Bump version: 0.24.2-beta.2 → 0.24.2

## Summary
Fixes IndexError when creating tables with empty list data and a
provided schema. Previously, `_into_pyarrow_reader()` would attempt to
access `data[0]` on empty lists, causing an IndexError. Now properly
handles empty lists by using the provided schema.

Also adds regression tests for GitHub issues #1968 and #303 to prevent
future regressions with empty table scenarios.

## Changes
- Fix IndexError in `_into_pyarrow_reader()` for empty list + schema
case
- Add Optional[pa.Schema] parameter to handle empty data gracefully  
- Add `test_create_table_empty_list_with_schema` for the IndexError fix
- Add `test_create_empty_then_add_data` for issue #1968
- Add `test_search_empty_table` for issue #303

## Test plan
- [x] All new regression tests pass
- [x] Existing tests continue to pass
- [x] Code formatted with `make format`

.bumpversion.toml

@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.20.0-beta.2"
+current_version = "0.21.2-beta.1"
 parse = """(?x)
     (?P<major>0|[1-9]\\d*)\\.
     (?P<minor>0|[1-9]\\d*)\\.

.github/workflows/cargo-publish.yml

@@ -5,8 +5,8 @@ on:
     tags-ignore:
       # We don't publish pre-releases for Rust. Crates.io is just a source
       # distribution, so we don't need to publish pre-releases.
-      - 'v*-beta*'
+      - "v*-beta*"
-      - '*-v*' # for example, python-vX.Y.Z
+      - "*-v*" # for example, python-vX.Y.Z
 
 env:
   # This env var is used by Swatinem/rust-cache@v2 for the cache
@@ -19,6 +19,8 @@ env:
 jobs:
   build:
     runs-on: ubuntu-22.04
+    permissions:
+      id-token: write
     timeout-minutes: 30
     # Only runs on tags that matches the make-release action
     if: startsWith(github.ref, 'refs/tags/v')
@@ -31,6 +33,8 @@ jobs:
         run: |
           sudo apt update
           sudo apt install -y protobuf-compiler libssl-dev
+      - uses: rust-lang/crates-io-auth-action@v1
+        id: auth
       - name: Publish the package
         run: |
-          cargo publish -p lancedb --all-features --token ${{ secrets.CARGO_REGISTRY_TOKEN }}
+          cargo publish -p lancedb --all-features --token ${{ steps.auth.outputs.token }}

.github/workflows/npm-publish.yml

@@ -539,10 +539,20 @@ jobs:
         # We need to deprecate the old package to avoid confusion.
         # Each time we publish a new version, it gets undeprecated.
         run: npm deprecate vectordb "Use @lancedb/lancedb instead."
+      - name: Checkout
+        uses: actions/checkout@v4
+        with:
+          ref: main
       - name: Update package-lock.json
-        run: bash ci/update_lockfiles.sh
+        run: |
+          git config user.name 'Lance Release'
+          git config user.email 'lance-dev@lancedb.com'
+          bash ci/update_lockfiles.sh
       - name: Push new commit
         uses: ad-m/github-push-action@master
+        with:
+          github_token: ${{ secrets.LANCEDB_RELEASE_TOKEN }}
+          branch: main
       - name: Notify Slack Action
         uses: ravsamhq/notify-slack-action@2.3.0
         if: ${{ always() }}

CLAUDE.md

@@ -0,0 +1,24 @@
+LanceDB is a database designed for retrieval, including vector, full-text, and hybrid search.
+It is a wrapper around Lance. There are two backends: local (in-process like SQLite) and
+remote (against LanceDB Cloud).
+
+The core of LanceDB is written in Rust. There are bindings in Python, Typescript, and Java.
+
+Project layout:
+
+* `rust/lancedb`: The LanceDB core Rust implementation.
+* `python`: The Python bindings, using PyO3.
+* `nodejs`: The Typescript bindings, using napi-rs
+* `java`: The Java bindings
+
+(`rust/ffi` and `node/` are for a deprecated package. You can ignore them.)
+
+Common commands:
+
+* Check for compiler errors: `cargo check --features remote --tests --examples`
+* Run tests: `cargo test --features remote --tests`
+* Run specific test: `cargo test --features remote -p <package_name> --test <test_name>`
+* Lint: `cargo clippy --features remote --tests --examples`
+* Format: `cargo fmt --all`
+
+Before committing changes, run formatting.

Cargo.toml

@@ -21,14 +21,14 @@ categories = ["database-implementations"]
 rust-version = "1.78.0"
 
 [workspace.dependencies]
-lance = { "version" = "=0.29.0", "features" = ["dynamodb"] }
+lance = { "version" = "=0.32.0", "features" = ["dynamodb"] }
-lance-io = "=0.29.0"
+lance-io = "=0.32.0"
-lance-index = "=0.29.0"
+lance-index = "=0.32.0"
-lance-linalg = "=0.29.0"
+lance-linalg = "=0.32.0"
-lance-table = "=0.29.0"
+lance-table = "=0.32.0"
-lance-testing = "=0.29.0"
+lance-testing = "=0.32.0"
-lance-datafusion = "=0.29.0"
+lance-datafusion = "=0.32.0"
-lance-encoding = "=0.29.0"
+lance-encoding = "=0.32.0"
 # Note that this one does not include pyarrow
 arrow = { version = "55.1", optional = false }
 arrow-array = "55.1"
@@ -39,20 +39,20 @@ arrow-schema = "55.1"
 arrow-arith = "55.1"
 arrow-cast = "55.1"
 async-trait = "0"
-datafusion = { version = "47.0", default-features = false }
+datafusion = { version = "48.0", default-features = false }
-datafusion-catalog = "47.0"
+datafusion-catalog = "48.0"
-datafusion-common = { version = "47.0", default-features = false }
+datafusion-common = { version = "48.0", default-features = false }
-datafusion-execution = "47.0"
+datafusion-execution = "48.0"
-datafusion-expr = "47.0"
+datafusion-expr = "48.0"
-datafusion-physical-plan = "47.0"
+datafusion-physical-plan = "48.0"
 env_logger = "0.11"
-half = { "version" = "=2.5.0", default-features = false, features = [
+half = { "version" = "2.6.0", default-features = false, features = [
     "num-traits",
 ] }
 futures = "0"
 log = "0.4"
 moka = { version = "0.12", features = ["future"] }
-object_store = "0.11.0"
+object_store = "0.12.0"
 pin-project = "1.0.7"
 snafu = "0.8"
 url = "2"

ci/set_lance_version.py

@@ -47,10 +47,10 @@ def extract_features(line: str) -> list:
     """
     import re
 
-    match = re.search(r'"features"\s*=\s*\[(.*?)\]', line)
+    match = re.search(r'"features"\s*=\s*\[\s*(.*?)\s*\]', line, re.DOTALL)
     if match:
         features_str = match.group(1)
-        return [f.strip('"') for f in features_str.split(",")]
+        return [f.strip('"') for f in features_str.split(",") if len(f) > 0]
     return []
 
 
@@ -63,10 +63,24 @@ def update_cargo_toml(line_updater):
         lines = f.readlines()
 
     new_lines = []
+    lance_line = ""
+    is_parsing_lance_line = False
     for line in lines:
         if line.startswith("lance"):
             # Update the line using the provided function
+            if line.strip().endswith("}"):
                 new_lines.append(line_updater(line))
+            else:
+                lance_line = line
+                is_parsing_lance_line = True
+        elif is_parsing_lance_line:
+            lance_line += line
+            if line.strip().endswith("}"):
+                new_lines.append(line_updater(lance_line))
+                lance_line = ""
+                is_parsing_lance_line = False
+            else:
+                print("doesn't end with }:", line)
         else:
             # Keep the line unchanged
             new_lines.append(line)

docs/package-lock.json

@@ -19,7 +19,7 @@
     },
     "../node": {
       "name": "vectordb",
-      "version": "0.12.0",
+      "version": "0.21.2-beta.0",
       "cpu": [
         "x64",
         "arm64"
@@ -65,11 +65,11 @@
         "uuid": "^9.0.0"
       },
       "optionalDependencies": {
-        "@lancedb/vectordb-darwin-arm64": "0.12.0",
+        "@lancedb/vectordb-darwin-arm64": "0.21.2-beta.0",
-        "@lancedb/vectordb-darwin-x64": "0.12.0",
+        "@lancedb/vectordb-darwin-x64": "0.21.2-beta.0",
-        "@lancedb/vectordb-linux-arm64-gnu": "0.12.0",
+        "@lancedb/vectordb-linux-arm64-gnu": "0.21.2-beta.0",
-        "@lancedb/vectordb-linux-x64-gnu": "0.12.0",
+        "@lancedb/vectordb-linux-x64-gnu": "0.21.2-beta.0",
-        "@lancedb/vectordb-win32-x64-msvc": "0.12.0"
+        "@lancedb/vectordb-win32-x64-msvc": "0.21.2-beta.0"
       },
       "peerDependencies": {
         "@apache-arrow/ts": "^14.0.2",

docs/src/guides/sql_querying.md

@@ -1,7 +1,9 @@
+# SQL Querying
+
 You can use DuckDB and Apache Datafusion to query your LanceDB tables using SQL.
 This guide will show how to query Lance tables them using both.
 
-We will re-use the dataset [created previously](./pandas_and_pyarrow.md):
+We will re-use the dataset [created previously](./tables.md):
 
 ```python
 import lancedb
@@ -27,21 +29,17 @@ arrow_table = table.to_lance()
 duckdb.query("SELECT * FROM arrow_table")
 ```
 
-```
+| vector      | item | price |
-┌─────────────┬─────────┬────────┐
+| ----------- | ---- | ----- |
-│   vector    │  item   │ price  │
+| [3.1, 4.1]  | foo  | 10.0  |
-│   float[]   │ varchar │ double │
+| [5.9, 26.5] | bar  | 20.0  |
-├─────────────┼─────────┼────────┤
-│ [3.1, 4.1]  │ foo     │   10.0 │
-│ [5.9, 26.5] │ bar     │   20.0 │
-└─────────────┴─────────┴────────┘
-```
 
 ## Querying a LanceDB Table with Apache Datafusion
 
 Have the required imports before doing any querying.
 
 === "Python"
+
     ```python
     --8<-- "python/python/tests/docs/test_guide_tables.py:import-lancedb"
     --8<-- "python/python/tests/docs/test_guide_tables.py:import-session-context"
@@ -51,16 +49,12 @@ Have the required imports before doing any querying.
 Register the table created with the Datafusion session context.
 
 === "Python"
+
     ```python
     --8<-- "python/python/tests/docs/test_guide_tables.py:lance_sql_basic"
     ```
 
-```
+| vector      | item | price |
-┌─────────────┬─────────┬────────┐
+| ----------- | ---- | ----- |
-│   vector    │  item   │ price  │
+| [3.1, 4.1]  | foo  | 10.0  |
-│   float[]   │ varchar │ double │
+| [5.9, 26.5] | bar  | 20.0  |
-├─────────────┼─────────┼────────┤
-│ [3.1, 4.1]  │ foo     │   10.0 │
-│ [5.9, 26.5] │ bar     │   20.0 │
-└─────────────┴─────────┴────────┘
-```

docs/src/js/classes/BooleanQuery.md

@@ -0,0 +1,53 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / BooleanQuery
+
+# Class: BooleanQuery
+
+Represents a full-text query interface.
+This interface defines the structure and behavior for full-text queries,
+including methods to retrieve the query type and convert the query to a dictionary format.
+
+## Implements
+
+- [`FullTextQuery`](../interfaces/FullTextQuery.md)
+
+## Constructors
+
+### new BooleanQuery()
+
+```ts
+new BooleanQuery(queries): BooleanQuery
+```
+
+Creates an instance of BooleanQuery.
+
+#### Parameters
+
+* **queries**: [[`Occur`](../enumerations/Occur.md), [`FullTextQuery`](../interfaces/FullTextQuery.md)][]
+    An array of (Occur, FullTextQuery objects) to combine.
+    Occur specifies whether the query must match, or should match.
+
+#### Returns
+
+[`BooleanQuery`](BooleanQuery.md)
+
+## Methods
+
+### queryType()
+
+```ts
+queryType(): FullTextQueryType
+```
+
+The type of the full-text query.
+
+#### Returns
+
+[`FullTextQueryType`](../enumerations/FullTextQueryType.md)
+
+#### Implementation of
+
+[`FullTextQuery`](../interfaces/FullTextQuery.md).[`queryType`](../interfaces/FullTextQuery.md#querytype)

docs/src/js/classes/MatchQuery.md

@@ -40,6 +40,8 @@ Creates an instance of MatchQuery.
     - `boost`: The boost factor for the query (default is 1.0).
     - `fuzziness`: The fuzziness level for the query (default is 0).
     - `maxExpansions`: The maximum number of terms to consider for fuzzy matching (default is 50).
+    - `operator`: The logical operator to use for combining terms in the query (default is "OR").
+    - `prefixLength`: The number of beginning characters being unchanged for fuzzy matching.
 
 * **options.boost?**: `number`
 
@@ -47,6 +49,10 @@ Creates an instance of MatchQuery.
 
 * **options.maxExpansions?**: `number`
 
+* **options.operator?**: [`Operator`](../enumerations/Operator.md)
+
+* **options.prefixLength?**: `number`
+
 #### Returns
 
 [`MatchQuery`](MatchQuery.md)

docs/src/js/classes/MultiMatchQuery.md

@@ -38,9 +38,12 @@ Creates an instance of MultiMatchQuery.
 * **options?**
     Optional parameters for the multi-match query.
     - `boosts`: An array of boost factors for each column (default is 1.0 for all).
+    - `operator`: The logical operator to use for combining terms in the query (default is "OR").
 
 * **options.boosts?**: `number`[]
 
+* **options.operator?**: [`Operator`](../enumerations/Operator.md)
+
 #### Returns
 
 [`MultiMatchQuery`](MultiMatchQuery.md)

docs/src/js/classes/PhraseQuery.md

@@ -19,7 +19,10 @@ including methods to retrieve the query type and convert the query to a dictiona
 ### new PhraseQuery()
 
 ```ts
-new PhraseQuery(query, column): PhraseQuery
+new PhraseQuery(
+   query,
+   column,
+   options?): PhraseQuery
 ```
 
 Creates an instance of `PhraseQuery`.
@@ -32,6 +35,12 @@ Creates an instance of `PhraseQuery`.
 * **column**: `string`
     The name of the column to search within.
 
+* **options?**
+    Optional parameters for the phrase query.
+    - `slop`: The maximum number of intervening unmatched positions allowed between words in the phrase (default is 0).
+
+* **options.slop?**: `number`
+
 #### Returns
 
 [`PhraseQuery`](PhraseQuery.md)

docs/src/js/classes/Session.md

@@ -0,0 +1,84 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / Session
+
+# Class: Session
+
+A session for managing caches and object stores across LanceDB operations.
+
+Sessions allow you to configure cache sizes for index and metadata caches,
+which can significantly impact performance for large datasets.
+
+## Constructors
+
+### new Session()
+
+```ts
+new Session(indexCacheSizeBytes?, metadataCacheSizeBytes?): Session
+```
+
+Create a new session with custom cache sizes.
+
+# Parameters
+
+- `index_cache_size_bytes`: The size of the index cache in bytes.
+  Defaults to 6GB if not specified.
+- `metadata_cache_size_bytes`: The size of the metadata cache in bytes.
+  Defaults to 1GB if not specified.
+
+#### Parameters
+
+* **indexCacheSizeBytes?**: `null` \| `bigint`
+
+* **metadataCacheSizeBytes?**: `null` \| `bigint`
+
+#### Returns
+
+[`Session`](Session.md)
+
+## Methods
+
+### approxNumItems()
+
+```ts
+approxNumItems(): number
+```
+
+Get the approximate number of items cached in the session.
+
+#### Returns
+
+`number`
+
+***
+
+### sizeBytes()
+
+```ts
+sizeBytes(): bigint
+```
+
+Get the current size of the session caches in bytes.
+
+#### Returns
+
+`bigint`
+
+***
+
+### default()
+
+```ts
+static default(): Session
+```
+
+Create a session with default cache sizes.
+
+This is equivalent to creating a session with 6GB index cache
+and 1GB metadata cache.
+
+#### Returns
+
+[`Session`](Session.md)

docs/src/js/classes/Table.md

@@ -612,7 +612,7 @@ of the given query
 
 #### Parameters
 
-* **query**: `string` \| [`IntoVector`](../type-aliases/IntoVector.md) \| [`FullTextQuery`](../interfaces/FullTextQuery.md)
+* **query**: `string` \| [`IntoVector`](../type-aliases/IntoVector.md) \| [`MultiVector`](../type-aliases/MultiVector.md) \| [`FullTextQuery`](../interfaces/FullTextQuery.md)
     the query, a vector or string
 
 * **queryType?**: `string`
@@ -799,7 +799,7 @@ by `query`.
 
 #### Parameters
 
-* **vector**: [`IntoVector`](../type-aliases/IntoVector.md)
+* **vector**: [`IntoVector`](../type-aliases/IntoVector.md) \| [`MultiVector`](../type-aliases/MultiVector.md)
 
 #### Returns
 

docs/src/js/classes/VectorQuery.md

@@ -386,6 +386,53 @@ called then every valid row from the table will be returned.
 
 ***
 
+### maximumNprobes()
+
+```ts
+maximumNprobes(maximumNprobes): VectorQuery
+```
+
+Set the maximum number of probes used.
+
+This controls the maximum number of partitions that will be searched.  If this
+number is greater than minimumNprobes then the excess partitions will _only_ be
+searched if we have not found enough results.  This can be useful when there is
+a narrow filter to allow these queries to spend more time searching and avoid
+potential false negatives.
+
+#### Parameters
+
+* **maximumNprobes**: `number`
+
+#### Returns
+
+[`VectorQuery`](VectorQuery.md)
+
+***
+
+### minimumNprobes()
+
+```ts
+minimumNprobes(minimumNprobes): VectorQuery
+```
+
+Set the minimum number of probes used.
+
+This controls the minimum number of partitions that will be searched.  This
+parameter will impact every query against a vector index, regardless of the
+filter.  See `nprobes` for more details.  Higher values will increase recall
+but will also increase latency.
+
+#### Parameters
+
+* **minimumNprobes**: `number`
+
+#### Returns
+
+[`VectorQuery`](VectorQuery.md)
+
+***
+
 ### nprobes()
 
 ```ts
@@ -413,6 +460,10 @@ For best results we recommend tuning this parameter with a benchmark against
 your actual data to find the smallest possible value that will still give
 you the desired recall.
 
+For more fine grained control over behavior when you have a very narrow filter
+you can use `minimumNprobes` and `maximumNprobes`.  This method sets both
+the minimum and maximum to the same value.
+
 #### Parameters
 
 * **nprobes**: `number`

docs/src/js/enumerations/FullTextQueryType.md

@@ -15,6 +15,14 @@ Enum representing the types of full-text queries supported.
 
 ## Enumeration Members
 
+### Boolean
+
+```ts
+Boolean: "boolean";
+```
+
+***
+
 ### Boost
 
 ```ts

docs/src/js/enumerations/Occur.md

@@ -0,0 +1,37 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / Occur
+
+# Enumeration: Occur
+
+Enum representing the occurrence of terms in full-text queries.
+
+- `Must`: The term must be present in the document.
+- `Should`: The term should contribute to the document score, but is not required.
+- `MustNot`: The term must not be present in the document.
+
+## Enumeration Members
+
+### Must
+
+```ts
+Must: "MUST";
+```
+
+***
+
+### MustNot
+
+```ts
+MustNot: "MUST_NOT";
+```
+
+***
+
+### Should
+
+```ts
+Should: "SHOULD";
+```

docs/src/js/enumerations/Operator.md

@@ -0,0 +1,28 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / Operator
+
+# Enumeration: Operator
+
+Enum representing the logical operators used in full-text queries.
+
+- `And`: All terms must match.
+- `Or`: At least one term must match.
+
+## Enumeration Members
+
+### And
+
+```ts
+And: "AND";
+```
+
+***
+
+### Or
+
+```ts
+Or: "OR";
+```

docs/src/js/functions/connect.md

@@ -6,10 +6,13 @@
 
 # Function: connect()
 
-## connect(uri, options)
+## connect(uri, options, session)
 
 ```ts
-function connect(uri, options?): Promise<Connection>
+function connect(
+   uri,
+   options?,
+   session?): Promise<Connection>
 ```
 
 Connect to a LanceDB instance at the given URI.
@@ -29,6 +32,8 @@ Accepted formats:
 * **options?**: `Partial`&lt;[`ConnectionOptions`](../interfaces/ConnectionOptions.md)&gt;
     The options to use when connecting to the database
 
+* **session?**: [`Session`](../classes/Session.md)
+
 ### Returns
 
 `Promise`&lt;[`Connection`](../classes/Connection.md)&gt;
@@ -77,7 +82,7 @@ Accepted formats:
 
 [ConnectionOptions](../interfaces/ConnectionOptions.md) for more details on the URI format.
 
-### Example
+### Examples
 
 ```ts
 const conn = await connect({
@@ -85,3 +90,11 @@ const conn = await connect({
   storageOptions: {timeout: "60s"}
 });
 ```
+
+```ts
+const session = Session.default();
+const conn = await connect({
+  uri: "/path/to/database",
+  session: session
+});
+```

docs/src/js/globals.md

@@ -12,9 +12,12 @@
 ## Enumerations
 
 - [FullTextQueryType](enumerations/FullTextQueryType.md)
+- [Occur](enumerations/Occur.md)
+- [Operator](enumerations/Operator.md)
 
 ## Classes
 
+- [BooleanQuery](classes/BooleanQuery.md)
 - [BoostQuery](classes/BoostQuery.md)
 - [Connection](classes/Connection.md)
 - [Index](classes/Index.md)
@@ -26,6 +29,7 @@
 - [Query](classes/Query.md)
 - [QueryBase](classes/QueryBase.md)
 - [RecordBatchIterator](classes/RecordBatchIterator.md)
+- [Session](classes/Session.md)
 - [Table](classes/Table.md)
 - [TagContents](classes/TagContents.md)
 - [Tags](classes/Tags.md)
@@ -81,6 +85,7 @@
 - [FieldLike](type-aliases/FieldLike.md)
 - [IntoSql](type-aliases/IntoSql.md)
 - [IntoVector](type-aliases/IntoVector.md)
+- [MultiVector](type-aliases/MultiVector.md)
 - [RecordBatchLike](type-aliases/RecordBatchLike.md)
 - [SchemaLike](type-aliases/SchemaLike.md)
 - [TableLike](type-aliases/TableLike.md)

docs/src/js/interfaces/ConnectionOptions.md

@@ -70,6 +70,17 @@ Defaults to 'us-east-1'.
 
 ***
 
+### session?
+
+```ts
+optional session: Session;
+```
+
+(For LanceDB OSS only): the session to use for this connection. Holds
+shared caches and other session-specific state.
+
+***
+
 ### storageOptions?
 
 ```ts

docs/src/js/interfaces/FtsOptions.md

@@ -23,7 +23,7 @@ whether to remove punctuation
 ### baseTokenizer?
 
 ```ts
-optional baseTokenizer: "raw" | "simple" | "whitespace";
+optional baseTokenizer: "raw" | "simple" | "whitespace" | "ngram";
 ```
 
 The tokenizer to use when building the index.
@@ -71,6 +71,36 @@ tokens longer than this length will be ignored
 
 ***
 
+### ngramMaxLength?
+
+```ts
+optional ngramMaxLength: number;
+```
+
+ngram max length
+
+***
+
+### ngramMinLength?
+
+```ts
+optional ngramMinLength: number;
+```
+
+ngram min length
+
+***
+
+### prefixOnly?
+
+```ts
+optional prefixOnly: boolean;
+```
+
+whether to only index the prefix of the token for ngram tokenizer
+
+***
+
 ### removeStopWords?
 
 ```ts

docs/src/js/interfaces/OpenTableOptions.md

@@ -8,7 +8,7 @@
 
 ## Properties
 
-### indexCacheSize?
+### ~~indexCacheSize?~~
 
 ```ts
 optional indexCacheSize: number;
@@ -16,6 +16,11 @@ optional indexCacheSize: number;
 
 Set the size of the index cache, specified as a number of entries
 
+#### Deprecated
+
+Use session-level cache configuration instead.
+Create a Session with custom cache sizes and pass it to the connect() function.
+
 The exact meaning of an "entry" will depend on the type of index:
 - IVF: there is one entry for each IVF partition
 - BTREE: there is one entry for the entire index

docs/src/js/interfaces/OptimizeOptions.md

@@ -24,10 +24,10 @@ The default is 7 days
 // Delete all versions older than 1 day
 const olderThan = new Date();
 olderThan.setDate(olderThan.getDate() - 1));
-tbl.cleanupOlderVersions(olderThan);
+tbl.optimize({cleanupOlderThan: olderThan});
 
 // Delete all versions except the current version
-tbl.cleanupOlderVersions(new Date());
+tbl.optimize({cleanupOlderThan: new Date()});
 ```
 
 ***

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

@@ -0,0 +1,11 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / MultiVector
+
+# Type Alias: MultiVector
+
+```ts
+type MultiVector: IntoVector[];
+```

docs/src/notebooks/Multivector_on_LanceDB.ipynb

@@ -428,7 +428,7 @@
         "\n",
         "**Why?**  \n",
         "Embedding the UFO dataset and ingesting it into LanceDB takes **~2 hours on a T4 GPU**. To save time:  \n",
-        "- **Use the pre-prepared table with index created ** (provided below) to proceed directly to step7: search.  \n",
+        "- **Use the pre-prepared table with index created** (provided below) to proceed directly to **Step 7**: search.  \n",
         "- **Step 5a** contains the full ingestion code for reference (run it only if necessary).  \n",
         "- **Step 6** contains the details on creating the index on the multivector column"
       ]

docs/test/md_testing.py

@@ -30,7 +30,8 @@ excluded_globs = [
     "../src/rag/advanced_techniques/*.md",
     "../src/guides/scalar_index.md",
     "../src/guides/storage.md",
-    "../src/search.md"
+    "../src/search.md",
+    "../src/guides/sql_querying.md",
 ]
 
 python_prefix = "py"

docs/test/requirements.txt

@@ -7,3 +7,4 @@ tantivy==0.20.1
 --extra-index-url https://download.pytorch.org/whl/cpu
 torch
 polars>=0.19, <=1.3.0
+datafusion

java/.mvn/wrapper/maven-wrapper.properties

@@ -0,0 +1,19 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you 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.
+wrapperVersion=3.3.2
+distributionType=only-script
+distributionUrl=https://repo.maven.apache.org/maven2/org/apache/maven/apache-maven/3.9.9/apache-maven-3.9.9-bin.zip

java/README.md

@@ -0,0 +1,37 @@
+# LanceDB Java SDK
+
+## Configuration and Initialization
+
+### LanceDB Cloud
+
+For LanceDB Cloud, use the simplified builder API:
+
+```java
+import com.lancedb.lance.namespace.LanceRestNamespace;
+
+// If your DB url is db://example-db, then your database here is example-db
+LanceRestNamespace namespace = LanceDBRestNamespaces.builder()
+    .apiKey("your_lancedb_cloud_api_key")
+    .database("your_database_name")
+    .build();
+```
+
+### LanceDB Enterprise
+
+For Enterprise deployments, use your VPC endpoint:
+
+```java
+LanceRestNamespace namespace = LanceDBRestNamespaces.builder()
+    .apiKey("your_lancedb_enterprise_api_key")
+    .database("your-top-dir") // Your top level folder under your cloud bucket, e.g. s3://your-bucket/your-top-dir/
+    .hostOverride("http://<vpc_endpoint_dns_name>:80")
+    .build();
+```
+
+## Development
+
+Build:
+
+```shell
+./mvnw install
+```

java/core/lancedb-jni/Cargo.toml

@@ -19,7 +19,7 @@ lancedb = { path = "../../../rust/lancedb" }
 lance = { workspace = true }
 arrow = { workspace = true, features = ["ffi"] }
 arrow-schema.workspace = true
-tokio = "1.23"
+tokio = "1.46"
 jni = "0.21.1"
 snafu.workspace = true
 lazy_static.workspace = true

java/core/pom.xml

@@ -8,18 +8,24 @@
     <parent>
         <groupId>com.lancedb</groupId>
         <artifactId>lancedb-parent</artifactId>
-        <version>0.20.0-beta.2</version>
+        <version>0.21.2-beta.1</version>
         <relativePath>../pom.xml</relativePath>
     </parent>
 
     <artifactId>lancedb-core</artifactId>
-    <name>LanceDB Core</name>
+    <name>${project.artifactId}</name>
+    <description>LanceDB Core</description>
     <packaging>jar</packaging>
     <properties>
         <rust.release.build>false</rust.release.build>
     </properties>
 
     <dependencies>
+        <dependency>
+            <groupId>com.lancedb</groupId>
+            <artifactId>lance-namespace-core</artifactId>
+            <version>0.0.1</version>
+        </dependency>
         <dependency>
             <groupId>org.apache.arrow</groupId>
             <artifactId>arrow-vector</artifactId>

java/lance-namespace/pom.xml

@@ -0,0 +1,26 @@
+<?xml version="1.0" encoding="UTF-8"?>
+
+<project xmlns="http://maven.apache.org/POM/4.0.0"
+    xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+    xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
+    <modelVersion>4.0.0</modelVersion>
+
+    <parent>
+        <groupId>com.lancedb</groupId>
+        <artifactId>lancedb-parent</artifactId>
+        <version>0.21.2-beta.1</version>
+        <relativePath>../pom.xml</relativePath>
+    </parent>
+
+    <artifactId>lancedb-lance-namespace</artifactId>
+    <name>${project.artifactId}</name>
+    <description>LanceDB Java Integration with Lance Namespace</description>
+    <packaging>jar</packaging>
+
+    <dependencies>
+        <dependency>
+            <groupId>com.lancedb</groupId>
+            <artifactId>lance-namespace-core</artifactId>
+        </dependency>
+    </dependencies>
+</project>

java/lance-namespace/src/main/java/com/lancedb/lancedb/LanceDbRestNamespaces.java

@@ -0,0 +1,146 @@
+/*
+ * 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.
+ */
+package com.lancedb.lancedb;
+
+import com.lancedb.lance.namespace.LanceRestNamespace;
+import com.lancedb.lance.namespace.client.apache.ApiClient;
+
+import java.util.HashMap;
+import java.util.Map;
+import java.util.Optional;
+
+/** Util class to help construct a {@link LanceRestNamespace} for LanceDB. */
+public class LanceDbRestNamespaces {
+  private static final String DEFAULT_REGION = "us-east-1";
+  private static final String CLOUD_URL_PATTERN = "https://%s.%s.api.lancedb.com";
+
+  private String apiKey;
+  private String database;
+  private Optional<String> hostOverride = Optional.empty();
+  private Optional<String> region = Optional.empty();
+  private Map<String, String> additionalConfig = new HashMap<>();
+
+  private LanceDbRestNamespaces() {}
+
+  /**
+   * Create a new builder instance.
+   *
+   * @return A new LanceRestNamespaceBuilder
+   */
+  public static LanceDbRestNamespaces builder() {
+    return new LanceDbRestNamespaces();
+  }
+
+  /**
+   * Set the API key (required).
+   *
+   * @param apiKey The LanceDB API key
+   * @return This builder
+   */
+  public LanceDbRestNamespaces apiKey(String apiKey) {
+    if (apiKey == null || apiKey.trim().isEmpty()) {
+      throw new IllegalArgumentException("API key cannot be null or empty");
+    }
+    this.apiKey = apiKey;
+    return this;
+  }
+
+  /**
+   * Set the database name (required).
+   *
+   * @param database The database name
+   * @return This builder
+   */
+  public LanceDbRestNamespaces database(String database) {
+    if (database == null || database.trim().isEmpty()) {
+      throw new IllegalArgumentException("Database cannot be null or empty");
+    }
+    this.database = database;
+    return this;
+  }
+
+  /**
+   * Set a custom host override (optional). When set, this overrides the default LanceDB Cloud URL
+   * construction. Use this for LanceDB Enterprise deployments.
+   *
+   * @param hostOverride The complete base URL (e.g., "http://your-vpc-endpoint:80")
+   * @return This builder
+   */
+  public LanceDbRestNamespaces hostOverride(String hostOverride) {
+    this.hostOverride = Optional.ofNullable(hostOverride);
+    return this;
+  }
+
+  /**
+   * Set the region for LanceDB Cloud (optional). Defaults to "us-east-1" if not specified. This is
+   * ignored when hostOverride is set.
+   *
+   * @param region The AWS region (e.g., "us-east-1", "eu-west-1")
+   * @return This builder
+   */
+  public LanceDbRestNamespaces region(String region) {
+    this.region = Optional.ofNullable(region);
+    return this;
+  }
+
+  /**
+   * Add additional configuration parameters.
+   *
+   * @param key The configuration key
+   * @param value The configuration value
+   * @return This builder
+   */
+  public LanceDbRestNamespaces config(String key, String value) {
+    this.additionalConfig.put(key, value);
+    return this;
+  }
+
+  /**
+   * Build the LanceRestNamespace instance.
+   *
+   * @return A configured LanceRestNamespace
+   * @throws IllegalStateException if required parameters are missing
+   */
+  public LanceRestNamespace build() {
+    // Validate required fields
+    if (apiKey == null) {
+      throw new IllegalStateException("API key is required");
+    }
+    if (database == null) {
+      throw new IllegalStateException("Database is required");
+    }
+
+    // Build configuration map
+    Map<String, String> config = new HashMap<>(additionalConfig);
+    config.put("headers.x-lancedb-database", database);
+    config.put("headers.x-api-key", apiKey);
+
+    // Determine base URL
+    String baseUrl;
+    if (hostOverride.isPresent()) {
+      baseUrl = hostOverride.get();
+      config.put("host_override", hostOverride.get());
+    } else {
+      String effectiveRegion = region.orElse(DEFAULT_REGION);
+      baseUrl = String.format(CLOUD_URL_PATTERN, database, effectiveRegion);
+      config.put("region", effectiveRegion);
+    }
+
+    // Create and configure ApiClient
+    ApiClient apiClient = new ApiClient();
+    apiClient.setBasePath(baseUrl);
+
+    return new LanceRestNamespace(apiClient, config);
+  }
+}

java/mvnw

@@ -0,0 +1,259 @@
+#!/bin/sh
+# ----------------------------------------------------------------------------
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you 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.
+# ----------------------------------------------------------------------------
+
+# ----------------------------------------------------------------------------
+# Apache Maven Wrapper startup batch script, version 3.3.2
+#
+# Optional ENV vars
+# -----------------
+#   JAVA_HOME - location of a JDK home dir, required when download maven via java source
+#   MVNW_REPOURL - repo url base for downloading maven distribution
+#   MVNW_USERNAME/MVNW_PASSWORD - user and password for downloading maven
+#   MVNW_VERBOSE - true: enable verbose log; debug: trace the mvnw script; others: silence the output
+# ----------------------------------------------------------------------------
+
+set -euf
+[ "${MVNW_VERBOSE-}" != debug ] || set -x
+
+# OS specific support.
+native_path() { printf %s\\n "$1"; }
+case "$(uname)" in
+CYGWIN* | MINGW*)
+  [ -z "${JAVA_HOME-}" ] || JAVA_HOME="$(cygpath --unix "$JAVA_HOME")"
+  native_path() { cygpath --path --windows "$1"; }
+  ;;
+esac
+
+# set JAVACMD and JAVACCMD
+set_java_home() {
+  # For Cygwin and MinGW, ensure paths are in Unix format before anything is touched
+  if [ -n "${JAVA_HOME-}" ]; then
+    if [ -x "$JAVA_HOME/jre/sh/java" ]; then
+      # IBM's JDK on AIX uses strange locations for the executables
+      JAVACMD="$JAVA_HOME/jre/sh/java"
+      JAVACCMD="$JAVA_HOME/jre/sh/javac"
+    else
+      JAVACMD="$JAVA_HOME/bin/java"
+      JAVACCMD="$JAVA_HOME/bin/javac"
+
+      if [ ! -x "$JAVACMD" ] || [ ! -x "$JAVACCMD" ]; then
+        echo "The JAVA_HOME environment variable is not defined correctly, so mvnw cannot run." >&2
+        echo "JAVA_HOME is set to \"$JAVA_HOME\", but \"\$JAVA_HOME/bin/java\" or \"\$JAVA_HOME/bin/javac\" does not exist." >&2
+        return 1
+      fi
+    fi
+  else
+    JAVACMD="$(
+      'set' +e
+      'unset' -f command 2>/dev/null
+      'command' -v java
+    )" || :
+    JAVACCMD="$(
+      'set' +e
+      'unset' -f command 2>/dev/null
+      'command' -v javac
+    )" || :
+
+    if [ ! -x "${JAVACMD-}" ] || [ ! -x "${JAVACCMD-}" ]; then
+      echo "The java/javac command does not exist in PATH nor is JAVA_HOME set, so mvnw cannot run." >&2
+      return 1
+    fi
+  fi
+}
+
+# hash string like Java String::hashCode
+hash_string() {
+  str="${1:-}" h=0
+  while [ -n "$str" ]; do
+    char="${str%"${str#?}"}"
+    h=$(((h * 31 + $(LC_CTYPE=C printf %d "'$char")) % 4294967296))
+    str="${str#?}"
+  done
+  printf %x\\n $h
+}
+
+verbose() { :; }
+[ "${MVNW_VERBOSE-}" != true ] || verbose() { printf %s\\n "${1-}"; }
+
+die() {
+  printf %s\\n "$1" >&2
+  exit 1
+}
+
+trim() {
+  # MWRAPPER-139:
+  #   Trims trailing and leading whitespace, carriage returns, tabs, and linefeeds.
+  #   Needed for removing poorly interpreted newline sequences when running in more
+  #   exotic environments such as mingw bash on Windows.
+  printf "%s" "${1}" | tr -d '[:space:]'
+}
+
+# parse distributionUrl and optional distributionSha256Sum, requires .mvn/wrapper/maven-wrapper.properties
+while IFS="=" read -r key value; do
+  case "${key-}" in
+  distributionUrl) distributionUrl=$(trim "${value-}") ;;
+  distributionSha256Sum) distributionSha256Sum=$(trim "${value-}") ;;
+  esac
+done <"${0%/*}/.mvn/wrapper/maven-wrapper.properties"
+[ -n "${distributionUrl-}" ] || die "cannot read distributionUrl property in ${0%/*}/.mvn/wrapper/maven-wrapper.properties"
+
+case "${distributionUrl##*/}" in
+maven-mvnd-*bin.*)
+  MVN_CMD=mvnd.sh _MVNW_REPO_PATTERN=/maven/mvnd/
+  case "${PROCESSOR_ARCHITECTURE-}${PROCESSOR_ARCHITEW6432-}:$(uname -a)" in
+  *AMD64:CYGWIN* | *AMD64:MINGW*) distributionPlatform=windows-amd64 ;;
+  :Darwin*x86_64) distributionPlatform=darwin-amd64 ;;
+  :Darwin*arm64) distributionPlatform=darwin-aarch64 ;;
+  :Linux*x86_64*) distributionPlatform=linux-amd64 ;;
+  *)
+    echo "Cannot detect native platform for mvnd on $(uname)-$(uname -m), use pure java version" >&2
+    distributionPlatform=linux-amd64
+    ;;
+  esac
+  distributionUrl="${distributionUrl%-bin.*}-$distributionPlatform.zip"
+  ;;
+maven-mvnd-*) MVN_CMD=mvnd.sh _MVNW_REPO_PATTERN=/maven/mvnd/ ;;
+*) MVN_CMD="mvn${0##*/mvnw}" _MVNW_REPO_PATTERN=/org/apache/maven/ ;;
+esac
+
+# apply MVNW_REPOURL and calculate MAVEN_HOME
+# maven home pattern: ~/.m2/wrapper/dists/{apache-maven-<version>,maven-mvnd-<version>-<platform>}/<hash>
+[ -z "${MVNW_REPOURL-}" ] || distributionUrl="$MVNW_REPOURL$_MVNW_REPO_PATTERN${distributionUrl#*"$_MVNW_REPO_PATTERN"}"
+distributionUrlName="${distributionUrl##*/}"
+distributionUrlNameMain="${distributionUrlName%.*}"
+distributionUrlNameMain="${distributionUrlNameMain%-bin}"
+MAVEN_USER_HOME="${MAVEN_USER_HOME:-${HOME}/.m2}"
+MAVEN_HOME="${MAVEN_USER_HOME}/wrapper/dists/${distributionUrlNameMain-}/$(hash_string "$distributionUrl")"
+
+exec_maven() {
+  unset MVNW_VERBOSE MVNW_USERNAME MVNW_PASSWORD MVNW_REPOURL || :
+  exec "$MAVEN_HOME/bin/$MVN_CMD" "$@" || die "cannot exec $MAVEN_HOME/bin/$MVN_CMD"
+}
+
+if [ -d "$MAVEN_HOME" ]; then
+  verbose "found existing MAVEN_HOME at $MAVEN_HOME"
+  exec_maven "$@"
+fi
+
+case "${distributionUrl-}" in
+*?-bin.zip | *?maven-mvnd-?*-?*.zip) ;;
+*) die "distributionUrl is not valid, must match *-bin.zip or maven-mvnd-*.zip, but found '${distributionUrl-}'" ;;
+esac
+
+# prepare tmp dir
+if TMP_DOWNLOAD_DIR="$(mktemp -d)" && [ -d "$TMP_DOWNLOAD_DIR" ]; then
+  clean() { rm -rf -- "$TMP_DOWNLOAD_DIR"; }
+  trap clean HUP INT TERM EXIT
+else
+  die "cannot create temp dir"
+fi
+
+mkdir -p -- "${MAVEN_HOME%/*}"
+
+# Download and Install Apache Maven
+verbose "Couldn't find MAVEN_HOME, downloading and installing it ..."
+verbose "Downloading from: $distributionUrl"
+verbose "Downloading to: $TMP_DOWNLOAD_DIR/$distributionUrlName"
+
+# select .zip or .tar.gz
+if ! command -v unzip >/dev/null; then
+  distributionUrl="${distributionUrl%.zip}.tar.gz"
+  distributionUrlName="${distributionUrl##*/}"
+fi
+
+# verbose opt
+__MVNW_QUIET_WGET=--quiet __MVNW_QUIET_CURL=--silent __MVNW_QUIET_UNZIP=-q __MVNW_QUIET_TAR=''
+[ "${MVNW_VERBOSE-}" != true ] || __MVNW_QUIET_WGET='' __MVNW_QUIET_CURL='' __MVNW_QUIET_UNZIP='' __MVNW_QUIET_TAR=v
+
+# normalize http auth
+case "${MVNW_PASSWORD:+has-password}" in
+'') MVNW_USERNAME='' MVNW_PASSWORD='' ;;
+has-password) [ -n "${MVNW_USERNAME-}" ] || MVNW_USERNAME='' MVNW_PASSWORD='' ;;
+esac
+
+if [ -z "${MVNW_USERNAME-}" ] && command -v wget >/dev/null; then
+  verbose "Found wget ... using wget"
+  wget ${__MVNW_QUIET_WGET:+"$__MVNW_QUIET_WGET"} "$distributionUrl" -O "$TMP_DOWNLOAD_DIR/$distributionUrlName" || die "wget: Failed to fetch $distributionUrl"
+elif [ -z "${MVNW_USERNAME-}" ] && command -v curl >/dev/null; then
+  verbose "Found curl ... using curl"
+  curl ${__MVNW_QUIET_CURL:+"$__MVNW_QUIET_CURL"} -f -L -o "$TMP_DOWNLOAD_DIR/$distributionUrlName" "$distributionUrl" || die "curl: Failed to fetch $distributionUrl"
+elif set_java_home; then
+  verbose "Falling back to use Java to download"
+  javaSource="$TMP_DOWNLOAD_DIR/Downloader.java"
+  targetZip="$TMP_DOWNLOAD_DIR/$distributionUrlName"
+  cat >"$javaSource" <<-END
+	public class Downloader extends java.net.Authenticator
+	{
+	  protected java.net.PasswordAuthentication getPasswordAuthentication()
+	  {
+	    return new java.net.PasswordAuthentication( System.getenv( "MVNW_USERNAME" ), System.getenv( "MVNW_PASSWORD" ).toCharArray() );
+	  }
+	  public static void main( String[] args ) throws Exception
+	  {
+	    setDefault( new Downloader() );
+	    java.nio.file.Files.copy( java.net.URI.create( args[0] ).toURL().openStream(), java.nio.file.Paths.get( args[1] ).toAbsolutePath().normalize() );
+	  }
+	}
+	END
+  # For Cygwin/MinGW, switch paths to Windows format before running javac and java
+  verbose " - Compiling Downloader.java ..."
+  "$(native_path "$JAVACCMD")" "$(native_path "$javaSource")" || die "Failed to compile Downloader.java"
+  verbose " - Running Downloader.java ..."
+  "$(native_path "$JAVACMD")" -cp "$(native_path "$TMP_DOWNLOAD_DIR")" Downloader "$distributionUrl" "$(native_path "$targetZip")"
+fi
+
+# If specified, validate the SHA-256 sum of the Maven distribution zip file
+if [ -n "${distributionSha256Sum-}" ]; then
+  distributionSha256Result=false
+  if [ "$MVN_CMD" = mvnd.sh ]; then
+    echo "Checksum validation is not supported for maven-mvnd." >&2
+    echo "Please disable validation by removing 'distributionSha256Sum' from your maven-wrapper.properties." >&2
+    exit 1
+  elif command -v sha256sum >/dev/null; then
+    if echo "$distributionSha256Sum  $TMP_DOWNLOAD_DIR/$distributionUrlName" | sha256sum -c >/dev/null 2>&1; then
+      distributionSha256Result=true
+    fi
+  elif command -v shasum >/dev/null; then
+    if echo "$distributionSha256Sum  $TMP_DOWNLOAD_DIR/$distributionUrlName" | shasum -a 256 -c >/dev/null 2>&1; then
+      distributionSha256Result=true
+    fi
+  else
+    echo "Checksum validation was requested but neither 'sha256sum' or 'shasum' are available." >&2
+    echo "Please install either command, or disable validation by removing 'distributionSha256Sum' from your maven-wrapper.properties." >&2
+    exit 1
+  fi
+  if [ $distributionSha256Result = false ]; then
+    echo "Error: Failed to validate Maven distribution SHA-256, your Maven distribution might be compromised." >&2
+    echo "If you updated your Maven version, you need to update the specified distributionSha256Sum property." >&2
+    exit 1
+  fi
+fi
+
+# unzip and move
+if command -v unzip >/dev/null; then
+  unzip ${__MVNW_QUIET_UNZIP:+"$__MVNW_QUIET_UNZIP"} "$TMP_DOWNLOAD_DIR/$distributionUrlName" -d "$TMP_DOWNLOAD_DIR" || die "failed to unzip"
+else
+  tar xzf${__MVNW_QUIET_TAR:+"$__MVNW_QUIET_TAR"} "$TMP_DOWNLOAD_DIR/$distributionUrlName" -C "$TMP_DOWNLOAD_DIR" || die "failed to untar"
+fi
+printf %s\\n "$distributionUrl" >"$TMP_DOWNLOAD_DIR/$distributionUrlNameMain/mvnw.url"
+mv -- "$TMP_DOWNLOAD_DIR/$distributionUrlNameMain" "$MAVEN_HOME" || [ -d "$MAVEN_HOME" ] || die "fail to move MAVEN_HOME"
+
+clean || :
+exec_maven "$@"

java/pom.xml

@@ -6,11 +6,10 @@
 
     <groupId>com.lancedb</groupId>
     <artifactId>lancedb-parent</artifactId>
-    <version>0.20.0-beta.2</version>
+    <version>0.21.2-beta.1</version>
     <packaging>pom</packaging>
-
+    <name>${project.artifactId}</name>
-    <name>LanceDB Parent</name>
+    <description>LanceDB Java SDK Parent POM</description>
-    <description>LanceDB vector database Java API</description>
     <url>http://lancedb.com/</url>
 
     <developers>
@@ -29,6 +28,7 @@
     <properties>
         <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
         <arrow.version>15.0.0</arrow.version>
+        <lance-namespace.verison>0.0.1</lance-namespace.verison>
         <spotless.skip>false</spotless.skip>
         <spotless.version>2.30.0</spotless.version>
         <spotless.java.googlejavaformat.version>1.7</spotless.java.googlejavaformat.version>
@@ -52,6 +52,7 @@
 
     <modules>
         <module>core</module>
+        <module>lance-namespace</module>
     </modules>
 
     <scm>
@@ -62,6 +63,11 @@
 
     <dependencyManagement>
         <dependencies>
+            <dependency>
+                <groupId>com.lancedb</groupId>
+                <artifactId>lance-namespace-core</artifactId>
+                <version>${lance-namespace.verison}</version>
+            </dependency>
             <dependency>
                 <groupId>org.apache.arrow</groupId>
                 <artifactId>arrow-vector</artifactId>

node/package-lock.json

@@ -1,12 +1,12 @@
 {
   "name": "vectordb",
-  "version": "0.20.0-beta.2",
+  "version": "0.21.2-beta.1",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
     "": {
       "name": "vectordb",
-      "version": "0.20.0-beta.2",
+      "version": "0.21.2-beta.1",
       "cpu": [
         "x64",
         "arm64"
@@ -52,11 +52,11 @@
         "uuid": "^9.0.0"
       },
       "optionalDependencies": {
-        "@lancedb/vectordb-darwin-arm64": "0.20.0-beta.2",
+        "@lancedb/vectordb-darwin-arm64": "0.21.2-beta.1",
-        "@lancedb/vectordb-darwin-x64": "0.20.0-beta.2",
+        "@lancedb/vectordb-darwin-x64": "0.21.2-beta.1",
-        "@lancedb/vectordb-linux-arm64-gnu": "0.20.0-beta.2",
+        "@lancedb/vectordb-linux-arm64-gnu": "0.21.2-beta.1",
-        "@lancedb/vectordb-linux-x64-gnu": "0.20.0-beta.2",
+        "@lancedb/vectordb-linux-x64-gnu": "0.21.2-beta.1",
-        "@lancedb/vectordb-win32-x64-msvc": "0.20.0-beta.2"
+        "@lancedb/vectordb-win32-x64-msvc": "0.21.2-beta.1"
       },
       "peerDependencies": {
         "@apache-arrow/ts": "^14.0.2",
@@ -327,9 +327,9 @@
       }
     },
     "node_modules/@lancedb/vectordb-darwin-arm64": {
-      "version": "0.20.0-beta.2",
+      "version": "0.21.2-beta.1",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-arm64/-/vectordb-darwin-arm64-0.20.0-beta.2.tgz",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-arm64/-/vectordb-darwin-arm64-0.21.2-beta.1.tgz",
-      "integrity": "sha512-H9PmJ/5KSvstVzR8Q7T22+eHRjJZ2ef3aA3gdFxXvoMi3xQ0MGIxz23HuKHGTRT4tfl1nNnpOPb2W7Na8etK9w==",
+      "integrity": "sha512-7QXVJNTei7PMuXRyyc+F3WGiudRNq9HfeOaMmMOJJpuCAO0zLq1pM9DCl5aPF5MddrodPHJxi+IWV+iAFH7zcg==",
       "cpu": [
         "arm64"
       ],
@@ -339,9 +339,9 @@
       ]
     },
     "node_modules/@lancedb/vectordb-darwin-x64": {
-      "version": "0.20.0-beta.2",
+      "version": "0.21.2-beta.1",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-x64/-/vectordb-darwin-x64-0.20.0-beta.2.tgz",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-x64/-/vectordb-darwin-x64-0.21.2-beta.1.tgz",
-      "integrity": "sha512-9AQkv4tIys+vg0cplZtSE48o61jd7EnmuMkUht+vLORL5/HAma84eAoU9lXHT7zAtPAQmL+98Bfvcsx7fJ6mVw==",
+      "integrity": "sha512-M/TWcJ3WVc6DNFgG/lWI7L5tQ05IF3WoWuZfRfbbimGhRvY7xf1O3uOt+jMcNJCa5mHFGCg2SZDA8mebd/mL7g==",
       "cpu": [
         "x64"
       ],
@@ -351,9 +351,9 @@
       ]
     },
     "node_modules/@lancedb/vectordb-linux-arm64-gnu": {
-      "version": "0.20.0-beta.2",
+      "version": "0.21.2-beta.1",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-arm64-gnu/-/vectordb-linux-arm64-gnu-0.20.0-beta.2.tgz",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-arm64-gnu/-/vectordb-linux-arm64-gnu-0.21.2-beta.1.tgz",
-      "integrity": "sha512-eQWoJz2ePml7NyEInTBeakWx56+5c6r2p3F+iHC5tsLuznn6eFX90koXJunRxH1WXHDN48ECUlEmKypgfEmn4w==",
+      "integrity": "sha512-OEsM9znf9DDmdwGuTg2EVu+ebwuWQ1lCx0cYy4+hNy3ntolwMC39ePg2H9WD9SsEnQ2vcGJgBJTQLPKgXww+iQ==",
       "cpu": [
         "arm64"
       ],
@@ -363,9 +363,9 @@
       ]
     },
     "node_modules/@lancedb/vectordb-linux-x64-gnu": {
-      "version": "0.20.0-beta.2",
+      "version": "0.21.2-beta.1",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-x64-gnu/-/vectordb-linux-x64-gnu-0.20.0-beta.2.tgz",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-x64-gnu/-/vectordb-linux-x64-gnu-0.21.2-beta.1.tgz",
-      "integrity": "sha512-/+84U+Dt07m8Jk0b8h+SvOzlrynITPP3SDBOlB+OonwmGSxirXhc8gkfNZctgXOJYKMyRIRSsMHP/QNjOp2ajA==",
+      "integrity": "sha512-7FTq/O1zNzD71rgX2PEVmkct4jk2wc+ADU3rss+0VqoBSO9XeMqZEVD2WgZWuSTg6bYai//FHGDHSaknHBNsdw==",
       "cpu": [
         "x64"
       ],
@@ -375,9 +375,9 @@
       ]
     },
     "node_modules/@lancedb/vectordb-win32-x64-msvc": {
-      "version": "0.20.0-beta.2",
+      "version": "0.21.2-beta.1",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-win32-x64-msvc/-/vectordb-win32-x64-msvc-0.20.0-beta.2.tgz",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-win32-x64-msvc/-/vectordb-win32-x64-msvc-0.21.2-beta.1.tgz",
-      "integrity": "sha512-bgdunAPnknBh/5oO+vr6RXMr6wb3hHugNPXcIidxYMQvgFa8uhaAKtgYkAKuoyUReOYo8DGtVkZxNUUpZbF7/A==",
+      "integrity": "sha512-mN1p/J0kdqy6MrlKtmA8set/PibqFPyytQJFAuxSLXC/rwD7vgqUCt0SI0zVWPGG7J5Y65kvdc99l7Yl7lJtwQ==",
       "cpu": [
         "x64"
       ],

node/package.json

@@ -1,6 +1,6 @@
 {
   "name": "vectordb",
-  "version": "0.20.0-beta.2",
+  "version": "0.21.2-beta.1",
   "description": " Serverless, low-latency vector database for AI applications",
   "private": false,
   "main": "dist/index.js",
@@ -89,10 +89,10 @@
     }
   },
   "optionalDependencies": {
-    "@lancedb/vectordb-darwin-x64": "0.20.0-beta.2",
+    "@lancedb/vectordb-darwin-x64": "0.21.2-beta.1",
-    "@lancedb/vectordb-darwin-arm64": "0.20.0-beta.2",
+    "@lancedb/vectordb-darwin-arm64": "0.21.2-beta.1",
-    "@lancedb/vectordb-linux-x64-gnu": "0.20.0-beta.2",
+    "@lancedb/vectordb-linux-x64-gnu": "0.21.2-beta.1",
-    "@lancedb/vectordb-linux-arm64-gnu": "0.20.0-beta.2",
+    "@lancedb/vectordb-linux-arm64-gnu": "0.21.2-beta.1",
-    "@lancedb/vectordb-win32-x64-msvc": "0.20.0-beta.2"
+    "@lancedb/vectordb-win32-x64-msvc": "0.21.2-beta.1"
   }
 }

node/src/integration_test/test.ts

@@ -49,7 +49,7 @@ describe('LanceDB Mirrored Store Integration test', function () {
   it('s3://...?mirroredStore=... param is processed correctly', async function () {
     this.timeout(600000)
 
-    const dir = tmpdir()
+    const dir = await fs.promises.mkdtemp(path.join(tmpdir(), 'lancedb-mirror-'))
     console.log(dir)
     const conn = await lancedb.connect({ uri: `s3://lancedb-integtest?mirroredStore=${dir}`, storageOptions: { allowHttp: 'true' } })
     const data = Array(200).fill({ vector: Array(128).fill(1.0), id: 0 })
@@ -63,118 +63,93 @@ describe('LanceDB Mirrored Store Integration test', function () {
     const t = await conn.createTable(tableName, data, { writeMode: lancedb.WriteMode.Overwrite })
 
     const mirroredPath = path.join(dir, `${tableName}.lance`)
-    fs.readdir(mirroredPath, { withFileTypes: true }, (err, files) => {
+
-      if (err != null) throw err
+    const files = await fs.promises.readdir(mirroredPath, { withFileTypes: true })
     // there should be three dirs
-      assert.equal(files.length, 3)
+    assert.equal(files.length, 3, 'files after table creation')
     assert.isTrue(files[0].isDirectory())
     assert.isTrue(files[1].isDirectory())
 
-      fs.readdir(path.join(mirroredPath, '_transactions'), { withFileTypes: true }, (err, files) => {
+    const transactionFiles = await fs.promises.readdir(path.join(mirroredPath, '_transactions'), { withFileTypes: true })
-        if (err != null) throw err
+    assert.equal(transactionFiles.length, 1, 'transactionFiles after table creation')
-        assert.equal(files.length, 1)
+    assert.isTrue(transactionFiles[0].name.endsWith('.txn'))
-        assert.isTrue(files[0].name.endsWith('.txn'))
-      })
 
-      fs.readdir(path.join(mirroredPath, '_versions'), { withFileTypes: true }, (err, files) => {
+    const versionFiles = await fs.promises.readdir(path.join(mirroredPath, '_versions'), { withFileTypes: true })
-        if (err != null) throw err
+    assert.equal(versionFiles.length, 1, 'versionFiles after table creation')
-        assert.equal(files.length, 1)
+    assert.isTrue(versionFiles[0].name.endsWith('.manifest'))
-        assert.isTrue(files[0].name.endsWith('.manifest'))
-      })
 
-      fs.readdir(path.join(mirroredPath, 'data'), { withFileTypes: true }, (err, files) => {
+    const dataFiles = await fs.promises.readdir(path.join(mirroredPath, 'data'), { withFileTypes: true })
-        if (err != null) throw err
+    assert.equal(dataFiles.length, 1, 'dataFiles after table creation')
-        assert.equal(files.length, 1)
+    assert.isTrue(dataFiles[0].name.endsWith('.lance'))
-        assert.isTrue(files[0].name.endsWith('.lance'))
-      })
-    })
 
     // try create index and check if it's mirrored
     await t.createIndex({ column: 'vector', type: 'ivf_pq' })
 
-    fs.readdir(mirroredPath, { withFileTypes: true }, (err, files) => {
+    const filesAfterIndex = await fs.promises.readdir(mirroredPath, { withFileTypes: true })
-      if (err != null) throw err
     // there should be four dirs
-      assert.equal(files.length, 4)
+    assert.equal(filesAfterIndex.length, 4, 'filesAfterIndex')
-      assert.isTrue(files[0].isDirectory())
+    assert.isTrue(filesAfterIndex[0].isDirectory())
-      assert.isTrue(files[1].isDirectory())
+    assert.isTrue(filesAfterIndex[1].isDirectory())
-      assert.isTrue(files[2].isDirectory())
+    assert.isTrue(filesAfterIndex[2].isDirectory())
 
     // Two TXs now
-      fs.readdir(path.join(mirroredPath, '_transactions'), { withFileTypes: true }, (err, files) => {
+    const transactionFilesAfterIndex = await fs.promises.readdir(path.join(mirroredPath, '_transactions'), { withFileTypes: true })
-        if (err != null) throw err
+    assert.equal(transactionFilesAfterIndex.length, 2, 'transactionFilesAfterIndex')
-        assert.equal(files.length, 2)
+    assert.isTrue(transactionFilesAfterIndex[0].name.endsWith('.txn'))
-        assert.isTrue(files[0].name.endsWith('.txn'))
+    assert.isTrue(transactionFilesAfterIndex[1].name.endsWith('.txn'))
-        assert.isTrue(files[1].name.endsWith('.txn'))
-      })
 
-      fs.readdir(path.join(mirroredPath, 'data'), { withFileTypes: true }, (err, files) => {
+    const dataFilesAfterIndex = await fs.promises.readdir(path.join(mirroredPath, 'data'), { withFileTypes: true })
-        if (err != null) throw err
+    assert.equal(dataFilesAfterIndex.length, 1, 'dataFilesAfterIndex')
-        assert.equal(files.length, 1)
+    assert.isTrue(dataFilesAfterIndex[0].name.endsWith('.lance'))
-        assert.isTrue(files[0].name.endsWith('.lance'))
-      })
 
-      fs.readdir(path.join(mirroredPath, '_indices'), { withFileTypes: true }, (err, files) => {
+    const indicesFiles = await fs.promises.readdir(path.join(mirroredPath, '_indices'), { withFileTypes: true })
-        if (err != null) throw err
+    assert.equal(indicesFiles.length, 1, 'indicesFiles')
-        assert.equal(files.length, 1)
+    assert.isTrue(indicesFiles[0].isDirectory())
-        assert.isTrue(files[0].isDirectory())
 
-        fs.readdir(path.join(mirroredPath, '_indices', files[0].name), { withFileTypes: true }, (err, files) => {
+    const indexFiles = await fs.promises.readdir(path.join(mirroredPath, '_indices', indicesFiles[0].name), { withFileTypes: true })
-          if (err != null) throw err
+    console.log(`DEBUG indexFiles in ${indicesFiles[0].name}:`, indexFiles.map(f => `${f.name} (${f.isFile() ? 'file' : 'dir'})`))
-
+    assert.equal(indexFiles.length, 2, 'indexFiles')
-          assert.equal(files.length, 1)
+    const fileNames = indexFiles.map(f => f.name).sort()
-          assert.isTrue(files[0].isFile())
+    assert.isTrue(fileNames.includes('auxiliary.idx'), 'auxiliary.idx should be present')
-          assert.isTrue(files[0].name.endsWith('.idx'))
+    assert.isTrue(fileNames.includes('index.idx'), 'index.idx should be present')
-        })
+    assert.isTrue(indexFiles.every(f => f.isFile()), 'all index files should be files')
-      })
-    })
 
     // try delete and check if it's mirrored
     await t.delete('id = 0')
 
-    fs.readdir(mirroredPath, { withFileTypes: true }, (err, files) => {
+    const filesAfterDelete = await fs.promises.readdir(mirroredPath, { withFileTypes: true })
-      if (err != null) throw err
     // there should be five dirs
-      assert.equal(files.length, 5)
+    assert.equal(filesAfterDelete.length, 5, 'filesAfterDelete')
-      assert.isTrue(files[0].isDirectory())
+    assert.isTrue(filesAfterDelete[0].isDirectory())
-      assert.isTrue(files[1].isDirectory())
+    assert.isTrue(filesAfterDelete[1].isDirectory())
-      assert.isTrue(files[2].isDirectory())
+    assert.isTrue(filesAfterDelete[2].isDirectory())
-      assert.isTrue(files[3].isDirectory())
+    assert.isTrue(filesAfterDelete[3].isDirectory())
-      assert.isTrue(files[4].isDirectory())
+    assert.isTrue(filesAfterDelete[4].isDirectory())
 
     // Three TXs now
-      fs.readdir(path.join(mirroredPath, '_transactions'), { withFileTypes: true }, (err, files) => {
+    const transactionFilesAfterDelete = await fs.promises.readdir(path.join(mirroredPath, '_transactions'), { withFileTypes: true })
-        if (err != null) throw err
+    assert.equal(transactionFilesAfterDelete.length, 3, 'transactionFilesAfterDelete')
-        assert.equal(files.length, 3)
+    assert.isTrue(transactionFilesAfterDelete[0].name.endsWith('.txn'))
-        assert.isTrue(files[0].name.endsWith('.txn'))
+    assert.isTrue(transactionFilesAfterDelete[1].name.endsWith('.txn'))
-        assert.isTrue(files[1].name.endsWith('.txn'))
-      })
 
-      fs.readdir(path.join(mirroredPath, 'data'), { withFileTypes: true }, (err, files) => {
+    const dataFilesAfterDelete = await fs.promises.readdir(path.join(mirroredPath, 'data'), { withFileTypes: true })
-        if (err != null) throw err
+    assert.equal(dataFilesAfterDelete.length, 1, 'dataFilesAfterDelete')
-        assert.equal(files.length, 1)
+    assert.isTrue(dataFilesAfterDelete[0].name.endsWith('.lance'))
-        assert.isTrue(files[0].name.endsWith('.lance'))
-      })
 
-      fs.readdir(path.join(mirroredPath, '_indices'), { withFileTypes: true }, (err, files) => {
+    const indicesFilesAfterDelete = await fs.promises.readdir(path.join(mirroredPath, '_indices'), { withFileTypes: true })
-        if (err != null) throw err
+    assert.equal(indicesFilesAfterDelete.length, 1, 'indicesFilesAfterDelete')
-        assert.equal(files.length, 1)
+    assert.isTrue(indicesFilesAfterDelete[0].isDirectory())
-        assert.isTrue(files[0].isDirectory())
 
-        fs.readdir(path.join(mirroredPath, '_indices', files[0].name), { withFileTypes: true }, (err, files) => {
+    const indexFilesAfterDelete = await fs.promises.readdir(path.join(mirroredPath, '_indices', indicesFilesAfterDelete[0].name), { withFileTypes: true })
-          if (err != null) throw err
+    console.log(`DEBUG indexFilesAfterDelete in ${indicesFilesAfterDelete[0].name}:`, indexFilesAfterDelete.map(f => `${f.name} (${f.isFile() ? 'file' : 'dir'})`))
+    assert.equal(indexFilesAfterDelete.length, 2, 'indexFilesAfterDelete')
+    const fileNamesAfterDelete = indexFilesAfterDelete.map(f => f.name).sort()
+    assert.isTrue(fileNamesAfterDelete.includes('auxiliary.idx'), 'auxiliary.idx should be present after delete')
+    assert.isTrue(fileNamesAfterDelete.includes('index.idx'), 'index.idx should be present after delete')
+    assert.isTrue(indexFilesAfterDelete.every(f => f.isFile()), 'all index files should be files after delete')
 
-          assert.equal(files.length, 1)
+    const deletionFiles = await fs.promises.readdir(path.join(mirroredPath, '_deletions'), { withFileTypes: true })
-          assert.isTrue(files[0].isFile())
+    assert.equal(deletionFiles.length, 1, 'deletionFiles')
-          assert.isTrue(files[0].name.endsWith('.idx'))
+    assert.isTrue(deletionFiles[0].name.endsWith('.arrow'))
-        })
-      })
-
-      fs.readdir(path.join(mirroredPath, '_deletions'), { withFileTypes: true }, (err, files) => {
-        if (err != null) throw err
-        assert.equal(files.length, 1)
-        assert.isTrue(files[0].name.endsWith('.arrow'))
-      })
-    })
   })
 })

nodejs/CLAUDE.md

@@ -0,0 +1,13 @@
+These are the typescript bindings of LanceDB.
+The core Rust library is in the `../rust/lancedb` directory, the rust binding
+code is in the `src/` directory and the typescript bindings are in
+the `lancedb/` directory.
+
+Whenever you change the Rust code, you will need to recompile: `npm run build`.
+
+Common commands:
+* Build: `npm run build`
+* Lint: `npm run lint`
+* Fix lints: `npm run lint-fix`
+* Test: `npm test`
+* Run single test file: `npm test __test__/arrow.test.ts`

nodejs/Cargo.toml

@@ -1,7 +1,7 @@
 [package]
 name = "lancedb-nodejs"
 edition.workspace = true
-version = "0.20.0-beta.2"
+version = "0.21.2-beta.1"
 license.workspace = true
 description.workspace = true
 repository.workspace = true

nodejs/__test__/arrow.test.ts

@@ -1,7 +1,7 @@
 // SPDX-License-Identifier: Apache-2.0
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
-import { Schema } from "apache-arrow";
+import { Bool, Field, Int32, List, Schema, Struct, Utf8 } from "apache-arrow";
 
 import * as arrow15 from "apache-arrow-15";
 import * as arrow16 from "apache-arrow-16";
@@ -11,10 +11,12 @@ import * as arrow18 from "apache-arrow-18";
 import {
   convertToTable,
   fromBufferToRecordBatch,
+  fromDataToBuffer,
   fromRecordBatchToBuffer,
   fromTableToBuffer,
   makeArrowTable,
   makeEmptyTable,
+  tableFromIPC,
 } from "../lancedb/arrow";
 import {
   EmbeddingFunction,
@@ -375,8 +377,221 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
         expect(table2.schema).toEqual(schema);
       });
 
+      it("will handle missing columns in schema alignment when using embeddings", async function () {
+        const schema = new Schema(
+          [
+            new Field("domain", new Utf8(), true),
+            new Field("name", new Utf8(), true),
+            new Field("description", new Utf8(), true),
+          ],
+          new Map([["embedding_functions", JSON.stringify([])]]),
+        );
+
+        const data = [
+          { domain: "google.com", name: "Google" },
+          { domain: "facebook.com", name: "Facebook" },
+        ];
+
+        const table = await convertToTable(data, undefined, { schema });
+
+        expect(table.numCols).toBe(3);
+        expect(table.numRows).toBe(2);
+
+        const descriptionColumn = table.getChild("description");
+        expect(descriptionColumn).toBeDefined();
+        expect(descriptionColumn?.nullCount).toBe(2);
+        expect(descriptionColumn?.toArray()).toEqual([null, null]);
+
+        expect(table.getChild("domain")?.toArray()).toEqual([
+          "google.com",
+          "facebook.com",
+        ]);
+        expect(table.getChild("name")?.toArray()).toEqual([
+          "Google",
+          "Facebook",
+        ]);
+      });
+
+      it("will handle completely missing nested struct columns", async function () {
+        const schema = new Schema(
+          [
+            new Field("id", new Utf8(), true),
+            new Field("name", new Utf8(), true),
+            new Field(
+              "metadata",
+              new Struct([
+                new Field("version", new Int32(), true),
+                new Field("author", new Utf8(), true),
+                new Field(
+                  "tags",
+                  new List(new Field("item", new Utf8(), true)),
+                  true,
+                ),
+              ]),
+              true,
+            ),
+          ],
+          new Map([["embedding_functions", JSON.stringify([])]]),
+        );
+
+        const data = [
+          { id: "doc1", name: "Document 1" },
+          { id: "doc2", name: "Document 2" },
+        ];
+
+        const table = await convertToTable(data, undefined, { schema });
+
+        expect(table.numCols).toBe(3);
+        expect(table.numRows).toBe(2);
+
+        const buf = await fromTableToBuffer(table);
+        const retrievedTable = tableFromIPC(buf);
+
+        const rows = [];
+        for (let i = 0; i < retrievedTable.numRows; i++) {
+          rows.push(retrievedTable.get(i));
+        }
+
+        expect(rows[0].metadata.version).toBe(null);
+        expect(rows[0].metadata.author).toBe(null);
+        expect(rows[0].metadata.tags).toBe(null);
+        expect(rows[0].id).toBe("doc1");
+        expect(rows[0].name).toBe("Document 1");
+      });
+
+      it("will handle partially missing nested struct fields", async function () {
+        const schema = new Schema(
+          [
+            new Field("id", new Utf8(), true),
+            new Field(
+              "metadata",
+              new Struct([
+                new Field("version", new Int32(), true),
+                new Field("author", new Utf8(), true),
+                new Field("created_at", new Utf8(), true),
+              ]),
+              true,
+            ),
+          ],
+          new Map([["embedding_functions", JSON.stringify([])]]),
+        );
+
+        const data = [
+          { id: "doc1", metadata: { version: 1, author: "Alice" } },
+          { id: "doc2", metadata: { version: 2 } },
+        ];
+
+        const table = await convertToTable(data, undefined, { schema });
+
+        expect(table.numCols).toBe(2);
+        expect(table.numRows).toBe(2);
+
+        const metadataColumn = table.getChild("metadata");
+        expect(metadataColumn).toBeDefined();
+        expect(metadataColumn?.type.toString()).toBe(
+          "Struct<{version:Int32, author:Utf8, created_at:Utf8}>",
+        );
+      });
+
+      it("will handle multiple levels of nested structures", async function () {
+        const schema = new Schema(
+          [
+            new Field("id", new Utf8(), true),
+            new Field(
+              "config",
+              new Struct([
+                new Field("database", new Utf8(), true),
+                new Field(
+                  "connection",
+                  new Struct([
+                    new Field("host", new Utf8(), true),
+                    new Field("port", new Int32(), true),
+                    new Field(
+                      "ssl",
+                      new Struct([
+                        new Field("enabled", new Bool(), true),
+                        new Field("cert_path", new Utf8(), true),
+                      ]),
+                      true,
+                    ),
+                  ]),
+                  true,
+                ),
+              ]),
+              true,
+            ),
+          ],
+          new Map([["embedding_functions", JSON.stringify([])]]),
+        );
+
+        const data = [
+          {
+            id: "config1",
+            config: {
+              database: "postgres",
+              connection: { host: "localhost" },
+            },
+          },
+          {
+            id: "config2",
+            config: { database: "mysql" },
+          },
+          {
+            id: "config3",
+          },
+        ];
+
+        const table = await convertToTable(data, undefined, { schema });
+
+        expect(table.numCols).toBe(2);
+        expect(table.numRows).toBe(3);
+
+        const configColumn = table.getChild("config");
+        expect(configColumn).toBeDefined();
+        expect(configColumn?.type.toString()).toBe(
+          "Struct<{database:Utf8, connection:Struct<{host:Utf8, port:Int32, ssl:Struct<{enabled:Bool, cert_path:Utf8}>}>}>",
+        );
+      });
+
+      it("will handle missing columns in Arrow table input when using embeddings", async function () {
+        const incompleteTable = makeArrowTable([
+          { domain: "google.com", name: "Google" },
+          { domain: "facebook.com", name: "Facebook" },
+        ]);
+
+        const schema = new Schema(
+          [
+            new Field("domain", new Utf8(), true),
+            new Field("name", new Utf8(), true),
+            new Field("description", new Utf8(), true),
+          ],
+          new Map([["embedding_functions", JSON.stringify([])]]),
+        );
+
+        const buf = await fromDataToBuffer(incompleteTable, undefined, schema);
+
+        expect(buf.byteLength).toBeGreaterThan(0);
+
+        const retrievedTable = tableFromIPC(buf);
+        expect(retrievedTable.numCols).toBe(3);
+        expect(retrievedTable.numRows).toBe(2);
+
+        const descriptionColumn = retrievedTable.getChild("description");
+        expect(descriptionColumn).toBeDefined();
+        expect(descriptionColumn?.nullCount).toBe(2);
+        expect(descriptionColumn?.toArray()).toEqual([null, null]);
+
+        expect(retrievedTable.getChild("domain")?.toArray()).toEqual([
+          "google.com",
+          "facebook.com",
+        ]);
+        expect(retrievedTable.getChild("name")?.toArray()).toEqual([
+          "Google",
+          "Facebook",
+        ]);
+      });
+
       it("should correctly retain values in nested struct fields", async function () {
-        // Define test data with nested struct
         const testData = [
           {
             id: "doc1",
@@ -400,10 +615,8 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
           },
         ];
 
-        // Create Arrow table from the data
         const table = makeArrowTable(testData);
 
-        // Verify schema has the nested struct fields
         const metadataField = table.schema.fields.find(
           (f) => f.name === "metadata",
         );
@@ -417,23 +630,17 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
           "text",
         ]);
 
-        // Convert to buffer and back (simulating storage and retrieval)
         const buf = await fromTableToBuffer(table);
         const retrievedTable = tableFromIPC(buf);
 
-        // Verify the retrieved table has the same structure
         const rows = [];
         for (let i = 0; i < retrievedTable.numRows; i++) {
           rows.push(retrievedTable.get(i));
         }
 
-        // Check values in the first row
         const firstRow = rows[0];
         expect(firstRow.id).toBe("doc1");
         expect(firstRow.vector.toJSON()).toEqual([1, 2, 3]);
-
-        // Verify metadata values are preserved (this is where the bug is)
-        expect(firstRow.metadata).toBeDefined();
         expect(firstRow.metadata.filePath).toBe("/path/to/file1.ts");
         expect(firstRow.metadata.startLine).toBe(10);
         expect(firstRow.metadata.endLine).toBe(20);
@@ -592,14 +799,14 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
         ).rejects.toThrow("column vector was missing");
       });
 
-      it("will provide a nice error if run twice", async function () {
+      it("will skip embedding application if already applied", async function () {
         const records = sampleRecords();
         const table = await convertToTable(records, dummyEmbeddingConfig);
 
         // fromTableToBuffer will try and apply the embeddings again
-        await expect(
+        // but should skip since the column already has non-null values
-          fromTableToBuffer(table, dummyEmbeddingConfig),
+        const result = await fromTableToBuffer(table, dummyEmbeddingConfig);
-        ).rejects.toThrow("already existed");
+        expect(result.byteLength).toBeGreaterThan(0);
       });
     });
 

nodejs/__test__/session.test.ts

@@ -0,0 +1,46 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+import * as tmp from "tmp";
+import { Session, connect } from "../lancedb";
+
+describe("Session", () => {
+  let tmpDir: tmp.DirResult;
+  beforeEach(() => {
+    tmpDir = tmp.dirSync({ unsafeCleanup: true });
+  });
+  afterEach(() => tmpDir.removeCallback());
+
+  it("should configure cache sizes and work with database operations", async () => {
+    // Create session with small cache limits for testing
+    const indexCacheSize = BigInt(1024 * 1024); // 1MB
+    const metadataCacheSize = BigInt(512 * 1024); // 512KB
+
+    const session = new Session(indexCacheSize, metadataCacheSize);
+
+    // Record initial cache state
+    const initialCacheSize = session.sizeBytes();
+    const initialCacheItems = session.approxNumItems();
+
+    // Test session works with database connection
+    const db = await connect({ uri: tmpDir.name, session: session });
+
+    // Create and use a table to exercise the session
+    const data = Array.from({ length: 100 }, (_, i) => ({
+      id: i,
+      text: `item ${i}`,
+    }));
+    const table = await db.createTable("test", data);
+    const results = await table.query().limit(5).toArray();
+
+    expect(results).toHaveLength(5);
+
+    // Verify cache usage increased after operations
+    const finalCacheSize = session.sizeBytes();
+    const finalCacheItems = session.approxNumItems();
+
+    expect(finalCacheSize).toBeGreaterThan(initialCacheSize); // Cache should have grown
+    expect(finalCacheItems).toBeGreaterThanOrEqual(initialCacheItems); // Items should not decrease
+    expect(initialCacheSize).toBeLessThan(indexCacheSize + metadataCacheSize); // Within limits
+  });
+});

nodejs/__test__/table.test.ts

@@ -33,7 +33,12 @@ import {
   register,
 } from "../lancedb/embedding";
 import { Index } from "../lancedb/indices";
-import { instanceOfFullTextQuery } from "../lancedb/query";
+import {
+  BooleanQuery,
+  Occur,
+  Operator,
+  instanceOfFullTextQuery,
+} from "../lancedb/query";
 import exp = require("constants");
 
 describe.each([arrow15, arrow16, arrow17, arrow18])(
@@ -363,9 +368,9 @@ describe("merge insert", () => {
       { a: 4, b: "z" },
     ];
 
-    expect(
+    const result = (await table.toArrow()).toArray().sort((a, b) => a.a - b.a);
-      JSON.parse(JSON.stringify((await table.toArrow()).toArray())),
+
-    ).toEqual(expected);
+    expect(result.map((row) => ({ ...row }))).toEqual(expected);
   });
   test("conditional update", async () => {
     const newData = [
@@ -554,6 +559,32 @@ describe("When creating an index", () => {
     rst = await tbl.query().limit(2).offset(1).nearestTo(queryVec).toArrow();
     expect(rst.numRows).toBe(1);
 
+    // test nprobes
+    rst = await tbl.query().nearestTo(queryVec).limit(2).nprobes(50).toArrow();
+    expect(rst.numRows).toBe(2);
+    rst = await tbl
+      .query()
+      .nearestTo(queryVec)
+      .limit(2)
+      .minimumNprobes(15)
+      .toArrow();
+    expect(rst.numRows).toBe(2);
+    rst = await tbl
+      .query()
+      .nearestTo(queryVec)
+      .limit(2)
+      .minimumNprobes(10)
+      .maximumNprobes(20)
+      .toArrow();
+    expect(rst.numRows).toBe(2);
+
+    expect(() => tbl.query().nearestTo(queryVec).minimumNprobes(0)).toThrow(
+      "Invalid input, minimum_nprobes must be greater than 0",
+    );
+    expect(() => tbl.query().nearestTo(queryVec).maximumNprobes(5)).toThrow(
+      "Invalid input, maximum_nprobes must be greater than minimum_nprobes",
+    );
+
     await tbl.dropIndex("vec_idx");
     const indices2 = await tbl.listIndices();
     expect(indices2.length).toBe(0);
@@ -1531,6 +1562,18 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
 
       const results = await table.search("hello").toArray();
       expect(results[0].text).toBe(data[0].text);
+
+      const results2 = await table
+        .search(new MatchQuery("hello world", "text"))
+        .toArray();
+      expect(results2.length).toBe(2);
+
+      const results3 = await table
+        .search(
+          new MatchQuery("hello world", "text", { operator: Operator.And }),
+        )
+        .toArray();
+      expect(results3.length).toBe(1);
     });
 
     test("full text search without lowercase", async () => {
@@ -1607,6 +1650,114 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
       expect(resultSet.has("fob")).toBe(true);
       expect(resultSet.has("fo")).toBe(true);
       expect(resultSet.has("food")).toBe(true);
+
+      const prefixResults = await table
+        .search(
+          new MatchQuery("foo", "text", { fuzziness: 3, prefixLength: 3 }),
+        )
+        .toArray();
+      expect(prefixResults.length).toBe(2);
+      const resultSet2 = new Set(prefixResults.map((r) => r.text));
+      expect(resultSet2.has("foo")).toBe(true);
+      expect(resultSet2.has("food")).toBe(true);
+    });
+
+    test("full text search boolean query", async () => {
+      const db = await connect(tmpDir.name);
+      const data = [
+        { text: "The cat and dog are playing" },
+        { text: "The cat is sleeping" },
+        { text: "The dog is barking" },
+        { text: "The dog chases the cat" },
+      ];
+      const table = await db.createTable("test", data);
+      await table.createIndex("text", {
+        config: Index.fts({ withPosition: false }),
+      });
+
+      const shouldResults = await table
+        .search(
+          new BooleanQuery([
+            [Occur.Should, new MatchQuery("cat", "text")],
+            [Occur.Should, new MatchQuery("dog", "text")],
+          ]),
+        )
+        .toArray();
+      expect(shouldResults.length).toBe(4);
+
+      const mustResults = await table
+        .search(
+          new BooleanQuery([
+            [Occur.Must, new MatchQuery("cat", "text")],
+            [Occur.Must, new MatchQuery("dog", "text")],
+          ]),
+        )
+        .toArray();
+      expect(mustResults.length).toBe(2);
+
+      const mustNotResults = await table
+        .search(
+          new BooleanQuery([
+            [Occur.Must, new MatchQuery("cat", "text")],
+            [Occur.MustNot, new MatchQuery("dog", "text")],
+          ]),
+        )
+        .toArray();
+      expect(mustNotResults.length).toBe(1);
+    });
+
+    test("full text search ngram", async () => {
+      const db = await connect(tmpDir.name);
+      const data = [
+        { text: "hello world", vector: [0.1, 0.2, 0.3] },
+        { text: "lance database", vector: [0.4, 0.5, 0.6] },
+        { text: "lance is cool", vector: [0.7, 0.8, 0.9] },
+      ];
+      const table = await db.createTable("test", data);
+      await table.createIndex("text", {
+        config: Index.fts({ baseTokenizer: "ngram" }),
+      });
+
+      const results = await table.search("lan").toArray();
+      expect(results.length).toBe(2);
+      const resultSet = new Set(results.map((r) => r.text));
+      expect(resultSet.has("lance database")).toBe(true);
+      expect(resultSet.has("lance is cool")).toBe(true);
+
+      const results2 = await table.search("nce").toArray(); // spellchecker:disable-line
+      expect(results2.length).toBe(2);
+      const resultSet2 = new Set(results2.map((r) => r.text));
+      expect(resultSet2.has("lance database")).toBe(true);
+      expect(resultSet2.has("lance is cool")).toBe(true);
+
+      // the default min_ngram_length is 3, so "la" should not match
+      const results3 = await table.search("la").toArray();
+      expect(results3.length).toBe(0);
+
+      // test setting min_ngram_length and prefix_only
+      await table.createIndex("text", {
+        config: Index.fts({
+          baseTokenizer: "ngram",
+          ngramMinLength: 2,
+          prefixOnly: true,
+        }),
+        replace: true,
+      });
+
+      const results4 = await table.search("lan").toArray();
+      expect(results4.length).toBe(2);
+      const resultSet4 = new Set(results4.map((r) => r.text));
+      expect(resultSet4.has("lance database")).toBe(true);
+      expect(resultSet4.has("lance is cool")).toBe(true);
+
+      const results5 = await table.search("nce").toArray(); // spellchecker:disable-line
+      expect(results5.length).toBe(0);
+
+      const results6 = await table.search("la").toArray();
+      expect(results6.length).toBe(2);
+      const resultSet6 = new Set(results6.map((r) => r.text));
+      expect(resultSet6.has("lance database")).toBe(true);
+      expect(resultSet6.has("lance is cool")).toBe(true);
     });
 
     test.each([
@@ -1712,4 +1863,43 @@ describe("column name options", () => {
     expect(results[0].query_index).toBe(0);
     expect(results[1].query_index).toBe(1);
   });
+
+  test("index and search multivectors", async () => {
+    const db = await connect(tmpDir.name);
+    const data = [];
+    // generate 512 random multivectors
+    for (let i = 0; i < 256; i++) {
+      data.push({
+        multivector: Array.from({ length: 10 }, () =>
+          Array(2).fill(Math.random()),
+        ),
+      });
+    }
+    const table = await db.createTable("multivectors", data, {
+      schema: new Schema([
+        new Field(
+          "multivector",
+          new List(
+            new Field(
+              "item",
+              new FixedSizeList(2, new Field("item", new Float32())),
+            ),
+          ),
+        ),
+      ]),
+    });
+
+    const results = await table.search(data[0].multivector).limit(10).toArray();
+    expect(results.length).toBe(10);
+
+    await table.createIndex("multivector", {
+      config: Index.ivfPq({ numPartitions: 2, distanceType: "cosine" }),
+    });
+
+    const results2 = await table
+      .search(data[0].multivector)
+      .limit(10)
+      .toArray();
+    expect(results2.length).toBe(10);
+  });
 });

nodejs/lancedb/arrow.ts

@@ -107,6 +107,20 @@ export type IntoVector =
   | number[]
   | Promise<Float32Array | Float64Array | number[]>;
 
+export type MultiVector = IntoVector[];
+
+export function isMultiVector(value: unknown): value is MultiVector {
+  return Array.isArray(value) && isIntoVector(value[0]);
+}
+
+export function isIntoVector(value: unknown): value is IntoVector {
+  return (
+    value instanceof Float32Array ||
+    value instanceof Float64Array ||
+    (Array.isArray(value) && !Array.isArray(value[0]))
+  );
+}
+
 export function isArrowTable(value: object): value is TableLike {
   if (value instanceof ArrowTable) return true;
   return "schema" in value && "batches" in value;
@@ -417,7 +431,9 @@ function inferSchema(
         } else {
           const inferredType = inferType(value, path, opts);
           if (inferredType === undefined) {
-            throw new Error(`Failed to infer data type for field ${path.join(".")} at row ${rowI}. \
+            throw new Error(`Failed to infer data type for field ${path.join(
+              ".",
+            )} at row ${rowI}. \
                              Consider providing an explicit schema.`);
           }
           pathTree.set(path, inferredType);
@@ -799,11 +815,17 @@ async function applyEmbeddingsFromMetadata(
         `Cannot apply embedding function because the source column '${functionEntry.sourceColumn}' was not present in the data`,
       );
     }
+
+    // Check if destination column exists and handle accordingly
     if (columns[destColumn] !== undefined) {
-      throw new Error(
+      const existingColumn = columns[destColumn];
-        `Attempt to apply embeddings to table failed because column ${destColumn} already existed`,
+      // If the column exists but is all null, we can fill it with embeddings
-      );
+      if (existingColumn.nullCount !== existingColumn.length) {
+        // Column has non-null values, skip embedding application
+        continue;
       }
+    }
+
     if (table.batches.length > 1) {
       throw new Error(
         "Internal error: `makeArrowTable` unexpectedly created a table with more than one batch",
@@ -831,6 +853,15 @@ async function applyEmbeddingsFromMetadata(
     const vector = makeVector(vectors, destType);
     columns[destColumn] = vector;
   }
+
+  // Add any missing columns from the schema as null vectors
+  for (const field of schema.fields) {
+    if (!(field.name in columns)) {
+      const nullValues = new Array(table.numRows).fill(null);
+      columns[field.name] = makeVector(nullValues, field.type);
+    }
+  }
+
   const newTable = new ArrowTable(columns);
   return alignTable(newTable, schema);
 }
@@ -903,11 +934,23 @@ async function applyEmbeddings<T>(
       );
     }
   } else {
+    // Check if destination column exists and handle accordingly
     if (Object.prototype.hasOwnProperty.call(newColumns, destColumn)) {
-      throw new Error(
+      const existingColumn = newColumns[destColumn];
-        `Attempt to apply embeddings to table failed because column ${destColumn} already existed`,
+      // If the column exists but is all null, we can fill it with embeddings
+      if (existingColumn.nullCount !== existingColumn.length) {
+        // Column has non-null values, skip embedding application and return table as-is
+        let newTable = new ArrowTable(newColumns);
+        if (schema != null) {
+          newTable = alignTable(newTable, schema as Schema);
+        }
+        return new ArrowTable(
+          new Schema(newTable.schema.fields, schemaMetadata),
+          newTable.batches,
         );
       }
+    }
+
     if (table.batches.length > 1) {
       throw new Error(
         "Internal error: `makeArrowTable` unexpectedly created a table with more than one batch",
@@ -967,7 +1010,21 @@ export async function convertToTable(
   embeddings?: EmbeddingFunctionConfig,
   makeTableOptions?: Partial<MakeArrowTableOptions>,
 ): Promise<ArrowTable> {
-  const table = makeArrowTable(data, makeTableOptions);
+  let processedData = data;
+
+  // If we have a schema with embedding metadata, we need to preprocess the data
+  // to ensure all nested fields are present
+  if (
+    makeTableOptions?.schema &&
+    makeTableOptions.schema.metadata?.has("embedding_functions")
+  ) {
+    processedData = ensureNestedFieldsExist(
+      data,
+      makeTableOptions.schema as Schema,
+    );
+  }
+
+  const table = makeArrowTable(processedData, makeTableOptions);
   return await applyEmbeddings(table, embeddings, makeTableOptions?.schema);
 }
 
@@ -1060,7 +1117,16 @@ export async function fromDataToBuffer(
     schema = sanitizeSchema(schema);
   }
   if (isArrowTable(data)) {
-    return fromTableToBuffer(sanitizeTable(data), embeddings, schema);
+    const table = sanitizeTable(data);
+    // If we have a schema with embedding functions, we need to ensure all columns exist
+    // before applying embeddings, since applyEmbeddingsFromMetadata expects all columns
+    // to be present in the table
+    if (schema && schema.metadata?.has("embedding_functions")) {
+      const alignedTable = alignTableToSchema(table, schema);
+      return fromTableToBuffer(alignedTable, embeddings, schema);
+    } else {
+      return fromTableToBuffer(table, embeddings, schema);
+    }
   } else {
     const table = await convertToTable(data, embeddings, { schema });
     return fromTableToBuffer(table);
@@ -1129,7 +1195,7 @@ function alignBatch(batch: RecordBatch, schema: Schema): RecordBatch {
     type: new Struct(schema.fields),
     length: batch.numRows,
     nullCount: batch.nullCount,
-    children: alignedChildren,
+    children: alignedChildren as unknown as ArrowData<DataType>[],
   });
   return new RecordBatch(schema, newData);
 }
@@ -1201,6 +1267,79 @@ function validateSchemaEmbeddings(
   return new Schema(fields, schema.metadata);
 }
 
+/**
+ * Ensures that all nested fields defined in the schema exist in the data,
+ * filling missing fields with null values.
+ */
+export function ensureNestedFieldsExist(
+  data: Array<Record<string, unknown>>,
+  schema: Schema,
+): Array<Record<string, unknown>> {
+  return data.map((row) => {
+    const completeRow: Record<string, unknown> = {};
+
+    for (const field of schema.fields) {
+      if (field.name in row) {
+        if (
+          field.type.constructor.name === "Struct" &&
+          row[field.name] !== null &&
+          row[field.name] !== undefined
+        ) {
+          // Handle nested struct
+          const nestedValue = row[field.name] as Record<string, unknown>;
+          completeRow[field.name] = ensureStructFieldsExist(
+            nestedValue,
+            field.type,
+          );
+        } else {
+          // Non-struct field or null struct value
+          completeRow[field.name] = row[field.name];
+        }
+      } else {
+        // Field is missing from the data - set to null
+        completeRow[field.name] = null;
+      }
+    }
+
+    return completeRow;
+  });
+}
+
+/**
+ * Recursively ensures that all fields in a struct type exist in the data,
+ * filling missing fields with null values.
+ */
+function ensureStructFieldsExist(
+  data: Record<string, unknown>,
+  structType: Struct,
+): Record<string, unknown> {
+  const completeStruct: Record<string, unknown> = {};
+
+  for (const childField of structType.children) {
+    if (childField.name in data) {
+      if (
+        childField.type.constructor.name === "Struct" &&
+        data[childField.name] !== null &&
+        data[childField.name] !== undefined
+      ) {
+        // Recursively handle nested struct
+        completeStruct[childField.name] = ensureStructFieldsExist(
+          data[childField.name] as Record<string, unknown>,
+          childField.type,
+        );
+      } else {
+        // Non-struct field or null struct value
+        completeStruct[childField.name] = data[childField.name];
+      }
+    } else {
+      // Field is missing - set to null
+      completeStruct[childField.name] = null;
+    }
+  }
+
+  return completeStruct;
+}
+
 interface JsonDataType {
   type: string;
   fields?: JsonField[];
@@ -1334,3 +1473,64 @@ function fieldToJson(field: Field): JsonField {
     metadata: field.metadata,
   };
 }
+
+function alignTableToSchema(
+  table: ArrowTable,
+  targetSchema: Schema,
+): ArrowTable {
+  const existingColumns = new Map<string, Vector>();
+
+  // Map existing columns
+  for (const field of table.schema.fields) {
+    existingColumns.set(field.name, table.getChild(field.name)!);
+  }
+
+  // Create vectors for all fields in target schema
+  const alignedColumns: Record<string, Vector> = {};
+
+  for (const field of targetSchema.fields) {
+    if (existingColumns.has(field.name)) {
+      // Column exists, use it
+      alignedColumns[field.name] = existingColumns.get(field.name)!;
+    } else {
+      // Column missing, create null vector
+      alignedColumns[field.name] = createNullVector(field, table.numRows);
+    }
+  }
+
+  // Create new table with aligned schema and columns
+  return new ArrowTable(targetSchema, alignedColumns);
+}
+
+function createNullVector(field: Field, numRows: number): Vector {
+  if (field.type.constructor.name === "Struct") {
+    // For struct types, create a struct with null fields
+    const structType = field.type as Struct;
+    const childVectors = structType.children.map((childField) =>
+      createNullVector(childField, numRows),
+    );
+
+    // Create struct data
+    const structData = makeData({
+      type: structType,
+      length: numRows,
+      nullCount: 0,
+      children: childVectors.map((v) => v.data[0]),
+    });
+
+    return arrowMakeVector(structData);
+  } else {
+    // For other types, create a vector of nulls
+    const nullBitmap = new Uint8Array(Math.ceil(numRows / 8));
+    // All bits are 0, meaning all values are null
+
+    const data = makeData({
+      type: field.type,
+      length: numRows,
+      nullCount: numRows,
+      nullBitmap,
+    });
+
+    return arrowMakeVector(data);
+  }
+}

nodejs/lancedb/connection.ts

@@ -85,6 +85,9 @@ export interface OpenTableOptions {
   /**
    * Set the size of the index cache, specified as a number of entries
    *
+   * @deprecated Use session-level cache configuration instead.
+   * Create a Session with custom cache sizes and pass it to the connect() function.
+   *
    * The exact meaning of an "entry" will depend on the type of index:
    * - IVF: there is one entry for each IVF partition
    * - BTREE: there is one entry for the entire index

nodejs/lancedb/index.ts

@@ -10,6 +10,7 @@ import {
 import {
   ConnectionOptions,
   Connection as LanceDbConnection,
+  Session,
 } from "./native.js";
 
 export {
@@ -51,6 +52,8 @@ export {
   OpenTableOptions,
 } from "./connection";
 
+export { Session } from "./native.js";
+
 export {
   ExecutableQuery,
   Query,
@@ -64,7 +67,10 @@ export {
   PhraseQuery,
   BoostQuery,
   MultiMatchQuery,
+  BooleanQuery,
   FullTextQueryType,
+  Operator,
+  Occur,
 } from "./query";
 
 export {
@@ -97,6 +103,7 @@ export {
   RecordBatchLike,
   DataLike,
   IntoVector,
+  MultiVector,
 } from "./arrow";
 export { IntoSql, packBits } from "./util";
 
@@ -127,6 +134,7 @@ export { IntoSql, packBits } from "./util";
 export async function connect(
   uri: string,
   options?: Partial<ConnectionOptions>,
+  session?: Session,
 ): Promise<Connection>;
 /**
  * Connect to a LanceDB instance at the given URI.
@@ -145,31 +153,43 @@ export async function connect(
  *   storageOptions: {timeout: "60s"}
  * });
  * ```
+ *
+ * @example
+ * ```ts
+ * const session = Session.default();
+ * const conn = await connect({
+ *   uri: "/path/to/database",
+ *   session: session
+ * });
+ * ```
  */
 export async function connect(
   options: Partial<ConnectionOptions> & { uri: string },
 ): Promise<Connection>;
 export async function connect(
   uriOrOptions: string | (Partial<ConnectionOptions> & { uri: string }),
-  options: Partial<ConnectionOptions> = {},
+  options?: Partial<ConnectionOptions>,
 ): Promise<Connection> {
   let uri: string | undefined;
+  let finalOptions: Partial<ConnectionOptions> = {};
+
   if (typeof uriOrOptions !== "string") {
     const { uri: uri_, ...opts } = uriOrOptions;
     uri = uri_;
-    options = opts;
+    finalOptions = opts;
   } else {
     uri = uriOrOptions;
+    finalOptions = options || {};
   }
 
   if (!uri) {
     throw new Error("uri is required");
   }
 
-  options = (options as ConnectionOptions) ?? {};
+  finalOptions = (finalOptions as ConnectionOptions) ?? {};
-  (<ConnectionOptions>options).storageOptions = cleanseStorageOptions(
+  (<ConnectionOptions>finalOptions).storageOptions = cleanseStorageOptions(
-    (<ConnectionOptions>options).storageOptions,
+    (<ConnectionOptions>finalOptions).storageOptions,
   );
-  const nativeConn = await LanceDbConnection.new(uri, options);
+  const nativeConn = await LanceDbConnection.new(uri, finalOptions);
   return new LocalConnection(nativeConn);
 }

nodejs/lancedb/indices.ts

@@ -439,7 +439,7 @@ export interface FtsOptions {
    *
    * "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";
+  baseTokenizer?: "simple" | "whitespace" | "raw" | "ngram";
 
   /**
    * language for stemming and stop words
@@ -472,6 +472,21 @@ export interface FtsOptions {
    * whether to remove punctuation
    */
   asciiFolding?: boolean;
+
+  /**
+   * ngram min length
+   */
+  ngramMinLength?: number;
+
+  /**
+   * ngram max length
+   */
+  ngramMaxLength?: number;
+
+  /**
+   * whether to only index the prefix of the token for ngram tokenizer
+   */
+  prefixOnly?: boolean;
 }
 
 export class Index {
@@ -608,6 +623,9 @@ export class Index {
         options?.stem,
         options?.removeStopWords,
         options?.asciiFolding,
+        options?.ngramMinLength,
+        options?.ngramMaxLength,
+        options?.prefixOnly,
       ),
     );
   }

nodejs/lancedb/query.ts

@@ -448,6 +448,10 @@ export class VectorQuery extends QueryBase<NativeVectorQuery> {
    * For best results we recommend tuning this parameter with a benchmark against
    * your actual data to find the smallest possible value that will still give
    * you the desired recall.
+   *
+   * For more fine grained control over behavior when you have a very narrow filter
+   * you can use `minimumNprobes` and `maximumNprobes`.  This method sets both
+   * the minimum and maximum to the same value.
    */
   nprobes(nprobes: number): VectorQuery {
     super.doCall((inner) => inner.nprobes(nprobes));
@@ -455,6 +459,33 @@ export class VectorQuery extends QueryBase<NativeVectorQuery> {
     return this;
   }
 
+  /**
+   * Set the minimum number of probes used.
+   *
+   * This controls the minimum number of partitions that will be searched.  This
+   * parameter will impact every query against a vector index, regardless of the
+   * filter.  See `nprobes` for more details.  Higher values will increase recall
+   * but will also increase latency.
+   */
+  minimumNprobes(minimumNprobes: number): VectorQuery {
+    super.doCall((inner) => inner.minimumNprobes(minimumNprobes));
+    return this;
+  }
+
+  /**
+   * Set the maximum number of probes used.
+   *
+   * This controls the maximum number of partitions that will be searched.  If this
+   * number is greater than minimumNprobes then the excess partitions will _only_ be
+   * searched if we have not found enough results.  This can be useful when there is
+   * a narrow filter to allow these queries to spend more time searching and avoid
+   * potential false negatives.
+   */
+  maximumNprobes(maximumNprobes: number): VectorQuery {
+    super.doCall((inner) => inner.maximumNprobes(maximumNprobes));
+    return this;
+  }
+
   /*
    * Set the distance range to use
    *
@@ -762,6 +793,31 @@ export enum FullTextQueryType {
   MatchPhrase = "match_phrase",
   Boost = "boost",
   MultiMatch = "multi_match",
+  Boolean = "boolean",
+}
+
+/**
+ * Enum representing the logical operators used in full-text queries.
+ *
+ * - `And`: All terms must match.
+ * - `Or`: At least one term must match.
+ */
+export enum Operator {
+  And = "AND",
+  Or = "OR",
+}
+
+/**
+ * Enum representing the occurrence of terms in full-text queries.
+ *
+ * - `Must`: The term must be present in the document.
+ * - `Should`: The term should contribute to the document score, but is not required.
+ * - `MustNot`: The term must not be present in the document.
+ */
+export enum Occur {
+  Should = "SHOULD",
+  Must = "MUST",
+  MustNot = "MUST_NOT",
 }
 
 /**
@@ -791,6 +847,7 @@ export function instanceOfFullTextQuery(obj: any): obj is FullTextQuery {
 export class MatchQuery implements FullTextQuery {
   /** @ignore */
   public readonly inner: JsFullTextQuery;
+
   /**
    * Creates an instance of MatchQuery.
    *
@@ -800,6 +857,8 @@ export class MatchQuery implements FullTextQuery {
    *   - `boost`: The boost factor for the query (default is 1.0).
    *   - `fuzziness`: The fuzziness level for the query (default is 0).
    *   - `maxExpansions`: The maximum number of terms to consider for fuzzy matching (default is 50).
+   *   - `operator`: The logical operator to use for combining terms in the query (default is "OR").
+   *   - `prefixLength`: The number of beginning characters being unchanged for fuzzy matching.
    */
   constructor(
     query: string,
@@ -808,6 +867,8 @@ export class MatchQuery implements FullTextQuery {
       boost?: number;
       fuzziness?: number;
       maxExpansions?: number;
+      operator?: Operator;
+      prefixLength?: number;
     },
   ) {
     let fuzziness = options?.fuzziness;
@@ -820,6 +881,8 @@ export class MatchQuery implements FullTextQuery {
       options?.boost ?? 1.0,
       fuzziness,
       options?.maxExpansions ?? 50,
+      options?.operator ?? Operator.Or,
+      options?.prefixLength ?? 0,
     );
   }
 
@@ -836,9 +899,11 @@ export class PhraseQuery implements FullTextQuery {
    *
    * @param query - The phrase to search for in the specified column.
    * @param column - The name of the column to search within.
+   * @param options - Optional parameters for the phrase query.
+   *   - `slop`: The maximum number of intervening unmatched positions allowed between words in the phrase (default is 0).
    */
-  constructor(query: string, column: string) {
+  constructor(query: string, column: string, options?: { slop?: number }) {
-    this.inner = JsFullTextQuery.phraseQuery(query, column);
+    this.inner = JsFullTextQuery.phraseQuery(query, column, options?.slop ?? 0);
   }
 
   queryType(): FullTextQueryType {
@@ -889,18 +954,21 @@ export class MultiMatchQuery implements FullTextQuery {
    * @param columns - An array of column names to search within.
    * @param options - Optional parameters for the multi-match query.
    *  - `boosts`: An array of boost factors for each column (default is 1.0 for all).
+   *  - `operator`: The logical operator to use for combining terms in the query (default is "OR").
    */
   constructor(
     query: string,
     columns: string[],
     options?: {
       boosts?: number[];
+      operator?: Operator;
     },
   ) {
     this.inner = JsFullTextQuery.multiMatchQuery(
       query,
       columns,
       options?.boosts,
+      options?.operator ?? Operator.Or,
     );
   }
 
@@ -908,3 +976,23 @@ export class MultiMatchQuery implements FullTextQuery {
     return FullTextQueryType.MultiMatch;
   }
 }
+
+export class BooleanQuery implements FullTextQuery {
+  /** @ignore */
+  public readonly inner: JsFullTextQuery;
+  /**
+   * Creates an instance of BooleanQuery.
+   *
+   * @param queries - An array of (Occur, FullTextQuery objects) to combine.
+   * Occur specifies whether the query must match, or should match.
+   */
+  constructor(queries: [Occur, FullTextQuery][]) {
+    this.inner = JsFullTextQuery.booleanQuery(
+      queries.map(([occur, query]) => [occur, query.inner]),
+    );
+  }
+
+  queryType(): FullTextQueryType {
+    return FullTextQueryType.Boolean;
+  }
+}

nodejs/lancedb/table.ts

@@ -6,9 +6,11 @@ import {
   Data,
   DataType,
   IntoVector,
+  MultiVector,
   Schema,
   dataTypeToJson,
   fromDataToBuffer,
+  isMultiVector,
   tableFromIPC,
 } from "./arrow";
 
@@ -75,10 +77,10 @@ export interface OptimizeOptions {
    * // Delete all versions older than 1 day
    * const olderThan = new Date();
    * olderThan.setDate(olderThan.getDate() - 1));
-   * tbl.cleanupOlderVersions(olderThan);
+   * tbl.optimize({cleanupOlderThan: olderThan});
    *
    * // Delete all versions except the current version
-   * tbl.cleanupOlderVersions(new Date());
+   * tbl.optimize({cleanupOlderThan: new Date()});
    */
   cleanupOlderThan: Date;
   deleteUnverified: boolean;
@@ -346,7 +348,7 @@ export abstract class Table {
    * if the query is a string and no embedding function is defined, it will be treated as a full text search query
    */
   abstract search(
-    query: string | IntoVector | FullTextQuery,
+    query: string | IntoVector | MultiVector | FullTextQuery,
     queryType?: string,
     ftsColumns?: string | string[],
   ): VectorQuery | Query;
@@ -357,7 +359,7 @@ export abstract class Table {
    * is the same thing as calling `nearestTo` on the builder returned
    * by `query`.  @see {@link Query#nearestTo} for more details.
    */
-  abstract vectorSearch(vector: IntoVector): VectorQuery;
+  abstract vectorSearch(vector: IntoVector | MultiVector): VectorQuery;
   /**
    * Add new columns with defined values.
    * @param {AddColumnsSql[]} newColumnTransforms pairs of column names and
@@ -668,7 +670,7 @@ export class LocalTable extends Table {
   }
 
   search(
-    query: string | IntoVector | FullTextQuery,
+    query: string | IntoVector | MultiVector | FullTextQuery,
     queryType: string = "auto",
     ftsColumns?: string | string[],
   ): VectorQuery | Query {
@@ -715,7 +717,15 @@ export class LocalTable extends Table {
     return this.query().nearestTo(queryPromise);
   }
 
-  vectorSearch(vector: IntoVector): VectorQuery {
+  vectorSearch(vector: IntoVector | MultiVector): VectorQuery {
+    if (isMultiVector(vector)) {
+      const query = this.query().nearestTo(vector[0]);
+      for (const v of vector.slice(1)) {
+        query.addQueryVector(v);
+      }
+      return query;
+    }
+
     return this.query().nearestTo(vector);
   }
 

nodejs/npm/darwin-arm64/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-darwin-arm64",
-	"version": "0.20.0-beta.2",
+	"version": "0.21.2-beta.1",
 	"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": "0.20.0-beta.2",
+	"version": "0.21.2-beta.1",
 	"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": "0.20.0-beta.2",
+	"version": "0.21.2-beta.1",
 	"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": "0.20.0-beta.2",
+	"version": "0.21.2-beta.1",
 	"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": "0.20.0-beta.2",
+	"version": "0.21.2-beta.1",
 	"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": "0.20.0-beta.2",
+	"version": "0.21.2-beta.1",
 	"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": "0.20.0-beta.2",
+  "version": "0.21.2-beta.1",
   "os": [
     "win32"
   ],

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

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-win32-x64-msvc",
-	"version": "0.20.0-beta.2",
+	"version": "0.21.2-beta.1",
 	"os": ["win32"],
 	"cpu": ["x64"],
 	"main": "lancedb.win32-x64-msvc.node",

nodejs/package-lock.json

@@ -1,12 +1,12 @@
 {
   "name": "@lancedb/lancedb",
-  "version": "0.20.0-beta.2",
+  "version": "0.21.2-beta.1",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
     "": {
       "name": "@lancedb/lancedb",
-      "version": "0.20.0-beta.2",
+      "version": "0.21.2-beta.1",
       "cpu": [
         "x64",
         "arm64"

nodejs/package.json

@@ -11,7 +11,7 @@
     "ann"
   ],
   "private": false,
-  "version": "0.20.0-beta.2",
+  "version": "0.21.2-beta.1",
   "main": "dist/index.js",
   "exports": {
     ".": "./dist/index.js",

nodejs/src/connection.rs

@@ -74,6 +74,10 @@ impl Connection {
             builder = builder.host_override(&host_override);
         }
 
+        if let Some(session) = options.session {
+            builder = builder.session(session.inner.clone());
+        }
+
         Ok(Self::inner_new(builder.execute().await.default_error()?))
     }
 

nodejs/src/index.rs

@@ -123,6 +123,9 @@ impl Index {
         stem: Option<bool>,
         remove_stop_words: Option<bool>,
         ascii_folding: Option<bool>,
+        ngram_min_length: Option<u32>,
+        ngram_max_length: Option<u32>,
+        prefix_only: Option<bool>,
     ) -> Self {
         let mut opts = FtsIndexBuilder::default();
         if let Some(with_position) = with_position {
@@ -149,6 +152,15 @@ impl Index {
         if let Some(ascii_folding) = ascii_folding {
             opts = opts.ascii_folding(ascii_folding);
         }
+        if let Some(ngram_min_length) = ngram_min_length {
+            opts = opts.ngram_min_length(ngram_min_length);
+        }
+        if let Some(ngram_max_length) = ngram_max_length {
+            opts = opts.ngram_max_length(ngram_max_length);
+        }
+        if let Some(prefix_only) = prefix_only {
+            opts = opts.ngram_prefix_only(prefix_only);
+        }
 
         Self {
             inner: Mutex::new(Some(LanceDbIndex::FTS(opts))),

nodejs/src/lib.rs

@@ -14,6 +14,7 @@ pub mod merge;
 mod query;
 pub mod remote;
 mod rerankers;
+mod session;
 mod table;
 mod util;
 
@@ -34,6 +35,9 @@ pub struct ConnectionOptions {
     ///
     /// The available options are described at https://lancedb.github.io/lancedb/guides/storage/
     pub storage_options: Option<HashMap<String, String>>,
+    /// (For LanceDB OSS only): the session to use for this connection. Holds
+    /// shared caches and other session-specific state.
+    pub session: Option<session::Session>,
 
     /// (For LanceDB cloud only): configuration for the remote HTTP client.
     pub client_config: Option<remote::ClientConfig>,

nodejs/src/query.rs

@@ -4,7 +4,8 @@
 use std::sync::Arc;
 
 use lancedb::index::scalar::{
-    BoostQuery, FtsQuery, FullTextSearchQuery, MatchQuery, MultiMatchQuery, PhraseQuery,
+    BooleanQuery, BoostQuery, FtsQuery, FullTextSearchQuery, MatchQuery, MultiMatchQuery, Occur,
+    Operator, PhraseQuery,
 };
 use lancedb::query::ExecutableQuery;
 use lancedb::query::Query as LanceDbQuery;
@@ -177,6 +178,31 @@ impl VectorQuery {
         self.inner = self.inner.clone().nprobes(nprobe as usize);
     }
 
+    #[napi]
+    pub fn minimum_nprobes(&mut self, minimum_nprobe: u32) -> napi::Result<()> {
+        self.inner = self
+            .inner
+            .clone()
+            .minimum_nprobes(minimum_nprobe as usize)
+            .default_error()?;
+        Ok(())
+    }
+
+    #[napi]
+    pub fn maximum_nprobes(&mut self, maximum_nprobes: u32) -> napi::Result<()> {
+        let maximum_nprobes = if maximum_nprobes == 0 {
+            None
+        } else {
+            Some(maximum_nprobes as usize)
+        };
+        self.inner = self
+            .inner
+            .clone()
+            .maximum_nprobes(maximum_nprobes)
+            .default_error()?;
+        Ok(())
+    }
+
     #[napi]
     pub fn distance_range(&mut self, lower_bound: Option<f64>, upper_bound: Option<f64>) {
         // napi doesn't support f32, so we have to convert to f32
@@ -308,6 +334,8 @@ impl JsFullTextQuery {
         boost: f64,
         fuzziness: Option<u32>,
         max_expansions: u32,
+        operator: String,
+        prefix_length: u32,
     ) -> napi::Result<Self> {
         Ok(Self {
             inner: MatchQuery::new(query)
@@ -315,14 +343,23 @@ impl JsFullTextQuery {
                 .with_boost(boost as f32)
                 .with_fuzziness(fuzziness)
                 .with_max_expansions(max_expansions as usize)
+                .with_operator(
+                    Operator::try_from(operator.as_str()).map_err(|e| {
+                        napi::Error::from_reason(format!("Invalid operator: {}", e))
+                    })?,
+                )
+                .with_prefix_length(prefix_length)
                 .into(),
         })
     }
 
     #[napi(factory)]
-    pub fn phrase_query(query: String, column: String) -> napi::Result<Self> {
+    pub fn phrase_query(query: String, column: String, slop: u32) -> napi::Result<Self> {
         Ok(Self {
-            inner: PhraseQuery::new(query).with_column(Some(column)).into(),
+            inner: PhraseQuery::new(query)
+                .with_column(Some(column))
+                .with_slop(slop)
+                .into(),
         })
     }
 
@@ -348,6 +385,7 @@ impl JsFullTextQuery {
         query: String,
         columns: Vec<String>,
         boosts: Option<Vec<f64>>,
+        operator: String,
     ) -> napi::Result<Self> {
         let q = match boosts {
             Some(boosts) => MultiMatchQuery::try_new(query, columns)
@@ -358,7 +396,37 @@ impl JsFullTextQuery {
             napi::Error::from_reason(format!("Failed to create multi match query: {}", e))
         })?;
 
-        Ok(Self { inner: q.into() })
+        let operator = Operator::try_from(operator.as_str()).map_err(|e| {
+            napi::Error::from_reason(format!("Invalid operator for multi match query: {}", e))
+        })?;
+
+        Ok(Self {
+            inner: q.with_operator(operator).into(),
+        })
+    }
+
+    #[napi(factory)]
+    pub fn boolean_query(queries: Vec<(String, &JsFullTextQuery)>) -> napi::Result<Self> {
+        let mut sub_queries = Vec::with_capacity(queries.len());
+        for (occur, q) in queries {
+            let occur = Occur::try_from(occur.as_str())
+                .map_err(|e| napi::Error::from_reason(e.to_string()))?;
+            sub_queries.push((occur, q.inner.clone()));
+        }
+        Ok(Self {
+            inner: BooleanQuery::new(sub_queries).into(),
+        })
+    }
+
+    #[napi(getter)]
+    pub fn query_type(&self) -> String {
+        match self.inner {
+            FtsQuery::Match(_) => "match".to_string(),
+            FtsQuery::Phrase(_) => "phrase".to_string(),
+            FtsQuery::Boost(_) => "boost".to_string(),
+            FtsQuery::MultiMatch(_) => "multi_match".to_string(),
+            FtsQuery::Boolean(_) => "boolean".to_string(),
+        }
     }
 }
 

nodejs/src/session.rs

@@ -0,0 +1,102 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+use std::sync::Arc;
+
+use lancedb::{ObjectStoreRegistry, Session as LanceSession};
+use napi::bindgen_prelude::*;
+use napi_derive::*;
+
+/// A session for managing caches and object stores across LanceDB operations.
+///
+/// Sessions allow you to configure cache sizes for index and metadata caches,
+/// which can significantly impact memory use and performance. They can
+/// also be re-used across multiple connections to share the same cache state.
+#[napi]
+#[derive(Clone)]
+pub struct Session {
+    pub(crate) inner: Arc<LanceSession>,
+}
+
+impl std::fmt::Debug for Session {
+    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+        f.debug_struct("Session")
+            .field("size_bytes", &self.inner.size_bytes())
+            .field("approx_num_items", &self.inner.approx_num_items())
+            .finish()
+    }
+}
+
+#[napi]
+impl Session {
+    /// Create a new session with custom cache sizes.
+    ///
+    /// # Parameters
+    ///
+    /// - `index_cache_size_bytes`: The size of the index cache in bytes.
+    ///   Index data is stored in memory in this cache to speed up queries.
+    ///   Defaults to 6GB if not specified.
+    /// - `metadata_cache_size_bytes`: The size of the metadata cache in bytes.
+    ///   The metadata cache stores file metadata and schema information in memory.
+    ///   This cache improves scan and write performance.
+    ///   Defaults to 1GB if not specified.
+    #[napi(constructor)]
+    pub fn new(
+        index_cache_size_bytes: Option<BigInt>,
+        metadata_cache_size_bytes: Option<BigInt>,
+    ) -> napi::Result<Self> {
+        let index_cache_size = index_cache_size_bytes
+            .map(|size| size.get_u64().1 as usize)
+            .unwrap_or(6 * 1024 * 1024 * 1024); // 6GB default
+
+        let metadata_cache_size = metadata_cache_size_bytes
+            .map(|size| size.get_u64().1 as usize)
+            .unwrap_or(1024 * 1024 * 1024); // 1GB default
+
+        let session = LanceSession::new(
+            index_cache_size,
+            metadata_cache_size,
+            Arc::new(ObjectStoreRegistry::default()),
+        );
+
+        Ok(Self {
+            inner: Arc::new(session),
+        })
+    }
+
+    /// Create a session with default cache sizes.
+    ///
+    /// This is equivalent to creating a session with 6GB index cache
+    /// and 1GB metadata cache.
+    #[napi(factory)]
+    pub fn default() -> Self {
+        Self {
+            inner: Arc::new(LanceSession::default()),
+        }
+    }
+
+    /// Get the current size of the session caches in bytes.
+    #[napi]
+    pub fn size_bytes(&self) -> BigInt {
+        BigInt::from(self.inner.size_bytes())
+    }
+
+    /// Get the approximate number of items cached in the session.
+    #[napi]
+    pub fn approx_num_items(&self) -> u32 {
+        self.inner.approx_num_items() as u32
+    }
+}
+
+// Implement FromNapiValue for Session to work with napi(object)
+impl napi::bindgen_prelude::FromNapiValue for Session {
+    unsafe fn from_napi_value(
+        env: napi::sys::napi_env,
+        napi_val: napi::sys::napi_value,
+    ) -> napi::Result<Self> {
+        let object: napi::bindgen_prelude::ClassInstance<Session> =
+            napi::bindgen_prelude::ClassInstance::from_napi_value(env, napi_val)?;
+        let copy = object.clone();
+        Ok(copy)
+    }
+}

python/.bumpversion.toml

@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.23.0"
+current_version = "0.24.2"
 parse = """(?x)
     (?P<major>0|[1-9]\\d*)\\.
     (?P<minor>0|[1-9]\\d*)\\.

python/CLAUDE.md

@@ -0,0 +1,19 @@
+These are the Python bindings of LanceDB.
+The core Rust library is in the `../rust/lancedb` directory, the rust binding
+code is in the `src/` directory and the Python bindings are in the `lancedb/` directory.
+
+Common commands:
+
+* Build: `make develop`
+* Format: `make format`
+* Lint: `make check`
+* Fix lints: `make fix`
+* Test: `make test`
+* Doc test: `make doctest`
+
+Before committing changes, run lints and then formatting.
+
+When you change the Rust code, you will need to recompile the Python bindings: `make develop`.
+
+When you export new types from Rust to Python, you must manually update `python/lancedb/_lancedb.pyi`
+with the corresponding type hints. You can run `pyright` to check for type errors in the Python code.

python/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "lancedb-python"
-version = "0.23.0"
+version = "0.24.2"
 edition.workspace = true
 description = "Python bindings for LanceDB"
 license.workspace = true

python/pyproject.toml

@@ -85,8 +85,8 @@ embeddings = [
     "boto3>=1.28.57",
     "awscli>=1.29.57",
     "botocore>=1.31.57",
-    "ollama",
+    'ibm-watsonx-ai>=1.1.2; python_version >= "3.10"',
-    "ibm-watsonx-ai>=1.1.2",
+    "ollama>=0.3.0",
 ]
 azure = ["adlfs>=2024.2.0"]
 

python/python/lancedb/__init__.py

@@ -18,6 +18,7 @@ from .remote import ClientConfig
 from .remote.db import RemoteDBConnection
 from .schema import vector
 from .table import AsyncTable
+from ._lancedb import Session
 
 
 def connect(
@@ -30,6 +31,7 @@ def connect(
     request_thread_pool: Optional[Union[int, ThreadPoolExecutor]] = None,
     client_config: Union[ClientConfig, Dict[str, Any], None] = None,
     storage_options: Optional[Dict[str, str]] = None,
+    session: Optional[Session] = None,
     **kwargs: Any,
 ) -> DBConnection:
     """Connect to a LanceDB database.
@@ -64,6 +66,12 @@ def connect(
     storage_options: dict, optional
         Additional options for the storage backend. See available options at
         <https://lancedb.github.io/lancedb/guides/storage/>
+    session: Session, optional
+        (For LanceDB OSS only)
+        A session to use for this connection. Sessions allow you to configure
+        cache sizes for index and metadata caches, which can significantly
+        impact memory use and performance. They can also be re-used across
+        multiple connections to share the same cache state.
 
     Examples
     --------
@@ -92,7 +100,7 @@ def connect(
         if api_key is None:
             api_key = os.environ.get("LANCEDB_API_KEY")
         if api_key is None:
-            raise ValueError(f"api_key is required to connected LanceDB cloud: {uri}")
+            raise ValueError(f"api_key is required to connect to LanceDB cloud: {uri}")
         if isinstance(request_thread_pool, int):
             request_thread_pool = ThreadPoolExecutor(request_thread_pool)
         return RemoteDBConnection(
@@ -113,6 +121,7 @@ def connect(
         uri,
         read_consistency_interval=read_consistency_interval,
         storage_options=storage_options,
+        session=session,
     )
 
 
@@ -125,6 +134,7 @@ async def connect_async(
     read_consistency_interval: Optional[timedelta] = None,
     client_config: Optional[Union[ClientConfig, Dict[str, Any]]] = None,
     storage_options: Optional[Dict[str, str]] = None,
+    session: Optional[Session] = None,
 ) -> AsyncConnection:
     """Connect to a LanceDB database.
 
@@ -158,6 +168,12 @@ async def connect_async(
     storage_options: dict, optional
         Additional options for the storage backend. See available options at
         <https://lancedb.github.io/lancedb/guides/storage/>
+    session: Session, optional
+        (For LanceDB OSS only)
+        A session to use for this connection. Sessions allow you to configure
+        cache sizes for index and metadata caches, which can significantly
+        impact memory use and performance. They can also be re-used across
+        multiple connections to share the same cache state.
 
     Examples
     --------
@@ -197,6 +213,7 @@ async def connect_async(
             read_consistency_interval_secs,
             client_config,
             storage_options,
+            session,
         )
     )
 
@@ -212,6 +229,7 @@ __all__ = [
     "DBConnection",
     "LanceDBConnection",
     "RemoteDBConnection",
+    "Session",
     "__version__",
 ]
 

python/python/lancedb/_lancedb.pyi

@@ -6,6 +6,19 @@ import pyarrow as pa
 from .index import BTree, IvfFlat, IvfPq, Bitmap, LabelList, HnswPq, HnswSq, FTS
 from .remote import ClientConfig
 
+class Session:
+    def __init__(
+        self,
+        index_cache_size_bytes: Optional[int] = None,
+        metadata_cache_size_bytes: Optional[int] = None,
+    ): ...
+    @staticmethod
+    def default() -> "Session": ...
+    @property
+    def size_bytes(self) -> int: ...
+    @property
+    def approx_num_items(self) -> int: ...
+
 class Connection(object):
     uri: str
     async def table_names(
@@ -89,6 +102,7 @@ async def connect(
     read_consistency_interval: Optional[float],
     client_config: Optional[Union[ClientConfig, Dict[str, Any]]],
     storage_options: Optional[Dict[str, str]],
+    session: Optional[Session],
 ) -> Connection: ...
 
 class RecordBatchStream:
@@ -143,6 +157,8 @@ class VectorQuery:
     def postfilter(self): ...
     def refine_factor(self, refine_factor: int): ...
     def nprobes(self, nprobes: int): ...
+    def minimum_nprobes(self, minimum_nprobes: int): ...
+    def maximum_nprobes(self, maximum_nprobes: int): ...
     def bypass_vector_index(self): ...
     def nearest_to_text(self, query: dict) -> HybridQuery: ...
     def to_query_request(self) -> PyQueryRequest: ...
@@ -158,6 +174,8 @@ class HybridQuery:
     def distance_type(self, distance_type: str): ...
     def refine_factor(self, refine_factor: int): ...
     def nprobes(self, nprobes: int): ...
+    def minimum_nprobes(self, minimum_nprobes: int): ...
+    def maximum_nprobes(self, maximum_nprobes: int): ...
     def bypass_vector_index(self): ...
     def to_vector_query(self) -> VectorQuery: ...
     def to_fts_query(self) -> FTSQuery: ...
@@ -165,23 +183,21 @@ class HybridQuery:
     def get_with_row_id(self) -> bool: ...
     def to_query_request(self) -> PyQueryRequest: ...
 
-class PyFullTextSearchQuery:
+class FullTextQuery:
-    columns: Optional[List[str]]
+    pass
-    query: str
-    limit: Optional[int]
-    wand_factor: Optional[float]
 
 class PyQueryRequest:
     limit: Optional[int]
     offset: Optional[int]
     filter: Optional[Union[str, bytes]]
-    full_text_search: Optional[PyFullTextSearchQuery]
+    full_text_search: Optional[FullTextQuery]
     select: Optional[Union[str, List[str]]]
     fast_search: Optional[bool]
     with_row_id: Optional[bool]
     column: Optional[str]
     query_vector: Optional[List[pa.Array]]
-    nprobes: Optional[int]
+    minimum_nprobes: Optional[int]
+    maximum_nprobes: Optional[int]
     lower_bound: Optional[float]
     upper_bound: Optional[float]
     ef: Optional[int]

python/python/lancedb/common.py

@@ -94,9 +94,9 @@ def data_to_reader(
     else:
         raise TypeError(
             f"Unknown data type {type(data)}. "
-            "Please check "
+            "Supported types: list of dicts, pandas DataFrame, polars DataFrame, "
-            "https://lancedb.github.io/lance/read_and_write.html "
+            "pyarrow Table/RecordBatch, or Pydantic models. "
-            "to see supported types."
+            "See https://lancedb.github.io/lancedb/guides/tables/ for examples."
         )
 
 

python/python/lancedb/db.py

@@ -37,6 +37,7 @@ if TYPE_CHECKING:
     from ._lancedb import Connection as LanceDbConnection
     from .common import DATA, URI
     from .embeddings import EmbeddingFunctionConfig
+    from ._lancedb import Session
 
 
 class DBConnection(EnforceOverrides):
@@ -247,6 +248,9 @@ class DBConnection(EnforceOverrides):
         name: str
             The name of the table.
         index_cache_size: int, default 256
+            **Deprecated**: Use session-level cache configuration instead.
+            Create a Session with custom cache sizes and pass it to lancedb.connect().
+
             Set the size of the index cache, specified as a number of entries
 
             The exact meaning of an "entry" will depend on the type of index:
@@ -354,6 +358,7 @@ class LanceDBConnection(DBConnection):
         *,
         read_consistency_interval: Optional[timedelta] = None,
         storage_options: Optional[Dict[str, str]] = None,
+        session: Optional[Session] = None,
     ):
         if not isinstance(uri, Path):
             scheme = get_uri_scheme(uri)
@@ -367,6 +372,7 @@ class LanceDBConnection(DBConnection):
         self._entered = False
         self.read_consistency_interval = read_consistency_interval
         self.storage_options = storage_options
+        self.session = session
 
         if read_consistency_interval is not None:
             read_consistency_interval_secs = read_consistency_interval.total_seconds()
@@ -382,6 +388,7 @@ class LanceDBConnection(DBConnection):
                 read_consistency_interval_secs,
                 None,
                 storage_options,
+                session,
             )
 
         self._conn = AsyncConnection(LOOP.run(do_connect()))
@@ -475,6 +482,17 @@ class LanceDBConnection(DBConnection):
         -------
         A LanceTable object representing the table.
         """
+        if index_cache_size is not None:
+            import warnings
+
+            warnings.warn(
+                "index_cache_size is deprecated. Use session-level cache "
+                "configuration instead. Create a Session with custom cache sizes "
+                "and pass it to lancedb.connect().",
+                DeprecationWarning,
+                stacklevel=2,
+            )
+
         return LanceTable.open(
             self,
             name,
@@ -820,6 +838,9 @@ class AsyncConnection(object):
             See available options at
             <https://lancedb.github.io/lancedb/guides/storage/>
         index_cache_size: int, default 256
+            **Deprecated**: Use session-level cache configuration instead.
+            Create a Session with custom cache sizes and pass it to lancedb.connect().
+
             Set the size of the index cache, specified as a number of entries
 
             The exact meaning of an "entry" will depend on the type of index:

python/python/lancedb/embeddings/__init__.py

@@ -11,7 +11,7 @@ from .instructor import InstructorEmbeddingFunction
 from .ollama import OllamaEmbeddings
 from .open_clip import OpenClipEmbeddings
 from .openai import OpenAIEmbeddings
-from .registry import EmbeddingFunctionRegistry, get_registry
+from .registry import EmbeddingFunctionRegistry, get_registry, register
 from .sentence_transformers import SentenceTransformerEmbeddings
 from .gte import GteEmbeddings
 from .transformers import TransformersEmbeddingFunction, ColbertEmbeddings

python/python/lancedb/embeddings/gte_mlx_model.py

@@ -9,11 +9,14 @@ from huggingface_hub import snapshot_download
 from pydantic import BaseModel
 from transformers import BertTokenizer
 
+from .utils import create_import_stub
+
 try:
     import mlx.core as mx
     import mlx.nn as nn
 except ImportError:
-    raise ImportError("You need to install MLX to use this model use - pip install mlx")
+    mx = create_import_stub("mlx.core", "mlx")
+    nn = create_import_stub("mlx.nn", "mlx")
 
 
 def average_pool(last_hidden_state: mx.array, attention_mask: mx.array) -> mx.array:
@@ -72,7 +75,7 @@ class TransformerEncoder(nn.Module):
         super().__init__()
         self.layers = [
             TransformerEncoderLayer(dims, num_heads, mlp_dims)
-            for i in range(num_layers)
+            for _ in range(num_layers)
         ]
 
     def __call__(self, x, mask):

python/python/lancedb/embeddings/ollama.py

@@ -2,14 +2,15 @@
 # SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
 from functools import cached_property
-from typing import TYPE_CHECKING, List, Optional, Union
+from typing import TYPE_CHECKING, List, Optional, Sequence, Union
+
+import numpy as np
 
 from ..util import attempt_import_or_raise
 from .base import TextEmbeddingFunction
 from .registry import register
 
 if TYPE_CHECKING:
-    import numpy as np
     import ollama
 
 
@@ -28,23 +29,21 @@ class OllamaEmbeddings(TextEmbeddingFunction):
     keep_alive: Optional[Union[float, str]] = None
     ollama_client_kwargs: Optional[dict] = {}
 
-    def ndims(self):
+    def ndims(self) -> int:
         return len(self.generate_embeddings(["foo"])[0])
 
-    def _compute_embedding(self, text) -> Union["np.array", None]:
+    def _compute_embedding(self, text: Sequence[str]) -> Sequence[Sequence[float]]:
-        return (
+        response = self._ollama_client.embed(
-            self._ollama_client.embeddings(
             model=self.name,
-                prompt=text,
+            input=text,
             options=self.options,
             keep_alive=self.keep_alive,
-            )["embedding"]
-            or None
         )
+        return response.embeddings
 
     def generate_embeddings(
-        self, texts: Union[List[str], "np.ndarray"]
+        self, texts: Union[List[str], np.ndarray]
-    ) -> list[Union["np.array", None]]:
+    ) -> list[Union[np.array, None]]:
         """
         Get the embeddings for the given texts
 
@@ -54,8 +53,8 @@ class OllamaEmbeddings(TextEmbeddingFunction):
             The texts to embed
         """
         # TODO retry, rate limit, token limit
-        embeddings = [self._compute_embedding(text) for text in texts]
+        embeddings = self._compute_embedding(texts)
-        return embeddings
+        return list(embeddings)
 
     @cached_property
     def _ollama_client(self) -> "ollama.Client":

python/python/lancedb/embeddings/registry.py

@@ -2,7 +2,7 @@
 # SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
 import json
-from typing import Dict, Optional
+from typing import Dict, Optional, Type
 
 from .base import EmbeddingFunction, EmbeddingFunctionConfig
 
@@ -43,7 +43,7 @@ class EmbeddingFunctionRegistry:
         self._functions = {}
         self._variables = {}
 
-    def register(self, alias: str = None):
+    def register(self, alias: Optional[str] = None):
         """
         This creates a decorator that can be used to register
         an EmbeddingFunction.
@@ -75,7 +75,7 @@ class EmbeddingFunctionRegistry:
         """
         self._functions = {}
 
-    def get(self, name: str):
+    def get(self, name: str) -> Type[EmbeddingFunction]:
         """
         Fetch an embedding function class by name
 

python/python/lancedb/embeddings/utils.py

@@ -21,6 +21,36 @@ from ..dependencies import pandas as pd
 from ..util import attempt_import_or_raise
 
 
+def create_import_stub(module_name: str, package_name: str = None):
+    """
+    Create a stub module that allows class definition but fails when used.
+    This allows modules to be imported for doctest collection even when
+    optional dependencies are not available.
+
+    Parameters
+    ----------
+    module_name : str
+        The name of the module to create a stub for
+    package_name : str, optional
+        The package name to suggest in the error message
+
+    Returns
+    -------
+    object
+        A stub object that can be used in place of the module
+    """
+
+    class _ImportStub:
+        def __getattr__(self, name):
+            return _ImportStub  # Return stub for chained access like nn.Module
+
+        def __call__(self, *args, **kwargs):
+            pkg = package_name or module_name
+            raise ImportError(f"You need to install {pkg} to use this functionality")
+
+    return _ImportStub()
+
+
 # ruff: noqa: PERF203
 def retry(tries=10, delay=1, max_delay=30, backoff=3, jitter=1):
     def wrapper(fn):

python/python/lancedb/index.py

@@ -137,6 +137,9 @@ class FTS:
     stem: bool = True
     remove_stop_words: bool = True
     ascii_folding: bool = True
+    ngram_min_length: int = 3
+    ngram_max_length: int = 3
+    prefix_only: bool = False
 
 
 @dataclass

python/python/lancedb/query.py

@@ -4,7 +4,6 @@
 from __future__ import annotations
 
 from abc import ABC, abstractmethod
-import abc
 from concurrent.futures import ThreadPoolExecutor
 from enum import Enum
 from datetime import timedelta
@@ -15,7 +14,7 @@ from typing import (
     Literal,
     Optional,
     Tuple,
-    Type,
+    TypeVar,
     Union,
     Any,
 )
@@ -59,6 +58,8 @@ if TYPE_CHECKING:
     else:
         from typing_extensions import Self
 
+T = TypeVar("T", bound="LanceModel")
+
 
 # Pydantic validation function for vector queries
 def ensure_vector_query(
@@ -88,15 +89,28 @@ def ensure_vector_query(
         return val
 
 
-class FullTextQueryType(Enum):
+class FullTextQueryType(str, Enum):
     MATCH = "match"
     MATCH_PHRASE = "match_phrase"
     BOOST = "boost"
     MULTI_MATCH = "multi_match"
+    BOOLEAN = "boolean"
 
 
-class FullTextQuery(abc.ABC, pydantic.BaseModel):
+class FullTextOperator(str, Enum):
-    @abc.abstractmethod
+    AND = "AND"
+    OR = "OR"
+
+
+class Occur(str, Enum):
+    SHOULD = "SHOULD"
+    MUST = "MUST"
+    MUST_NOT = "MUST_NOT"
+
+
+@pydantic.dataclasses.dataclass
+class FullTextQuery(ABC):
+    @abstractmethod
     def query_type(self) -> FullTextQueryType:
         """
         Get the query type of the query.
@@ -106,35 +120,43 @@ class FullTextQuery(abc.ABC, pydantic.BaseModel):
         str
             The type of the query.
         """
+        pass
 
-    @abc.abstractmethod
+    def __and__(self, other: "FullTextQuery") -> "FullTextQuery":
-    def to_dict(self) -> dict:
         """
-        Convert the query to a dictionary.
+        Combine two queries with a logical AND operation.
+
+        Parameters
+        ----------
+        other : FullTextQuery
+            The other query to combine with.
 
         Returns
         -------
-        dict
+        FullTextQuery
-            The query as a dictionary.
+            A new query that combines both queries with AND.
         """
+        return BooleanQuery([(Occur.MUST, self), (Occur.MUST, other)])
+
+    def __or__(self, other: "FullTextQuery") -> "FullTextQuery":
+        """
+        Combine two queries with a logical OR operation.
+
+        Parameters
+        ----------
+        other : FullTextQuery
+            The other query to combine with.
+
+        Returns
+        -------
+        FullTextQuery
+            A new query that combines both queries with OR.
+        """
+        return BooleanQuery([(Occur.SHOULD, self), (Occur.SHOULD, other)])
 
 
+@pydantic.dataclasses.dataclass
 class MatchQuery(FullTextQuery):
-    query: str
-    column: str
-    boost: float = 1.0
-    fuzziness: int = 0
-    max_expansions: int = 50
-
-    def __init__(
-        self,
-        query: str,
-        column: str,
-        *,
-        boost: float = 1.0,
-        fuzziness: int = 0,
-        max_expansions: int = 50,
-    ):
     """
     Match query for full-text search.
 
@@ -157,36 +179,30 @@ class MatchQuery(FullTextQuery):
     max_expansions : int, optional
         The maximum number of terms to consider for fuzzy matching.
         Defaults to 50.
+    operator : FullTextOperator, default OR
+        The operator to use for combining the query results.
+        Can be either `AND` or `OR`.
+        If `AND`, all terms in the query must match.
+        If `OR`, at least one term in the query must match.
+    prefix_length : int, optional
+        The number of beginning characters being unchanged for fuzzy matching.
+        This is useful to achieve prefix matching.
     """
-        super().__init__(
+
-            query=query,
+    query: str
-            column=column,
+    column: str
-            boost=boost,
+    boost: float = pydantic.Field(1.0, kw_only=True)
-            fuzziness=fuzziness,
+    fuzziness: int = pydantic.Field(0, kw_only=True)
-            max_expansions=max_expansions,
+    max_expansions: int = pydantic.Field(50, kw_only=True)
-        )
+    operator: FullTextOperator = pydantic.Field(FullTextOperator.OR, kw_only=True)
+    prefix_length: int = pydantic.Field(0, kw_only=True)
 
     def query_type(self) -> FullTextQueryType:
         return FullTextQueryType.MATCH
 
-    def to_dict(self) -> dict:
-        return {
-            "match": {
-                self.column: {
-                    "query": self.query,
-                    "boost": self.boost,
-                    "fuzziness": self.fuzziness,
-                    "max_expansions": self.max_expansions,
-                }
-            }
-        }
-
 
+@pydantic.dataclasses.dataclass
 class PhraseQuery(FullTextQuery):
-    query: str
-    column: str
-
-    def __init__(self, query: str, column: str):
     """
     Phrase query for full-text search.
 
@@ -197,31 +213,17 @@ class PhraseQuery(FullTextQuery):
     column : str
         The name of the column to match against.
     """
-        super().__init__(query=query, column=column)
+
+    query: str
+    column: str
+    slop: int = pydantic.Field(0, kw_only=True)
 
     def query_type(self) -> FullTextQueryType:
         return FullTextQueryType.MATCH_PHRASE
 
-    def to_dict(self) -> dict:
-        return {
-            "match_phrase": {
-                self.column: self.query,
-            }
-        }
-
 
+@pydantic.dataclasses.dataclass
 class BoostQuery(FullTextQuery):
-    positive: FullTextQuery
-    negative: FullTextQuery
-    negative_boost: float = 0.5
-
-    def __init__(
-        self,
-        positive: FullTextQuery,
-        negative: FullTextQuery,
-        *,
-        negative_boost: float = 0.5,
-    ):
     """
     Boost query for full-text search.
 
@@ -231,68 +233,65 @@ class BoostQuery(FullTextQuery):
         The positive query object.
     negative : dict
         The negative query object.
-        negative_boost : float
+    negative_boost : float, default 0.5
         The boost factor for the negative query.
     """
-        super().__init__(
+
-            positive=positive, negative=negative, negative_boost=negative_boost
+    positive: FullTextQuery
-        )
+    negative: FullTextQuery
+    negative_boost: float = pydantic.Field(0.5, kw_only=True)
 
     def query_type(self) -> FullTextQueryType:
         return FullTextQueryType.BOOST
 
-    def to_dict(self) -> dict:
-        return {
-            "boost": {
-                "positive": self.positive.to_dict(),
-                "negative": self.negative.to_dict(),
-                "negative_boost": self.negative_boost,
-            }
-        }
-
 
+@pydantic.dataclasses.dataclass
 class MultiMatchQuery(FullTextQuery):
-    query: str
-    columns: list[str]
-    boosts: list[float]
-
-    def __init__(
-        self,
-        query: str,
-        columns: list[str],
-        *,
-        boosts: Optional[list[float]] = None,
-    ):
     """
     Multi-match query for full-text search.
 
     Parameters
     ----------
-        query : str
+    query : str | list[Query]
-            The query string to match against.
+        If a string, the query string to match against.
-
     columns : list[str]
         The list of columns to match against.
-
     boosts : list[float], optional
         The list of boost factors for each column. If not provided,
         all columns will have the same boost factor.
+    operator : FullTextOperator, default OR
+        The operator to use for combining the query results.
+        Can be either `AND` or `OR`.
+        It would be applied to all columns individually.
+        For example, if the operator is `AND`,
+        then the query "hello world" is equal to
+        `match("hello AND world", column1) OR match("hello AND world", column2)`.
     """
-        if boosts is None:
+
-            boosts = [1.0] * len(columns)
+    query: str
-        super().__init__(query=query, columns=columns, boosts=boosts)
+    columns: list[str]
+    boosts: Optional[list[float]] = pydantic.Field(None, kw_only=True)
+    operator: FullTextOperator = pydantic.Field(FullTextOperator.OR, kw_only=True)
 
     def query_type(self) -> FullTextQueryType:
         return FullTextQueryType.MULTI_MATCH
 
-    def to_dict(self) -> dict:
+
-        return {
+@pydantic.dataclasses.dataclass
-            "multi_match": {
+class BooleanQuery(FullTextQuery):
-                "query": self.query,
+    """
-                "columns": self.columns,
+    Boolean query for full-text search.
-                "boost": self.boosts,
+
-            }
+    Parameters
-        }
+    ----------
+    queries : list[tuple(Occur, FullTextQuery)]
+        The list of queries with their occurrence requirements.
+    """
+
+    queries: list[tuple[Occur, FullTextQuery]]
+
+    def query_type(self) -> FullTextQueryType:
+        return FullTextQueryType.BOOLEAN
 
 
 class FullTextSearchQuery(pydantic.BaseModel):
@@ -445,8 +444,18 @@ class Query(pydantic.BaseModel):
     # which columns to return in the results
     columns: Optional[Union[List[str], Dict[str, str]]] = None
 
-    # number of IVF partitions to search
+    # minimum number of IVF partitions to search
-    nprobes: Optional[int] = None
+    #
+    # If None then a default value (20) will be used.
+    minimum_nprobes: Optional[int] = None
+
+    # maximum number of IVF partitions to search
+    #
+    # If None then a default value (20) will be used.
+    #
+    # If 0 then no limit will be applied and all partitions could be searched
+    # if needed to satisfy the limit.
+    maximum_nprobes: Optional[int] = None
 
     # lower bound for distance search
     lower_bound: Optional[float] = None
@@ -484,7 +493,8 @@ class Query(pydantic.BaseModel):
         query.vector_column = req.column
         query.vector = req.query_vector
         query.distance_type = req.distance_type
-        query.nprobes = req.nprobes
+        query.minimum_nprobes = req.minimum_nprobes
+        query.maximum_nprobes = req.maximum_nprobes
         query.lower_bound = req.lower_bound
         query.upper_bound = req.upper_bound
         query.ef = req.ef
@@ -493,10 +503,8 @@ class Query(pydantic.BaseModel):
         query.postfilter = req.postfilter
         if req.full_text_search is not None:
             query.full_text_query = FullTextSearchQuery(
-                columns=req.full_text_search.columns,
+                columns=None,
-                query=req.full_text_search.query,
+                query=req.full_text_search,
-                limit=req.full_text_search.limit,
-                wand_factor=req.full_text_search.wand_factor,
             )
         return query
 
@@ -740,8 +748,8 @@ class LanceQueryBuilder(ABC):
         return self.to_arrow(timeout=timeout).to_pylist()
 
     def to_pydantic(
-        self, model: Type[LanceModel], *, timeout: Optional[timedelta] = None
+        self, model: type[T], *, timeout: Optional[timedelta] = None
-    ) -> List[LanceModel]:
+    ) -> list[T]:
         """Return the table as a list of pydantic models.
 
         Parameters
@@ -904,7 +912,7 @@ class LanceQueryBuilder(ABC):
             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
+                  LanceRead: uri=..., projection=[vector], ...
 
         Parameters
         ----------
@@ -934,6 +942,7 @@ class LanceQueryBuilder(ABC):
         >>> plan = table.search(query).analyze_plan()
         >>> print(plan)  # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
         AnalyzeExec verbose=true, metrics=[]
+          TracedExec, metrics=[]
             ProjectionExec: expr=[...], metrics=[...]
               GlobalLimitExec: skip=0, fetch=10, metrics=[...]
                 FilterExec: _distance@2 IS NOT NULL,
@@ -943,8 +952,7 @@ class LanceQueryBuilder(ABC):
                   metrics=[output_rows=..., elapsed_compute=..., row_replacements=...]
                     KNNVectorDistance: metric=l2,
                     metrics=[output_rows=..., elapsed_compute=..., output_batches=...]
-                    LanceScan: uri=..., projection=[vector], row_id=true,
+                      LanceRead: uri=..., projection=[vector], ...
-                    row_addr=false, ordered=false,
                       metrics=[output_rows=..., elapsed_compute=...,
                       bytes_read=..., iops=..., requests=...]
 
@@ -1047,7 +1055,8 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
         super().__init__(table)
         self._query = query
         self._distance_type = None
-        self._nprobes = None
+        self._minimum_nprobes = None
+        self._maximum_nprobes = None
         self._lower_bound = None
         self._upper_bound = None
         self._refine_factor = None
@@ -1110,6 +1119,10 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
         See discussion in [Querying an ANN Index][querying-an-ann-index] for
         tuning advice.
 
+        This method sets both the minimum and maximum number of probes to the same
+        value. See `minimum_nprobes` and `maximum_nprobes` for more fine-grained
+        control.
+
         Parameters
         ----------
         nprobes: int
@@ -1120,7 +1133,36 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
         LanceVectorQueryBuilder
             The LanceQueryBuilder object.
         """
-        self._nprobes = nprobes
+        self._minimum_nprobes = nprobes
+        self._maximum_nprobes = nprobes
+        return self
+
+    def minimum_nprobes(self, minimum_nprobes: int) -> LanceVectorQueryBuilder:
+        """Set the minimum number of probes to use.
+
+        See `nprobes` for more details.
+
+        These partitions will be searched on every vector query and will increase recall
+        at the expense of latency.
+        """
+        self._minimum_nprobes = minimum_nprobes
+        return self
+
+    def maximum_nprobes(self, maximum_nprobes: int) -> LanceVectorQueryBuilder:
+        """Set the maximum number of probes to use.
+
+        See `nprobes` for more details.
+
+        If this value is greater than `minimum_nprobes` then the excess partitions
+        will be searched only if we have not found enough results.
+
+        This can be useful when there is a narrow filter to allow these queries to
+        spend more time searching and avoid potential false negatives.
+
+        If this value is 0 then no limit will be applied and all partitions could be
+        searched if needed to satisfy the limit.
+        """
+        self._maximum_nprobes = maximum_nprobes
         return self
 
     def distance_range(
@@ -1224,7 +1266,8 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
             limit=self._limit,
             distance_type=self._distance_type,
             columns=self._columns,
-            nprobes=self._nprobes,
+            minimum_nprobes=self._minimum_nprobes,
+            maximum_nprobes=self._maximum_nprobes,
             lower_bound=self._lower_bound,
             upper_bound=self._upper_bound,
             refine_factor=self._refine_factor,
@@ -1333,6 +1376,8 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
         if query_string is not None and not isinstance(query_string, str):
             raise ValueError("Reranking currently only supports string queries")
         self._str_query = query_string if query_string is not None else self._str_query
+        if reranker.score == "all":
+            self.with_row_id(True)
         return self
 
     def bypass_vector_index(self) -> LanceVectorQueryBuilder:
@@ -1410,10 +1455,13 @@ class LanceFtsQueryBuilder(LanceQueryBuilder):
 
         query = self._query
         if self._phrase_query:
-            raise NotImplementedError(
+            if isinstance(query, str):
-                "Phrase query is not yet supported in Lance FTS. "
+                if not query.startswith('"') or not query.endswith('"'):
-                "Use tantivy-based index instead for now."
+                    query = f'"{query}"'
-            )
+            elif isinstance(query, FullTextQuery) and not isinstance(
+                query, PhraseQuery
+            ):
+                raise TypeError("Please use PhraseQuery for phrase queries.")
         query = self.to_query_object()
         results = self._table._execute_query(query, timeout=timeout)
         results = results.read_all()
@@ -1525,6 +1573,8 @@ class LanceFtsQueryBuilder(LanceQueryBuilder):
             The LanceQueryBuilder object.
         """
         self._reranker = reranker
+        if reranker.score == "all":
+            self.with_row_id(True)
         return self
 
 
@@ -1588,7 +1638,8 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
         self._fts_columns = fts_columns
         self._norm = None
         self._reranker = None
-        self._nprobes = None
+        self._minimum_nprobes = None
+        self._maximum_nprobes = None
         self._refine_factor = None
         self._distance_type = None
         self._phrase_query = None
@@ -1800,6 +1851,8 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
 
         self._norm = normalize
         self._reranker = reranker
+        if reranker.score == "all":
+            self.with_row_id(True)
 
         return self
 
@@ -1820,7 +1873,24 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
         LanceHybridQueryBuilder
             The LanceHybridQueryBuilder object.
         """
-        self._nprobes = nprobes
+        self._minimum_nprobes = nprobes
+        self._maximum_nprobes = nprobes
+        return self
+
+    def minimum_nprobes(self, minimum_nprobes: int) -> LanceHybridQueryBuilder:
+        """Set the minimum number of probes to use.
+
+        See `nprobes` for more details.
+        """
+        self._minimum_nprobes = minimum_nprobes
+        return self
+
+    def maximum_nprobes(self, maximum_nprobes: int) -> LanceHybridQueryBuilder:
+        """Set the maximum number of probes to use.
+
+        See `nprobes` for more details.
+        """
+        self._maximum_nprobes = maximum_nprobes
         return self
 
     def distance_range(
@@ -1975,7 +2045,7 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
           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
+                LanceRead: uri=..., projection=[vector], ...
 
         Parameters
         ----------
@@ -2049,8 +2119,10 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
             self._fts_query.phrase_query(True)
         if self._distance_type:
             self._vector_query.metric(self._distance_type)
-        if self._nprobes:
+        if self._minimum_nprobes:
-            self._vector_query.nprobes(self._nprobes)
+            self._vector_query.minimum_nprobes(self._minimum_nprobes)
+        if self._maximum_nprobes is not None:
+            self._vector_query.maximum_nprobes(self._maximum_nprobes)
         if self._refine_factor:
             self._vector_query.refine_factor(self._refine_factor)
         if self._ef:
@@ -2359,7 +2431,7 @@ class AsyncQueryBase(object):
             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
+                  LanceRead: uri=..., projection=[vector], ...
 
         Parameters
         ----------
@@ -2513,7 +2585,7 @@ class AsyncQuery(AsyncQueryBase):
                 self._inner.nearest_to_text({"query": query, "columns": columns})
             )
         # FullTextQuery object
-        return AsyncFTSQuery(self._inner.nearest_to_text({"query": query.to_dict()}))
+        return AsyncFTSQuery(self._inner.nearest_to_text({"query": query}))
 
 
 class AsyncFTSQuery(AsyncQueryBase):
@@ -2661,6 +2733,34 @@ class AsyncVectorQueryBase:
         self._inner.nprobes(nprobes)
         return self
 
+    def minimum_nprobes(self, minimum_nprobes: int) -> Self:
+        """Set the minimum number of probes to use.
+
+        See `nprobes` for more details.
+
+        These partitions will be searched on every indexed vector query and will
+        increase recall at the expense of latency.
+        """
+        self._inner.minimum_nprobes(minimum_nprobes)
+        return self
+
+    def maximum_nprobes(self, maximum_nprobes: int) -> Self:
+        """Set the maximum number of probes to use.
+
+        See `nprobes` for more details.
+
+        If this value is greater than `minimum_nprobes` then the excess partitions
+        will be searched only if we have not found enough results.
+
+        This can be useful when there is a narrow filter to allow these queries to
+        spend more time searching and avoid potential false negatives.
+
+        If this value is 0 then no limit will be applied and all partitions could be
+        searched if needed to satisfy the limit.
+        """
+        self._inner.maximum_nprobes(maximum_nprobes)
+        return self
+
     def distance_range(
         self, lower_bound: Optional[float] = None, upper_bound: Optional[float] = None
     ) -> Self:
@@ -2835,7 +2935,7 @@ class AsyncVectorQuery(AsyncQueryBase, AsyncVectorQueryBase):
                 self._inner.nearest_to_text({"query": query, "columns": columns})
             )
         # FullTextQuery object
-        return AsyncHybridQuery(self._inner.nearest_to_text({"query": query.to_dict()}))
+        return AsyncHybridQuery(self._inner.nearest_to_text({"query": query}))
 
     async def to_batches(
         self,
@@ -2956,9 +3056,15 @@ class AsyncHybridQuery(AsyncQueryBase, AsyncVectorQueryBase):
                 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
+                      LanceRead: uri=..., projection=[vector], ...
+        <BLANKLINE>
         FTS Search Plan:
-        LanceScan: uri=..., projection=[vector, text], row_id=false, row_addr=false, ordered=true
+        ProjectionExec: expr=[vector@2 as vector, text@3 as text, _score@1 as _score]
+          Take: columns="_rowid, _score, (vector), (text)"
+            CoalesceBatchesExec: target_batch_size=1024
+              GlobalLimitExec: skip=0, fetch=10
+                MatchQuery: query=hello
+        <BLANKLINE>
 
         Parameters
         ----------

python/python/lancedb/remote/table.py

@@ -18,7 +18,7 @@ from lancedb._lancedb import (
     UpdateResult,
 )
 from lancedb.embeddings.base import EmbeddingFunctionConfig
-from lancedb.index import FTS, BTree, Bitmap, HnswPq, HnswSq, IvfFlat, IvfPq, LabelList
+from lancedb.index import FTS, BTree, Bitmap, HnswSq, IvfFlat, IvfPq, LabelList
 from lancedb.remote.db import LOOP
 import pyarrow as pa
 
@@ -89,7 +89,7 @@ class RemoteTable(Table):
 
     def to_pandas(self):
         """to_pandas() is not yet supported on LanceDB cloud."""
-        return NotImplementedError("to_pandas() is not yet supported on LanceDB cloud.")
+        raise NotImplementedError("to_pandas() is not yet supported on LanceDB cloud.")
 
     def checkout(self, version: Union[int, str]):
         return LOOP.run(self._table.checkout(version))
@@ -158,6 +158,9 @@ class RemoteTable(Table):
         stem: bool = True,
         remove_stop_words: bool = True,
         ascii_folding: bool = True,
+        ngram_min_length: int = 3,
+        ngram_max_length: int = 3,
+        prefix_only: bool = False,
     ):
         config = FTS(
             with_position=with_position,
@@ -168,6 +171,9 @@ class RemoteTable(Table):
             stem=stem,
             remove_stop_words=remove_stop_words,
             ascii_folding=ascii_folding,
+            ngram_min_length=ngram_min_length,
+            ngram_max_length=ngram_max_length,
+            prefix_only=prefix_only,
         )
         LOOP.run(
             self._table.create_index(
@@ -186,6 +192,8 @@ class RemoteTable(Table):
         accelerator: Optional[str] = None,
         index_type="vector",
         wait_timeout: Optional[timedelta] = None,
+        *,
+        num_bits: int = 8,
     ):
         """Create an index on the table.
         Currently, the only parameters that matter are
@@ -220,11 +228,6 @@ class RemoteTable(Table):
         >>> table.create_index("l2", "vector") # doctest: +SKIP
         """
 
-        if num_partitions is not None:
-            logging.warning(
-                "num_partitions is not supported on LanceDB cloud."
-                "This parameter will be tuned automatically."
-            )
         if num_sub_vectors is not None:
             logging.warning(
                 "num_sub_vectors is not supported on LanceDB cloud."
@@ -244,13 +247,21 @@ class RemoteTable(Table):
 
         index_type = index_type.upper()
         if index_type == "VECTOR" or index_type == "IVF_PQ":
-            config = IvfPq(distance_type=metric)
+            config = IvfPq(
+                distance_type=metric,
+                num_partitions=num_partitions,
+                num_sub_vectors=num_sub_vectors,
+                num_bits=num_bits,
+            )
         elif index_type == "IVF_HNSW_PQ":
-            config = HnswPq(distance_type=metric)
+            raise ValueError(
+                "IVF_HNSW_PQ is not supported on LanceDB cloud."
+                "Please use IVF_HNSW_SQ instead."
+            )
         elif index_type == "IVF_HNSW_SQ":
-            config = HnswSq(distance_type=metric)
+            config = HnswSq(distance_type=metric, num_partitions=num_partitions)
         elif index_type == "IVF_FLAT":
-            config = IvfFlat(distance_type=metric)
+            config = IvfFlat(distance_type=metric, num_partitions=num_partitions)
         else:
             raise ValueError(
                 f"Unknown vector index type: {index_type}. Valid options are"

python/python/lancedb/rerankers/answerdotai.py

@@ -74,9 +74,7 @@ class AnswerdotaiRerankers(Reranker):
         if self.score == "relevance":
             combined_results = self._keep_relevance_score(combined_results)
         elif self.score == "all":
-            raise NotImplementedError(
+            combined_results = self._merge_and_keep_scores(vector_results, fts_results)
-                "Answerdotai Reranker does not support score='all' yet"
-            )
         combined_results = combined_results.sort_by(
             [("_relevance_score", "descending")]
         )

python/python/lancedb/rerankers/base.py

@@ -232,6 +232,39 @@ class Reranker(ABC):
 
         return deduped_table
 
+    def _merge_and_keep_scores(self, vector_results: pa.Table, fts_results: pa.Table):
+        """
+        Merge the results from the vector and FTS search and keep the scores.
+        This op is slower than just keeping relevance score but can be useful
+        for debugging.
+        """
+        # add nulls to fts results for _distance
+        if "_distance" not in fts_results.column_names:
+            fts_results = fts_results.append_column(
+                "_distance",
+                pa.array([None] * len(fts_results), type=pa.float32()),
+            )
+        # add nulls to vector results for _score
+        if "_score" not in vector_results.column_names:
+            vector_results = vector_results.append_column(
+                "_score",
+                pa.array([None] * len(vector_results), type=pa.float32()),
+            )
+
+        # combine them and fill the scores
+        vector_results_dict = {row["_rowid"]: row for row in vector_results.to_pylist()}
+        fts_results_dict = {row["_rowid"]: row for row in fts_results.to_pylist()}
+
+        # merge them into vector_results
+        for key, value in fts_results_dict.items():
+            if key in vector_results_dict:
+                vector_results_dict[key]["_score"] = value["_score"]
+            else:
+                vector_results_dict[key] = value
+
+        combined = pa.Table.from_pylist(list(vector_results_dict.values()))
+        return combined
+
     def _keep_relevance_score(self, combined_results: pa.Table):
         if self.score == "relevance":
             if "_score" in combined_results.column_names:

python/python/lancedb/rerankers/cohere.py

@@ -92,14 +92,14 @@ class CohereReranker(Reranker):
         vector_results: pa.Table,
         fts_results: pa.Table,
     ):
+        if self.score == "all":
+            combined_results = self._merge_and_keep_scores(vector_results, fts_results)
+        else:
             combined_results = self.merge_results(vector_results, fts_results)
         combined_results = self._rerank(combined_results, query)
         if self.score == "relevance":
             combined_results = self._keep_relevance_score(combined_results)
-        elif self.score == "all":
+
-            raise NotImplementedError(
-                "return_score='all' not implemented for cohere reranker"
-            )
         return combined_results
 
     def rerank_vector(self, query: str, vector_results: pa.Table):

python/python/lancedb/rerankers/cross_encoder.py

@@ -81,15 +81,15 @@ class CrossEncoderReranker(Reranker):
         vector_results: pa.Table,
         fts_results: pa.Table,
     ):
+        if self.score == "all":
+            combined_results = self._merge_and_keep_scores(vector_results, fts_results)
+        else:
             combined_results = self.merge_results(vector_results, fts_results)
         combined_results = self._rerank(combined_results, query)
         # sort the results by _score
         if self.score == "relevance":
             combined_results = self._keep_relevance_score(combined_results)
-        elif self.score == "all":
+
-            raise NotImplementedError(
-                "return_score='all' not implemented for CrossEncoderReranker"
-            )
         combined_results = combined_results.sort_by(
             [("_relevance_score", "descending")]
         )

python/python/lancedb/rerankers/jinaai.py

@@ -97,14 +97,14 @@ class JinaReranker(Reranker):
         vector_results: pa.Table,
         fts_results: pa.Table,
     ):
+        if self.score == "all":
+            combined_results = self._merge_and_keep_scores(vector_results, fts_results)
+        else:
             combined_results = self.merge_results(vector_results, fts_results)
         combined_results = self._rerank(combined_results, query)
         if self.score == "relevance":
             combined_results = self._keep_relevance_score(combined_results)
-        elif self.score == "all":
+
-            raise NotImplementedError(
-                "return_score='all' not implemented for JinaReranker"
-            )
         return combined_results
 
     def rerank_vector(self, query: str, vector_results: pa.Table):

python/python/lancedb/rerankers/openai.py

@@ -88,14 +88,13 @@ class OpenaiReranker(Reranker):
         vector_results: pa.Table,
         fts_results: pa.Table,
     ):
+        if self.score == "all":
+            combined_results = self._merge_and_keep_scores(vector_results, fts_results)
+        else:
             combined_results = self.merge_results(vector_results, fts_results)
         combined_results = self._rerank(combined_results, query)
         if self.score == "relevance":
             combined_results = self._keep_relevance_score(combined_results)
-        elif self.score == "all":
-            raise NotImplementedError(
-                "OpenAI Reranker does not support score='all' yet"
-            )
 
         combined_results = combined_results.sort_by(
             [("_relevance_score", "descending")]

python/python/lancedb/rerankers/voyageai.py

@@ -94,14 +94,14 @@ class VoyageAIReranker(Reranker):
         vector_results: pa.Table,
         fts_results: pa.Table,
     ):
+        if self.score == "all":
+            combined_results = self._merge_and_keep_scores(vector_results, fts_results)
+        else:
             combined_results = self.merge_results(vector_results, fts_results)
         combined_results = self._rerank(combined_results, query)
         if self.score == "relevance":
             combined_results = self._keep_relevance_score(combined_results)
-        elif self.score == "all":
+
-            raise NotImplementedError(
-                "return_score='all' not implemented for voyageai reranker"
-            )
         return combined_results
 
     def rerank_vector(self, query: str, vector_results: pa.Table):

python/python/lancedb/table.py

@@ -102,7 +102,9 @@ if TYPE_CHECKING:
     )
 
 
-def _into_pyarrow_reader(data) -> pa.RecordBatchReader:
+def _into_pyarrow_reader(
+    data, schema: Optional[pa.Schema] = None
+) -> pa.RecordBatchReader:
     from lancedb.dependencies import datasets
 
     if _check_for_hugging_face(data):
@@ -123,6 +125,12 @@ def _into_pyarrow_reader(data) -> pa.RecordBatchReader:
         raise ValueError("Cannot add a single dictionary to a table. Use a list.")
 
     if isinstance(data, list):
+        # Handle empty list case
+        if not data:
+            if schema is None:
+                raise ValueError("Cannot create table from empty list without a schema")
+            return pa.Table.from_pylist(data, schema=schema).to_reader()
+
         # convert to list of dict if data is a bunch of LanceModels
         if isinstance(data[0], LanceModel):
             schema = data[0].__class__.to_arrow_schema()
@@ -165,9 +173,9 @@ def _into_pyarrow_reader(data) -> pa.RecordBatchReader:
     else:
         raise TypeError(
             f"Unknown data type {type(data)}. "
-            "Please check "
+            "Supported types: list of dicts, pandas DataFrame, polars DataFrame, "
-            "https://lancedb.github.io/lancedb/python/python/ "
+            "pyarrow Table/RecordBatch, or Pydantic models. "
-            "to see supported types."
+            "See https://lancedb.github.io/lancedb/guides/tables/ for examples."
         )
 
 
@@ -236,7 +244,7 @@ def _sanitize_data(
     # 1. There might be embedding columns missing that will be added
     #    in the add_embeddings step.
     # 2. If `allow_subschemas` is True, there might be columns missing.
-    reader = _into_pyarrow_reader(data)
+    reader = _into_pyarrow_reader(data, target_schema)
 
     reader = _append_vector_columns(reader, target_schema, metadata=metadata)
 
@@ -827,7 +835,7 @@ class Table(ABC):
         ordering_field_names: Optional[Union[str, List[str]]] = None,
         replace: bool = False,
         writer_heap_size: Optional[int] = 1024 * 1024 * 1024,
-        use_tantivy: bool = True,
+        use_tantivy: bool = False,
         tokenizer_name: Optional[str] = None,
         with_position: bool = False,
         # tokenizer configs:
@@ -838,6 +846,9 @@ class Table(ABC):
         stem: bool = True,
         remove_stop_words: bool = True,
         ascii_folding: bool = True,
+        ngram_min_length: int = 3,
+        ngram_max_length: int = 3,
+        prefix_only: bool = False,
         wait_timeout: Optional[timedelta] = None,
     ):
         """Create a full-text search index on the table.
@@ -864,7 +875,7 @@ class Table(ABC):
             The tokenizer to use for the index. Can be "raw", "default" or the 2 letter
             language code followed by "_stem". So for english it would be "en_stem".
             For available languages see: https://docs.rs/tantivy/latest/tantivy/tokenizer/enum.Language.html
-        use_tantivy: bool, default True
+        use_tantivy: bool, default False
             If True, use the legacy full-text search implementation based on tantivy.
             If False, use the new full-text search implementation based on lance-index.
         with_position: bool, default False
@@ -877,6 +888,7 @@ class Table(ABC):
             - "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.
+            - "ngram": N-Gram tokenizer.
         language : str, default "English"
             The language to use for tokenization.
         max_token_length : int, default 40
@@ -894,6 +906,12 @@ class Table(ABC):
         ascii_folding : bool, default True
             Whether to fold ASCII characters. This converts accented characters to
             their ASCII equivalent. For example, "café" would be converted to "cafe".
+        ngram_min_length: int, default 3
+            The minimum length of an n-gram.
+        ngram_max_length: int, default 3
+            The maximum length of an n-gram.
+        prefix_only: bool, default False
+            Whether to only index the prefix of the token for ngram tokenizer.
         wait_timeout: timedelta, optional
             The timeout to wait if indexing is asynchronous.
         """
@@ -1970,7 +1988,7 @@ class LanceTable(Table):
         ordering_field_names: Optional[Union[str, List[str]]] = None,
         replace: bool = False,
         writer_heap_size: Optional[int] = 1024 * 1024 * 1024,
-        use_tantivy: bool = True,
+        use_tantivy: bool = False,
         tokenizer_name: Optional[str] = None,
         with_position: bool = False,
         # tokenizer configs:
@@ -1981,6 +1999,9 @@ class LanceTable(Table):
         stem: bool = True,
         remove_stop_words: bool = True,
         ascii_folding: bool = True,
+        ngram_min_length: int = 3,
+        ngram_max_length: int = 3,
+        prefix_only: bool = False,
     ):
         if not use_tantivy:
             if not isinstance(field_names, str):
@@ -1996,6 +2017,9 @@ class LanceTable(Table):
                     "stem": stem,
                     "remove_stop_words": remove_stop_words,
                     "ascii_folding": ascii_folding,
+                    "ngram_min_length": ngram_min_length,
+                    "ngram_max_length": ngram_max_length,
+                    "prefix_only": prefix_only,
                 }
             else:
                 tokenizer_configs = self.infer_tokenizer_configs(tokenizer_name)
@@ -2065,6 +2089,9 @@ class LanceTable(Table):
                 "stem": False,
                 "remove_stop_words": False,
                 "ascii_folding": False,
+                "ngram_min_length": 3,
+                "ngram_max_length": 3,
+                "prefix_only": False,
             }
         elif tokenizer_name == "raw":
             return {
@@ -2075,6 +2102,9 @@ class LanceTable(Table):
                 "stem": False,
                 "remove_stop_words": False,
                 "ascii_folding": False,
+                "ngram_min_length": 3,
+                "ngram_max_length": 3,
+                "prefix_only": False,
             }
         elif tokenizer_name == "whitespace":
             return {
@@ -2085,6 +2115,9 @@ class LanceTable(Table):
                 "stem": False,
                 "remove_stop_words": False,
                 "ascii_folding": False,
+                "ngram_min_length": 3,
+                "ngram_max_length": 3,
+                "prefix_only": False,
             }
 
         # or it's with language stemming with pattern like "en_stem"
@@ -2103,6 +2136,9 @@ class LanceTable(Table):
             "stem": True,
             "remove_stop_words": False,
             "ascii_folding": False,
+            "ngram_min_length": 3,
+            "ngram_max_length": 3,
+            "prefix_only": False,
         }
 
     def add(
@@ -3637,8 +3673,10 @@ class AsyncTable:
             )
             if query.distance_type is not None:
                 async_query = async_query.distance_type(query.distance_type)
-            if query.nprobes is not None:
+            if query.minimum_nprobes is not None:
-                async_query = async_query.nprobes(query.nprobes)
+                async_query = async_query.minimum_nprobes(query.minimum_nprobes)
+            if query.maximum_nprobes is not None:
+                async_query = async_query.maximum_nprobes(query.maximum_nprobes)
             if query.refine_factor is not None:
                 async_query = async_query.refine_factor(query.refine_factor)
             if query.vector_column:

python/python/lancedb/types.py

@@ -25,4 +25,4 @@ IndexType = Literal[
 ]
 
 # Tokenizer literals
-BaseTokenizerType = Literal["simple", "raw", "whitespace"]
+BaseTokenizerType = Literal["simple", "raw", "whitespace", "ngram"]

python/python/tests/docs/test_search.py

@@ -6,7 +6,7 @@ import lancedb
 
 # --8<-- [end:import-lancedb]
 # --8<-- [start:import-numpy]
-from lancedb.query import BoostQuery, MatchQuery
+from lancedb.query import BooleanQuery, BoostQuery, MatchQuery, Occur
 import numpy as np
 import pyarrow as pa
 
@@ -191,6 +191,15 @@ def test_fts_fuzzy_query():
         "food",  # 1 insertion
     }
 
+    results = table.search(
+        MatchQuery("foo", "text", fuzziness=1, prefix_length=3)
+    ).to_pandas()
+    assert len(results) == 2
+    assert set(results["text"].to_list()) == {
+        "foo",
+        "food",
+    }
+
 
 @pytest.mark.skipif(
     os.name == "nt", reason="Need to fix https://github.com/lancedb/lance/issues/3905"
@@ -240,6 +249,60 @@ def test_fts_boost_query():
     )
 
 
+@pytest.mark.skipif(
+    os.name == "nt", reason="Need to fix https://github.com/lancedb/lance/issues/3905"
+)
+def test_fts_boolean_query(tmp_path):
+    uri = tmp_path / "boolean-example"
+    db = lancedb.connect(uri)
+    table = db.create_table(
+        "my_table_fts_boolean",
+        data=[
+            {"text": "The cat and dog are playing"},
+            {"text": "The cat is sleeping"},
+            {"text": "The dog is barking"},
+            {"text": "The dog chases the cat"},
+        ],
+        mode="overwrite",
+    )
+    table.create_fts_index("text", use_tantivy=False, replace=True)
+
+    # SHOULD
+    results = table.search(
+        MatchQuery("cat", "text") | MatchQuery("dog", "text")
+    ).to_pandas()
+    assert len(results) == 4
+    assert set(results["text"].to_list()) == {
+        "The cat and dog are playing",
+        "The cat is sleeping",
+        "The dog is barking",
+        "The dog chases the cat",
+    }
+    # MUST
+    results = table.search(
+        MatchQuery("cat", "text") & MatchQuery("dog", "text")
+    ).to_pandas()
+    assert len(results) == 2
+    assert set(results["text"].to_list()) == {
+        "The cat and dog are playing",
+        "The dog chases the cat",
+    }
+
+    # MUST NOT
+    results = table.search(
+        BooleanQuery(
+            [
+                (Occur.MUST, MatchQuery("cat", "text")),
+                (Occur.MUST_NOT, MatchQuery("dog", "text")),
+            ]
+        )
+    ).to_pandas()
+    assert len(results) == 1
+    assert set(results["text"].to_list()) == {
+        "The cat is sleeping",
+    }
+
+
 @pytest.mark.skipif(
     os.name == "nt", reason="Need to fix https://github.com/lancedb/lance/issues/3905"
 )

python/python/tests/test_fts.py

@@ -33,8 +33,11 @@ tantivy = pytest.importorskip("tantivy")
 
 @pytest.fixture
 def table(tmp_path) -> ldb.table.LanceTable:
+    # Use local random state to avoid affecting other tests
+    rng = np.random.RandomState(42)
+    local_random = random.Random(42)
     db = ldb.connect(tmp_path)
-    vectors = [np.random.randn(128) for _ in range(100)]
+    vectors = [rng.randn(128) for _ in range(100)]
 
     text_nouns = ("puppy", "car")
     text2_nouns = ("rabbit", "girl", "monkey")
@@ -44,10 +47,10 @@ def table(tmp_path) -> ldb.table.LanceTable:
     text = [
         " ".join(
             [
-                text_nouns[random.randrange(0, len(text_nouns))],
+                text_nouns[local_random.randrange(0, len(text_nouns))],
-                verbs[random.randrange(0, 5)],
+                verbs[local_random.randrange(0, 5)],
-                adv[random.randrange(0, 5)],
+                adv[local_random.randrange(0, 5)],
-                adj[random.randrange(0, 5)],
+                adj[local_random.randrange(0, 5)],
             ]
         )
         for _ in range(100)
@@ -55,15 +58,15 @@ def table(tmp_path) -> ldb.table.LanceTable:
     text2 = [
         " ".join(
             [
-                text2_nouns[random.randrange(0, len(text2_nouns))],
+                text2_nouns[local_random.randrange(0, len(text2_nouns))],
-                verbs[random.randrange(0, 5)],
+                verbs[local_random.randrange(0, 5)],
-                adv[random.randrange(0, 5)],
+                adv[local_random.randrange(0, 5)],
-                adj[random.randrange(0, 5)],
+                adj[local_random.randrange(0, 5)],
             ]
         )
         for _ in range(100)
     ]
-    count = [random.randint(1, 10000) for _ in range(100)]
+    count = [local_random.randint(1, 10000) for _ in range(100)]
     table = db.create_table(
         "test",
         data=pd.DataFrame(
@@ -82,8 +85,11 @@ def table(tmp_path) -> ldb.table.LanceTable:
 
 @pytest.fixture
 async def async_table(tmp_path) -> ldb.table.AsyncTable:
+    # Use local random state to avoid affecting other tests
+    rng = np.random.RandomState(42)
+    local_random = random.Random(42)
     db = await ldb.connect_async(tmp_path)
-    vectors = [np.random.randn(128) for _ in range(100)]
+    vectors = [rng.randn(128) for _ in range(100)]
 
     text_nouns = ("puppy", "car")
     text2_nouns = ("rabbit", "girl", "monkey")
@@ -93,10 +99,10 @@ async def async_table(tmp_path) -> ldb.table.AsyncTable:
     text = [
         " ".join(
             [
-                text_nouns[random.randrange(0, len(text_nouns))],
+                text_nouns[local_random.randrange(0, len(text_nouns))],
-                verbs[random.randrange(0, 5)],
+                verbs[local_random.randrange(0, 5)],
-                adv[random.randrange(0, 5)],
+                adv[local_random.randrange(0, 5)],
-                adj[random.randrange(0, 5)],
+                adj[local_random.randrange(0, 5)],
             ]
         )
         for _ in range(100)
@@ -104,15 +110,15 @@ async def async_table(tmp_path) -> ldb.table.AsyncTable:
     text2 = [
         " ".join(
             [
-                text2_nouns[random.randrange(0, len(text2_nouns))],
+                text2_nouns[local_random.randrange(0, len(text2_nouns))],
-                verbs[random.randrange(0, 5)],
+                verbs[local_random.randrange(0, 5)],
-                adv[random.randrange(0, 5)],
+                adv[local_random.randrange(0, 5)],
-                adj[random.randrange(0, 5)],
+                adj[local_random.randrange(0, 5)],
             ]
         )
         for _ in range(100)
     ]
-    count = [random.randint(1, 10000) for _ in range(100)]
+    count = [local_random.randint(1, 10000) for _ in range(100)]
     table = await db.create_table(
         "test",
         data=pd.DataFrame(
@@ -215,6 +221,19 @@ def test_search_fts(table, use_tantivy):
         assert len(results) == 5
         assert len(results[0]) == 3  # id, text, _score
 
+        # Test boolean query
+        results = (
+            table.search(MatchQuery("puppy", "text") & MatchQuery("runs", "text"))
+            .select(["id", "text"])
+            .limit(5)
+            .to_list()
+        )
+        assert len(results) == 5
+        assert len(results[0]) == 3  # id, text, _score
+        for r in results:
+            assert "puppy" in r["text"]
+            assert "runs" in r["text"]
+
 
 @pytest.mark.asyncio
 async def test_fts_select_async(async_table):
@@ -656,3 +675,46 @@ def test_fts_on_list(mem_db: DBConnection):
 
     res = table.search(PhraseQuery("lance database", "text")).limit(5).to_list()
     assert len(res) == 2
+
+
+def test_fts_ngram(mem_db: DBConnection):
+    data = pa.table({"text": ["hello world", "lance database", "lance is cool"]})
+    table = mem_db.create_table("test", data=data)
+    table.create_fts_index("text", use_tantivy=False, base_tokenizer="ngram")
+
+    results = table.search("lan", query_type="fts").limit(10).to_list()
+    assert len(results) == 2
+    assert set(r["text"] for r in results) == {"lance database", "lance is cool"}
+
+    results = (
+        table.search("nce", query_type="fts").limit(10).to_list()
+    )  # spellchecker:disable-line
+    assert len(results) == 2
+    assert set(r["text"] for r in results) == {"lance database", "lance is cool"}
+
+    # the default min_ngram_length is 3, so "la" should not match
+    results = table.search("la", query_type="fts").limit(10).to_list()
+    assert len(results) == 0
+
+    # test setting min_ngram_length and prefix_only
+    table.create_fts_index(
+        "text",
+        use_tantivy=False,
+        base_tokenizer="ngram",
+        replace=True,
+        ngram_min_length=2,
+        prefix_only=True,
+    )
+
+    results = table.search("lan", query_type="fts").limit(10).to_list()
+    assert len(results) == 2
+    assert set(r["text"] for r in results) == {"lance database", "lance is cool"}
+
+    results = (
+        table.search("nce", query_type="fts").limit(10).to_list()
+    )  # spellchecker:disable-line
+    assert len(results) == 0
+
+    results = table.search("la", query_type="fts").limit(10).to_list()
+    assert len(results) == 2
+    assert set(r["text"] for r in results) == {"lance database", "lance is cool"}

python/python/tests/test_hybrid_query.py

@@ -166,7 +166,7 @@ async def test_explain_plan(table: AsyncTable):
     assert "Vector Search Plan" in plan
     assert "KNNVectorDistance" in plan
     assert "FTS Search Plan" in plan
-    assert "LanceScan" in plan
+    assert "LanceRead" in plan
 
 
 @pytest.mark.asyncio

python/python/tests/test_query.py

@@ -25,6 +25,8 @@ from lancedb.query import (
     AsyncQueryBase,
     AsyncVectorQuery,
     LanceVectorQueryBuilder,
+    MatchQuery,
+    PhraseQuery,
     Query,
     FullTextSearchQuery,
 )
@@ -270,7 +272,9 @@ async def test_distance_range_with_new_rows_async():
     # append more rows so that execution plan would be mixed with ANN & Flat KNN
     new_data = pa.table(
         {
-            "vector": pa.FixedShapeTensorArray.from_numpy_ndarray(np.random.rand(4, 2)),
+            "vector": pa.FixedShapeTensorArray.from_numpy_ndarray(
+                np.random.rand(4, 2) + 1
+            ),
         }
     )
     await table.add(new_data)
@@ -437,6 +441,33 @@ def test_query_builder_with_filter(table):
     assert all(np.array(rs[0]["vector"]) == [3, 4])
 
 
+def test_invalid_nprobes_sync(table):
+    with pytest.raises(ValueError, match="minimum_nprobes must be greater than 0"):
+        LanceVectorQueryBuilder(table, [0, 0], "vector").minimum_nprobes(0).to_list()
+    with pytest.raises(
+        ValueError, match="maximum_nprobes must be greater than minimum_nprobes"
+    ):
+        LanceVectorQueryBuilder(table, [0, 0], "vector").maximum_nprobes(5).to_list()
+    with pytest.raises(
+        ValueError, match="minimum_nprobes must be less or equal to maximum_nprobes"
+    ):
+        LanceVectorQueryBuilder(table, [0, 0], "vector").minimum_nprobes(100).to_list()
+
+
+@pytest.mark.asyncio
+async def test_invalid_nprobes_async(table_async: AsyncTable):
+    with pytest.raises(ValueError, match="minimum_nprobes must be greater than 0"):
+        await table_async.vector_search([0, 0]).minimum_nprobes(0).to_list()
+    with pytest.raises(
+        ValueError, match="maximum_nprobes must be greater than minimum_nprobes"
+    ):
+        await table_async.vector_search([0, 0]).maximum_nprobes(5).to_list()
+    with pytest.raises(
+        ValueError, match="minimum_nprobes must be less or equal to maximum_nprobes"
+    ):
+        await table_async.vector_search([0, 0]).minimum_nprobes(100).to_list()
+
+
 def test_query_builder_with_prefilter(table):
     df = (
         LanceVectorQueryBuilder(table, [0, 0], "vector")
@@ -583,6 +614,21 @@ async def test_query_async(table_async: AsyncTable):
         table_async.query().nearest_to(pa.array([1, 2])).nprobes(10),
         expected_num_rows=2,
     )
+    await check_query(
+        table_async.query().nearest_to(pa.array([1, 2])).minimum_nprobes(10),
+        expected_num_rows=2,
+    )
+    await check_query(
+        table_async.query().nearest_to(pa.array([1, 2])).maximum_nprobes(30),
+        expected_num_rows=2,
+    )
+    await check_query(
+        table_async.query()
+        .nearest_to(pa.array([1, 2]))
+        .minimum_nprobes(10)
+        .maximum_nprobes(20),
+        expected_num_rows=2,
+    )
     await check_query(
         table_async.query().nearest_to(pa.array([1, 2])).bypass_vector_index(),
         expected_num_rows=2,
@@ -731,6 +777,83 @@ async def test_explain_plan_async(table_async: AsyncTable):
     assert "KNN" in plan
 
 
+@pytest.mark.asyncio
+async def test_explain_plan_fts(table_async: AsyncTable):
+    """Test explain plan for FTS queries"""
+    # Create FTS index
+    from lancedb.index import FTS
+
+    await table_async.create_index("text", config=FTS())
+
+    # Test pure FTS query
+    query = await table_async.search("dog", query_type="fts", fts_columns="text")
+    plan = await query.explain_plan()
+    # Should show FTS details (issue #2465 is now fixed)
+    assert "MatchQuery: query=dog" in plan
+    assert "GlobalLimitExec" in plan  # Default limit
+
+    # Test FTS query with limit
+    query_with_limit = await table_async.search(
+        "dog", query_type="fts", fts_columns="text"
+    )
+    plan_with_limit = await query_with_limit.limit(1).explain_plan()
+    assert "MatchQuery: query=dog" in plan_with_limit
+    assert "GlobalLimitExec: skip=0, fetch=1" in plan_with_limit
+
+    # Test FTS query with offset and limit
+    query_with_offset = await table_async.search(
+        "dog", query_type="fts", fts_columns="text"
+    )
+    plan_with_offset = await query_with_offset.offset(1).limit(1).explain_plan()
+    assert "MatchQuery: query=dog" in plan_with_offset
+    assert "GlobalLimitExec: skip=1, fetch=1" in plan_with_offset
+
+
+@pytest.mark.asyncio
+async def test_explain_plan_vector_with_limit_offset(table_async: AsyncTable):
+    """Test explain plan for vector queries with limit and offset"""
+    # Test vector query with limit
+    plan_with_limit = await (
+        table_async.query().nearest_to(pa.array([1, 2])).limit(1).explain_plan()
+    )
+    assert "KNN" in plan_with_limit
+    assert "GlobalLimitExec: skip=0, fetch=1" in plan_with_limit
+
+    # Test vector query with offset and limit
+    plan_with_offset = await (
+        table_async.query()
+        .nearest_to(pa.array([1, 2]))
+        .offset(1)
+        .limit(1)
+        .explain_plan()
+    )
+    assert "KNN" in plan_with_offset
+    assert "GlobalLimitExec: skip=1, fetch=1" in plan_with_offset
+
+
+@pytest.mark.asyncio
+async def test_explain_plan_with_filters(table_async: AsyncTable):
+    """Test explain plan for queries with filters"""
+    # Test vector query with filter
+    plan_with_filter = await (
+        table_async.query().nearest_to(pa.array([1, 2])).where("id = 1").explain_plan()
+    )
+    assert "KNN" in plan_with_filter
+    assert "LanceRead" in plan_with_filter
+
+    # Test FTS query with filter
+    from lancedb.index import FTS
+
+    await table_async.create_index("text", config=FTS())
+    query_fts_filter = await table_async.search(
+        "dog", query_type="fts", fts_columns="text"
+    )
+    plan_fts_filter = await query_fts_filter.where("id = 1").explain_plan()
+    assert "MatchQuery: query=dog" in plan_fts_filter
+    assert "LanceRead" in plan_fts_filter
+    assert "full_filter=id = Int64(1)" in plan_fts_filter  # Should show filter details
+
+
 @pytest.mark.asyncio
 async def test_query_camelcase_async(tmp_path):
     db = await lancedb.connect_async(tmp_path)
@@ -909,7 +1032,39 @@ def test_query_serialization_sync(table: lancedb.table.Table):
 
     q = table.search([5.0, 6.0]).nprobes(10).refine_factor(5).to_query_object()
     check_set_props(
-        q, vector_column="vector", vector=[5.0, 6.0], nprobes=10, refine_factor=5
+        q,
+        vector_column="vector",
+        vector=[5.0, 6.0],
+        minimum_nprobes=10,
+        maximum_nprobes=10,
+        refine_factor=5,
+    )
+
+    q = table.search([5.0, 6.0]).minimum_nprobes(10).to_query_object()
+    check_set_props(
+        q,
+        vector_column="vector",
+        vector=[5.0, 6.0],
+        minimum_nprobes=10,
+        maximum_nprobes=None,
+    )
+
+    q = table.search([5.0, 6.0]).nprobes(50).to_query_object()
+    check_set_props(
+        q,
+        vector_column="vector",
+        vector=[5.0, 6.0],
+        minimum_nprobes=50,
+        maximum_nprobes=50,
+    )
+
+    q = table.search([5.0, 6.0]).maximum_nprobes(10).to_query_object()
+    check_set_props(
+        q,
+        vector_column="vector",
+        vector=[5.0, 6.0],
+        maximum_nprobes=10,
+        minimum_nprobes=None,
     )
 
     q = table.search([5.0, 6.0]).distance_range(0.0, 1.0).to_query_object()
@@ -961,7 +1116,8 @@ async def test_query_serialization_async(table_async: AsyncTable):
         limit=10,
         vector=sample_vector,
         postfilter=False,
-        nprobes=20,
+        minimum_nprobes=20,
+        maximum_nprobes=20,
         with_row_id=False,
         bypass_vector_index=False,
     )
@@ -971,7 +1127,20 @@ async def test_query_serialization_async(table_async: AsyncTable):
         q,
         vector=sample_vector,
         postfilter=False,
-        nprobes=20,
+        minimum_nprobes=20,
+        maximum_nprobes=20,
+        with_row_id=False,
+        bypass_vector_index=False,
+        limit=10,
+    )
+
+    q = (await table_async.search([5.0, 6.0])).nprobes(50).to_query_object()
+    check_set_props(
+        q,
+        vector=sample_vector,
+        postfilter=False,
+        minimum_nprobes=50,
+        maximum_nprobes=50,
         with_row_id=False,
         bypass_vector_index=False,
         limit=10,
@@ -990,7 +1159,8 @@ async def test_query_serialization_async(table_async: AsyncTable):
         filter="id = 1",
         postfilter=True,
         vector=sample_vector,
-        nprobes=20,
+        minimum_nprobes=20,
+        maximum_nprobes=20,
         with_row_id=False,
         bypass_vector_index=False,
     )
@@ -1004,7 +1174,8 @@ async def test_query_serialization_async(table_async: AsyncTable):
     check_set_props(
         q,
         vector=sample_vector,
-        nprobes=10,
+        minimum_nprobes=10,
+        maximum_nprobes=10,
         refine_factor=5,
         postfilter=False,
         with_row_id=False,
@@ -1012,6 +1183,18 @@ async def test_query_serialization_async(table_async: AsyncTable):
         limit=10,
     )
 
+    q = (await table_async.search([5.0, 6.0])).minimum_nprobes(5).to_query_object()
+    check_set_props(
+        q,
+        vector=sample_vector,
+        minimum_nprobes=5,
+        maximum_nprobes=20,
+        postfilter=False,
+        with_row_id=False,
+        bypass_vector_index=False,
+        limit=10,
+    )
+
     q = (
         (await table_async.search([5.0, 6.0]))
         .distance_range(0.0, 1.0)
@@ -1023,7 +1206,8 @@ async def test_query_serialization_async(table_async: AsyncTable):
         lower_bound=0.0,
         upper_bound=1.0,
         postfilter=False,
-        nprobes=20,
+        minimum_nprobes=20,
+        maximum_nprobes=20,
         with_row_id=False,
         bypass_vector_index=False,
         limit=10,
@@ -1035,7 +1219,8 @@ async def test_query_serialization_async(table_async: AsyncTable):
         distance_type="cosine",
         vector=sample_vector,
         postfilter=False,
-        nprobes=20,
+        minimum_nprobes=20,
+        maximum_nprobes=20,
         with_row_id=False,
         bypass_vector_index=False,
         limit=10,
@@ -1047,7 +1232,8 @@ async def test_query_serialization_async(table_async: AsyncTable):
         ef=7,
         vector=sample_vector,
         postfilter=False,
-        nprobes=20,
+        minimum_nprobes=20,
+        maximum_nprobes=20,
         with_row_id=False,
         bypass_vector_index=False,
         limit=10,
@@ -1059,24 +1245,34 @@ async def test_query_serialization_async(table_async: AsyncTable):
         bypass_vector_index=True,
         vector=sample_vector,
         postfilter=False,
-        nprobes=20,
+        minimum_nprobes=20,
+        maximum_nprobes=20,
         with_row_id=False,
         limit=10,
     )
 
     # FTS queries
-    q = (await table_async.search("foo")).limit(10).to_query_object()
+    match_query = MatchQuery("foo", "text")
+    q = (await table_async.search(match_query)).limit(10).to_query_object()
     check_set_props(
         q,
         limit=10,
-        full_text_query=FullTextSearchQuery(columns=[], query="foo"),
+        full_text_query=FullTextSearchQuery(columns=None, query=match_query),
         with_row_id=False,
     )
 
-    q = (await table_async.search("foo", query_type="fts")).to_query_object()
+    q = (await table_async.search(match_query)).to_query_object()
     check_set_props(
         q,
-        full_text_query=FullTextSearchQuery(columns=[], query="foo"),
+        full_text_query=FullTextSearchQuery(columns=None, query=match_query),
+        with_row_id=False,
+    )
+
+    phrase_query = PhraseQuery("foo", "text", slop=1)
+    q = (await table_async.search(phrase_query)).to_query_object()
+    check_set_props(
+        q,
+        full_text_query=FullTextSearchQuery(columns=None, query=phrase_query),
         with_row_id=False,
     )
 
@@ -1143,3 +1339,20 @@ async def test_query_timeout_async(tmp_path):
             .nearest_to([0.0, 0.0])
             .to_list(timeout=timedelta(0))
         )
+
+
+def test_search_empty_table(mem_db):
+    """Test searching on empty table should not crash
+
+    Regression test for issue #303:
+    https://github.com/lancedb/lancedb/issues/303
+    Searching on empty table produces scary error message
+    """
+    schema = pa.schema(
+        [pa.field("vector", pa.list_(pa.float32(), 2)), pa.field("id", pa.int64())]
+    )
+    table = mem_db.create_table("test_empty_search", schema=schema)
+
+    # Search on empty table should return empty results, not crash
+    results = table.search([1.0, 2.0]).limit(5).to_list()
+    assert results == []

python/python/tests/test_remote_db.py

@@ -210,6 +210,25 @@ async def test_retry_error():
         assert cause.status_code == 429
 
 
+def test_table_unimplemented_functions():
+    def handler(request):
+        if 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"{}")
+        else:
+            request.send_response(404)
+            request.end_headers()
+
+    with mock_lancedb_connection(handler) as db:
+        table = db.create_table("test", [{"id": 1}])
+        with pytest.raises(NotImplementedError):
+            table.to_arrow()
+        with pytest.raises(NotImplementedError):
+            table.to_pandas()
+
+
 def test_table_add_in_threadpool():
     def handler(request):
         if request.path == "/v1/table/test/insert/":
@@ -496,6 +515,8 @@ def test_query_sync_minimal():
             "ef": None,
             "vector": [1.0, 2.0, 3.0],
             "nprobes": 20,
+            "minimum_nprobes": 20,
+            "maximum_nprobes": 20,
             "version": None,
         }
 
@@ -536,6 +557,8 @@ def test_query_sync_maximal():
             "refine_factor": 10,
             "vector": [1.0, 2.0, 3.0],
             "nprobes": 5,
+            "minimum_nprobes": 5,
+            "maximum_nprobes": 5,
             "lower_bound": None,
             "upper_bound": None,
             "ef": None,
@@ -564,6 +587,66 @@ def test_query_sync_maximal():
         )
 
 
+def test_query_sync_nprobes():
+    def handler(body):
+        assert body == {
+            "distance_type": "l2",
+            "k": 10,
+            "prefilter": True,
+            "fast_search": True,
+            "vector_column": "vector2",
+            "refine_factor": None,
+            "lower_bound": None,
+            "upper_bound": None,
+            "ef": None,
+            "vector": [1.0, 2.0, 3.0],
+            "nprobes": 5,
+            "minimum_nprobes": 5,
+            "maximum_nprobes": 15,
+            "version": None,
+        }
+
+        return pa.table({"id": [1, 2, 3], "name": ["a", "b", "c"]})
+
+    with query_test_table(handler) as table:
+        (
+            table.search([1, 2, 3], vector_column_name="vector2", fast_search=True)
+            .minimum_nprobes(5)
+            .maximum_nprobes(15)
+            .to_list()
+        )
+
+
+def test_query_sync_no_max_nprobes():
+    def handler(body):
+        assert body == {
+            "distance_type": "l2",
+            "k": 10,
+            "prefilter": True,
+            "fast_search": True,
+            "vector_column": "vector2",
+            "refine_factor": None,
+            "lower_bound": None,
+            "upper_bound": None,
+            "ef": None,
+            "vector": [1.0, 2.0, 3.0],
+            "nprobes": 5,
+            "minimum_nprobes": 5,
+            "maximum_nprobes": 0,
+            "version": None,
+        }
+
+        return pa.table({"id": [1, 2, 3], "name": ["a", "b", "c"]})
+
+    with query_test_table(handler) as table:
+        (
+            table.search([1, 2, 3], vector_column_name="vector2", fast_search=True)
+            .minimum_nprobes(5)
+            .maximum_nprobes(0)
+            .to_list()
+        )
+
+
 @pytest.mark.parametrize("server_version", [Version("0.1.0"), Version("0.2.0")])
 def test_query_sync_batch_queries(server_version):
     def handler(body):
@@ -666,6 +749,8 @@ def test_query_sync_hybrid():
                 "refine_factor": None,
                 "vector": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
                 "nprobes": 20,
+                "minimum_nprobes": 20,
+                "maximum_nprobes": 20,
                 "lower_bound": None,
                 "upper_bound": None,
                 "ef": None,

python/python/tests/test_rerankers.py

@@ -499,3 +499,19 @@ def test_empty_result_reranker():
             .rerank(reranker)
             .to_arrow()
         )
+
+
+@pytest.mark.parametrize("use_tantivy", [True, False])
+def test_cross_encoder_reranker_return_all(tmp_path, use_tantivy):
+    pytest.importorskip("sentence_transformers")
+    reranker = CrossEncoderReranker(return_score="all")
+    table, schema = get_test_table(tmp_path, use_tantivy)
+    query = "single player experience"
+    result = (
+        table.search(query, query_type="hybrid", vector_column_name="vector")
+        .rerank(reranker=reranker)
+        .to_arrow()
+    )
+    assert "_relevance_score" in result.column_names
+    assert "_score" in result.column_names
+    assert "_distance" in result.column_names

python/python/tests/test_s3.py

@@ -245,7 +245,7 @@ def test_s3_dynamodb_sync(s3_bucket: str, commit_table: str, monkeypatch):
         NotImplementedError,
         match="Full-text search is only supported on the local filesystem",
     ):
-        table.create_fts_index("x")
+        table.create_fts_index("x", use_tantivy=True)
 
     # make sure list tables still works
     assert db.table_names() == ["test_ddb_sync"]

python/python/tests/test_session.py

@@ -0,0 +1,38 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+import lancedb
+
+
+def test_session_cache_configuration(tmp_path):
+    """Test Session cache configuration and basic functionality."""
+    # Create session with small cache limits for testing
+    index_cache_size = 1024 * 1024  # 1MB
+    metadata_cache_size = 512 * 1024  # 512KB
+
+    session = lancedb.Session(
+        index_cache_size_bytes=index_cache_size,
+        metadata_cache_size_bytes=metadata_cache_size,
+    )
+
+    # Record initial cache state
+    initial_cache_size = session.size_bytes
+    initial_cache_items = session.approx_num_items
+
+    # Test session works with database connection
+    db = lancedb.connect(tmp_path, session=session)
+
+    # Create and use a table to exercise the session
+    data = [{"id": i, "text": f"item {i}"} for i in range(100)]
+    table = db.create_table("test", data)
+    results = list(table.to_arrow().to_pylist())
+
+    assert len(results) == 100
+
+    # Verify cache usage increased after operations
+    final_cache_size = session.size_bytes
+    final_cache_items = session.approx_num_items
+
+    assert final_cache_size > initial_cache_size  # Cache should have grown
+    assert final_cache_items >= initial_cache_items  # Items should not decrease
+    assert initial_cache_size < index_cache_size + metadata_cache_size

python/python/tests/test_table.py

@@ -1804,3 +1804,45 @@ def test_stats(mem_db: DBConnection):
             },
         },
     }
+
+
+def test_create_table_empty_list_with_schema(mem_db: DBConnection):
+    """Test creating table with empty list data and schema
+
+    Regression test for IndexError: list index out of range
+    when calling create_table(name, data=[], schema=schema)
+    """
+    schema = pa.schema(
+        [pa.field("vector", pa.list_(pa.float32(), 2)), pa.field("id", pa.int64())]
+    )
+    table = mem_db.create_table("test_empty_list", data=[], schema=schema)
+    assert table.count_rows() == 0
+    assert table.schema == schema
+
+
+def test_create_table_empty_list_no_schema_error(mem_db: DBConnection):
+    """Test that creating table with empty list and no schema raises error"""
+    with pytest.raises(
+        ValueError, match="Cannot create table from empty list without a schema"
+    ):
+        mem_db.create_table("test_empty_no_schema", data=[])
+
+
+def test_add_table_with_empty_embeddings(tmp_path):
+    """Test exact scenario from issue #1968
+
+    Regression test for issue #1968:
+    https://github.com/lancedb/lancedb/issues/1968
+    """
+    db = lancedb.connect(tmp_path)
+
+    class MySchema(LanceModel):
+        text: str
+        embedding: Vector(16)
+
+    table = db.create_table("test", schema=MySchema)
+    table.add(
+        [{"text": "bar", "embedding": [0.1] * 16}],
+        on_bad_vectors="drop",
+    )
+    assert table.count_rows() == 1