+    /// Whether to store the position of the tokens
+    /// This is used for phrase queries
+    pub with_position: bool,
+
     pub tokenizer_configs: TokenizerConfig,
 }
 

rust/lancedb/src/index/vector.rs

@@ -132,6 +132,10 @@ macro_rules! impl_pq_params_setter {
             self.num_sub_vectors = Some(num_sub_vectors);
             self
         }
+        pub fn num_bits(mut self, num_bits: u32) -> Self {
+            self.num_bits = Some(num_bits);
+            self
+        }
     };
 }
 
@@ -189,6 +193,7 @@ pub struct IvfPqIndexBuilder {
 
     // PQ
     pub(crate) num_sub_vectors: Option<u32>,
+    pub(crate) num_bits: Option<u32>,
 }
 
 impl Default for IvfPqIndexBuilder {
@@ -197,6 +202,7 @@ impl Default for IvfPqIndexBuilder {
             distance_type: DistanceType::L2,
             num_partitions: None,
             num_sub_vectors: None,
+            num_bits: None,
             sample_rate: 256,
             max_iterations: 50,
         }
@@ -256,6 +262,7 @@ pub struct IvfHnswPqIndexBuilder {
 
     // PQ
     pub(crate) num_sub_vectors: Option<u32>,
+    pub(crate) num_bits: Option<u32>,
 }
 
 impl Default for IvfHnswPqIndexBuilder {
@@ -264,6 +271,7 @@ impl Default for IvfHnswPqIndexBuilder {
             distance_type: DistanceType::L2,
             num_partitions: None,
             num_sub_vectors: None,
+            num_bits: None,
             sample_rate: 256,
             max_iterations: 50,
             m: 20,

rust/lancedb/src/query.rs

@@ -573,7 +573,7 @@ pub struct Query {
     parent: Arc<dyn TableInternal>,
 
     /// limit the number of rows to return.
-    pub(crate) limit: Option<usize>,
+    pub limit: Option<usize>,
 
     /// Offset of the query.
     pub(crate) offset: Option<usize>,
@@ -596,7 +596,7 @@ pub struct Query {
     /// If set to true, the query will return the `_rowid` meta column.
     ///
     /// By default, this is false.
-    pub(crate) with_row_id: bool,
+    pub with_row_id: bool,
 
     /// If set to false, the filter will be applied after the vector search.
     pub(crate) prefilter: bool,

rust/lancedb/src/remote/db.rs

@@ -271,7 +271,7 @@ impl From<StorageOptions> for RemoteOptions {
                 filtered.insert(opt.to_string(), v.to_string());
             }
         }
-        RemoteOptions::new(filtered)
+        Self::new(filtered)
     }
 }
 

rust/lancedb/src/remote/table.rs

@@ -145,10 +145,8 @@ impl<S: HttpSend> RemoteTable<S> {
     }
 
     fn apply_query_params(body: &mut serde_json::Value, params: &Query) -> Result<()> {
-        if params.offset.is_some() {
-            return Err(Error::NotSupported {
-                message: "Offset is not yet supported in LanceDB Cloud".into(),
-            });
+        if let Some(offset) = params.offset {
+            body["offset"] = serde_json::Value::Number(serde_json::Number::from(offset));
         }
 
         if let Some(limit) = params.limit {
@@ -570,7 +568,19 @@ impl<S: HttpSend> TableInternal for RemoteTable<S> {
             Index::BTree(_) => ("BTREE", None),
             Index::Bitmap(_) => ("BITMAP", None),
             Index::LabelList(_) => ("LABEL_LIST", None),
-            Index::FTS(_) => ("FTS", None),
+            Index::FTS(fts) => {
+                let with_position = fts.with_position;
+                let configs = serde_json::to_value(fts.tokenizer_configs).map_err(|e| {
+                    Error::InvalidInput {
+                        message: format!("failed to serialize FTS index params {:?}", e),
+                    }
+                })?;
+                for (key, value) in configs.as_object().unwrap() {
+                    body[key] = value.clone();
+                }
+                body["with_position"] = serde_json::Value::Bool(with_position);
+                ("FTS", None)
+            }
             Index::Auto => {
                 let schema = self.schema().await?;
                 let field = schema
@@ -1336,6 +1346,7 @@ mod tests {
                 "vector_column": "my_vector",
                 "prefilter": false,
                 "k": 42,
+                "offset": 10,
                 "distance_type": "cosine",
                 "bypass_vector_index": true,
                 "columns": ["a", "b"],
@@ -1364,6 +1375,7 @@ mod tests {
         let _ = table
             .query()
             .limit(42)
+            .offset(10)
             .select(Select::columns(&["a", "b"]))
             .nearest_to(vec![0.1, 0.2, 0.3])
             .unwrap()
@@ -1496,6 +1508,7 @@ mod tests {
         ];
 
         for (index_type, distance_type, index) in cases {
+            let params = index.clone();
             let table = Table::new_with_handler("my_table", move |request| {
                 assert_eq!(request.method(), "POST");
                 assert_eq!(request.url().path(), "/v1/table/my_table/create_index/");
@@ -1512,6 +1525,17 @@ mod tests {
                 if let Some(distance_type) = distance_type {
                     expected_body["metric_type"] = distance_type.to_lowercase().into();
                 }
+                if let Index::FTS(fts) = &params {
+                    expected_body["with_position"] = fts.with_position.into();
+                    expected_body["base_tokenizer"] = "simple".into();
+                    expected_body["language"] = "English".into();
+                    expected_body["max_token_length"] = 40.into();
+                    expected_body["lower_case"] = true.into();
+                    expected_body["stem"] = false.into();
+                    expected_body["remove_stop_words"] = false.into();
+                    expected_body["ascii_folding"] = false.into();
+                }
+
                 assert_eq!(body, expected_body);
 
                 http::Response::builder().status(200).body("{}").unwrap()