Merge remote-tracking branch 'origin/main' into gatekeeper/fix-2468-1

fix: preserve binary batch vector queries
chore: update lance dependency to v12.0.0-beta.2 (#4056 )
2026-08-26 07:58:31 +00:00 · 2026-08-26 03:01:31 +00:00 · 2026-08-26 03:01:14 +00:00 · 2026-08-25 21:56:26 -05:00 · 2026-08-26 02:43:02 +00:00 · 2026-08-26 10:08:48 +08:00
30 changed files with 2183 additions and 235 deletions
@@ -3455,8 +3455,8 @@ checksum = "42703706b716c37f96a77aea830392ad231f44c9e9a67872fa5548707e11b11c"

 [[package]]
 name = "fsst"
-version = "11.0.0-beta.22"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.22#ea3cb4d799c468232735e9bcb43959487aca5c20"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
 "arrow-array",
 "rand 0.9.5",
@@ -4815,8 +4815,8 @@ checksum = "e037a2e1d8d5fdbd49b16a4ea09d5d6401c1f29eca5ff29d03d3824dba16256a"

 [[package]]
 name = "lance"
-version = "11.0.0-beta.22"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.22#ea3cb4d799c468232735e9bcb43959487aca5c20"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
 "arc-swap",
 "arrow",
@@ -4888,8 +4888,8 @@ dependencies = [

 [[package]]
 name = "lance-arrow"
-version = "11.0.0-beta.22"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.22#ea3cb4d799c468232735e9bcb43959487aca5c20"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
 "arrow-array",
 "arrow-buffer",
@@ -4911,7 +4911,7 @@ dependencies = [
 [[package]]
 name = "lance-arrow-scalar"
 version = "58.0.0"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.22#ea3cb4d799c468232735e9bcb43959487aca5c20"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
 "arrow-array",
 "arrow-buffer",
@@ -4925,7 +4925,7 @@ dependencies = [
 [[package]]
 name = "lance-arrow-stats"
 version = "58.0.0"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.22#ea3cb4d799c468232735e9bcb43959487aca5c20"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
 "arrow-array",
 "arrow-schema",
@@ -4934,8 +4934,8 @@ dependencies = [

 [[package]]
 name = "lance-bitpacking"
-version = "11.0.0-beta.22"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.22#ea3cb4d799c468232735e9bcb43959487aca5c20"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
 "arrayref",
 "crunchy",
@@ -4945,8 +4945,8 @@ dependencies = [

 [[package]]
 name = "lance-core"
-version = "11.0.0-beta.22"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.22#ea3cb4d799c468232735e9bcb43959487aca5c20"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
 "arrow-array",
 "arrow-buffer",
@@ -4983,8 +4983,8 @@ dependencies = [

 [[package]]
 name = "lance-datafusion"
-version = "11.0.0-beta.22"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.22#ea3cb4d799c468232735e9bcb43959487aca5c20"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
 "arrow",
 "arrow-array",
@@ -5013,8 +5013,8 @@ dependencies = [

 [[package]]
 name = "lance-datagen"
-version = "11.0.0-beta.22"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.22#ea3cb4d799c468232735e9bcb43959487aca5c20"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
 "arrow",
 "arrow-array",
@@ -5031,8 +5031,8 @@ dependencies = [

 [[package]]
 name = "lance-derive"
-version = "11.0.0-beta.22"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.22#ea3cb4d799c468232735e9bcb43959487aca5c20"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
 "proc-macro2",
 "quote",
@@ -5041,8 +5041,8 @@ dependencies = [

 [[package]]
 name = "lance-encoding"
-version = "11.0.0-beta.22"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.22#ea3cb4d799c468232735e9bcb43959487aca5c20"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
 "arrow-arith",
 "arrow-array",
@@ -5075,8 +5075,8 @@ dependencies = [

 [[package]]
 name = "lance-file"
-version = "11.0.0-beta.22"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.22#ea3cb4d799c468232735e9bcb43959487aca5c20"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
 "arrow-arith",
 "arrow-array",
@@ -5107,8 +5107,8 @@ dependencies = [

 [[package]]
 name = "lance-index"
-version = "11.0.0-beta.22"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.22#ea3cb4d799c468232735e9bcb43959487aca5c20"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
 "arc-swap",
 "arrow",
@@ -5172,8 +5172,8 @@ dependencies = [

 [[package]]
 name = "lance-index-core"
-version = "11.0.0-beta.22"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.22#ea3cb4d799c468232735e9bcb43959487aca5c20"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
 "arrow-array",
 "arrow-schema",
@@ -5195,8 +5195,8 @@ dependencies = [

 [[package]]
 name = "lance-io"
-version = "11.0.0-beta.22"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.22#ea3cb4d799c468232735e9bcb43959487aca5c20"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
 "arrow",
 "arrow-array",
@@ -5236,8 +5236,8 @@ dependencies = [

 [[package]]
 name = "lance-linalg"
-version = "11.0.0-beta.22"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.22#ea3cb4d799c468232735e9bcb43959487aca5c20"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
 "arrow-array",
 "arrow-schema",
@@ -5251,8 +5251,8 @@ dependencies = [

 [[package]]
 name = "lance-namespace"
-version = "11.0.0-beta.22"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.22#ea3cb4d799c468232735e9bcb43959487aca5c20"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
 "arrow",
 "async-trait",
@@ -5264,8 +5264,8 @@ dependencies = [

 [[package]]
 name = "lance-namespace-impls"
-version = "11.0.0-beta.22"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.22#ea3cb4d799c468232735e9bcb43959487aca5c20"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
 "arrow",
 "arrow-ipc",
@@ -5318,8 +5318,8 @@ dependencies = [

 [[package]]
 name = "lance-select"
-version = "11.0.0-beta.22"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.22#ea3cb4d799c468232735e9bcb43959487aca5c20"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
 "arrow-array",
 "arrow-buffer",
@@ -5333,8 +5333,8 @@ dependencies = [

 [[package]]
 name = "lance-table"
-version = "11.0.0-beta.22"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.22#ea3cb4d799c468232735e9bcb43959487aca5c20"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
 "arrow",
 "arrow-array",
@@ -5374,8 +5374,8 @@ dependencies = [

 [[package]]
 name = "lance-testing"
-version = "11.0.0-beta.22"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.22#ea3cb4d799c468232735e9bcb43959487aca5c20"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
 "arrow-array",
 "arrow-schema",
@@ -5388,8 +5388,8 @@ dependencies = [

 [[package]]
 name = "lance-tokenizer"
-version = "11.0.0-beta.22"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.22#ea3cb4d799c468232735e9bcb43959487aca5c20"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
 "frostem",
 "icu_segmenter",
@@ -13,20 +13,20 @@ categories = ["database-implementations"]
 rust-version = "1.91.0"

 [workspace.dependencies]
-lance = { "version" = "=11.0.0-beta.22", default-features = false, "tag" = "v11.0.0-beta.22", "git" = "https://github.com/lance-format/lance.git" }
-lance-core = { "version" = "=11.0.0-beta.22", "tag" = "v11.0.0-beta.22", "git" = "https://github.com/lance-format/lance.git" }
-lance-datagen = { "version" = "=11.0.0-beta.22", "tag" = "v11.0.0-beta.22", "git" = "https://github.com/lance-format/lance.git" }
-lance-file = { "version" = "=11.0.0-beta.22", "tag" = "v11.0.0-beta.22", "git" = "https://github.com/lance-format/lance.git" }
-lance-io = { "version" = "=11.0.0-beta.22", default-features = false, "tag" = "v11.0.0-beta.22", "git" = "https://github.com/lance-format/lance.git" }
-lance-index = { "version" = "=11.0.0-beta.22", "tag" = "v11.0.0-beta.22", "git" = "https://github.com/lance-format/lance.git" }
-lance-linalg = { "version" = "=11.0.0-beta.22", "tag" = "v11.0.0-beta.22", "git" = "https://github.com/lance-format/lance.git" }
-lance-namespace = { "version" = "=11.0.0-beta.22", "tag" = "v11.0.0-beta.22", "git" = "https://github.com/lance-format/lance.git" }
-lance-namespace-impls = { "version" = "=11.0.0-beta.22", default-features = false, "tag" = "v11.0.0-beta.22", "git" = "https://github.com/lance-format/lance.git" }
-lance-table = { "version" = "=11.0.0-beta.22", "tag" = "v11.0.0-beta.22", "git" = "https://github.com/lance-format/lance.git" }
-lance-testing = { "version" = "=11.0.0-beta.22", "tag" = "v11.0.0-beta.22", "git" = "https://github.com/lance-format/lance.git" }
-lance-datafusion = { "version" = "=11.0.0-beta.22", "tag" = "v11.0.0-beta.22", "git" = "https://github.com/lance-format/lance.git" }
-lance-encoding = { "version" = "=11.0.0-beta.22", "tag" = "v11.0.0-beta.22", "git" = "https://github.com/lance-format/lance.git" }
-lance-arrow = { "version" = "=11.0.0-beta.22", "tag" = "v11.0.0-beta.22", "git" = "https://github.com/lance-format/lance.git" }
+lance = { "version" = "=12.0.0-beta.2", default-features = false, "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-core = { "version" = "=12.0.0-beta.2", "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-datagen = { "version" = "=12.0.0-beta.2", "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-file = { "version" = "=12.0.0-beta.2", "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-io = { "version" = "=12.0.0-beta.2", default-features = false, "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-index = { "version" = "=12.0.0-beta.2", "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-linalg = { "version" = "=12.0.0-beta.2", "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-namespace = { "version" = "=12.0.0-beta.2", "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-namespace-impls = { "version" = "=12.0.0-beta.2", default-features = false, "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-table = { "version" = "=12.0.0-beta.2", "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-testing = { "version" = "=12.0.0-beta.2", "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-datafusion = { "version" = "=12.0.0-beta.2", "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-encoding = { "version" = "=12.0.0-beta.2", "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-arrow = { "version" = "=12.0.0-beta.2", "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
 lancedb = { path = "rust/lancedb", default-features = false }
 ahash = "0.8"
 # Note that this one does not include pyarrow
@@ -0,0 +1,518 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / AutoQuery
+
+# Class: AutoQuery
+
+A builder for automatic string searches.
+
+Automatic search determines whether to use full-text or vector search from
+the table revision selected for each execution. This builder exposes the
+common operations supported by both query families.
+
+## Extends
+
+- `StandardQueryBase`&lt;`NativeQuery` \| `NativeVectorQuery`&gt;
+
+## Properties
+
+### inner
+
+```ts
+protected inner: Query | VectorQuery | Promise<Query | VectorQuery>;
+```
+
+#### Inherited from
+
+`StandardQueryBase.inner`
+
+## Methods
+
+### analyzePlan()
+
+```ts
+analyzePlan(distributedMetrics?): Promise<string>
+```
+
+Executes the query and returns the physical query plan annotated with runtime metrics.
+
+This is useful for debugging and performance analysis, as it shows how the query was executed
+and includes metrics such as elapsed time, rows processed, and I/O statistics.
+
+#### Parameters
+
+* **distributedMetrics?**: [`AnalyzePlanDistributedMetrics`](../type-aliases/AnalyzePlanDistributedMetrics.md)
+    How distributed worker metrics are displayed for remote query plans.
+    Defaults to `"aggregate"`.
+
+#### Returns
+
+`Promise`&lt;`string`&gt;
+
+A query execution plan with runtime metrics for each step.
+
+#### Example
+
+```ts
+import * as lancedb from "@lancedb/lancedb"
+
+const db = await lancedb.connect("./.lancedb");
+const table = await db.createTable("my_table", [
+  { vector: [1.1, 0.9], id: "1" },
+]);
+
+const plan = await table.query().nearestTo([0.5, 0.2]).analyzePlan();
+
+Example output (with runtime metrics inlined):
+AnalyzeExec verbose=true, metrics=[]
+ ProjectionExec: expr=[id@3 as id, vector@0 as vector, _distance@2 as _distance], metrics=[output_rows=1, elapsed_compute=3.292µs]
+  Take: columns="vector, _rowid, _distance, (id)", metrics=[output_rows=1, elapsed_compute=66.001µs, batches_processed=1, bytes_read=8, iops=1, requests=1]
+   CoalesceBatchesExec: target_batch_size=1024, metrics=[output_rows=1, elapsed_compute=3.333µs]
+    GlobalLimitExec: skip=0, fetch=10, metrics=[output_rows=1, elapsed_compute=167ns]
+     FilterExec: _distance@2 IS NOT NULL, metrics=[output_rows=1, elapsed_compute=8.542µs]
+      SortExec: TopK(fetch=10), expr=[_distance@2 ASC NULLS LAST], metrics=[output_rows=1, elapsed_compute=63.25µs, row_replacements=1]
+       KNNVectorDistance: metric=l2, metrics=[output_rows=1, elapsed_compute=114.333µs, output_batches=1]
+        LanceScan: uri=/path/to/data, projection=[vector], row_id=true, row_addr=false, ordered=false, metrics=[output_rows=1, elapsed_compute=103.626µs, bytes_read=549, iops=2, requests=2]
+```
+
+#### Inherited from
+
+`StandardQueryBase.analyzePlan`
+
+***
+
+### execute()
+
+```ts
+protected execute(options?): AsyncGenerator<RecordBatch<any>, void, unknown>
+```
+
+Execute the query and return the results as an
+
+#### Parameters
+
+* **options?**: `Partial`&lt;[`QueryExecutionOptions`](../interfaces/QueryExecutionOptions.md)&gt;
+
+#### Returns
+
+`AsyncGenerator`&lt;`RecordBatch`&lt;`any`&gt;, `void`, `unknown`&gt;
+
+#### See
+
+ - AsyncIterator
+of
+ - RecordBatch.
+
+By default, LanceDb will use many threads to calculate results and, when
+the result set is large, multiple batches will be processed at one time.
+This readahead is limited however and backpressure will be applied if this
+stream is consumed slowly (this constrains the maximum memory used by a
+single query)
+
+#### Inherited from
+
+`StandardQueryBase.execute`
+
+***
+
+### explainPlan()
+
+```ts
+explainPlan(verbose): Promise<string>
+```
+
+Generates an explanation of the query execution plan.
+
+#### Parameters
+
+* **verbose**: `boolean` = `false`
+    If true, provides a more detailed explanation. Defaults to false.
+
+#### Returns
+
+`Promise`&lt;`string`&gt;
+
+A Promise that resolves to a string containing the query execution plan explanation.
+
+#### Example
+
+```ts
+import * as lancedb from "@lancedb/lancedb"
+const db = await lancedb.connect("./.lancedb");
+const table = await db.createTable("my_table", [
+  { vector: [1.1, 0.9], id: "1" },
+]);
+const plan = await table.query().nearestTo([0.5, 0.2]).explainPlan();
+```
+
+#### Inherited from
+
+`StandardQueryBase.explainPlan`
+
+***
+
+### fastSearch()
+
+```ts
+fastSearch(): this
+```
+
+Skip searching un-indexed data. This can make search faster, but will miss
+any data that is not yet indexed.
+
+Use [Table#optimize](Table.md#optimize) to index all un-indexed data.
+
+#### Returns
+
+`this`
+
+#### Inherited from
+
+`StandardQueryBase.fastSearch`
+
+***
+
+### ~~filter()~~
+
+```ts
+filter(predicate): this
+```
+
+A filter statement to be applied to this query.
+
+#### Parameters
+
+* **predicate**: `string`
+
+#### Returns
+
+`this`
+
+#### See
+
+where
+
+#### Deprecated
+
+Use `where` instead
+
+#### Inherited from
+
+`StandardQueryBase.filter`
+
+***
+
+### fullTextSearch()
+
+```ts
+fullTextSearch(query, options?): this
+```
+
+#### Parameters
+
+* **query**: `string` \| [`FullTextQuery`](../interfaces/FullTextQuery.md)
+
+* **options?**: `Partial`&lt;[`FullTextSearchOptions`](../interfaces/FullTextSearchOptions.md)&gt;
+
+#### Returns
+
+`this`
+
+#### Inherited from
+
+`StandardQueryBase.fullTextSearch`
+
+***
+
+### limit()
+
+```ts
+limit(limit): this
+```
+
+Set the maximum number of results to return.
+
+By default, a plain search has no limit.  If this method is not
+called then every valid row from the table will be returned.
+
+#### Parameters
+
+* **limit**: `number`
+
+#### Returns
+
+`this`
+
+#### Inherited from
+
+`StandardQueryBase.limit`
+
+***
+
+### offset()
+
+```ts
+offset(offset): this
+```
+
+Set the number of rows to skip before returning results.
+
+This is useful for pagination.
+
+#### Parameters
+
+* **offset**: `number`
+
+#### Returns
+
+`this`
+
+#### Inherited from
+
+`StandardQueryBase.offset`
+
+***
+
+### orderBy()
+
+```ts
+orderBy(ordering): this
+```
+
+Sort the results by the specified column(s).
+
+#### Parameters
+
+* **ordering**: [`ColumnOrdering`](../interfaces/ColumnOrdering.md) \| [`ColumnOrdering`](../interfaces/ColumnOrdering.md)[]
+
+#### Returns
+
+`this`
+
+This query builder.
+
+#### Inherited from
+
+`StandardQueryBase.orderBy`
+
+***
+
+### outputSchema()
+
+```ts
+outputSchema(): Promise<Schema<any>>
+```
+
+Returns the schema of the output that will be returned by this query.
+
+This can be used to inspect the types and names of the columns that will be
+returned by the query before executing it.
+
+#### Returns
+
+`Promise`&lt;`Schema`&lt;`any`&gt;&gt;
+
+An Arrow Schema describing the output columns.
+
+#### Inherited from
+
+`StandardQueryBase.outputSchema`
+
+***
+
+### select()
+
+```ts
+select(columns): this
+```
+
+Return only the specified columns.
+
+By default a query will return all columns from the table.  However, this can have
+a very significant impact on latency.  LanceDb stores data in a columnar fashion.  This
+means we can finely tune our I/O to select exactly the columns we need.
+
+As a best practice you should always limit queries to the columns that you need.  If you
+pass in an array of column names then only those columns will be returned.
+
+You can also use this method to create new "dynamic" columns based on your existing columns.
+For example, you may not care about "a" or "b" but instead simply want "a + b".  This is often
+seen in the SELECT clause of an SQL query (e.g. `SELECT a+b FROM my_table`).
+
+To create dynamic columns you can pass in a Map<string, string>.  A column will be returned
+for each entry in the map.  The key provides the name of the column.  The value is
+an SQL string used to specify how the column is calculated.
+
+For example, an SQL query might state `SELECT a + b AS combined, c`.  The equivalent
+input to this method would be:
+
+#### Parameters
+
+* **columns**: `string` \| `string`[] \| `Record`&lt;`string`, `string`&gt; \| `Map`&lt;`string`, `string`&gt;
+
+#### Returns
+
+`this`
+
+#### Example
+
+```ts
+new Map([["combined", "a + b"], ["c", "c"]])
+
+Columns will always be returned in the order given, even if that order is different than
+the order used when adding the data.
+
+Note that you can pass in a `Record<string, string>` (e.g. an object literal). This method
+uses `Object.entries` which should preserve the insertion order of the object.  However,
+object insertion order is easy to get wrong and `Map` is more foolproof.
+```
+
+#### Inherited from
+
+`StandardQueryBase.select`
+
+***
+
+### toArray()
+
+```ts
+toArray(options?): Promise<any[]>
+```
+
+Collect the results as an array of objects.
+
+#### Parameters
+
+* **options?**: `Partial`&lt;[`QueryExecutionOptions`](../interfaces/QueryExecutionOptions.md)&gt;
+
+#### Returns
+
+`Promise`&lt;`any`[]&gt;
+
+#### Inherited from
+
+`StandardQueryBase.toArray`
+
+***
+
+### toArrow()
+
+```ts
+toArrow(options?): Promise<Table<any>>
+```
+
+Collect the results as an Arrow
+
+#### Parameters
+
+* **options?**: `Partial`&lt;[`QueryExecutionOptions`](../interfaces/QueryExecutionOptions.md)&gt;
+
+#### Returns
+
+`Promise`&lt;`Table`&lt;`any`&gt;&gt;
+
+#### See
+
+ArrowTable.
+
+#### Inherited from
+
+`StandardQueryBase.toArrow`
+
+***
+
+### useLsm()
+
+```ts
+useLsm(enable): this
+```
+
+Control MemWAL read routing for this query.
+
+By default (unset), when the table carries a MemWAL write spec (see
+[Table#setLsmWriteSpec](Table.md#setlsmwritespec)), reads are routed through the LSM scanner so
+they also return data written via the `mergeInsert` LSM path that has not yet
+been compacted into the base table (the active/frozen in-memory memtables and
+the flushed generations), deduplicated by primary key; a table without a spec
+reads the base table.
+
+#### Parameters
+
+* **enable**: `boolean`
+    `true` forces the LSM scanner and errors if the table has no
+    MemWAL write spec. `false` bypasses the MemWAL and reads the base table only,
+    even when a spec is present.
+    Note: the LSM scanner does not support every query shape (e.g. reranking,
+    hybrid search, `orderBy`). On a MemWAL table those shapes error unless
+    `useLsm(false)` is set, because a base-only read would silently exclude
+    un-compacted MemWAL data.
+
+#### Returns
+
+`this`
+
+#### Inherited from
+
+`StandardQueryBase.useLsm`
+
+***
+
+### where()
+
+```ts
+where(predicate): this
+```
+
+A filter statement to be applied to this query.
+
+The filter should be supplied as an SQL query string.  For example:
+
+#### Parameters
+
+* **predicate**: `string`
+
+#### Returns
+
+`this`
+
+#### Example
+
+```ts
+x > 10
+y > 0 AND y < 100
+x > 5 OR y = 'test'
+
+Filtering performance can often be improved by creating a scalar index
+on the filter column(s).
+
+Calling this multiple times combines the filters with a logical AND rather
+than replacing the previous filter.
+```
+
+#### Inherited from
+
+`StandardQueryBase.where`
+
+***
+
+### withRowId()
+
+```ts
+withRowId(): this
+```
+
+Whether to return the row id in the results.
+
+This column can be used to match results between different queries. For
+example, to match results from a full text search and a vector search in
+order to perform hybrid search.
+
+#### Returns
+
+`this`
+
+#### Inherited from
+
+`StandardQueryBase.withRowId`
@@ -942,7 +942,7 @@ Get the schema of the table.
 abstract search(
   query,
   queryType?,
-   ftsColumns?): Query | VectorQuery
+   ftsColumns?): Query | VectorQuery | AutoQuery
 ```

 Create a search query to find the nearest neighbors
@@ -964,7 +964,7 @@ of the given query

 #### Returns

-[`Query`](Query.md) \| [`VectorQuery`](VectorQuery.md)
+[`Query`](Query.md) \| [`VectorQuery`](VectorQuery.md) \| [`AutoQuery`](AutoQuery.md)

 ***

@@ -18,6 +18,7 @@

 ## Classes

+- [AutoQuery](classes/AutoQuery.md)
 - [BooleanQuery](classes/BooleanQuery.md)
 - [BoostQuery](classes/BoostQuery.md)
 - [BranchContents](classes/BranchContents.md)
@@ -10,16 +10,12 @@
 function getRegistry(): EmbeddingFunctionRegistry
 ```

-Utility function to get the global instance of the registry
+Get the global embedding function registry.
+
+LanceDB built-in providers are initialized when this public API is first
+used, so importing the root package does not change automatic search
+selection for tables without embedding metadata.

 ## Returns

 [`EmbeddingFunctionRegistry`](../classes/EmbeddingFunctionRegistry.md)
-
-`EmbeddingFunctionRegistry` The global instance of the registry
-
-## Example
-
-```ts
-const registry = getRegistry();
-const openai = registry.get("openai").create();
@@ -28,7 +28,7 @@
    <properties>
        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
        <arrow.version>15.0.0</arrow.version>
-        <lance-core.version>11.0.0-beta.22</lance-core.version>
+        <lance-core.version>12.0.0-beta.2</lance-core.version>
        <spotless.skip>false</spotless.skip>
        <spotless.version>2.30.0</spotless.version>
        <spotless.java.googlejavaformat.version>1.7</spotless.java.googlejavaformat.version>
@@ -0,0 +1,95 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+import { execFileSync } from "node:child_process";
+import { resolve } from "node:path";
+
+import type { OpenAIEmbeddingFunction } from "../lancedb/embedding/openai";
+import type { EmbeddingFunctionRegistry } from "../lancedb/embedding/registry";
+
+type EmbeddingModule = typeof import("../lancedb/embedding");
+type OpenAIModule = typeof import("../lancedb/embedding/openai");
+type RegistryModule = typeof import("../lancedb/embedding/registry");
+
+describe("embedding function registry", () => {
+  const registries: EmbeddingFunctionRegistry[] = [];
+
+  afterEach(() => {
+    for (const registry of registries) {
+      registry.reset();
+    }
+    registries.length = 0;
+  });
+
+  it("defers built-in providers until the public registry API is used", () => {
+    jest.isolateModules(() => {
+      const embedding = require("../lancedb/embedding") as EmbeddingModule;
+      const { getRegistry: getInternalRegistry } =
+        require("../lancedb/embedding/registry") as RegistryModule;
+      const registry = getInternalRegistry();
+      registries.push(registry);
+
+      expect(registry.length()).toBe(0);
+      expect(embedding.getRegistry()).toBe(registry);
+      expect(registry.get("openai")).toBeDefined();
+      expect(registry.get("huggingface")).toBeDefined();
+    });
+  });
+
+  it("preserves automatic FTS search in a fresh process", () => {
+    execFileSync(
+      process.execPath,
+      [resolve(__dirname, "fixtures", "auto_fts_search.cjs")],
+      { stdio: "pipe" },
+    );
+  });
+
+  it("shares registrations across duplicated provider module graphs", () => {
+    let registeringRegistry: EmbeddingFunctionRegistry | undefined;
+    let latestOpenAIConstructor: typeof OpenAIEmbeddingFunction | undefined;
+
+    jest.isolateModules(() => {
+      require("../lancedb/embedding/openai");
+      const { getRegistry } =
+        require("../lancedb/embedding/registry") as RegistryModule;
+      registeringRegistry = getRegistry();
+      registries.push(registeringRegistry);
+      expect(registeringRegistry.get("openai")).toBeDefined();
+    });
+
+    expect(() => {
+      jest.isolateModules(() => {
+        const { OpenAIEmbeddingFunction } =
+          require("../lancedb/embedding/openai") as OpenAIModule;
+        latestOpenAIConstructor = OpenAIEmbeddingFunction;
+        const { getRegistry } =
+          require("../lancedb/embedding/registry") as RegistryModule;
+        registries.push(getRegistry());
+      });
+    }).not.toThrow();
+
+    const previousApiKey = process.env.OPENAI_API_KEY;
+    process.env.OPENAI_API_KEY = "test";
+    try {
+      const latestOpenAI = registeringRegistry!
+        .get<OpenAIEmbeddingFunction>("openai")!
+        .create();
+      expect(latestOpenAI).toBeInstanceOf(latestOpenAIConstructor!);
+    } finally {
+      if (previousApiKey === undefined) {
+        delete process.env.OPENAI_API_KEY;
+      } else {
+        process.env.OPENAI_API_KEY = previousApiKey;
+      }
+    }
+
+    jest.isolateModules(() => {
+      const { getRegistry } =
+        require("../lancedb/embedding") as EmbeddingModule;
+      const publicRegistry = getRegistry();
+      registries.push(publicRegistry);
+      expect(publicRegistry).toBe(registeringRegistry);
+      expect(publicRegistry.get("openai")).toBeDefined();
+    });
+  });
+});
@@ -0,0 +1,33 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+const assert = require("node:assert/strict");
+const tmp = require("tmp");
+const { connect, embedding, Index } = require("../../dist");
+const { getRegistry } = require("../../dist/embedding/registry");
+
+async function main() {
+  assert.equal(typeof embedding.getRegistry, "function");
+  assert.equal(getRegistry().length(), 0);
+  assert.equal(embedding.getRegistry(), getRegistry());
+  assert.equal(getRegistry().length(), 2);
+
+  const dir = tmp.dirSync({ unsafeCleanup: true });
+  let db;
+  try {
+    db = await connect(dir.name);
+    const table = await db.createTable("docs", [{ text: "hello world" }]);
+    await table.createIndex("text", { config: Index.fts() });
+
+    const rows = await table.search("hello").toArray();
+    assert.equal(rows[0].text, "hello world");
+  } finally {
+    db?.close();
+    dir.removeCallback();
+  }
+}
+
+main().catch((error) => {
+  console.error(error);
+  process.exitCode = 1;
+});
@@ -11,10 +11,13 @@ import * as arrow17 from "apache-arrow-17";
 import * as arrow18 from "apache-arrow-18";

 import {
+  AutoQuery,
  Connection,
  MatchQuery,
  PhraseQuery,
+  Query,
  Table,
+  VectorQuery,
  connect,
  tokenize,
 } from "../lancedb";
@@ -682,6 +685,56 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
  },
 );

+// https://github.com/lancedb/lancedb/issues/1963
+it("should query documents with LangChain PDF metadata", async () => {
+  const tmpDir = tmp.dirSync({ unsafeCleanup: true });
+  try {
+    const db = await connect(tmpDir.name);
+    const documents = [
+      {
+        text: "first page",
+        vector: [1, 0],
+        source: "first.pdf",
+        loc: { pageNumber: 1, lines: { from: 1, to: 12 } },
+        pdf: {
+          version: "1.10.100",
+          info: {
+            format: "PDF 1.7",
+            producer: "pdf.js",
+            creator: "Writer",
+          },
+          totalPages: 2,
+        },
+      },
+      {
+        text: "second page",
+        vector: [0, 1],
+        source: "second.pdf",
+        loc: { pageNumber: 2, lines: { from: 13, to: 24 } },
+        pdf: {
+          version: "1.10.100",
+          info: {
+            format: "PDF 1.7",
+            producer: "pdf.js",
+            creator: "Writer",
+          },
+          totalPages: 2,
+        },
+      },
+    ];
+    const documentsTable = await db.createTable("documents", documents);
+
+    const results = await documentsTable.query().toArray();
+
+    expect(results).toHaveLength(2);
+    expect(results[0].source).toBe("first.pdf");
+    expect(results[0].pdf.info.producer).toBe("pdf.js");
+    expect(results[1].loc.pageNumber).toBe(2);
+  } finally {
+    tmpDir.removeCallback();
+  }
+});
+
 describe("merge insert", () => {
  let tmpDir: tmp.DirResult;
  let table: Table;
@@ -1777,6 +1830,194 @@ describe("Read consistency interval", () => {
  });
 });

+describe("automatic search schema consistency", () => {
+  let tmpDir: tmp.DirResult;
+
+  class SchemaRefreshEmbedding extends EmbeddingFunction<string> {
+    ndims() {
+      return 2;
+    }
+
+    embeddingDataType() {
+      return new Float32();
+    }
+
+    async computeSourceEmbeddings(data: string[]) {
+      return data.map((value) => [value.length, 1]);
+    }
+
+    async computeQueryEmbeddings(value: string) {
+      return [value.length, 1];
+    }
+  }
+
+  function embeddingSchema() {
+    const func = new SchemaRefreshEmbedding();
+    return LanceSchema({
+      text: func.sourceField(new Utf8()),
+      vector: func.vectorField(),
+    });
+  }
+
+  beforeEach(() => {
+    getRegistry().reset();
+    register("schema-refresh")(SchemaRefreshEmbedding);
+    tmpDir = tmp.dirSync({ unsafeCleanup: true });
+  });
+
+  afterEach(() => {
+    getRegistry().reset();
+    tmpDir.removeCallback();
+  });
+
+  it("uses the schema refreshed from another connection", async () => {
+    const first = await connect(tmpDir.name, { readConsistencyInterval: 0 });
+    const second = await connect(tmpDir.name, { readConsistencyInterval: 0 });
+
+    try {
+      const stale = await first.createTable("docs", [{ text: "before" }], {
+        schema: embeddingSchema(),
+      });
+      const replacement = await second.createTable(
+        "docs",
+        [{ text: "after hello" }],
+        { mode: "overwrite" },
+      );
+      await replacement.createIndex("text", { config: Index.fts() });
+
+      const search = stale.search("hello");
+      expect(search).toBeInstanceOf(AutoQuery);
+      expect(search).not.toBeInstanceOf(Query);
+      expect(search).not.toBeInstanceOf(VectorQuery);
+      expect("nprobes" in search).toBe(false);
+
+      const rows = await search.toArray();
+      expect(rows[0].text).toBe("after hello");
+      expect((await stale.schema()).metadata.has("embedding_functions")).toBe(
+        false,
+      );
+    } finally {
+      first.close();
+      second.close();
+    }
+  });
+
+  it("tracks embedding metadata across checkout and restore", async () => {
+    const first = await connect(tmpDir.name, { readConsistencyInterval: 0 });
+    const second = await connect(tmpDir.name, { readConsistencyInterval: 0 });
+
+    try {
+      await first.createTable("docs", [{ text: "before" }], {
+        schema: embeddingSchema(),
+      });
+      const table = await second.createTable(
+        "docs",
+        [{ text: "after hello" }],
+        { mode: "overwrite" },
+      );
+      await table.createIndex("text", { config: Index.fts() });
+
+      await table.checkout(1);
+      expect((await table.search("before").toArray())[0].text).toBe("before");
+
+      await table.checkoutLatest();
+      expect((await table.search("hello").toArray())[0].text).toBe(
+        "after hello",
+      );
+
+      await table.checkout(1);
+      await table.restore();
+      expect((await table.search("before").toArray())[0].text).toBe("before");
+    } finally {
+      first.close();
+      second.close();
+    }
+  });
+
+  it("pins automatic search while computing an embedding", async () => {
+    let markStarted!: () => void;
+    let releaseEmbedding!: () => void;
+    const started = new Promise<void>((resolve) => {
+      markStarted = resolve;
+    });
+    const released = new Promise<void>((resolve) => {
+      releaseEmbedding = resolve;
+    });
+
+    class BlockingEmbedding extends SchemaRefreshEmbedding {
+      async computeQueryEmbeddings(value: string) {
+        markStarted();
+        await released;
+        return [value.length, 1];
+      }
+    }
+
+    register("schema-refresh-blocking")(BlockingEmbedding);
+    const func = new BlockingEmbedding();
+    const schema = LanceSchema({
+      text: func.sourceField(new Utf8()),
+      vector: func.vectorField(),
+    });
+    const first = await connect(tmpDir.name, { readConsistencyInterval: 0 });
+    const second = await connect(tmpDir.name, { readConsistencyInterval: 0 });
+
+    try {
+      const table = await first.createTable(
+        "docs",
+        [{ text: "hello before" }],
+        { schema },
+      );
+      const pending = table.search("hello").toArray();
+      await started;
+
+      const replacement = await second.createTable(
+        "docs",
+        [{ text: "hello after" }],
+        { mode: "overwrite" },
+      );
+      await replacement.createIndex("text", { config: Index.fts() });
+      releaseEmbedding();
+
+      expect((await pending)[0].text).toBe("hello before");
+    } finally {
+      releaseEmbedding();
+      first.close();
+      second.close();
+    }
+  });
+
+  it("refreshes a reused automatic search for every execution", async () => {
+    const first = await connect(tmpDir.name, { readConsistencyInterval: 0 });
+    const second = await connect(tmpDir.name, { readConsistencyInterval: 0 });
+
+    try {
+      const table = await first.createTable("docs", [
+        { text: "hello before", marker: "before" },
+      ]);
+      await table.createIndex("text", { config: Index.fts() });
+      const search = table.search("hello").select(["text"]);
+
+      const before = (await search.toArray())[0];
+      expect(before.text).toBe("hello before");
+      expect(before.marker).toBeUndefined();
+
+      const replacement = await second.createTable(
+        "docs",
+        [{ text: "hello after", marker: "after" }],
+        { mode: "overwrite" },
+      );
+      await replacement.createIndex("text", { config: Index.fts() });
+
+      const after = (await search.toArray())[0];
+      expect(after.text).toBe("hello after");
+      expect(after.marker).toBeUndefined();
+    } finally {
+      first.close();
+      second.close();
+    }
+  });
+});
+
 describe("schema evolution", function () {
  let tmpDir: tmp.DirResult;
  beforeEach(() => {
@@ -2344,7 +2585,24 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
      );
    });

-    test("full text search if no embedding function provided", async () => {
+    test("full text search if only an unrelated embedding function is registered", async () => {
+      register("unused")(
+        class extends EmbeddingFunction<string> {
+          ndims() {
+            return 3;
+          }
+          embeddingDataType() {
+            return new Float32();
+          }
+          async computeQueryEmbeddings(_data: string) {
+            return [1, 2, 3];
+          }
+          async computeSourceEmbeddings(data: string[]) {
+            return data.map(() => [1, 2, 3]);
+          }
+        },
+      );
+
      const db = await connect(tmpDir.name);
      const data = [
        { text: "hello world", vector: [0.1, 0.2, 0.3] },
@@ -2366,6 +2624,306 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
      expect(results2[0].text).toBe(data[1].text);
    });

+    test("auto search stays consistent with the active revision", async () => {
+      let initCalls = 0;
+      let queryCalls = 0;
+      let markStarted!: () => void;
+      const started = new Promise<void>((resolve) => {
+        markStarted = resolve;
+      });
+      let releaseEmbedding!: () => void;
+      const embeddingReleased = new Promise<void>((resolve) => {
+        releaseEmbedding = resolve;
+      });
+
+      @register("refresh-test")
+      class TestEmbedding extends EmbeddingFunction<string> {
+        async init() {
+          initCalls += 1;
+        }
+        ndims() {
+          return 1;
+        }
+        embeddingDataType() {
+          return new arrow.Float32();
+        }
+        async computeQueryEmbeddings(value: string) {
+          queryCalls += 1;
+          if (value === "blocked") {
+            markStarted();
+            await embeddingReleased;
+          }
+          return value === "greetings" ? [0.1] : [0.2];
+        }
+        async computeSourceEmbeddings(values: string[]) {
+          return values.map((value) =>
+            value === "hello world" ? [0.1] : [0.2],
+          );
+        }
+      }
+
+      const writer = await connect(tmpDir.name);
+      await writer.createTable("test", [{ text: "plain", vector: [0.0] }]);
+      const reader = await connect(tmpDir.name, {
+        readConsistencyInterval: 0,
+      });
+      const tracked = await reader.openTable("test");
+      type SnapshotCountingNative = {
+        querySnapshot: () => Promise<unknown>;
+      };
+      const native = (tracked as unknown as { inner: SnapshotCountingNative })
+        .inner;
+      const querySnapshot = native.querySnapshot.bind(native);
+      let snapshotCalls = 0;
+      native.querySnapshot = async () => {
+        snapshotCalls += 1;
+        return await querySnapshot();
+      };
+      const autoQuery = tracked.search("greetings").select(["text"]).limit(1);
+
+      const func = new TestEmbedding();
+      const schema = LanceSchema({
+        text: func.sourceField(new arrow.Utf8()),
+        vector: func.vectorField(),
+      });
+      const data = [{ text: "hello world" }, { text: "goodbye world" }];
+      await writer.createTable("test", data, { mode: "overwrite", schema });
+      const baselineInitCalls = initCalls;
+
+      expect(
+        (await tracked.schema()).metadata.get("embedding_functions"),
+      ).toBeDefined();
+      const results = await autoQuery.toArray();
+      expect(results[0].text).toBe(data[0].text);
+      expect(initCalls).toBe(baselineInitCalls + 1);
+      expect(queryCalls).toBe(1);
+      expect(snapshotCalls).toBe(1);
+
+      const repeatedResults = await autoQuery.toArray();
+      expect(repeatedResults[0].text).toBe(data[0].text);
+      expect(initCalls).toBe(baselineInitCalls + 1);
+      expect(queryCalls).toBe(1);
+      expect(snapshotCalls).toBe(2);
+
+      const pending = tracked
+        .search("blocked")
+        .select(["text"])
+        .limit(1)
+        .toArray();
+      await started;
+
+      const ftsData = [
+        { text: "greetings from full text", vector: [0.0] },
+        { text: "blocked from full text", vector: [0.0] },
+      ];
+      const ftsTable = await writer.createTable("test", ftsData, {
+        mode: "overwrite",
+      });
+      await ftsTable.createIndex("text", { config: Index.fts() });
+      releaseEmbedding();
+
+      const pendingResults = await pending;
+      expect(pendingResults[0].text).toBe(data[1].text);
+
+      expect(
+        (await tracked.schema()).metadata.get("embedding_functions"),
+      ).toBeUndefined();
+      const ftsResults = await autoQuery.toArray();
+      expect(ftsResults[0].text).toBe(ftsData[0].text);
+    });
+
+    test("auto search keeps newer preparation during a revision race", async () => {
+      let aCalls = 0;
+      let bCalls = 0;
+      let markAStarted!: () => void;
+      const aStarted = new Promise<void>((resolve) => {
+        markAStarted = resolve;
+      });
+      let releaseA!: () => void;
+      const aReleased = new Promise<void>((resolve) => {
+        releaseA = resolve;
+      });
+      let markBStarted!: () => void;
+      const bStarted = new Promise<void>((resolve) => {
+        markBStarted = resolve;
+      });
+      let releaseB!: () => void;
+      const bReleased = new Promise<void>((resolve) => {
+        releaseB = resolve;
+      });
+
+      @register("race-a")
+      class EmbeddingA extends EmbeddingFunction<string> {
+        ndims() {
+          return 1;
+        }
+        embeddingDataType() {
+          return new arrow.Float32();
+        }
+        async computeQueryEmbeddings() {
+          aCalls += 1;
+          markAStarted();
+          await aReleased;
+          return [0.1];
+        }
+        async computeSourceEmbeddings(values: string[]) {
+          return values.map(() => [0.1]);
+        }
+      }
+
+      @register("race-b")
+      class EmbeddingB extends EmbeddingFunction<string> {
+        ndims() {
+          return 1;
+        }
+        embeddingDataType() {
+          return new arrow.Float32();
+        }
+        async computeQueryEmbeddings() {
+          bCalls += 1;
+          markBStarted();
+          await bReleased;
+          return [0.2];
+        }
+        async computeSourceEmbeddings(values: string[]) {
+          return values.map(() => [0.2]);
+        }
+      }
+
+      const writer = await connect(tmpDir.name);
+      const embeddingA = new EmbeddingA();
+      const schemaA = LanceSchema({
+        text: embeddingA.sourceField(new arrow.Utf8()),
+        vector: embeddingA.vectorField(),
+      });
+      await writer.createTable("race", [{ text: "revision a" }], {
+        schema: schemaA,
+      });
+      const reader = await connect(tmpDir.name, {
+        readConsistencyInterval: 0,
+      });
+      const tracked = await reader.openTable("race");
+      const query = tracked.search("query");
+
+      const first = query.toArray();
+      await aStarted;
+
+      const embeddingB = new EmbeddingB();
+      const schemaB = LanceSchema({
+        text: embeddingB.sourceField(new arrow.Utf8()),
+        vector: embeddingB.vectorField(),
+      });
+      await writer.createTable("race", [{ text: "revision b" }], {
+        mode: "overwrite",
+        schema: schemaB,
+      });
+      const second = query.toArray();
+      await bStarted;
+
+      releaseA();
+      releaseB();
+      await Promise.all([first, second]);
+      expect(aCalls).toBe(1);
+      expect(bCalls).toBe(1);
+    });
+
+    test("stale FTS routing keeps newer vector preparation", async () => {
+      let vectorCalls = 0;
+      let markVectorStarted!: () => void;
+      const vectorStarted = new Promise<void>((resolve) => {
+        markVectorStarted = resolve;
+      });
+      let releaseVector!: () => void;
+      const vectorReleased = new Promise<void>((resolve) => {
+        releaseVector = resolve;
+      });
+
+      @register("stale-fts-race")
+      class RaceEmbedding extends EmbeddingFunction<string> {
+        ndims() {
+          return 1;
+        }
+        embeddingDataType() {
+          return new arrow.Float32();
+        }
+        async computeQueryEmbeddings() {
+          vectorCalls += 1;
+          markVectorStarted();
+          await vectorReleased;
+          return [0.1];
+        }
+        async computeSourceEmbeddings(values: string[]) {
+          return values.map(() => [0.1]);
+        }
+      }
+
+      const writer = await connect(tmpDir.name);
+      const ftsTable = await writer.createTable("stale_fts", [
+        { text: "hello", vector: [0.0] },
+      ]);
+      await ftsTable.createIndex("text", { config: Index.fts() });
+
+      const reader = await connect(tmpDir.name, {
+        readConsistencyInterval: 0,
+      });
+      const tracked = await reader.openTable("stale_fts");
+      type Snapshot = {
+        schema: () => Promise<Buffer>;
+      };
+      type NativeWithSnapshot = {
+        querySnapshot: () => Promise<Snapshot>;
+      };
+      const native = (tracked as unknown as { inner: NativeWithSnapshot })
+        .inner;
+      const querySnapshot = native.querySnapshot.bind(native);
+      let snapshotCalls = 0;
+      let markStaleSchemaStarted!: () => void;
+      const staleSchemaStarted = new Promise<void>((resolve) => {
+        markStaleSchemaStarted = resolve;
+      });
+      let releaseStaleSchema!: () => void;
+      const staleSchemaReleased = new Promise<void>((resolve) => {
+        releaseStaleSchema = resolve;
+      });
+      native.querySnapshot = async () => {
+        const snapshot = await querySnapshot();
+        snapshotCalls += 1;
+        if (snapshotCalls === 1) {
+          const schema = snapshot.schema.bind(snapshot);
+          snapshot.schema = async () => {
+            markStaleSchemaStarted();
+            await staleSchemaReleased;
+            return await schema();
+          };
+        }
+        return snapshot;
+      };
+
+      const query = tracked.search("hello");
+      const staleFtsExecution = query.toArray();
+      await staleSchemaStarted;
+
+      const embedding = new RaceEmbedding();
+      const vectorSchema = LanceSchema({
+        text: embedding.sourceField(new arrow.Utf8()),
+        vector: embedding.vectorField(),
+      });
+      await writer.createTable("stale_fts", [{ text: "hello" }], {
+        mode: "overwrite",
+        schema: vectorSchema,
+      });
+
+      const vectorExecution = query.toArray();
+      await vectorStarted;
+      releaseStaleSchema();
+      await staleFtsExecution;
+      releaseVector();
+      await vectorExecution;
+
+      await query.toArray();
+      expect(vectorCalls).toBe(1);
+    });
+
    test("tokenizes FTS queries by column or index name", async () => {
      const db = await connect(tmpDir.name);
      const data = [
@@ -2916,6 +3474,30 @@ describe("column name options", () => {
    expect(results[1].query_index).toBe(1);
  });

+  test("observes promised additional vectors while the query is pending", async () => {
+    const initialVector = new Promise<number[]>(() => undefined);
+    const query = table.query().nearestTo(initialVector);
+    const unhandled: unknown[] = [];
+    const onUnhandled = (reason: unknown) => unhandled.push(reason);
+    process.on("unhandledRejection", onUnhandled);
+
+    try {
+      query.addQueryVector(Promise.reject(new Error("extra vector failed")));
+      await new Promise<void>((resolve) => setImmediate(resolve));
+      expect(unhandled).toEqual([]);
+
+      const rejectedQuery = table
+        .query()
+        .nearestTo([0.1, 0.2])
+        .addQueryVector(Promise.reject(new Error("consumed vector failed")));
+      await expect(rejectedQuery.toArray()).rejects.toThrow(
+        "consumed vector failed",
+      );
+    } finally {
+      process.off("unhandledRejection", onUnhandled);
+    }
+  });
+
  test("index and search multivectors", async () => {
    const db = await connect(tmpDir.name);
    const data = [];
@@ -4,7 +4,15 @@
 import { Field, Schema } from "../arrow";
 import { sanitizeType } from "../sanitize";
 import { EmbeddingFunction } from "./embedding_function";
-import { EmbeddingFunctionConfig, getRegistry } from "./registry";
+import {
+  EmbeddingFunctionConfig,
+  EmbeddingFunctionRegistry,
+  getRegistry as getGlobalRegistry,
+  registerBuiltIn,
+} from "./registry";
+
+type OpenAIModule = typeof import("./openai");
+type TransformersModule = typeof import("./transformers");

 export {
  FieldOptions,
@@ -14,7 +22,39 @@ export {
  EmbeddingFunctionConstructor,
 } from "./embedding_function";

-export * from "./registry";
+export {
+  EmbeddingFunctionRegistry,
+  parseEmbeddingMetadata,
+  register,
+} from "./registry";
+export type {
+  CreateReturnType,
+  EmbeddingFunctionConfig,
+  EmbeddingFunctionCreate,
+  EmbeddingMetadataEntry,
+  ResolvedEmbeddingFunctionConfig,
+} from "./registry";
+
+function initializeBuiltInProviders() {
+  const { OpenAIEmbeddingFunction } = require("./openai") as OpenAIModule;
+  const { TransformersEmbeddingFunction } =
+    require("./transformers") as TransformersModule;
+
+  registerBuiltIn("openai", OpenAIEmbeddingFunction);
+  registerBuiltIn("huggingface", TransformersEmbeddingFunction);
+}
+
+/**
+ * Get the global embedding function registry.
+ *
+ * LanceDB built-in providers are initialized when this public API is first
+ * used, so importing the root package does not change automatic search
+ * selection for tables without embedding metadata.
+ */
+export function getRegistry(): EmbeddingFunctionRegistry {
+  initializeBuiltInProviders();
+  return getGlobalRegistry();
+}

 /**
 * Create a schema with embedding functions.
@@ -5,14 +5,13 @@ import type OpenAI from "openai";
 import type { EmbeddingCreateParams } from "openai/resources/index";
 import { Float, Float32 } from "../arrow";
 import { EmbeddingFunction } from "./embedding_function";
-import { register } from "./registry";
+import { registerBuiltIn } from "./registry";

 export type OpenAIOptions = {
  apiKey: string;
  model: EmbeddingCreateParams["model"];
 };

-@register("openai")
 export class OpenAIEmbeddingFunction extends EmbeddingFunction<
  string,
  Partial<OpenAIOptions>
@@ -100,3 +99,5 @@ export class OpenAIEmbeddingFunction extends EmbeddingFunction<
    return response.data[0].embedding;
  }
 }
+
+registerBuiltIn("openai", OpenAIEmbeddingFunction);
@@ -7,6 +7,10 @@ import {
 } from "./embedding_function";
 import "reflect-metadata";

+const builtInFunctionsKey = Symbol.for(
+  "@lancedb/lancedb::embedding-built-in-functions::v1",
+);
+
 export type CreateReturnType<T> = T extends { init: () => Promise<void> }
  ? Promise<T>
  : T;
@@ -59,6 +63,15 @@ export class EmbeddingFunctionRegistry {
    };
  }

+  /** @ignore */
+  setBuiltIn<
+    T extends EmbeddingFunctionConstructor = EmbeddingFunctionConstructor,
+  >(name: string, ctor: T): T {
+    this.#functions.set(name, ctor);
+    Reflect.defineMetadata("lancedb::embedding::name", name, ctor);
+    return ctor;
+  }
+
  get<T extends EmbeddingFunction<unknown>>(
    name: string,
  ): EmbeddingFunctionCreate<T> | undefined;
@@ -96,6 +109,7 @@ export class EmbeddingFunctionRegistry {
   */
  reset(this: EmbeddingFunctionRegistry) {
    this.#functions.clear();
+    getBuiltInFunctions(this).clear();
  }

  /**
@@ -183,12 +197,56 @@ export class EmbeddingFunctionRegistry {
  }
 }

-const _REGISTRY = new EmbeddingFunctionRegistry();
+function getBuiltInFunctions(registry: EmbeddingFunctionRegistry): Set<string> {
+  const registryWithBuiltIns = registry as EmbeddingFunctionRegistry & {
+    [key: symbol]: Set<string> | undefined;
+  };
+  let builtInFunctions = registryWithBuiltIns[builtInFunctionsKey];
+  if (builtInFunctions === undefined) {
+    builtInFunctions = new Set<string>();
+    registryWithBuiltIns[builtInFunctionsKey] = builtInFunctions;
+  }
+  return builtInFunctions;
+}
+
+// Server bundlers can load the side-effect embedding entry points and the public
+// embedding API from separate module graphs. Keep their registry shared.
+const registryKey = Symbol.for(
+  "@lancedb/lancedb::embedding-function-registry::v1",
+);
+const registryGlobal = globalThis as typeof globalThis & {
+  [key: symbol]: EmbeddingFunctionRegistry | undefined;
+};
+
+function getGlobalRegistry(): EmbeddingFunctionRegistry {
+  const existingRegistry = registryGlobal[registryKey];
+  if (existingRegistry !== undefined) {
+    return existingRegistry;
+  }
+  const registry = new EmbeddingFunctionRegistry();
+  registryGlobal[registryKey] = registry;
+  return registry;
+}
+
+const _REGISTRY = getGlobalRegistry();

 export function register(name?: string) {
  return _REGISTRY.register(name);
 }

+/** @ignore */
+export function registerBuiltIn<
+  T extends EmbeddingFunctionConstructor = EmbeddingFunctionConstructor,
+>(name: string, ctor: T): T {
+  const builtInFunctions = getBuiltInFunctions(_REGISTRY);
+  if (builtInFunctions.has(name)) {
+    return _REGISTRY.setBuiltIn(name, ctor);
+  }
+  _REGISTRY.register(name)(ctor);
+  builtInFunctions.add(name);
+  return ctor;
+}
+
 /**
 * Utility function to get the global instance of the registry
 * @returns `EmbeddingFunctionRegistry` The global instance of the registry
@@ -3,7 +3,7 @@

 import { Float, Float32 } from "../arrow";
 import { EmbeddingFunction } from "./embedding_function";
-import { register } from "./registry";
+import { registerBuiltIn } from "./registry";

 export type XenovaTransformerOptions = {
  /** The wasm compatible model to use */
@@ -31,7 +31,6 @@ export type XenovaTransformerOptions = {
  };
 };

-@register("huggingface")
 export class TransformersEmbeddingFunction extends EmbeddingFunction<
  string,
  Partial<XenovaTransformerOptions>
@@ -158,6 +157,8 @@ export class TransformersEmbeddingFunction extends EmbeddingFunction<
  }
 }

+registerBuiltIn("huggingface", TransformersEmbeddingFunction);
+
 const tensorDiv = (
  src: import("@huggingface/transformers").Tensor,
  divBy: number,
@@ -103,6 +103,7 @@ export {
 } from "./native.js";

 export {
+  AutoQuery,
  ExecutableQuery,
  Query,
  QueryBase,
@@ -100,6 +100,29 @@ export interface FullTextSearchOptions {
  columns?: string | string[];
 }

+function nearestToNative(
+  inner: NativeQuery,
+  vector: Awaited<IntoVector>,
+): NativeVectorQuery {
+  const raw = Array.isArray(vector) ? null : extractVectorBuffer(vector);
+  if (raw) {
+    return inner.nearestToRaw(raw.data, raw.dtype);
+  }
+  return inner.nearestTo(Float32Array.from(vector as number[]));
+}
+
+function addQueryVectorToNative(
+  inner: NativeVectorQuery,
+  vector: Awaited<IntoVector>,
+) {
+  const raw = Array.isArray(vector) ? null : extractVectorBuffer(vector);
+  if (raw) {
+    inner.addQueryVectorRaw(raw.data, raw.dtype);
+  } else {
+    inner.addQueryVector(Float32Array.from(vector as number[]));
+  }
+}
+
 /** Common methods supported by all query types
 *
 * @see {@link Query}
@@ -111,13 +134,15 @@ export class QueryBase<
  NativeQueryType extends NativeQuery | NativeVectorQuery | NativeTakeQuery,
 > implements AsyncIterable<RecordBatch>
 {
+  protected inner!: NativeQueryType | Promise<NativeQueryType>;
+
  /**
   * @hidden
   */
-  protected constructor(
-    protected inner: NativeQueryType | Promise<NativeQueryType>,
-  ) {
-    // intentionally empty
+  protected constructor(inner?: NativeQueryType | Promise<NativeQueryType>) {
+    if (inner !== undefined) {
+      this.inner = inner;
+    }
  }

  // call a function on the inner (either a promise or the actual object)
@@ -135,6 +160,15 @@ export class QueryBase<
    }
  }

+  /**
+   * Return the native query used by the next terminal operation.
+   *
+   * @hidden
+   */
+  protected async getInner(): Promise<NativeQueryType> {
+    return this.inner;
+  }
+
  /**
   * Return only the specified columns.
   *
@@ -207,16 +241,11 @@ export class QueryBase<
  /**
   * @hidden
   */
-  protected nativeExecute(
+  protected async nativeExecute(
    options?: Partial<QueryExecutionOptions>,
  ): Promise<NativeBatchIterator> {
-    if (this.inner instanceof Promise) {
-      return this.inner.then((inner) =>
-        inner.execute(options?.maxBatchLength, options?.timeoutMs),
-      );
-    } else {
-      return this.inner.execute(options?.maxBatchLength, options?.timeoutMs);
-    }
+    const inner = await this.getInner();
+    return inner.execute(options?.maxBatchLength, options?.timeoutMs);
  }

  /**
@@ -245,12 +274,7 @@ export class QueryBase<
  /** Collect the results as an Arrow @see {@link ArrowTable}. */
  async toArrow(options?: Partial<QueryExecutionOptions>): Promise<ArrowTable> {
    const batches = [];
-    let inner;
-    if (this.inner instanceof Promise) {
-      inner = await this.inner;
-    } else {
-      inner = this.inner;
-    }
+    const inner = await this.getInner();
    for await (const batch of new RecordBatchIterable(inner, options)) {
      batches.push(batch);
    }
@@ -279,11 +303,8 @@ export class QueryBase<
   * @returns A Promise that resolves to a string containing the query execution plan explanation.
   */
  async explainPlan(verbose = false): Promise<string> {
-    if (this.inner instanceof Promise) {
-      return this.inner.then((inner) => inner.explainPlan(verbose));
-    } else {
-      return this.inner.explainPlan(verbose);
-    }
+    const inner = await this.getInner();
+    return inner.explainPlan(verbose);
  }

  /**
@@ -321,13 +342,8 @@ export class QueryBase<
    distributedMetrics?: AnalyzePlanDistributedMetrics,
  ): Promise<string> {
    const distributedMetricsMode = distributedMetrics ?? "aggregate";
-    if (this.inner instanceof Promise) {
-      return this.inner.then((inner) =>
-        inner.analyzePlan(distributedMetricsMode),
-      );
-    } else {
-      return this.inner.analyzePlan(distributedMetricsMode);
-    }
+    const inner = await this.getInner();
+    return inner.analyzePlan(distributedMetricsMode);
  }

  /**
@@ -339,12 +355,8 @@ export class QueryBase<
   * @returns An Arrow Schema describing the output columns.
   */
  async outputSchema(): Promise<import("./arrow").Schema> {
-    let schemaBuffer: Buffer;
-    if (this.inner instanceof Promise) {
-      schemaBuffer = await this.inner.then((inner) => inner.outputSchema());
-    } else {
-      schemaBuffer = await this.inner.outputSchema();
-    }
+    const inner = await this.getInner();
+    const schemaBuffer = await inner.outputSchema();
    const schema = tableFromIPC(schemaBuffer).schema;
    return schema;
  }
@@ -356,7 +368,7 @@ export class StandardQueryBase<
  extends QueryBase<NativeQueryType>
  implements ExecutableQuery
 {
-  constructor(inner: NativeQueryType | Promise<NativeQueryType>) {
+  constructor(inner?: NativeQueryType | Promise<NativeQueryType>) {
    super(inner);
  }

@@ -510,6 +522,13 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {
    super(inner);
  }

+  /**
+   * @hidden
+   */
+  protected doVectorCall(fn: (inner: NativeVectorQuery) => void) {
+    super.doCall(fn);
+  }
+
  /**
   * Set the number of partitions to search (probe)
   *
@@ -537,7 +556,7 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {
   * the minimum and maximum to the same value.
   */
  nprobes(nprobes: number): VectorQuery {
-    super.doCall((inner) => inner.nprobes(nprobes));
+    this.doVectorCall((inner) => inner.nprobes(nprobes));

    return this;
  }
@@ -551,7 +570,7 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {
   * but will also increase latency.
   */
  minimumNprobes(minimumNprobes: number): VectorQuery {
-    super.doCall((inner) => inner.minimumNprobes(minimumNprobes));
+    this.doVectorCall((inner) => inner.minimumNprobes(minimumNprobes));
    return this;
  }

@@ -565,7 +584,7 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {
   * potential false negatives.
   */
  maximumNprobes(maximumNprobes: number): VectorQuery {
-    super.doCall((inner) => inner.maximumNprobes(maximumNprobes));
+    this.doVectorCall((inner) => inner.maximumNprobes(maximumNprobes));
    return this;
  }

@@ -578,7 +597,7 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {
   * `undefined` means no lower or upper bound.
   */
  distanceRange(lowerBound?: number, upperBound?: number): VectorQuery {
-    super.doCall((inner) => inner.distanceRange(lowerBound, upperBound));
+    this.doVectorCall((inner) => inner.distanceRange(lowerBound, upperBound));
    return this;
  }

@@ -592,7 +611,7 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {
   * also increase the latency of your query. The default value is 1.5*limit.
   */
  ef(ef: number): VectorQuery {
-    super.doCall((inner) => inner.ef(ef));
+    this.doVectorCall((inner) => inner.ef(ef));
    return this;
  }

@@ -606,7 +625,7 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {
   * whose data type is a fixed-size-list of floats.
   */
  column(column: string): VectorQuery {
-    super.doCall((inner) => inner.column(column));
+    this.doVectorCall((inner) => inner.column(column));
    return this;
  }

@@ -627,7 +646,7 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {
  distanceType(
    distanceType: Required<IvfPqOptions>["distanceType"],
  ): VectorQuery {
-    super.doCall((inner) => inner.distanceType(distanceType));
+    this.doVectorCall((inner) => inner.distanceType(distanceType));
    return this;
  }

@@ -661,7 +680,7 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {
   * distance between the query vector and the actual uncompressed vector.
   */
  refineFactor(refineFactor: number): VectorQuery {
-    super.doCall((inner) => inner.refineFactor(refineFactor));
+    this.doVectorCall((inner) => inner.refineFactor(refineFactor));
    return this;
  }

@@ -686,7 +705,7 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {
   * factor can often help restore some of the results lost by post filtering.
   */
  postfilter(): VectorQuery {
-    super.doCall((inner) => inner.postfilter());
+    this.doVectorCall((inner) => inner.postfilter());
    return this;
  }

@@ -700,7 +719,7 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {
   * calculate your recall to select an appropriate value for nprobes.
   */
  bypassVectorIndex(): VectorQuery {
-    super.doCall((inner) => inner.bypassVectorIndex());
+    this.doVectorCall((inner) => inner.bypassVectorIndex());
    return this;
  }

@@ -708,43 +727,39 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {
   * Add a query vector to the search
   *
   * This method can be called multiple times to add multiple query vectors
-   * to the search. If multiple query vectors are added, then they will be searched
-   * in parallel, and the results will be concatenated. A column called `query_index`
-   * will be added to indicate the index of the query vector that produced the result.
-   *
-   * Performance wise, this is equivalent to running multiple queries concurrently.
+   * to the search. A column called `query_index` will be added to indicate the index
+   * of the query vector that produced the result. Flat searches share one table scan
+   * across the query vectors, avoiding the scan and memory amplification of running
+   * multiple queries concurrently. Indexed searches may still perform per-vector
+   * index work.
   */
  addQueryVector(vector: IntoVector): VectorQuery {
    if (vector instanceof Promise) {
+      // Observe the promise as soon as it is accepted. The existing native
+      // query may still be pending, and delaying observation until it resolves
+      // can otherwise surface a fast rejection as unhandled.
+      const settledVector = vector.then(
+        (value) => ({ status: "fulfilled" as const, value }),
+        (reason) => ({ status: "rejected" as const, reason }),
+      );
      const res = (async () => {
-        try {
-          const v = await vector;
-          // biome-ignore lint/suspicious/noExplicitAny: we need to get the `inner`, but js has no package scoping
-          const value: any = this.addQueryVector(v);
-          const inner = value.inner as
-            | NativeVectorQuery
-            | Promise<NativeVectorQuery>;
-          return inner;
-        } catch (e) {
-          return Promise.reject(e);
+        const inner = await this.getInner();
+        const outcome = await settledVector;
+        if (outcome.status === "rejected") {
+          throw outcome.reason;
        }
+        addQueryVectorToNative(inner, outcome.value);
+        return inner;
      })();
      return new VectorQuery(res);
    } else {
-      super.doCall((inner) => {
-        const raw = Array.isArray(vector) ? null : extractVectorBuffer(vector);
-        if (raw) {
-          inner.addQueryVectorRaw(raw.data, raw.dtype);
-        } else {
-          inner.addQueryVector(Float32Array.from(vector as number[]));
-        }
-      });
+      this.doVectorCall((inner) => addQueryVectorToNative(inner, vector));
      return this;
    }
  }

  rerank(reranker: Reranker): VectorQuery {
-    super.doCall((inner) =>
+    this.doVectorCall((inner) =>
      inner.rerank(async (args) => {
        const vecResults = await fromBufferToRecordBatch(args.vecResults);
        const ftsResults = await fromBufferToRecordBatch(args.ftsResults);
@@ -763,6 +778,71 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {
  }
 }

+/**
+ * Create a string query whose vector/FTS routing is resolved against the active
+ * table schema when the query executes.
+ *
+ * @hidden
+ */
+export function createAutoQuery(
+  table: NativeTable,
+  query: string,
+  columns: string[] | null,
+  getVector: (metadata: string) => Promise<Awaited<IntoVector>>,
+): AutoQuery {
+  type RouteSnapshot = {
+    table: NativeTable;
+    embeddingMetadata: string | undefined;
+  };
+  type CachedPreparation = {
+    metadata: string;
+    vector: Promise<Awaited<IntoVector>>;
+  };
+
+  let cachedPreparation: CachedPreparation | undefined;
+
+  const snapshotRoute = async (): Promise<RouteSnapshot> => {
+    const snapshot = await table.querySnapshot();
+    const schema = tableFromIPC(await snapshot.schema()).schema;
+    return {
+      table: snapshot,
+      embeddingMetadata: schema.metadata.get("embedding_functions"),
+    };
+  };
+
+  const createInner = async (): Promise<NativeQuery | NativeVectorQuery> => {
+    const route = await snapshotRoute();
+    if (route.embeddingMetadata === undefined) {
+      const inner = route.table.query();
+      inner.fullTextSearch({ query, columns });
+      return inner;
+    }
+
+    const metadata = route.embeddingMetadata;
+    if (cachedPreparation?.metadata !== metadata) {
+      cachedPreparation = {
+        metadata,
+        vector: Promise.resolve().then(() => getVector(metadata)),
+      };
+    }
+
+    const preparation = cachedPreparation;
+    let vector: Awaited<IntoVector>;
+    try {
+      vector = await preparation.vector;
+    } catch (error) {
+      if (cachedPreparation === preparation) {
+        cachedPreparation = undefined;
+      }
+      throw error;
+    }
+
+    return nearestToNative(route.table.query(), vector);
+  };
+
+  return new AutoQuery(createInner);
+}
+
 /**
 * A query that returns a subset of the rows in the table.
 *
@@ -788,6 +868,51 @@ export class TakeQuery extends QueryBase<NativeTakeQuery> {
  }
 }

+/**
+ * A builder for automatic string searches.
+ *
+ * Automatic search determines whether to use full-text or vector search from
+ * the table revision selected for each execution. This builder exposes the
+ * common operations supported by both query families.
+ *
+ * @hideconstructor
+ */
+export class AutoQuery extends StandardQueryBase<
+  NativeQuery | NativeVectorQuery
+> {
+  private readonly calls: Array<
+    (inner: NativeQuery | NativeVectorQuery) => void
+  > = [];
+
+  /** @hidden */
+  constructor(
+    private readonly createInner: () => Promise<
+      NativeQuery | NativeVectorQuery
+    >,
+  ) {
+    super();
+  }
+
+  /** @hidden */
+  protected override doCall(
+    fn: (inner: NativeQuery | NativeVectorQuery) => void,
+  ) {
+    this.calls.push(fn);
+  }
+
+  /** @hidden */
+  protected override async getInner(): Promise<
+    NativeQuery | NativeVectorQuery
+  > {
+    const calls = [...this.calls];
+    const inner = await this.createInner();
+    for (const call of calls) {
+      call(inner);
+    }
+    return inner;
+  }
+}
+
 /** A builder for LanceDB queries.
 *
 * @see {@link Table#query}, {@link Table#search}
@@ -840,45 +965,19 @@ export class Query extends StandardQueryBase<NativeQuery> {
   * a default `limit` of 10 will be used.  @see {@link Query#limit}
   */
  nearestTo(vector: IntoVector): VectorQuery {
-    const callNearestTo = (
-      inner: NativeQuery,
-      resolved: Float32Array | Float64Array | Uint8Array | number[],
-    ): NativeVectorQuery => {
-      const raw = Array.isArray(resolved)
-        ? null
-        : extractVectorBuffer(resolved);
-      if (raw) {
-        return inner.nearestToRaw(raw.data, raw.dtype);
-      }
-      return inner.nearestTo(Float32Array.from(resolved as number[]));
-    };
-
-    if (this.inner instanceof Promise) {
-      const nativeQuery = this.inner.then(async (inner) => {
-        const resolved = vector instanceof Promise ? await vector : vector;
-        return callNearestTo(inner, resolved);
-      });
+    const inner = this.inner;
+    if (inner instanceof Promise) {
+      const nativeQuery = inner.then(async (resolvedInner) =>
+        nearestToNative(resolvedInner, await vector),
+      );
      return new VectorQuery(nativeQuery);
    }
    if (vector instanceof Promise) {
-      const res = (async () => {
-        try {
-          const v = await vector;
-          // biome-ignore lint/suspicious/noExplicitAny: we need to get the `inner`, but js has no package scoping
-          const value: any = this.nearestTo(v);
-          const inner = value.inner as
-            | NativeVectorQuery
-            | Promise<NativeVectorQuery>;
-          return inner;
-        } catch (e) {
-          return Promise.reject(e);
-        }
-      })();
-      return new VectorQuery(res);
-    } else {
-      const vectorQuery = callNearestTo(this.inner, vector);
-      return new VectorQuery(vectorQuery);
+      return new VectorQuery(
+        vector.then((resolvedVector) => nearestToNative(inner, resolvedVector)),
+      );
    }
+    return new VectorQuery(nearestToNative(inner, vector));
  }

  nearestToText(query: string | FullTextQuery, columns?: string[]): Query {
@@ -43,10 +43,12 @@ import {
  Table as _NativeTable,
 } from "./native";
 import {
+  AutoQuery,
  FullTextQuery,
  Query,
  TakeQuery,
  VectorQuery,
+  createAutoQuery,
  instanceOfFullTextQuery,
 } from "./query";
 import { sanitizeType } from "./sanitize";
@@ -523,7 +525,7 @@ export abstract class Table {
    query: string | IntoVector | MultiVector | FullTextQuery,
    queryType?: string,
    ftsColumns?: string | string[],
-  ): VectorQuery | Query;
+  ): VectorQuery | Query | AutoQuery;
  /**
   * Search the table with a given query vector.
   *
@@ -975,10 +977,11 @@ export class LocalTable extends Table {
    return this.inner.display();
  }

-  private async getEmbeddingFunctions(): Promise<
-    Map<string, EmbeddingFunctionConfig>
-  > {
-    const schema = await this.schema();
+  private async getEmbeddingFunctions(
+    inner: _NativeTable = this.inner,
+  ): Promise<Map<string, EmbeddingFunctionConfig>> {
+    const schemaBuf = await inner.schema();
+    const schema = tableFromIPC(schemaBuf).schema;
    const registry = getRegistry();
    return registry.parseFunctions(schema.metadata);
  }
@@ -1160,7 +1163,7 @@ export class LocalTable extends Table {
    query: string | IntoVector | MultiVector | FullTextQuery,
    queryType: string = "auto",
    ftsColumns?: string | string[],
-  ): VectorQuery | Query {
+  ): VectorQuery | Query | AutoQuery {
    if (typeof query !== "string" && !instanceOfFullTextQuery(query)) {
      if (queryType === "fts") {
        throw new Error("Cannot perform full text search on a vector query");
@@ -1175,14 +1178,28 @@ export class LocalTable extends Table {
      });
    }

-    // The query type is auto or vector
-    // fall back to full text search if no embedding functions are defined and the query is a string
-    if (
-      queryType === "auto" &&
-      (getRegistry().length() === 0 || instanceOfFullTextQuery(query))
-    ) {
-      return this.query().fullTextSearch(query, {
-        columns: ftsColumns,
+    if (queryType === "auto") {
+      if (instanceOfFullTextQuery(query)) {
+        return this.query().fullTextSearch(query, {
+          columns: ftsColumns,
+        });
+      }
+
+      const columns =
+        typeof ftsColumns === "string" ? [ftsColumns] : (ftsColumns ?? null);
+      return createAutoQuery(this.inner, query, columns, async (metadata) => {
+        const functions = await getRegistry().parseFunctions(
+          new Map([["embedding_functions", metadata]]),
+        );
+        // TODO: Support multiple embedding functions
+        const embeddingFunc: EmbeddingFunctionConfig | undefined = functions
+          .values()
+          .next().value;
+        // The route only calls this callback when embedding metadata exists.
+        // parseFunctions either yields a provider or reports malformed metadata.
+        if (!embeddingFunc)
+          throw new Error("Invalid embedding function metadata");
+        return await embeddingFunc.function.computeQueryEmbeddings(query);
      });
    }

@@ -278,6 +278,13 @@ impl Table {
        Ok(Query::new(self.inner_ref()?.query()))
    }

+    /// Return a read-only table handle pinned to the current query revision.
+    #[napi(catch_unwind)]
+    pub async fn query_snapshot(&self) -> napi::Result<Self> {
+        let snapshot = self.inner_ref()?.query_snapshot().await.default_error()?;
+        Ok(Self::new(snapshot))
+    }
+
    #[napi(catch_unwind)]
    pub fn take_offsets(&self, offsets: Vec<i64>) -> napi::Result<TakeQuery> {
        Ok(TakeQuery::new(
@@ -554,6 +561,12 @@ impl Table {
            .default_error()
    }

+    #[napi(catch_unwind)]
+    pub async fn checkout_current(&self) -> napi::Result<Self> {
+        let table = self.inner_ref()?.checkout_current().await.default_error()?;
+        Ok(Self::new(table))
+    }
+
    #[napi(catch_unwind)]
    pub async fn checkout(&self, version: i64) -> napi::Result<()> {
        self.inner_ref()?
@@ -179,6 +179,18 @@ def connect(
    ...     },
    ... )

+    For Azure Blob Storage, credentials can be passed directly without setting
+    environment variables:
+
+    >>> azure_storage_options = {
+    ...     "account_name": "some-account",
+    ...     "account_key": "some-key",
+    ... }
+    >>> db = lancedb.connect(  # doctest: +SKIP
+    ...     "az://my-container/my-database",
+    ...     storage_options=azure_storage_options,
+    ... )
+
    For tests and temporary data, use an in-memory database:

    >>> db = lancedb.connect("memory://")
@@ -465,6 +477,10 @@ async def connect_async(
    --------

    >>> import lancedb
+    >>> azure_storage_options = {
+    ...     "account_name": "some-account",
+    ...     "account_key": "some-key",
+    ... }
    >>> async def doctest_example():
    ...     # For a local directory, provide a path to the database
    ...     db = await lancedb.connect_async("~/.lancedb")
@@ -472,6 +488,11 @@ async def connect_async(
    ...     db = await lancedb.connect_async("s3://my-bucket/lancedb",
    ...                                      storage_options={
    ...                                          "aws_access_key_id": "***"})
+    ...     # Azure credentials can also be passed directly
+    ...     db = await lancedb.connect_async(
+    ...         "az://my-container/my-database",
+    ...         storage_options=azure_storage_options,
+    ...     )
    ...     # For tests and temporary data, use an in-memory database
    ...     db = await lancedb.connect_async("memory://")
    ...     # Connect to LanceDB cloud
@@ -3378,9 +3378,10 @@ class AsyncQuery(AsyncStandardQuery):
        pass in multiple vectors. When multiple vectors are passed in, if the vector
        column is with multivector type, then the vectors will be treated as a single
        query. Or the vectors will be treated as multiple queries, this can be useful
-        if you want to find the nearest vectors to multiple query vectors.
-        This is not expected to be faster than making multiple queries concurrently;
-        it is just a convenience method. If multiple vectors are passed in then
+        if you want to find the nearest vectors to multiple query vectors. Flat
+        searches share one table scan across the query vectors, avoiding the scan
+        and memory amplification of making multiple queries concurrently. If
+        multiple vectors are passed in then
        an additional column `query_index` will be added to the results. This column
        will contain the index of the query vector that the result is nearest to.
        """
@@ -3509,8 +3510,8 @@ class AsyncFTSQuery(AsyncStandardQuery):

        Typically, a single vector is passed in as the query. However, you can also
        pass in multiple vectors.  This can be useful if you want to find the nearest
-        vectors to multiple query vectors. This is not expected to be faster than
-        making multiple queries concurrently; it is just a convenience method.
+        vectors to multiple query vectors. Flat searches share one table scan across
+        the query vectors instead of issuing concurrent full scans.
        If multiple vectors are passed in then an additional column `query_index`
        will be added to the results.  This column will contain the index of the
        query vector that the result is nearest to.
@@ -897,6 +897,23 @@ def test_query_builder_batches(table):
    assert rs_list["id"][1] == 2


+def test_batch_vector_query_shares_filtered_flat_scan(table):
+    query = (
+        table.search([[1.0, 2.0], [3.0, 4.0]])
+        .where("id > 0", prefilter=True)
+        .limit(1)
+        .select(["id"])
+    )
+
+    plan = query.explain_plan(verbose=True)
+    assert "KNNVectorDistance: queries=2" in plan
+    assert "UnionExec" not in plan
+
+    results = query.to_arrow()
+    assert len(results) == 2
+    assert results["query_index"].to_pylist() == [0, 1]
+
+
 def test_dynamic_projection(table):
    rs = (
        LanceVectorQueryBuilder(table, [0, 0], "vector")
@@ -10,7 +10,7 @@ use lance::io::WrappingObjectStore;
 use object_store::{
    CopyOptions, Error, GetOptions, GetResult, ListResult, MultipartUpload, ObjectMeta,
    ObjectStore, ObjectStoreExt, PutMultipartOptions, PutOptions, PutPayload, PutResult, Result,
-    UploadPart, path::Path,
+    UploadPart, list::PaginatedListStore, path::Path,
 };

 use async_trait::async_trait;
@@ -187,6 +187,14 @@ impl WrappingObjectStore for MirroringObjectStoreWrapper {
            secondary: self.secondary.clone(),
        })
    }
+
+    fn wrap_paginated(
+        &self,
+        _store_prefix: &str,
+        original: Arc<dyn PaginatedListStore>,
+    ) -> Option<Arc<dyn PaginatedListStore>> {
+        Some(original)
+    }
 }

 // windows pathing can't be simply concatenated
@@ -12,7 +12,7 @@ use lance::io::WrappingObjectStore;
 use object_store::{
    CopyOptions, GetOptions, GetResult, ListResult, MultipartUpload, ObjectMeta, ObjectStore,
    PutMultipartOptions, PutOptions, PutPayload, PutResult, RenameOptions, Result as OSResult,
-    UploadPart, path::Path,
+    UploadPart, list::PaginatedListStore, path::Path,
 };

 #[derive(Debug, Default)]
@@ -57,6 +57,14 @@ impl WrappingObjectStore for IoStatsHolder {
            stats: self.0.clone(),
        })
    }
+
+    fn wrap_paginated(
+        &self,
+        _store_prefix: &str,
+        original: Arc<dyn PaginatedListStore>,
+    ) -> Option<Arc<dyn PaginatedListStore>> {
+        Some(original)
+    }
 }

 impl IoTrackingStore {
@@ -1174,12 +1174,12 @@ impl VectorQuery {

    /// Add another query vector to the search.
    ///
-    /// Multiple searches will be dispatched as part of the query.
-    /// This is a convenience method for adding multiple query vectors
-    /// to the search. It is not expected to be faster than issuing
-    /// multiple queries concurrently.
+    /// Multiple searches will be dispatched as a batch. Flat searches share
+    /// one table scan across the query vectors, avoiding the scan and memory
+    /// amplification of issuing the searches concurrently. Indexed searches
+    /// may still perform per-vector index work.
    ///
-    /// The output data will contain an additional columns `query_index` which
+    /// The output data will contain an additional column `query_index` which
    /// will contain the index of the query vector that was used to generate the
    /// result.
    pub fn add_query_vector(mut self, vector: impl IntoQueryVector) -> Result<Self> {
@@ -1646,7 +1646,11 @@ mod tests {
    use std::{collections::HashSet, sync::Arc};

    use super::*;
-    use arrow::{array::downcast_array, compute::concat_batches, datatypes::Int32Type};
+    use arrow::{
+        array::downcast_array,
+        compute::concat_batches,
+        datatypes::{Int32Type, UInt8Type},
+    };
    use arrow_array::{
        FixedSizeListArray, Float32Array, Int32Array, RecordBatch, StringArray, cast::AsArray,
        types::Float32Type,
@@ -2334,7 +2338,8 @@ mod tests {
            .limit(1);

        let plan = query.explain_plan(true).await.unwrap();
-        assert!(plan.contains("UnionExec"));
+        assert!(plan.contains("KNNVectorDistance: queries=2"));
+        assert!(!plan.contains("UnionExec"));

        let results = query
            .execute()
@@ -2349,6 +2354,100 @@ mod tests {
        // We don't guarantee order.
        assert!(query_index.values().contains(&0));
        assert!(query_index.values().contains(&1));
+
+        // Batch KNN does not support a per-query offset, so offset queries keep
+        // the legacy per-vector plan to preserve their result semantics.
+        let offset_query = table
+            .query()
+            .nearest_to(&[0.1, 0.2, 0.3, 0.4])
+            .unwrap()
+            .add_query_vector(&[0.5, 0.6, 0.7, 0.8])
+            .unwrap()
+            .limit(1)
+            .offset(1);
+        assert!(
+            offset_query
+                .explain_plan(true)
+                .await
+                .unwrap()
+                .contains("UnionExec")
+        );
+        let offset_results = offset_query
+            .execute()
+            .await
+            .unwrap()
+            .try_collect::<Vec<_>>()
+            .await
+            .unwrap();
+        assert_eq!(
+            offset_results
+                .iter()
+                .map(RecordBatch::num_rows)
+                .sum::<usize>(),
+            2
+        );
+    }
+
+    #[tokio::test]
+    async fn test_multiple_binary_query_vectors() {
+        let vectors = FixedSizeListArray::from_iter_primitive::<UInt8Type, _, _>(
+            vec![
+                Some(vec![Some(0), Some(0)]),
+                Some(vec![Some(255), Some(255)]),
+            ],
+            2,
+        );
+        let schema = Arc::new(ArrowSchema::new(vec![
+            ArrowField::new("id", DataType::Int32, false),
+            ArrowField::new("vector", vectors.data_type().clone(), false),
+        ]));
+        let batch = RecordBatch::try_new(
+            schema,
+            vec![Arc::new(Int32Array::from(vec![0, 1])), Arc::new(vectors)],
+        )
+        .unwrap();
+
+        let conn = connect("memory://").execute().await.unwrap();
+        let table = conn
+            .create_table("binary_batch", batch)
+            .execute()
+            .await
+            .unwrap();
+        let query = table
+            .query()
+            .nearest_to(&[0.0, 0.0])
+            .unwrap()
+            .add_query_vector(&[255.0, 255.0])
+            .unwrap()
+            .distance_type(DistanceType::Hamming)
+            .limit(1);
+
+        // Binary queries retain the per-vector plan because Lance's binary
+        // nearest path requires primitive UInt8 query arrays.
+        assert!(
+            query
+                .explain_plan(true)
+                .await
+                .unwrap()
+                .contains("UnionExec")
+        );
+
+        let results = query
+            .execute()
+            .await
+            .unwrap()
+            .try_collect::<Vec<_>>()
+            .await
+            .unwrap();
+        let results = concat_batches(&results[0].schema(), &results).unwrap();
+        assert_eq!(results.num_rows(), 2);
+
+        let ids = results["id"].as_primitive::<Int32Type>();
+        assert!(ids.values().contains(&0));
+        assert!(ids.values().contains(&1));
+        let query_index = results["query_index"].as_primitive::<Int32Type>();
+        assert!(query_index.values().contains(&0));
+        assert!(query_index.values().contains(&1));
    }

    #[tokio::test]
@@ -1722,9 +1722,39 @@ impl<S: HttpSend> BaseTable for RemoteTable<S> {
    fn id(&self) -> &str {
        &self.identifier
    }
+    async fn query_snapshot(&self) -> Result<Arc<dyn BaseTable>> {
+        let description = self.describe().await?;
+        let TableDescription {
+            version,
+            schema,
+            location,
+        } = description;
+        let schema = Arc::new(arrow_schema::Schema::try_from(schema)?);
+        let snapshot = self.with_branch(self.branch.clone());
+        *snapshot.version.write().await = Some(version);
+        *snapshot.location.write().await = location;
+        snapshot.schema_cache.seed(schema);
+        Ok(Arc::new(snapshot))
+    }
    async fn version(&self) -> Result<u64> {
        self.describe().await.map(|desc| desc.version)
    }
+
+    async fn checkout_current(&self) -> Result<Arc<dyn BaseTable>> {
+        let description = self.describe().await?;
+        let TableDescription {
+            version,
+            schema,
+            location,
+        } = description;
+        let schema = Arc::new(arrow_schema::Schema::try_from(schema)?);
+        let snapshot = self.with_branch(self.branch.clone());
+        *snapshot.version.write().await = Some(version);
+        *snapshot.location.write().await = location;
+        snapshot.schema_cache.seed(schema);
+        Ok(Arc::new(snapshot))
+    }
+
    async fn checkout(&self, version: u64) -> Result<()> {
        // Validate the version exists. The describe is sent without freshness
        // headers so a stale `min_version` from a previous write doesn't ride
@@ -8739,6 +8769,28 @@ mod tests {
        }
    }

+    /// A pinned snapshot should reuse the version and schema returned by its
+    /// initial describe instead of issuing two more describe requests.
+    #[tokio::test]
+    async fn test_checkout_current_seeds_schema_from_single_describe() {
+        let describe_calls = Arc::new(AtomicUsize::new(0));
+        let calls = describe_calls.clone();
+        let table = Table::new_with_handler("my_table", move |request| {
+            assert_eq!(request.url().path(), "/v1/table/my_table/describe/");
+            calls.fetch_add(1, Ordering::SeqCst);
+            http::Response::builder()
+                .status(200)
+                .body(
+                    r#"{"version":42,"schema":{"fields":[{"name":"a","type":{"type":"int32"},"nullable":false}]}}"#,
+                )
+                .unwrap()
+        });
+
+        let snapshot = table.checkout_current().await.unwrap();
+        assert_eq!(snapshot.schema().await.unwrap().fields().len(), 1);
+        assert_eq!(describe_calls.load(Ordering::SeqCst), 1);
+    }
+
    /// Test that schema cache is invalidated after checkout
    #[tokio::test]
    async fn test_schema_cache_invalidation_on_checkout() {
@@ -560,6 +560,13 @@ pub trait BaseTable: std::fmt::Display + std::fmt::Debug + Send + Sync {
    fn id(&self) -> &str;
    /// Get the arrow [Schema] of the table.
    async fn schema(&self) -> Result<SchemaRef>;
+    /// Create a read-only handle pinned to the table's current active revision.
+    ///
+    /// The returned handle is independent from later refreshes or checkouts on
+    /// this handle.  This is used by bindings that must prepare client-side
+    /// query state from the same revision that the query will execute against.
+    #[doc(hidden)]
+    async fn query_snapshot(&self) -> Result<Arc<dyn BaseTable>>;
    /// Count the number of rows in this table.
    async fn count_rows(&self, filter: Option<Filter>) -> Result<usize>;
    /// Create a physical plan for the query.
@@ -785,6 +792,12 @@ pub trait BaseTable: std::fmt::Display + std::fmt::Debug + Send + Sync {
    async fn drop_columns(&self, columns: &[&str]) -> Result<DropColumnsResult>;
    /// Get the version of the table.
    async fn version(&self) -> Result<u64>;
+    /// Return a new table handle pinned to the exact revision currently visible.
+    async fn checkout_current(&self) -> Result<Arc<dyn BaseTable>> {
+        Err(Error::NotSupported {
+            message: "checkout_current is not supported on this table type".into(),
+        })
+    }
    /// Checkout a specific version of the table.
    async fn checkout(&self, version: u64) -> Result<()>;
    /// Checkout a table version referenced by a tag.
@@ -1133,6 +1146,16 @@ impl Table {
        self.inner.schema().await
    }

+    /// Create a read-only handle pinned to the current active revision.
+    #[doc(hidden)]
+    pub async fn query_snapshot(&self) -> Result<Self> {
+        Ok(Self {
+            inner: self.inner.query_snapshot().await?,
+            database: self.database.clone(),
+            embedding_registry: self.embedding_registry.clone(),
+        })
+    }
+
    /// Count the number of rows in this dataset.
    ///
    /// # Arguments
@@ -1944,6 +1967,20 @@ impl Table {
        self.inner.version().await
    }

+    /// Return a new table handle pinned to the exact revision currently visible.
+    ///
+    /// This is used when asynchronous preparation must remain consistent with
+    /// the revision used for a later read.
+    #[doc(hidden)]
+    pub async fn checkout_current(&self) -> Result<Self> {
+        let inner = self.inner.checkout_current().await?;
+        Ok(Self {
+            inner,
+            database: self.database.clone(),
+            embedding_registry: self.embedding_registry.clone(),
+        })
+    }
+
    /// Checks out a specific version of the Table
    ///
    /// Any read operation on the table will now access the data at the checked out version.
@@ -3039,10 +3076,33 @@ impl BaseTable for NativeTable {
        &self.id
    }

+    async fn query_snapshot(&self) -> Result<Arc<dyn BaseTable>> {
+        let snapshot = self.dataset.new_query_snapshot().await?;
+        let mut table = self.with_dataset(snapshot);
+        // QueryTable requests do not carry a revision. A pinned snapshot must
+        // execute locally until the namespace API can accept that revision.
+        table
+            .pushdown_operations
+            .remove(&NamespaceClientPushdownOperation::QueryTable);
+        Ok(Arc::new(table))
+    }
+
    async fn version(&self) -> Result<u64> {
        Ok(self.dataset.get().await?.version().version)
    }

+    async fn checkout_current(&self) -> Result<Arc<dyn BaseTable>> {
+        let current = self.dataset.get().await?;
+        let dataset = dataset::DatasetConsistencyWrapper::new_time_travel(
+            current.as_ref().clone(),
+            self.read_consistency_interval,
+        );
+        Ok(Arc::new(Self {
+            dataset,
+            ..self.clone()
+        }))
+    }
+
    async fn checkout(&self, version: u64) -> Result<()> {
        self.dataset.as_time_travel(version).await
    }
@@ -4086,6 +4146,14 @@ mod tests {
                parent_list_calls: self.parent_list_calls.clone(),
            })
        }
+
+        fn wrap_paginated(
+            &self,
+            _store_prefix: &str,
+            _original: Arc<dyn object_store::list::PaginatedListStore>,
+        ) -> Option<Arc<dyn object_store::list::PaginatedListStore>> {
+            None
+        }
    }

    #[tokio::test]
@@ -4189,6 +4257,14 @@ mod tests {
            self.called.store(true, Ordering::Relaxed);
            original
        }
+
+        fn wrap_paginated(
+            &self,
+            _store_prefix: &str,
+            original: Arc<dyn object_store::list::PaginatedListStore>,
+        ) -> Option<Arc<dyn object_store::list::PaginatedListStore>> {
+            Some(original)
+        }
    }

    #[tokio::test]
@@ -32,6 +32,10 @@ struct DatasetState {
    /// `Some(version)` = pinned to a specific version (time travel),
    /// `None` = tracking latest.
    pinned_version: Option<u64>,
+    /// Whether the pin is an internal query snapshot rather than user-visible
+    /// time travel. Query snapshots remain read-only but preserve MemWAL read
+    /// semantics.
+    query_snapshot: bool,
 }

 #[derive(Debug, Clone)]
@@ -70,6 +74,7 @@ impl DatasetConsistencyWrapper {
            state: Arc::new(Mutex::new(DatasetState {
                dataset,
                pinned_version: None,
+                query_snapshot: false,
            })),
            consistency,
            shard_writer: Arc::new(ShardWriterCache::default()),
@@ -93,6 +98,36 @@ impl DatasetConsistencyWrapper {
        wrapper
    }

+    /// Create an independent read-only wrapper pinned to the current dataset
+    /// while retaining this wrapper's live MemWAL read context.
+    pub async fn new_query_snapshot(&self) -> Result<Self> {
+        // Apply the configured consistency policy before taking the snapshot.
+        // The returned dataset is intentionally discarded: a checkout may race
+        // after this await, so the dataset and its pin provenance must instead
+        // be cloned together from one authoritative state sample below.
+        self.get().await?;
+
+        let (dataset, query_snapshot) = {
+            let state = self.state.lock()?;
+            // Preserve user time travel so the MemWAL safety guard still sees
+            // it. Latest and already-internal snapshots remain internal pins.
+            (
+                state.dataset.clone(),
+                state.query_snapshot || state.pinned_version.is_none(),
+            )
+        };
+        let version = dataset.version().version;
+        Ok(Self {
+            state: Arc::new(Mutex::new(DatasetState {
+                dataset,
+                pinned_version: Some(version),
+                query_snapshot,
+            })),
+            consistency: ConsistencyMode::Lazy,
+            shard_writer: self.shard_writer.clone(),
+        })
+    }
+
    /// The MemWAL `ShardWriter` cache co-located with this dataset.
    pub(crate) fn shard_writer(&self) -> &Arc<ShardWriterCache> {
        &self.shard_writer
@@ -169,6 +204,7 @@ impl DatasetConsistencyWrapper {
        let mut state = self.state.lock()?;
        state.dataset = Arc::new(new_dataset);
        state.pinned_version = None;
+        state.query_snapshot = false;
        drop(state);
        if let ConsistencyMode::Eventual(bg_cache) = &self.consistency {
            bg_cache.invalidate();
@@ -202,10 +238,10 @@ impl DatasetConsistencyWrapper {

    /// Returns the version, if in time travel mode, or None otherwise.
    pub fn time_travel_version(&self) -> Option<u64> {
-        self.state
-            .lock()
-            .unwrap_or_else(|e| e.into_inner())
-            .pinned_version
+        let state = self.state.lock().unwrap_or_else(|e| e.into_inner());
+        (!state.query_snapshot)
+            .then_some(state.pinned_version)
+            .flatten()
    }

    /// Convert into a wrapper in latest version mode.
@@ -225,6 +261,7 @@ impl DatasetConsistencyWrapper {
        if state.pinned_version.is_some() {
            state.dataset = Arc::new(new_dataset);
            state.pinned_version = None;
+            state.query_snapshot = false;
        }
        drop(state);
        if let ConsistencyMode::Eventual(bg_cache) = &self.consistency {
@@ -260,6 +297,7 @@ impl DatasetConsistencyWrapper {
        let mut state = self.state.lock()?;
        state.dataset = Arc::new(new_dataset);
        state.pinned_version = Some(version_value);
+        state.query_snapshot = false;
        Ok(())
    }

@@ -461,6 +499,29 @@ mod tests {
        assert_eq!(wrapper.time_travel_version(), Some(1));
    }

+    #[tokio::test]
+    async fn test_query_snapshot_samples_dataset_and_pin_together() {
+        let dir = tempfile::tempdir().unwrap();
+        let uri = dir.path().to_str().unwrap();
+        let ds = create_test_dataset(uri).await;
+
+        let wrapper = DatasetConsistencyWrapper::new_latest(ds, None);
+        wrapper.as_time_travel(1u64).await.unwrap();
+        let stale_time_travel_dataset = wrapper.get().await.unwrap();
+
+        append_to_dataset(uri).await;
+        wrapper.as_latest().await.unwrap();
+
+        let snapshot = wrapper.new_query_snapshot().await.unwrap();
+        let snapshot_dataset = snapshot.get().await.unwrap();
+        assert_eq!(snapshot_dataset.version().version, 2);
+        assert_ne!(
+            snapshot_dataset.version().version,
+            stale_time_travel_dataset.version().version
+        );
+        assert_eq!(snapshot.time_travel_version(), None);
+    }
+
    #[tokio::test]
    async fn test_as_latest_from_time_travel() {
        let dir = tempfile::tempdir().unwrap();
@@ -1056,6 +1056,44 @@ mod lsm_tests {
        );
    }

+    #[tokio::test]
+    async fn query_snapshot_preserves_lsm_read_semantics() {
+        let dir = tempdir().unwrap();
+        let table = id_value_table(&dir).await;
+        table
+            .set_lsm_write_spec(LsmWriteSpec::unsharded())
+            .await
+            .unwrap();
+        lsm_upsert(&table, vec![4, 5]).await;
+
+        let snapshot = table.query_snapshot().await.unwrap();
+        let rows = collect_id_value(snapshot.query().execute().await.unwrap()).await;
+        assert_eq!(
+            rows.iter().map(|(id, _)| *id).collect::<Vec<_>>(),
+            vec![1, 2, 3, 4, 5]
+        );
+    }
+
+    #[tokio::test]
+    async fn query_snapshot_preserves_time_travel_lsm_guard() {
+        let dir = tempdir().unwrap();
+        let table = id_value_table(&dir).await;
+        table
+            .set_lsm_write_spec(LsmWriteSpec::unsharded())
+            .await
+            .unwrap();
+        lsm_upsert(&table, vec![4]).await;
+
+        let version = table.version().await.unwrap();
+        table.checkout(version).await.unwrap();
+        let direct_error = table.query().execute().await.err().unwrap();
+        assert!(matches!(direct_error, Error::NotSupported { .. }));
+
+        let snapshot = table.query_snapshot().await.unwrap();
+        let snapshot_error = snapshot.query().execute().await.err().unwrap();
+        assert!(matches!(snapshot_error, Error::NotSupported { .. }));
+    }
+
    #[tokio::test]
    async fn lsm_read_dedup_newest_wins() {
        let dir = tempdir().unwrap();
@@ -21,7 +21,6 @@ use datafusion_physical_plan::ExecutionPlan;
 use datafusion_physical_plan::projection::ProjectionExec;
 use datafusion_physical_plan::repartition::RepartitionExec;
 use datafusion_physical_plan::union::UnionExec;
-use futures::future::try_join_all;
 use lance::dataset::mem_wal::DatasetMemWalExt;
 use lance::dataset::scanner::DatasetRecordBatchStream;
 use lance::dataset::scanner::Scanner;
@@ -170,6 +169,7 @@ pub async fn create_plan(
    let mut column = query.column.clone();

    let mut query_vector = query.query_vector.first().cloned();
+    let mut is_batch_query = false;
    if query.query_vector.len() > 1 {
        if column.is_none() {
            // Infer a vector column with the same dimension of the query vector.
@@ -180,16 +180,37 @@ pub async fn create_plan(
            )?);
        }
        let vector_field = schema.field(column.as_ref().unwrap()).unwrap();
-        if let DataType::List(_) = vector_field.data_type() {
-            // Multivector handling: concatenate into FixedSizeList<FixedSizeList<_>>
+        let (_, element_type) =
+            lance::index::vector::utils::get_vector_type(schema, column.as_ref().unwrap())?;
+        let is_binary = matches!(element_type, DataType::UInt8);
+        if matches!(vector_field.data_type(), DataType::List(_))
+            || (query.base.offset.unwrap_or(0) == 0 && !is_binary)
+        {
+            // Lance distinguishes these cases from the vector column type: a
+            // list-like query against a List column is one multivector query,
+            // while the same query against a FixedSizeList column is a batch of
+            // independent queries. The batch path shares a single flat scan and
+            // bounds retained candidate data instead of running one scan per
+            // query vector.
            let vectors = query
                .query_vector
                .iter()
                .map(|arr| arr.as_ref())
                .collect::<Vec<_>>();
            let dim = vectors[0].len();
+            if let Some((query_index, actual_dim)) = vectors
+                .iter()
+                .enumerate()
+                .find_map(|(index, vector)| (vector.len() != dim).then_some((index, vector.len())))
+            {
+                return Err(Error::InvalidInput {
+                    message: format!(
+                        "query vector at index {query_index} has dimension {actual_dim}, expected {dim}"
+                    ),
+                });
+            }
            let mut fsl_builder = FixedSizeListBuilder::with_capacity(
-                Float32Builder::with_capacity(dim),
+                Float32Builder::with_capacity(dim * vectors.len()),
                dim as i32,
                vectors.len(),
            );
@@ -200,8 +221,12 @@ pub async fn create_plan(
                fsl_builder.append(true);
            }
            query_vector = Some(Arc::new(fsl_builder.finish()));
+            is_batch_query = !matches!(vector_field.data_type(), DataType::List(_));
        } else {
-            // Multiple query vectors: create a plan for each and union them
+            // Lance's batch path has no per-query offset, and its binary path
+            // requires primitive UInt8 queries rather than a fixed-size list.
+            // Keep the prior plan shape for these cases so offsets are applied
+            // per query and binary query vectors retain their primitive shape.
            let query_vecs = query.query_vector.clone();
            let plan_futures = query_vecs
                .into_iter()
@@ -214,7 +239,7 @@ pub async fn create_plan(
                    }
                })
                .collect::<Vec<_>>();
-            let plans = try_join_all(plan_futures).await?;
+            let plans = futures::future::try_join_all(plan_futures).await?;
            return create_multi_vector_plan(plans);
        }
    }
@@ -251,10 +276,14 @@ pub async fn create_plan(
        }
    }

-    scanner.limit(
-        query.base.limit.map(|limit| limit as i64),
-        query.base.offset.map(|offset| offset as i64),
-    )?;
+    // For a batch query, `nearest` already applies k to each query vector.
+    // Adding Scanner's global limit would truncate the combined result to k rows.
+    if !is_batch_query {
+        scanner.limit(
+            query.base.limit.map(|limit| limit as i64),
+            query.base.offset.map(|offset| offset as i64),
+        )?;
+    }

    if let Some(ef) = query.ef {
        scanner.ef(ef);
@@ -697,6 +726,7 @@ mod tests {

    use super::*;
    use crate::query::{QueryExecutionOptions, QueryRequest};
+    use crate::table::BaseTable;

    fn fixed_size_list_array(values: Vec<f32>, dimension: i32) -> FixedSizeListArray {
        FixedSizeListArray::try_new_from_values(Float32Array::from(values), dimension).unwrap()
@@ -889,10 +919,56 @@ mod tests {

        async fn query_table(&self, _request: NsQueryTableRequest) -> lance::Result<bytes::Bytes> {
            self.query_table_calls.fetch_add(1, Ordering::SeqCst);
-            panic!("approx_mode queries must not be pushed down to namespace query_table");
+            panic!("query must not be pushed down to namespace query_table");
        }
    }

+    #[tokio::test]
+    async fn test_execute_query_pinned_snapshot_with_namespace_pushdown_runs_locally() {
+        use crate::connect;
+        use arrow_array::{Int32Array, RecordBatch};
+        use arrow_schema::{DataType, Field, Schema};
+
+        let conn = connect("memory://").execute().await.unwrap();
+        let schema = Arc::new(Schema::new(vec![Field::new("id", DataType::Int32, false)]));
+        let batch = RecordBatch::try_new(
+            schema,
+            vec![Arc::new(Int32Array::from(vec![1, 2, 3, 4, 5]))],
+        )
+        .unwrap();
+        let table = conn
+            .create_table("test_pinned_namespace_fallback", vec![batch])
+            .execute()
+            .await
+            .unwrap();
+
+        let namespace_client = Arc::new(CountingNamespaceClient::default());
+        let mut native_table = table.as_native().unwrap().clone();
+        native_table.namespace_client = Some(namespace_client.clone());
+        native_table
+            .pushdown_operations
+            .insert(NamespaceClientPushdownOperation::QueryTable);
+
+        let snapshot = native_table.checkout_current().await.unwrap();
+        let snapshot = snapshot.as_any().downcast_ref::<NativeTable>().unwrap();
+        assert!(snapshot.dataset.time_travel_version().is_some());
+
+        let query = AnyQuery::Query(QueryRequest {
+            filter: Some(QueryFilter::Sql("id > 3".to_string())),
+            ..Default::default()
+        });
+        let stream = execute_query(snapshot, &query, QueryExecutionOptions::default())
+            .await
+            .unwrap();
+        let batches = stream.try_collect::<Vec<_>>().await.unwrap();
+
+        assert_eq!(
+            batches.iter().map(|batch| batch.num_rows()).sum::<usize>(),
+            2
+        );
+        assert_eq!(namespace_client.query_table_calls.load(Ordering::SeqCst), 0);
+    }
+
    #[tokio::test]
    async fn test_execute_query_approx_mode_with_namespace_pushdown_runs_locally() {
        use crate::connect;
@@ -1010,7 +1086,38 @@ mod tests {
    }

    #[tokio::test]
-    async fn test_create_plan_multivector_structure() {
+    async fn test_query_snapshot_disables_namespace_pushdown() {
+        use crate::connect;
+        use crate::table::BaseTable;
+        use arrow_array::{Int32Array, RecordBatch};
+        use arrow_schema::{DataType, Field, Schema};
+
+        let conn = connect("memory://").execute().await.unwrap();
+        let schema = Arc::new(Schema::new(vec![Field::new("id", DataType::Int32, false)]));
+        let batch =
+            RecordBatch::try_new(schema, vec![Arc::new(Int32Array::from(vec![1, 2, 3]))]).unwrap();
+        let table = conn
+            .create_table("test_snapshot_namespace_fallback", vec![batch])
+            .execute()
+            .await
+            .unwrap();
+        let mut native_table = table.as_native().unwrap().clone();
+        native_table.namespace_client = Some(Arc::new(CountingNamespaceClient::default()));
+        native_table
+            .pushdown_operations
+            .insert(NamespaceClientPushdownOperation::QueryTable);
+
+        let snapshot = BaseTable::query_snapshot(&native_table).await.unwrap();
+        let snapshot = snapshot.as_any().downcast_ref::<NativeTable>().unwrap();
+        assert!(
+            !can_execute_namespace_query(snapshot, &AnyQuery::Query(QueryRequest::default()),)
+                .await
+                .unwrap()
+        );
+    }
+
+    #[tokio::test]
+    async fn test_create_plan_batch_vector_uses_shared_scan() {
        use arrow_array::{Float32Array, RecordBatch};
        use arrow_schema::{DataType, Field, Schema};
        use datafusion_physical_plan::display::DisplayableExecutionPlan;
@@ -1037,11 +1144,18 @@ mod tests {
            .unwrap();
        let native_table = table.as_native().unwrap();

-        // This triggers the "create_multi_vector_plan" logic branch
+        // A batch of vectors against a fixed-size vector column should use
+        // Lance's native batch KNN path instead of independent scan plans.
        let q1 = Arc::new(Float32Array::from(vec![1.0, 2.0]));
        let q2 = Arc::new(Float32Array::from(vec![3.0, 4.0]));

        let req = VectorQueryRequest {
+            base: QueryRequest {
+                filter: Some(QueryFilter::Sql("id >= 0".to_string())),
+                limit: Some(1),
+                select: Select::Columns(vec!["id".to_string()]),
+                ..Default::default()
+            },
            column: Some("vector".to_string()),
            query_vector: vec![q1, q2],
            ..Default::default()
@@ -1058,19 +1172,17 @@ mod tests {
            .indent(true)
            .to_string();

-        // We expect a RepartitionExec wrapping a UnionExec
        assert!(
-            display.contains("RepartitionExec"),
-            "Plan should include Repartitioning"
+            display.contains("KNNVectorDistance: queries=2"),
+            "plan should use native batch KNN, got:\n{display}"
        );
        assert!(
-            display.contains("UnionExec"),
-            "Plan should include a Union of multiple searches"
+            !display.contains("UnionExec"),
+            "flat batch KNN should share one scan, got:\n{display}"
        );
-        // We expect the projection to add the 'query_index' column (logic inside multi_vector_plan)
        assert!(
            display.contains("query_index"),
-            "Plan should add query_index column"
+            "plan should add query_index column, got:\n{display}"
        );
    }

@@ -298,7 +298,7 @@ async fn build_read_context(
    for shard_id in shard_ids {
        let manifest_store =
            ShardManifestStore::new(store.clone(), &base_path, shard_id, scan_batch_size);
-        if let Some(manifest) = manifest_store.read_latest().await? {
+        if let Some(manifest) = manifest_store.latest().await? {
            snapshots.push(snapshot_from_manifest(shard_id, &manifest, &exclude));
        }
    }
Author	SHA1	Message	Date
Gatefixer	c2d251619a	Merge remote-tracking branch 'origin/main' into gatekeeper/fix-2468-1	2026-08-26 03:01:31 +00:00
Gatefixer	09f8aa0267	fix: preserve binary batch vector queries	2026-08-26 03:01:14 +00:00
LanceDB Robot	21530432a0	chore: update lance dependency to v12.0.0-beta.2 (#4056 ) Updates the Rust workspace Lance crates and Java lance-core dependency to [v12.0.0-beta.2](https://github.com/lance-format/lance/releases/tag/v12.0.0-beta.2). No compatibility fixes were required; full-workspace Clippy passes with all features and warnings denied.	2026-08-25 21:56:26 -05:00
Gatefixer	6b90ddd1b6	Merge remote-tracking branch 'origin/main' into gatekeeper/fix-2468-1 # Conflicts: # rust/lancedb/src/table/query.rs	2026-08-26 02:43:02 +00:00
lancedb-gatefixer[bot]	9b825c5f29	fix(node): route auto search using table embeddings (#3832 ) ## Summary - Resolve automatic string-search routing from the active table schema whenever the query executes. - Defer embedding-provider construction while leaving explicit vector and FTS routes unchanged. - Cover unrelated global registrations and metadata transitions across repeated executions of one query builder. ## Root cause LocalTable.search used the number of globally registered embedding providers to choose between vector and full-text search. A provider registered for any other table therefore sent a plain FTS table down the vector path. A wrapper-lifetime metadata snapshot avoided that contamination but became stale after time travel or read-consistency refreshes. The query now records fluent builder operations and creates the appropriate native vector or FTS query from the active schema on each execution. ## Validation - pnpm build - pnpm tsc - pnpm lint - pnpm run docs - pnpm test --runInBand (681 passed, 5 skipped) Fixes #1557 <!-- lance-gatekeeper-fix:v1 agent=b6183df8296db4aabdc5d19a2256b029 generation=1 --> --------- Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com> Co-authored-by: Xuanwo <github@xuanwo.io>	2026-08-26 10:08:48 +08:00
LanceDB Robot	8083232dd5	chore: update lance dependency to v12.0.0-beta.1 (#4055 ) Updates the Lance Rust workspace dependencies and Java lance-core dependency to [v12.0.0-beta.1](https://github.com/lance-format/lance/releases/tag/v12.0.0-beta.1). Includes compatibility updates for the renamed shard-manifest API and paginated object-store wrappers.	2026-08-25 16:49:17 -07:00
lancedb-gatefixer[bot]	302b21aa94	test(node): cover nested PDF metadata queries (#3827 ) ## Summary - add an end-to-end Node regression matching LangChain PDFLoader metadata - verify create/query round trips rich nested `loc` and `pdf.info` fields against the currently configured Apache Arrow peer ## Root cause LanceDB v0.14 delegated nested object inference to Apache Arrow. Nested strings were dictionary-encoded with colliding dictionary IDs, so serializing query results as an IPC file failed with a dictionary-replacement error. Current `main` recursively infers nested fields and avoids those invalid dictionaries, but the reported LangChain path had no end-to-end regression coverage. ## Validation - `pnpm build` - `pnpm lint` - `pnpm run docs` - `pnpm test --runInBand` (678 passed, 5 skipped) Fixes #1963 <!-- lance-gatekeeper-fix:v1 agent=bf8d489db7db2e17678b143f9f0a36d2 generation=1 --> --------- Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com> Co-authored-by: Xuanwo <github@xuanwo.io>	2026-08-26 06:42:29 +08:00
lancedb-gatefixer[bot]	35b5d015ac	fix(node): preserve embedding registration in server bundles (#3806 ) ## Summary - lazily initialize built-in OpenAI and Hugging Face providers when consumers call the public embedding registry API - choose automatic vector versus FTS search from embedding metadata on a fresh pinned table revision for every execution - expose automatic string searches as an `AutoQuery` with only operations common to both native query families - keep the registry shared and built-in registration safe across duplicated module graphs ## Root cause Nitro treats dependency modules as side-effect-free and removes the bare OpenAI provider import from its generated route. Registration therefore never runs, so `getRegistry().get("openai")` remains undefined even when the registry itself is shared globally. Bundlers may also duplicate the provider and registry module graphs. The public embedding entry point now initializes built-in providers only when `getRegistry()` is explicitly called, keeping initialization on a live path that Nitro retains. Each terminal automatic-search execution pins the exact table revision visible at dispatch, reads embedding metadata and computes an embedding from that snapshot, replays the builder operations, and constructs and executes the selected native query against the same snapshot. Pinned native snapshots execute locally when namespace pushdown cannot carry their revision, while remote snapshots are seeded directly from one version-and-schema response. The public `AutoQuery` builder exposes only the operations shared by FTS and vector search, so runtime class narrowing cannot expose invalid vector-only methods. Repeated built-in registration replaces stale constructors from duplicated module graphs while public `register()` retains its duplicate-alias error. ## Validation - `cargo fmt --all` - `cargo check --quiet --features remote --tests --examples` - `cargo clippy --quiet --features remote --tests --examples` - `pnpm build` - `pnpm lint` - `pnpm run docs` - `pnpm test --runInBand` (783 passed, 5 skipped) - serial examples suite with a local OpenAI mock (11 passed), including `sentence-transformers.test.ts` - packaged Nitro 2.13.4 server route using the reported imports returned `{"registered":true}` - fresh-process FTS fixture initialized both public built-ins and confirmed automatic string search still returned the indexed row - schema-consistency regressions cover read-consistency refresh, checkout, checkoutLatest, restore, runtime class narrowing, concurrent overwrite during embedding computation, and reused automatic-search builders - focused regressions confirm pinned native snapshots bypass unversioned namespace pushdown and remote snapshots use one describe request Fixes #2429 <!-- lance-gatekeeper-fix:v1 agent=2adf0f21b8bfb634606ed8897a849e30 generation=1 --> --------- Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com> Co-authored-by: Xuanwo <github@xuanwo.io>	2026-08-26 04:17:32 +08:00
lancedb-gatefixer[bot]	a57fb68891	docs(python): fix Azure storage options examples (#3899 ) ## Summary - document that Azure Blob Storage credentials can be passed directly through `storage_options` - provide valid quoted `account_name` and `account_key` examples for both sync and async Python connections - execute the option dictionaries during doctests so the original unquoted-key mistake is caught ## Root cause The historical Python storage guide used `account_name` and `account_key` as bare identifiers in dictionary literals. Following that example either raised `NameError` or, when those names were predefined, produced incorrect option keys. The runtime already accepts direct Azure credentials, but the current Python API reference did not contain a corrected Azure example. ## Validation - `python/.venv/bin/ruff format --check python/python/lancedb/__init__.py` - `python/.venv/bin/ruff check .` - `cd python && uv run --no-sync pytest --doctest-modules python/lancedb/__init__.py -q` - `cd python && uv run --no-sync pytest python/tests/test_import.py -q` Fixes #2236 <!-- lance-gatekeeper-fix:v1 agent=7a7a9e009eb2ebe52ac9b1adf2e8afb2 generation=1 --> Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com> Co-authored-by: Xuanwo <github@xuanwo.io>	2026-08-26 00:31:14 +08:00
Gatefixer	de54965ece	fix: share scans across batched vector queries	2026-08-05 21:36:34 +00:00