Bump version: 0.17.1-beta.5 → 0.17.1-beta.6

* Add `See Also` section to `cleanup_old_files` and `compact_files` so
they know it's linked to `optimize`.
* Fixes link to `compact_files` arguments
* Improves formatting of note.

.bumpversion.toml

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

.github/workflows/make-release-commit.yml

@@ -97,3 +97,7 @@ jobs:
         if: ${{ !inputs.dry_run && inputs.other }}
         with:
           github_token: ${{ secrets.GITHUB_TOKEN }}
+      - uses: ./.github/workflows/update_package_lock_nodejs
+        if: ${{ !inputs.dry_run && inputs.other }}
+        with:
+          github_token: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/npm-publish.yml

@@ -571,7 +571,7 @@ jobs:
         uses: actions/checkout@v4
         with:
           ref: main
-          persist-credentials: false
+          token: ${{ secrets.LANCEDB_RELEASE_TOKEN }}
           fetch-depth: 0
           lfs: true
       - uses: ./.github/workflows/update_package_lock
@@ -589,7 +589,7 @@ jobs:
         uses: actions/checkout@v4
         with:
           ref: main
-          persist-credentials: false
+          token: ${{ secrets.LANCEDB_RELEASE_TOKEN }}
           fetch-depth: 0
           lfs: true
       - uses: ./.github/workflows/update_package_lock_nodejs

.github/workflows/rust.yml

@@ -185,7 +185,7 @@ jobs:
           Add-Content $env:GITHUB_PATH "C:\BuildTools\VC\Tools\Llvm\x64\bin"
 
           # Add MSVC runtime libraries to LIB
-          $env:LIB = "C:\BuildTools\VC\Tools\MSVC\$latestVersion\lib\arm64;" + 
+          $env:LIB = "C:\BuildTools\VC\Tools\MSVC\$latestVersion\lib\arm64;" +
                      "C:\Program Files (x86)\Windows Kits\10\Lib\10.0.22621.0\um\arm64;" +
                      "C:\Program Files (x86)\Windows Kits\10\Lib\10.0.22621.0\ucrt\arm64"
           Add-Content $env:GITHUB_ENV "LIB=$env:LIB"
@@ -238,3 +238,41 @@ jobs:
           $env:VCPKG_ROOT = $env:VCPKG_INSTALLATION_ROOT
           cargo build --target aarch64-pc-windows-msvc
           cargo test --target aarch64-pc-windows-msvc
+
+  msrv:
+    # Check the minimum supported Rust version
+    name: MSRV Check - Rust v${{ matrix.msrv }}
+    runs-on: ubuntu-24.04
+    strategy:
+      matrix:
+        msrv: ["1.78.0"] # This should match up with rust-version in Cargo.toml
+    env:
+      # Need up-to-date compilers for kernels
+      CC: clang-18
+      CXX: clang++-18
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          submodules: true
+      - name: Install dependencies
+        run: |
+          sudo apt update
+          sudo apt install -y protobuf-compiler libssl-dev
+      - name: Install ${{ matrix.msrv }}
+        uses: dtolnay/rust-toolchain@master
+        with:
+          toolchain: ${{ matrix.msrv }}
+      - name: Downgrade  dependencies
+        # These packages have newer requirements for MSRV
+        run: |
+          cargo update -p aws-sdk-bedrockruntime --precise 1.64.0
+          cargo update -p aws-sdk-dynamodb --precise 1.55.0
+          cargo update -p aws-config --precise 1.5.10
+          cargo update -p aws-sdk-kms --precise 1.51.0
+          cargo update -p aws-sdk-s3 --precise 1.65.0
+          cargo update -p aws-sdk-sso --precise 1.50.0
+          cargo update -p aws-sdk-ssooidc --precise 1.51.0
+          cargo update -p aws-sdk-sts --precise 1.51.0
+          cargo update -p home --precise 0.5.9
+      - name: cargo +${{ matrix.msrv }} check
+        run: cargo check --workspace --tests --benches --all-features

.github/workflows/upload_wheel/action.yml

@@ -22,7 +22,7 @@ runs:
     shell: bash
     id: choose_repo
     run: |
-      if [ ${{ github.ref }} == "*beta*" ]; then
+      if [[ ${{ github.ref }} == *beta* ]]; then
         echo "repo=fury" >> $GITHUB_OUTPUT
       else
         echo "repo=pypi" >> $GITHUB_OUTPUT
@@ -33,7 +33,7 @@ runs:
       FURY_TOKEN: ${{ inputs.fury_token }}
       PYPI_TOKEN: ${{ inputs.pypi_token }}
     run: |
-      if [ ${{ steps.choose_repo.outputs.repo }} == "fury" ]; then
+      if [[ ${{ steps.choose_repo.outputs.repo }} == fury ]]; then
         WHEEL=$(ls target/wheels/lancedb-*.whl 2> /dev/null | head -n 1)
         echo "Uploading $WHEEL to Fury"
         curl -f -F package=@$WHEEL https://$FURY_TOKEN@push.fury.io/lancedb/

Cargo.toml

@@ -18,19 +18,19 @@ repository = "https://github.com/lancedb/lancedb"
 description = "Serverless, low-latency vector database for AI applications"
 keywords = ["lancedb", "lance", "database", "vector", "search"]
 categories = ["database-implementations"]
-rust-version = "1.80.0"                                                     # TODO: lower this once we upgrade Lance again.
+rust-version = "1.78.0"
 
 [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.5" }
+lance-io = { version = "=0.21.0", git = "https://github.com/lancedb/lance.git", tag = "v0.21.0-beta.5" }
+lance-index = { version = "=0.21.0", git = "https://github.com/lancedb/lance.git", tag = "v0.21.0-beta.5" }
+lance-linalg = { version = "=0.21.0", git = "https://github.com/lancedb/lance.git", tag = "v0.21.0-beta.5" }
+lance-table = { version = "=0.21.0", git = "https://github.com/lancedb/lance.git", tag = "v0.21.0-beta.5" }
+lance-testing = { version = "=0.21.0", git = "https://github.com/lancedb/lance.git", tag = "v0.21.0-beta.5" }
+lance-datafusion = { version = "=0.21.0", git = "https://github.com/lancedb/lance.git", tag = "v0.21.0-beta.5" }
+lance-encoding = { version = "=0.21.0", git = "https://github.com/lancedb/lance.git", tag = "v0.21.0-beta.5" }
 # Note that this one does not include pyarrow
 arrow = { version = "53.2", optional = false }
 arrow-array = "53.2"

docs/mkdocs.yml

@@ -62,6 +62,7 @@ plugins:
             # for cross references
             - https://arrow.apache.org/docs/objects.inv
             - https://pandas.pydata.org/docs/objects.inv
+            - https://lancedb.github.io/lance/objects.inv
   - mkdocs-jupyter
   - render_swagger:
       allow_arbitrary_locations: true
@@ -231,6 +232,7 @@ nav:
               - 🐍 Python: python/saas-python.md
               - 👾 JavaScript: javascript/modules.md
               - REST API: cloud/rest.md
+          - FAQs: cloud/cloud_faq.md
 
   - Quick start: basic.md
   - Concepts:
@@ -357,6 +359,7 @@ nav:
           - 🐍 Python: python/saas-python.md
           - 👾 JavaScript: javascript/modules.md
           - REST API: cloud/rest.md
+      - FAQs: cloud/cloud_faq.md
 
 extra_css:
   - styles/global.css

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/basic.md

@@ -141,14 +141,6 @@ recommend switching to stable releases.
     --8<-- "python/python/tests/docs/test_basic.py:connect_async"
     ```
 
-    !!! note "Asynchronous Python API"
-
-        The asynchronous Python API is new and has some slight differences compared
-        to the synchronous API.  Feel free to start using the asynchronous version.
-        Once all features have migrated we will start to move the synchronous API to
-        use the same syntax as the asynchronous API.  To help with this migration we
-        have created a [migration guide](migration.md) detailing the differences.
-
 === "Typescript[^1]"
 
     === "@lancedb/lancedb"

docs/src/cloud/cloud_faq.md

@@ -0,0 +1,34 @@
+This section provides answers to the most common questions asked about LanceDB Cloud. By following these guidelines, you can ensure a smooth, performant experience with LanceDB Cloud.
+
+### Should I reuse the database connection?
+Yes! It is recommended to establish a single database connection and maintain it throughout your interaction with the tables within. 
+
+LanceDB uses HTTP connections to communicate with the servers. By re-using the Connection object, you avoid the overhead of repeatedly establishing HTTP connections, significantly improving efficiency.
+
+### Should I re-use the `Table` object?
+`table = db.open_table()` should be called once and used for all subsequent table operations. If there are changes to the opened table, `table` always reflect the **latest version** of the data. 
+
+### What should I do if I need to search for rows by `id`?
+LanceDB Cloud currently does not support an ID or primary key column. You are recommended to add a 
+user-defined ID column. To significantly improve the query performance with SQL causes, a scalar BITMAP/BTREE index should be created on this column. 
+
+### What are the vector indexing types supported by LanceDB Cloud?
+We support `IVF_PQ` and `IVF_HNSW_SQ` as the `index_type` which is passed to `create_index`. LanceDB Cloud tunes the indexing parameters automatically to achieve the best tradeoff between query latency and query quality.
+
+### When I add new rows to a table, do I need to manually update the index?
+No! LanceDB Cloud triggers an asynchronous background job to index the new vectors.
+
+Even though indexing is asynchronous, your vectors will still be immediately searchable. LanceDB uses brute-force search to search over unindexed rows. This makes you new data is immediately available, but does increase latency temporarily. To disable the brute-force part of search, set the `fast_search` flag in your query to `true`.
+
+### Do I need to reindex the whole dataset if only a small portion of the data is deleted or updated?
+No! Similar to adding data to the table, LanceDB Cloud triggers an asynchronous background job to update the existing indices. Therefore, no action is needed from users and there is absolutely no 
+downtime expected.
+
+### How do I know whether an index has been created?
+While index creation in LanceDB Cloud is generally fast, querying immediately after a `create_index` call may result in errors. It's recommended to use `list_indices` to verify index creation before querying.
+
+### Why is my query latency higher than expected?
+Multiple factors can impact query latency. To reduce query latency, consider the following:
+- Send pre-warm queries: send a few queries to warm up the cache before an actual user query.
+- Check network latency: LanceDB Cloud is hosted in AWS `us-east-1` region. It is recommended to run queries from an EC2 instance that is in the same region.
+- Create scalar indices: If you are filtering on metadata, it is recommended to create scalar indices on those columns. This will speedup searches with metadata filtering. See [here](../guides/scalar_index.md) for more details on creating a scalar index.

docs/src/guides/tables.md

@@ -804,12 +804,13 @@ a table:
 
 You can add new columns to the table with the `add_columns` method. New columns
 are filled with values based on a SQL expression. For example, you can add a new
-column `y` to the table and fill it with the value of `x + 1`.
+column `y` to the table, fill it with the value of `x * 2` and set the expected 
+data type for it.
 
 === "Python"
 
     ```python
-    table.add_columns({"double_price": "price * 2"})
+    --8<-- "python/python/tests/docs/test_basic.py:add_columns"
     ```
     **API Reference:** [lancedb.table.Table.add_columns][]
 
@@ -849,8 +850,7 @@ rewriting the column, which can be a heavy operation.
 
     ```python
     import pyarrow as pa
-    table.alter_column({"path": "double_price", "rename": "dbl_price",
-                        "data_type": pa.float32(), "nullable": False})
+    --8<-- "python/python/tests/docs/test_basic.py:alter_columns"
     ```
     **API Reference:** [lancedb.table.Table.alter_columns][]
 
@@ -873,7 +873,7 @@ will remove the column from the schema.
 === "Python"
 
     ```python
-    table.drop_columns(["dbl_price"])
+     --8<-- "python/python/tests/docs/test_basic.py:drop_columns"
     ```
     **API Reference:** [lancedb.table.Table.drop_columns][]
 

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

docs/src/migration.md

@@ -1,81 +1,14 @@
 # Rust-backed Client Migration Guide
 
-In an effort to ensure all clients have the same set of capabilities we have begun migrating the
-python and node clients onto a common Rust base library. In python, this new client is part of
-the same lancedb package, exposed as an asynchronous client. Once the asynchronous client has
-reached full functionality we will begin migrating the synchronous library to be a thin wrapper
-around the asynchronous client.
+In an effort to ensure all clients have the same set of capabilities we have
+migrated the Python and Node clients onto a common Rust base library. In Python,
+both the synchronous and asynchronous clients are based on this implementation.
+In Node, the new client is available as `@lancedb/lancedb`, which replaces
+the existing `vectordb` package.
 
-This guide describes the differences between the two APIs and will hopefully assist users
+This guide describes the differences between the two Node APIs and will hopefully assist users
 that would like to migrate to the new API.
 
-## Python
-### Closeable Connections
-
-The Connection now has a `close` method. You can call this when
-you are done with the connection to eagerly free resources. Currently
-this is limited to freeing/closing the HTTP connection for remote
-connections. In the future we may add caching or other resources to
-native connections so this is probably a good practice even if you
-aren't using remote connections.
-
-In addition, the connection can be used as a context manager which may
-be a more convenient way to ensure the connection is closed.
-
-```python
-import lancedb
-
-async def my_async_fn():
-    with await lancedb.connect_async("my_uri") as db:
-        print(await db.table_names())
-```
-
-It is not mandatory to call the `close` method. If you do not call it
-then the connection will be closed when the object is garbage collected.
-
-### Closeable Table
-
-The Table now also has a `close` method, similar to the connection. This
-can be used to eagerly free the cache used by a Table object. Similar to
-the connection, it can be used as a context manager and it is not mandatory
-to call the `close` method.
-
-#### Changes to Table APIs
-
-- Previously `Table.schema` was a property. Now it is an async method.
-- The method `Table.__len__` was removed and `len(table)` will no longer
-  work. Use `Table.count_rows` instead.
-
-#### Creating Indices
-
-The `Table.create_index` method is now used for creating both vector indices
-and scalar indices. It currently requires a column name to be specified (the
-column to index). Vector index defaults are now smarter and scale better with
-the size of the data.
-
-To specify index configuration details you will need to specify which kind of
-index you are using.
-
-#### Querying
-
-The `Table.search` method has been renamed to `AsyncTable.vector_search` for
-clarity.
-
-### Features not yet supported
-
-The following features are not yet supported by the asynchronous API. However,
-we plan to support them soon.
-
-- You cannot specify an embedding function when creating or opening a table.
-  You must calculate embeddings yourself if using the asynchronous API
-- The merge insert operation is not supported in the asynchronous API
-- Cleanup / compact / optimize indices are not supported in the asynchronous API
-- add / alter columns is not supported in the asynchronous API
-- The asynchronous API does not yet support any full text search or reranking
-  search
-- Remote connections to LanceDb Cloud are not yet supported.
-- The method Table.head is not yet supported.
-
 ## TypeScript/JavaScript
 
 For JS/TS users, we offer a brand new SDK [@lancedb/lancedb](https://www.npmjs.com/package/@lancedb/lancedb)

docs/src/python/python.md

@@ -47,6 +47,8 @@ is also an [asynchronous API client](#connections-asynchronous).
 
 ::: lancedb.embeddings.registry.EmbeddingFunctionRegistry
 
+::: lancedb.embeddings.base.EmbeddingFunctionConfig
+
 ::: lancedb.embeddings.base.EmbeddingFunction
 
 ::: lancedb.embeddings.base.TextEmbeddingFunction

docs/src/python/saas-python.md

@@ -17,4 +17,8 @@ pip install lancedb
 ## Table
 
 ::: lancedb.remote.table.RemoteTable
-
+    options:
+        filters:
+            - "!cleanup_old_versions"
+            - "!compact_files"
+            - "!optimize"

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.5</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.5</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.5",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
     "": {
       "name": "vectordb",
-      "version": "0.14.0-beta.2",
+      "version": "0.14.1-beta.5",
       "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.5",
+        "@lancedb/vectordb-darwin-x64": "0.14.1-beta.5",
+        "@lancedb/vectordb-linux-arm64-gnu": "0.14.1-beta.5",
+        "@lancedb/vectordb-linux-arm64-musl": "0.14.1-beta.5",
+        "@lancedb/vectordb-linux-x64-gnu": "0.14.1-beta.5",
+        "@lancedb/vectordb-linux-x64-musl": "0.14.1-beta.5",
+        "@lancedb/vectordb-win32-arm64-msvc": "0.14.1-beta.5",
+        "@lancedb/vectordb-win32-x64-msvc": "0.14.1-beta.5"
       },
       "peerDependencies": {
         "@apache-arrow/ts": "^14.0.2",
@@ -329,6 +329,102 @@
         "@jridgewell/sourcemap-codec": "^1.4.10"
       }
     },
+    "node_modules/@lancedb/vectordb-darwin-arm64": {
+      "version": "0.14.1-beta.5",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-arm64/-/vectordb-darwin-arm64-0.14.1-beta.5.tgz",
+      "integrity": "sha512-AgDIcrFhFlDi1loVpZpJz7pweFh5JU1p/V18xtlzwpc8ABViFEoqGCPUaRZTaFPMpcgjIWflee5MH0oDRoqwVg==",
+      "cpu": [
+        "arm64"
+      ],
+      "optional": true,
+      "os": [
+        "darwin"
+      ]
+    },
+    "node_modules/@lancedb/vectordb-darwin-x64": {
+      "version": "0.14.1-beta.5",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-x64/-/vectordb-darwin-x64-0.14.1-beta.5.tgz",
+      "integrity": "sha512-SBLTiIK+BDHzg1+Judy0qJK949jL0lDOIuSC7kfsq3EFFUCbsTYEJTv1q2dWmh/MEUVIr/HTFDyxhajhRVYLkA==",
+      "cpu": [
+        "x64"
+      ],
+      "optional": true,
+      "os": [
+        "darwin"
+      ]
+    },
+    "node_modules/@lancedb/vectordb-linux-arm64-gnu": {
+      "version": "0.14.1-beta.5",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-arm64-gnu/-/vectordb-linux-arm64-gnu-0.14.1-beta.5.tgz",
+      "integrity": "sha512-1Osls8wy5Wz1Pk6JX9XeM++GdF6BVF8XuD48j+EV97AgEaY63Ya7VTk299cvMzD2RdQzlOGOwD+yuwfYakY7Kw==",
+      "cpu": [
+        "arm64"
+      ],
+      "optional": true,
+      "os": [
+        "linux"
+      ]
+    },
+    "node_modules/@lancedb/vectordb-linux-arm64-musl": {
+      "version": "0.14.1-beta.5",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-arm64-musl/-/vectordb-linux-arm64-musl-0.14.1-beta.5.tgz",
+      "integrity": "sha512-50NnvLASNh3KN2gFFQWiqtoGFJHORrCjISijJwevKE4LatUCXLG582Eh4Fc+pkjQ5wQY259nu1I/vwl0ZBEIUg==",
+      "cpu": [
+        "arm64"
+      ],
+      "optional": true,
+      "os": [
+        "linux"
+      ]
+    },
+    "node_modules/@lancedb/vectordb-linux-x64-gnu": {
+      "version": "0.14.1-beta.5",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-x64-gnu/-/vectordb-linux-x64-gnu-0.14.1-beta.5.tgz",
+      "integrity": "sha512-smkVkuEDl/sqVtVtHLgi2Pfpe6tmW0pbnUJEICR7rpqeCgb2Yk+JHy8BJ1oB75zm3jT+WG3pQ5gdseijBlGifg==",
+      "cpu": [
+        "x64"
+      ],
+      "optional": true,
+      "os": [
+        "linux"
+      ]
+    },
+    "node_modules/@lancedb/vectordb-linux-x64-musl": {
+      "version": "0.14.1-beta.5",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-x64-musl/-/vectordb-linux-x64-musl-0.14.1-beta.5.tgz",
+      "integrity": "sha512-sOCPHc/6PZ7UimTI00zrYTo6mO948fbJz2YLJoqEm310Uz8UDG5UAgSPjYfOBEzDlMiXJhVx48K7QdFWnV3ATw==",
+      "cpu": [
+        "x64"
+      ],
+      "optional": true,
+      "os": [
+        "linux"
+      ]
+    },
+    "node_modules/@lancedb/vectordb-win32-arm64-msvc": {
+      "version": "0.14.1-beta.5",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-win32-arm64-msvc/-/vectordb-win32-arm64-msvc-0.14.1-beta.5.tgz",
+      "integrity": "sha512-whGNb+djCTPJG1xU1Lq8J5KQjzIBS3RGTDugSULnS8U9zgzLgEl8Bm/slT357Wy3GlE+P4y7OOmBaK/pHgRvzQ==",
+      "cpu": [
+        "arm64"
+      ],
+      "optional": true,
+      "os": [
+        "win32"
+      ]
+    },
+    "node_modules/@lancedb/vectordb-win32-x64-msvc": {
+      "version": "0.14.1-beta.5",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-win32-x64-msvc/-/vectordb-win32-x64-msvc-0.14.1-beta.5.tgz",
+      "integrity": "sha512-Kq53smSxmKmrlQlSzPL8RUVlEnJbhFKM1/ZvQJVgipuMCHJLXkLHGeLOIacudlFjO+6olR96i/FBCdHuGTsXbw==",
+      "cpu": [
+        "x64"
+      ],
+      "optional": true,
+      "os": [
+        "win32"
+      ]
+    },
     "node_modules/@neon-rs/cli": {
       "version": "0.0.160",
       "resolved": "https://registry.npmjs.org/@neon-rs/cli/-/cli-0.0.160.tgz",

node/package.json

@@ -1,7 +1,8 @@
 {
   "name": "vectordb",
-  "version": "0.14.0-beta.2",
+  "version": "0.14.1-beta.5",
   "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.5",
+    "@lancedb/vectordb-darwin-arm64": "0.14.1-beta.5",
+    "@lancedb/vectordb-linux-x64-gnu": "0.14.1-beta.5",
+    "@lancedb/vectordb-linux-arm64-gnu": "0.14.1-beta.5",
+    "@lancedb/vectordb-linux-x64-musl": "0.14.1-beta.5",
+    "@lancedb/vectordb-linux-arm64-musl": "0.14.1-beta.5",
+    "@lancedb/vectordb-win32-x64-msvc": "0.14.1-beta.5",
+    "@lancedb/vectordb-win32-arm64-msvc": "0.14.1-beta.5"
   }
 }

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.5"
 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) => {
@@ -1051,6 +1058,26 @@ describe.each([arrow13, arrow14, arrow15, arrow16, arrow17])(
       expect(results[0].text).toBe(data[0].text);
     });
 
+    test("full text search without lowercase", async () => {
+      const db = await connect(tmpDir.name);
+      const data = [
+        { text: "hello world", vector: [0.1, 0.2, 0.3] },
+        { text: "Hello World", vector: [0.4, 0.5, 0.6] },
+      ];
+      const table = await db.createTable("test", data);
+      await table.createIndex("text", {
+        config: Index.fts({ withPosition: false }),
+      });
+      const results = await table.search("hello").toArray();
+      expect(results.length).toBe(2);
+
+      await table.createIndex("text", {
+        config: Index.fts({ withPosition: false, lowercase: false }),
+      });
+      const results2 = await table.search("hello").toArray();
+      expect(results2.length).toBe(1);
+    });
+
     test("full text search phrase query", async () => {
       const db = await connect(tmpDir.name);
       const data = [

nodejs/examples/basic.test.ts

@@ -119,7 +119,9 @@ test("basic table examples", async () => {
 
     {
       // --8<-- [start:add_columns]
-      await tbl.addColumns([{ name: "double_price", valueSql: "price * 2" }]);
+      await tbl.addColumns([
+        { name: "double_price", valueSql: "cast((price * 2) as Float)" },
+      ]);
       // --8<-- [end:add_columns]
       // --8<-- [start:alter_columns]
       await tbl.alterColumns([

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.
    *
@@ -339,6 +349,52 @@ export interface FtsOptions {
    * which will make the index smaller and faster to build, but will not support phrase queries.
    */
   withPosition?: boolean;
+
+  /**
+   * The tokenizer to use when building the index.
+   * The default is "simple".
+   *
+   * The following tokenizers are available:
+   *
+   * "simple" - Simple tokenizer. This tokenizer splits the text into tokens using whitespace and punctuation as a delimiter.
+   *
+   * "whitespace" - Whitespace tokenizer. This tokenizer splits the text into tokens using whitespace as a delimiter.
+   *
+   * "raw" - Raw tokenizer. This tokenizer does not split the text into tokens and indexes the entire text as a single token.
+   */
+  baseTokenizer?: "simple" | "whitespace" | "raw";
+
+  /**
+   * language for stemming and stop words
+   * this is only used when `stem` or `remove_stop_words` is true
+   */
+  language?: string;
+
+  /**
+   * maximum token length
+   * tokens longer than this length will be ignored
+   */
+  maxTokenLength?: number;
+
+  /**
+   * whether to lowercase tokens
+   */
+  lowercase?: boolean;
+
+  /**
+   * whether to stem tokens
+   */
+  stem?: boolean;
+
+  /**
+   * whether to remove stop words
+   */
+  removeStopWords?: boolean;
+
+  /**
+   * whether to remove punctuation
+   */
+  asciiFolding?: boolean;
 }
 
 export class Index {
@@ -440,7 +496,18 @@ export class Index {
    * For now, the full text search index only supports English, and doesn't support phrase search.
    */
   static fts(options?: Partial<FtsOptions>) {
-    return new Index(LanceDbIndex.fts(options?.withPosition));
+    return new Index(
+      LanceDbIndex.fts(
+        options?.withPosition,
+        options?.baseTokenizer,
+        options?.language,
+        options?.maxTokenLength,
+        options?.lowercase,
+        options?.stem,
+        options?.removeStopWords,
+        options?.asciiFolding,
+      ),
+    );
   }
 
   /**

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.5",
 	"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.5",
 	"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.5",
 	"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.5",
 	"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.5",
 	"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.5",
 	"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.5",
   "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.5",
 	"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": "0.14.1-beta.5",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
     "": {
       "name": "@lancedb/lancedb",
-      "version": "0.13.0",
+      "version": "0.14.1-beta.5",
       "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.5",
   "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);
         }
@@ -92,11 +96,45 @@ impl Index {
     }
 
     #[napi(factory)]
-    pub fn fts(with_position: Option<bool>) -> Self {
+    #[allow(clippy::too_many_arguments)]
+    pub fn fts(
+        with_position: Option<bool>,
+        base_tokenizer: Option<String>,
+        language: Option<String>,
+        max_token_length: Option<u32>,
+        lower_case: Option<bool>,
+        stem: Option<bool>,
+        remove_stop_words: Option<bool>,
+        ascii_folding: Option<bool>,
+    ) -> Self {
         let mut opts = FtsIndexBuilder::default();
+        let mut tokenizer_configs = opts.tokenizer_configs.clone();
         if let Some(with_position) = with_position {
             opts = opts.with_position(with_position);
         }
+        if let Some(base_tokenizer) = base_tokenizer {
+            tokenizer_configs = tokenizer_configs.base_tokenizer(base_tokenizer);
+        }
+        if let Some(language) = language {
+            tokenizer_configs = tokenizer_configs.language(&language).unwrap();
+        }
+        if let Some(max_token_length) = max_token_length {
+            tokenizer_configs = tokenizer_configs.max_token_length(Some(max_token_length as usize));
+        }
+        if let Some(lower_case) = lower_case {
+            tokenizer_configs = tokenizer_configs.lower_case(lower_case);
+        }
+        if let Some(stem) = stem {
+            tokenizer_configs = tokenizer_configs.stem(stem);
+        }
+        if let Some(remove_stop_words) = remove_stop_words {
+            tokenizer_configs = tokenizer_configs.remove_stop_words(remove_stop_words);
+        }
+        if let Some(ascii_folding) = ascii_folding {
+            tokenizer_configs = tokenizer_configs.ascii_folding(ascii_folding);
+        }
+        opts.tokenizer_configs = tokenizer_configs;
+
         Self {
             inner: Mutex::new(Some(LanceDbIndex::FTS(opts))),
         }

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.6"
 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.6"
 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==0.21.0b5",
     "tqdm>=4.27.0",
     "pydantic>=1.10",
     "packaging",

python/python/lancedb/__init__.py

@@ -70,7 +70,7 @@ def connect(
         default configuration is used.
     storage_options: dict, optional
         Additional options for the storage backend. See available options at
-        https://lancedb.github.io/lancedb/guides/storage/
+        <https://lancedb.github.io/lancedb/guides/storage/>
 
     Examples
     --------
@@ -82,11 +82,13 @@ def connect(
 
     For object storage, use a URI prefix:
 
-    >>> db = lancedb.connect("s3://my-bucket/lancedb")
+    >>> db = lancedb.connect("s3://my-bucket/lancedb",
+    ...                      storage_options={"aws_access_key_id": "***"})
 
     Connect to LanceDB cloud:
 
-    >>> db = lancedb.connect("db://my_database", api_key="ldb_...")
+    >>> db = lancedb.connect("db://my_database", api_key="ldb_...",
+    ...                      client_config={"retry_config": {"retries": 5}})
 
     Returns
     -------
@@ -110,6 +112,7 @@ def connect(
             # TODO: remove this (deprecation warning downstream)
             request_thread_pool=request_thread_pool,
             client_config=client_config,
+            storage_options=storage_options,
             **kwargs,
         )
 
@@ -163,7 +166,7 @@ async def connect_async(
         default configuration is used.
     storage_options: dict, optional
         Additional options for the storage backend. See available options at
-        https://lancedb.github.io/lancedb/guides/storage/
+        <https://lancedb.github.io/lancedb/guides/storage/>
 
     Examples
     --------

python/python/lancedb/_lancedb.pyi

@@ -2,19 +2,8 @@ from typing import Dict, List, Optional, Tuple
 
 import pyarrow as pa
 
-class Index:
-    @staticmethod
-    def ivf_pq(
-        distance_type: Optional[str],
-        num_partitions: Optional[int],
-        num_sub_vectors: Optional[int],
-        max_iterations: Optional[int],
-        sample_rate: Optional[int],
-    ) -> Index: ...
-    @staticmethod
-    def btree() -> Index: ...
-
 class Connection(object):
+    uri: str
     async def table_names(
         self, start_after: Optional[str], limit: Optional[int]
     ) -> list[str]: ...
@@ -46,9 +35,7 @@ class Table:
     async def add(self, data: pa.RecordBatchReader, mode: str) -> None: ...
     async def update(self, updates: Dict[str, str], where: Optional[str]) -> None: ...
     async def count_rows(self, filter: Optional[str]) -> int: ...
-    async def create_index(
-        self, column: str, config: Optional[Index], replace: Optional[bool]
-    ): ...
+    async def create_index(self, column: str, config, replace: Optional[bool]): ...
     async def version(self) -> int: ...
     async def checkout(self, version): ...
     async def checkout_latest(self): ...
@@ -79,9 +66,21 @@ 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 +94,24 @@ 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/background_loop.py

@@ -23,3 +23,6 @@ class BackgroundEventLoop:
 
     def run(self, future):
         return asyncio.run_coroutine_threadsafe(future, self.loop).result()
+
+
+LOOP = BackgroundEventLoop()

python/python/lancedb/db.py

@@ -17,10 +17,11 @@ from abc import abstractmethod
 from pathlib import Path
 from typing import TYPE_CHECKING, Dict, Iterable, List, Literal, Optional, Union
 
+from lancedb.embeddings.registry import EmbeddingFunctionRegistry
 from overrides import EnforceOverrides, override
 
 from lancedb.common import data_to_reader, sanitize_uri, validate_schema
-from lancedb.background_loop import BackgroundEventLoop
+from lancedb.background_loop import LOOP
 
 from ._lancedb import connect as lancedb_connect
 from .table import (
@@ -43,8 +44,6 @@ if TYPE_CHECKING:
     from .common import DATA, URI
     from .embeddings import EmbeddingFunctionConfig
 
-LOOP = BackgroundEventLoop()
-
 
 class DBConnection(EnforceOverrides):
     """An active LanceDB connection interface."""
@@ -82,6 +81,10 @@ class DBConnection(EnforceOverrides):
         on_bad_vectors: str = "error",
         fill_value: float = 0.0,
         embedding_functions: Optional[List[EmbeddingFunctionConfig]] = None,
+        *,
+        storage_options: Optional[Dict[str, str]] = None,
+        data_storage_version: Optional[str] = None,
+        enable_v2_manifest_paths: Optional[bool] = None,
     ) -> Table:
         """Create a [Table][lancedb.table.Table] in the database.
 
@@ -119,6 +122,24 @@ class DBConnection(EnforceOverrides):
             One of "error", "drop", "fill".
         fill_value: float
             The value to use when filling vectors. Only used if on_bad_vectors="fill".
+        storage_options: dict, optional
+            Additional options for the storage backend. Options already set on the
+            connection will be inherited by the table, but can be overridden here.
+            See available options at
+            <https://lancedb.github.io/lancedb/guides/storage/>
+        data_storage_version: optional, str, default "stable"
+            The version of the data storage format to use. Newer versions are more
+            efficient but require newer versions of lance to read.  The default is
+            "stable" which will use the legacy v2 version.  See the user guide
+            for more details.
+        enable_v2_manifest_paths: bool, optional, default False
+            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 0.13.0). To migrate an existing dataset, instead
+            use the
+            [Table.migrate_manifest_paths_v2][lancedb.table.Table.migrate_v2_manifest_paths]
+            method.
 
         Returns
         -------
@@ -140,7 +161,7 @@ class DBConnection(EnforceOverrides):
         >>> data = [{"vector": [1.1, 1.2], "lat": 45.5, "long": -122.7},
         ...         {"vector": [0.2, 1.8], "lat": 40.1, "long":  -74.1}]
         >>> db.create_table("my_table", data)
-        LanceTable(connection=..., name="my_table")
+        LanceTable(name='my_table', version=1, ...)
         >>> db["my_table"].head()
         pyarrow.Table
         vector: fixed_size_list<item: float>[2]
@@ -161,7 +182,7 @@ class DBConnection(EnforceOverrides):
         ...    "long": [-122.7, -74.1]
         ... })
         >>> db.create_table("table2", data)
-        LanceTable(connection=..., name="table2")
+        LanceTable(name='table2', version=1, ...)
         >>> db["table2"].head()
         pyarrow.Table
         vector: fixed_size_list<item: float>[2]
@@ -184,7 +205,7 @@ class DBConnection(EnforceOverrides):
         ...   pa.field("long", pa.float32())
         ... ])
         >>> db.create_table("table3", data, schema = custom_schema)
-        LanceTable(connection=..., name="table3")
+        LanceTable(name='table3', version=1, ...)
         >>> db["table3"].head()
         pyarrow.Table
         vector: fixed_size_list<item: float>[2]
@@ -218,7 +239,7 @@ class DBConnection(EnforceOverrides):
         ...     pa.field("price", pa.float32()),
         ... ])
         >>> db.create_table("table4", make_batches(), schema=schema)
-        LanceTable(connection=..., name="table4")
+        LanceTable(name='table4', version=1, ...)
 
         """
         raise NotImplementedError
@@ -226,7 +247,13 @@ class DBConnection(EnforceOverrides):
     def __getitem__(self, name: str) -> LanceTable:
         return self.open_table(name)
 
-    def open_table(self, name: str, *, index_cache_size: Optional[int] = None) -> Table:
+    def open_table(
+        self,
+        name: str,
+        *,
+        storage_options: Optional[Dict[str, str]] = None,
+        index_cache_size: Optional[int] = None,
+    ) -> Table:
         """Open a Lance Table in the database.
 
         Parameters
@@ -243,6 +270,11 @@ class DBConnection(EnforceOverrides):
             This cache applies to the entire opened table, across all indices.
             Setting this value higher will increase performance on larger datasets
             at the expense of more RAM
+        storage_options: dict, optional
+            Additional options for the storage backend. Options already set on the
+            connection will be inherited by the table, but can be overridden here.
+            See available options at
+            <https://lancedb.github.io/lancedb/guides/storage/>
 
         Returns
         -------
@@ -309,15 +341,15 @@ class LanceDBConnection(DBConnection):
     >>> db = lancedb.connect("./.lancedb")
     >>> db.create_table("my_table", data=[{"vector": [1.1, 1.2], "b": 2},
     ...                                   {"vector": [0.5, 1.3], "b": 4}])
-    LanceTable(connection=..., name="my_table")
+    LanceTable(name='my_table', version=1, ...)
     >>> db.create_table("another_table", data=[{"vector": [0.4, 0.4], "b": 6}])
-    LanceTable(connection=..., name="another_table")
+    LanceTable(name='another_table', version=1, ...)
     >>> sorted(db.table_names())
     ['another_table', 'my_table']
     >>> len(db)
     2
     >>> db["my_table"]
-    LanceTable(connection=..., name="my_table")
+    LanceTable(name='my_table', version=1, ...)
     >>> "my_table" in db
     True
     >>> db.drop_table("my_table")
@@ -363,7 +395,7 @@ class LanceDBConnection(DBConnection):
         self._conn = AsyncConnection(LOOP.run(do_connect()))
 
     def __repr__(self) -> str:
-        val = f"{self.__class__.__name__}({self._uri}"
+        val = f"{self.__class__.__name__}(uri={self._uri!r}"
         if self.read_consistency_interval is not None:
             val += f", read_consistency_interval={repr(self.read_consistency_interval)}"
         val += ")"
@@ -403,6 +435,10 @@ class LanceDBConnection(DBConnection):
         on_bad_vectors: str = "error",
         fill_value: float = 0.0,
         embedding_functions: Optional[List[EmbeddingFunctionConfig]] = None,
+        *,
+        storage_options: Optional[Dict[str, str]] = None,
+        data_storage_version: Optional[str] = None,
+        enable_v2_manifest_paths: Optional[bool] = None,
     ) -> LanceTable:
         """Create a table in the database.
 
@@ -424,12 +460,19 @@ class LanceDBConnection(DBConnection):
             on_bad_vectors=on_bad_vectors,
             fill_value=fill_value,
             embedding_functions=embedding_functions,
+            storage_options=storage_options,
+            data_storage_version=data_storage_version,
+            enable_v2_manifest_paths=enable_v2_manifest_paths,
         )
         return tbl
 
     @override
     def open_table(
-        self, name: str, *, index_cache_size: Optional[int] = None
+        self,
+        name: str,
+        *,
+        storage_options: Optional[Dict[str, str]] = None,
+        index_cache_size: Optional[int] = None,
     ) -> LanceTable:
         """Open a table in the database.
 
@@ -442,7 +485,12 @@ class LanceDBConnection(DBConnection):
         -------
         A LanceTable object representing the table.
         """
-        return LanceTable.open(self, name, index_cache_size=index_cache_size)
+        return LanceTable.open(
+            self,
+            name,
+            storage_options=storage_options,
+            index_cache_size=index_cache_size,
+        )
 
     @override
     def drop_table(self, name: str, ignore_missing: bool = False):
@@ -524,6 +572,10 @@ class AsyncConnection(object):
         Any attempt to use the connection after it is closed will result in an error."""
         self._inner.close()
 
+    @property
+    def uri(self) -> str:
+        return self._inner.uri
+
     async def table_names(
         self, *, start_after: Optional[str] = None, limit: Optional[int] = None
     ) -> Iterable[str]:
@@ -557,6 +609,7 @@ class AsyncConnection(object):
         fill_value: Optional[float] = None,
         storage_options: Optional[Dict[str, str]] = None,
         *,
+        embedding_functions: List[EmbeddingFunctionConfig] = None,
         data_storage_version: Optional[str] = None,
         use_legacy_format: Optional[bool] = None,
         enable_v2_manifest_paths: Optional[bool] = None,
@@ -601,7 +654,7 @@ class AsyncConnection(object):
             Additional options for the storage backend. Options already set on the
             connection will be inherited by the table, but can be overridden here.
             See available options at
-            https://lancedb.github.io/lancedb/guides/storage/
+            <https://lancedb.github.io/lancedb/guides/storage/>
         data_storage_version: optional, str, default "stable"
             The version of the data storage format to use. Newer versions are more
             efficient but require newer versions of lance to read.  The default is
@@ -730,6 +783,17 @@ class AsyncConnection(object):
         """
         metadata = None
 
+        if embedding_functions is not None:
+            # If we passed in embedding functions explicitly
+            # then we'll override any schema metadata that
+            # may was implicitly specified by the LanceModel schema
+            registry = EmbeddingFunctionRegistry.get_instance()
+            metadata = registry.get_table_metadata(embedding_functions)
+
+        data, schema = sanitize_create_table(
+            data, schema, metadata, on_bad_vectors, fill_value
+        )
+
         # Defining defaults here and not in function prototype.  In the future
         # these defaults will move into rust so better to keep them as None.
         if on_bad_vectors is None:
@@ -791,7 +855,7 @@ class AsyncConnection(object):
             Additional options for the storage backend. Options already set on the
             connection will be inherited by the table, but can be overridden here.
             See available options at
-            https://lancedb.github.io/lancedb/guides/storage/
+            <https://lancedb.github.io/lancedb/guides/storage/>
         index_cache_size: int, default 256
             Set the size of the index cache, specified as a number of entries
 

python/python/lancedb/index.py

@@ -1,8 +1,6 @@
-from typing import Optional
+from dataclasses import dataclass
+from typing import Literal, Optional
 
-from ._lancedb import (
-    Index as LanceDbIndex,
-)
 from ._lancedb import (
     IndexConfig,
 )
@@ -29,6 +27,7 @@ lang_mapping = {
 }
 
 
+@dataclass
 class BTree:
     """Describes a btree index configuration
 
@@ -50,10 +49,10 @@ class BTree:
     the block size may be added in the future.
     """
 
-    def __init__(self):
-        self._inner = LanceDbIndex.btree()
+    pass
 
 
+@dataclass
 class Bitmap:
     """Describe a Bitmap index configuration.
 
@@ -73,10 +72,10 @@ class Bitmap:
     requires 128 / 8 * 1Bi bytes on disk.
     """
 
-    def __init__(self):
-        self._inner = LanceDbIndex.bitmap()
+    pass
 
 
+@dataclass
 class LabelList:
     """Describe a LabelList index configuration.
 
@@ -87,41 +86,57 @@ class LabelList:
     For example, it works with `tags`, `categories`, `keywords`, etc.
     """
 
-    def __init__(self):
-        self._inner = LanceDbIndex.label_list()
+    pass
 
 
+@dataclass
 class FTS:
     """Describe a FTS index configuration.
 
     `FTS` is a full-text search index that can be used on `String` columns
 
     For example, it works with `title`, `description`, `content`, etc.
+
+    Attributes
+    ----------
+    with_position : bool, default True
+        Whether to store the position of the token in the document. Setting this
+        to False can reduce the size of the index and improve indexing speed,
+        but it will disable support for phrase queries.
+    base_tokenizer : str, default "simple"
+        The base tokenizer to use for tokenization. Options are:
+        - "simple": Splits text by whitespace and punctuation.
+        - "whitespace": Split text by whitespace, but not punctuation.
+        - "raw": No tokenization. The entire text is treated as a single token.
+    language : str, default "English"
+        The language to use for tokenization.
+    max_token_length : int, default 40
+        The maximum token length to index. Tokens longer than this length will be
+        ignored.
+    lower_case : bool, default True
+        Whether to convert the token to lower case. This makes queries case-insensitive.
+    stem : bool, default False
+        Whether to stem the token. Stemming reduces words to their root form.
+        For example, in English "running" and "runs" would both be reduced to "run".
+    remove_stop_words : bool, default False
+        Whether to remove stop words. Stop words are common words that are often
+        removed from text before indexing. For example, in English "the" and "and".
+    ascii_folding : bool, default False
+        Whether to fold ASCII characters. This converts accented characters to
+        their ASCII equivalent. For example, "café" would be converted to "cafe".
     """
 
-    def __init__(
-        self,
-        with_position: bool = True,
-        base_tokenizer: str = "simple",
-        language: str = "English",
-        max_token_length: Optional[int] = 40,
-        lower_case: bool = True,
-        stem: bool = False,
-        remove_stop_words: bool = False,
-        ascii_folding: bool = False,
-    ):
-        self._inner = LanceDbIndex.fts(
-            with_position=with_position,
-            base_tokenizer=base_tokenizer,
-            language=language,
-            max_token_length=max_token_length,
-            lower_case=lower_case,
-            stem=stem,
-            remove_stop_words=remove_stop_words,
-            ascii_folding=ascii_folding,
-        )
+    with_position: bool = True
+    base_tokenizer: Literal["simple", "raw", "whitespace"] = "simple"
+    language: str = "English"
+    max_token_length: Optional[int] = 40
+    lower_case: bool = True
+    stem: bool = False
+    remove_stop_words: bool = False
+    ascii_folding: bool = False
 
 
+@dataclass
 class HnswPq:
     """Describe a HNSW-PQ index configuration.
 
@@ -178,6 +193,12 @@ 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.
@@ -226,28 +247,17 @@ class HnswPq:
         search phase.
     """
 
-    def __init__(
-        self,
-        *,
-        distance_type: Optional[str] = None,
-        num_partitions: Optional[int] = None,
-        num_sub_vectors: Optional[int] = None,
-        max_iterations: Optional[int] = None,
-        sample_rate: Optional[int] = None,
-        m: Optional[int] = None,
-        ef_construction: Optional[int] = None,
-    ):
-        self._inner = LanceDbIndex.hnsw_pq(
-            distance_type=distance_type,
-            num_partitions=num_partitions,
-            num_sub_vectors=num_sub_vectors,
-            max_iterations=max_iterations,
-            sample_rate=sample_rate,
-            m=m,
-            ef_construction=ef_construction,
-        )
+    distance_type: Literal["l2", "cosine", "dot"] = "l2"
+    num_partitions: Optional[int] = None
+    num_sub_vectors: Optional[int] = None
+    num_bits: int = 8
+    max_iterations: int = 50
+    sample_rate: int = 256
+    m: int = 20
+    ef_construction: int = 300
 
 
+@dataclass
 class HnswSq:
     """Describe a HNSW-SQ index configuration.
 
@@ -337,26 +347,15 @@ class HnswSq:
 
     """
 
-    def __init__(
-        self,
-        *,
-        distance_type: Optional[str] = None,
-        num_partitions: Optional[int] = None,
-        max_iterations: Optional[int] = None,
-        sample_rate: Optional[int] = None,
-        m: Optional[int] = None,
-        ef_construction: Optional[int] = None,
-    ):
-        self._inner = LanceDbIndex.hnsw_sq(
-            distance_type=distance_type,
-            num_partitions=num_partitions,
-            max_iterations=max_iterations,
-            sample_rate=sample_rate,
-            m=m,
-            ef_construction=ef_construction,
-        )
+    distance_type: Literal["l2", "cosine", "dot"] = "l2"
+    num_partitions: Optional[int] = None
+    max_iterations: int = 50
+    sample_rate: int = 256
+    m: int = 20
+    ef_construction: int = 300
 
 
+@dataclass
 class IvfPq:
     """Describes an IVF PQ Index
 
@@ -379,112 +378,103 @@ class IvfPq:
 
     Note that training an IVF PQ index on a large dataset is a slow operation and
     currently is also a memory intensive operation.
+
+    Attributes
+    ----------
+    distance_type: str, default "L2"
+        The distance metric used to train the index
+
+        This is used when training the index to calculate the IVF partitions
+        (vectors are grouped in partitions with similar vectors according to this
+        distance type) and to calculate a subvector's code during quantization.
+
+        The distance type used to train an index MUST match the distance type used
+        to search the index.  Failure to do so will yield inaccurate results.
+
+        The following distance types are available:
+
+        "l2" - Euclidean distance. This is a very common distance metric that
+        accounts for both magnitude and direction when determining the distance
+        between vectors. L2 distance has a range of [0, ∞).
+
+        "cosine" - Cosine distance.  Cosine distance is a distance metric
+        calculated from the cosine similarity between two vectors. Cosine
+        similarity is a measure of similarity between two non-zero vectors of an
+        inner product space. It is defined to equal the cosine of the angle
+        between them.  Unlike L2, the cosine distance is not affected by the
+        magnitude of the vectors.  Cosine distance has a range of [0, 2].
+
+        Note: the cosine distance is undefined when one (or both) of the vectors
+        are all zeros (there is no direction).  These vectors are invalid and may
+        never be returned from a vector search.
+
+        "dot" - Dot product. Dot distance is the dot product of two vectors. Dot
+        distance has a range of (-∞, ∞). If the vectors are normalized (i.e. their
+        L2 norm is 1), then dot distance is equivalent to the cosine distance.
+    num_partitions: int, default sqrt(num_rows)
+        The number of IVF partitions to create.
+
+        This value should generally scale with the number of rows in the dataset.
+        By default the number of partitions is the square root of the number of
+        rows.
+
+        If this value is too large then the first part of the search (picking the
+        right partition) will be slow.  If this value is too small then the second
+        part of the search (searching within a partition) will be slow.
+    num_sub_vectors: int, default is vector dimension / 16
+        Number of sub-vectors of PQ.
+
+        This value controls how much the vector is compressed during the
+        quantization step.  The more sub vectors there are the less the vector is
+        compressed.  The default is the dimension of the vector divided by 16.  If
+        the dimension is not evenly divisible by 16 we use the dimension divded by
+        8.
+
+        The above two cases are highly preferred.  Having 8 or 16 values per
+        subvector allows us to use efficient SIMD instructions.
+
+        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.
+
+        When training an IVF PQ index we use kmeans to calculate the partitions.
+        This parameter controls how many iterations of kmeans to run.
+
+        Increasing this might improve the quality of the index but in most cases
+        these extra iterations have diminishing returns.
+
+        The default value is 50.
+    sample_rate: int, default 256
+        The rate used to calculate the number of training vectors for kmeans.
+
+        When an IVF PQ index is trained, we need to calculate partitions.  These
+        are groups of vectors that are similar to each other.  To do this we use an
+        algorithm called kmeans.
+
+        Running kmeans on a large dataset can be slow.  To speed this up we run
+        kmeans on a random sample of the data.  This parameter controls the size of
+        the sample.  The total number of vectors used to train the index is
+        `sample_rate * num_partitions`.
+
+        Increasing this value might improve the quality of the index but in most
+        cases the default should be sufficient.
+
+        The default value is 256.
     """
 
-    def __init__(
-        self,
-        *,
-        distance_type: Optional[str] = None,
-        num_partitions: Optional[int] = None,
-        num_sub_vectors: Optional[int] = None,
-        max_iterations: Optional[int] = None,
-        sample_rate: Optional[int] = None,
-    ):
-        """
-        Create an IVF PQ index config
-
-        Parameters
-        ----------
-        distance_type: str, default "L2"
-            The distance metric used to train the index
-
-            This is used when training the index to calculate the IVF partitions
-            (vectors are grouped in partitions with similar vectors according to this
-            distance type) and to calculate a subvector's code during quantization.
-
-            The distance type used to train an index MUST match the distance type used
-            to search the index.  Failure to do so will yield inaccurate results.
-
-            The following distance types are available:
-
-            "l2" - Euclidean distance. This is a very common distance metric that
-            accounts for both magnitude and direction when determining the distance
-            between vectors. L2 distance has a range of [0, ∞).
-
-            "cosine" - Cosine distance.  Cosine distance is a distance metric
-            calculated from the cosine similarity between two vectors. Cosine
-            similarity is a measure of similarity between two non-zero vectors of an
-            inner product space. It is defined to equal the cosine of the angle
-            between them.  Unlike L2, the cosine distance is not affected by the
-            magnitude of the vectors.  Cosine distance has a range of [0, 2].
-
-            Note: the cosine distance is undefined when one (or both) of the vectors
-            are all zeros (there is no direction).  These vectors are invalid and may
-            never be returned from a vector search.
-
-            "dot" - Dot product. Dot distance is the dot product of two vectors. Dot
-            distance has a range of (-∞, ∞). If the vectors are normalized (i.e. their
-            L2 norm is 1), then dot distance is equivalent to the cosine distance.
-        num_partitions: int, default sqrt(num_rows)
-            The number of IVF partitions to create.
-
-            This value should generally scale with the number of rows in the dataset.
-            By default the number of partitions is the square root of the number of
-            rows.
-
-            If this value is too large then the first part of the search (picking the
-            right partition) will be slow.  If this value is too small then the second
-            part of the search (searching within a partition) will be slow.
-        num_sub_vectors: int, default is vector dimension / 16
-            Number of sub-vectors of PQ.
-
-            This value controls how much the vector is compressed during the
-            quantization step.  The more sub vectors there are the less the vector is
-            compressed.  The default is the dimension of the vector divided by 16.  If
-            the dimension is not evenly divisible by 16 we use the dimension divded by
-            8.
-
-            The above two cases are highly preferred.  Having 8 or 16 values per
-            subvector allows us to use efficient SIMD instructions.
-
-            If the dimension is not visible by 8 then we use 1 subvector.  This is not
-            ideal and will likely result in poor performance.
-        max_iterations: int, default 50
-            Max iteration to train kmeans.
-
-            When training an IVF PQ index we use kmeans to calculate the partitions.
-            This parameter controls how many iterations of kmeans to run.
-
-            Increasing this might improve the quality of the index but in most cases
-            these extra iterations have diminishing returns.
-
-            The default value is 50.
-        sample_rate: int, default 256
-            The rate used to calculate the number of training vectors for kmeans.
-
-            When an IVF PQ index is trained, we need to calculate partitions.  These
-            are groups of vectors that are similar to each other.  To do this we use an
-            algorithm called kmeans.
-
-            Running kmeans on a large dataset can be slow.  To speed this up we run
-            kmeans on a random sample of the data.  This parameter controls the size of
-            the sample.  The total number of vectors used to train the index is
-            `sample_rate * num_partitions`.
-
-            Increasing this value might improve the quality of the index but in most
-            cases the default should be sufficient.
-
-            The default value is 256.
-        """
-        if distance_type is not None:
-            distance_type = distance_type.lower()
-        self._inner = LanceDbIndex.ivf_pq(
-            distance_type=distance_type,
-            num_partitions=num_partitions,
-            num_sub_vectors=num_sub_vectors,
-            max_iterations=max_iterations,
-            sample_rate=sample_rate,
-        )
+    distance_type: Literal["l2", "cosine", "dot"] = "l2"
+    num_partitions: Optional[int] = None
+    num_sub_vectors: Optional[int] = None
+    num_bits: int = 8
+    max_iterations: int = 50
+    sample_rate: int = 256
 
 
-__all__ = ["BTree", "IvfPq", "IndexConfig"]
+__all__ = ["BTree", "IvfPq", "HnswPq", "HnswSq", "IndexConfig"]

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,
             )
         )
 
@@ -120,7 +121,13 @@ class RemoteDBConnection(DBConnection):
         return LOOP.run(self._conn.table_names(start_after=page_token, limit=limit))
 
     @override
-    def open_table(self, name: str, *, index_cache_size: Optional[int] = None) -> Table:
+    def open_table(
+        self,
+        name: str,
+        *,
+        storage_options: Optional[Dict[str, str]] = None,
+        index_cache_size: Optional[int] = None,
+    ) -> Table:
         """Open a Lance Table in the database.
 
         Parameters

python/python/lancedb/remote/table.py

@@ -15,7 +15,10 @@ from datetime import timedelta
 import logging
 from functools import cached_property
 from typing import Dict, Iterable, List, Optional, Union, Literal
+import warnings
 
+from lancedb._lancedb import IndexConfig
+from lancedb.embeddings.base import EmbeddingFunctionConfig
 from lancedb.index import FTS, BTree, Bitmap, HnswPq, HnswSq, IvfPq, LabelList
 from lancedb.remote.db import LOOP
 import pyarrow as pa
@@ -25,7 +28,7 @@ from lancedb.merge import LanceMergeInsertBuilder
 from lancedb.embeddings import EmbeddingFunctionRegistry
 
 from ..query import LanceVectorQueryBuilder, LanceQueryBuilder
-from ..table import AsyncTable, Query, Table
+from ..table import AsyncTable, IndexStatistics, Query, Table
 
 
 class RemoteTable(Table):
@@ -62,7 +65,7 @@ class RemoteTable(Table):
         return LOOP.run(self._table.version())
 
     @cached_property
-    def embedding_functions(self) -> dict:
+    def embedding_functions(self) -> Dict[str, EmbeddingFunctionConfig]:
         """
         Get the embedding functions for the table
 
@@ -78,7 +81,7 @@ 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."""
@@ -89,16 +92,16 @@ 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):
+    def list_indices(self) -> Iterable[IndexConfig]:
         """List all the indices on the table"""
         return LOOP.run(self._table.list_indices())
 
-    def index_stats(self, index_uuid: str):
+    def index_stats(self, index_uuid: str) -> Optional[IndexStatistics]:
         """List all the stats of a specified index"""
         return LOOP.run(self._table.index_stats(index_uuid))
 
@@ -157,9 +160,7 @@ 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,
@@ -481,16 +482,28 @@ class RemoteTable(Table):
         )
 
     def cleanup_old_versions(self, *_):
-        """cleanup_old_versions() is not supported on the LanceDB cloud"""
-        raise NotImplementedError(
-            "cleanup_old_versions() is not supported on the LanceDB cloud"
+        """
+        cleanup_old_versions() is a no-op on LanceDB Cloud.
+
+        Tables are automatically cleaned up and optimized.
+        """
+        warnings.warn(
+            "cleanup_old_versions() is a no-op on LanceDB Cloud. "
+            "Tables are automatically cleaned up and optimized."
         )
+        pass
 
     def compact_files(self, *_):
-        """compact_files() is not supported on the LanceDB cloud"""
-        raise NotImplementedError(
-            "compact_files() is not supported on the LanceDB cloud"
+        """
+        compact_files() is a no-op on LanceDB Cloud.
+
+        Tables are automatically compacted and optimized.
+        """
+        warnings.warn(
+            "compact_files() is a no-op on LanceDB Cloud. "
+            "Tables are automatically compacted and optimized."
         )
+        pass
 
     def optimize(
         self,
@@ -498,12 +511,16 @@ class RemoteTable(Table):
         cleanup_older_than: Optional[timedelta] = None,
         delete_unverified: bool = False,
     ):
-        """optimize() is not supported on the LanceDB cloud.
-        Indices are optimized automatically."""
-        raise NotImplementedError(
-            "optimize() is not supported on the LanceDB cloud. "
+        """
+        optimize() is a no-op on LanceDB Cloud.
+
+        Indices are optimized automatically.
+        """
+        warnings.warn(
+            "optimize() is a no-op on LanceDB Cloud. "
             "Indices are optimized automatically."
         )
+        pass
 
     def count_rows(self, filter: Optional[str] = None) -> int:
         return LOOP.run(self._table.count_rows(filter))
@@ -517,6 +534,16 @@ class RemoteTable(Table):
     def drop_columns(self, columns: Iterable[str]):
         return LOOP.run(self._table.drop_columns(columns))
 
+    def uses_v2_manifest_paths(self) -> bool:
+        raise NotImplementedError(
+            "uses_v2_manifest_paths() is not supported on the LanceDB Cloud"
+        )
+
+    def migrate_v2_manifest_paths(self):
+        raise NotImplementedError(
+            "migrate_v2_manifest_paths() is not supported on the LanceDB Cloud"
+        )
+
 
 def add_index(tbl: pa.Table, i: int) -> pa.Table:
     return tbl.add_column(

python/python/lancedb/util.py

@@ -314,3 +314,15 @@ def deprecated(func):
 def validate_table_name(name: str):
     """Verify the table name is valid."""
     native_validate_table_name(name)
+
+
+def add_note(base_exception: BaseException, note: str):
+    if hasattr(base_exception, "add_note"):
+        base_exception.add_note(note)
+    elif isinstance(base_exception.args[0], str):
+        base_exception.args = (
+            base_exception.args[0] + "\n" + note,
+            *base_exception.args[1:],
+        )
+    else:
+        raise ValueError("Cannot add note to exception")

python/python/tests/conftest.py

@@ -0,0 +1,32 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+from datetime import timedelta
+from lancedb.db import AsyncConnection, DBConnection
+import lancedb
+import pytest
+import pytest_asyncio
+
+
+# Use an in-memory database for most tests.
+@pytest.fixture
+def mem_db() -> DBConnection:
+    return lancedb.connect("memory://")
+
+
+# Use a temporary directory when we need to inspect the database files.
+@pytest.fixture
+def tmp_db(tmp_path) -> DBConnection:
+    return lancedb.connect(tmp_path)
+
+
+@pytest_asyncio.fixture
+async def mem_db_async() -> AsyncConnection:
+    return await lancedb.connect_async("memory://")
+
+
+@pytest_asyncio.fixture
+async def tmp_db_async(tmp_path) -> AsyncConnection:
+    return await lancedb.connect_async(
+        tmp_path, read_consistency_interval=timedelta(seconds=0)
+    )

python/python/tests/docs/test_basic.py

@@ -75,6 +75,22 @@ def test_quickstart():
             for _ in range(1000)
         ]
     )
+    # --8<-- [start:add_columns]
+    tbl.add_columns({"double_price": "cast((price * 2) as float)"})
+    # --8<-- [end:add_columns]
+    # --8<-- [start:alter_columns]
+    tbl.alter_columns(
+        {
+            "path": "double_price",
+            "rename": "dbl_price",
+            "data_type": pa.float64(),
+            "nullable": True,
+        }
+    )
+    # --8<-- [end:alter_columns]
+    # --8<-- [start:drop_columns]
+    tbl.drop_columns(["dbl_price"])
+    # --8<-- [end:drop_columns]
     # --8<-- [start:create_index]
     # Synchronous client
     tbl.create_index(num_sub_vectors=1)

python/python/tests/test_db.py

@@ -98,7 +98,7 @@ def test_ingest_pd(tmp_path):
     assert db.open_table("test").name == db["test"].name
 
 
-def test_ingest_iterator(tmp_path):
+def test_ingest_iterator(mem_db: lancedb.DBConnection):
     class PydanticSchema(LanceModel):
         vector: Vector(2)
         item: str
@@ -156,8 +156,7 @@ def test_ingest_iterator(tmp_path):
             ]
 
     def run_tests(schema):
-        db = lancedb.connect(tmp_path)
-        tbl = db.create_table("table2", make_batches(), schema=schema, mode="overwrite")
+        tbl = mem_db.create_table("table2", make_batches(), schema=schema)
         tbl.to_pandas()
         assert tbl.search([3.1, 4.1]).limit(1).to_pandas()["_distance"][0] == 0.0
         assert tbl.search([5.9, 26.5]).limit(1).to_pandas()["_distance"][0] == 0.0
@@ -165,15 +164,14 @@ def test_ingest_iterator(tmp_path):
         tbl.add(make_batches())
         assert tbl_len == 50
         assert len(tbl) == tbl_len * 2
-        assert len(tbl.list_versions()) == 3
-        db.drop_database()
+        assert len(tbl.list_versions()) == 2
+        mem_db.drop_database()
 
     run_tests(arrow_schema)
     run_tests(PydanticSchema)
 
 
-def test_table_names(tmp_path):
-    db = lancedb.connect(tmp_path)
+def test_table_names(tmp_db: lancedb.DBConnection):
     data = pd.DataFrame(
         {
             "vector": [[3.1, 4.1], [5.9, 26.5]],
@@ -181,10 +179,10 @@ def test_table_names(tmp_path):
             "price": [10.0, 20.0],
         }
     )
-    db.create_table("test2", data=data)
-    db.create_table("test1", data=data)
-    db.create_table("test3", data=data)
-    assert db.table_names() == ["test1", "test2", "test3"]
+    tmp_db.create_table("test2", data=data)
+    tmp_db.create_table("test1", data=data)
+    tmp_db.create_table("test3", data=data)
+    assert tmp_db.table_names() == ["test1", "test2", "test3"]
 
 
 @pytest.mark.asyncio
@@ -209,8 +207,7 @@ async def test_table_names_async(tmp_path):
     assert await db.table_names(start_after="test1") == ["test2", "test3"]
 
 
-def test_create_mode(tmp_path):
-    db = lancedb.connect(tmp_path)
+def test_create_mode(tmp_db: lancedb.DBConnection):
     data = pd.DataFrame(
         {
             "vector": [[3.1, 4.1], [5.9, 26.5]],
@@ -218,10 +215,10 @@ def test_create_mode(tmp_path):
             "price": [10.0, 20.0],
         }
     )
-    db.create_table("test", data=data)
+    tmp_db.create_table("test", data=data)
 
     with pytest.raises(Exception):
-        db.create_table("test", data=data)
+        tmp_db.create_table("test", data=data)
 
     new_data = pd.DataFrame(
         {
@@ -230,13 +227,11 @@ def test_create_mode(tmp_path):
             "price": [10.0, 20.0],
         }
     )
-    tbl = db.create_table("test", data=new_data, mode="overwrite")
+    tbl = tmp_db.create_table("test", data=new_data, mode="overwrite")
     assert tbl.to_pandas().item.tolist() == ["fizz", "buzz"]
 
 
-def test_create_table_from_iterator(tmp_path):
-    db = lancedb.connect(tmp_path)
-
+def test_create_table_from_iterator(mem_db: lancedb.DBConnection):
     def gen_data():
         for _ in range(10):
             yield pa.RecordBatch.from_arrays(
@@ -248,14 +243,12 @@ def test_create_table_from_iterator(tmp_path):
                 ["vector", "item", "price"],
             )
 
-    table = db.create_table("test", data=gen_data())
+    table = mem_db.create_table("test", data=gen_data())
     assert table.count_rows() == 10
 
 
 @pytest.mark.asyncio
-async def test_create_table_from_iterator_async(tmp_path):
-    db = await lancedb.connect_async(tmp_path)
-
+async def test_create_table_from_iterator_async(mem_db_async: lancedb.AsyncConnection):
     def gen_data():
         for _ in range(10):
             yield pa.RecordBatch.from_arrays(
@@ -267,12 +260,11 @@ async def test_create_table_from_iterator_async(tmp_path):
                 ["vector", "item", "price"],
             )
 
-    table = await db.create_table("test", data=gen_data())
+    table = await mem_db_async.create_table("test", data=gen_data())
     assert await table.count_rows() == 10
 
 
-def test_create_exist_ok(tmp_path):
-    db = lancedb.connect(tmp_path)
+def test_create_exist_ok(tmp_db: lancedb.DBConnection):
     data = pd.DataFrame(
         {
             "vector": [[3.1, 4.1], [5.9, 26.5]],
@@ -280,13 +272,13 @@ def test_create_exist_ok(tmp_path):
             "price": [10.0, 20.0],
         }
     )
-    tbl = db.create_table("test", data=data)
+    tbl = tmp_db.create_table("test", data=data)
 
-    with pytest.raises(OSError):
-        db.create_table("test", data=data)
+    with pytest.raises(ValueError):
+        tmp_db.create_table("test", data=data)
 
     # open the table but don't add more rows
-    tbl2 = db.create_table("test", data=data, exist_ok=True)
+    tbl2 = tmp_db.create_table("test", data=data, exist_ok=True)
     assert tbl.name == tbl2.name
     assert tbl.schema == tbl2.schema
     assert len(tbl) == len(tbl2)
@@ -298,7 +290,7 @@ def test_create_exist_ok(tmp_path):
             pa.field("price", pa.float64()),
         ]
     )
-    tbl3 = db.create_table("test", schema=schema, exist_ok=True)
+    tbl3 = tmp_db.create_table("test", schema=schema, exist_ok=True)
     assert tbl3.schema == schema
 
     bad_schema = pa.schema(
@@ -310,7 +302,7 @@ def test_create_exist_ok(tmp_path):
         ]
     )
     with pytest.raises(ValueError):
-        db.create_table("test", schema=bad_schema, exist_ok=True)
+        tmp_db.create_table("test", schema=bad_schema, exist_ok=True)
 
 
 @pytest.mark.asyncio
@@ -325,26 +317,24 @@ async def test_connect(tmp_path):
 
 
 @pytest.mark.asyncio
-async def test_close(tmp_path):
-    db = await lancedb.connect_async(tmp_path)
-    assert db.is_open()
-    db.close()
-    assert not db.is_open()
+async def test_close(mem_db_async: lancedb.AsyncConnection):
+    assert mem_db_async.is_open()
+    mem_db_async.close()
+    assert not mem_db_async.is_open()
 
     with pytest.raises(RuntimeError, match="is closed"):
-        await db.table_names()
+        await mem_db_async.table_names()
 
 
 @pytest.mark.asyncio
-async def test_context_manager(tmp_path):
-    with await lancedb.connect_async(tmp_path) as db:
+async def test_context_manager():
+    with await lancedb.connect_async("memory://") as db:
         assert db.is_open()
     assert not db.is_open()
 
 
 @pytest.mark.asyncio
-async def test_create_mode_async(tmp_path):
-    db = await lancedb.connect_async(tmp_path)
+async def test_create_mode_async(tmp_db_async: lancedb.AsyncConnection):
     data = pd.DataFrame(
         {
             "vector": [[3.1, 4.1], [5.9, 26.5]],
@@ -352,10 +342,10 @@ async def test_create_mode_async(tmp_path):
             "price": [10.0, 20.0],
         }
     )
-    await db.create_table("test", data=data)
+    await tmp_db_async.create_table("test", data=data)
 
     with pytest.raises(ValueError, match="already exists"):
-        await db.create_table("test", data=data)
+        await tmp_db_async.create_table("test", data=data)
 
     new_data = pd.DataFrame(
         {
@@ -364,15 +354,14 @@ async def test_create_mode_async(tmp_path):
             "price": [10.0, 20.0],
         }
     )
-    _tbl = await db.create_table("test", data=new_data, mode="overwrite")
+    _tbl = await tmp_db_async.create_table("test", data=new_data, mode="overwrite")
 
     # MIGRATION: to_pandas() is not available in async
     # assert tbl.to_pandas().item.tolist() == ["fizz", "buzz"]
 
 
 @pytest.mark.asyncio
-async def test_create_exist_ok_async(tmp_path):
-    db = await lancedb.connect_async(tmp_path)
+async def test_create_exist_ok_async(tmp_db_async: lancedb.AsyncConnection):
     data = pd.DataFrame(
         {
             "vector": [[3.1, 4.1], [5.9, 26.5]],
@@ -380,13 +369,13 @@ async def test_create_exist_ok_async(tmp_path):
             "price": [10.0, 20.0],
         }
     )
-    tbl = await db.create_table("test", data=data)
+    tbl = await tmp_db_async.create_table("test", data=data)
 
     with pytest.raises(ValueError, match="already exists"):
-        await db.create_table("test", data=data)
+        await tmp_db_async.create_table("test", data=data)
 
     # open the table but don't add more rows
-    tbl2 = await db.create_table("test", data=data, exist_ok=True)
+    tbl2 = await tmp_db_async.create_table("test", data=data, exist_ok=True)
     assert tbl.name == tbl2.name
     assert await tbl.schema() == await tbl2.schema()
 
@@ -397,7 +386,7 @@ async def test_create_exist_ok_async(tmp_path):
             pa.field("price", pa.float64()),
         ]
     )
-    tbl3 = await db.create_table("test", schema=schema, exist_ok=True)
+    tbl3 = await tmp_db_async.create_table("test", schema=schema, exist_ok=True)
     assert await tbl3.schema() == schema
 
     # Migration: When creating a table, but the table already exists, but
@@ -448,13 +437,12 @@ async def test_create_table_v2_manifest_paths_async(tmp_path):
         assert re.match(r"\d{20}\.manifest", manifest)
 
 
-def test_open_table_sync(tmp_path):
-    db = lancedb.connect(tmp_path)
-    db.create_table("test", data=[{"id": 0}])
-    assert db.open_table("test").count_rows() == 1
-    assert db.open_table("test", index_cache_size=0).count_rows() == 1
-    with pytest.raises(FileNotFoundError, match="does not exist"):
-        db.open_table("does_not_exist")
+def test_open_table_sync(tmp_db: lancedb.DBConnection):
+    tmp_db.create_table("test", data=[{"id": 0}])
+    assert tmp_db.open_table("test").count_rows() == 1
+    assert tmp_db.open_table("test", index_cache_size=0).count_rows() == 1
+    with pytest.raises(ValueError, match="Table 'does_not_exist' was not found"):
+        tmp_db.open_table("does_not_exist")
 
 
 @pytest.mark.asyncio
@@ -494,8 +482,7 @@ async def test_open_table(tmp_path):
         await db.open_table("does_not_exist")
 
 
-def test_delete_table(tmp_path):
-    db = lancedb.connect(tmp_path)
+def test_delete_table(tmp_db: lancedb.DBConnection):
     data = pd.DataFrame(
         {
             "vector": [[3.1, 4.1], [5.9, 26.5]],
@@ -503,26 +490,25 @@ def test_delete_table(tmp_path):
             "price": [10.0, 20.0],
         }
     )
-    db.create_table("test", data=data)
+    tmp_db.create_table("test", data=data)
 
     with pytest.raises(Exception):
-        db.create_table("test", data=data)
+        tmp_db.create_table("test", data=data)
 
-    assert db.table_names() == ["test"]
+    assert tmp_db.table_names() == ["test"]
 
-    db.drop_table("test")
-    assert db.table_names() == []
+    tmp_db.drop_table("test")
+    assert tmp_db.table_names() == []
 
-    db.create_table("test", data=data)
-    assert db.table_names() == ["test"]
+    tmp_db.create_table("test", data=data)
+    assert tmp_db.table_names() == ["test"]
 
     # dropping a table that does not exist should pass
     # if ignore_missing=True
-    db.drop_table("does_not_exist", ignore_missing=True)
+    tmp_db.drop_table("does_not_exist", ignore_missing=True)
 
 
-def test_drop_database(tmp_path):
-    db = lancedb.connect(tmp_path)
+def test_drop_database(tmp_db: lancedb.DBConnection):
     data = pd.DataFrame(
         {
             "vector": [[3.1, 4.1], [5.9, 26.5]],
@@ -537,51 +523,50 @@ def test_drop_database(tmp_path):
             "price": [12.0, 17.0],
         }
     )
-    db.create_table("test", data=data)
+    tmp_db.create_table("test", data=data)
     with pytest.raises(Exception):
-        db.create_table("test", data=data)
+        tmp_db.create_table("test", data=data)
 
-    assert db.table_names() == ["test"]
+    assert tmp_db.table_names() == ["test"]
 
-    db.create_table("new_test", data=new_data)
-    db.drop_database()
-    assert db.table_names() == []
+    tmp_db.create_table("new_test", data=new_data)
+    tmp_db.drop_database()
+    assert tmp_db.table_names() == []
 
     # it should pass when no tables are present
-    db.create_table("test", data=new_data)
-    db.drop_table("test")
-    assert db.table_names() == []
-    db.drop_database()
-    assert db.table_names() == []
+    tmp_db.create_table("test", data=new_data)
+    tmp_db.drop_table("test")
+    assert tmp_db.table_names() == []
+    tmp_db.drop_database()
+    assert tmp_db.table_names() == []
 
     # creating an empty database with schema
     schema = pa.schema([pa.field("vector", pa.list_(pa.float32(), list_size=2))])
-    db.create_table("empty_table", schema=schema)
+    tmp_db.create_table("empty_table", schema=schema)
     # dropping a empty database should pass
-    db.drop_database()
-    assert db.table_names() == []
+    tmp_db.drop_database()
+    assert tmp_db.table_names() == []
 
 
-def test_empty_or_nonexistent_table(tmp_path):
-    db = lancedb.connect(tmp_path)
+def test_empty_or_nonexistent_table(mem_db: lancedb.DBConnection):
     with pytest.raises(Exception):
-        db.create_table("test_with_no_data")
+        mem_db.create_table("test_with_no_data")
 
     with pytest.raises(Exception):
-        db.open_table("does_not_exist")
+        mem_db.open_table("does_not_exist")
 
     schema = pa.schema([pa.field("a", pa.int64(), nullable=False)])
-    test = db.create_table("test", schema=schema)
+    test = mem_db.create_table("test", schema=schema)
 
     class TestModel(LanceModel):
         a: int
 
-    test2 = db.create_table("test2", schema=TestModel)
+    test2 = mem_db.create_table("test2", schema=TestModel)
     assert test.schema == test2.schema
 
 
 @pytest.mark.asyncio
-async def test_create_in_v2_mode(tmp_path):
+async def test_create_in_v2_mode(mem_db_async: lancedb.AsyncConnection):
     def make_data():
         for i in range(10):
             yield pa.record_batch([pa.array([x for x in range(1024)])], names=["x"])
@@ -591,10 +576,8 @@ async def test_create_in_v2_mode(tmp_path):
 
     schema = pa.schema([pa.field("x", pa.int64())])
 
-    db = await lancedb.connect_async(tmp_path)
-
     # Create table in v1 mode
-    tbl = await db.create_table(
+    tbl = await mem_db_async.create_table(
         "test", data=make_data(), schema=schema, data_storage_version="legacy"
     )
 
@@ -610,7 +593,7 @@ async def test_create_in_v2_mode(tmp_path):
     assert not await is_in_v2_mode(tbl)
 
     # Create table in v2 mode
-    tbl = await db.create_table(
+    tbl = await mem_db_async.create_table(
         "test_v2", data=make_data(), schema=schema, use_legacy_format=False
     )
 
@@ -622,7 +605,7 @@ async def test_create_in_v2_mode(tmp_path):
     assert await is_in_v2_mode(tbl)
 
     # Create empty table in v2 mode and add data
-    tbl = await db.create_table(
+    tbl = await mem_db_async.create_table(
         "test_empty_v2", data=None, schema=schema, use_legacy_format=False
     )
     await tbl.add(make_table())
@@ -630,7 +613,7 @@ async def test_create_in_v2_mode(tmp_path):
     assert await is_in_v2_mode(tbl)
 
     # Create empty table uses v1 mode by default
-    tbl = await db.create_table(
+    tbl = await mem_db_async.create_table(
         "test_empty_v2_default", data=None, schema=schema, data_storage_version="legacy"
     )
     await tbl.add(make_table())
@@ -638,18 +621,17 @@ async def test_create_in_v2_mode(tmp_path):
     assert not await is_in_v2_mode(tbl)
 
 
-def test_replace_index(tmp_path):
-    db = lancedb.connect(uri=tmp_path)
-    table = db.create_table(
+def test_replace_index(mem_db: lancedb.DBConnection):
+    table = mem_db.create_table(
         "test",
         [
-            {"vector": np.random.rand(128), "item": "foo", "price": float(i)}
-            for i in range(1000)
+            {"vector": np.random.rand(32), "item": "foo", "price": float(i)}
+            for i in range(512)
         ],
     )
     table.create_index(
         num_partitions=2,
-        num_sub_vectors=4,
+        num_sub_vectors=2,
     )
 
     with pytest.raises(Exception):
@@ -660,27 +642,26 @@ def test_replace_index(tmp_path):
         )
 
     table.create_index(
-        num_partitions=2,
-        num_sub_vectors=4,
+        num_partitions=1,
+        num_sub_vectors=2,
         replace=True,
         index_cache_size=10,
     )
 
 
-def test_prefilter_with_index(tmp_path):
-    db = lancedb.connect(uri=tmp_path)
+def test_prefilter_with_index(mem_db: lancedb.DBConnection):
     data = [
-        {"vector": np.random.rand(128), "item": "foo", "price": float(i)}
-        for i in range(1000)
+        {"vector": np.random.rand(32), "item": "foo", "price": float(i)}
+        for i in range(512)
     ]
     sample_key = data[100]["vector"]
-    table = db.create_table(
+    table = mem_db.create_table(
         "test",
         data,
     )
     table.create_index(
         num_partitions=2,
-        num_sub_vectors=4,
+        num_sub_vectors=2,
     )
     table = (
         table.search(sample_key)
@@ -691,13 +672,12 @@ def test_prefilter_with_index(tmp_path):
     assert table.num_rows == 1
 
 
-def test_create_table_with_invalid_names(tmp_path):
-    db = lancedb.connect(uri=tmp_path)
+def test_create_table_with_invalid_names(tmp_db: lancedb.DBConnection):
     data = [{"vector": np.random.rand(128), "item": "foo"} for i in range(10)]
     with pytest.raises(ValueError):
-        db.create_table("foo/bar", data)
+        tmp_db.create_table("foo/bar", data)
     with pytest.raises(ValueError):
-        db.create_table("foo bar", data)
+        tmp_db.create_table("foo bar", data)
     with pytest.raises(ValueError):
-        db.create_table("foo$$bar", data)
-    db.create_table("foo.bar", data)
+        tmp_db.create_table("foo$$bar", data)
+    tmp_db.create_table("foo.bar", data)

python/python/tests/test_fts.py

@@ -15,10 +15,12 @@ import random
 from unittest import mock
 
 import lancedb as ldb
+from lancedb.db import DBConnection
 from lancedb.index import FTS
 import numpy as np
 import pandas as pd
 import pytest
+from utils import exception_output
 
 pytest.importorskip("lancedb.fts")
 tantivy = pytest.importorskip("tantivy")
@@ -458,3 +460,44 @@ def test_syntax(table):
     table.search('the cats OR dogs were not really "pets" at all').phrase_query().limit(
         10
     ).to_list()
+
+
+def test_language(mem_db: DBConnection):
+    sentences = [
+        "Il n'y a que trois routes qui traversent la ville.",
+        "Je veux prendre la route vers l'est.",
+        "Je te retrouve au café au bout de la route.",
+    ]
+    data = [{"text": s} for s in sentences]
+    table = mem_db.create_table("test", data=data)
+
+    with pytest.raises(ValueError) as e:
+        table.create_fts_index("text", use_tantivy=False, language="klingon")
+
+    assert exception_output(e) == (
+        "ValueError: LanceDB does not support the requested language: 'klingon'\n"
+        "Supported languages: Arabic, Danish, Dutch, English, Finnish, French, "
+        "German, Greek, Hungarian, Italian, Norwegian, Portuguese, Romanian, "
+        "Russian, Spanish, Swedish, Tamil, Turkish"
+    )
+
+    table.create_fts_index(
+        "text",
+        use_tantivy=False,
+        language="French",
+        stem=True,
+        ascii_folding=True,
+        remove_stop_words=True,
+    )
+
+    # Can get "routes" and "route" from the same root
+    results = table.search("route", query_type="fts").limit(5).to_list()
+    assert len(results) == 3
+
+    # Can find "café", without needing to provide accent
+    results = table.search("cafe", query_type="fts").limit(5).to_list()
+    assert len(results) == 1
+
+    # Stop words -> no results
+    results = table.search("la", query_type="fts").limit(5).to_list()
+    assert len(results) == 0

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)