Merge branch 'main' into gatekeeper/fix-2369-1

test: cover HNSW index optimization
2026-08-28 00:48:40 +00:00 · 2026-08-21 16:12:55 -07:00 · 2026-08-05 21:57:08 +00:00
161 changed files with 2182 additions and 22418 deletions
@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.38.0-beta.11"
+current_version = "0.38.0-beta.3"
 parse = """(?x)
    (?P<major>0|[1-9]\\d*)\\.
    (?P<minor>0|[1-9]\\d*)\\.
@@ -3455,8 +3455,8 @@ checksum = "42703706b716c37f96a77aea830392ad231f44c9e9a67872fa5548707e11b11c"

 [[package]]
 name = "fsst"
-version = "12.0.0-beta.2"
-source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
+version = "11.0.0-beta.18"
+source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.18#7b6e2d3586e9c1b99326313533aba2150557ed65"
 dependencies = [
 "arrow-array",
 "rand 0.9.5",
@@ -4815,8 +4815,8 @@ checksum = "e037a2e1d8d5fdbd49b16a4ea09d5d6401c1f29eca5ff29d03d3824dba16256a"

 [[package]]
 name = "lance"
-version = "12.0.0-beta.2"
-source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
+version = "11.0.0-beta.18"
+source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.18#7b6e2d3586e9c1b99326313533aba2150557ed65"
 dependencies = [
 "arc-swap",
 "arrow",
@@ -4888,8 +4888,8 @@ dependencies = [

 [[package]]
 name = "lance-arrow"
-version = "12.0.0-beta.2"
-source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
+version = "11.0.0-beta.18"
+source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.18#7b6e2d3586e9c1b99326313533aba2150557ed65"
 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=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
+source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.18#7b6e2d3586e9c1b99326313533aba2150557ed65"
 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=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
+source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.18#7b6e2d3586e9c1b99326313533aba2150557ed65"
 dependencies = [
 "arrow-array",
 "arrow-schema",
@@ -4934,8 +4934,8 @@ dependencies = [

 [[package]]
 name = "lance-bitpacking"
-version = "12.0.0-beta.2"
-source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
+version = "11.0.0-beta.18"
+source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.18#7b6e2d3586e9c1b99326313533aba2150557ed65"
 dependencies = [
 "arrayref",
 "crunchy",
@@ -4945,8 +4945,8 @@ dependencies = [

 [[package]]
 name = "lance-core"
-version = "12.0.0-beta.2"
-source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
+version = "11.0.0-beta.18"
+source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.18#7b6e2d3586e9c1b99326313533aba2150557ed65"
 dependencies = [
 "arrow-array",
 "arrow-buffer",
@@ -4983,8 +4983,8 @@ dependencies = [

 [[package]]
 name = "lance-datafusion"
-version = "12.0.0-beta.2"
-source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
+version = "11.0.0-beta.18"
+source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.18#7b6e2d3586e9c1b99326313533aba2150557ed65"
 dependencies = [
 "arrow",
 "arrow-array",
@@ -5013,8 +5013,8 @@ dependencies = [

 [[package]]
 name = "lance-datagen"
-version = "12.0.0-beta.2"
-source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
+version = "11.0.0-beta.18"
+source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.18#7b6e2d3586e9c1b99326313533aba2150557ed65"
 dependencies = [
 "arrow",
 "arrow-array",
@@ -5031,8 +5031,8 @@ dependencies = [

 [[package]]
 name = "lance-derive"
-version = "12.0.0-beta.2"
-source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
+version = "11.0.0-beta.18"
+source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.18#7b6e2d3586e9c1b99326313533aba2150557ed65"
 dependencies = [
 "proc-macro2",
 "quote",
@@ -5041,8 +5041,8 @@ dependencies = [

 [[package]]
 name = "lance-encoding"
-version = "12.0.0-beta.2"
-source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
+version = "11.0.0-beta.18"
+source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.18#7b6e2d3586e9c1b99326313533aba2150557ed65"
 dependencies = [
 "arrow-arith",
 "arrow-array",
@@ -5075,8 +5075,8 @@ dependencies = [

 [[package]]
 name = "lance-file"
-version = "12.0.0-beta.2"
-source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
+version = "11.0.0-beta.18"
+source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.18#7b6e2d3586e9c1b99326313533aba2150557ed65"
 dependencies = [
 "arrow-arith",
 "arrow-array",
@@ -5107,8 +5107,8 @@ dependencies = [

 [[package]]
 name = "lance-index"
-version = "12.0.0-beta.2"
-source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
+version = "11.0.0-beta.18"
+source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.18#7b6e2d3586e9c1b99326313533aba2150557ed65"
 dependencies = [
 "arc-swap",
 "arrow",
@@ -5172,8 +5172,8 @@ dependencies = [

 [[package]]
 name = "lance-index-core"
-version = "12.0.0-beta.2"
-source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
+version = "11.0.0-beta.18"
+source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.18#7b6e2d3586e9c1b99326313533aba2150557ed65"
 dependencies = [
 "arrow-array",
 "arrow-schema",
@@ -5195,8 +5195,8 @@ dependencies = [

 [[package]]
 name = "lance-io"
-version = "12.0.0-beta.2"
-source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
+version = "11.0.0-beta.18"
+source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.18#7b6e2d3586e9c1b99326313533aba2150557ed65"
 dependencies = [
 "arrow",
 "arrow-array",
@@ -5222,11 +5222,7 @@ dependencies = [
 "pin-project",
 "prost",
 "rand 0.9.5",
- "reqsign-core",
- "reqsign-file-read-tokio",
- "reqsign-google",
 "serde",
- "serde_json",
 "tempfile",
 "tokio",
 "tracing",
@@ -5236,8 +5232,8 @@ dependencies = [

 [[package]]
 name = "lance-linalg"
-version = "12.0.0-beta.2"
-source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
+version = "11.0.0-beta.18"
+source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.18#7b6e2d3586e9c1b99326313533aba2150557ed65"
 dependencies = [
 "arrow-array",
 "arrow-schema",
@@ -5251,8 +5247,8 @@ dependencies = [

 [[package]]
 name = "lance-namespace"
-version = "12.0.0-beta.2"
-source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
+version = "11.0.0-beta.18"
+source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.18#7b6e2d3586e9c1b99326313533aba2150557ed65"
 dependencies = [
 "arrow",
 "async-trait",
@@ -5264,8 +5260,8 @@ dependencies = [

 [[package]]
 name = "lance-namespace-impls"
-version = "12.0.0-beta.2"
-source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
+version = "11.0.0-beta.18"
+source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.18#7b6e2d3586e9c1b99326313533aba2150557ed65"
 dependencies = [
 "arrow",
 "arrow-ipc",
@@ -5318,8 +5314,8 @@ dependencies = [

 [[package]]
 name = "lance-select"
-version = "12.0.0-beta.2"
-source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
+version = "11.0.0-beta.18"
+source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.18#7b6e2d3586e9c1b99326313533aba2150557ed65"
 dependencies = [
 "arrow-array",
 "arrow-buffer",
@@ -5333,8 +5329,8 @@ dependencies = [

 [[package]]
 name = "lance-table"
-version = "12.0.0-beta.2"
-source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
+version = "11.0.0-beta.18"
+source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.18#7b6e2d3586e9c1b99326313533aba2150557ed65"
 dependencies = [
 "arrow",
 "arrow-array",
@@ -5374,8 +5370,8 @@ dependencies = [

 [[package]]
 name = "lance-testing"
-version = "12.0.0-beta.2"
-source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
+version = "11.0.0-beta.18"
+source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.18#7b6e2d3586e9c1b99326313533aba2150557ed65"
 dependencies = [
 "arrow-array",
 "arrow-schema",
@@ -5388,8 +5384,8 @@ dependencies = [

 [[package]]
 name = "lance-tokenizer"
-version = "12.0.0-beta.2"
-source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
+version = "11.0.0-beta.18"
+source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.18#7b6e2d3586e9c1b99326313533aba2150557ed65"
 dependencies = [
 "frostem",
 "icu_segmenter",
@@ -5402,7 +5398,7 @@ dependencies = [

 [[package]]
 name = "lancedb"
-version = "0.38.0-beta.11"
+version = "0.38.0-beta.3"
 dependencies = [
 "ahash",
 "anyhow",
@@ -5490,7 +5486,7 @@ dependencies = [

 [[package]]
 name = "lancedb-nodejs"
-version = "0.38.0-beta.11"
+version = "0.38.0-beta.3"
 dependencies = [
 "arrow-array",
 "arrow-buffer",
@@ -5515,7 +5511,7 @@ dependencies = [

 [[package]]
 name = "lancedb-python"
-version = "0.38.0-beta.11"
+version = "0.38.0-beta.3"
 dependencies = [
 "arrow",
 "async-trait",
@@ -13,20 +13,20 @@ categories = ["database-implementations"]
 rust-version = "1.91.0"

 [workspace.dependencies]
-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" }
+lance = { "version" = "=11.0.0-beta.18", default-features = false, "tag" = "v11.0.0-beta.18", "git" = "https://github.com/lance-format/lance.git" }
+lance-core = { "version" = "=11.0.0-beta.18", "tag" = "v11.0.0-beta.18", "git" = "https://github.com/lance-format/lance.git" }
+lance-datagen = { "version" = "=11.0.0-beta.18", "tag" = "v11.0.0-beta.18", "git" = "https://github.com/lance-format/lance.git" }
+lance-file = { "version" = "=11.0.0-beta.18", "tag" = "v11.0.0-beta.18", "git" = "https://github.com/lance-format/lance.git" }
+lance-io = { "version" = "=11.0.0-beta.18", default-features = false, "tag" = "v11.0.0-beta.18", "git" = "https://github.com/lance-format/lance.git" }
+lance-index = { "version" = "=11.0.0-beta.18", "tag" = "v11.0.0-beta.18", "git" = "https://github.com/lance-format/lance.git" }
+lance-linalg = { "version" = "=11.0.0-beta.18", "tag" = "v11.0.0-beta.18", "git" = "https://github.com/lance-format/lance.git" }
+lance-namespace = { "version" = "=11.0.0-beta.18", "tag" = "v11.0.0-beta.18", "git" = "https://github.com/lance-format/lance.git" }
+lance-namespace-impls = { "version" = "=11.0.0-beta.18", default-features = false, "tag" = "v11.0.0-beta.18", "git" = "https://github.com/lance-format/lance.git" }
+lance-table = { "version" = "=11.0.0-beta.18", "tag" = "v11.0.0-beta.18", "git" = "https://github.com/lance-format/lance.git" }
+lance-testing = { "version" = "=11.0.0-beta.18", "tag" = "v11.0.0-beta.18", "git" = "https://github.com/lance-format/lance.git" }
+lance-datafusion = { "version" = "=11.0.0-beta.18", "tag" = "v11.0.0-beta.18", "git" = "https://github.com/lance-format/lance.git" }
+lance-encoding = { "version" = "=11.0.0-beta.18", "tag" = "v11.0.0-beta.18", "git" = "https://github.com/lance-format/lance.git" }
+lance-arrow = { "version" = "=11.0.0-beta.18", "tag" = "v11.0.0-beta.18", "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
@@ -14,7 +14,7 @@ Add the following dependency to your `pom.xml`:
 <dependency>
    <groupId>com.lancedb</groupId>
    <artifactId>lancedb-core</artifactId>
-    <version>0.38.0-beta.11</version>
+    <version>0.38.0-beta.3</version>
 </dependency>
 ```

@@ -1,518 +0,0 @@
-[**@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`
@@ -37,31 +37,6 @@ latest and stays writable.

 ***

-### cherryPick()
-
-```ts
-cherryPick(fromBranch, dryRun): Promise<CherryPickResult>
-```
-
-Cherry-pick a branch onto main.
-
-Set `dryRun` to `true` to preview. A failed cherry-pick resolves
-with `status: "failed"` instead of throwing.
-
-#### Parameters
-
-* **fromBranch**: `string`
-    Branch to cherry-pick from.
-
-* **dryRun**: `boolean` = `false`
-    When true, only preview. Defaults to false.
-
-#### Returns
-
-`Promise`&lt;[`CherryPickResult`](../interfaces/CherryPickResult.md)&gt;
-
-***
-
 ### create()

 ```ts
@@ -137,3 +112,28 @@ List all branches, mapping name to branch metadata.
 #### Returns

 `Promise`&lt;`Record`&lt;`string`, [`BranchContents`](BranchContents.md)&gt;&gt;
+
+***
+
+### merge()
+
+```ts
+merge(fromBranch, dryRun): Promise<MergeBranchResult>
+```
+
+Merge a branch into main.
+
+Set `dryRun` to `true` to preview the merge. A rejected merge resolves
+with `status: "rejected"` instead of throwing.
+
+#### Parameters
+
+* **fromBranch**: `string`
+    Branch to merge from.
+
+* **dryRun**: `boolean` = `false`
+    When true, only preview the merge. Defaults to false.
+
+#### Returns
+
+`Promise`&lt;[`MergeBranchResult`](../interfaces/MergeBranchResult.md)&gt;
@@ -169,45 +169,6 @@ Creates a new empty Table

 ***

-### createMaterializedView()
-
-```ts
-abstract createMaterializedView(
-   name,
-   source,
-   options?): Promise<MaterializedView>
-```
-
-Define a materialized view named `name` over the table `source`.
-
-The view is created empty, with the query recorded in its schema
-metadata; `view.refresh()` computes the rows. The view is a normal
-table: it can be queried, indexed and searched, and it appears in
-`tableNames`. The source table must have stable row ids (create it with
-the `newTableEnableStableRowIds` storage option); they keep the view's
-provenance valid across source compactions and cannot be enabled after
-a table exists. Local databases only.
-
-#### Parameters
-
-* **name**: `string`
-
-* **source**: `string`
-
-* **options?**
-
-* **options.limit?**: `number`
-
-* **options.select?**: [`MaterializedViewSelect`](../type-aliases/MaterializedViewSelect.md)
-
-* **options.where?**: `string`
-
-#### Returns
-
-`Promise`&lt;[`MaterializedView`](MaterializedView.md)&gt;
-
-***
-
 ### createNamespace()

 ```ts
@@ -538,22 +499,6 @@ List server-side jobs across the database's tables.

 ***

-### listMaterializedViews()
-
-```ts
-abstract listMaterializedViews(): Promise<string[]>
-```
-
-The names of the materialized views in this database.
-
-Found by reading every table's schema, so this costs an open per table.
-
-#### Returns
-
-`Promise`&lt;`string`[]&gt;
-
-***
-
 ### listNamespaces()

 ```ts
@@ -584,90 +529,6 @@ Child namespace names and

 ***

-### listTables()
-
-#### listTables(options)
-
-```ts
-abstract listTables(options?): Promise<ListTablesResponse>
-```
-
-List a page of the tables in this database.
-
-To retrieve the tables after the page, pass the `pageToken` the response
-carries back in. A page can be shorter than `limit` without being the last
-one, so walk until a response carries no page token:
-
-```ts
-const names = [];
-let pageToken = undefined;
-do {
-  const page = await conn.listTables({ pageToken, limit: 100 });
-  names.push(...page.tables);
-  pageToken = page.pageToken;
-} while (pageToken);
-```
-
-##### Parameters
-
-* **options?**: `Partial`&lt;[`ListTablesOptions`](../interfaces/ListTablesOptions.md)&gt;
-    Pagination options
-    (`pageToken`, `limit`).
-
-##### Returns
-
-`Promise`&lt;[`ListTablesResponse`](../interfaces/ListTablesResponse.md)&gt;
-
-A page of table names and an
-  optional token for the tables after it.
-
-#### listTables(namespacePath, options)
-
-```ts
-abstract listTables(namespacePath?, options?): Promise<ListTablesResponse>
-```
-
-List a page of the tables in this database.
-
-##### Parameters
-
-* **namespacePath?**: `string`[]
-    The namespace path to list tables from
-    (defaults to root namespace)
-
-* **options?**: `Partial`&lt;[`ListTablesOptions`](../interfaces/ListTablesOptions.md)&gt;
-    Pagination options
-    (`pageToken`, `limit`).
-
-##### Returns
-
-`Promise`&lt;[`ListTablesResponse`](../interfaces/ListTablesResponse.md)&gt;
-
-A page of table names and an
-  optional token for the tables after it.
-
-***
-
-### openMaterializedView()
-
-```ts
-abstract openMaterializedView(name): Promise<MaterializedView>
-```
-
-Open the materialized view named `name`.
-
-Rejects a table that exists but is not a materialized view.
-
-#### Parameters
-
-* **name**: `string`
-
-#### Returns
-
-`Promise`&lt;[`MaterializedView`](MaterializedView.md)&gt;
-
-***
-
 ### openTable()

 ```ts
@@ -677,13 +538,18 @@ abstract openTable(
   options?): Promise<Table>
 ```

+Open a table in the database.
+
 #### Parameters

 * **name**: `string`
+    The name of the table

 * **namespacePath?**: `string`[]
+    The namespace path of the table (defaults to root namespace)

 * **options?**: `Partial`&lt;[`OpenTableOptions`](../interfaces/OpenTableOptions.md)&gt;
+    Additional options

 #### Returns

@@ -724,7 +590,7 @@ a "not supported" error.

 ***

-### ~~tableNames()~~
+### tableNames()

 #### tableNames(options)

@@ -746,10 +612,6 @@ Tables will be returned in lexicographical order.

 `Promise`&lt;`string`[]&gt;

-##### Deprecated
-
-Use [Connection.listTables](Connection.md#listtables) instead.
-
 #### tableNames(namespacePath, options)

 ```ts
@@ -772,7 +634,3 @@ Tables will be returned in lexicographical order.
 ##### Returns

 `Promise`&lt;`string`[]&gt;
-
-##### Deprecated
-
-Use [Connection.listTables](Connection.md#listtables) instead.
@@ -1,101 +0,0 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / MaterializedView
-
-# Class: MaterializedView
-
-A handle on a materialized view: its table plus its definition.
-
-Obtained from [Connection#createMaterializedView](Connection.md#creatematerializedview) or
-[Connection#openMaterializedView](Connection.md#openmaterializedview). The view is a normal table --
-queries, indexes and search all apply through [MaterializedView#table](MaterializedView.md#table)
-- whose contents are maintained by [MaterializedView#refresh](MaterializedView.md#refresh).
-
-## Constructors
-
-### new MaterializedView()
-
-```ts
-new MaterializedView(table): MaterializedView
-```
-
-#### Parameters
-
-* **table**: [`Table`](Table.md)
-
-#### Returns
-
-[`MaterializedView`](MaterializedView.md)
-
-## Accessors
-
-### name
-
-```ts
-get name(): string
-```
-
-#### Returns
-
-`string`
-
-## Methods
-
-### definition()
-
-```ts
-definition(): Promise<MaterializedViewDefinition>
-```
-
-The query that defines the view, read from its stored schema.
-
-#### Returns
-
-`Promise`&lt;[`MaterializedViewDefinition`](../interfaces/MaterializedViewDefinition.md)&gt;
-
-***
-
-### refresh()
-
-```ts
-refresh(options?): Promise<RefreshMaterializedViewResult>
-```
-
-Recompute the view from its source.
-
-The refresh is incremental when the source's changes can be reconciled
-into the view -- rows added, changed or removed since the last one --
-and otherwise rebuilds. `full` forces a rebuild; `sourceVersion`
-refreshes to that source version instead of the latest.
-
-Concurrent refreshes of one view do not duplicate its rows. Two that
-plan the same source rows conflict on commit, and the loser throws
-rather than writing them a second time.
-
-#### Parameters
-
-* **options?**
-
-* **options.full?**: `boolean`
-
-* **options.sourceVersion?**: `number`
-
-#### Returns
-
-`Promise`&lt;[`RefreshMaterializedViewResult`](../interfaces/RefreshMaterializedViewResult.md)&gt;
-
-***
-
-### table()
-
-```ts
-table(): Table
-```
-
-The view, as the table it is.
-
-#### Returns
-
-[`Table`](Table.md)
@@ -942,7 +942,7 @@ Get the schema of the table.
 abstract search(
   query,
   queryType?,
-   ftsColumns?): Query | VectorQuery | AutoQuery
+   ftsColumns?): Query | VectorQuery
 ```

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

 #### Returns

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

 ***

@@ -1292,18 +1292,6 @@ abstract updateFieldMetadata(updates): Promise<UpdateFieldMetadataResult>

 Update per-field (column) metadata.

-The following keys are treated specially, by convention, and should be
-used when appropriate:
-
- `lancedb:description`: for a human-readable description of a field.
- `lancedb:tag:<name>`: for a user-defined key-value tag, where the suffix
-  names the tag category; e.g. `lancedb:tag:model: "clip"`.
- `lancedb:logical-column`: for a column grouping; e.g. `feature_v1` and
-  `feature_v2` might be in the same logical column.
- `lancedb:status`: for status options (`production`, `candidate`,
-  `deprecated`, `archived`) to designate the current life cycle state of
-  this column.
-
 #### Parameters

 * **updates**: [`FieldMetadataUpdate`](../interfaces/FieldMetadataUpdate.md)[]
@@ -18,7 +18,6 @@

 ## Classes

- [AutoQuery](classes/AutoQuery.md)
 - [BooleanQuery](classes/BooleanQuery.md)
 - [BoostQuery](classes/BoostQuery.md)
 - [BranchContents](classes/BranchContents.md)
@@ -29,7 +28,6 @@
 - [Job](classes/Job.md)
 - [MakeArrowTableOptions](classes/MakeArrowTableOptions.md)
 - [MatchQuery](classes/MatchQuery.md)
- [MaterializedView](classes/MaterializedView.md)
 - [MergeInsertBuilder](classes/MergeInsertBuilder.md)
 - [MultiMatchQuery](classes/MultiMatchQuery.md)
 - [NativeJsHeaderProvider](classes/NativeJsHeaderProvider.md)
@@ -61,9 +59,6 @@
 - [BranchIndexSummary](interfaces/BranchIndexSummary.md)
 - [BranchRowCountSummary](interfaces/BranchRowCountSummary.md)
 - [BucketStats](interfaces/BucketStats.md)
- [CherryPickError](interfaces/CherryPickError.md)
- [CherryPickPreview](interfaces/CherryPickPreview.md)
- [CherryPickResult](interfaces/CherryPickResult.md)
 - [ClientConfig](interfaces/ClientConfig.md)
 - [ColumnAlteration](interfaces/ColumnAlteration.md)
 - [ColumnOrdering](interfaces/ColumnOrdering.md)
@@ -101,12 +96,12 @@
 - [JobInfo](interfaces/JobInfo.md)
 - [ListNamespacesOptions](interfaces/ListNamespacesOptions.md)
 - [ListNamespacesResponse](interfaces/ListNamespacesResponse.md)
- [ListTablesOptions](interfaces/ListTablesOptions.md)
- [ListTablesResponse](interfaces/ListTablesResponse.md)
 - [LsmStats](interfaces/LsmStats.md)
 - [LsmWriteSpec](interfaces/LsmWriteSpec.md)
- [MaterializedViewDefinition](interfaces/MaterializedViewDefinition.md)
 - [MemtableStats](interfaces/MemtableStats.md)
+- [MergeBlocker](interfaces/MergeBlocker.md)
+- [MergeBranchResult](interfaces/MergeBranchResult.md)
+- [MergePreview](interfaces/MergePreview.md)
 - [MergeResult](interfaces/MergeResult.md)
 - [NativeOAuthConfig](interfaces/NativeOAuthConfig.md)
 - [OAuthConfig](interfaces/OAuthConfig.md)
@@ -115,7 +110,6 @@
 - [OptimizeStats](interfaces/OptimizeStats.md)
 - [QueryExecutionOptions](interfaces/QueryExecutionOptions.md)
 - [RefreshColumnResult](interfaces/RefreshColumnResult.md)
- [RefreshMaterializedViewResult](interfaces/RefreshMaterializedViewResult.md)
 - [RemovalStats](interfaces/RemovalStats.md)
 - [RenameTableOptions](interfaces/RenameTableOptions.md)
 - [RestNamespaceConfig](interfaces/RestNamespaceConfig.md)
@@ -148,7 +142,6 @@
 - [FieldLike](type-aliases/FieldLike.md)
 - [IntoSql](type-aliases/IntoSql.md)
 - [IntoVector](type-aliases/IntoVector.md)
- [MaterializedViewSelect](type-aliases/MaterializedViewSelect.md)
 - [MultiVector](type-aliases/MultiVector.md)
 - [RecordBatchLike](type-aliases/RecordBatchLike.md)
 - [SchemaLike](type-aliases/SchemaLike.md)
@@ -50,14 +50,6 @@ changedColumns: BranchColumnChange[];

 ***

-### errors
-
-```ts
-errors: CherryPickError[];
-```
-
-***
-
 ### fromBranch

 ```ts
@@ -74,6 +66,22 @@ mainVersion: number;

 ***

+### mergeBlockers
+
+```ts
+mergeBlockers: MergeBlocker[];
+```
+
+***
+
+### mergeable
+
+```ts
+mergeable: boolean;
+```
+
+***
+
 ### parentVersion

 ```ts
@@ -1,17 +0,0 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / CherryPickPreview
-
-# Interface: CherryPickPreview
-
-Changes that would be, or were, promoted by a cherry-pick.
-
-## Properties
-
-### promotedColumns
-
-```ts
-promotedColumns: string[];
-```
@@ -17,8 +17,7 @@ metadata: Record<string, null | string>;
 ```

 Metadata key/value pairs. Merged into the field's existing metadata by
-default; a value of `null` deletes that key. See
-[Table.updateFieldMetadata](../classes/Table.md#updatefieldmetadata) for the conventional `lancedb:*` keys.
+default; a value of `null` deletes that key.

 ***

@@ -1,34 +0,0 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / ListTablesOptions
-
-# Interface: ListTablesOptions
-
-## Properties
-
-### limit?
-
-```ts
-optional limit: number;
-```
-
-An upper bound on how many tables to return.
-
-A page may hold fewer than this and still not be the last one, so keep
-going while the response carries a page token rather than while pages are
-full.
-
-***
-
-### pageToken?
-
-```ts
-optional pageToken: string;
-```
-
-Token from a previous response, to resume listing where it left off.
-
-The token is opaque: it carries whatever the database needs to resume, and
-callers should not construct or interpret one.
@@ -1,23 +0,0 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / ListTablesResponse
-
-# Interface: ListTablesResponse
-
-## Properties
-
-### pageToken?
-
-```ts
-optional pageToken: string;
-```
-
-***
-
-### tables
-
-```ts
-tables: string[];
-```
@@ -1,59 +0,0 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / MaterializedViewDefinition
-
-# Interface: MaterializedViewDefinition
-
-The query that defines a materialized view.
-
-## Properties
-
-### filter?
-
-```ts
-optional filter: string;
-```
-
-SQL predicate selecting the source rows the view holds.
-
-***
-
-### inputs
-
-```ts
-inputs: string[];
-```
-
-Source columns the projections and filter read.
-
-***
-
-### limit?
-
-```ts
-optional limit: number;
-```
-
-Cap on the number of rows the view holds.
-
-***
-
-### projections
-
-```ts
-projections: [string, string][];
-```
-
-`[output column, SQL expression]` pairs, in view schema order.
-
-***
-
-### sourceTable
-
-```ts
-sourceTable: string;
-```
-
-Name of the source table, in the same database as the view.
@@ -2,11 +2,11 @@

 ***

-[@lancedb/lancedb](../globals.md) / CherryPickError
+[@lancedb/lancedb](../globals.md) / MergeBlocker

-# Interface: CherryPickError
+# Interface: MergeBlocker

-A reason why a cherry-pick cannot currently land.
+A reason why a branch cannot currently be merged.

 ## Properties

@@ -2,11 +2,11 @@

 ***

-[@lancedb/lancedb](../globals.md) / CherryPickResult
+[@lancedb/lancedb](../globals.md) / MergeBranchResult

-# Interface: CherryPickResult
+# Interface: MergeBranchResult

-Result of previewing or attempting a cherry-pick.
+Result of previewing or attempting a branch merge.

 ## Properties

@@ -29,7 +29,7 @@ optional mainVersionAfter: number;
 ### preview

 ```ts
-preview: CherryPickPreview;
+preview: MergePreview;
 ```

 ***
@@ -38,9 +38,9 @@ preview: CherryPickPreview;

 ```ts
 status:
-  | "failed"
  | "unknown"
+  | "rejected"
  | "ready"
  | "notImplemented"
-  | "cherryPicked";
+  | "merged";
 ```
@@ -0,0 +1,17 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / MergePreview
+
+# Interface: MergePreview
+
+Changes that would be, or were, promoted by a branch merge.
+
+## Properties
+
+### promotedColumns
+
+```ts
+promotedColumns: string[];
+```
@@ -1,41 +0,0 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / RefreshMaterializedViewResult
-
-# Interface: RefreshMaterializedViewResult
-
-## Properties
-
-### mode
-
-```ts
-mode: string;
-```
-
-How the view was brought up to date: "rebuild", "incremental" or "no_op".
-
-***
-
-### rowsWritten
-
-```ts
-rowsWritten: number;
-```
-
-***
-
-### sourceVersion
-
-```ts
-sourceVersion: number;
-```
-
-***
-
-### version
-
-```ts
-version: number;
-```
@@ -4,16 +4,11 @@

 [@lancedb/lancedb](../globals.md) / TableNamesOptions

-# Interface: ~~TableNamesOptions~~
-
-## Deprecated
-
-Use [ListTablesOptions](ListTablesOptions.md) with [Connection.listTables](../classes/Connection.md#listtables)
-instead.
+# Interface: TableNamesOptions

 ## Properties

-### ~~limit?~~
+### limit?

 ```ts
 optional limit: number;
@@ -23,7 +18,7 @@ An optional limit to the number of results to return.

 ***

-### ~~startAfter?~~
+### startAfter?

 ```ts
 optional startAfter: string;
@@ -10,12 +10,16 @@
 function getRegistry(): EmbeddingFunctionRegistry
 ```

-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.
+Utility function to get the global instance of the registry

 ## Returns

 [`EmbeddingFunctionRegistry`](../classes/EmbeddingFunctionRegistry.md)
+
+`EmbeddingFunctionRegistry` The global instance of the registry
+
+## Example
+
+```ts
+const registry = getRegistry();
+const openai = registry.get("openai").create();
@@ -1,14 +0,0 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / MaterializedViewSelect
-
-# Type Alias: MaterializedViewSelect
-
-```ts
-type MaterializedViewSelect: (string | [string, string])[] | Record<string, string>;
-```
-
-The view's columns: column names, `[alias, SQL expression]` pairs, or a
-record of the same. A bare name projects itself.
@@ -102,12 +102,6 @@ listing a storage directory.

 ::: lancedb.job.AsyncJob

-## Materialized Views (Synchronous)
-
-::: lancedb.materialized_view.MaterializedView
-
-::: lancedb.materialized_view.MaterializedViewDefinition
-
 ## Expressions

 Type-safe expression builder for filters and projections. Use these instead
@@ -159,8 +153,6 @@ and combined with [BooleanQuery][lancedb.query.BooleanQuery].

 ::: lancedb.query.FullTextOperator

-::: lancedb.query.DocumentGranularity
-
 ::: lancedb.query.Occur

 ## Embeddings
@@ -223,14 +215,10 @@ tokens = list(
 Blob columns store large binary values out of line so they can be read lazily
 instead of being materialized with the rest of the row.

-`lancedb.BlobType` is `lance.blob.BlobType` when pylance is installed. Without
-pylance, LanceDB uses a matching `lance.blob.v2` extension type so blob columns
-still work. Queries return descriptors. Call
-[`fetch_blob_files`][lancedb.table.Table.fetch_blob_files] for lazy reads or
-[`fetch_blobs`][lancedb.table.Table.fetch_blobs] for eager bytes.
-
 ::: lancedb.blob

+::: lancedb.BlobType
+
 ::: lancedb._blob.BlobFile
    options:
      show_root_full_path: false
@@ -267,8 +255,6 @@ still work. Queries return descriptors. Call

 ::: lancedb.streaming.StreamingDataset

-::: lancedb.streaming.StreamingDataLoader
-
 ::: lancedb.permutation.permutation_builder

 ::: lancedb.permutation.PermutationBuilder
@@ -309,10 +295,6 @@ Table hold your actual data as a collection of records / rows.

 ::: lancedb.table.AsyncBranches

-## Materialized Views (Asynchronous)
-
-::: lancedb.materialized_view.AsyncMaterializedView
-
 ## Indices (Asynchronous)

 Indices can be created on a table to speed up queries. This section
@@ -8,7 +8,7 @@
    <parent>
      <groupId>com.lancedb</groupId>
      <artifactId>lancedb-parent</artifactId>
-      <version>0.38.0-beta.11</version>
+      <version>0.38.0-beta.3</version>
      <relativePath>../pom.xml</relativePath>
    </parent>

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

    <groupId>com.lancedb</groupId>
    <artifactId>lancedb-parent</artifactId>
-    <version>0.38.0-beta.11</version>
+    <version>0.38.0-beta.3</version>
    <packaging>pom</packaging>
    <name>${project.artifactId}</name>
    <description>LanceDB Java SDK Parent POM</description>
@@ -28,7 +28,7 @@
    <properties>
        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
        <arrow.version>15.0.0</arrow.version>
-        <lance-core.version>12.0.0-beta.2</lance-core.version>
+        <lance-core.version>11.0.0-beta.18</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>
@@ -1,7 +1,7 @@
 [package]
 name = "lancedb-nodejs"
 edition.workspace = true
-version = "0.38.0-beta.11"
+version = "0.38.0-beta.3"
 publish = false
 license.workspace = true
 description.workspace = true
@@ -1,16 +1,11 @@
 // SPDX-License-Identifier: Apache-2.0
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors
-import * as fs from "node:fs";
-import * as vm from "node:vm";
 import * as arrow15 from "apache-arrow-15";
 import * as arrow16 from "apache-arrow-16";
 import * as arrow17 from "apache-arrow-17";
 import * as arrow18 from "apache-arrow-18";

 import {
-  Field as CurrentField,
-  LargeBinary as CurrentLargeBinary,
-  Schema as CurrentSchema,
  Vector as CurrentVector,
  convertToTable,
  tableFromIPC as currentTableFromIPC,
@@ -41,59 +36,6 @@ function sampleRecords(): Array<Record<string, any>> {
    },
  ];
 }
-
-it("serializes an Arrow Table created in another JavaScript realm", async () => {
-  const context = vm.createContext({
-    TextDecoder,
-    TextEncoder,
-    console,
-    setTimeout,
-    clearTimeout,
-  });
-  vm.runInContext(
-    fs.readFileSync(
-      require.resolve("apache-arrow-15/Arrow.es2015.min"),
-      "utf8",
-    ),
-    context,
-  );
-  const foreignTable: unknown = vm.runInContext(
-    "Arrow.tableFromArrays({ id: new Int32Array([1, 2, 3]), text: ['foo', 'bar', 'baz'] })",
-    context,
-  );
-
-  const foreignMetadata = (
-    foreignTable as { schema: { metadata: Map<string, string> } }
-  ).schema.metadata;
-  expect(foreignMetadata).not.toBeInstanceOf(Map);
-
-  const buf = await fromDataToBuffer(
-    foreignTable as Parameters<typeof fromDataToBuffer>[0],
-  );
-  const actual = currentTableFromIPC(buf);
-
-  expect(actual.numRows).toBe(3);
-  expect(actual.getChild("id")?.toJSON()).toEqual([1, 2, 3]);
-  expect(actual.getChild("text")?.toJSON()).toEqual(["foo", "bar", "baz"]);
-});
-
-it("preserves field metadata from a provided schema", async function () {
-  const jsonMetadata = new Map([["ARROW:extension:name", "lance.json"]]);
-  const schema = new CurrentSchema([
-    new CurrentField("meta", new CurrentLargeBinary(), true, jsonMetadata),
-  ]);
-
-  const table = makeArrowTable(
-    [{ meta: Buffer.from(JSON.stringify({ source: "test" })) }],
-    { schema },
-  );
-
-  expect(table.schema.fields[0].metadata).toEqual(jsonMetadata);
-
-  const roundTripped = currentTableFromIPC(await fromTableToBuffer(table));
-  expect(roundTripped.schema.fields[0].metadata).toEqual(jsonMetadata);
-});
-
 describe.each([arrow15, arrow16, arrow17, arrow18])(
  "Arrow",
  (
@@ -573,137 +515,6 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
        );
      });

-      it("will allow matching inferred types across records", function () {
-        expect(() =>
-          makeArrowTable([{ value: 1 }, { value: 2 }]),
-        ).not.toThrow();
-      });
-
-      it("will reject mismatched inferred types across records", function () {
-        expect(() => makeArrowTable([{ value: 1 }, { value: "two" }])).toThrow(
-          "Failed to infer schema for data. Previously inferred type Float64 but found Utf8 for field value at row 1. Consider providing an explicit schema.",
-        );
-      });
-
-      it("will ignore generated dictionary IDs when comparing inferred types", function () {
-        const table = makeArrowTable([{ str: "a" }, { str: "b" }], {
-          dictionaryEncodeStrings: true,
-        });
-
-        expect(table.getChild("str")?.toJSON()).toEqual(["a", "b"]);
-      });
-
-      it("will preserve null values without treating them as type mismatches", function () {
-        for (const records of [
-          [{ vector: [1, 2, 3] }, { vector: null }],
-          [{ vector: null }, { vector: [1, 2, 3] }],
-        ]) {
-          const table = makeArrowTable(records);
-
-          expect(table.numRows).toBe(2);
-          expect(table.getChild("vector")?.nullCount).toBe(1);
-        }
-      });
-
-      it("will preserve empty variable-size lists", function () {
-        for (const records of [
-          [{ items: [1] }, { items: [] }],
-          [{ items: [] }, { items: [1] }],
-        ]) {
-          const table = makeArrowTable(records);
-          expect(
-            table
-              .getChild("items")
-              ?.toJSON()
-              .map((value) => value.toJSON()),
-          ).toEqual(records.map((record) => record.items));
-        }
-      });
-
-      it("will propagate deferred evidence through nested lists", function () {
-        for (const records of [
-          [{ items: [1] }, { items: [null] }],
-          [{ items: [null] }, { items: [1] }],
-          [{ items: [null, 1] }, { items: [2, null] }],
-        ]) {
-          const table = makeArrowTable(records);
-          expect(
-            table
-              .getChild("items")
-              ?.toJSON()
-              .map((value) => value.toJSON()),
-          ).toEqual(records.map((record) => record.items));
-        }
-
-        const nestedRecords = [{ items: [[1]] }, { items: [[null]] }];
-        const nestedTable = makeArrowTable(nestedRecords);
-        expect(
-          nestedTable
-            .getChild("items")
-            ?.toJSON()
-            .map((value) =>
-              value
-                .toJSON()
-                .map((nestedValue: { toJSON: () => unknown[] }) =>
-                  nestedValue.toJSON(),
-                ),
-            ),
-        ).toEqual(nestedRecords.map((record) => record.items));
-      });
-
-      it("will reject incompatible deferred evidence within a list", function () {
-        for (const items of [
-          [[], 1],
-          [1, []],
-          [[null], 1],
-          [1, [null]],
-        ]) {
-          expect(() => makeArrowTable([{ items }])).toThrow(
-            "Failed to infer data type for field items at row 0.",
-          );
-        }
-      });
-
-      it("will reject empty fixed-size lists", function () {
-        expect(() =>
-          makeArrowTable([{ vector: [1, 2, 3] }, { vector: [] }]),
-        ).toThrow(
-          "Failed to infer schema for data. Previously inferred type FixedSizeList[3]<Float32> but found List[0] for field vector at row 1.",
-        );
-      });
-
-      it("will reject inferred leaf and branch shape changes", function () {
-        expect(() =>
-          makeArrowTable([{ value: 1 }, { value: { nested: 2 } }]),
-        ).toThrow(
-          "Failed to infer schema for data. Previously inferred type Float64 but found Struct for field value at row 1.",
-        );
-        expect(() =>
-          makeArrowTable([{ value: { nested: 1 } }, { value: 2 }]),
-        ).toThrow(
-          "Failed to infer schema for data. Previously inferred type Struct but found Float64 for field value at row 1.",
-        );
-      });
-
-      it("will allow null values around inferred struct values", function () {
-        for (const { records, nullIndex } of [
-          {
-            records: [{ value: null }, { value: { nested: 2 } }],
-            nullIndex: 0,
-          },
-          {
-            records: [{ value: { nested: 1 } }, { value: null }],
-            nullIndex: 1,
-          },
-        ]) {
-          const table = makeArrowTable(records);
-          const values = table.getChild("value");
-
-          expect(values?.nullCount).toBe(1);
-          expect(values?.get(nullIndex)).toBeNull();
-        }
-      });
-
      it("will allow a schema to be provided", async function () {
        await checkTableCreation(
          async (records, _, schema) =>
@@ -4,13 +4,7 @@
 import { readdirSync } from "fs";
 import { Field, Float64, Schema } from "apache-arrow";
 import * as tmp from "tmp";
-import {
-  Connection,
-  ListTablesResponse,
-  Table,
-  connect,
-  connectNamespace,
-} from "../lancedb";
+import { Connection, Table, connect, connectNamespace } from "../lancedb";
 import { LocalTable } from "../lancedb/table";

 describe("when connecting", () => {
@@ -53,7 +47,6 @@ describe("given a connection", () => {
    await db.close();
    expect(db.isOpen()).toBe(false);
    await expect(db.tableNames()).rejects.toThrow("Connection is closed");
-    await expect(db.listTables()).rejects.toThrow("Connection is closed");
    await expect(db.renameTable("a", "b")).rejects.toThrow(
      "Connection is closed",
    );
@@ -136,66 +129,6 @@ describe("given a connection", () => {
    expect(tables).toEqual(["b", "c"]);
  });

-  it("should respect limit and page token when listing tables", async () => {
-    const db = await connect(tmpDir.name);
-
-    await db.createTable("b", [{ id: 1 }]);
-    await db.createTable("a", [{ id: 1 }]);
-    await db.createTable("c", [{ id: 1 }]);
-
-    const all = await db.listTables();
-    expect(all.tables).toEqual(["a", "b", "c"]);
-    expect(all.pageToken).toBeUndefined();
-
-    const first = await db.listTables({ limit: 1 });
-    expect(first.tables).toEqual(["a"]);
-    expect(first.pageToken).toBeDefined();
-
-    const second = await db.listTables({
-      limit: 1,
-      pageToken: first.pageToken,
-    });
-    expect(second.tables).toEqual(["b"]);
-  });
-
-  it("should visit every table exactly once when walking pages", async () => {
-    const db = await connect(tmpDir.name);
-
-    const created = ["a", "b", "c", "d", "e"];
-    for (const name of created) {
-      await db.createTable(name, [{ id: 1 }]);
-    }
-
-    const seen: string[] = [];
-    let pageToken: string | undefined = undefined;
-    do {
-      const page: ListTablesResponse = await db.listTables({
-        limit: 2,
-        pageToken,
-      });
-      seen.push(...page.tables);
-      pageToken = page.pageToken;
-    } while (pageToken);
-
-    expect(seen).toEqual(created);
-  });
-
-  it("should list tables in a namespace", async () => {
-    const db = await connect(tmpDir.name, {
-      // biome-ignore lint/style/useNamingConvention: opaque backend property key, must match Rust
-      namespaceClientProperties: { manifest_enabled: "true" },
-    });
-    await db.createNamespace(["child"]);
-    await db.createTable("nested", [{ id: 1 }], ["child"]);
-
-    await expect(db.listTables(["child"])).resolves.toEqual(
-      expect.objectContaining({ tables: ["nested"] }),
-    );
-    await expect(db.listTables()).resolves.toEqual(
-      expect.objectContaining({ tables: [] }),
-    );
-  });
-
  it("should create tables in v2 mode", async () => {
    const db = await connect(tmpDir.name);
    const data = [...Array(10000).keys()].map((i) => ({ id: i }));
@@ -187,58 +187,6 @@ describe("embedding functions", () => {
    const vector0 = JSON.parse(JSON.stringify(arr[0].vector));
    expect(vector0).toEqual([1, 2, 3]);
  });
-  it("should append multiple Python embeddings with the same alias", async () => {
-    @register("python-mock")
-    // biome-ignore lint/correctness/noUnusedVariables: the decorator registers this class
-    class MockEmbeddingFunction extends EmbeddingFunction<string> {
-      ndims() {
-        return 3;
-      }
-      embeddingDataType(): Float {
-        return new Float32();
-      }
-      async computeQueryEmbeddings(_data: string) {
-        return [1, 2, 3];
-      }
-      async computeSourceEmbeddings(data: string[]) {
-        return data.map((value) =>
-          value === "hello world" ? [1, 2, 3] : [4, 5, 6],
-        );
-      }
-    }
-
-    const metadata = new Map([
-      [
-        "embedding_functions",
-        '[{"source_column":"text1","vector_column":"vector1","name":"python-mock","model":{}},{"source_column":"text2","vector_column":"vector2","name":"python-mock","model":{}}]',
-      ],
-    ]);
-    const schema = new Schema(
-      [
-        new Field("text1", new Utf8(), true),
-        new Field("text2", new Utf8(), true),
-        new Field(
-          "vector1",
-          new FixedSizeList(3, new Field("item", new Float32(), true)),
-          true,
-        ),
-        new Field(
-          "vector2",
-          new FixedSizeList(3, new Field("item", new Float32(), true)),
-          true,
-        ),
-      ],
-      metadata,
-    );
-
-    const db = await connect(tmpDir.name);
-    const table = await db.createEmptyTable("test", schema);
-    await table.add([{ text1: "hello world", text2: "goodbye world" }]);
-
-    const rows = await table.query().toArray();
-    expect(JSON.parse(JSON.stringify(rows[0].vector1))).toEqual([1, 2, 3]);
-    expect(JSON.parse(JSON.stringify(rows[0].vector2))).toEqual([4, 5, 6]);
-  });

  it("should append generated vectors to a non-nullable schema", async () => {
    @register("non_nullable_schema_test")
@@ -539,52 +487,4 @@ describe("embedding functions", () => {
      expect(stringSchema3).toEqual(stringExpectedSchema);
    },
  );
-  test("parses one function writing several vector columns", async () => {
-    class MockEmbeddingFunction extends EmbeddingFunction<string> {
-      ndims() {
-        return 3;
-      }
-      embeddingDataType(): Float {
-        return new Float32();
-      }
-      async computeQueryEmbeddings(_data: string) {
-        return [1, 2, 3];
-      }
-      async computeSourceEmbeddings(data: string[]) {
-        return Array.from({ length: data.length }).fill([
-          1, 2, 3,
-        ]) as number[][];
-      }
-    }
-    const registry = getRegistry();
-    registry.register("multi_output_mock")(MockEmbeddingFunction);
-
-    // A materialized view can project one source vector column under two
-    // names, so a table's configuration names the same function twice.
-    const parsed = await registry.parseFunctions(
-      new Map([
-        [
-          "embedding_functions",
-          JSON.stringify([
-            {
-              name: "multi_output_mock",
-              sourceColumn: "text",
-              vectorColumn: "vector_a",
-              model: {},
-            },
-            {
-              name: "multi_output_mock",
-              sourceColumn: "text",
-              vectorColumn: "vector_b",
-              model: {},
-            },
-          ]),
-        ],
-      ]),
-    );
-
-    expect(
-      [...parsed.values()].map(({ vectorColumn }) => vectorColumn).sort(),
-    ).toEqual(["vector_a", "vector_b"]);
-  });
 });
@@ -1,95 +0,0 @@
-// 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();
-    });
-  });
-});
@@ -1,33 +0,0 @@
-// 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;
-});
@@ -1,147 +0,0 @@
-// SPDX-License-Identifier: Apache-2.0
-// SPDX-FileCopyrightText: Copyright The LanceDB Authors
-
-import * as tmp from "tmp";
-
-import { Connection, connect } from "../lancedb";
-import {
-  DEFINITION_META_KEY,
-  definitionFromMetadata,
-} from "../lancedb/materialized_view";
-
-describe("materialized views", () => {
-  let tmpDir: tmp.DirResult;
-  let db: Connection;
-
-  beforeEach(async () => {
-    tmpDir = tmp.dirSync({ unsafeCleanup: true });
-    db = await connect(tmpDir.name);
-    await db.createTable(
-      "people",
-      [
-        { name: "ada", age: 36 },
-        { name: "kid", age: 7 },
-        { name: "grace", age: 85 },
-      ],
-      { storageOptions: { newTableEnableStableRowIds: "true" } },
-    );
-  });
-  afterEach(() => tmpDir.removeCallback());
-
-  it("rejects a stored limit a number cannot carry", () => {
-    const big = new Map([
-      [
-        DEFINITION_META_KEY,
-        '{"kind":"select","source_table":"people","limit":9007199254740993}',
-      ],
-    ]);
-    expect(() => definitionFromMetadata(big, "v")).toThrow(
-      /too large to represent exactly/,
-    );
-
-    const safe = new Map([
-      [
-        DEFINITION_META_KEY,
-        '{"kind":"select","source_table":"people","limit":42}',
-      ],
-    ]);
-    expect(definitionFromMetadata(safe, "v").limit).toBe(42);
-  });
-
-  it("creates, refreshes and queries a view", async () => {
-    const view = await db.createMaterializedView("adults", "people", {
-      select: ["name", ["shout", "upper(name)"]],
-      where: "age >= 18",
-    });
-    expect(view.name).toBe("adults");
-    expect(await view.table().countRows()).toBe(0);
-
-    const result = await view.refresh();
-    expect(result.mode).toBe("rebuild");
-    expect(Number(result.rowsWritten)).toBe(2);
-
-    const rows = await view.table().query().toArray();
-    expect(rows.map((r) => r.shout).sort()).toEqual(["ADA", "GRACE"]);
-  });
-
-  it("round-trips the definition", async () => {
-    await db.createMaterializedView("adults", "people", {
-      where: "age >= 18",
-    });
-    const view = await db.openMaterializedView("adults");
-    const definition = await view.definition();
-    expect(definition.sourceTable).toBe("people");
-    expect(definition.filter).toBe("age >= 18");
-    expect(definition.projections).toEqual([
-      ["name", "`name`"],
-      ["age", "`age`"],
-    ]);
-    expect(definition.inputs).toEqual(["age", "name"]);
-  });
-
-  it("refreshes incrementally after an append", async () => {
-    const view = await db.createMaterializedView("copy", "people");
-    await view.refresh();
-
-    const people = await db.openTable("people");
-    await people.add([{ name: "alan", age: 41 }]);
-    const result = await view.refresh();
-    expect(result.mode).toBe("incremental");
-    expect(Number(result.rowsWritten)).toBe(1);
-    expect(await view.table().countRows()).toBe(4);
-
-    expect((await view.refresh()).mode).toBe("no_op");
-  });
-
-  it("lists views and rejects non-views", async () => {
-    await db.createMaterializedView("adults", "people", {
-      where: "age >= 18",
-    });
-    expect(await db.listMaterializedViews()).toEqual(["adults"]);
-    await expect(db.openMaterializedView("people")).rejects.toThrow(
-      "not a materialized view",
-    );
-  });
-
-  it("rejects an invalid expression at create time", async () => {
-    await expect(
-      db.createMaterializedView("bad", "people", {
-        select: [["x", "missing + 1"]],
-      }),
-    ).rejects.toThrow("missing");
-  });
-
-  it("rejects invalid numeric options before creating anything", async () => {
-    for (const limit of [-5, 1.5, Infinity, NaN]) {
-      await expect(
-        db.createMaterializedView("bad", "people", { limit }),
-      ).rejects.toThrow("non-negative integer");
-    }
-    expect(await db.listMaterializedViews()).toEqual([]);
-
-    const view = await db.createMaterializedView("copy", "people");
-    for (const sourceVersion of [-1, 1.5, Infinity, NaN]) {
-      await expect(view.refresh({ sourceVersion })).rejects.toThrow(
-        "non-negative integer",
-      );
-    }
-  });
-
-  it("quotes bare select names", async () => {
-    await db.createTable("odd_names", [{ "order item": "widget" }], {
-      storageOptions: { newTableEnableStableRowIds: "true" },
-    });
-    const view = await db.createMaterializedView("quoted", "odd_names", {
-      select: ["order item"],
-    });
-    const result = await view.refresh();
-    expect(Number(result.rowsWritten)).toBe(1);
-  });
-
-  it("requires stable row ids on the source", async () => {
-    await db.createTable("plain", [{ x: 1 }]);
-    await expect(db.createMaterializedView("v", "plain")).rejects.toThrow(
-      "stable row ids",
-    );
-  });
-});
@@ -75,25 +75,6 @@ async function withMockDatabase(
 }

 describe("remote connection", () => {
-  it("refuses materialized views before issuing any request", async () => {
-    const paths: string[] = [];
-    await withMockDatabase(
-      (req, res) => {
-        paths.push(req.url ?? "");
-        res.writeHead(404).end();
-      },
-      async (db) => {
-        await expect(db.openMaterializedView("secret_table")).rejects.toThrow(
-          /only on local databases/,
-        );
-        await expect(db.listMaterializedViews()).rejects.toThrow(
-          /only on local databases/,
-        );
-        expect(paths).toEqual([]);
-      },
-    );
-  });
-
  it("should accept partial connection options", async () => {
    await connect("db://test", {
      apiKey: "fake",
@@ -330,7 +311,7 @@ describe("remote connection", () => {
    expect(createIndexBody?.["custom_stop_words"]).toEqual(["the"]);
  });

-  it("diffs and cherry-picks remote branches", async () => {
+  it("diffs and merges remote branches", async () => {
    const sampleDiff = {
      fromBranch: "exp",
      parentVersion: 1,
@@ -352,9 +333,10 @@ describe("remote connection", () => {
      changedColumns: [],
      addedIndexes: [],
      removedIndexes: [],
-      errors: [],
+      mergeable: true,
+      mergeBlockers: [],
    };
-    const cherryPickBodies: Record<string, unknown>[] = [];
+    const mergeBodies: Record<string, unknown>[] = [];

    await withMockDatabase(
      (req, res) => {
@@ -384,16 +366,17 @@ describe("remote connection", () => {
              .end(JSON.stringify(sampleDiff));
            return;
          }
-          if (path.endsWith("/branches/cherry_pick/")) {
-            cherryPickBodies.push(body);
+          if (path.endsWith("/branches/merge/")) {
+            mergeBodies.push(body);
            const dryRun = body["dry_run"] === true;
            const response = {
-              status: dryRun ? "ready" : "failed",
+              status: dryRun ? "ready" : "rejected",
              diff: dryRun
                ? sampleDiff
                : {
                    ...sampleDiff,
-                    errors: [
+                    mergeable: false,
+                    mergeBlockers: [
                      { code: "baseMoved", message: "main has advanced" },
                    ],
                  },
@@ -415,19 +398,19 @@ describe("remote connection", () => {

        await expect(branches.diff("exp")).resolves.toEqual(sampleDiff);

-        const failed = await branches.cherryPick("exp");
-        expect(failed.status).toBe("failed");
-        expect(failed.diff.errors).toEqual([
+        const rejected = await branches.merge("exp");
+        expect(rejected.status).toBe("rejected");
+        expect(rejected.diff.mergeBlockers).toEqual([
          { code: "baseMoved", message: "main has advanced" },
        ]);

-        const preview = await branches.cherryPick("exp", true);
+        const preview = await branches.merge("exp", true);
        expect(preview.status).toBe("ready");
        expect(preview.preview.promotedColumns).toEqual(["tag"]);
      },
    );

-    expect(cherryPickBodies).toEqual([
+    expect(mergeBodies).toEqual([
      // biome-ignore lint/style/useNamingConvention: snake_case mandated by the server wire format
      { from_branch: "exp", dry_run: false },
      // biome-ignore lint/style/useNamingConvention: snake_case mandated by the server wire format
@@ -11,13 +11,10 @@ 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";
@@ -685,56 +682,6 @@ 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;
@@ -1830,194 +1777,6 @@ 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(() => {
@@ -2585,24 +2344,7 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
      );
    });

-    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]);
-          }
-        },
-      );
-
+    test("full text search if no embedding function provided", async () => {
      const db = await connect(tmpDir.name);
      const data = [
        { text: "hello world", vector: [0.1, 0.2, 0.3] },
@@ -2624,306 +2366,6 @@ 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 = [
@@ -3474,30 +2916,6 @@ 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 = [];
@@ -3535,7 +2953,7 @@ describe("column name options", () => {
      .limit(10)
      .toArray();
    expect(results2.length).toBe(10);
-  }, 30_000);
+  });
 });

 describe("when creating an empty table", () => {
@@ -3561,27 +2979,6 @@ describe("when creating an empty table", () => {
    expect((actualSchema.fields[1].type as Float64).precision).toBe(2);
  });

-  it("can add and query JSON data", async () => {
-    const schema = new Schema([
-      new Field("id", new Int32(), true),
-      new Field(
-        "meta",
-        new Utf8(),
-        true,
-        new Map([["ARROW:extension:name", "arrow.json"]]),
-      ),
-    ]);
-    const table = await con.createEmptyTable("json", schema);
-    const meta = JSON.stringify({ x: 1 });
-
-    await table.add([{ id: 1, meta }]);
-
-    const rows = await table.query().toArray();
-    expect(rows).toHaveLength(1);
-    expect(rows[0].id).toBe(1);
-    expect(rows[0].meta).toBe(meta);
-  });
-
  it("can create an empty table from schema that specifies field types by name", async () => {
    const schemaLike = {
      fields: [
@@ -170,7 +170,7 @@ test("basic table examples", async () => {
    // --8<-- [end:create_index]

    // --8<-- [start:delete_rows]
-    await tbl.delete("item = 'fizz'");
+    await tbl.delete('item = "fizz"');
    // --8<-- [end:delete_rows]

    // --8<-- [start:drop_table]
@@ -5,6 +5,7 @@ import {
  Data as ArrowData,
  Table as ArrowTable,
  Binary,
+  Bool,
  BufferType,
  DataType,
  DateUnit,
@@ -17,7 +18,12 @@ import {
  FixedSizeList,
  Float,
  Float32,
+  Float64,
  Int,
+  Int8,
+  Int16,
+  Int32,
+  Int64,
  LargeBinary,
  List,
  Null,
@@ -30,16 +36,17 @@ import {
  Struct,
  Timestamp,
  Type,
+  Uint8,
+  Uint16,
+  Uint32,
  Utf8,
  Vector,
  makeVector as arrowMakeVector,
-  util as arrowUtil,
  vectorFromArray as badVectorFromArray,
  makeBuilder,
  makeData,
 } from "apache-arrow";
 import { Buffers } from "apache-arrow/data";
-import { typedArrayToArrowType } from "./arrow_type";
 import { type EmbeddingFunction } from "./embedding/embedding_function";
 import {
  EmbeddingFunctionConfig,
@@ -52,7 +59,14 @@ import {
  sanitizeTable,
  sanitizeType,
 } from "./sanitize";
-import { inferSchema } from "./schema";
+
+/**
+ * Check if a field name indicates a vector column.
+ */
+function nameSuggestsVectorColumn(fieldName: string): boolean {
+  const nameLower = fieldName.toLowerCase();
+  return nameLower.includes("vector") || nameLower.includes("embedding");
+}

 export * from "apache-arrow";
 export type SchemaLike =
@@ -72,7 +86,8 @@ export type FieldLike =
    };

 export type DataLike =
-  | import("apache-arrow").Data
+  // biome-ignore lint/suspicious/noExplicitAny: <explanation>
+  | import("apache-arrow").Data<Struct<any>>
  | {
      // biome-ignore lint/suspicious/noExplicitAny: <explanation>
      type: any;
@@ -81,7 +96,6 @@ export type DataLike =
      stride: number;
      nullable: boolean;
      children: DataLike[];
-      dictionary?: { data: readonly DataLike[] };
      get nullCount(): number;
      // biome-ignore lint/suspicious/noExplicitAny: <explanation>
      values: Buffers<any>[BufferType.DATA];
@@ -445,6 +459,110 @@ export function makeArrowTable(
  return new ArrowTable(inferredSchema, finalColumns);
 }

+function inferSchema(
+  data: Array<Record<string, unknown>>,
+  schema: Schema | undefined,
+  opts: MakeArrowTableOptions,
+): Schema {
+  // We will collect all fields we see in the data.
+  const pathTree = new PathTree<DataType>();
+
+  for (const [rowI, row] of data.entries()) {
+    for (const [path, value] of rowPathsAndValues(row)) {
+      if (!pathTree.has(path)) {
+        // First time seeing this field.
+        if (schema !== undefined) {
+          const field = getFieldForPath(schema, path);
+          if (field === undefined) {
+            throw new Error(
+              `Found field not in schema: ${path.join(".")} at row ${rowI}`,
+            );
+          } else {
+            pathTree.set(path, field.type);
+          }
+        } else {
+          const inferredType = inferType(value, path, opts);
+          if (inferredType === undefined) {
+            throw new Error(`Failed to infer data type for field ${path.join(
+              ".",
+            )} at row ${rowI}. \
+                             Consider providing an explicit schema.`);
+          }
+          pathTree.set(path, inferredType);
+        }
+      } else if (schema === undefined) {
+        const currentType = pathTree.get(path);
+        const newType = inferType(value, path, opts);
+        if (currentType !== newType) {
+          new Error(`Failed to infer schema for data. Previously inferred type \
+                     ${currentType} but found ${newType} at row ${rowI}. Consider \
+                     providing an explicit schema.`);
+        }
+      }
+    }
+  }
+
+  if (schema === undefined) {
+    function fieldsFromPathTree(pathTree: PathTree<DataType>): Field[] {
+      const fields = [];
+      for (const [name, value] of pathTree.map.entries()) {
+        if (value instanceof PathTree) {
+          const children = fieldsFromPathTree(value);
+          fields.push(new Field(name, new Struct(children), true));
+        } else {
+          fields.push(new Field(name, value, true));
+        }
+      }
+      return fields;
+    }
+    const fields = fieldsFromPathTree(pathTree);
+    return new Schema(fields);
+  } else {
+    function takeMatchingFields(
+      fields: Field[],
+      pathTree: PathTree<DataType>,
+    ): Field[] {
+      const outFields = [];
+      for (const field of fields) {
+        if (pathTree.map.has(field.name)) {
+          const value = pathTree.get([field.name]);
+          if (value instanceof PathTree) {
+            const struct = field.type as Struct;
+            const children = takeMatchingFields(struct.children, value);
+            outFields.push(
+              new Field(field.name, new Struct(children), field.nullable),
+            );
+          } else {
+            outFields.push(
+              new Field(field.name, value as DataType, field.nullable),
+            );
+          }
+        }
+      }
+      return outFields;
+    }
+    const fields = takeMatchingFields(schema.fields, pathTree);
+    return new Schema(fields);
+  }
+}
+
+function* rowPathsAndValues(
+  row: Record<string, unknown>,
+  basePath: string[] = [],
+): Generator<[string[], unknown]> {
+  for (const [key, value] of Object.entries(row)) {
+    if (isObject(value)) {
+      yield* rowPathsAndValues(value, [...basePath, key]);
+    } else {
+      // Skip undefined values - they should be treated the same as missing fields
+      // for embedding function purposes
+      if (value !== undefined) {
+        yield [[...basePath, key], value];
+      }
+    }
+  }
+}
+
 function isObject(value: unknown): value is Record<string, unknown> {
  return (
    typeof value === "object" &&
@@ -459,19 +577,146 @@ function isObject(value: unknown): value is Record<string, unknown> {
  );
 }

-function valueAtPath(datum: Record<string, unknown>, path: string[]): unknown {
-  let current: unknown = datum;
+function getFieldForPath(schema: Schema, path: string[]): Field | undefined {
+  let current: Field | Schema = schema;
  for (const key of path) {
-    if (current == null) {
-      return null;
-    }
-    if (isObject(current) && (Object.hasOwn(current, key) || key in current)) {
-      current = current[key];
+    if (current instanceof Schema) {
+      const field: Field | undefined = current.fields.find(
+        (f) => f.name === key,
+      );
+      if (field === undefined) {
+        return undefined;
+      }
+      current = field;
+    } else if (current instanceof Field && DataType.isStruct(current.type)) {
+      const struct: Struct = current.type;
+      const field = struct.children.find((f) => f.name === key);
+      if (field === undefined) {
+        return undefined;
+      }
+      current = field;
    } else {
      return undefined;
    }
  }
-  return current;
+  if (current instanceof Field) {
+    return current;
+  } else {
+    return undefined;
+  }
+}
+
+/**
+ * Try to infer which Arrow type to use for a given value.
+ *
+ * May return undefined if the type cannot be inferred.
+ */
+function inferType(
+  value: unknown,
+  path: string[],
+  opts: MakeArrowTableOptions,
+): DataType | undefined {
+  if (typeof value === "bigint") {
+    return new Int64();
+  } else if (typeof value === "number") {
+    // Even if it's an integer, it's safer to assume Float64. Users can
+    // always provide an explicit schema or use BigInt if they mean integer.
+    return new Float64();
+  } else if (typeof value === "string") {
+    if (opts.dictionaryEncodeStrings) {
+      return new Dictionary(new Utf8(), new Int32());
+    } else {
+      return new Utf8();
+    }
+  } else if (typeof value === "boolean") {
+    return new Bool();
+  } else if (value instanceof Buffer) {
+    return new Binary();
+  } else if (ArrayBuffer.isView(value) && !(value instanceof DataView)) {
+    const info = typedArrayToArrowType(value);
+    if (info !== undefined) {
+      const child = new Field("item", info.elementType, true);
+      return new FixedSizeList(info.length, child);
+    }
+    return undefined;
+  } else if (Array.isArray(value)) {
+    if (value.length === 0) {
+      return undefined; // Without any values we can't infer the type
+    }
+    if (path.length === 1 && Object.hasOwn(opts.vectorColumns, path[0])) {
+      const floatType = sanitizeType(opts.vectorColumns[path[0]].type);
+      return new FixedSizeList(
+        value.length,
+        new Field("item", floatType, true),
+      );
+    }
+    const valueType = inferType(value[0], path, opts);
+    if (valueType === undefined) {
+      return undefined;
+    }
+    // Try to automatically detect embedding columns.
+    if (nameSuggestsVectorColumn(path[path.length - 1])) {
+      // Check if value is a Uint8Array for integer vector type determination
+      if (value instanceof Uint8Array) {
+        // For integer vectors, we default to Uint8 (matching Python implementation)
+        const child = new Field("item", new Uint8(), true);
+        return new FixedSizeList(value.length, child);
+      } else {
+        // For float vectors, we default to Float32
+        const child = new Field("item", new Float32(), true);
+        return new FixedSizeList(value.length, child);
+      }
+    } else {
+      const child = new Field("item", valueType, true);
+      return new List(child);
+    }
+  } else {
+    // TODO: timestamp
+    return undefined;
+  }
+}
+
+class PathTree<V> {
+  map: Map<string, V | PathTree<V>>;
+
+  constructor(entries?: [string[], V][]) {
+    this.map = new Map();
+    if (entries !== undefined) {
+      for (const [path, value] of entries) {
+        this.set(path, value);
+      }
+    }
+  }
+  has(path: string[]): boolean {
+    let ref: PathTree<V> = this;
+    for (const part of path) {
+      if (!(ref instanceof PathTree) || !ref.map.has(part)) {
+        return false;
+      }
+      ref = ref.map.get(part) as PathTree<V>;
+    }
+    return true;
+  }
+  get(path: string[]): V | undefined {
+    let ref: PathTree<V> = this;
+    for (const part of path) {
+      if (!(ref instanceof PathTree) || !ref.map.has(part)) {
+        return undefined;
+      }
+      ref = ref.map.get(part) as PathTree<V>;
+    }
+    return ref as V;
+  }
+  set(path: string[], value: V): void {
+    let ref: PathTree<V> = this;
+    for (const part of path.slice(0, path.length - 1)) {
+      if (!ref.map.has(part)) {
+        ref.map.set(part, new PathTree<V>());
+      }
+      ref = ref.map.get(part) as PathTree<V>;
+    }
+    ref.map.set(path[path.length - 1], value);
+  }
 }

 function transposeData(
@@ -479,26 +724,37 @@ function transposeData(
  field: Field,
  path: string[] = [],
 ): Vector {
-  const valuesPath = [...path, field.name];
-  const values = data.map((datum) => valueAtPath(datum, valuesPath));
  if (field.type instanceof Struct) {
    const childFields = field.type.children;
+    const fullPath = [...path, field.name];
    const childVectors = childFields.map((child) => {
-      return transposeData(data, child, valuesPath);
+      return transposeData(data, child, fullPath);
    });
-    const nullCount = values.filter((value) => value === null).length;
    const structData = makeData({
      type: field.type,
-      length: values.length,
-      nullCount,
-      nullBitmap:
-        nullCount > 0
-          ? arrowUtil.packBools(values.map((value) => value !== null))
-          : undefined,
      children: childVectors as unknown as ArrowData<DataType>[],
    });
    return arrowMakeVector(structData);
  } else {
+    const valuesPath = [...path, field.name];
+    const values = data.map((datum) => {
+      let current: unknown = datum;
+      for (const key of valuesPath) {
+        if (current == null) {
+          return null;
+        }
+
+        if (
+          isObject(current) &&
+          (Object.hasOwn(current, key) || key in current)
+        ) {
+          current = current[key];
+        } else {
+          return null;
+        }
+      }
+      return current;
+    });
    return makeVector(values, field.type, undefined, field.nullable);
  }
 }
@@ -541,6 +797,32 @@ function makeListVector(lists: unknown[][]): Vector<unknown> {
  return listBuilder.finish().toVector();
 }

+/**
+ * Map a JS TypedArray instance to the corresponding Arrow element DataType
+ * and its length. Returns undefined if the value is not a recognized TypedArray.
+ */
+function typedArrayToArrowType(
+  value: ArrayBufferView,
+): { elementType: DataType; length: number } | undefined {
+  if (value instanceof Float32Array)
+    return { elementType: new Float32(), length: value.length };
+  if (value instanceof Float64Array)
+    return { elementType: new Float64(), length: value.length };
+  if (value instanceof Uint8Array)
+    return { elementType: new Uint8(), length: value.length };
+  if (value instanceof Uint16Array)
+    return { elementType: new Uint16(), length: value.length };
+  if (value instanceof Uint32Array)
+    return { elementType: new Uint32(), length: value.length };
+  if (value instanceof Int8Array)
+    return { elementType: new Int8(), length: value.length };
+  if (value instanceof Int16Array)
+    return { elementType: new Int16(), length: value.length };
+  if (value instanceof Int32Array)
+    return { elementType: new Int32(), length: value.length };
+  return undefined;
+}
+
 /** Helper function to convert an Array of JS values to an Arrow Vector */
 function makeVector(
  values: unknown[],
@@ -1180,12 +1462,8 @@ export function ensureNestedFieldsExist(
          completeRow[field.name] = row[field.name];
        }
      } else {
-        // Keep a missing struct valid while filling each of its children with
-        // null. This is distinct from an explicitly null struct value.
-        completeRow[field.name] =
-          field.type.constructor.name === "Struct"
-            ? ensureStructFieldsExist({}, field.type as Struct)
-            : null;
+        // Field is missing from the data - set to null
+        completeRow[field.name] = null;
      }
    }

@@ -1220,12 +1498,8 @@ function ensureStructFieldsExist(
        completeStruct[childField.name] = data[childField.name];
      }
    } else {
-      // Keep a missing struct valid while filling each of its children with
-      // null. This is distinct from an explicitly null struct value.
-      completeStruct[childField.name] =
-        childField.type.constructor.name === "Struct"
-          ? ensureStructFieldsExist({}, childField.type as Struct)
-          : null;
+      // Field is missing - set to null
+      completeStruct[childField.name] = null;
    }
  }

@@ -1,40 +0,0 @@
-// SPDX-License-Identifier: Apache-2.0
-// SPDX-FileCopyrightText: Copyright The LanceDB Authors
-
-import {
-  type DataType,
-  Float32,
-  Float64,
-  Int8,
-  Int16,
-  Int32,
-  Uint8,
-  Uint16,
-  Uint32,
-} from "apache-arrow";
-
-/**
- * Map a JS TypedArray instance to the corresponding Arrow element type and
- * length. Returns undefined when the view is not a supported TypedArray.
- */
-export function typedArrayToArrowType(
-  value: ArrayBufferView,
-): { elementType: DataType; length: number } | undefined {
-  if (value instanceof Float32Array)
-    return { elementType: new Float32(), length: value.length };
-  if (value instanceof Float64Array)
-    return { elementType: new Float64(), length: value.length };
-  if (value instanceof Uint8Array)
-    return { elementType: new Uint8(), length: value.length };
-  if (value instanceof Uint16Array)
-    return { elementType: new Uint16(), length: value.length };
-  if (value instanceof Uint32Array)
-    return { elementType: new Uint32(), length: value.length };
-  if (value instanceof Int8Array)
-    return { elementType: new Int8(), length: value.length };
-  if (value instanceof Int16Array)
-    return { elementType: new Int16(), length: value.length };
-  if (value instanceof Int32Array)
-    return { elementType: new Int32(), length: value.length };
-  return undefined;
-}
@@ -16,12 +16,6 @@ import {
  makeEmptyTable,
 } from "./arrow";
 import { EmbeddingFunctionConfig, getRegistry } from "./embedding/registry";
-import {
-  MaterializedView,
-  MaterializedViewSelect,
-  normalizeSelect,
-  validateNonNegativeInteger,
-} from "./materialized_view";
 import { Connection as LanceDbConnection } from "./native";
 import type {
  CreateNamespaceResponse,
@@ -31,14 +25,12 @@ import type {
  JobDescription,
  JobInfo,
  ListNamespacesResponse,
-  ListTablesResponse,
 } from "./native";
 export type {
  CreateNamespaceResponse,
  DescribeNamespaceResponse,
  DropNamespaceResponse,
  ListNamespacesResponse,
-  ListTablesResponse,
 };
 import { sanitizeTable } from "./sanitize";
 import { LocalTable, Table } from "./table";
@@ -136,10 +128,6 @@ export interface OpenTableOptions {
  indexCacheSize?: number;
 }

-/**
- * @deprecated Use {@link ListTablesOptions} with {@link Connection.listTables}
- * instead.
- */
 export interface TableNamesOptions {
  /**
   * If present, only return names that come lexicographically after the
@@ -153,24 +141,6 @@ export interface TableNamesOptions {
  limit?: number;
 }

-export interface ListTablesOptions {
-  /**
-   * Token from a previous response, to resume listing where it left off.
-   *
-   * The token is opaque: it carries whatever the database needs to resume, and
-   * callers should not construct or interpret one.
-   */
-  pageToken?: string;
-  /**
-   * An upper bound on how many tables to return.
-   *
-   * A page may hold fewer than this and still not be the last one, so keep
-   * going while the response carries a page token rather than while pages are
-   * full.
-   */
-  limit?: number;
-}
-
 export interface ListNamespacesOptions {
  /** Token from a previous response for pagination. */
  pageToken?: string;
@@ -255,7 +225,6 @@ export abstract class Connection {
   * @param {Partial<TableNamesOptions>} options - options to control the
   * paging / start point (backwards compatibility)
   *
-   * @deprecated Use {@link Connection.listTables} instead.
   */
  abstract tableNames(options?: Partial<TableNamesOptions>): Promise<string[]>;
  /**
@@ -266,94 +235,18 @@ export abstract class Connection {
   * @param {Partial<TableNamesOptions>} options - options to control the
   * paging / start point
   *
-   * @deprecated Use {@link Connection.listTables} instead.
   */
  abstract tableNames(
    namespacePath?: string[],
    options?: Partial<TableNamesOptions>,
  ): Promise<string[]>;

-  /**
-   * List a page of the tables in this database.
-   *
-   * To retrieve the tables after the page, pass the `pageToken` the response
-   * carries back in. A page can be shorter than `limit` without being the last
-   * one, so walk until a response carries no page token:
-   *
-   * ```ts
-   * const names = [];
-   * let pageToken = undefined;
-   * do {
-   *   const page = await conn.listTables({ pageToken, limit: 100 });
-   *   names.push(...page.tables);
-   *   pageToken = page.pageToken;
-   * } while (pageToken);
-   * ```
-   *
-   * @param {Partial<ListTablesOptions>} options - Pagination options
-   *   (`pageToken`, `limit`).
-   * @returns {Promise<ListTablesResponse>} A page of table names and an
-   *   optional token for the tables after it.
-   */
-  abstract listTables(
-    options?: Partial<ListTablesOptions>,
-  ): Promise<ListTablesResponse>;
-  /**
-   * List a page of the tables in this database.
-   *
-   * @param {string[]} namespacePath - The namespace path to list tables from
-   *   (defaults to root namespace)
-   * @param {Partial<ListTablesOptions>} options - Pagination options
-   *   (`pageToken`, `limit`).
-   * @returns {Promise<ListTablesResponse>} A page of table names and an
-   *   optional token for the tables after it.
-   */
-  abstract listTables(
-    namespacePath?: string[],
-    options?: Partial<ListTablesOptions>,
-  ): Promise<ListTablesResponse>;
-
  /**
   * Open a table in the database.
   * @param {string} name - The name of the table
   * @param {string[]} namespacePath - The namespace path of the table (defaults to root namespace)
   * @param {Partial<OpenTableOptions>} options - Additional options
   */
-  /**
-   * Define a materialized view named `name` over the table `source`.
-   *
-   * The view is created empty, with the query recorded in its schema
-   * metadata; `view.refresh()` computes the rows. The view is a normal
-   * table: it can be queried, indexed and searched, and it appears in
-   * `tableNames`. The source table must have stable row ids (create it with
-   * the `newTableEnableStableRowIds` storage option); they keep the view's
-   * provenance valid across source compactions and cannot be enabled after
-   * a table exists. Local databases only.
-   */
-  abstract createMaterializedView(
-    name: string,
-    source: string,
-    options?: {
-      select?: MaterializedViewSelect;
-      where?: string;
-      limit?: number;
-    },
-  ): Promise<MaterializedView>;
-
-  /**
-   * Open the materialized view named `name`.
-   *
-   * Rejects a table that exists but is not a materialized view.
-   */
-  abstract openMaterializedView(name: string): Promise<MaterializedView>;
-
-  /**
-   * The names of the materialized views in this database.
-   *
-   * Found by reading every table's schema, so this costs an open per table.
-   */
-  abstract listMaterializedViews(): Promise<string[]>;
-
  abstract openTable(
    name: string,
    namespacePath?: string[],
@@ -638,54 +531,6 @@ export class LocalConnection extends Connection {
    );
  }

-  async createMaterializedView(
-    name: string,
-    source: string,
-    options?: {
-      select?: MaterializedViewSelect;
-      where?: string;
-      limit?: number;
-    },
-  ): Promise<MaterializedView> {
-    validateNonNegativeInteger(options?.limit, "limit");
-    const innerTable = await this.inner.createMaterializedView(
-      name,
-      source,
-      normalizeSelect(options?.select),
-      options?.where,
-      options?.limit,
-    );
-    return new MaterializedView(new LocalTable(innerTable));
-  }
-
-  async openMaterializedView(name: string): Promise<MaterializedView> {
-    const innerTable = await this.inner.openMaterializedView(name);
-    return new MaterializedView(new LocalTable(innerTable));
-  }
-
-  async listMaterializedViews(): Promise<string[]> {
-    return await this.inner.listMaterializedViews();
-  }
-
-  async listTables(
-    namespacePathOrOptions?: string[] | Partial<ListTablesOptions>,
-    options?: Partial<ListTablesOptions>,
-  ): Promise<ListTablesResponse> {
-    // Detect if first argument is namespacePath array or options object
-    const namespacePath = Array.isArray(namespacePathOrOptions)
-      ? namespacePathOrOptions
-      : undefined;
-    const listTablesOptions = Array.isArray(namespacePathOrOptions)
-      ? options
-      : namespacePathOrOptions;
-
-    return this.inner.listTables(
-      namespacePath ?? [],
-      listTablesOptions?.pageToken,
-      listTablesOptions?.limit,
-    );
-  }
-
  async openTable(
    name: string,
    namespacePath?: string[],
@@ -4,15 +4,7 @@
 import { Field, Schema } from "../arrow";
 import { sanitizeType } from "../sanitize";
 import { EmbeddingFunction } from "./embedding_function";
-import {
-  EmbeddingFunctionConfig,
-  EmbeddingFunctionRegistry,
-  getRegistry as getGlobalRegistry,
-  registerBuiltIn,
-} from "./registry";
-
-type OpenAIModule = typeof import("./openai");
-type TransformersModule = typeof import("./transformers");
+import { EmbeddingFunctionConfig, getRegistry } from "./registry";

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

-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();
-}
+export * from "./registry";

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

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

+@register("openai")
 export class OpenAIEmbeddingFunction extends EmbeddingFunction<
  string,
  Partial<OpenAIOptions>
@@ -99,5 +100,3 @@ export class OpenAIEmbeddingFunction extends EmbeddingFunction<
    return response.data[0].embedding;
  }
 }
-
-registerBuiltIn("openai", OpenAIEmbeddingFunction);
@@ -7,10 +7,6 @@ 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;
@@ -63,15 +59,6 @@ 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;
@@ -109,7 +96,6 @@ export class EmbeddingFunctionRegistry {
   */
  reset(this: EmbeddingFunctionRegistry) {
    this.#functions.clear();
-    getBuiltInFunctions(this).clear();
  }

  /**
@@ -197,56 +183,12 @@ export class 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();
+const _REGISTRY = new EmbeddingFunctionRegistry();

 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 { registerBuiltIn } from "./registry";
+import { register } from "./registry";

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

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

-registerBuiltIn("huggingface", TransformersEmbeddingFunction);
-
 const tensorDiv = (
  src: import("@huggingface/transformers").Tensor,
  divBy: number,
@@ -21,11 +21,6 @@ import type { BaseTokenizer } from "./indices";
 import type { FtsToken } from "./table";

 // Re-export native header provider for use with connectWithHeaderProvider
-export {
-  MaterializedView,
-  MaterializedViewDefinition,
-  MaterializedViewSelect,
-} from "./materialized_view";
 export { JsHeaderProvider as NativeJsHeaderProvider } from "./native.js";

 // OpenTelemetry metrics bridge. Only the high-level entry point is public; the
@@ -56,7 +51,6 @@ export {
  AddResult,
  AddColumnsResult,
  RefreshColumnResult,
-  RefreshMaterializedViewResult,
  AlterColumnsResult,
  UpdateFieldMetadataResult,
  DeleteResult,
@@ -81,13 +75,11 @@ export {
  Connection,
  CreateTableOptions,
  TableNamesOptions,
-  ListTablesOptions,
  OpenTableOptions,
  ListNamespacesOptions,
  CreateNamespaceOptions,
  DropNamespaceOptions,
  ListNamespacesResponse,
-  ListTablesResponse,
  CreateNamespaceResponse,
  DropNamespaceResponse,
  DescribeNamespaceResponse,
@@ -103,7 +95,6 @@ export {
 } from "./native.js";

 export {
-  AutoQuery,
  ExecutableQuery,
  Query,
  QueryBase,
@@ -144,10 +135,10 @@ export {
  BranchColumnChange,
  BranchIndexSummary,
  BranchRowCountSummary,
-  CherryPickError,
+  MergeBlocker,
  BranchDiff,
-  CherryPickPreview,
-  CherryPickResult,
+  MergePreview,
+  MergeBranchResult,
  AddDataOptions,
  UpdateOptions,
  OptimizeOptions,
@@ -1,161 +0,0 @@
-// SPDX-License-Identifier: Apache-2.0
-// SPDX-FileCopyrightText: Copyright The LanceDB Authors
-
-import { RefreshMaterializedViewResult } from "./native";
-import { Table } from "./table";
-
-/** Schema metadata key holding a materialized view's definition. */
-export const DEFINITION_META_KEY = "mv.definition";
-
-/** The query that defines a materialized view. */
-export interface MaterializedViewDefinition {
-  /** Name of the source table, in the same database as the view. */
-  sourceTable: string;
-  /** `[output column, SQL expression]` pairs, in view schema order. */
-  projections: [string, string][];
-  /** SQL predicate selecting the source rows the view holds. */
-  filter?: string;
-  /** Cap on the number of rows the view holds. */
-  limit?: number;
-  /** Source columns the projections and filter read. */
-  inputs: string[];
-}
-
-/**
- * The view's columns: column names, `[alias, SQL expression]` pairs, or a
- * record of the same. A bare name projects itself.
- */
-export type MaterializedViewSelect =
-  | (string | [string, string])[]
-  | Record<string, string>;
-
-/**
- * @internal Reject a numeric option N-API would otherwise silently coerce:
- * `Infinity` reaches Rust as 0, `1.5` as 1.
- */
-export function validateNonNegativeInteger(
-  value: number | undefined,
-  name: string,
-): void {
-  if (value !== undefined && !(Number.isSafeInteger(value) && value >= 0)) {
-    throw new Error(`${name} must be a non-negative integer`);
-  }
-}
-
-/** @internal Quote a column name as a Lance SQL identifier (backticks). */
-function quoteIdentifier(name: string): string {
-  return "`" + name.replace(/`/g, "``") + "`";
-}
-
-/**
- * @internal Normalize a select argument into `[alias, expression]` pairs.
- * A bare name projects itself and is quoted, so any valid column name works;
- * pair and record entries are kept verbatim because their right side is an
- * expression.
- */
-export function normalizeSelect(
-  select?: MaterializedViewSelect,
-): [string, string][] | undefined {
-  if (select === undefined) {
-    return undefined;
-  }
-  if (Array.isArray(select)) {
-    return select.map((item) =>
-      typeof item === "string" ? [item, quoteIdentifier(item)] : item,
-    );
-  }
-  return Object.entries(select);
-}
-
-/** @internal Parse a definition off a table's stored schema metadata. */
-export function definitionFromMetadata(
-  metadata: Map<string, string>,
-  name: string,
-): MaterializedViewDefinition {
-  const raw = metadata.get(DEFINITION_META_KEY);
-  if (raw === undefined) {
-    throw new Error(`Table '${name}' is not a materialized view`);
-  }
-  // biome-ignore lint/suspicious/noExplicitAny: raw JSON
-  const value: any = JSON.parse(raw);
-  if (value.kind !== "select") {
-    throw new Error(
-      `materialized view '${name}' is defined by '${value.kind}', which this ` +
-        "version of lancedb cannot refresh",
-    );
-  }
-  const limit = value.limit ?? undefined;
-  // JSON.parse rounds integers past 2^53; every exact u64 parses to a safe
-  // integer and every rounded one does not, so this rejects precisely the
-  // values a number cannot carry.
-  if (limit !== undefined && !Number.isSafeInteger(limit)) {
-    throw new Error(
-      `materialized view '${name}' has a stored limit too large to represent exactly`,
-    );
-  }
-  return {
-    sourceTable: value.source_table,
-    // biome-ignore lint/suspicious/noExplicitAny: raw JSON
-    projections: (value.projections ?? []).map((p: any) => [
-      p.output,
-      p.expression,
-    ]),
-    filter: value.filter ?? undefined,
-    limit,
-    inputs: value.inputs ?? [],
-  };
-}
-
-/**
- * A handle on a materialized view: its table plus its definition.
- *
- * Obtained from {@link Connection#createMaterializedView} or
- * {@link Connection#openMaterializedView}. The view is a normal table --
- * queries, indexes and search all apply through {@link MaterializedView#table}
- * -- whose contents are maintained by {@link MaterializedView#refresh}.
- */
-export class MaterializedView {
-  private readonly inner: Table;
-
-  constructor(table: Table) {
-    this.inner = table;
-  }
-
-  get name(): string {
-    return this.inner.name;
-  }
-
-  /** The view, as the table it is. */
-  table(): Table {
-    return this.inner;
-  }
-
-  /** The query that defines the view, read from its stored schema. */
-  async definition(): Promise<MaterializedViewDefinition> {
-    const schema = await this.inner.schema();
-    return definitionFromMetadata(schema.metadata, this.name);
-  }
-
-  /**
-   * Recompute the view from its source.
-   *
-   * The refresh is incremental when the source's changes can be reconciled
-   * into the view -- rows added, changed or removed since the last one --
-   * and otherwise rebuilds. `full` forces a rebuild; `sourceVersion`
-   * refreshes to that source version instead of the latest.
-   *
-   * Concurrent refreshes of one view do not duplicate its rows. Two that
-   * plan the same source rows conflict on commit, and the loser throws
-   * rather than writing them a second time.
-   */
-  async refresh(options?: {
-    full?: boolean;
-    sourceVersion?: number;
-  }): Promise<RefreshMaterializedViewResult> {
-    validateNonNegativeInteger(options?.sourceVersion, "sourceVersion");
-    return await this.inner.refreshMaterializedView(
-      options?.full,
-      options?.sourceVersion,
-    );
-  }
-}
@@ -100,29 +100,6 @@ 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}
@@ -134,15 +111,13 @@ export class QueryBase<
  NativeQueryType extends NativeQuery | NativeVectorQuery | NativeTakeQuery,
 > implements AsyncIterable<RecordBatch>
 {
-  protected inner!: NativeQueryType | Promise<NativeQueryType>;
-
  /**
   * @hidden
   */
-  protected constructor(inner?: NativeQueryType | Promise<NativeQueryType>) {
-    if (inner !== undefined) {
-      this.inner = inner;
-    }
+  protected constructor(
+    protected inner: NativeQueryType | Promise<NativeQueryType>,
+  ) {
+    // intentionally empty
  }

  // call a function on the inner (either a promise or the actual object)
@@ -160,15 +135,6 @@ 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.
   *
@@ -241,11 +207,16 @@ export class QueryBase<
  /**
   * @hidden
   */
-  protected async nativeExecute(
+  protected nativeExecute(
    options?: Partial<QueryExecutionOptions>,
  ): Promise<NativeBatchIterator> {
-    const inner = await this.getInner();
-    return inner.execute(options?.maxBatchLength, options?.timeoutMs);
+    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);
+    }
  }

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

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

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

@@ -522,13 +510,6 @@ 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)
   *
@@ -556,7 +537,7 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {
   * the minimum and maximum to the same value.
   */
  nprobes(nprobes: number): VectorQuery {
-    this.doVectorCall((inner) => inner.nprobes(nprobes));
+    super.doCall((inner) => inner.nprobes(nprobes));

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

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

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

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

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

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

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

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

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

@@ -727,39 +708,43 @@ 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. 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.
+   * 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.
   */
  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 () => {
-        const inner = await this.getInner();
-        const outcome = await settledVector;
-        if (outcome.status === "rejected") {
-          throw outcome.reason;
+        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);
        }
-        addQueryVectorToNative(inner, outcome.value);
-        return inner;
      })();
      return new VectorQuery(res);
    } else {
-      this.doVectorCall((inner) => addQueryVectorToNative(inner, vector));
+      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[]));
+        }
+      });
      return this;
    }
  }

  rerank(reranker: Reranker): VectorQuery {
-    this.doVectorCall((inner) =>
+    super.doCall((inner) =>
      inner.rerank(async (args) => {
        const vecResults = await fromBufferToRecordBatch(args.vecResults);
        const ftsResults = await fromBufferToRecordBatch(args.ftsResults);
@@ -778,71 +763,6 @@ 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.
 *
@@ -868,51 +788,6 @@ 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}
@@ -965,19 +840,45 @@ export class Query extends StandardQueryBase<NativeQuery> {
   * a default `limit` of 10 will be used.  @see {@link Query#limit}
   */
  nearestTo(vector: IntoVector): VectorQuery {
-    const inner = this.inner;
-    if (inner instanceof Promise) {
-      const nativeQuery = inner.then(async (resolvedInner) =>
-        nearestToNative(resolvedInner, await vector),
-      );
+    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);
+      });
      return new VectorQuery(nativeQuery);
    }
    if (vector instanceof Promise) {
-      return new VectorQuery(
-        vector.then((resolvedVector) => nearestToNative(inner, resolvedVector)),
-      );
+      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(nearestToNative(inner, vector));
  }

  nearestToText(query: string | FullTextQuery, columns?: string[]): Query {
@@ -94,24 +94,17 @@ export function sanitizeMetadata(
  if (metadataLike === undefined || metadataLike === null) {
    return undefined;
  }
-
-  let entries: IterableIterator<[unknown, unknown]>;
-  try {
-    entries = Map.prototype.entries.call(metadataLike);
-  } catch {
+  if (!(metadataLike instanceof Map)) {
    throw Error("Expected metadata, if present, to be a Map<string, string>");
  }
-
-  const metadata = new Map<string, string>();
-  for (const [key, value] of entries) {
-    if (typeof key !== "string" || typeof value !== "string") {
+  for (const item of metadataLike) {
+    if (typeof item[0] !== "string" || typeof item[1] !== "string") {
      throw Error(
        "Expected metadata, if present, to be a Map<string, string> but it had non-string keys or values",
      );
    }
-    metadata.set(key, value);
  }
-  return metadata;
+  return metadataLike as Map<string, string>;
 }

 export function sanitizeInt(typeLike: object) {
@@ -1,567 +0,0 @@
-// SPDX-License-Identifier: Apache-2.0
-// SPDX-FileCopyrightText: Copyright The LanceDB Authors
-
-import {
-  Binary,
-  Bool,
-  DataType,
-  Dictionary,
-  Field,
-  FixedSizeList,
-  Float32,
-  Float64,
-  Int32,
-  Int64,
-  List,
-  Schema,
-  Struct,
-  Utf8,
-  util as arrowUtil,
-} from "apache-arrow";
-import { typedArrayToArrowType } from "./arrow_type";
-import { sanitizeType } from "./sanitize";
-
-type InferenceOptions = {
-  dictionaryEncodeStrings: boolean;
-  vectorColumns: Record<string, { type: unknown }>;
-};
-
-/**
- * Infer the Arrow schema represented by a set of records.
- *
- * This is the intentionally small interface to schema inference. The stateful
- * details of combining partial type evidence are encapsulated below so callers
- * only need to provide records, an optional schema, and inference options.
- */
-export function inferSchema(
-  data: Array<Record<string, unknown>>,
-  schema: Schema | undefined,
-  options: InferenceOptions,
-): Schema {
-  return new SchemaInferrer(schema, options).infer(data);
-}
-
-class SchemaInferrer {
-  private readonly fields = new FieldTree();
-
-  constructor(
-    private readonly providedSchema: Schema | undefined,
-    private readonly options: InferenceOptions,
-  ) {}
-
-  infer(data: Array<Record<string, unknown>>): Schema {
-    for (const [row, record] of data.entries()) {
-      for (const [path, value] of recordPathsAndValues(record)) {
-        this.observe(path, value, row);
-      }
-    }
-
-    return this.providedSchema === undefined
-      ? new Schema(fieldsFromTree(this.fields))
-      : new Schema(matchingFields(this.providedSchema.fields, this.fields));
-  }
-
-  private observe(path: string[], value: unknown, row: number): void {
-    const current = this.fields.get(path);
-    if (current === undefined) {
-      this.addField(path, value, row);
-    } else if (this.providedSchema === undefined) {
-      this.updateInferredField(path, value, row, current);
-    }
-  }
-
-  private addField(path: string[], value: unknown, row: number): void {
-    if (this.providedSchema !== undefined) {
-      this.addSchemaField(this.providedSchema, path, row);
-      return;
-    }
-
-    const evidence =
-      this.inferType(value, path) ?? DeferredTypeEvidence.from(value, row);
-    if (evidence === undefined) {
-      throw typeInferenceError(path, row);
-    }
-
-    const conflict = this.fields.set(
-      path,
-      evidence,
-      (existing) =>
-        existing instanceof DeferredTypeEvidence && existing.isOnlyNulls(),
-    );
-    if (conflict !== undefined) {
-      throw branchConflictError(conflict, row, "Struct");
-    }
-  }
-
-  private addSchemaField(schema: Schema, path: string[], row: number): void {
-    const field = fieldAtPath(schema, path);
-    if (field === undefined) {
-      throw new Error(
-        `Found field not in schema: ${path.join(".")} at row ${row}`,
-      );
-    }
-
-    const conflict = this.fields.set(path, field.type);
-    if (conflict !== undefined) {
-      throw branchConflictError(conflict, row, "Struct");
-    }
-  }
-
-  private updateInferredField(
-    path: string[],
-    value: unknown,
-    row: number,
-    current: FieldNode,
-  ): void {
-    const newType = this.inferType(value, path);
-    const deferred = DeferredTypeEvidence.from(value, row);
-
-    if (current instanceof FieldTree) {
-      if (deferred?.isOnlyNulls()) {
-        return;
-      }
-      throw schemaInferenceError(
-        path,
-        row,
-        "Struct",
-        describeEvidence(newType ?? deferred),
-      );
-    }
-
-    if (current instanceof DeferredTypeEvidence) {
-      this.resolveDeferredField(path, row, current, newType, deferred);
-      return;
-    }
-
-    if (newType !== undefined) {
-      if (!inferredTypesEqual(current, newType)) {
-        throw schemaInferenceError(
-          path,
-          row,
-          describeEvidence(current),
-          describeEvidence(newType),
-        );
-      }
-      return;
-    }
-
-    if (deferred === undefined || !deferred.matches(current)) {
-      throw schemaInferenceError(
-        path,
-        row,
-        describeEvidence(current),
-        describeEvidence(deferred),
-      );
-    }
-  }
-
-  private resolveDeferredField(
-    path: string[],
-    row: number,
-    current: DeferredTypeEvidence,
-    newType: DataType | undefined,
-    deferred: DeferredTypeEvidence | undefined,
-  ): void {
-    if (newType !== undefined) {
-      if (!current.matches(newType)) {
-        throw schemaInferenceError(
-          path,
-          row,
-          current.describe(),
-          describeEvidence(newType),
-        );
-      }
-      this.fields.set(path, newType);
-      return;
-    }
-
-    if (deferred !== undefined) {
-      this.fields.set(path, current.merge(deferred));
-      return;
-    }
-
-    throw schemaInferenceError(
-      path,
-      row,
-      current.describe(),
-      describeEvidence(newType),
-    );
-  }
-
-  private inferType(value: unknown, path: string[]): DataType | undefined {
-    if (typeof value === "bigint") {
-      return new Int64();
-    }
-    if (typeof value === "number") {
-      return new Float64();
-    }
-    if (typeof value === "string") {
-      return this.options.dictionaryEncodeStrings
-        ? new Dictionary(new Utf8(), new Int32())
-        : new Utf8();
-    }
-    if (typeof value === "boolean") {
-      return new Bool();
-    }
-    if (value instanceof Buffer) {
-      return new Binary();
-    }
-    if (ArrayBuffer.isView(value) && !(value instanceof DataView)) {
-      const typedArray = typedArrayToArrowType(value);
-      return typedArray === undefined
-        ? undefined
-        : new FixedSizeList(
-            typedArray.length,
-            new Field("item", typedArray.elementType, true),
-          );
-    }
-    if (!Array.isArray(value) || value.length === 0) {
-      return undefined;
-    }
-
-    const configuredVector =
-      path.length === 1 ? this.options.vectorColumns[path[0]] : undefined;
-    if (configuredVector !== undefined) {
-      return new FixedSizeList(
-        value.length,
-        new Field("item", sanitizeType(configuredVector.type), true),
-      );
-    }
-
-    const itemType = this.inferArrayItemType(value, path);
-    if (itemType === undefined) {
-      return undefined;
-    }
-
-    return nameSuggestsVectorColumn(path[path.length - 1])
-      ? new FixedSizeList(value.length, new Field("item", new Float32(), true))
-      : new List(new Field("item", itemType, true));
-  }
-
-  private inferArrayItemType(
-    values: unknown[],
-    path: string[],
-  ): DataType | undefined {
-    let itemType: DataType | undefined;
-    const deferredItems: unknown[] = [];
-
-    for (const value of values) {
-      const candidate = this.inferType(value, path);
-      if (candidate === undefined) {
-        if (!isDeferredValue(value)) {
-          return undefined;
-        }
-        deferredItems.push(value);
-      } else if (itemType === undefined) {
-        itemType = candidate;
-      } else if (!inferredTypesEqual(itemType, candidate)) {
-        return undefined;
-      }
-    }
-
-    if (itemType === undefined) {
-      return undefined;
-    }
-    return deferredItems.every((value) =>
-      deferredValueMatchesType(value, itemType),
-    )
-      ? itemType
-      : undefined;
-  }
-}
-
-/** Nulls and empty/all-null lists that do not determine a type by themselves. */
-class DeferredTypeEvidence {
-  private constructor(
-    private readonly values: Array<{ value: unknown; row: number }>,
-  ) {}
-
-  static from(value: unknown, row: number): DeferredTypeEvidence | undefined {
-    return isDeferredValue(value)
-      ? new DeferredTypeEvidence([{ value, row }])
-      : undefined;
-  }
-
-  isOnlyNulls(): boolean {
-    return this.values.every(({ value }) => value == null);
-  }
-
-  matches(type: DataType): boolean {
-    return this.values.every(({ value }) =>
-      deferredValueMatchesType(value, type),
-    );
-  }
-
-  merge(other: DeferredTypeEvidence): DeferredTypeEvidence {
-    return new DeferredTypeEvidence([...this.values, ...other.values]);
-  }
-
-  describe(): string {
-    const list = this.values.find(({ value }) => Array.isArray(value));
-    return list === undefined
-      ? "null"
-      : `List[${(list.value as unknown[]).length}]`;
-  }
-
-  firstRow(): number {
-    return this.values[0].row;
-  }
-}
-
-type FieldNode = DataType | DeferredTypeEvidence | FieldTree;
-type LeafNode = Exclude<FieldNode, FieldTree>;
-type FieldConflict = { path: string[]; value: FieldNode };
-
-/** Nested field state, kept separate from Arrow's eventual Struct types. */
-class FieldTree {
-  private readonly children = new Map<string, FieldNode>();
-
-  get(path: string[]): FieldNode | undefined {
-    let current: FieldNode = this;
-    for (const part of path) {
-      if (!(current instanceof FieldTree)) {
-        return undefined;
-      }
-      const child = current.children.get(part);
-      if (child === undefined) {
-        return undefined;
-      }
-      current = child;
-    }
-    return current;
-  }
-
-  set(
-    path: string[],
-    value: LeafNode,
-    canReplaceLeaf: (value: LeafNode) => boolean = () => false,
-  ): FieldConflict | undefined {
-    let branch: FieldTree = this;
-    for (const [index, part] of path.slice(0, -1).entries()) {
-      const child = branch.children.get(part);
-      if (child === undefined || (isLeaf(child) && canReplaceLeaf(child))) {
-        const nextBranch = new FieldTree();
-        branch.children.set(part, nextBranch);
-        branch = nextBranch;
-      } else if (child instanceof FieldTree) {
-        branch = child;
-      } else {
-        return { path: path.slice(0, index + 1), value: child };
-      }
-    }
-
-    const name = path[path.length - 1];
-    const current = branch.children.get(name);
-    if (current instanceof FieldTree) {
-      return { path, value: current };
-    }
-    branch.children.set(name, value);
-    return undefined;
-  }
-
-  entries(): IterableIterator<[string, FieldNode]> {
-    return this.children.entries();
-  }
-
-  has(name: string): boolean {
-    return this.children.has(name);
-  }
-}
-
-function isLeaf(value: FieldNode): value is LeafNode {
-  return !(value instanceof FieldTree);
-}
-
-function fieldsFromTree(tree: FieldTree, path: string[] = []): Field[] {
-  const fields: Field[] = [];
-  for (const [name, value] of tree.entries()) {
-    if (value instanceof FieldTree) {
-      fields.push(
-        new Field(
-          name,
-          new Struct(fieldsFromTree(value, [...path, name])),
-          true,
-        ),
-      );
-    } else if (value instanceof DeferredTypeEvidence) {
-      throw typeInferenceError([...path, name], value.firstRow());
-    } else {
-      fields.push(new Field(name, value, true));
-    }
-  }
-  return fields;
-}
-
-function matchingFields(fields: Field[], tree: FieldTree): Field[] {
-  const matches: Field[] = [];
-  for (const field of fields) {
-    if (!tree.has(field.name)) {
-      continue;
-    }
-    const value = tree.get([field.name]);
-    if (value instanceof FieldTree) {
-      const struct = field.type as Struct;
-      matches.push(
-        new Field(
-          field.name,
-          new Struct(matchingFields(struct.children, value)),
-          field.nullable,
-          field.metadata,
-        ),
-      );
-    } else {
-      matches.push(field);
-    }
-  }
-  return matches;
-}
-
-function* recordPathsAndValues(
-  record: Record<string, unknown>,
-  path: string[] = [],
-): Generator<[string[], unknown]> {
-  for (const [name, value] of Object.entries(record)) {
-    if (isRecord(value)) {
-      yield* recordPathsAndValues(value, [...path, name]);
-    } else if (value !== undefined) {
-      yield [[...path, name], value];
-    }
-  }
-}
-
-function isRecord(value: unknown): value is Record<string, unknown> {
-  return (
-    typeof value === "object" &&
-    value !== null &&
-    !Array.isArray(value) &&
-    !(value instanceof RegExp) &&
-    !(value instanceof Date) &&
-    !(value instanceof Set) &&
-    !(value instanceof Map) &&
-    !(value instanceof Buffer) &&
-    !ArrayBuffer.isView(value)
-  );
-}
-
-function fieldAtPath(schema: Schema, path: string[]): Field | undefined {
-  let fields = schema.fields;
-  let field: Field | undefined;
-  for (const [index, name] of path.entries()) {
-    field = fields.find((candidate) => candidate.name === name);
-    if (field === undefined || index === path.length - 1) {
-      return field;
-    }
-    if (!DataType.isStruct(field.type)) {
-      return undefined;
-    }
-    fields = field.type.children;
-  }
-  return field;
-}
-
-function isDeferredValue(value: unknown): boolean {
-  return (
-    value == null || (Array.isArray(value) && value.every(isDeferredValue))
-  );
-}
-
-function deferredValueMatchesType(value: unknown, type: DataType): boolean {
-  if (value == null) {
-    return true;
-  }
-  if (!Array.isArray(value)) {
-    return false;
-  }
-  if (DataType.isList(type)) {
-    return value.every((item) =>
-      deferredValueMatchesType(item, type.valueType),
-    );
-  }
-  if (DataType.isFixedSizeList(type)) {
-    return (
-      value.length === type.listSize &&
-      value.every((item) => deferredValueMatchesType(item, type.valueType))
-    );
-  }
-  return false;
-}
-
-function inferredTypesEqual(current: DataType, candidate: DataType): boolean {
-  if (DataType.isDictionary(current)) {
-    return (
-      DataType.isDictionary(candidate) &&
-      current.isOrdered === candidate.isOrdered &&
-      inferredTypesEqual(current.indices, candidate.indices) &&
-      inferredTypesEqual(current.dictionary, candidate.dictionary)
-    );
-  }
-  if (DataType.isList(current)) {
-    return (
-      DataType.isList(candidate) &&
-      current.valueField.name === candidate.valueField.name &&
-      current.valueField.nullable === candidate.valueField.nullable &&
-      inferredTypesEqual(current.valueType, candidate.valueType)
-    );
-  }
-  if (DataType.isFixedSizeList(current)) {
-    return (
-      DataType.isFixedSizeList(candidate) &&
-      current.listSize === candidate.listSize &&
-      current.valueField.name === candidate.valueField.name &&
-      current.valueField.nullable === candidate.valueField.nullable &&
-      inferredTypesEqual(current.valueType, candidate.valueType)
-    );
-  }
-  return arrowUtil.compareTypes(current, candidate);
-}
-
-function describeEvidence(
-  evidence: DataType | DeferredTypeEvidence | undefined,
-): string {
-  if (evidence === undefined) {
-    return "an unsupported value";
-  }
-  return evidence instanceof DeferredTypeEvidence
-    ? evidence.describe()
-    : evidence.toString();
-}
-
-function branchConflictError(
-  conflict: FieldConflict,
-  row: number,
-  candidate: string,
-): Error {
-  return schemaInferenceError(
-    conflict.path,
-    row,
-    conflict.value instanceof FieldTree
-      ? "Struct"
-      : describeEvidence(conflict.value),
-    candidate,
-  );
-}
-
-function schemaInferenceError(
-  path: string[],
-  row: number,
-  currentType: string,
-  newType: string,
-): Error {
-  return new Error(
-    `Failed to infer schema for data. Previously inferred type ${currentType} ` +
-      `but found ${newType} for field ${path.join(".")} at row ${row}. ` +
-      "Consider providing an explicit schema.",
-  );
-}
-
-function typeInferenceError(path: string[], row: number): Error {
-  return new Error(
-    `Failed to infer data type for field ${path.join(".")} at row ${row}. ` +
-      "Consider providing an explicit schema.",
-  );
-}
-
-function nameSuggestsVectorColumn(name: string): boolean {
-  const normalized = name.toLowerCase();
-  return normalized.includes("vector") || normalized.includes("embedding");
-}
@@ -35,7 +35,6 @@ import {
  Branches as NativeBranches,
  OptimizeStats,
  RefreshColumnResult,
-  RefreshMaterializedViewResult,
  TableStatistics,
  Tags,
  UpdateFieldMetadataResult,
@@ -43,12 +42,10 @@ import {
  Table as _NativeTable,
 } from "./native";
 import {
-  AutoQuery,
  FullTextQuery,
  Query,
  TakeQuery,
  VectorQuery,
-  createAutoQuery,
  instanceOfFullTextQuery,
 } from "./query";
 import { sanitizeType } from "./sanitize";
@@ -525,7 +522,7 @@ export abstract class Table {
    query: string | IntoVector | MultiVector | FullTextQuery,
    queryType?: string,
    ftsColumns?: string | string[],
-  ): VectorQuery | Query | AutoQuery;
+  ): VectorQuery | Query;
  /**
   * Search the table with a given query vector.
   *
@@ -605,18 +602,6 @@ export abstract class Table {
   */
  abstract refreshColumnAsync(column: string): Promise<Job>;

-  /**
-   * Recompute this table's contents from its materialized-view definition.
-   *
-   * Plumbing for {@link MaterializedView.refresh}, which is the way to call
-   * it: rejects tables that carry no view definition. Local tables only.
-   * @ignore
-   */
-  abstract refreshMaterializedView(
-    full?: boolean,
-    sourceVersion?: number,
-  ): Promise<RefreshMaterializedViewResult>;
-
  /**
   * Alter the name or nullability of columns.
   * @param {ColumnAlteration[]} columnAlterations One or more alterations to
@@ -630,18 +615,6 @@ export abstract class Table {

  /**
   * Update per-field (column) metadata.
-   *
-   * The following keys are treated specially, by convention, and should be
-   * used when appropriate:
-   *
-   * - `lancedb:description`: for a human-readable description of a field.
-   * - `lancedb:tag:<name>`: for a user-defined key-value tag, where the suffix
-   *   names the tag category; e.g. `lancedb:tag:model: "clip"`.
-   * - `lancedb:logical-column`: for a column grouping; e.g. `feature_v1` and
-   *   `feature_v2` might be in the same logical column.
-   * - `lancedb:status`: for status options (`production`, `candidate`,
-   *   `deprecated`, `archived`) to designate the current life cycle state of
-   *   this column.
   * @param {FieldMetadataUpdate[]} updates One or more per-field updates. Each
   * update's metadata is merged into the field's existing metadata by default;
   * a value of `null` deletes that key, and `replace: true` swaps the whole map.
@@ -989,11 +962,10 @@ export class LocalTable extends Table {
    return this.inner.display();
  }

-  private async getEmbeddingFunctions(
-    inner: _NativeTable = this.inner,
-  ): Promise<Map<string, EmbeddingFunctionConfig>> {
-    const schemaBuf = await inner.schema();
-    const schema = tableFromIPC(schemaBuf).schema;
+  private async getEmbeddingFunctions(): Promise<
+    Map<string, EmbeddingFunctionConfig>
+  > {
+    const schema = await this.schema();
    const registry = getRegistry();
    return registry.parseFunctions(schema.metadata);
  }
@@ -1175,7 +1147,7 @@ export class LocalTable extends Table {
    query: string | IntoVector | MultiVector | FullTextQuery,
    queryType: string = "auto",
    ftsColumns?: string | string[],
-  ): VectorQuery | Query | AutoQuery {
+  ): VectorQuery | Query {
    if (typeof query !== "string" && !instanceOfFullTextQuery(query)) {
      if (queryType === "fts") {
        throw new Error("Cannot perform full text search on a vector query");
@@ -1190,28 +1162,14 @@ export class LocalTable extends Table {
      });
    }

-    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);
+    // 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,
      });
    }

@@ -1306,13 +1264,6 @@ export class LocalTable extends Table {
    return await this.inner.refreshColumnAsync(column);
  }

-  async refreshMaterializedView(
-    full?: boolean,
-    sourceVersion?: number,
-  ): Promise<RefreshMaterializedViewResult> {
-    return await this.inner.refreshMaterializedView(full, sourceVersion);
-  }
-
  async alterColumns(
    columnAlterations: ColumnAlteration[],
  ): Promise<AlterColumnsResult> {
@@ -1567,8 +1518,7 @@ export interface FieldMetadataUpdate {
  path: string;
  /**
   * Metadata key/value pairs. Merged into the field's existing metadata by
-   * default; a value of `null` deletes that key. See
-   * {@link Table.updateFieldMetadata} for the conventional `lancedb:*` keys.
+   * default; a value of `null` deletes that key.
   */
  metadata: Record<string, string | null>;
  /** If true, replace the field's entire metadata map instead of merging. */
@@ -1607,8 +1557,8 @@ export interface BranchRowCountSummary {
  deltaAvailable: boolean;
 }

-/** A reason why a cherry-pick cannot currently land. */
-export interface CherryPickError {
+/** A reason why a branch cannot currently be merged. */
+export interface MergeBlocker {
  code: string;
  message: string;
 }
@@ -1628,19 +1578,20 @@ export interface BranchDiff {
  changedColumns: BranchColumnChange[];
  addedIndexes: BranchIndexSummary[];
  removedIndexes: BranchIndexSummary[];
-  errors: CherryPickError[];
+  mergeable: boolean;
+  mergeBlockers: MergeBlocker[];
 }

-/** Changes that would be, or were, promoted by a cherry-pick. */
-export interface CherryPickPreview {
+/** Changes that would be, or were, promoted by a branch merge. */
+export interface MergePreview {
  promotedColumns: string[];
 }

-/** Result of previewing or attempting a cherry-pick. */
-export interface CherryPickResult {
-  status: "ready" | "failed" | "notImplemented" | "cherryPicked" | "unknown";
+/** Result of previewing or attempting a branch merge. */
+export interface MergeBranchResult {
+  status: "ready" | "rejected" | "notImplemented" | "merged" | "unknown";
  diff: BranchDiff;
-  preview: CherryPickPreview;
+  preview: MergePreview;
  mainVersionAfter?: number;
 }

@@ -1703,21 +1654,21 @@ export class Branches {
  }

  /**
-   * Cherry-pick a branch onto main.
+   * Merge a branch into main.
   *
-   * Set `dryRun` to `true` to preview. A failed cherry-pick resolves
-   * with `status: "failed"` instead of throwing.
+   * Set `dryRun` to `true` to preview the merge. A rejected merge resolves
+   * with `status: "rejected"` instead of throwing.
   *
-   * @param fromBranch Branch to cherry-pick from.
-   * @param dryRun When true, only preview. Defaults to false.
+   * @param fromBranch Branch to merge from.
+   * @param dryRun When true, only preview the merge. Defaults to false.
   */
-  async cherryPick(
+  async merge(
    fromBranch: string,
    dryRun: boolean = false,
-  ): Promise<CherryPickResult> {
-    return (await this.#inner.cherryPick(
+  ): Promise<MergeBranchResult> {
+    return (await this.#inner.merge(
      fromBranch,
      dryRun,
-    )) as unknown as CherryPickResult;
+    )) as unknown as MergeBranchResult;
  }
 }
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-darwin-arm64",
-	"version": "0.38.0-beta.11",
+	"version": "0.38.0-beta.3",
 	"os": ["darwin"],
 	"cpu": ["arm64"],
 	"main": "lancedb.darwin-arm64.node",
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-arm64-gnu",
-	"version": "0.38.0-beta.11",
+	"version": "0.38.0-beta.3",
 	"os": ["linux"],
 	"cpu": ["arm64"],
 	"main": "lancedb.linux-arm64-gnu.node",
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-arm64-musl",
-	"version": "0.38.0-beta.11",
+	"version": "0.38.0-beta.3",
 	"os": ["linux"],
 	"cpu": ["arm64"],
 	"main": "lancedb.linux-arm64-musl.node",
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-x64-gnu",
-	"version": "0.38.0-beta.11",
+	"version": "0.38.0-beta.3",
 	"os": ["linux"],
 	"cpu": ["x64"],
 	"main": "lancedb.linux-x64-gnu.node",
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-x64-musl",
-	"version": "0.38.0-beta.11",
+	"version": "0.38.0-beta.3",
 	"os": ["linux"],
 	"cpu": ["x64"],
 	"main": "lancedb.linux-x64-musl.node",
@@ -1,6 +1,6 @@
 {
  "name": "@lancedb/lancedb-win32-arm64-msvc",
-  "version": "0.38.0-beta.11",
+  "version": "0.38.0-beta.3",
  "os": [
    "win32"
  ],
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-win32-x64-msvc",
-	"version": "0.38.0-beta.11",
+	"version": "0.38.0-beta.3",
 	"os": ["win32"],
 	"cpu": ["x64"],
 	"main": "lancedb.win32-x64-msvc.node",
@@ -1,12 +1,12 @@
 {
  "name": "@lancedb/lancedb",
-  "version": "0.38.0-beta.11",
+  "version": "0.38.0-beta.3",
  "lockfileVersion": 3,
  "requires": true,
  "packages": {
    "": {
      "name": "@lancedb/lancedb",
-      "version": "0.38.0-beta.11",
+      "version": "0.38.0-beta.3",
      "cpu": [
        "x64",
        "arm64"
@@ -11,7 +11,7 @@
    "ann"
  ],
  "private": false,
-  "version": "0.38.0-beta.11",
+  "version": "0.38.0-beta.3",
  "main": "dist/index.js",
  "exports": {
    ".": "./dist/index.js",
@@ -17,7 +17,6 @@ use lancedb::connection::{ConnectBuilder, Connection as LanceDBConnection, conne

 use lance_namespace::models::{
    CreateNamespaceRequest, DescribeNamespaceRequest, DropNamespaceRequest, ListNamespacesRequest,
-    ListTablesRequest,
 };
 use lancedb::ipc::{ipc_file_to_batches, ipc_file_to_schema};

@@ -37,12 +36,6 @@ pub struct ListNamespacesResponse {
    pub page_token: Option<String>,
 }

-#[napi(object)]
-pub struct ListTablesResponse {
-    pub tables: Vec<String>,
-    pub page_token: Option<String>,
-}
-
 #[napi(object)]
 pub struct CreateNamespaceResponse {
    pub properties: Option<HashMap<String, String>>,
@@ -213,33 +206,6 @@ impl Connection {
        op.execute().await.default_error()
    }

-    /// List a page of tables in the database.
-    #[napi(catch_unwind)]
-    pub async fn list_tables(
-        &self,
-        namespace_path: Option<Vec<String>>,
-        page_token: Option<String>,
-        limit: Option<u32>,
-    ) -> napi::Result<ListTablesResponse> {
-        let request = ListTablesRequest {
-            // The root namespace is an empty path, not an absent one: a namespace-backed
-            // database rejects a request that names no namespace.
-            id: Some(namespace_path.unwrap_or_default()),
-            page_token,
-            limit: limit.map(|limit| i32::try_from(limit).unwrap_or(i32::MAX)),
-            ..Default::default()
-        };
-        let response = self
-            .get_inner()?
-            .list_tables(request)
-            .await
-            .default_error()?;
-        Ok(ListTablesResponse {
-            tables: response.tables,
-            page_token: response.page_token,
-        })
-    }
-
    /// Create table from a Apache Arrow IPC (file) buffer.
    ///
    /// Parameters:
@@ -300,58 +266,6 @@ impl Connection {
        Ok(Table::new(tbl))
    }

-    #[napi(catch_unwind)]
-    pub async fn create_materialized_view(
-        &self,
-        name: String,
-        source: String,
-        projections: Option<Vec<Vec<String>>>,
-        filter: Option<String>,
-        limit: Option<i64>,
-    ) -> napi::Result<Table> {
-        let mut builder = self.get_inner()?.create_materialized_view(name, source);
-        if let Some(projections) = projections {
-            let mut pairs = Vec::with_capacity(projections.len());
-            for pair in projections {
-                let [output, expression]: [String; 2] = pair.try_into().map_err(|_| {
-                    napi::Error::from_reason("each projection must be an [output, expression] pair")
-                })?;
-                pairs.push((output, expression));
-            }
-            builder = builder.select(pairs);
-        }
-        if let Some(filter) = filter {
-            builder = builder.only_if(filter);
-        }
-        if let Some(limit) = limit {
-            let limit = u64::try_from(limit)
-                .map_err(|_| napi::Error::from_reason("limit must be a non-negative integer"))?;
-            builder = builder.limit(limit);
-        }
-        let view = builder.execute().await.default_error()?;
-        Ok(Table::new(view.table().clone()))
-    }
-
-    #[napi(catch_unwind)]
-    pub async fn open_materialized_view(&self, name: String) -> napi::Result<Table> {
-        let view = self
-            .get_inner()?
-            .open_materialized_view(&name)
-            .await
-            .default_error()?;
-        Ok(Table::new(view.table().clone()))
-    }
-
-    #[napi(catch_unwind)]
-    pub async fn list_materialized_views(&self) -> napi::Result<Vec<String>> {
-        let views = self
-            .get_inner()?
-            .list_materialized_views()
-            .await
-            .default_error()?;
-        Ok(views.into_iter().map(|v| v.name).collect())
-    }
-
    #[napi(catch_unwind)]
    pub async fn open_table(
        &self,
@@ -14,12 +14,9 @@ pub struct Job {
 }

 impl Job {
-    pub(crate) fn new<T>(inner: lancedb::Job<T>) -> Self
-    where
-        T: Clone + Send + Sync + 'static,
-    {
+    pub(crate) fn new(inner: lancedb::Job) -> Self {
        Self {
-            inner: Arc::new(inner.map(|_| ())),
+            inner: Arc::new(inner),
        }
    }
 }
@@ -1,10 +1,6 @@
 // SPDX-License-Identifier: Apache-2.0
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors

-// The materialized-view refresh future deepens the type graph past the
-// default trait-recursion depth; same raise as the core crate applies.
-#![recursion_limit = "256"]
-
 use std::collections::HashMap;

 use env_logger::Env;
@@ -278,13 +278,6 @@ 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(
@@ -388,26 +381,6 @@ impl Table {
        Ok(crate::job::Job::new(job))
    }

-    #[napi(catch_unwind)]
-    pub async fn refresh_materialized_view(
-        &self,
-        full: Option<bool>,
-        source_version: Option<i64>,
-    ) -> napi::Result<RefreshMaterializedViewResult> {
-        let view = lancedb::MaterializedView::from_table(self.inner_ref()?.clone())
-            .await
-            .default_error()?;
-        let mut builder = view.refresh().full(full.unwrap_or(false));
-        if let Some(version) = source_version {
-            let version = u64::try_from(version).map_err(|_| {
-                napi::Error::from_reason("sourceVersion must be a non-negative integer")
-            })?;
-            builder = builder.source_version(version);
-        }
-        let result = builder.execute().await.default_error()?;
-        Ok(result.into())
-    }
-
    #[napi(catch_unwind)]
    pub async fn add_columns_with_schema(
        &self,
@@ -561,12 +534,6 @@ 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()?
@@ -1420,31 +1387,6 @@ pub struct RefreshColumnResult {
    pub version: i64,
 }

-#[napi(object)]
-pub struct RefreshMaterializedViewResult {
-    /// How the view was brought up to date: "rebuild", "incremental" or "no_op".
-    pub mode: String,
-    pub rows_written: i64,
-    pub source_version: i64,
-    pub version: i64,
-}
-
-impl From<lancedb::RefreshMaterializedViewResult> for RefreshMaterializedViewResult {
-    fn from(value: lancedb::RefreshMaterializedViewResult) -> Self {
-        let mode = match value.mode {
-            lancedb::RefreshMode::Rebuild => "rebuild",
-            lancedb::RefreshMode::Incremental => "incremental",
-            lancedb::RefreshMode::NoOp => "no_op",
-        };
-        Self {
-            mode: mode.to_string(),
-            rows_written: value.rows_written as i64,
-            source_version: value.source_version as i64,
-            version: value.version as i64,
-        }
-    }
-}
-
 impl From<lancedb::table::RefreshColumnResult> for RefreshColumnResult {
    fn from(value: lancedb::table::RefreshColumnResult) -> Self {
        Self {
@@ -1663,18 +1605,18 @@ impl Branches {
    }

    #[napi(ts_return_type = "Promise<Record<string, unknown>>")]
-    pub async fn cherry_pick(
+    pub async fn merge(
        &self,
        from_branch: String,
        dry_run: Option<bool>,
    ) -> napi::Result<serde_json::Value> {
        let result = self
            .inner
-            .cherry_pick(&from_branch, dry_run.unwrap_or(false))
+            .merge_branch(&from_branch, dry_run.unwrap_or(false))
            .await
            .default_error()?;
        serde_json::to_value(result).map_err(|err| {
-            napi::Error::from_reason(format!("failed to serialize cherry-pick result: {err}"))
+            napi::Error::from_reason(format!("failed to serialize branch merge result: {err}"))
        })
    }
 }
@@ -1,6 +1,6 @@
 [package]
 name = "lancedb-python"
-version = "0.38.0-beta.11"
+version = "0.38.0-beta.3"
 publish = false
 edition.workspace = true
 description = "Python bindings for LanceDB"
@@ -26,9 +26,7 @@ lance-namespace-impls.workspace = true
 lance-io.workspace = true
 env_logger.workspace = true
 log.workspace = true
-# Maturin enables extension-module mode for Python builds. Keeping it out of
-# Cargo features lets Rust unit tests link against libpython.
-pyo3 = { version = "0.28", features = ["abi3-py310", "chrono"] }
+pyo3 = { version = "0.28", features = ["extension-module", "abi3-py310", "chrono"] }
 chrono.workspace = true
 pyo3-async-runtimes = { version = "0.28", features = [
    "attributes",
@@ -43,7 +41,10 @@ tokio.workspace = true
 libc = "0.2"

 [build-dependencies]
-pyo3-build-config = { version = "0.28", features = ["abi3-py310"] }
+pyo3-build-config = { version = "0.28", features = [
+    "extension-module",
+    "abi3-py310",
+] }

 [features]
 default = ["remote", "lancedb/aws", "lancedb/gcs", "lancedb/azure", "lancedb/dynamodb", "lancedb/oss", "lancedb/huggingface", "lancedb/cos", "lancedb/goosefs", "lancedb/metrics-otel"]
@@ -38,25 +38,6 @@ Stable releases are created about every 2 weeks. For the latest features and bug
 pip install --pre --extra-index-url https://pypi.fury.io/lancedb/ lancedb
 ```

-### Threading in CPU-limited containers
-
-LanceDB uses separate pools for compute work and storage I/O. On a container with
-two visible CPUs, current releases intentionally use one compute worker by default;
-no manual configuration is needed. If every query logs an I/O core reservation
-warning on a two-CPU container, upgrade from LanceDB 0.21.1 or earlier.
-
-The two commonly tuned environment variables control different resources:
-
- `LANCE_CPU_THREADS` overrides the number of compute workers. One worker is the
-  appropriate setting for a two-CPU container when an explicit override is needed.
- `LANCE_IO_THREADS` controls concurrent storage operations, not reserved CPU
-  cores. Its default can be greater than the number of CPUs because I/O workers
-  spend much of their time waiting for storage.
-
-Keep the defaults unless measurements show that the workload benefits from an
-override. See the [Lance threading model](https://lance.org/guide/performance/#threading-model)
-for the current defaults and tuning guidance.
-
 ## Usage

 ### Basic Example
@@ -101,12 +101,9 @@ azure = ["adlfs>=2024.2.0"]
 [tool.maturin]
 python-source = "python"
 module-name = "lancedb._lancedb"
-# uv installs the project as an editable package before `uv run`, so keep that
-# bootstrap build consistent with `maturin develop`.
-editable-profile = "dev"

 [build-system]
-requires = ["maturin>=1.10"]
+requires = ["maturin>=1.4"]
 build-backend = "maturin"

 [tool.ruff.lint]
@@ -6,7 +6,7 @@ import importlib.metadata
 import os
 from concurrent.futures import ThreadPoolExecutor
 from datetime import timedelta
-from typing import Dict, Optional, Union, Any, List, Iterable, TYPE_CHECKING
+from typing import Dict, Optional, Union, Any, List, Iterable

 __version__ = importlib.metadata.version("lancedb")

@@ -20,7 +20,7 @@ from .db import AsyncConnection, DBConnection, LanceDBConnection
 from .remote import ClientConfig
 from .remote.db import RemoteDBConnection
 from .expr import Expr, col, lit, func
-from .schema import blob, vector
+from .schema import blob, vector, BlobType
 from .job import AsyncJob, Job
 from .functions import (
    FunctionArtifactRequest as FunctionArtifactRequest,
@@ -29,15 +29,9 @@ from .functions import (
    FunctionRegistrationRequest as FunctionRegistrationRequest,
    FunctionVersion as FunctionVersion,
    PythonRuntimeSpec as PythonRuntimeSpec,
-    RefreshColumnResult as RefreshColumnResult,
    UdfDefinition as UdfDefinition,
    udf as udf,
 )
-from .materialized_view import (
-    AsyncMaterializedView,
-    MaterializedView,
-    MaterializedViewDefinition,
-)
 from .table import AsyncTable, Table
 from .types import BaseTokenizerType
 from ._lancedb import Session
@@ -49,19 +43,6 @@ from .namespace import (
 )


-if TYPE_CHECKING:
-    from lance.blob import BlobType as BlobType
-
-
-def __getattr__(name: str):
-    if name == "BlobType":
-        from .schema import BlobType
-
-        globals()["BlobType"] = BlobType
-        return BlobType
-    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
-
-
 def _check_s3_bucket_with_dots(
    uri: str, storage_options: Optional[Dict[str, str]]
 ) -> None:
@@ -192,18 +173,6 @@ 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://")
@@ -490,10 +459,6 @@ 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")
@@ -501,11 +466,6 @@ 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
@@ -546,9 +506,6 @@ async def connect_async(


 __all__ = [
-    "AsyncMaterializedView",
-    "MaterializedView",
-    "MaterializedViewDefinition",
    "connect",
    "connect_async",
    "tokenize",
@@ -12,7 +12,7 @@ from typing import TYPE_CHECKING, Optional, Union
 import pyarrow as pa

 from .expr import Expr
-from .schema import row_addressable_blob_v2_paths
+from .schema import blob_v2_column_paths
 from .types import BlobMode, QueryProjection, QueryProjectionSpec

 if TYPE_CHECKING:
@@ -119,7 +119,7 @@ def blob_v2_projection_sources(
    schema: pa.Schema,
    projection: QueryProjection,
 ) -> dict[str, str]:
-    blob_columns = row_addressable_blob_v2_paths(schema)
+    blob_columns = blob_v2_column_paths(schema)
    if not blob_columns:
        return {}
    columns = set(blob_columns)
@@ -140,9 +140,7 @@ def v2_projection_needs_row_id(
 ) -> bool:
    if with_row_id:
        return False
-    return projection_includes_blob_column(
-        projection, row_addressable_blob_v2_paths(schema)
-    )
+    return projection_includes_blob_column(projection, blob_v2_column_paths(schema))


 def blob_auto_row_id_for_scan(
@@ -272,8 +270,7 @@ def _iter_projection_pairs(
            if isinstance(expr, str):
                yield name, expr
            elif isinstance(expr, Expr):
-                source = expr._column_name()
-                yield name, source if source is not None else expr.to_sql()
+                yield name, expr.to_sql()
        return
    for column in projection:
        if isinstance(column, str):
@@ -283,8 +280,7 @@ def _iter_projection_pairs(
            if isinstance(expr, str):
                yield name, expr
            elif isinstance(expr, Expr):
-                source = expr._column_name()
-                yield name, source if source is not None else expr.to_sql()
+                yield name, expr.to_sql()


 def _set_blob_column(tbl: pa.Table, output_name: str, blobs: pa.Array) -> pa.Table:
@@ -87,7 +87,6 @@ class PyExpr:
    def contains(self, substr: "PyExpr") -> "PyExpr": ...
    def isin(self, values: List["PyExpr"]) -> "PyExpr": ...
    def cast(self, data_type: pa.DataType) -> "PyExpr": ...
-    def column_name(self) -> Optional[str]: ...
    def to_sql(self) -> str: ...

 def expr_col(name: str) -> PyExpr: ...
@@ -148,7 +147,7 @@ class Connection(object):
        limit: Optional[int],
    ) -> list[str]: ...  # Deprecated: Use list_tables instead
    def job(self, job_id: str) -> Job: ...
-    async def create_function_async(self, request_json: str) -> Job: ...
+    async def create_function_async(self, request_json: str) -> FunctionJob: ...
    async def get_function(self, name: str, version: str) -> str: ...
    async def list_jobs(self) -> List[JobInfo]: ...
    async def get_job(self, job_id: str) -> Optional[JobDescription]: ...
@@ -198,15 +197,6 @@ class Connection(object):
        cur_namespace_path: Optional[List[str]] = None,
        new_namespace_path: Optional[List[str]] = None,
    ) -> None: ...
-    async def create_materialized_view(
-        self,
-        name: str,
-        source: str,
-        projections: Optional[List[Tuple[str, str]]] = None,
-        filter: Optional[str] = None,
-        limit: Optional[int] = None,
-    ) -> Table: ...
-    async def list_materialized_views(self) -> List[str]: ...
    async def drop_table(
        self, name: str, namespace_path: Optional[List[str]] = None
    ) -> None: ...
@@ -235,7 +225,14 @@ class Job:
    @property
    def id(self) -> Optional[str]: ...
    async def status(self) -> str: ...
-    async def wait(self) -> Optional[str]: ...
+    async def wait(self) -> None: ...
+    async def cancel(self) -> None: ...
+
+class FunctionJob:
+    @property
+    def id(self) -> Optional[str]: ...
+    async def status(self) -> str: ...
+    async def wait(self) -> str: ...
    async def cancel(self) -> None: ...

 class JobInfo:
@@ -284,7 +281,6 @@ class Table:
        mode: Literal["append", "overwrite"],
        progress: Optional[Any] = None,
        write_parallelism: Optional[int] = None,
-        allow_external_blob_outside_bases: bool = False,
    ) -> AddResult: ...
    async def update(
        self, updates: Dict[str, str], where: Optional[str]
@@ -359,9 +355,6 @@ class Table:
    ) -> AddColumnsResult: ...
    async def refresh_column(self, column: str) -> RefreshColumnResult: ...
    async def refresh_column_async(self, column: str) -> Job: ...
-    async def refresh_materialized_view(
-        self, full: bool = False, source_version: Optional[int] = None
-    ) -> RefreshMaterializedViewResult: ...
    async def add_columns_with_schema(self, schema: pa.Schema) -> AddColumnsResult: ...
    async def alter_columns(
        self, columns: list[dict[str, Any]]
@@ -427,7 +420,7 @@ class Branches:
    async def checkout(self, name: str, version: Optional[int] = None) -> Table: ...
    async def delete(self, name: str) -> None: ...
    async def diff(self, from_branch: str) -> Dict[str, Any]: ...
-    async def cherry_pick(
+    async def merge(
        self, from_branch: str, dry_run: bool = False
    ) -> Dict[str, Any]: ...

@@ -609,7 +602,6 @@ class PyQueryRequest:
    filter: Optional[Union[str, bytes]]
    full_text_search: Optional[FullTextQuery]
    select: Optional[Union[str, List[str]]]
-    select_source_columns: Optional[Dict[str, str]]
    fast_search: Optional[bool]
    with_row_id: Optional[bool]
    use_lsm: Optional[bool]
@@ -712,12 +704,6 @@ class RefreshColumnResult:
    rows_filled: int
    version: int

-class RefreshMaterializedViewResult:
-    mode: str
-    rows_written: int
-    source_version: int
-    version: int
-
 class AlterColumnsResult:
    version: int

@@ -16,7 +16,6 @@ from typing import (
    Iterable,
    List,
    Literal,
-    Mapping,
    Optional,
    Union,
 )
@@ -47,13 +46,7 @@ from lance_namespace.errors import NamespaceNotEmptyError, TableNotFoundError
 from . import __version__
 from ._lancedb import connect as lancedb_connect  # type: ignore
 from .functions import FunctionVersion, UdfDefinition
-from .job import AsyncJob, Job, _typed_job
-from .materialized_view import (
-    AsyncMaterializedView,
-    MaterializedView,
-    SelectArg,
-    normalize_select,
-)
+from .job import AsyncJob, Job, _function_job
 from .table import (
    AsyncTable,
    LanceTable,
@@ -517,70 +510,6 @@ class DBConnection(EnforceOverrides):
        """
        raise NotImplementedError

-    def create_materialized_view(
-        self,
-        name: str,
-        source: str,
-        *,
-        select: SelectArg = None,
-        where: Optional[str] = None,
-        limit: Optional[int] = None,
-    ) -> MaterializedView:
-        """Define a materialized view named ``name`` over the table ``source``.
-
-        The view is created empty, with the query recorded in its schema
-        metadata; ``view.refresh()`` computes the rows. The view is a normal
-        table: it can be queried, indexed and searched, and it appears in
-        ``table_names``. Local databases only.
-
-        The source table must have stable row ids (create it with the
-        ``new_table_enable_stable_row_ids`` storage option): they keep the
-        view's provenance valid across source compactions, and cannot be
-        enabled after a table exists.
-
-        Parameters
-        ----------
-        name: str
-            The name of the view.
-        source: str
-            The name of the source table, in this database.
-        select: list or dict, optional
-            The view's columns: column names, ``(alias, SQL expression)``
-            pairs, or a dict of the same. Omitting it selects every source
-            column, expanded against the source schema at creation time.
-        where: str, optional
-            SQL predicate; only matching source rows appear in the view.
-        limit: int, optional
-            Cap the view at this many rows, in materialization order.
-
-        Returns
-        -------
-        MaterializedView
-        """
-        raise NotImplementedError(
-            "materialized views are not supported on this connection type"
-        )
-
-    def open_materialized_view(self, name: str) -> MaterializedView:
-        """Open the materialized view named ``name``.
-
-        Raises ``ValueError`` if the table exists but is not a materialized
-        view.
-        """
-        raise NotImplementedError(
-            "materialized views are not supported on this connection type"
-        )
-
-    def list_materialized_views(self) -> List[str]:
-        """The names of the materialized views in this database.
-
-        Found by reading every table's schema, so this costs an open per
-        table.
-        """
-        raise NotImplementedError(
-            "materialized views are not supported on this connection type"
-        )
-
    def drop_table(self, name: str, namespace_path: Optional[List[str]] = None):
        """Drop a table from the database.

@@ -688,35 +617,17 @@ class DBConnection(EnforceOverrides):
        """
        raise NotImplementedError("serialize is not supported for this connection type")

-    def create_function(
-        self,
-        definition: UdfDefinition,
-        *,
-        secrets: Optional[Mapping[str, str]] = None,
-    ) -> FunctionVersion:
+    def create_function(self, definition: UdfDefinition) -> FunctionVersion:
        """Register a scalar Python UDF and wait for its immutable version.

-        ``secrets`` must contain exactly the names declared by
-        ``@udf(secrets=[...])``. Values are sent in the create request and
-        stored server-side in the private execution artifact; returned
-        Function and Job metadata contain only the declared names.
        This is the blocking counterpart of :meth:`create_function_async`.
        Local connections raise ``NotImplementedError``.
        """
-        return self.create_function_async(definition, secrets=secrets).wait()
+        return self.create_function_async(definition).wait()

-    def create_function_async(
-        self,
-        definition: UdfDefinition,
-        *,
-        secrets: Optional[Mapping[str, str]] = None,
-    ) -> Job[FunctionVersion]:
+    def create_function_async(self, definition: UdfDefinition) -> Job[FunctionVersion]:
        """Register a scalar Python UDF through the remote Function catalog.

-        ``secrets`` must contain exactly the names declared by
-        ``@udf(secrets=[...])``. Values are sent in the create request and
-        stored server-side in the private execution artifact; returned
-        Function and Job metadata contain only the declared names.
        Submission returns a typed job. The immutable Function version becomes
        available only when :meth:`Job.wait` succeeds. Local connections raise
        ``NotImplementedError``.
@@ -1225,58 +1136,6 @@ class LanceDBConnection(DBConnection):
            tbl.checkout(version)
        return tbl

-    @override
-    def create_materialized_view(
-        self,
-        name: str,
-        source: str,
-        *,
-        select: SelectArg = None,
-        where: Optional[str] = None,
-        limit: Optional[int] = None,
-    ) -> MaterializedView:
-        """Define a materialized view named ``name`` over the table ``source``.
-        See
-        [DBConnection.create_materialized_view][lancedb.DBConnection.create_materialized_view].
-
-        Examples
-        --------
-        >>> import lancedb
-        >>> db = lancedb.connect(
-        ...     "./.lancedb",
-        ...     storage_options={"new_table_enable_stable_row_ids": "true"},
-        ... )
-        >>> data = [{"name": "ada", "age": 36}, {"name": "kid", "age": 7}]
-        >>> table = db.create_table("people", data)
-        >>> view = db.create_materialized_view(
-        ...     "adults",
-        ...     "people",
-        ...     select=["name", ("shout", "upper(name)")],
-        ...     where="age >= 18",
-        ... )
-        >>> result = view.refresh()
-        >>> result.rows_written
-        1
-        """
-        LOOP.run(
-            self._conn.create_materialized_view(
-                name, source, select=select, where=where, limit=limit
-            )
-        )
-        return MaterializedView(self.open_table(name))
-
-    @override
-    def open_materialized_view(self, name: str) -> MaterializedView:
-        """Open the materialized view named ``name``."""
-        view = MaterializedView(self.open_table(name))
-        view.definition
-        return view
-
-    @override
-    def list_materialized_views(self) -> List[str]:
-        """The names of the materialized views in this database."""
-        return LOOP.run(self._conn.list_materialized_views())
-
    def clone_table(
        self,
        target_table_name: str,
@@ -1424,13 +1283,8 @@ class LanceDBConnection(DBConnection):
        return Job(self._conn.job(job_id))

    @override
-    def create_function_async(
-        self,
-        definition: UdfDefinition,
-        *,
-        secrets: Optional[Mapping[str, str]] = None,
-    ) -> Job[FunctionVersion]:
-        job = LOOP.run(self._conn.create_function_async(definition, secrets=secrets))
+    def create_function_async(self, definition: UdfDefinition) -> Job[FunctionVersion]:
+        job = LOOP.run(self._conn.create_function_async(definition))
        return Job(job)

    @override
@@ -2052,50 +1906,6 @@ class AsyncConnection(object):
            await tbl.checkout(version)
        return tbl

-    async def create_materialized_view(
-        self,
-        name: str,
-        source: str,
-        *,
-        select: SelectArg = None,
-        where: Optional[str] = None,
-        limit: Optional[int] = None,
-    ) -> AsyncMaterializedView:
-        """Define a materialized view named ``name`` over the table ``source``.
-        See
-        [DBConnection.create_materialized_view][lancedb.DBConnection.create_materialized_view].
-        """
-        inner = await self._inner.create_materialized_view(
-            name,
-            source,
-            projections=normalize_select(select),
-            filter=where,
-            limit=limit,
-        )
-        return AsyncMaterializedView(AsyncTable(inner))
-
-    async def open_materialized_view(self, name: str) -> AsyncMaterializedView:
-        """Open the materialized view named ``name``.
-
-        Raises ``ValueError`` if the table exists but is not a materialized
-        view.
-        """
-        if self.uri.startswith("db://"):
-            raise NotImplementedError(
-                "materialized views are supported only on local databases"
-            )
-        view = AsyncMaterializedView(await self.open_table(name))
-        await view.definition()
-        return view
-
-    async def list_materialized_views(self) -> List[str]:
-        """The names of the materialized views in this database.
-
-        Found by reading every table's schema, so this costs an open per
-        table.
-        """
-        return await self._inner.list_materialized_views()
-
    async def clone_table(
        self,
        target_table_name: str,
@@ -2249,26 +2059,19 @@ class AsyncConnection(object):
        return AsyncJob(self._inner.job(job_id))

    async def create_function_async(
-        self,
-        definition: UdfDefinition,
-        *,
-        secrets: Optional[Mapping[str, str]] = None,
+        self, definition: UdfDefinition
    ) -> AsyncJob[FunctionVersion]:
        """Register a scalar Python UDF through the remote Function catalog.

-        ``secrets`` must contain exactly the names declared by
-        ``@udf(secrets=[...])``. Values are sent in the create request and
-        stored server-side in the private execution artifact; returned
-        Function and Job metadata contain only the declared names.
        The returned typed job resolves to the immutable Function version.
        Local connections raise ``NotImplementedError``.
        """
        if not isinstance(definition, UdfDefinition):
            raise TypeError("create_function_async requires a @udf definition")
        inner = await self._inner.create_function_async(
-            definition._submission_json(secrets)
+            definition.registration_request.to_canonical_json()
        )
-        return _typed_job(inner, FunctionVersion.from_json)
+        return _function_job(inner)

    async def get_function(self, name: str, *, version: str) -> FunctionVersion:
        """Open one exact immutable Function version from the remote catalog."""
@@ -249,10 +249,6 @@ class Expr:

    # ── utilities ────────────────────────────────────────────────────────────

-    def _column_name(self) -> str | None:
-        """Return the source name when this is a bare column expression."""
-        return self._inner.column_name()
-
    def to_sql(self) -> str:
        """Render the expression as a SQL string (useful for debugging)."""
        return self._inner.to_sql()
@@ -316,7 +312,7 @@ def func(name: str, *args: ExprLike) -> Expr:
    --------
    >>> from lancedb.expr import col, func
    >>> func("lower", col("name"))
-    Expr(lower(`name`))
+    Expr(lower(name))
    """
    inner_args = [_coerce(a)._inner for a in args]
    return Expr(expr_func(name, inner_args))
@@ -1,30 +1,26 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright The LanceDB Authors

-"""Canonical Function values exchanged with LanceDB Enterprise services.
+"""Canonical values exchanged with LanceDB Enterprise Function services.

 These immutable models contain client/wire state only. Catalog persistence,
 environment bake, secret resolution, and execution are owned by Sophon.
-``RefreshColumnResult`` is also the backend-neutral result of a local
-expression-backed refresh job.
 """

 from __future__ import annotations

 import ast
-import builtins
 import base64
 import functools
 import hashlib
-import importlib
 import inspect
-import symtable
 import json
 import math
 import re
 import sys
 import textwrap
 import types
+import uuid
 from collections.abc import Mapping
 from datetime import date, datetime
 from typing import (
@@ -222,7 +218,6 @@ class PythonEnvironmentSpec(_RemoteValue):

    kind: str
    packages: tuple[str, ...] = ()
-    channels: tuple[str, ...] = ()
    path: Optional[str] = None
    modules: tuple[str, ...] = ()
    image: Optional[str] = None
@@ -276,7 +271,7 @@ class FunctionVersion(_RemoteValue):

        Every input must be a direct [lancedb.col][lancedb.expr.col]
        reference. The returned application is immutable and retains a
-        named-struct output as one binding, so every row's sibling values
+        named-struct output as one sibling group, so every row's sibling values
        come from one logical Function evaluation. Map result fields to table
        columns with
        [FunctionApplication.rename][lancedb.functions.FunctionApplication.rename],
@@ -326,14 +321,15 @@ class FunctionVersion(_RemoteValue):
            function=FunctionVersionRef(name=self.name, version=self.version),
            inputs=tuple(bindings),
            output=self.signature.output,
+            group_id=f"fg_{uuid.uuid4().hex}",
        )


 class FunctionRegistrationRequest(_RemoteValue):
    """Stable remote registration envelope produced by :func:`udf`.

-    Only secret names are represented. Secret values are supplied separately
-    when the definition is submitted and are not part of this durable value.
+    Only secret names are represented. Secret values are resolved inside the
+    remote service and have no client request field.
    """

    name: str
@@ -369,7 +365,7 @@ class ApplicationInput(_OpenRemoteValue):
 class FunctionApplication(_OpenRemoteValue):
    """Immutable pre-declaration application of an exact Function version.

-    A named-struct output remains one application through table
+    A named-struct output remains one grouped application through table
    declaration and execution.
    [FunctionApplication.rename][lancedb.functions.FunctionApplication.rename]
    records the result-field to table-column mapping without splitting sibling
@@ -379,6 +375,7 @@ class FunctionApplication(_OpenRemoteValue):
    function: FunctionVersionRef
    inputs: tuple[ApplicationInput, ...]
    output: FunctionOutput
+    group_id: str
    columns: Mapping[str, str] = Field(default_factory=dict)

    def _known_dict(self) -> dict[str, Any]:
@@ -450,10 +447,12 @@ class OutputMapping(_RemoteValue):


 class FunctionBinding(_RemoteValue):
-    """Immutable Function binding persisted by the Enterprise table service."""
+    """Immutable grouped binding persisted by the Enterprise table service."""

    binding_id: str
+    revision: _UInt64
    function: FunctionVersionRef
+    group_id: str
    inputs: tuple[InputBinding, ...]
    outputs: tuple[OutputMapping, ...]
    input_schema: Optional[Mapping[str, Any]] = None
@@ -461,11 +460,7 @@ class FunctionBinding(_RemoteValue):


 class RefreshColumnResult(_RemoteValue):
-    """Terminal result of an expression-backed or Function-backed refresh Job.
-
-    Local jobs produce this value in process. LanceDB Cloud and Enterprise
-    decode the same value from the durable server-job terminal payload.
-    """
+    """Terminal result of a remote Function-column refresh Job."""

    rows_assigned: _UInt64
    rows_failed: _UInt64
@@ -486,80 +481,61 @@ class RefreshColumnResult(_RemoteValue):

 _FUNCTION_NAME = re.compile(r"^[A-Za-z_][A-Za-z0-9_.-]*$")
 _SECRET_NAME = re.compile(r"^[A-Za-z_][A-Za-z0-9_]*$")
-# Keep this byte limit aligned with Sophon's MAX_FUNCTION_SECRET_VALUE_BYTES.
-_MAX_FUNCTION_SECRET_VALUE_BYTES = 64 * 1024
-_MAX_FUNCTION_SECRET_VALUES_BYTES = 512 * 1024
-
-
-def _validate_secret_value(name: str, value: Any) -> str:
-    """Validate one secret value before building the create request."""
-    if not isinstance(value, str):
-        raise TypeError(f"Function secret {name!r} value must be a string")
-    if not value:
-        raise ValueError(f"Function secret {name!r} value must be non-empty")
-    if "\0" in value:
-        raise ValueError(f"Function secret {name!r} value must not contain NUL")
-    value_bytes = len(value.encode("utf-8"))
-    if value_bytes > _MAX_FUNCTION_SECRET_VALUE_BYTES:
-        raise ValueError(
-            f"Function secret {name!r} value exceeds the "
-            f"{_MAX_FUNCTION_SECRET_VALUE_BYTES}-byte limit"
-        )
-    return value
-
-
-_GRAMMAR_PRIMITIVES = (
-    (pa.bool_(), "bool"),
-    (pa.int8(), "int8"),
-    (pa.int16(), "int16"),
-    (pa.int32(), "int32"),
-    (pa.int64(), "int64"),
-    (pa.uint8(), "uint8"),
-    (pa.uint16(), "uint16"),
-    (pa.uint32(), "uint32"),
-    (pa.uint64(), "uint64"),
-    (pa.float16(), "float16"),
-    (pa.float32(), "float32"),
-    (pa.float64(), "float64"),
-    (pa.string(), "utf8"),
-    (pa.binary(), "binary"),
-    (pa.date32(), "date32"),
-    (pa.date64(), "date64"),
-)


 def _canonical_arrow_type(data_type: pa.DataType) -> str:
-    """The server's V1 Function type grammar. Anything outside it is rejected
-    here rather than at registration."""
-    for candidate, name in _GRAMMAR_PRIMITIVES:
+    primitive_types = (
+        (pa.bool_(), "bool"),
+        (pa.int8(), "int8"),
+        (pa.int16(), "int16"),
+        (pa.int32(), "int32"),
+        (pa.int64(), "int64"),
+        (pa.uint8(), "uint8"),
+        (pa.uint16(), "uint16"),
+        (pa.uint32(), "uint32"),
+        (pa.uint64(), "uint64"),
+        (pa.float16(), "float16"),
+        (pa.float32(), "float32"),
+        (pa.float64(), "float64"),
+        (pa.string(), "utf8"),
+        (pa.large_utf8(), "large_utf8"),
+        (pa.binary(), "binary"),
+        (pa.large_binary(), "large_binary"),
+        (pa.date32(), "date32"),
+        (pa.date64(), "date64"),
+    )
+    for candidate, name in primitive_types:
        if data_type == candidate:
            return name
-    if pa.types.is_list(data_type) or pa.types.is_large_list(data_type):
-        prefix = "list" if pa.types.is_list(data_type) else "large_list"
-        return f"{prefix}<{_canonical_list_item(data_type)}>"
-    if pa.types.is_fixed_size_list(data_type) and data_type.list_size > 0:
+    if pa.types.is_fixed_size_binary(data_type):
+        return f"fixed_size_binary[{data_type.byte_width}]"
+    if pa.types.is_list(data_type):
+        return f"list<{_canonical_arrow_type(data_type.value_type)}>"
+    if pa.types.is_large_list(data_type):
+        return f"large_list<{_canonical_arrow_type(data_type.value_type)}>"
+    if pa.types.is_fixed_size_list(data_type):
        return (
-            f"fixed_size_list<{_canonical_list_item(data_type)}, {data_type.list_size}>"
+            f"fixed_size_list<{_canonical_arrow_type(data_type.value_type)}>"
+            f"[{data_type.list_size}]"
        )
+    if pa.types.is_struct(data_type):
+        fields = ",".join(
+            f"{field.name}:{_canonical_arrow_type(field.type)}" for field in data_type
+        )
+        return f"struct<{fields}>"
+    if pa.types.is_timestamp(data_type):
+        timezone = f",tz={data_type.tz}" if data_type.tz is not None else ""
+        return f"timestamp[{data_type.unit}{timezone}]"
+    if pa.types.is_time32(data_type) or pa.types.is_time64(data_type):
+        return f"time[{data_type.unit}]"
+    if pa.types.is_duration(data_type):
+        return f"duration[{data_type.unit}]"
+    if pa.types.is_decimal(data_type):
+        bit_width = data_type.bit_width
+        return f"decimal{bit_width}({data_type.precision},{data_type.scale})"
    raise TypeError(f"unsupported Arrow type for Function signature: {data_type}")


-def _canonical_list_item(data_type: pa.DataType) -> str:
-    """The grammar names only the item type; it always means a non-nullable
-    child called `item`, so any other child metadata cannot be represented."""
-    child = data_type.value_field
-    if child.name != "item" or child.nullable or child.metadata:
-        raise TypeError(
-            "unsupported Arrow type for Function signature: list items must be a "
-            f"non-nullable field named 'item', got {child}"
-        )
-    return _canonical_arrow_type(child.type)
-
-
-def _list_of(item: pa.DataType) -> pa.DataType:
-    return pa.list_(pa.field("item", item, nullable=False))
-
-
 def _annotation_type(annotation: Any) -> tuple[pa.DataType, bool]:
    nullable = False
    origin = get_origin(annotation)
@@ -607,7 +583,7 @@ def _annotation_type(annotation: Any) -> tuple[pa.DataType, bool]:
        value_type, value_nullable = _annotation_type(arguments[0])
        if value_nullable:
            raise TypeError("nullable Function list elements are not supported")
-        return _list_of(value_type), nullable
+        return pa.list_(value_type), nullable
    raise TypeError(f"unsupported Function annotation: {annotation!r}")


@@ -754,104 +730,6 @@ def _literal_source(value: Any) -> str:
    )


-_DYNAMIC_NAMESPACE_ACCESS = frozenset(
-    {"globals", "locals", "vars", "eval", "exec", "compile", "__import__"}
-)
-# Modules that hand out namespaces (`sys.modules`, `builtins`, importers,
-# introspection). The artifact's module namespace holds only the names it was
-# packaged with, so reaching around it cannot be represented.
-_NAMESPACE_MODULES = frozenset(
-    {"sys", "builtins", "importlib", "inspect", "gc", "ctypes", "types"}
-)
-
-
-def _namespace_acquisition(
-    definition: ast.FunctionDef, references: set[str]
-) -> list[str]:
-    found = set(references & _DYNAMIC_NAMESPACE_ACCESS)
-    for node in ast.walk(definition):
-        if isinstance(node, ast.Import):
-            found.update(
-                alias.name
-                for alias in node.names
-                if alias.name.split(".")[0] in _NAMESPACE_MODULES
-            )
-        elif isinstance(node, ast.ImportFrom) and node.module:
-            if node.module.split(".")[0] in _NAMESPACE_MODULES:
-                found.add(node.module)
-    return sorted(found)
-
-
-def _module_references(module_source: str) -> set[str]:
-    """Names any scope in `module_source` binds or loads at module scope.
-    Python's own scope analysis on the exact text that ships: free variables
-    belong to an enclosing scope inside the function, and postponed
-    annotations are not runtime loads."""
-
-    def visit(table: symtable.SymbolTable, found: set[str]) -> None:
-        for symbol in table.get_symbols():
-            if symbol.is_global() and (
-                symbol.is_referenced() or symbol.is_declared_global()
-            ):
-                found.add(symbol.get_name())
-        for child in table.get_children():
-            visit(child, found)
-
-    found: set[str] = set()
-    for table in symtable.symtable(module_source, "<udf>", "exec").get_children():
-        visit(table, found)
-    return found
-
-
-def _global_source(name: str, value: Any) -> str:
-    """One module-level line that rebinds `name` to `value` in the artifact:
-    an import for modules and importable classes/functions, a literal otherwise."""
-    if isinstance(value, types.ModuleType):
-        if value.__name__.split(".")[0] in _NAMESPACE_MODULES:
-            raise ValueError(
-                f"@udf cannot package dynamic namespace access: {value.__name__!r}"
-            )
-        try:
-            imported = importlib.import_module(value.__name__)
-        except ImportError:
-            imported = None
-        if imported is not value:
-            raise TypeError(
-                f"Function source references module {name!r} that does not import "
-                f"as {value.__name__!r}"
-            )
-        return f"import {value.__name__} as {name}"
-    module_name = getattr(value, "__module__", None)
-    qualname = getattr(value, "__qualname__", None)
-    if (
-        isinstance(module_name, str)
-        and isinstance(qualname, str)
-        and module_name != "__main__"
-        and "." not in qualname
-        and "<" not in qualname
-    ):
-        try:
-            imported = getattr(importlib.import_module(module_name), qualname)
-        except (ImportError, AttributeError):
-            imported = None
-        if imported is value:
-            return f"from {module_name} import {qualname} as {name}"
-    return f"{name} = {_literal_source(value)}"
-
-
-def _is_recursive_reference(function: Callable[..., Any], name: str) -> bool:
-    """`name` inside the body means the function itself unless the module has
-    since bound it to something else."""
-    if name != function.__name__:
-        return False
-    bound = function.__globals__.get(name, function)
-    if bound is function:
-        return True
-    # The decorator's own result is the one wrapper known to call `function`
-    # unchanged; any other binding may behave differently from a self-call.
-    return type(bound) is UdfDefinition and bound._function is function
-
-
 def _package_source(function: Callable[..., Any]) -> bytes:
    if not inspect.isfunction(function) or inspect.iscoroutinefunction(function):
        raise TypeError("@udf requires a synchronous Python function")
@@ -876,46 +754,23 @@ def _package_source(function: Callable[..., Any]) -> bytes:
    closure = inspect.getclosurevars(function)
    if closure.nonlocals:
        raise ValueError("@udf cannot package functions that capture closure values")
-    function_source = ast.unparse(definition)
-    module_header = "from __future__ import annotations"
-    references = _module_references(f"{module_header}\n\n{function_source}\n")
-    dynamic = _namespace_acquisition(definition, references)
-    if dynamic:
-        raise ValueError(f"@udf cannot package dynamic namespace access: {dynamic!r}")
-    # Resolve every module-scope reference the way the interpreter would: the
-    # function's own globals first (a module global may shadow a builtin, and
-    # nested scopes are not visible to getclosurevars), then its builtins.
-    # The artifact runs under the standard builtins; only the exact mapping is
-    # provably equivalent (a subclass or copy can change lookups and hooks).
-    if function.__builtins__ is not vars(builtins):
-        raise ValueError("@udf cannot package a non-standard builtins environment")
-    globals_source = []
-    unresolved = []
-    for name in sorted(references):
-        if name == function.__name__:
-            if not _is_recursive_reference(function, name):
-                raise ValueError(
-                    f"@udf cannot package {name!r}: the module binds that name to "
-                    "another value, which the artifact's own definition would shadow"
-                )
-            continue
-        if name in function.__globals__:
-            globals_source.append(_global_source(name, function.__globals__[name]))
-        elif hasattr(builtins, name):
-            pass
-        else:
-            unresolved.append(name)
-    if unresolved:
+    if closure.unbound:
        raise ValueError(
-            f"@udf source contains unresolved global names: {unresolved!r}"
+            f"@udf source contains unresolved global names: {sorted(closure.unbound)!r}"
        )
+    globals_source = []
+    for name, value in sorted(closure.globals.items()):
+        if isinstance(value, types.ModuleType):
+            globals_source.append(f"import {value.__name__} as {name}")
+        else:
+            globals_source.append(f"{name} = {_literal_source(value)}")

-    parts = [module_header]
+    function_source = ast.unparse(definition)
+    parts = ["from __future__ import annotations"]
    if globals_source:
        parts.extend(["", *globals_source])
    parts.extend(["", function_source, ""])
-    packaged = "\n".join(parts)
-    return packaged.encode("utf-8")
+    return "\n".join(parts).encode("utf-8")


 class UdfDefinition:
@@ -938,25 +793,13 @@ class UdfDefinition:
        env: Mapping[str, str],
        secrets: tuple[str, ...],
        python_version: Optional[str],
-        conda: tuple[str, ...] = (),
-        conda_channels: tuple[str, ...] = (),
    ):
        function_name = name or function.__name__
        if not _FUNCTION_NAME.fullmatch(function_name):
            raise ValueError(f"invalid Function name: {function_name!r}")
-        if pip and conda:
-            raise ValueError("a Function environment is pip or conda, not both")
-        if conda_channels and not conda:
-            raise ValueError("conda_channels requires conda packages")
-        packages = tuple(sorted(set(conda if conda else pip)))
+        packages = tuple(sorted(set(pip)))
        if any(not package or package != package.strip() for package in packages):
-            raise ValueError("package requirements must be non-empty and trimmed")
-        if conda:
-            environment_spec = PythonEnvironmentSpec(
-                kind="conda", packages=packages, channels=tuple(conda_channels)
-            )
-        else:
-            environment_spec = PythonEnvironmentSpec(kind="pip", packages=packages)
+            raise ValueError("pip requirements must be non-empty and trimmed")
        environment = dict(env)
        if any(
            not isinstance(key, str) or not isinstance(value, str)
@@ -974,6 +817,7 @@ class UdfDefinition:
            raise ValueError(
                f"Function env and secret names must be disjoint: {sorted(overlap)!r}"
            )
+
        signature = _infer_signature(function, input_schema, output_schema)
        source = _package_source(function)
        digest = f"sha256:{hashlib.sha256(source).hexdigest()}"
@@ -981,7 +825,7 @@ class UdfDefinition:
            kind="python",
            python_version=python_version
            or f"{sys.version_info.major}.{sys.version_info.minor}",
-            environment=environment_spec,
+            environment=PythonEnvironmentSpec(kind="pip", packages=packages),
            env=environment,
        )
        self._function = function
@@ -1008,59 +852,9 @@ class UdfDefinition:

    @property
    def registration_request(self) -> FunctionRegistrationRequest:
-        """The immutable, value-free client model for a Function submission."""
+        """The immutable request sent by ``create_function_async``."""
        return self._request

-    def _submission_json(self, secrets: Optional[Mapping[str, str]]) -> str:
-        """Build one registration submission without retaining values on self."""
-        if secrets is None:
-            secret_values: Mapping[str, str] = {}
-        elif not isinstance(secrets, Mapping):
-            raise TypeError("Function secrets must be a mapping of names to strings")
-        else:
-            secret_values = secrets
-
-        if any(not isinstance(name, str) for name in secret_values):
-            raise TypeError("Function secret names must be strings")
-        expected = set(self._request.required_secrets)
-        provided = set(secret_values)
-        if provided != expected:
-            missing = sorted(expected - provided)
-            unexpected = sorted(provided - expected)
-            details = []
-            if missing:
-                details.append(f"missing: {missing!r}")
-            if unexpected:
-                details.append(f"unexpected: {unexpected!r}")
-            raise ValueError(
-                "Function secret values must exactly match the declared secrets ("
-                + "; ".join(details)
-                + ")"
-            )
-
-        canonical_values = {}
-        total_bytes = 0
-        for name in sorted(secret_values):
-            value = _validate_secret_value(name, secret_values[name])
-            total_bytes += len(value.encode("utf-8"))
-            if total_bytes > _MAX_FUNCTION_SECRET_VALUES_BYTES:
-                raise ValueError(
-                    "Function secret values exceed the "
-                    f"{_MAX_FUNCTION_SECRET_VALUES_BYTES}-byte request limit"
-                )
-            canonical_values[name] = value
-
-        submission = self._request._known_dict()
-        if canonical_values:
-            submission["secret_values"] = canonical_values
-        return json.dumps(
-            submission,
-            ensure_ascii=False,
-            allow_nan=False,
-            sort_keys=True,
-            separators=(",", ":"),
-        )
-
    def __call__(self, *args, **kwargs):
        return self._function(*args, **kwargs)

@@ -1080,8 +874,6 @@ def udf(
    env: Optional[Mapping[str, str]] = None,
    secrets: tuple[str, ...] | list[str] = (),
    python_version: Optional[str] = None,
-    conda: tuple[str, ...] | list[str] = (),
-    conda_channels: tuple[str, ...] | list[str] = (),
 ) -> Callable[[Callable[..., Any]], UdfDefinition]: ...


@@ -1095,8 +887,6 @@ def udf(
    env: Optional[Mapping[str, str]] = None,
    secrets: tuple[str, ...] | list[str] = (),
    python_version: Optional[str] = None,
-    conda: tuple[str, ...] | list[str] = (),
-    conda_channels: tuple[str, ...] | list[str] = (),
 ):
    """Prepare a scalar Python callable for remote Function registration.

@@ -1119,25 +909,14 @@ def udf(
        provided together with ``input_schema``.
    pip : sequence of str, optional
        Pip requirements for the remote environment.
-    conda : sequence of str, optional
-        Conda packages for the remote environment, instead of ``pip``.
-    conda_channels : sequence of str, optional
-        Conda channels in priority order; requires ``conda``.
    env : mapping of str to str, optional
        Non-secret environment variables. Use ``secrets`` for credentials.
    secrets : sequence of str, optional
-        Names of secrets required by the callable. Supply their values separately
-        to ``create_function`` or ``create_function_async``.
+        Names of secrets resolved by the remote service. Secret values are not
+        accepted by this API or included in the registration request.
    python_version : str, optional
        Remote Python major/minor version. Defaults to the client version.

-    The packaged artifact is a snapshot: the function source plus exactly
-    the module-level names it references (modules as imports, importable
-    classes and functions as imports, literals inline). Code that reaches the
-    module namespace another way -- ``globals()``/``eval``, ``sys.modules``,
-    ``builtins`` -- is rejected where it can be seen and otherwise
-    unsupported; closures and a non-standard ``__builtins__`` are rejected.
-
    Returns
    -------
    UdfDefinition
@@ -1154,10 +933,6 @@ def udf(
    ...     return value * 2
    >>> score(1.5)
    3.0
-    >>> db.create_function(  # doctest: +SKIP
-    ...     score, secrets={"MODEL_TOKEN": "user-secret-value"}
-    ... )
-
    """

    def decorate(target: Callable[..., Any]) -> UdfDefinition:
@@ -1170,8 +945,6 @@ def udf(
            env={} if env is None else env,
            secrets=tuple(secrets),
            python_version=python_version,
-            conda=tuple(conda),
-            conda_channels=tuple(conda_channels),
        )

    if function is None:
@@ -7,7 +7,6 @@ from typing import List, Literal, Optional
 from ._lancedb import (
    IndexConfig,
 )
-from .query import DocumentGranularity
 from .types import BaseTokenizerType

 lang_mapping = {
@@ -122,11 +121,6 @@ class FTS:

    >>> config = FTS(block_size=256)

-    Create an index that treats each deepest-list element as one document:
-
-    >>> from lancedb.query import DocumentGranularity
-    >>> config = FTS(document_granularity=DocumentGranularity.LIST_ELEMENT)
-
    Attributes
    ----------
    with_position : bool, default False
@@ -169,20 +163,6 @@ class FTS:
        The number of documents per compressed posting block. Supported values
        are 128 and 256. A value of 256 uses the experimental FTS V3 format
        and may introduce breaking changes.
-    memory_limit : int, optional
-        The total memory limit in MiB for the local FTS build stage. The limit
-        is divided evenly among indexing workers. This build-only setting is
-        not persisted with the index and does not apply to remote tables.
-    num_workers : int, optional
-        The number of workers for a local FTS build. By default Lance uses
-        roughly half of the available CPU cores. The effective value is
-        limited by the available compute capacity. This build-only setting is
-        not persisted with the index and does not apply to remote tables.
-    document_granularity : DocumentGranularity, default ROW
-        ``ROW`` treats the selected text in one table row as one document.
-        ``LIST_ELEMENT`` treats each element of the deepest list on the indexed
-        field path as one document and returns its physical coordinates in
-        ``_doc_index`` for matching queries.

    Notes
    -----
@@ -205,9 +185,6 @@ class FTS:
    prefix_only: bool = False
    block_size: int = 128
    custom_stop_words: Optional[List[str]] = None
-    memory_limit: Optional[int] = None
-    num_workers: Optional[int] = None
-    document_granularity: DocumentGranularity = DocumentGranularity.ROW


@dataclass
@@ -5,11 +5,12 @@

 import asyncio
 from datetime import timedelta
-from typing import Any, Callable, Generic, Optional, TypeVar, cast
+from typing import Any, Generic, Optional, TypeVar, cast

 from lancedb.background_loop import LOOP

 from . import _lancedb
+from .functions import FunctionVersion

 T = TypeVar("T")

@@ -17,18 +18,11 @@ T = TypeVar("T")
 class AsyncJob(Generic[T]):
    """A handle to an operation that may still be running.

-    The operation may already be complete when the handle is created. ``T``
-    is the endpoint's terminal result type; unit-result jobs resolve to
-    ``None``.
+    The operation may already be complete when the handle is created.
    """

-    def __init__(
-        self,
-        inner: Optional[Any],
-        result_decoder: Optional[Callable[[Any], T]] = None,
-    ):
+    def __init__(self, inner: Optional[Any]):
        self._inner = inner
-        self._result_decoder = result_decoder

    @property
    def id(self) -> Optional[str]:
@@ -56,21 +50,17 @@ class AsyncJob(Generic[T]):
    async def wait(self, timeout: Optional[timedelta] = None) -> T:
        """Wait until the operation reaches a terminal state.

-        Returns the endpoint's typed result, or ``None`` for a unit-result
-        job.
-
        Raises `JobFailedError` if the operation failed, `JobCancelledError`
        if it was cancelled, and `TimeoutError` if `timeout` elapses first.
        """
        if self._inner is None:
            return cast(T, None)
        if timeout is None:
-            result = await self._inner.wait()
-        else:
-            result = await asyncio.wait_for(self._inner.wait(), timeout.total_seconds())
-        if self._result_decoder is not None:
-            return self._result_decoder(result)
-        return cast(T, result)
+            return cast(T, await self._inner.wait())
+        return cast(
+            T,
+            await asyncio.wait_for(self._inner.wait(), timeout.total_seconds()),
+        )

    async def cancel(self):
        """Request cancellation. Cancelling a finished operation is a no-op."""
@@ -80,7 +70,7 @@ class AsyncJob(Generic[T]):


 class Job(Generic[T]):
-    """Synchronous counterpart of `AsyncJob` with the same result type."""
+    """Synchronous counterpart of `AsyncJob`."""

    def __init__(self, inner: Optional[AsyncJob[T]]):
        self._inner = inner
@@ -106,9 +96,6 @@ class Job(Generic[T]):
    def wait(self, timeout: Optional[timedelta] = None) -> T:
        """Block until the operation reaches a terminal state.

-        Returns the endpoint's typed result, or ``None`` for a unit-result
-        job.
-
        Raises `JobFailedError` if the operation failed, `JobCancelledError`
        if it was cancelled, and `TimeoutError` if `timeout` elapses first.
        """
@@ -123,8 +110,23 @@ class Job(Generic[T]):
        LOOP.run(self._inner.cancel())


-def _typed_job(
-    inner: "_lancedb.Job", result_decoder: Callable[[str], T]
-) -> AsyncJob[T]:
-    """Bind an internal JSON-producing job to its public result model."""
-    return AsyncJob(inner, result_decoder)
+class _FunctionJobAdapter:
+    def __init__(self, inner: "_lancedb.FunctionJob"):
+        self._inner = inner
+
+    @property
+    def id(self) -> Optional[str]:
+        return self._inner.id
+
+    async def status(self) -> str:
+        return await self._inner.status()
+
+    async def wait(self) -> FunctionVersion:
+        return FunctionVersion.from_json(await self._inner.wait())
+
+    async def cancel(self):
+        await self._inner.cancel()
+
+
+def _function_job(inner: "_lancedb.FunctionJob") -> AsyncJob[FunctionVersion]:
+    return AsyncJob(_FunctionJobAdapter(inner))
@@ -1,178 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright The LanceDB Authors
-
-"""Materialized views: tables defined by a query over a source table and
-maintained by refresh. See ``DBConnection.create_materialized_view``."""
-
-from __future__ import annotations
-
-import json
-from dataclasses import dataclass, field
-from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, Tuple, Union
-
-from .background_loop import LOOP
-
-if TYPE_CHECKING:
-    import pyarrow as pa
-
-    from ._lancedb import RefreshMaterializedViewResult
-    from .table import AsyncTable, LanceTable
-
-DEFINITION_META_KEY = b"mv.definition"
-
-SelectArg = Union[
-    str,
-    Sequence[Union[str, Tuple[str, str]]],
-    Dict[str, str],
-    None,
-]
-
-
-@dataclass
-class MaterializedViewDefinition:
-    """The query that defines a materialized view."""
-
-    source_table: str
-    """Name of the source table, in the same database as the view."""
-    projections: List[Tuple[str, str]]
-    """``(output column, SQL expression)`` pairs, in view schema order."""
-    filter: Optional[str] = None
-    """SQL predicate selecting the source rows the view holds."""
-    limit: Optional[int] = None
-    """Cap on the number of rows the view holds."""
-    inputs: List[str] = field(default_factory=list)
-    """Source columns the projections and filter read."""
-
-
-def _definition_from_schema(
-    schema: "pa.Schema", name: str
-) -> MaterializedViewDefinition:
-    metadata = schema.metadata or {}
-    raw = metadata.get(DEFINITION_META_KEY)
-    if raw is None:
-        raise ValueError(f"Table '{name}' is not a materialized view")
-    value = json.loads(raw)
-    kind = value.get("kind")
-    if kind != "select":
-        raise NotImplementedError(
-            f"materialized view '{name}' is defined by '{kind}', which this "
-            "version of lancedb cannot refresh"
-        )
-    return MaterializedViewDefinition(
-        source_table=value["source_table"],
-        projections=[
-            (p["output"], p["expression"]) for p in value.get("projections", [])
-        ],
-        filter=value.get("filter"),
-        limit=value.get("limit"),
-        inputs=value.get("inputs", []),
-    )
-
-
-def _quote_identifier(name: str) -> str:
-    """Quote a column name as a Lance SQL identifier (backticks)."""
-    escaped = name.replace("`", "``")
-    return f"`{escaped}`"
-
-
-def normalize_select(select: SelectArg) -> Optional[List[Tuple[str, str]]]:
-    """``select`` items may be a column name, an ``(alias, expression)`` pair,
-    or a dict of the same. A bare name projects itself and is quoted, so any
-    valid column name works; dict and pair entries are kept verbatim because
-    their right side is an expression.
-
-    A lone string is one column, not a sequence of its characters."""
-    if select is None:
-        return None
-    if isinstance(select, str):
-        select = [select]
-    if isinstance(select, dict):
-        return list(select.items())
-    normalized = []
-    for item in select:
-        if isinstance(item, str):
-            normalized.append((item, _quote_identifier(item)))
-        else:
-            alias, expression = item
-            normalized.append((alias, expression))
-    return normalized
-
-
-class AsyncMaterializedView:
-    """A handle on a materialized view: its table plus its definition.
-
-    Obtained from ``AsyncConnection.create_materialized_view`` or
-    ``AsyncConnection.open_materialized_view``.
-    """
-
-    def __init__(self, table: "AsyncTable"):
-        self._table = table
-
-    def __repr__(self) -> str:
-        return f"AsyncMaterializedView(name={self.name!r})"
-
-    @property
-    def name(self) -> str:
-        return self._table.name
-
-    @property
-    def table(self) -> "AsyncTable":
-        """The view, as the table it is. Queries, indexes and search all
-        apply; writes are not blocked, but a rebuild replaces them."""
-        return self._table
-
-    async def definition(self) -> MaterializedViewDefinition:
-        """The query that defines the view, read from its stored schema."""
-        return _definition_from_schema(await self._table.schema(), self.name)
-
-    async def refresh(
-        self, *, full: bool = False, source_version: Optional[int] = None
-    ) -> "RefreshMaterializedViewResult":
-        """Recompute the view from its source.
-
-        The refresh is incremental when the source's changes can be
-        reconciled into the view -- rows added, changed or removed since the
-        last one -- and otherwise rebuilds. ``full=True`` forces a rebuild;
-        ``source_version`` refreshes to that source version instead of the
-        latest.
-
-        Concurrent refreshes of one view do not duplicate its rows. Two that
-        plan the same source rows conflict on commit, and the loser raises
-        rather than writing them a second time.
-        """
-        return await self._table._inner.refresh_materialized_view(
-            full=full, source_version=source_version
-        )
-
-
-class MaterializedView:
-    """Synchronous variant of
-    [AsyncMaterializedView][lancedb.materialized_view.AsyncMaterializedView]."""
-
-    def __init__(self, table: "LanceTable"):
-        self._table = table
-        self._async = AsyncMaterializedView(table._table)
-
-    def __repr__(self) -> str:
-        return f"MaterializedView(name={self.name!r})"
-
-    @property
-    def name(self) -> str:
-        return self._table.name
-
-    @property
-    def table(self) -> "LanceTable":
-        """The view, as the table it is."""
-        return self._table
-
-    @property
-    def definition(self) -> MaterializedViewDefinition:
-        """The query that defines the view, read from its stored schema."""
-        return _definition_from_schema(self._table.schema, self.name)
-
-    def refresh(
-        self, *, full: bool = False, source_version: Optional[int] = None
-    ) -> "RefreshMaterializedViewResult":
-        """Recompute the view from its source. See
-        [AsyncMaterializedView.refresh][lancedb.materialized_view.AsyncMaterializedView.refresh]."""
-        return LOOP.run(self._async.refresh(full=full, source_version=source_version))
@@ -61,11 +61,6 @@ from lance_namespace import (
    NamespaceExistsRequest,
    TableExistsRequest,
 )
-from lancedb.materialized_view import (
-    AsyncMaterializedView,
-    MaterializedView,
-    SelectArg,
-)
 from lancedb.table import AsyncTable, LanceTable, Table
 from lancedb.util import validate_table_name
 from lancedb.common import DATA
@@ -624,42 +619,6 @@ class LanceNamespaceDBConnection(DBConnection):
            tbl.checkout(version)
        return tbl

-    @override
-    def create_materialized_view(
-        self,
-        name: str,
-        source: str,
-        *,
-        select: "SelectArg" = None,
-        where: Optional[str] = None,
-        limit: Optional[int] = None,
-    ) -> "MaterializedView":
-        """Define a materialized view over a table in the root namespace.
-        See
-        [DBConnection.create_materialized_view][lancedb.DBConnection.create_materialized_view].
-        """
-        return MaterializedView(
-            self.open_table(
-                LOOP.run(
-                    self._inner.create_materialized_view(
-                        name, source, select=select, where=where, limit=limit
-                    )
-                ).name
-            )
-        )
-
-    @override
-    def open_materialized_view(self, name: str) -> "MaterializedView":
-        """Open the materialized view named ``name``."""
-        view = MaterializedView(self.open_table(name))
-        view.definition
-        return view
-
-    @override
-    def list_materialized_views(self) -> List[str]:
-        """The names of the materialized views in the root namespace."""
-        return LOOP.run(self._inner.list_materialized_views())
-
    @override
    def drop_table(self, name: str, namespace_path: Optional[List[str]] = None):
        if namespace_path is None:
@@ -1182,33 +1141,6 @@ class AsyncLanceNamespaceDBConnection:
            route_pushdown_to_rust=self._route_pushdown_to_rust,
        )

-    async def create_materialized_view(
-        self,
-        name: str,
-        source: str,
-        *,
-        select: "SelectArg" = None,
-        where: Optional[str] = None,
-        limit: Optional[int] = None,
-    ) -> "AsyncMaterializedView":
-        """Define a materialized view over a table in the root namespace."""
-        view = await self._inner.create_materialized_view(
-            name, source, select=select, where=where, limit=limit
-        )
-        # Reopen through the namespace so the view's table carries the
-        # namespace client and pushdown configuration a bare inner table lacks.
-        return AsyncMaterializedView(await self.open_table(view.name))
-
-    async def open_materialized_view(self, name: str) -> "AsyncMaterializedView":
-        """Open the materialized view named ``name``."""
-        view = AsyncMaterializedView(await self.open_table(name))
-        await view.definition()
-        return view
-
-    async def list_materialized_views(self) -> List[str]:
-        """The names of the materialized views in the root namespace."""
-        return await self._inner.list_materialized_views()
-
    async def drop_table(self, name: str, namespace_path: Optional[List[str]] = None):
        """Drop a table from the namespace."""
        if namespace_path is None:
@@ -391,15 +391,6 @@ def _table_to_pickle_state(table: Table) -> dict[str, Any]:
    }


-def _drop_base_version(permutation_data: pa.Table) -> pa.Table:
-    """Strip the recorded base version so the reader leaves the base table unpinned."""
-    metadata = dict(permutation_data.schema.metadata or {})
-    if metadata.pop(b"base_version", None) is None:
-        return permutation_data
-    metadata.pop(b"base_branch", None)
-    return permutation_data.replace_schema_metadata(metadata)
-
-
 def _table_from_pickle_state(state: dict[str, Any]) -> Table:
    from . import connect

@@ -688,15 +679,11 @@ class Permutation:
        from . import connect

        connection_factory = state["connection_factory"]
-        rebuilt_base = False
        if connection_factory is not None:
            base_table = connection_factory(state["base_table_name"])
        elif "base_table_state" in state:
-            base_state = state["base_table_state"]
-            rebuilt_base = base_state["kind"] == "memory"
-            base_table = _table_from_pickle_state(base_state)
+            base_table = _table_from_pickle_state(state["base_table_state"])
        elif "base_table_data" in state:
-            rebuilt_base = True
            # In-memory base table inlined into the pickle; rebuild the same
            # way we rebuild the in-memory permutation table.
            mem_db = connect("memory://")
@@ -714,14 +701,11 @@ class Permutation:
            )

        permutation_table: Optional[Table] = None
-        permutation_data = state["permutation_data"]
-        if permutation_data is not None:
-            if rebuilt_base:
-                # The base table was materialized from Arrow, so it is a fresh
-                # single-version dataset and the recorded pin cannot resolve on it.
-                permutation_data = _drop_base_version(permutation_data)
+        if state["permutation_data"] is not None:
            mem_db = connect("memory://")
-            permutation_table = mem_db.create_table("permutation", permutation_data)
+            permutation_table = mem_db.create_table(
+                "permutation", state["permutation_data"]
+            )

        self.base_table = base_table
        self.permutation_table = permutation_table
@@ -167,12 +167,6 @@ def _projection_to_scanner_kwargs(columns: QueryProjection) -> Dict[str, Any]:
    return {"columns": projection}


-def _query_request_projection(req: "PyQueryRequest") -> QueryProjection:
-    if req.select_source_columns is not None:
-        return req.select_source_columns
-    return req.select
-
-
 def _scanner_kwargs_for_query(
    query: Query,
    blob_mode: BlobMode,
@@ -381,13 +375,6 @@ class FullTextOperator(str, Enum):
    OR = "OR"


-class DocumentGranularity(str, Enum):
-    """The unit treated as one full-text-search document."""
-
-    ROW = "row"
-    LIST_ELEMENT = "list_element"
-
-
 class Occur(str, Enum):
    SHOULD = "SHOULD"
    MUST = "MUST"
@@ -491,10 +478,6 @@ class MatchQuery(FullTextQuery):
    prefix_length : int, optional
        The number of beginning characters being unchanged for fuzzy matching.
        This is useful to achieve prefix matching.
-    document_granularity : DocumentGranularity, optional
-        Explicitly select row or deepest-list-element documents. If omitted,
-        the indexed granularity is inferred. When both granularities are indexed
-        for the field, this must be specified. With no index, row granularity is used.
    """

    query: str
@@ -504,9 +487,6 @@ class MatchQuery(FullTextQuery):
    max_expansions: int = pydantic.Field(50, kw_only=True)
    operator: FullTextOperator = pydantic.Field(FullTextOperator.OR, kw_only=True)
    prefix_length: int = pydantic.Field(0, kw_only=True)
-    document_granularity: Optional[DocumentGranularity] = pydantic.Field(
-        None, kw_only=True
-    )

    def query_type(self) -> FullTextQueryType:
        return FullTextQueryType.MATCH
@@ -523,20 +503,11 @@ class PhraseQuery(FullTextQuery):
        The query string to match against.
    column : str
        The name of the column to match against.
-    slop : int, default 0
-        The maximum number of intervening positions permitted in the phrase.
-    document_granularity : DocumentGranularity, optional
-        Explicitly select row or deepest-list-element documents. If omitted,
-        the indexed granularity is inferred. When both granularities are indexed
-        for the field, this must be specified. With no index, row granularity is used.
    """

    query: str
    column: str
    slop: int = pydantic.Field(0, kw_only=True)
-    document_granularity: Optional[DocumentGranularity] = pydantic.Field(
-        None, kw_only=True
-    )

    def query_type(self) -> FullTextQueryType:
        return FullTextQueryType.MATCH_PHRASE
@@ -2805,16 +2776,15 @@ class AsyncQueryBase(object):

        req = self._inner.to_query_request()
        schema = await self._table.schema()
-        projection = _query_request_projection(req)
        self._blob_auto_row_id = blob_auto_row_id_for_scan(
            schema,
-            projection,
+            req.select,
            with_row_id=self._with_row_id,
        )
        if not self._blob_auto_row_id:
            self._blob_paths = ()
            return
-        self._blob_paths = tuple(blob_v2_projection_sources(schema, projection).keys())
+        self._blob_paths = tuple(blob_v2_projection_sources(schema, req.select).keys())
        self._inner.with_row_id()

    def select(self, columns: Union[List[str], dict[str, str]]) -> Self:
@@ -3408,10 +3378,9 @@ 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. 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
+        if you want to find the nearest vectors to multiple query vectors.
+        This is not expected to be faster than making multiple queries concurrently;
+        it is just a convenience method. If multiple vectors are passed in then
        an additional column `query_index` will be added to the results. This column
        will contain the index of the query vector that the result is nearest to.
        """
@@ -3540,8 +3509,8 @@ 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. Flat searches share one table scan across
-        the query vectors instead of issuing concurrent full scans.
+        vectors to multiple query vectors. This is not expected to be faster than
+        making multiple queries concurrently; it is just a convenience method.
        If multiple vectors are passed in then an additional column `query_index`
        will be added to the results.  This column will contain the index of the
        query vector that the result is nearest to.
@@ -3901,15 +3870,14 @@ class AsyncHybridQuery(AsyncStandardQuery, AsyncVectorQueryBase):
        blob_paths: tuple[str, ...] = ()
        if self._table is not None:
            schema = await self._table.schema()
-            projection = _query_request_projection(req)
            blob_auto_row_id = blob_auto_row_id_for_scan(
                schema,
-                projection,
+                req.select,
                with_row_id=self._with_row_id,
            )
            if blob_auto_row_id:
                blob_paths = tuple(
-                    blob_v2_projection_sources(schema, projection).keys()
+                    blob_v2_projection_sources(schema, req.select).keys()
                )
        self._blob_auto_row_id = blob_auto_row_id
        self._blob_paths = blob_paths
@@ -7,7 +7,7 @@ import json
 import logging
 from concurrent.futures import ThreadPoolExecutor
 import sys
-from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Mapping, Optional, Union
+from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Union
 from urllib.parse import urlparse
 import warnings

@@ -25,7 +25,6 @@ from ..common import DATA
 from ..db import DBConnection, LOOP
 from ..functions import FunctionVersion, UdfDefinition
 from ..job import AsyncJob, Job
-from ..materialized_view import MaterializedView, SelectArg

 if TYPE_CHECKING:
    from .._lancedb import JobDescription, JobInfo
@@ -649,32 +648,6 @@ class RemoteDBConnection(DBConnection):
            namespace_path=namespace_path,
        )

-    @override
-    def create_materialized_view(
-        self,
-        name: str,
-        source: str,
-        *,
-        select: SelectArg = None,
-        where: Optional[str] = None,
-        limit: Optional[int] = None,
-    ) -> MaterializedView:
-        raise NotImplementedError(
-            "materialized views are supported only on local databases"
-        )
-
-    @override
-    def open_materialized_view(self, name: str) -> MaterializedView:
-        raise NotImplementedError(
-            "materialized views are supported only on local databases"
-        )
-
-    @override
-    def list_materialized_views(self) -> List[str]:
-        raise NotImplementedError(
-            "materialized views are supported only on local databases"
-        )
-
    @override
    def drop_table(self, name: str, namespace_path: Optional[List[str]] = None):
        """Drop a table from the database.
@@ -742,15 +715,8 @@ class RemoteDBConnection(DBConnection):
        return Job(self._conn.job(job_id))

    @override
-    def create_function_async(
-        self,
-        definition: UdfDefinition,
-        *,
-        secrets: Optional[Mapping[str, str]] = None,
-    ) -> Job[FunctionVersion]:
-        return Job(
-            LOOP.run(self._conn.create_function_async(definition, secrets=secrets))
-        )
+    def create_function_async(self, definition: UdfDefinition) -> Job[FunctionVersion]:
+        return Job(LOOP.run(self._conn.create_function_async(definition)))

    @override
    def get_function(self, name: str, *, version: str) -> FunctionVersion:
@@ -36,7 +36,6 @@ from lancedb._lancedb import (
    UpdateResult,
 )
 from lancedb.embeddings.base import EmbeddingFunctionConfig
-from lancedb.expr import Expr
 from lancedb.index import (
    FTS,
    BTree,
@@ -50,7 +49,7 @@ from lancedb.index import (
    LabelList,
 )
 from lancedb.job import Job
-from lancedb.functions import FunctionApplication, RefreshColumnResult
+from lancedb.functions import FunctionApplication
 from lancedb.remote.db import LOOP
 from lancedb.table import IndexConfigType, KNOWN_METRICS
 import pyarrow as pa
@@ -62,7 +61,6 @@ from lancedb.table import _normalize_progress

 from ..query import (
    AnalyzePlanDistributedMetrics,
-    DocumentGranularity,
    LanceQueryBuilder,
    LanceTakeQueryBuilder,
    LanceVectorQueryBuilder,
@@ -351,7 +349,6 @@ class RemoteTable(Table):
        ngram_max_length: int = 3,
        prefix_only: bool = False,
        block_size: int = 128,
-        document_granularity: DocumentGranularity = DocumentGranularity.ROW,
        name: Optional[str] = None,
    ):
        """Create a full-text search index on a column.
@@ -374,7 +371,6 @@ class RemoteTable(Table):
            ngram_max_length=ngram_max_length,
            prefix_only=prefix_only,
            block_size=block_size,
-            document_granularity=document_granularity,
        )
        LOOP.run(
            self._table.create_index(
@@ -614,7 +610,6 @@ class RemoteTable(Table):
        fill_value: float = 0.0,
        progress: Optional[Union[bool, Callable, Any]] = None,
        write_parallelism: Optional[int] = None,
-        allow_external_blob_outside_bases: bool = False,
    ) -> AddResult:
        """Add more data to the [Table][lancedb.table.Table].

@@ -647,8 +642,6 @@ class RemoteTable(Table):
            data in flight. Defaults to an estimate based on the data size,
            capped at the number of CPU cores. Lower this if bulk ingestion is
            using too much memory.
-        allow_external_blob_outside_bases: bool, default False
-            Not supported on LanceDB Cloud. Setting this raises.

        Returns
        -------
@@ -665,7 +658,6 @@ class RemoteTable(Table):
                    fill_value=fill_value,
                    progress=progress,
                    write_parallelism=write_parallelism,
-                    allow_external_blob_outside_bases=allow_external_blob_outside_bases,
                )
            )
        finally:
@@ -864,7 +856,7 @@ class RemoteTable(Table):

    def update(
        self,
-        where: Optional[Union[str, Expr]] = None,
+        where: Optional[str] = None,
        values: Optional[dict] = None,
        *,
        values_sql: Optional[Dict[str, str]] = None,
@@ -875,11 +867,9 @@ class RemoteTable(Table):

        Parameters
        ----------
-        where: str or [Expr][lancedb.expr.Expr], optional
-            The filter condition. Can be a SQL string or a type-safe
-            [Expr][lancedb.expr.Expr] built with [col][lancedb.expr.col] and
-            [lit][lancedb.expr.lit]. The filter must not be empty, or it will
-            error.
+        where: str, optional
+            The SQL where clause to use when updating rows. For example, 'x = 2'
+            or 'x IN (1, 2, 3)'. The filter must not be empty, or it will error.
        values: dict, optional
            The values to update. The keys are the column names and the values
            are the values to set.
@@ -982,7 +972,7 @@ class RemoteTable(Table):
    def refresh_column(self, column: str):
        return LOOP.run(self._table.refresh_column(column))

-    def refresh_column_async(self, column: str) -> Job[RefreshColumnResult]:
+    def refresh_column_async(self, column: str) -> Job:
        return Job(LOOP.run(self._table.refresh_column_async(column)))

    def alter_columns(
@@ -4,34 +4,30 @@

 """Schema helpers for Lance blob columns."""

-import importlib
-from typing import TYPE_CHECKING
-
 import pyarrow as pa
-import pyarrow.ipc
-
-if TYPE_CHECKING:
-    from lance.blob import BlobType as BlobType

 _BLOB_EXTENSION_NAME = "lance.blob.v2"
 _BLOB_V1_KEY = "lance-encoding:blob"
 _ARROW_EXT_NAME_KEY = "ARROW:extension:name"
-_BLOB_V2_STORAGE_TYPE = pa.struct(
-    [
-        pa.field("data", pa.large_binary(), nullable=True),
-        pa.field("uri", pa.utf8(), nullable=True),
-        pa.field("position", pa.uint64(), nullable=True),
-        pa.field("size", pa.uint64(), nullable=True),
-    ]
-)
-_resolved_blob_type = None


-class _FallbackBlobType(pa.ExtensionType):
-    """lance.blob.v2 extension type used when pylance is not installed."""
+class BlobType(pa.ExtensionType):
+    """PyArrow extension type for a Lance blob v2 column.
+
+    Queries return descriptors; call :meth:`~lancedb.table.Table.fetch_blob_files`
+    for lazy reads or :meth:`~lancedb.table.Table.fetch_blobs` for eager bytes.
+    """

    def __init__(self) -> None:
-        pa.ExtensionType.__init__(self, _BLOB_V2_STORAGE_TYPE, _BLOB_EXTENSION_NAME)
+        storage_type = pa.struct(
+            [
+                pa.field("data", pa.large_binary(), nullable=True),
+                pa.field("uri", pa.utf8(), nullable=True),
+                pa.field("position", pa.uint64(), nullable=True),
+                pa.field("size", pa.uint64(), nullable=True),
+            ]
+        )
+        super().__init__(storage_type, _BLOB_EXTENSION_NAME)

    def __arrow_ext_serialize__(self) -> bytes:
        return b""
@@ -39,16 +35,23 @@ class _FallbackBlobType(pa.ExtensionType):
    @classmethod
    def __arrow_ext_deserialize__(
        cls, storage_type: pa.DataType, serialized: bytes
-    ) -> "_FallbackBlobType":
+    ) -> "BlobType":
        return cls()

    def __reduce__(self):
+        # Ensure pickle round-trips on older pyarrow (apache/arrow#35599).
        return type(self).__arrow_ext_deserialize__, (
            self.storage_type,
            self.__arrow_ext_serialize__(),
        )


+try:
+    pa.register_extension_type(BlobType())  # type: ignore[arg-type]
+except pa.ArrowKeyError:
+    pass
+
+
 def _metadata_value(metadata: dict, key: str):
    return metadata.get(key.encode()) or metadata.get(key)

@@ -89,105 +92,43 @@ def is_blob_like_field(field: pa.Field) -> bool:
    return is_blob_v2_field(field) or _metadata_marks_legacy_blob(field.metadata or {})


-def _collect_blob_paths(schema: pa.Schema, is_blob) -> list[tuple[str, bool]]:
-    """Walk the schema and return (path, has_list_ancestor) for each blob field."""
-    paths: list[tuple[str, bool]] = []
+def _collect_blob_paths(schema: pa.Schema, is_blob) -> list[str]:
+    paths: list[str] = []

-    def walk(fields, prefix: str, has_list_ancestor: bool) -> None:
+    def walk(fields, prefix: str) -> None:
        for field in fields:
            path = f"{prefix}.{field.name}" if prefix else field.name
            if is_blob(field):
-                paths.append((path, has_list_ancestor))
+                paths.append(path)
            elif pa.types.is_struct(field.type):
-                walk(field.type, path, has_list_ancestor)
+                walk(field.type, path)
            elif (
                pa.types.is_list(field.type)
                or pa.types.is_large_list(field.type)
                or pa.types.is_fixed_size_list(field.type)
            ):
-                walk([field.type.value_field], path, True)
+                walk([field.type.value_field], path)

-    walk(schema, "", False)
+    walk(schema, "")
    return paths


 def blob_column_paths(schema: pa.Schema) -> list[str]:
    """Dotted paths of blob-like columns (v2 extension or legacy metadata)."""
-    return [path for path, _ in _collect_blob_paths(schema, is_blob_like_field)]
+    return _collect_blob_paths(schema, is_blob_like_field)


 def blob_v2_column_paths(schema: pa.Schema) -> list[str]:
-    return [path for path, _ in _collect_blob_paths(schema, is_blob_v2_field)]
-
-
-def row_addressable_blob_v2_paths(schema: pa.Schema) -> list[str]:
-    """Blob v2 paths with one blob addressable by table row id.
-
-    ``fetch_blobs`` and the descriptor row-id ride-along address one blob per
-    row, so a blob inside a list container has no row-id slot and no fetch
-    path. Those columns still store and query as raw descriptors.
-    """
-    return [
-        path
-        for path, has_list_ancestor in _collect_blob_paths(schema, is_blob_v2_field)
-        if not has_list_ancestor
-    ]
+    return _collect_blob_paths(schema, is_blob_v2_field)


 def schema_has_blob_field(schema: pa.Schema) -> bool:
    return bool(blob_column_paths(schema))


-def _deserialize_registered_type(extension_type: pa.ExtensionType) -> pa.DataType:
-    """Return the type Arrow reconstructs for this extension name."""
-    schema = pa.schema([pa.field("value", extension_type)])
-    restored = pa.ipc.read_schema(schema.serialize())
-    return restored.field("value").type
-
-
-def _resolve_blob_type():
-    """Return the BlobType class this process should use.
-
-    pylance's class when it owns the lance.blob.v2 registry entry,
-    otherwise LanceDB's fallback. A different registered class is an error.
-    """
-    global _resolved_blob_type
-    if _resolved_blob_type is not None:
-        return _resolved_blob_type
-    try:
-        blob_module = importlib.import_module("lance.blob")
-    except ModuleNotFoundError as err:
-        if err.name not in ("lance", "lance.blob"):
-            raise
-    else:
-        blob_type = getattr(blob_module, "BlobType", None)
-        if blob_type is not None:
-            registered_type = _deserialize_registered_type(blob_type())
-            if type(registered_type) is not blob_type:
-                registered_cls = type(registered_type)
-                raise ValueError(
-                    "lance.blob.v2 is already registered by "
-                    f"{registered_cls.__module__}.{registered_cls.__qualname__}"
-                )
-            _resolved_blob_type = blob_type
-            return blob_type
-    try:
-        pa.register_extension_type(_FallbackBlobType())  # type: ignore[arg-type]
-    except pa.ArrowKeyError as err:
-        raise ValueError(
-            "lance.blob.v2 is already registered by another extension class"
-        ) from err
-    _resolved_blob_type = _FallbackBlobType
-    return _resolved_blob_type
-
-
 def blob(name: str, nullable: bool = True) -> pa.Field:
-    """Create a Lance blob v2 column field.
-
-    When pylance is installed this is ``lance.blob.BlobType``.
-    """
-    blob_type = _resolve_blob_type()
-    return pa.field(name, blob_type(), nullable=nullable)
+    """Create a Lance blob v2 column field."""
+    return pa.field(name, BlobType(), nullable=nullable)


 def vector(dimension: int, value_type: pa.DataType = pa.float32()) -> pa.DataType:
@@ -214,11 +155,3 @@ def vector(dimension: int, value_type: pa.DataType = pa.float32()) -> pa.DataTyp
    ... ])
    """
    return pa.list_(value_type, dimension)
-
-
-def __getattr__(name: str):
-    if name == "BlobType":
-        blob_type = _resolve_blob_type()
-        globals()["BlobType"] = blob_type
-        return blob_type
-    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
@@ -40,7 +40,7 @@ from ._blob import (
 from .types import BlobMode
 from lancedb.arrow import peek_reader
 from lancedb.background_loop import LOOP, embedding_executor
-from lancedb.job import AsyncJob, Job, _typed_job
+from lancedb.job import AsyncJob, Job
 from .dependencies import (
    _check_for_hugging_face,
    _check_for_lance,
@@ -72,10 +72,7 @@ from .index import (
    FTS,
 )
 from .expr import Expr
-from .functions import (
-    FunctionApplication,
-    RefreshColumnResult as RefreshColumnJobResult,
-)
+from .functions import FunctionApplication
 from .merge import LanceMergeInsertBuilder
 from .pydantic import LanceModel, model_to_dict
 from .query import (
@@ -85,7 +82,6 @@ from .query import (
    AsyncQuery,
    AsyncTakeQuery,
    AsyncVectorQuery,
-    DocumentGranularity,
    FullTextQuery,
    LanceEmptyQueryBuilder,
    LanceFtsQueryBuilder,
@@ -104,12 +100,7 @@ from .util import (
    value_to_sql,
 )
 from .index import lang_mapping
-from .schema import (
-    blob_v2_column_paths,
-    is_blob_v2_field,
-    row_addressable_blob_v2_paths,
-    schema_has_blob_field,
-)
+from .schema import blob_v2_column_paths, schema_has_blob_field


 def _should_push_down_query_table(
@@ -431,7 +422,6 @@ def _cast_to_target_schema(

    def gen():
        for batch in reader:
-            batch = _coerce_blob_write_columns(batch, reordered_schema)
            # Table but not RecordBatch has cast.
            cast_batches = (
                pa.Table.from_batches([batch]).cast(reordered_schema).to_batches()
@@ -444,166 +434,6 @@ def _cast_to_target_schema(
    return pa.RecordBatchReader.from_batches(reordered_schema, gen())


-def _coerce_blob_write_columns(
-    batch: pa.RecordBatch, target_schema: pa.Schema
-) -> pa.RecordBatch:
-    """Materialize blob storage structs before the stream leaves Python.
-
-    merge_insert requires its source reader to already match the table's
-    physical schema. Unlike add and insert, it does not pass through
-    LanceDB's Rust blob coercion, so preserving binary input here would
-    reach Lance as binary and fail the schema check.
-    """
-    columns = []
-    fields = []
-    changed = False
-    for field, column in zip(batch.schema, batch.columns):
-        target_field = target_schema.field(field.name)
-        coerced = _coerce_blob_value(column, target_field)
-        if coerced is not column:
-            column = coerced
-            field = pa.field(
-                field.name,
-                coerced.type,
-                field.nullable,
-                target_field.metadata,
-            )
-            changed = True
-        columns.append(column)
-        fields.append(field)
-    if not changed:
-        return batch
-    return pa.RecordBatch.from_arrays(
-        columns, schema=pa.schema(fields, metadata=batch.schema.metadata)
-    )
-
-
-def _coerce_blob_value(column: pa.Array, target_field: pa.Field) -> pa.Array:
-    if is_blob_v2_field(target_field) and _can_coerce_to_blob(column.type):
-        return _coerce_value_to_blob(column, target_field)
-
-    target_type = target_field.type
-    if pa.types.is_struct(target_type) and pa.types.is_struct(column.type):
-        children = []
-        fields = []
-        changed = False
-        for source_field in column.type:
-            source_column = column.field(source_field.name)
-            nested_target = next(
-                (field for field in target_type if field.name == source_field.name),
-                None,
-            )
-            if nested_target is None:
-                children.append(source_column)
-                fields.append(source_field)
-                continue
-            coerced = _coerce_blob_value(source_column, nested_target)
-            if coerced is not source_column:
-                changed = True
-            child_array, child_type = _physical_array_and_type(coerced)
-            children.append(child_array)
-            fields.append(
-                pa.field(
-                    source_field.name,
-                    child_type,
-                    source_field.nullable,
-                    nested_target.metadata,
-                )
-            )
-        if not changed:
-            return column
-        return pa.StructArray.from_arrays(
-            children,
-            fields=fields,
-            mask=column.is_null() if column.null_count else None,
-        )
-
-    if _is_list_like(target_type) and _is_list_like(column.type):
-        return _coerce_blob_list_values(column, target_type.value_field)
-
-    return column
-
-
-def _coerce_blob_list_values(
-    column: pa.Array, target_value_field: pa.Field
-) -> pa.Array:
-    """Coerce blob values inside a list column, preserving offsets and nulls.
-
-    Works on the raw child values window instead of ``pc.list_flatten`` because
-    flatten drops values spanned by null slots, which would misalign offsets.
-    """
-    mask = column.is_null() if column.null_count else None
-    if pa.types.is_fixed_size_list(column.type):
-        list_size = column.type.list_size
-        values = column.values.slice(column.offset * list_size, len(column) * list_size)
-        coerced = _coerce_blob_value(values, target_value_field)
-        if coerced is values:
-            return column
-        physical_values, _ = _physical_array_and_type(coerced)
-        return pa.FixedSizeListArray.from_arrays(physical_values, list_size, mask=mask)
-    offsets = column.offsets
-    first_offset = offsets[0].as_py()
-    values = column.values.slice(
-        first_offset,
-        offsets[-1].as_py() - first_offset,
-    )
-    coerced = _coerce_blob_value(values, target_value_field)
-    if coerced is values:
-        return column
-    physical_values, _ = _physical_array_and_type(coerced)
-    if first_offset:
-        offsets = pc.subtract(offsets, pa.scalar(first_offset, offsets.type))
-    if pa.types.is_large_list(column.type):
-        return pa.LargeListArray.from_arrays(offsets, physical_values, mask=mask)
-    return pa.ListArray.from_arrays(offsets, physical_values, mask=mask)
-
-
-def _coerce_value_to_blob(values: pa.Array, target_field: pa.Field) -> pa.Array:
-    if pa.types.is_null(values.type):
-        data = pa.nulls(len(values), type=pa.large_binary())
-    elif pa.types.is_large_binary(values.type):
-        data = values
-    else:
-        data = values.cast(pa.large_binary())
-    length = len(values)
-    storage_type = target_field.type
-    if isinstance(storage_type, pa.ExtensionType):
-        storage_type = storage_type.storage_type
-    storage_fields = list(storage_type)
-    children = []
-    for storage_field in storage_fields:
-        if storage_field.name == "data":
-            children.append(data)
-        else:
-            children.append(pa.nulls(length, type=storage_field.type))
-    storage = pa.StructArray.from_arrays(
-        children,
-        fields=storage_fields,
-        mask=values.is_null() if values.null_count else None,
-    )
-    if isinstance(target_field.type, pa.ExtensionType):
-        return pa.ExtensionArray.from_storage(target_field.type, storage)
-    return storage
-
-
-def _physical_array_and_type(array: pa.Array) -> tuple[pa.Array, pa.DataType]:
-    if isinstance(array.type, pa.ExtensionType):
-        return array.storage, array.type.storage_type
-    return array, array.type
-
-
-def _can_coerce_to_blob(data_type: pa.DataType) -> bool:
-    return _is_binary_like(data_type) or pa.types.is_null(data_type)
-
-
-def _is_binary_like(data_type: pa.DataType) -> bool:
-    return (
-        pa.types.is_binary(data_type)
-        or pa.types.is_large_binary(data_type)
-        or pa.types.is_binary_view(data_type)
-    )
-
-
 def _field_extension_name(field: pa.Field) -> Optional[str]:
    extension_name = getattr(field.type, "extension_name", None)
    if extension_name is not None:
@@ -630,73 +460,65 @@ def _align_field_types(
        target_field = next((f for f in target_fields if f.name == field.name), None)
        if target_field is None:
            raise ValueError(f"Field '{field.name}' not found in target schema")
-        new_fields.append(_align_field(field, target_field))
+        # Preserve arrow.json input until it reaches Lance. LanceDB exposes stored
+        # JSON columns as lance.json (JSONB-backed LargeBinary), but casting the
+        # input to that storage type here merely relabels the raw JSON bytes as
+        # JSONB. Lance must see arrow.json so it can perform the JSONB encoding.
+        if (
+            _field_extension_name(field) == "arrow.json"
+            and _field_extension_name(target_field) == "lance.json"
+        ):
+            new_fields.append(field)
+            continue
+        if pa.types.is_struct(target_field.type):
+            if pa.types.is_struct(field.type):
+                new_type = pa.struct(
+                    _align_field_types(
+                        field.type.fields,
+                        target_field.type.fields,
+                    )
+                )
+            else:
+                new_type = target_field.type
+        elif pa.types.is_list(target_field.type):
+            if _is_list_like(field.type):
+                new_type = pa.list_(
+                    _align_field_types(
+                        [field.type.value_field],
+                        [target_field.type.value_field],
+                    )[0]
+                )
+            else:
+                new_type = target_field.type
+        elif pa.types.is_large_list(target_field.type):
+            if _is_list_like(field.type):
+                new_type = pa.large_list(
+                    _align_field_types(
+                        [field.type.value_field],
+                        [target_field.type.value_field],
+                    )[0]
+                )
+            else:
+                new_type = target_field.type
+        elif pa.types.is_fixed_size_list(target_field.type):
+            if _is_list_like(field.type):
+                new_type = pa.list_(
+                    _align_field_types(
+                        [field.type.value_field],
+                        [target_field.type.value_field],
+                    )[0],
+                    target_field.type.list_size,
+                )
+            else:
+                new_type = target_field.type
+        else:
+            new_type = target_field.type
+        new_fields.append(
+            pa.field(field.name, new_type, field.nullable, target_field.metadata)
+        )
    return new_fields


-def _align_list_value_field(
-    value_field: pa.Field, target_value_field: pa.Field
-) -> pa.Field:
-    # A list has exactly one child, so the inferred child name ("item") aligns
-    # positionally and adopts the table's child name; pa.Table.cast renames it.
-    return _align_field(value_field, target_value_field).with_name(
-        target_value_field.name
-    )
-
-
-def _align_field(field: pa.Field, target_field: pa.Field) -> pa.Field:
-    # Preserve arrow.json input until it reaches Lance. LanceDB exposes stored
-    # JSON columns as lance.json (JSONB-backed LargeBinary), but casting the
-    # input to that storage type here merely relabels the raw JSON bytes as
-    # JSONB. Lance must see arrow.json so it can perform the JSONB encoding.
-    if (
-        _field_extension_name(field) == "arrow.json"
-        and _field_extension_name(target_field) == "lance.json"
-    ):
-        return field
-    if pa.types.is_struct(target_field.type):
-        if pa.types.is_struct(field.type):
-            new_type = pa.struct(
-                _align_field_types(
-                    field.type.fields,
-                    target_field.type.fields,
-                )
-            )
-        else:
-            new_type = target_field.type
-    elif pa.types.is_list(target_field.type):
-        if _is_list_like(field.type):
-            new_type = pa.list_(
-                _align_list_value_field(
-                    field.type.value_field, target_field.type.value_field
-                )
-            )
-        else:
-            new_type = target_field.type
-    elif pa.types.is_large_list(target_field.type):
-        if _is_list_like(field.type):
-            new_type = pa.large_list(
-                _align_list_value_field(
-                    field.type.value_field, target_field.type.value_field
-                )
-            )
-        else:
-            new_type = target_field.type
-    elif pa.types.is_fixed_size_list(target_field.type):
-        if _is_list_like(field.type):
-            new_type = pa.list_(
-                _align_list_value_field(
-                    field.type.value_field, target_field.type.value_field
-                ),
-                target_field.type.list_size,
-            )
-        else:
-            new_type = target_field.type
-    else:
-        new_type = target_field.type
-    return pa.field(field.name, new_type, field.nullable, target_field.metadata)
-
-
 def _infer_subschema(
    schema: List[pa.Field],
    reference_fields: List[pa.Field],
@@ -763,7 +585,7 @@ def sanitize_create_table(
        schema = data.schema
    else:
        if schema is not None:
-            data = pa.Table.from_batches([], schema=schema)
+            data = pa.Table.from_pylist([], schema)
    if schema is None:
        if data is None:
            raise ValueError("Either data or schema must be provided")
@@ -1343,7 +1165,6 @@ class Table(ABC):
        ngram_max_length: int = 3,
        prefix_only: bool = False,
        block_size: int = 128,
-        document_granularity: DocumentGranularity = DocumentGranularity.ROW,
        wait_timeout: Optional[timedelta] = None,
        name: Optional[str] = None,
    ):
@@ -1422,11 +1243,6 @@ class Table(ABC):
            The number of documents per compressed posting block. Must be 128
            or 256. A value of 256 uses the experimental FTS V3 format and
            may introduce breaking changes.
-        document_granularity: DocumentGranularity, default ROW
-            ``ROW`` treats the selected text in one table row as one document.
-            ``LIST_ELEMENT`` treats each element of the deepest list on the field
-            path as one document and returns its physical coordinates in
-            ``_doc_index`` for matching queries.
        wait_timeout: timedelta, optional
            The timeout to wait if indexing is asynchronous.
        name: str, optional
@@ -1450,7 +1266,6 @@ class Table(ABC):
        fill_value: float = 0.0,
        progress: Optional[Union[bool, Callable, Any]] = None,
        write_parallelism: Optional[int] = None,
-        allow_external_blob_outside_bases: bool = False,
    ) -> AddResult:
        """Add more data to the [Table][lancedb.table.Table].

@@ -1502,10 +1317,6 @@ class Table(ABC):
            data in flight. Defaults to an estimate based on the data size,
            capped at the number of CPU cores. Lower this if bulk ingestion is
            using too much memory.
-        allow_external_blob_outside_bases: bool, default False
-            Store blob URIs that sit outside registered blob bases. The row
-            keeps a reference, so the object has to stay readable. Local
-            tables only.

        Returns
        -------
@@ -1918,7 +1729,7 @@ class Table(ABC):
    @abstractmethod
    def update(
        self,
-        where: Optional[Union[str, Expr]] = None,
+        where: Optional[str] = None,
        values: Optional[dict] = None,
        *,
        values_sql: Optional[Dict[str, str]] = None,
@@ -1933,11 +1744,9 @@ class Table(ABC):

        Parameters
        ----------
-        where: str or [Expr][lancedb.expr.Expr], optional
-            The filter condition. Can be a SQL string or a type-safe
-            [Expr][lancedb.expr.Expr] built with [col][lancedb.expr.col] and
-            [lit][lancedb.expr.lit]. The filter must not be empty, or it will
-            error.
+        where: str, optional
+            The SQL where clause to use when updating rows. For example, 'x = 2'
+            or 'x IN (1, 2, 3)'. The filter must not be empty, or it will error.
        values: dict, optional
            The values to update. The keys are the column names and the values
            are the values to set.
@@ -1955,7 +1764,6 @@ class Table(ABC):
        Examples
        --------
        >>> import lancedb
-        >>> from lancedb.expr import col
        >>> import pandas as pd
        >>> data = pd.DataFrame({"x": [1, 2, 3], "vector": [[1.0, 2], [3, 4], [5, 6]]})
        >>> db = lancedb.connect("./.lancedb")
@@ -1965,7 +1773,7 @@ class Table(ABC):
        0  1  [1.0, 2.0]
        1  2  [3.0, 4.0]
        2  3  [5.0, 6.0]
-        >>> table.update(where=col("x") == 2, values={"vector": [10.0, 10]})
+        >>> table.update(where="x = 2", values={"vector": [10.0, 10]})
        UpdateResult(rows_updated=1, version=2)
        >>> table.to_pandas()
           x        vector
@@ -2161,7 +1969,7 @@ class Table(ABC):
            A mapping with one ``FunctionApplication`` value keeps its scalar
            or named-struct result in the named table column. A bare
            named-struct application expands its ordered result fields as one
-            atomic binding; aliases come from ``rename(columns=...)``.
+            atomic sibling group; aliases come from ``rename(columns=...)``.
            Function columns are supported only on LanceDB Cloud and
            Enterprise.
        computed: Dict[str, str], optional
@@ -2231,7 +2039,7 @@ class Table(ABC):
        """

    @abstractmethod
-    def refresh_column_async(self, column: str) -> Job[RefreshColumnJobResult]:
+    def refresh_column_async(self, column: str) -> Job:
        """
        Like :meth:`refresh_column`, but returns a handle to the refresh job
        instead of blocking until it completes.
@@ -2242,12 +2050,6 @@ class Table(ABC):
        than failing the job. On local tables the job runs in-process; on
        LanceDB Cloud and Enterprise it is the server's backfill job.

-        Returns
-        -------
-        Job[RefreshColumnResult]
-            A job whose successful ``wait`` returns row counts plus the source
-            and published table versions.
-
        Examples
        --------
        >>> import lancedb
@@ -2256,9 +2058,7 @@ class Table(ABC):
        >>> table.add_columns(computed={"doubled": "x * 2"})
        AddColumnsResult(version=2)
        >>> job = table.refresh_column_async("doubled")
-        >>> result = job.wait()
-        >>> result.rows_assigned
-        2
+        >>> job.wait()
        >>> job.status()
        'finished'
        """
@@ -2304,25 +2104,12 @@ class Table(ABC):
        ----------
        updates : dict
            One or more dicts, each with:
-
            - "path": str — dot-path to the field (e.g. "embedding" or "a.b.c").
            - "metadata": dict[str, str | None] — keys to set; a value of ``None``
              deletes that key.
            - "replace": bool, optional — replace the field's whole metadata map
              instead of merging (default False).

-            The following keys are treated specially, by convention, and should
-            be used when appropriate:
-
-            - "lancedb:description": for a human-readable description of a field.
-            - ``"lancedb:tag:<name>"`` for a user-defined key-value tag, where the
-                suffix names the tag category; e.g. "lancedb:tag:model": "clip".
-            - "lancedb:logical-column" for a column grouping; e.g. "feature_v1"
-                and "feature_v2" might be in the same logical column.
-            - "lancedb:status" for status options ("production", "candidate",
-                "deprecated", "archived") to designate the current life cycle
-                state of this column.
-
        Returns
        -------
        UpdateFieldMetadataResult
@@ -2872,7 +2659,7 @@ class LanceTable(Table):
            arrow_tbl = self.to_arrow()
            if blob_mode == "descriptions":
                arrow_tbl = strip_auto_row_ids(
-                    arrow_tbl, row_addressable_blob_v2_paths(self.schema)
+                    arrow_tbl, blob_v2_column_paths(self.schema)
                )
            return arrow_tbl.to_pandas(**kwargs)

@@ -3470,7 +3257,6 @@ class LanceTable(Table):
        ngram_max_length: int = 3,
        prefix_only: bool = False,
        block_size: int = 128,
-        document_granularity: DocumentGranularity = DocumentGranularity.ROW,
        name: Optional[str] = None,
    ):
        """Create a full-text search index on a column.
@@ -3522,11 +3308,7 @@ class LanceTable(Table):
            tokenizer_configs = self.infer_tokenizer_configs(tokenizer_name)
            tokenizer_configs["custom_stop_words"] = custom_stop_words

-        config = FTS(
-            block_size=block_size,
-            document_granularity=document_granularity,
-            **tokenizer_configs,
-        )
+        config = FTS(block_size=block_size, **tokenizer_configs)

        try:
            LOOP.run(
@@ -3616,7 +3398,6 @@ class LanceTable(Table):
        fill_value: float = 0.0,
        progress: Optional[Union[bool, Callable, Any]] = None,
        write_parallelism: Optional[int] = None,
-        allow_external_blob_outside_bases: bool = False,
    ) -> AddResult:
        """Add data to the table.
        If vector columns are missing and the table
@@ -3644,9 +3425,6 @@ class LanceTable(Table):
            data in flight. Defaults to an estimate based on the data size,
            capped at the number of CPU cores. Lower this if bulk ingestion is
            using too much memory.
-        allow_external_blob_outside_bases: bool, default False
-            Allow blob URIs outside registered bases. See :meth:`Table.add`.
-            Local tables only.

        Returns
        -------
@@ -3663,7 +3441,6 @@ class LanceTable(Table):
                    fill_value=fill_value,
                    progress=progress,
                    write_parallelism=write_parallelism,
-                    allow_external_blob_outside_bases=allow_external_blob_outside_bases,
                )
            )
        finally:
@@ -4018,7 +3795,7 @@ class LanceTable(Table):

    def update(
        self,
-        where: Optional[Union[str, Expr]] = None,
+        where: Optional[str] = None,
        values: Optional[dict] = None,
        *,
        values_sql: Optional[Dict[str, str]] = None,
@@ -4029,11 +3806,9 @@ class LanceTable(Table):

        Parameters
        ----------
-        where: str or [Expr][lancedb.expr.Expr], optional
-            The filter condition. Can be a SQL string or a type-safe
-            [Expr][lancedb.expr.Expr] built with [col][lancedb.expr.col] and
-            [lit][lancedb.expr.lit]. The filter must not be empty, or it will
-            error.
+        where: str, optional
+            The SQL where clause to use when updating rows. For example, 'x = 2'
+            or 'x IN (1, 2, 3)'. The filter must not be empty, or it will error.
        values: dict, optional
            The values to update. The keys are the column names and the values
            are the values to set.
@@ -4051,7 +3826,6 @@ class LanceTable(Table):
        Examples
        --------
        >>> import lancedb
-        >>> from lancedb.expr import col
        >>> import pandas as pd
        >>> data = pd.DataFrame({"x": [1, 2, 3], "vector": [[1.0, 2], [3, 4], [5, 6]]})
        >>> db = lancedb.connect("./.lancedb")
@@ -4061,7 +3835,7 @@ class LanceTable(Table):
        0  1  [1.0, 2.0]
        1  2  [3.0, 4.0]
        2  3  [5.0, 6.0]
-        >>> table.update(where=col("x") == 2, values={"vector": [10.0, 10]})
+        >>> table.update(where="x = 2", values={"vector": [10.0, 10]})
        UpdateResult(rows_updated=1, version=2)
        >>> table.to_pandas()
           x        vector
@@ -4308,7 +4082,7 @@ class LanceTable(Table):
        [`AsyncTable.refresh_column`][lancedb.AsyncTable.refresh_column]."""
        return LOOP.run(self._table.refresh_column(column))

-    def refresh_column_async(self, column: str) -> Job[RefreshColumnJobResult]:
+    def refresh_column_async(self, column: str) -> Job:
        """Fill a computed column's unfilled rows, returning a handle to the
        refresh job. See
        [`Table.refresh_column_async`][lancedb.table.Table.refresh_column_async].
@@ -5276,9 +5050,7 @@ class AsyncTable:
        if blob_mode == "descriptions" or not schema_has_blob_field(schema):
            arrow_tbl = await self.to_arrow()
            if blob_mode == "descriptions":
-                arrow_tbl = strip_auto_row_ids(
-                    arrow_tbl, row_addressable_blob_v2_paths(schema)
-                )
+                arrow_tbl = strip_auto_row_ids(arrow_tbl, blob_v2_column_paths(schema))
            return arrow_tbl.to_pandas(**kwargs)

        if blob_mode == "lazy" and get_uri_scheme(await self.uri()) == "memory":
@@ -5582,7 +5354,6 @@ class AsyncTable:
        fill_value: Optional[float] = None,
        progress: Optional[Union[bool, Callable, Any]] = None,
        write_parallelism: Optional[int] = None,
-        allow_external_blob_outside_bases: bool = False,
    ) -> AddResult:
        """Add more data to the [AsyncTable][lancedb.table.AsyncTable].

@@ -5613,9 +5384,6 @@ class AsyncTable:
            data in flight. Defaults to an estimate based on the data size,
            capped at the number of CPU cores. Lower this if bulk ingestion is
            using too much memory.
-        allow_external_blob_outside_bases: bool, default False
-            Allow blob URIs outside registered bases. See :meth:`Table.add`.
-            Local tables only.

        """
        schema = await self.schema()
@@ -5652,7 +5420,6 @@ class AsyncTable:
                mode or "append",
                progress=progress,
                write_parallelism=write_parallelism,
-                allow_external_blob_outside_bases=allow_external_blob_outside_bases,
            )
        except RuntimeError as e:
            if "Cast error" in str(e):
@@ -6177,7 +5944,7 @@ class AsyncTable:
        self,
        updates: Optional[Dict[str, Any]] = None,
        *,
-        where: Optional[Union[str, Expr]] = None,
+        where: Optional[str] = None,
        updates_sql: Optional[Dict[str, str]] = None,
    ) -> UpdateResult:
        """
@@ -6192,11 +5959,9 @@ class AsyncTable:
            The updates to apply.  The keys should be the name of the column to
            update.  The values should be the new values to assign.  This is
            required unless updates_sql is supplied.
-        where: str or [Expr][lancedb.expr.Expr], optional
-            The filter condition. Can be a SQL string or a type-safe
-            [Expr][lancedb.expr.Expr] built with [col][lancedb.expr.col] and
-            [lit][lancedb.expr.lit]. Only rows that satisfy this filter will
-            be updated.
+        where: str, optional
+            An SQL filter that controls which rows are updated. For example, 'x = 2'
+            or 'x IN (1, 2, 3)'.  Only rows that satisfy this filter will be udpated.
        updates_sql: dict, optional
            The updates to apply, expressed as SQL expression strings.  The keys should
            be column names. The values should be SQL expressions.  These can be SQL
@@ -6214,14 +5979,13 @@ class AsyncTable:
        --------
        >>> import asyncio
        >>> import lancedb
-        >>> from lancedb.expr import col
        >>> import pandas as pd
        >>> async def demo_update():
        ...     data = pd.DataFrame({"x": [1, 2], "vector": [[1, 2], [3, 4]]})
        ...     db = await lancedb.connect_async("./.lancedb")
        ...     table = await db.create_table("my_table", data)
        ...     # x is [1, 2], vector is [[1, 2], [3, 4]]
-        ...     await table.update({"vector": [10, 10]}, where=col("x") == 2)
+        ...     await table.update({"vector": [10, 10]}, where="x = 2")
        ...     # x is [1, 2], vector is [[1, 2], [10, 10]]
        ...     await table.update(updates_sql={"x": "x + 1"})
        ...     # x is [2, 3], vector is [[1, 2], [10, 10]]
@@ -6235,8 +5999,7 @@ class AsyncTable:
        if updates is not None:
            updates_sql = {k: value_to_sql(v) for k, v in updates.items()}

-        predicate = where.to_sql() if isinstance(where, Expr) else where
-        return await self._inner.update(updates_sql, predicate)
+        return await self._inner.update(updates_sql, where)

    async def add_columns(
        self,
@@ -6264,7 +6027,7 @@ class AsyncTable:
            A mapping with one ``FunctionApplication`` value keeps its scalar
            or named-struct result in the named table column. A bare
            named-struct application expands its ordered result fields as one
-            atomic binding; aliases come from ``rename(columns=...)``.
+            atomic sibling group; aliases come from ``rename(columns=...)``.
            Function columns are supported only on LanceDB Cloud and
            Enterprise.
        computed: Dict[str, str], optional
@@ -6301,7 +6064,7 @@ class AsyncTable:
                isinstance(value, FunctionApplication) for value in transforms.values()
            ):
                raise ValueError(
-                    "one add_columns call declares exactly one Function binding"
+                    "one add_columns call declares exactly one Function sibling group"
                )
            function_output_name, function_application = next(iter(transforms.items()))

@@ -6359,9 +6122,7 @@ class AsyncTable:
        """
        return await self._inner.refresh_column(column)

-    async def refresh_column_async(
-        self, column: str
-    ) -> AsyncJob[RefreshColumnJobResult]:
+    async def refresh_column_async(self, column: str) -> AsyncJob:
        """
        Like :meth:`refresh_column`, but returns a handle to the refresh job
        instead of blocking until it completes.
@@ -6373,12 +6134,6 @@ class AsyncTable:
        in-process; on LanceDB Cloud and Enterprise it is the server's
        backfill job.

-        Returns
-        -------
-        AsyncJob[RefreshColumnResult]
-            A job whose successful ``wait`` returns row counts plus the source
-            and published table versions.
-
        Examples
        --------
        >>> import asyncio
@@ -6388,16 +6143,12 @@ class AsyncTable:
        ...     table = await db.create_table("computed_job_async_demo", [{"x": 1}])
        ...     await table.add_columns(computed={"doubled": "x * 2"})
        ...     job = await table.refresh_column_async("doubled")
-        ...     result = await job.wait()
-        ...     assert result.rows_assigned == 1
+        ...     await job.wait()
        ...     return await job.status()
        >>> asyncio.run(refresh_in_background())
        'finished'
        """
-        return _typed_job(
-            await self._inner.refresh_column_async(column),
-            RefreshColumnJobResult.from_json,
-        )
+        return AsyncJob(await self._inner.refresh_column_async(column))

    async def alter_columns(
        self, *alterations: Iterable[dict[str, Any]]
@@ -7050,21 +6801,21 @@ class Branches:
        """Diff a branch against main."""
        return LOOP.run(self._table.branches.diff(from_branch))

-    def cherry_pick(self, from_branch: str, dry_run: bool = False) -> Dict[str, Any]:
-        """Cherry-pick a branch onto main, or dry-run.
+    def merge(self, from_branch: str, dry_run: bool = False) -> Dict[str, Any]:
+        """Merge a branch into main, or dry-run.

        Parameters
        ----------
        from_branch: str
-            Branch to cherry-pick from.
+            Branch to merge from.
        dry_run: bool, default False
-            When True, only preview. When False, attempt the cherry-pick.
+            When True, only preview. When False, attempt the merge.

        Notes
        -----
-        A failed cherry-pick returns ``status="failed"`` instead of raising.
+        A rejected merge returns ``status="rejected"`` instead of raising.
        """
-        return LOOP.run(self._table.branches.cherry_pick(from_branch, dry_run))
+        return LOOP.run(self._table.branches.merge(from_branch, dry_run))

    def _wrap(
        self, async_table: "AsyncTable", version: Optional[int] = None
@@ -7200,11 +6951,9 @@ class AsyncBranches:
        """Diff a branch against main."""
        return await self._table.branches.diff(from_branch)

-    async def cherry_pick(
-        self, from_branch: str, dry_run: bool = False
-    ) -> Dict[str, Any]:
-        """Cherry-pick a branch onto main, or dry-run.
+    async def merge(self, from_branch: str, dry_run: bool = False) -> Dict[str, Any]:
+        """Merge a branch into main, or dry-run.

-        A failed cherry-pick returns ``status="failed"`` instead of raising.
+        A rejected merge returns ``status="rejected"`` instead of raising.
        """
-        return await self._table.branches.cherry_pick(from_branch, dry_run)
+        return await self._table.branches.merge(from_branch, dry_run)
@@ -105,7 +105,7 @@ def test_quickstart(tmp_path):
    tbl.create_index(num_sub_vectors=1)
    # --8<-- [end:create_index]
    # --8<-- [start:delete_rows]
-    tbl.delete("item = 'fizz'")
+    tbl.delete('item = "fizz"')
    # --8<-- [end:delete_rows]
    # --8<-- [start:drop_table]
    db.drop_table("my_table")
@@ -201,7 +201,7 @@ async def test_quickstart_async(tmp_path):
    await tbl.create_index("vector")
    # --8<-- [end:create_index_async]
    # --8<-- [start:delete_rows_async]
-    await tbl.delete("item = 'fizz'")
+    await tbl.delete('item = "fizz"')
    # --8<-- [end:delete_rows_async]
    # --8<-- [start:drop_table_async]
    await db.drop_table("my_table_async")
@@ -266,7 +266,7 @@ def test_table():
    tbl.add(pydantic_model_items)
    # --8<-- [end:add_table_from_pydantic]
    # --8<-- [start:delete_row]
-    tbl.delete("item = 'fizz'")
+    tbl.delete('item = "fizz"')
    # --8<-- [end:delete_row]
    # --8<-- [start:delete_specific_row]
    data = [
@@ -538,7 +538,7 @@ async def test_table_async():
    await async_tbl.add(pydantic_model_items)
    # --8<-- [end:add_table_async_from_pydantic]
    # --8<-- [start:delete_row_async]
-    await async_tbl.delete("item = 'fizz'")
+    await async_tbl.delete('item = "fizz"')
    # --8<-- [end:delete_row_async]
    # --8<-- [start:delete_specific_row_async]
    data = [
@@ -2,41 +2,17 @@
 # SPDX-FileCopyrightText: Copyright The LanceDB Authors

 import io
-import subprocess
-import sys
-import textwrap

-import lance
 import pyarrow as pa
 import pyarrow.compute as pc
 import pytest
-from lance.blob import BlobType as LanceBlobType

 import lancedb
-from lancedb._blob import (
-    blob_v2_projection_sources,
-    read_row_ids_from_hits,
-    stash_auto_row_ids,
-)
-from lancedb.expr import col
+from lancedb._blob import read_row_ids_from_hits, stash_auto_row_ids
 from lancedb.index import FTS
 from lancedb.schema import blob_column_paths, blob_v2_column_paths


-_HIDE_LANCE_BLOB = """\
-import importlib.abc
-import sys
-
-class _MissingLanceBlob(importlib.abc.MetaPathFinder):
-    def find_spec(self, fullname, path, target=None):
-        if fullname == "lance.blob" or fullname.startswith("lance.blob."):
-            raise ModuleNotFoundError(fullname, name="lance.blob")
-
-sys.modules.pop("lance.blob", None)
-sys.meta_path.insert(0, _MissingLanceBlob())
-"""
-
-
 def _blob_table(name, rows):
    db = lancedb.connect("memory:///")
    schema = pa.schema([pa.field("id", pa.int64()), lancedb.blob("image")])
@@ -70,181 +46,6 @@ def test_blob_factory_declares_v2_field():
    field = lancedb.blob("image")
    assert isinstance(field.type, pa.ExtensionType)
    assert field.type.extension_name == "lance.blob.v2"
-    assert lancedb.BlobType is LanceBlobType
-    assert type(field.type) is LanceBlobType
-
-
-def test_blob_type_works_without_pylance():
-    script = _HIDE_LANCE_BLOB + textwrap.dedent(
-        """\
-        import lancedb
-        import pyarrow as pa
-
-        field = lancedb.blob("image")
-        if not isinstance(field.type, pa.ExtensionType):
-            raise SystemExit("expected an extension type")
-        if field.type.extension_name != "lance.blob.v2":
-            raise SystemExit(field.type.extension_name)
-        if lancedb.BlobType is not type(field.type):
-            raise SystemExit("BlobType is not the field type class")
-        if lancedb.BlobType.__module__ != "lancedb.schema":
-            raise SystemExit(lancedb.BlobType.__module__)
-
-        db = lancedb.connect("memory:///")
-        table = db.create_table(
-            "images",
-            schema=pa.schema([pa.field("id", pa.int64()), field]),
-        )
-        table.add([{"id": 1, "image": b"hello"}])
-        result = (
-            table.merge_insert("id")
-            .when_matched_update_all()
-            .when_not_matched_insert_all()
-            .execute([{"id": 1, "image": b"updated"}, {"id": 2, "image": b"inserted"}])
-        )
-        if result.num_updated_rows != 1 or result.num_inserted_rows != 1:
-            raise SystemExit(
-                f"merge_insert rows updated={result.num_updated_rows} "
-                f"inserted={result.num_inserted_rows}"
-            )
-        """
-    )
-    result = subprocess.run(
-        [sys.executable, "-c", script],
-        capture_output=True,
-        text=True,
-        check=False,
-    )
-    assert result.returncode == 0, result.stderr
-
-
-def test_blob_resolves_pylance_type_without_eager_import():
-    script = textwrap.dedent(
-        """\
-        import sys
-        import lancedb
-
-        if "lance.blob" in sys.modules:
-            raise SystemExit("import lancedb imported lance.blob")
-        field = lancedb.blob("image")
-        from lance.blob import BlobType
-
-        if type(field.type) is not BlobType:
-            raise SystemExit(f"{type(field.type)} is not {BlobType}")
-        import lance
-
-        image = lance.blob_array([b"x"])
-        if type(image.type) is not BlobType:
-            raise SystemExit("blob_array used a different class")
-        if type(image.type) is not type(field.type):
-            raise SystemExit("field and array classes differ")
-        """
-    )
-    result = subprocess.run(
-        [sys.executable, "-c", script],
-        capture_output=True,
-        text=True,
-        check=False,
-    )
-    assert result.returncode == 0, result.stderr
-
-
-def test_blob_fallback_fails_if_name_already_registered():
-    script = _HIDE_LANCE_BLOB + textwrap.dedent(
-        """\
-        import pyarrow as pa
-
-        class OtherBlobType(pa.ExtensionType):
-            def __init__(self):
-                super().__init__(
-                    pa.struct([pa.field("data", pa.large_binary())]),
-                    "lance.blob.v2",
-                )
-
-            def __arrow_ext_serialize__(self):
-                return b""
-
-            @classmethod
-            def __arrow_ext_deserialize__(cls, storage_type, serialized):
-                return cls()
-
-        pa.register_extension_type(OtherBlobType())
-        import lancedb
-
-        try:
-            lancedb.blob("image")
-        except ValueError as err:
-            if "already registered" not in str(err):
-                raise SystemExit(err)
-        else:
-            raise SystemExit("expected ValueError")
-        """
-    )
-    result = subprocess.run(
-        [sys.executable, "-c", script],
-        capture_output=True,
-        text=True,
-        check=False,
-    )
-    assert result.returncode == 0, result.stderr
-
-
-def test_blob_type_rejects_competing_registration_with_pylance():
-    script = textwrap.dedent(
-        """\
-        import pyarrow as pa
-        import pyarrow.ipc
-
-        class OtherBlobType(pa.ExtensionType):
-            def __init__(self):
-                super().__init__(
-                    pa.struct(
-                        [
-                            pa.field("data", pa.large_binary()),
-                            pa.field("uri", pa.utf8()),
-                            pa.field("position", pa.uint64()),
-                            pa.field("size", pa.uint64()),
-                        ]
-                    ),
-                    "lance.blob.v2",
-                )
-
-            def __arrow_ext_serialize__(self):
-                return b""
-
-            @classmethod
-            def __arrow_ext_deserialize__(cls, storage_type, serialized):
-                return cls()
-
-        pa.register_extension_type(OtherBlobType())
-
-        from lance.blob import BlobType
-
-        if BlobType is OtherBlobType:
-            raise SystemExit("pylance BlobType was replaced")
-        schema = pa.schema([pa.field("value", BlobType())])
-        restored = pa.ipc.read_schema(schema.serialize())
-        if type(restored.field("value").type) is not OtherBlobType:
-            raise SystemExit(type(restored.field("value").type))
-
-        import lancedb
-
-        try:
-            lancedb.blob("image")
-        except ValueError as err:
-            if "__main__.OtherBlobType" not in str(err):
-                raise SystemExit(err)
-        else:
-            raise SystemExit("expected ValueError")
-        """
-    )
-    result = subprocess.run(
-        [sys.executable, "-c", script],
-        capture_output=True,
-        text=True,
-        check=False,
-    )
-    assert result.returncode == 0, result.stderr


 def test_blob_v2_column_paths_include_list_children():
@@ -269,14 +70,6 @@ def test_blob_v2_column_paths_include_list_children():
    ]


-def test_blob_v2_projection_sources_use_typed_column_name():
-    schema = pa.schema([lancedb.blob("blob")])
-
-    assert blob_v2_projection_sources(schema, {"blob_alias": col("blob")}) == {
-        "blob_alias": "blob"
-    }
-
-
 def _legacy_v1_table(name):
    db = lancedb.connect("memory:///")
    schema = pa.schema(
@@ -373,20 +166,6 @@ async def test_async_table_to_pandas_descriptions_mode_omits_row_id():
    assert set(descriptor.keys()) == {"kind", "position", "size", "blob_id", "blob_uri"}


-@pytest.mark.asyncio
-async def test_async_typed_blob_projection_preserves_source_column():
-    db = await lancedb.connect_async("memory:///typed_blob_projection")
-    schema = pa.schema([pa.field("id", pa.int64()), lancedb.blob("blob")])
-    table = await db.create_table("typed_blob_projection", schema=schema)
-    await table.add([{"id": 1, "blob": b"alpha"}])
-
-    hits = await table.query().select({"blob_alias": col("blob")}).to_arrow()
-
-    assert "_lance_row_id" in hits.schema.field("blob_alias").type.names
-    blobs = await table.fetch_blobs("blob", hits)
-    assert blobs.to_pylist() == [b"alpha"]
-
-
 def test_fetch_blobs_round_trip():
    table = _blob_table(
        "round_trip",
@@ -397,292 +176,6 @@ def test_fetch_blobs_round_trip():
    assert [blobs[0].as_py(), blobs[1].as_py()] == [b"alpha", b"beta"]


-def test_merge_insert_writes_python_bytes():
-    table = _blob_table("merge_bytes", [{"id": 1, "image": b"before"}])
-    result = (
-        table.merge_insert("id")
-        .when_matched_update_all()
-        .when_not_matched_insert_all()
-        .execute([{"id": 1, "image": b"updated"}, {"id": 2, "image": b"inserted"}])
-    )
-    assert result.num_updated_rows == 1
-    assert result.num_inserted_rows == 1
-    by_id = _row_ids_by_id(table)
-    blobs = table.fetch_blobs("image", [by_id[1], by_id[2]])
-    assert blobs.to_pylist() == [b"updated", b"inserted"]
-
-
-def test_merge_insert_bytes_after_reopen_without_touching_blob_type(tmp_path):
-    db = lancedb.connect(tmp_path)
-    schema = pa.schema([pa.field("id", pa.int64()), lancedb.blob("image")])
-    table = db.create_table("images", schema=schema)
-    table.add([{"id": 1, "image": b"hello"}])
-
-    script = textwrap.dedent(
-        f"""\
-        import lancedb
-
-        db = lancedb.connect({str(tmp_path)!r})
-        table = db.open_table("images")
-        image_type = table.schema.field("image").type
-        if type(image_type).__name__ != "StructType":
-            raise SystemExit(f"expected StructType, got {{type(image_type)}}")
-        result = (
-            table.merge_insert("id")
-            .when_matched_update_all()
-            .when_not_matched_insert_all()
-            .execute(
-                [{{"id": 1, "image": b"updated"}}, {{"id": 2, "image": b"inserted"}}]
-            )
-        )
-        if result.num_updated_rows != 1 or result.num_inserted_rows != 1:
-            raise SystemExit(
-                f"rows updated={{result.num_updated_rows}} "
-                f"inserted={{result.num_inserted_rows}}"
-            )
-        hits = table.search().with_row_id(True).limit(10).to_arrow()
-        by_id = dict(zip(hits["id"].to_pylist(), hits["_rowid"].to_pylist()))
-        blobs = table.fetch_blobs("image", [by_id[1], by_id[2]])
-        if blobs.to_pylist() != [b"updated", b"inserted"]:
-            raise SystemExit(blobs.to_pylist())
-        """
-    )
-    result = subprocess.run(
-        [sys.executable, "-c", script],
-        capture_output=True,
-        text=True,
-        check=False,
-    )
-    assert result.returncode == 0, result.stderr
-
-
-def test_merge_insert_bytes_after_reopen_without_pylance(tmp_path):
-    db = lancedb.connect(tmp_path)
-    schema = pa.schema([pa.field("id", pa.int64()), lancedb.blob("image")])
-    table = db.create_table("images", schema=schema)
-    table.add([{"id": 1, "image": b"hello"}])
-
-    script = _HIDE_LANCE_BLOB + textwrap.dedent(
-        f"""\
-        import lancedb
-
-        db = lancedb.connect({str(tmp_path)!r})
-        table = db.open_table("images")
-        image_type = table.schema.field("image").type
-        if type(image_type).__name__ != "StructType":
-            raise SystemExit(f"expected StructType, got {{type(image_type)}}")
-        result = (
-            table.merge_insert("id")
-            .when_matched_update_all()
-            .when_not_matched_insert_all()
-            .execute(
-                [{{"id": 1, "image": b"updated"}}, {{"id": 2, "image": b"inserted"}}]
-            )
-        )
-        if result.num_updated_rows != 1 or result.num_inserted_rows != 1:
-            raise SystemExit(
-                f"rows updated={{result.num_updated_rows}} "
-                f"inserted={{result.num_inserted_rows}}"
-            )
-        hits = table.search().with_row_id(True).limit(10).to_arrow()
-        by_id = dict(zip(hits["id"].to_pylist(), hits["_rowid"].to_pylist()))
-        blobs = table.fetch_blobs("image", [by_id[1], by_id[2]])
-        if blobs.to_pylist() != [b"updated", b"inserted"]:
-            raise SystemExit(blobs.to_pylist())
-        """
-    )
-    result = subprocess.run(
-        [sys.executable, "-c", script],
-        capture_output=True,
-        text=True,
-        check=False,
-    )
-    assert result.returncode == 0, result.stderr
-
-
-def test_merge_insert_blob_array_into_reopened_unregistered_table(tmp_path):
-    db = lancedb.connect(tmp_path)
-    schema = pa.schema([pa.field("id", pa.int64()), lancedb.blob("image")])
-    table = db.create_table("images", schema=schema)
-    table.add([{"id": 1, "image": b"before"}])
-
-    script = textwrap.dedent(
-        f"""\
-        import pyarrow as pa
-        import lancedb
-
-        db = lancedb.connect({str(tmp_path)!r})
-        table = db.open_table("images")
-        image_type = table.schema.field("image").type
-        if type(image_type).__name__ != "StructType":
-            raise SystemExit(
-                f"expected StructType before lance import, got {{type(image_type)}}"
-            )
-
-        import lance
-
-        updates = pa.Table.from_arrays(
-            [
-                pa.array([1, 2], type=pa.int64()),
-                lance.blob_array([b"updated", b"inserted"]),
-            ],
-            names=["id", "image"],
-        )
-        result = (
-            table.merge_insert("id")
-            .when_matched_update_all()
-            .when_not_matched_insert_all()
-            .execute(updates)
-        )
-        if result.num_updated_rows != 1 or result.num_inserted_rows != 1:
-            raise SystemExit(
-                f"rows updated={{result.num_updated_rows}} "
-                f"inserted={{result.num_inserted_rows}}"
-            )
-        hits = table.search().with_row_id(True).limit(10).to_arrow()
-        by_id = dict(zip(hits["id"].to_pylist(), hits["_rowid"].to_pylist()))
-        blobs = table.fetch_blobs("image", [by_id[1], by_id[2]])
-        if blobs.to_pylist() != [b"updated", b"inserted"]:
-            raise SystemExit(blobs.to_pylist())
-        """
-    )
-    result = subprocess.run(
-        [sys.executable, "-c", script],
-        capture_output=True,
-        text=True,
-        check=False,
-    )
-    assert result.returncode == 0, result.stderr
-
-
-def test_add_all_null_blob_column():
-    db = lancedb.connect("memory:///")
-    schema = pa.schema([pa.field("id", pa.int64()), lancedb.blob("image")])
-    table = db.create_table("all_null", schema=schema)
-    table.add([{"id": 1, "image": None}, {"id": 2, "image": None}])
-    by_id = _row_ids_by_id(table)
-    blobs = table.fetch_blobs("image", [by_id[1], by_id[2]])
-    assert blobs.to_pylist() == [None, None]
-
-
-def test_create_table_nested_blob_schema_without_rows():
-    db = lancedb.connect("memory:///")
-    schema = pa.schema(
-        [
-            pa.field("id", pa.int64()),
-            pa.field("info", pa.struct([lancedb.blob("blob")])),
-            pa.field("images", pa.list_(lancedb.blob("image"))),
-        ]
-    )
-    table = db.create_table("nested_empty", schema=schema)
-    assert table.count_rows() == 0
-
-
-def test_merge_insert_nested_blob_dicts():
-    db = lancedb.connect("memory:///")
-    info = pa.StructArray.from_arrays(
-        [
-            pa.array(["first"], type=pa.string()),
-            _blob_array("blob", [b"before"]),
-        ],
-        names=["name", "blob"],
-    )
-    data = pa.Table.from_arrays(
-        [pa.array([1], type=pa.int64()), info],
-        names=["id", "info"],
-    )
-    table = db.create_table("nested_merge", data=data)
-    result = (
-        table.merge_insert("id")
-        .when_matched_update_all()
-        .execute([{"id": 1, "info": {"name": "first", "blob": b"after"}}])
-    )
-    assert result.num_updated_rows == 1
-    by_id = _row_ids_by_id(table)
-    blobs = table.fetch_blobs("info.blob", [by_id[1]])
-    assert blobs.to_pylist() == [b"after"]
-
-
-def _list_blob_table(name):
-    db = lancedb.connect("memory:///")
-    blob_field = lancedb.blob("image")
-    images = pa.ListArray.from_arrays(
-        pa.array([0, 1], type=pa.int32()), _blob_array("image", [b"before"])
-    )
-    data = pa.Table.from_arrays(
-        [pa.array([1], type=pa.int64()), images],
-        schema=pa.schema(
-            [pa.field("id", pa.int64()), pa.field("images", pa.list_(blob_field))]
-        ),
-    )
-    return db.create_table(name, data=data)
-
-
-def test_merge_insert_list_blob_dicts():
-    table = _list_blob_table("list_merge")
-    result = (
-        table.merge_insert("id")
-        .when_matched_update_all()
-        .when_not_matched_insert_all()
-        .execute([{"id": 1, "images": [b"one", b"two"]}, {"id": 2, "images": None}])
-    )
-    assert result.num_updated_rows == 1
-    assert result.num_inserted_rows == 1
-    hits = table.search().limit(10).to_arrow()
-    sizes = {
-        row["id"]: None if row["images"] is None else [d["size"] for d in row["images"]]
-        for row in hits.to_pylist()
-    }
-    assert sizes == {1: [3, 3], 2: None}
-
-
-def test_list_blob_column_queries_as_raw_descriptors():
-    table = _list_blob_table("list_query")
-    hits = table.search().limit(10).to_arrow()
-    element = hits.schema.field("images").type.value_type
-    assert pa.types.is_struct(element)
-    assert "_lance_row_id" not in element.names
-    with pytest.raises(ValueError, match="expected struct before segment"):
-        table.fetch_blobs("images.image", [0])
-
-
-def test_row_addressable_paths_exclude_list_children():
-    from lancedb.schema import row_addressable_blob_v2_paths
-
-    schema = pa.schema(
-        [
-            pa.field("id", pa.int64()),
-            pa.field("info", pa.struct([lancedb.blob("blob")])),
-            pa.field("images", pa.list_(lancedb.blob("image"))),
-        ]
-    )
-    assert blob_v2_column_paths(schema) == ["info.blob", "images.image"]
-    assert row_addressable_blob_v2_paths(schema) == ["info.blob"]
-
-
-def test_merge_insert_writes_pylance_blob_array():
-    table = _blob_table("merge_pylance", [{"id": 1, "image": b"before"}])
-    image = lance.blob_array([b"updated", b"inserted"])
-    assert type(image.type) is LanceBlobType
-    assert type(image.type) is type(lancedb.BlobType())
-    updates = pa.Table.from_arrays(
-        [pa.array([1, 2], type=pa.int64()), image], names=["id", "image"]
-    )
-
-    result = (
-        table.merge_insert("id")
-        .when_matched_update_all()
-        .when_not_matched_insert_all()
-        .execute(updates)
-    )
-
-    assert result.num_updated_rows == 1
-    assert result.num_inserted_rows == 1
-    by_id = _row_ids_by_id(table)
-    blobs = table.fetch_blobs("image", [by_id[1], by_id[2]])
-    assert blobs.to_pylist() == [b"updated", b"inserted"]
-
-
 def test_fetch_blobs_accepts_query_result():
    table = _blob_table("from_result", [{"id": 1, "image": b"gamma"}])
    hits = table.search().limit(10).to_arrow()
@@ -910,50 +403,6 @@ async def test_blob_v2_hybrid_fetch_blobs_async():
    assert {blobs[i].as_py() for i in range(len(blobs))} == {b"alpha", b"beta"}


-@pytest.mark.asyncio
-async def test_async_hybrid_typed_blob_projection_preserves_source_column():
-    db = await lancedb.connect_async("memory:///hybrid_typed_blob")
-    schema = pa.schema(
-        [
-            pa.field("id", pa.int64()),
-            pa.field("text", pa.utf8()),
-            pa.field("vector", pa.list_(pa.float32(), list_size=2)),
-            lancedb.blob("blob"),
-        ]
-    )
-    table = await db.create_table("hybrid_typed_blob", schema=schema)
-    await table.add(
-        [
-            {
-                "id": 1,
-                "text": "hello alpha",
-                "vector": [1.0, 0.0],
-                "blob": b"alpha",
-            },
-            {
-                "id": 2,
-                "text": "hello beta",
-                "vector": [0.9, 0.1],
-                "blob": b"beta",
-            },
-        ]
-    )
-    await table.create_index("text", config=FTS(with_position=False))
-
-    hits = await (
-        table.query()
-        .nearest_to([1.0, 0.0])
-        .nearest_to_text("hello")
-        .select({"blob_alias": col("blob")})
-        .limit(2)
-        .to_arrow()
-    )
-
-    assert "_lance_row_id" in hits.schema.field("blob_alias").type.names
-    blobs = await table.fetch_blobs("blob", hits)
-    assert {blobs[i].as_py() for i in range(len(blobs))} == {b"alpha", b"beta"}
-
-
 def test_blob_file_seek_read_and_read_range():
    payload = _identifiable_payload(1024)
    table = _blob_table("seek_read", [{"id": 1, "image": payload}])
@@ -1168,71 +617,3 @@ def test_fetch_blobs_nested_path_survives_sort_after_query():
 def _identifiable_payload(size: int) -> bytes:
    block = 256
    return b"".join(bytes([i % 256]) * block for i in range(size // block))
-
-
-def _external_uri_blob_array(uris):
-    blob_type = lancedb.blob("image").type
-    storage_type = blob_type.storage_type
-    child_names = [field.name for field in storage_type]
-    assert "uri" in child_names, "blob layout no longer has a uri child"
-    children = [
-        pa.array(uris if field.name == "uri" else [None] * len(uris), type=field.type)
-        for field in storage_type
-    ]
-    storage = pa.StructArray.from_arrays(children, fields=list(storage_type))
-    return pa.ExtensionArray.from_storage(blob_type, storage)
-
-
-def _external_uri_table_and_rows(name, uris):
-    db = lancedb.connect("memory:///")
-    schema = pa.schema([pa.field("id", pa.int64()), lancedb.blob("image")])
-    table = db.create_table(name, schema=schema)
-    rows = pa.Table.from_arrays(
-        [
-            pa.array(range(len(uris)), type=pa.int64()),
-            _external_uri_blob_array(uris),
-        ],
-        schema=schema,
-    )
-    return table, rows
-
-
-def test_add_external_uri_struct_round_trips_with_flag(tmp_path):
-    payload = b"external-uri-bytes"
-    blob_path = tmp_path / "payload.bin"
-    blob_path.write_bytes(payload)
-
-    table, rows = _external_uri_table_and_rows("external_struct", [blob_path.as_uri()])
-    table.add(rows, allow_external_blob_outside_bases=True)
-
-    hits = table.search().to_arrow()
-    blobs = table.fetch_blobs("image", hits)
-    assert blobs[0].as_py() == payload
-
-
-def test_add_external_uri_without_flag_raises(tmp_path):
-    blob_path = tmp_path / "payload.bin"
-    blob_path.write_bytes(b"unreachable")
-
-    table, rows = _external_uri_table_and_rows("external_no_flag", [blob_path.as_uri()])
-    with pytest.raises(ValueError, match="allow_external_blob_outside_bases"):
-        table.add(rows)
-    assert table.count_rows() == 0
-
-
-def test_add_external_uri_string_round_trips_with_flag(tmp_path):
-    payload = b"external-uri-bytes"
-    blob_path = tmp_path / "payload.bin"
-    blob_path.write_bytes(payload)
-
-    db = lancedb.connect("memory:///")
-    schema = pa.schema([pa.field("id", pa.int64()), lancedb.blob("image")])
-    table = db.create_table("external_string", schema=schema)
-    table.add(
-        [{"id": 1, "image": blob_path.as_uri()}],
-        allow_external_blob_outside_bases=True,
-    )
-
-    hits = table.search().to_arrow()
-    blobs = table.fetch_blobs("image", hits)
-    assert blobs[0].as_py() == payload
@@ -774,7 +774,7 @@ def test_drop_table_async(tmp_db: lancedb.DBConnection):
    job = tmp_db.drop_table_async("test")
    assert job.id is None
    assert job.status() == "finished"
-    assert job.wait() is None
+    job.wait()
    assert tmp_db.table_names() == []

    tmp_db.create_table("test", data=data)
@@ -790,7 +790,7 @@ async def test_drop_table_async_connection(tmp_db_async: lancedb.AsyncConnection
    job = await tmp_db_async.drop_table_async("test")
    assert job.id is None
    assert await job.status() == "finished"
-    assert await job.wait() is None
+    await job.wait()
    assert await tmp_db_async.table_names() == []


@@ -52,7 +52,7 @@ class TestExprConstruction:
    def test_func(self):
        e = func("lower", col("name"))
        assert isinstance(e, Expr)
-        assert e.to_sql() == "lower(`name`)"
+        assert e.to_sql() == "lower(name)"

    def test_func_unknown_raises(self):
        with pytest.raises(Exception):
@@ -115,7 +115,7 @@ class TestExprOperators:
    def test_and_operator(self):
        e = (col("age") > lit(18)) & (col("status") == lit("active"))
        assert isinstance(e, Expr)
-        assert e.to_sql() == "((age > 18) AND (`status` = 'active'))"
+        assert e.to_sql() == "((age > 18) AND (status = 'active'))"

    def test_or_operator(self):
        e = (col("a") == lit(1)) | (col("b") == lit(2))
@@ -166,7 +166,7 @@ class TestExprOperators:
    def test_coerce_plain_str(self):
        e = col("name") == "alice"
        assert isinstance(e, Expr)
-        assert e.to_sql() == "(`name` = 'alice')"
+        assert e.to_sql() == "(name = 'alice')"

    def test_reflexive_comparisons(self):
        # 10 < col("age") swaps to col("age") > 10
@@ -198,85 +198,85 @@ class TestExprBytesLiteral:

    def test_bytes_equality_expr_sql(self):
        e = col("data") == lit(b"\xca\xfe")
-        assert e.to_sql() == "(`data` = X'CAFE')"
+        assert e.to_sql() == "(data = X'CAFE')"

    def test_bytes_ne_expr_sql(self):
        e = col("data") != lit(b"\xff")
-        assert e.to_sql() == "(`data` <> X'FF')"
+        assert e.to_sql() == "(data <> X'FF')"

    def test_bytes_compound_expr_sql(self):
        e = (col("data") == lit(b"\x01")) & (col("id") > lit(5))
-        assert e.to_sql() == "((`data` = X'01') AND (id > 5))"
+        assert e.to_sql() == "((data = X'01') AND (id > 5))"

    def test_bytes_in_function_call(self):
        # Regression test: binary literals inside scalar function calls
        # used to fail because DataFusion's unparser does not support Binary
        # scalars.  Now handled via a placeholder-substitution rewrite.
        e = func("contains", col("data"), lit(b"\xff"))
-        assert e.to_sql() == "contains(`data`, X'FF')"
+        assert e.to_sql() == "contains(data, X'FF')"

    def test_bytes_in_not(self):
        e = ~(col("data") == lit(b"\xff"))
-        assert e.to_sql() == "NOT (`data` = X'FF')"
+        assert e.to_sql() == "NOT (data = X'FF')"


 class TestExprStringMethods:
    def test_lower(self):
        e = col("name").lower()
        assert isinstance(e, Expr)
-        assert e.to_sql() == "lower(`name`)"
+        assert e.to_sql() == "lower(name)"

    def test_upper(self):
        e = col("name").upper()
        assert isinstance(e, Expr)
-        assert e.to_sql() == "upper(`name`)"
+        assert e.to_sql() == "upper(name)"

    def test_contains(self):
        e = col("text").contains(lit("hello"))
        assert isinstance(e, Expr)
-        assert e.to_sql() == "contains(`text`, 'hello')"
+        assert e.to_sql() == "contains(text, 'hello')"

    def test_contains_with_str_coerce(self):
        e = col("text").contains("hello")
        assert isinstance(e, Expr)
-        assert e.to_sql() == "contains(`text`, 'hello')"
+        assert e.to_sql() == "contains(text, 'hello')"

    def test_chained_lower_eq(self):
        e = col("name").lower() == lit("alice")
        assert isinstance(e, Expr)
-        assert e.to_sql() == "(lower(`name`) = 'alice')"
+        assert e.to_sql() == "(lower(name) = 'alice')"


 class TestExprCast:
    def test_cast_string(self):
        e = col("id").cast("string")
        assert isinstance(e, Expr)
-        assert e.to_sql() == "arrow_cast(id, 'Utf8')"
+        assert e.to_sql() == "CAST(id AS VARCHAR)"

    def test_cast_int32(self):
        e = col("score").cast("int32")
        assert isinstance(e, Expr)
-        assert e.to_sql() == "arrow_cast(score, 'Int32')"
+        assert e.to_sql() == "CAST(score AS INTEGER)"

    def test_cast_float64(self):
        e = col("val").cast("float64")
        assert isinstance(e, Expr)
-        assert e.to_sql() == "arrow_cast(val, 'Float64')"
+        assert e.to_sql() == "CAST(val AS DOUBLE)"

    def test_cast_pyarrow_type(self):
        e = col("score").cast(pa.int32())
        assert isinstance(e, Expr)
-        assert e.to_sql() == "arrow_cast(score, 'Int32')"
+        assert e.to_sql() == "CAST(score AS INTEGER)"

    def test_cast_pyarrow_float64(self):
        e = col("val").cast(pa.float64())
        assert isinstance(e, Expr)
-        assert e.to_sql() == "arrow_cast(val, 'Float64')"
+        assert e.to_sql() == "CAST(val AS DOUBLE)"

    def test_cast_pyarrow_string(self):
        e = col("id").cast(pa.string())
        assert isinstance(e, Expr)
-        assert e.to_sql() == "arrow_cast(id, 'Utf8')"
+        assert e.to_sql() == "CAST(id AS VARCHAR)"

    def test_cast_pyarrow_and_string_equivalent(self):
        # pa.int32() and "int32" should produce equivalent SQL
@@ -597,14 +597,14 @@ class TestExprIsin:
    def test_isin_strs(self):
        assert (
            col("status").isin(["active", "pending"]).to_sql()
-            == "`status` IN ('active', 'pending')"
+            == "status IN ('active', 'pending')"
        )

    def test_isin_coerces_and_mixes(self):
        assert col("id").isin([lit(1), 2]).to_sql() == "id IN (1, 2)"

    def test_isin_empty(self):
-        assert col("id").isin([]).to_sql() == "false"
+        assert col("id").isin([]).to_sql() == "id IN ()"

    def test_isin_filter(self, simple_table):
        result = simple_table.search().where(col("id").isin([1, 3, 5])).to_arrow()
@@ -121,7 +121,7 @@ def test_function_version_identity_is_immutable_and_exact():
    assert FunctionVersion(**changed) != version


-def test_function_version_binds_named_columns_as_one_immutable_application():
+def test_function_version_binds_named_columns_as_one_immutable_group():
    version = FunctionVersion.from_json(
        json.dumps(job_result("remote_function_job.json"))
    )
@@ -131,10 +131,13 @@ def test_function_version_binds_named_columns_as_one_immutable_application():
    assert application.function.name == version.name
    assert application.function.version == version.version
    assert application.output is version.signature.output
+    assert application.group_id.startswith("fg_")
    assert [
        (value.parameter, value.kind, value.value["path"])
        for value in application.inputs
    ] == [("text", "column", "documents.body")]
+    with pytest.raises((TypeError, ValueError)):
+        application.group_id = "fg_changed"


 def test_function_version_binding_validates_names_and_direct_columns():
@@ -153,7 +156,7 @@ def test_function_version_binding_validates_names_and_direct_columns():
 def test_function_version_keeps_named_struct_outputs_in_one_application():
    value = job_result("remote_function_job.json")
    value["name"] = "text_features"
-    value["version"] = "fv_multi_output"
+    value["version"] = "fv_grouped"
    value["signature"] = {
        "inputs": [
            {"name": "title", "arrow_type": "utf8", "nullable": True},
@@ -218,6 +221,7 @@ def test_function_application_uses_rename_columns_only():
    assert application.columns["normalized_text"] == "search_text"
    assert renamed.columns["normalized_text"] == "body_normalized"
    assert renamed.function == application.function
+    assert renamed.group_id == application.group_id
    assert not hasattr(application, "rename_outputs")
    with pytest.raises(TypeError, match="immutable"):
        renamed.columns["normalized_text"] = "changed"
@@ -238,6 +242,7 @@ def test_function_application_uses_rename_columns_only():

 def test_binding_and_refresh_result_keep_stable_remote_fields():
    binding = FunctionBinding.from_json(fixture("remote_function_binding.json"))
+    assert binding.revision == 3
    assert binding.function.version == "fv_01K3TEXT"
    assert [output.output_ordinal for output in binding.outputs] == [0, 1]
    assert binding.input_schema is not None
@@ -317,7 +322,7 @@ def known_application() -> FunctionApplication:


@pytest.mark.asyncio
-async def test_add_columns_routes_struct_as_one_and_multi_output_binding_atomically():
+async def test_add_columns_routes_struct_as_one_and_grouped_expansion_atomically():
    inner = _FunctionDeclarationInner()
    table = AsyncTable(inner)
    application = known_application()
@@ -338,12 +343,12 @@ async def test_add_columns_routes_struct_as_one_and_multi_output_binding_atomica


@pytest.mark.asyncio
-async def test_add_columns_rejects_multiple_bindings_and_unknown_newer_application():
+async def test_add_columns_rejects_mixed_groups_and_unknown_newer_application():
    inner = _FunctionDeclarationInner()
    table = AsyncTable(inner)
    application = known_application()

-    with pytest.raises(ValueError, match="exactly one Function binding"):
+    with pytest.raises(ValueError, match="exactly one Function sibling group"):
        await table.add_columns({"a": application, "b": application})

    future = json.loads(fixture("remote_function_application.json"))
@@ -371,6 +376,7 @@ def test_rename_requires_named_struct_and_keeps_partial_mapping_immutable():
                    "arrow_type": "list<float32>",
                    "nullable": False,
                },
+                "group_id": "fg_scalar",
            }
        )
    )
@@ -3,12 +3,7 @@

 from __future__ import annotations

-import base64
 import contextlib
-import functools
-import importlib.util
-import types
-from datetime import date
 import http.server
 import json
 from pathlib import Path
@@ -19,16 +14,7 @@ import pyarrow as pa
 import pytest

 import lancedb
-from lancedb.functions import (
-    _MAX_FUNCTION_SECRET_VALUE_BYTES,
-    _MAX_FUNCTION_SECRET_VALUES_BYTES,
-    FunctionRegistrationRequest,
-    UdfDefinition,
-    udf,
-)
-
-THRESHOLD = 20
-_CACHE = None
+from lancedb.functions import UdfDefinition, udf


 FIXTURES = (
@@ -85,416 +71,9 @@ def test_scalar_udf_matches_shared_registration_golden_and_remains_callable():
    _assert_no_secret_values(request)


-def _run_packaged(definition, *args):
-    """Execute the shipped artifact in a fresh namespace, as a worker would."""
-    source = base64.b64decode(definition.registration_request.artifact.content.data)
-    namespace: dict = {}
-    exec(compile(source, "<udf>", "exec"), namespace)
-    return namespace[definition.registration_request.artifact.entrypoint](*args)
-
-
-def test_udf_conda_environment():
-    @udf(conda=["scipy", "numpy"], conda_channels=["conda-forge", "defaults"])
-    def halve(value: float) -> float:
-        return value / 2
-
-    request = json.loads(halve.registration_request.to_canonical_json())
-    assert request["runtime"]["environment"] == {
-        "kind": "conda",
-        "packages": ["numpy", "scipy"],
-        "channels": ["conda-forge", "defaults"],
-    }
-    pip_request = json.loads(normalize_score.registration_request.to_canonical_json())
-    assert "channels" not in pip_request["runtime"]["environment"]
-
-    with pytest.raises(ValueError, match="not both"):
-        udf(name="both", pip=["numpy"], conda=["numpy"])(lambda value: value)
-    with pytest.raises(ValueError, match="requires conda"):
-        udf(name="channels", conda_channels=["conda-forge"])(lambda value: value)
-
-
-def test_udf_packages_attribute_access_and_body_imports():
-    @udf
-    def word_norm(body: str) -> float:
-        import numpy as np
-
-        try:
-            words = body.split()
-        except AttributeError as error:
-            raise ValueError(str(error)) from error
-        return float(np.linalg.norm([len(w) for w in words]))
-
-    assert _run_packaged(word_norm, "aa bb") == pytest.approx(8**0.5)
-
-
-def test_udf_packages_module_globals_and_global_caches():
-    @udf
-    def label(value: int) -> str:
-        return "big" if value >= THRESHOLD else "small"
-
-    assert _run_packaged(label, 21) == "big"
-
-    @udf
-    def cached(value: int) -> int:
-        global _CACHE
-        if _CACHE is None:
-            _CACHE = 40
-        return _CACHE + value
-
-    assert _run_packaged(cached, 2) == 42
-
-
-def test_udf_annotations_are_not_runtime_names():
-    @udf
-    def identity(value: date) -> date:
-        return value
-
-    assert _run_packaged(identity, date(2026, 8, 25)) == date(2026, 8, 25)
-
-
-def test_udf_nested_scopes_resolve_lexically():
-    @udf
-    def score(value: int) -> int:
-        offset = 2
-
-        def add_offset() -> int:
-            return value + offset
-
-        return add_offset() + sum(v for v in [0])
-
-    assert _run_packaged(score, 3) == 5
-
-
-def test_udf_resolves_module_globals_before_builtins(tmp_path):
-    module_path = tmp_path / "shadowing_udfs.py"
-    module_path.write_text(
-        "max = 7\n"
-        "len = lambda _: 99\n"
-        "\n"
-        "def uses_literal_shadow(value: int) -> int:\n"
-        "    def nested() -> int:\n"
-        "        return max\n"
-        "    return nested() + value\n"
-        "\n"
-        "def uses_callable_shadow(value: int) -> int:\n"
-        "    def nested() -> int:\n"
-        "        return len([1])\n"
-        "    return nested() + value\n"
-    )
-    spec = importlib.util.spec_from_file_location("shadowing_udfs", module_path)
-    module = importlib.util.module_from_spec(spec)
-    spec.loader.exec_module(module)
-
-    # The module's `max = 7` is what the interpreter would use, so it ships.
-    assert _run_packaged(udf(module.uses_literal_shadow), 1) == 8
-    # A callable global cannot ship; it must not be silently swapped for the builtin.
-    with pytest.raises(TypeError, match="unsupported global value of type function"):
-        udf(module.uses_callable_shadow)
-
-
-def test_canonical_arrow_type_is_exactly_the_grammar():
-    from lancedb.functions import _GRAMMAR_PRIMITIVES, _canonical_arrow_type
-
-    golden = json.loads(
-        (
-            Path(__file__).parents[3]
-            / "rust/lancedb/tests/fixtures/first_class_functions/v1/arrow_types.json"
-        ).read_text()
-    )
-    primitives = [
-        case["arrow_type"] for case in golden["valid"] if "<" not in case["arrow_type"]
-    ]
-    assert [name for _, name in _GRAMMAR_PRIMITIVES] == primitives
-    for outside in [
-        pa.timestamp("us"),
-        pa.decimal128(10, 2),
-        pa.large_string(),
-        pa.large_binary(),
-        pa.binary(4),
-        pa.duration("s"),
-        pa.struct([pa.field("a", pa.int32())]),
-        pa.list_(pa.float32(), 0),
-        pa.list_(pa.timestamp("us")),
-    ]:
-        with pytest.raises(TypeError, match="unsupported Arrow type"):
-            _canonical_arrow_type(outside)
-
-
-def test_udf_nested_annotations_are_postponed_in_the_artifact():
-    @udf
-    def score(value: int) -> int:
-        def identity(item: date) -> date:
-            return item
-
-        identity(date(2026, 8, 25))
-        return value
-
-    assert _run_packaged(score, 3) == 3
-
-
-def test_udf_ships_globals_the_body_deletes():
-    @udf
-    def clear(value: int) -> int:
-        global _CACHE
-        del _CACHE
-        return value
-
-    assert _run_packaged(clear, 3) == 3
-
-
-def test_udf_rejects_a_module_global_that_does_not_import_as_itself(tmp_path):
-    module_path = tmp_path / "fake_module_udfs.py"
-    module_path.write_text(
-        "import types\n"
-        "np = types.ModuleType('numpy')\n"
-        "np.sqrt = lambda x: 0\n"
-        "\n"
-        "def score(value: int) -> int:\n"
-        "    return int(np.sqrt(value))\n"
-    )
-    spec = importlib.util.spec_from_file_location("fake_module_udfs", module_path)
-    module = importlib.util.module_from_spec(spec)
-    spec.loader.exec_module(module)
-    with pytest.raises(TypeError, match="does not import as 'numpy'"):
-        udf(module.score)
-
-
-def test_udf_rejects_a_module_level_namespace_alias(tmp_path):
-    module_path = tmp_path / "aliasing_udfs.py"
-    module_path.write_text(
-        "import builtins as b\n"
-        "THRESHOLD = 5\n"
-        "\n"
-        "def score(value: int) -> int:\n"
-        "    return value + b.vars(b.__import__('aliasing_udfs'))['THRESHOLD']\n"
-    )
-    spec = importlib.util.spec_from_file_location("aliasing_udfs", module_path)
-    module = importlib.util.module_from_spec(spec)
-    spec.loader.exec_module(module)
-    with pytest.raises(ValueError, match="dynamic namespace access"):
-        udf(module.score)
-
-
-@pytest.mark.parametrize(
-    "access",
-    [
-        "globals()['THRESHOLD']",
-        "eval('THRESHOLD')",
-        "(lambda g: g()['THRESHOLD'])(globals)",
-        "__import__('sys').modules[__name__].THRESHOLD",
-        "sys.modules[__name__].THRESHOLD",
-    ],
-)
-def test_udf_rejects_dynamic_namespace_access(access):
-    namespace: dict = {}
-    exec(
-        f"def score(value: int) -> int:\n    return value + {access}\n",
-        {"THRESHOLD": 5},
-        namespace,
-    )
-    with pytest.raises(ValueError, match="dynamic namespace access"):
-        _package_from_text(
-            "def score(value: int) -> int:\n"
-            "    import sys\n"
-            f"    return value + {access}\n"
-        )
-
-
-def _package_from_text(source: str, module_globals: dict | None = None):
-    """Load `source` as a real module file so the packager can inspect it."""
-    import tempfile
-
-    directory = tempfile.mkdtemp()
-    path = Path(directory) / "generated_udf_module.py"
-    path.write_text(source)
-    spec = importlib.util.spec_from_file_location(f"generated_udf_{id(source)}", path)
-    module = importlib.util.module_from_spec(spec)
-    if module_globals:
-        module.__dict__.update(module_globals)
-    spec.loader.exec_module(module)
-    functions = [
-        value
-        for value in vars(module).values()
-        if callable(value) and getattr(value, "__module__", None) == module.__name__
-    ]
-    return udf(functions[0])
-
-
-def test_udf_rejects_a_non_standard_builtins_environment():
-    def score(value: int) -> int:
-        return len([1]) + value
-
-    score.__globals__  # noqa: B018 -- real function, real globals
-    import builtins
-
-    patched = types.FunctionType(
-        score.__code__,
-        {"__builtins__": {**vars(builtins), "len": lambda _: 99}},
-        "score",
-    )
-    patched.__annotations__ = score.__annotations__
-    assert patched(3) == 102
-    with pytest.raises(ValueError, match="non-standard builtins environment"):
-        udf(patched)
-
-    class ReportingDict(dict):  # reports standard entries, resolves differently
-        def __missing__(self, key):
-            return vars(builtins)[key]
-
-    disguised = types.FunctionType(
-        score.__code__, {"__builtins__": ReportingDict(len=lambda _: 99)}, "score"
-    )
-    disguised.__annotations__ = score.__annotations__
-    assert disguised(3) == 102
-    with pytest.raises(ValueError, match="non-standard builtins environment"):
-        udf(disguised)
-
-    hooked = types.FunctionType(
-        score.__code__,
-        {"__builtins__": {**vars(builtins), "__import__": lambda *a, **k: None}},
-        "score",
-    )
-    hooked.__annotations__ = score.__annotations__
-    with pytest.raises(ValueError, match="non-standard builtins environment"):
-        udf(hooked)
-
-
-def test_udf_recursion_versus_a_rebound_module_name(tmp_path):
-    module_path = tmp_path / "rebound_udfs.py"
-    module_path.write_text(
-        "def fact(value: int) -> int:\n"
-        "    return 1 if value <= 1 else value * fact(value - 1)\n"
-        "\n"
-        "def score(value: int) -> int:\n"
-        "    return score + value\n"
-    )
-    spec = importlib.util.spec_from_file_location("rebound_udfs", module_path)
-    module = importlib.util.module_from_spec(spec)
-    spec.loader.exec_module(module)
-    assert _run_packaged(udf(module.fact), 5) == 120
-    raw = module.score
-    module.score = 10
-    with pytest.raises(ValueError, match="binds that name to another value"):
-        udf(raw)
-    # A wrapper that merely exposes __wrapped__ is not the function.
-    module.score = functools.wraps(raw)(lambda value: 41)
-    with pytest.raises(ValueError, match="binds that name to another value"):
-        udf(raw)
-    # The decorator's own result is; a subclass of it is not.
-    module.fact = udf(module.fact)
-    assert _run_packaged(module.fact, 4) == 24
-
-    class Twisted(UdfDefinition):
-        def __call__(self, *args, **kwargs):
-            return 41
-
-    raw_fact = module.fact._function
-    module.fact = Twisted(
-        raw_fact,
-        name=None,
-        input_schema=None,
-        output_schema=None,
-        pip=(),
-        env={},
-        secrets=(),
-        python_version=None,
-    )
-    with pytest.raises(ValueError, match="binds that name to another value"):
-        udf(raw_fact)
-
-
-def test_canonical_arrow_type_rejects_unrepresentable_list_children():
-    from lancedb.functions import _canonical_arrow_type
-
-    for outside in [
-        pa.list_(pa.float32()),  # pyarrow default: nullable child
-        pa.list_(pa.field("custom", pa.float32(), nullable=False)),
-        pa.list_(pa.field("item", pa.float32(), nullable=False, metadata={"k": "v"})),
-        pa.list_(pa.field("item", pa.float32(), nullable=False), 0),
-    ]:
-        with pytest.raises(TypeError, match="unsupported Arrow type"):
-            _canonical_arrow_type(outside)
-    assert (
-        _canonical_arrow_type(
-            pa.list_(pa.field("item", pa.float32(), nullable=False), 3)
-        )
-        == "fixed_size_list<float32, 3>"
-    )
-
-
-def _calls_missing(value: int) -> int:
-    return missing(value)  # noqa: F821
-
-
-def _shadows_missing_in_a_comprehension(value: int) -> int:
-    return missing(value) + sum(missing for missing in ())  # noqa: F821
-
-
-def _shadows_missing_in_a_lambda(value: int) -> int:
-    return (lambda missing: missing)(value) + missing  # noqa: F821
-
-
-@pytest.mark.parametrize(
-    "function",
-    [_calls_missing, _shadows_missing_in_a_comprehension, _shadows_missing_in_a_lambda],
-)
-def test_udf_rejects_a_truly_unresolved_global(function):
-    with pytest.raises(ValueError, match=r"unresolved global names: \['missing'\]"):
-        udf(function)
-
-
-def _arrow_type_from_golden(spec: dict) -> pa.DataType:
-    kind = spec["type"]
-    if kind in ("list", "large_list", "fixed_size_list"):
-        item = _arrow_type_from_golden(spec["fields"][0]["type"])
-        field = pa.field("item", item, nullable=False)
-        if kind == "list":
-            return pa.list_(field)
-        if kind == "large_list":
-            return pa.large_list(field)
-        return pa.list_(field, spec["length"])
-    return {
-        "null": pa.null(),
-        "bool": pa.bool_(),
-        "utf8": pa.string(),
-        "binary": pa.binary(),
-        "float16": pa.float16(),
-        "float32": pa.float32(),
-        "float64": pa.float64(),
-        "date32": pa.date32(),
-        "date64": pa.date64(),
-    }.get(kind) or getattr(pa, kind)()
-
-
-def test_arrow_type_grammar_matches_the_shared_golden():
-    golden = json.loads(
-        (
-            Path(__file__).parents[3]
-            / "rust/lancedb/tests/fixtures/first_class_functions/v1/arrow_types.json"
-        ).read_text()
-    )
-    from lancedb.functions import _canonical_arrow_type
-
-    emitted = {
-        case["arrow_type"]: _canonical_arrow_type(_arrow_type_from_golden(case["json"]))
-        for case in golden["valid"]
-    }
-    assert emitted == {
-        case["arrow_type"]: case["arrow_type"] for case in golden["valid"]
-    }
-    assert not set(emitted) & set(golden["invalid"])
-    for case in golden["server_only"]:
-        with pytest.raises(TypeError, match="unsupported Arrow type"):
-            _canonical_arrow_type(_arrow_type_from_golden(case["json"]))
-
-
 def test_explicit_arrow_schema_is_deterministic():
    input_schema = pa.schema([pa.field("value", pa.float32(), nullable=True)])
-    output_schema = pa.field(
-        "embedding",
-        pa.list_(pa.field("item", pa.float32(), nullable=False), 3),
-        nullable=False,
-    )
+    output_schema = pa.field("embedding", pa.list_(pa.float32(), 3), nullable=False)

    @udf(input_schema=input_schema, output_schema=output_schema)
    def explicit(value):
@@ -503,7 +82,7 @@ def test_explicit_arrow_schema_is_deterministic():
    signature = explicit.registration_request.signature
    assert signature.inputs[0].arrow_type == "float32"
    assert signature.inputs[0].nullable is True
-    assert signature.output.arrow_type == "fixed_size_list<float32, 3>"
+    assert signature.output.arrow_type == "fixed_size_list<float32>[3]"
    assert signature.output.nullable is False


@@ -551,16 +130,7 @@ def test_annotation_and_explicit_schema_validation_fail_closed():
            return value


-def test_secret_names_are_canonical_and_disjoint_from_environment():
-    @udf(secrets=["Z_TOKEN", "A_TOKEN", "Z_TOKEN"])
-    def canonical_secrets(value: int) -> int:
-        return value
-
-    assert canonical_secrets.registration_request.required_secrets == (
-        "A_TOKEN",
-        "Z_TOKEN",
-    )
-
+def test_environment_rejects_secret_value_overlap():
    with pytest.raises(ValueError, match="must be disjoint"):

        @udf(env={"TOKEN": "plaintext"}, secrets=["TOKEN"])
@@ -568,37 +138,13 @@ def test_secret_names_are_canonical_and_disjoint_from_environment():
            return value


-def test_declared_secret_api_still_requires_explicit_create_values():
-    @udf(secrets=["API_TOKEN"])
-    def declared_secret(value: int) -> int:
-        return value
-
-    with pytest.raises(ValueError, match="missing"):
-        declared_secret._submission_json(None)
-    submission = json.loads(
-        declared_secret._submission_json({"API_TOKEN": "explicit-secret"})
-    )
-    assert submission["required_secrets"] == ["API_TOKEN"]
-    assert submission["secret_values"] == {"API_TOKEN": "explicit-secret"}
-
-
-def test_no_secrets_preserve_canonical_registration_shape():
-    @udf
-    def no_secrets(value: int) -> int:
-        return value
-
-    canonical = json.loads(no_secrets.registration_request.to_canonical_json())
-    assert "required_secrets" not in canonical
-    assert json.loads(no_secrets._submission_json(None)) == canonical
-
-
 def test_local_function_catalog_operations_are_not_supported(tmp_path):
    db = lancedb.connect(tmp_path)
    message = "Function catalog operations are not supported by this database"
    with pytest.raises(NotImplementedError, match=message):
-        db.create_function(normalize_score, secrets={"API_TOKEN": "value"})
+        db.create_function(normalize_score)
    with pytest.raises(NotImplementedError, match=message):
-        db.create_function_async(normalize_score, secrets={"API_TOKEN": "value"})
+        db.create_function_async(normalize_score)
    with pytest.raises(NotImplementedError, match=message):
        db.get_function("normalize_score", version="fv_exact")

@@ -641,7 +187,7 @@ def _mock_remote_function_catalog():
                    "job_state": "DONE",
                    "result": state["version"],
                }
-            elif self.path == "/v1/functions/describe":
+            elif self.path == "/v1/functions/get":
                assert body == {
                    "name": "normalize_score",
                    "version": "fv_exact",
@@ -675,9 +221,7 @@ def test_remote_registration_job_and_exact_version_reopen_round_trip():
            host_override=host,
            client_config={"retry_config": {"retries": 0}},
        )
-        registration = db.create_function_async(
-            normalize_score, secrets={"API_TOKEN": "secret-value"}
-        )
+        registration = db.create_function_async(normalize_score)
        assert registration.id == "job-register"
        created = registration.wait()
        reopened = db.get_function("normalize_score", version=created.version)
@@ -686,18 +230,10 @@ def test_remote_registration_job_and_exact_version_reopen_round_trip():
    assert reopened.name == "normalize_score"
    assert reopened.version == "fv_exact"
    create_request = state["requests"][0][1]
-    expected = json.loads(normalize_score.registration_request.to_canonical_json())
-    expected["secret_values"] = {"API_TOKEN": "secret-value"}
-    assert create_request == expected
-    durable_request = FunctionRegistrationRequest.from_json(json.dumps(create_request))
-    assert not hasattr(durable_request, "secret_values")
-    assert "secret_values" not in json.loads(durable_request.to_canonical_json())
-    assert "secret_values" not in json.loads(
+    assert create_request == json.loads(
        normalize_score.registration_request.to_canonical_json()
    )
-    assert "secret-value" not in repr(normalize_score)
-    assert "secret-value" not in repr(normalize_score.registration_request)
-    assert not hasattr(created, "secret_values")
+    _assert_no_secret_values(create_request)


 def test_blocking_remote_registration_returns_function_version():
@@ -708,9 +244,7 @@ def test_blocking_remote_registration_returns_function_version():
            host_override=host,
            client_config={"retry_config": {"retries": 0}},
        )
-        created = db.create_function(
-            normalize_score, secrets={"API_TOKEN": "blocking-secret"}
-        )
+        created = db.create_function(normalize_score)

    assert created.name == "normalize_score"
    assert created.version == "fv_exact"
@@ -718,121 +252,3 @@ def test_blocking_remote_registration_returns_function_version():
        "/v1/functions/create",
        "/v1/jobs/describe",
    ]
-    assert state["requests"][0][1]["secret_values"] == {"API_TOKEN": "blocking-secret"}
-
-
-@pytest.mark.parametrize(
-    ("secret_values", "error_type", "message"),
-    [
-        (None, ValueError, "missing"),
-        ({}, ValueError, "missing"),
-        ({"OTHER": "value"}, ValueError, "missing.*unexpected"),
-        ({"API_TOKEN": ""}, ValueError, "non-empty"),
-        ({"API_TOKEN": "bad\0value"}, ValueError, "NUL"),
-        ({"API_TOKEN": 123}, TypeError, "must be a string"),
-        ([("API_TOKEN", "value")], TypeError, "must be a mapping"),
-    ],
-)
-def test_secret_values_are_validated_before_remote_request(
-    secret_values, error_type, message
-):
-    with _mock_remote_function_catalog() as (host, state):
-        db = lancedb.connect(
-            "db://dev",
-            api_key="fake",
-            host_override=host,
-            client_config={"retry_config": {"retries": 0}},
-        )
-        with pytest.raises(error_type, match=message):
-            db.create_function_async(normalize_score, secrets=secret_values)
-        assert state["requests"] == []
-
-
-@pytest.mark.parametrize(
-    "value",
-    [
-        "x" * _MAX_FUNCTION_SECRET_VALUE_BYTES,
-        "é" * (_MAX_FUNCTION_SECRET_VALUE_BYTES // len("é".encode("utf-8"))),
-    ],
-    ids=["ascii", "multibyte"],
-)
-def test_secret_value_accepts_exact_utf8_byte_limit(value):
-    submission = json.loads(normalize_score._submission_json({"API_TOKEN": value}))
-    assert submission["secret_values"]["API_TOKEN"] == value
-    assert len(value.encode("utf-8")) == _MAX_FUNCTION_SECRET_VALUE_BYTES
-
-
-@pytest.mark.parametrize(
-    "value",
-    [
-        "x" * (_MAX_FUNCTION_SECRET_VALUE_BYTES + 1),
-        "é" * (_MAX_FUNCTION_SECRET_VALUE_BYTES // len("é".encode("utf-8")) + 1),
-    ],
-    ids=["ascii", "multibyte"],
-)
-def test_secret_value_rejects_over_utf8_byte_limit_before_json_construction(
-    monkeypatch, value
-):
-    def fail_if_json_construction_starts(self):
-        pytest.fail("oversized secret reached JSON construction")
-
-    monkeypatch.setattr(
-        FunctionRegistrationRequest, "_known_dict", fail_if_json_construction_starts
-    )
-    with pytest.raises(ValueError, match=r"exceeds the 65536-byte limit"):
-        normalize_score._submission_json({"API_TOKEN": value})
-
-
-def test_secret_values_accept_exact_aggregate_utf8_byte_limit(monkeypatch):
-    names = tuple(f"SECRET_{index}" for index in range(8))
-    value = "é" * (_MAX_FUNCTION_SECRET_VALUE_BYTES // len("é".encode("utf-8")))
-    values = {name: value for name in names}
-    monkeypatch.setattr(
-        normalize_score,
-        "_request",
-        normalize_score._request._copy(update={"required_secrets": names}),
-    )
-
-    submission = json.loads(normalize_score._submission_json(values))
-
-    assert submission["secret_values"] == values
-    assert sum(len(item.encode("utf-8")) for item in values.values()) == (
-        _MAX_FUNCTION_SECRET_VALUES_BYTES
-    )
-
-
-def test_secret_values_reject_aggregate_over_limit_before_construction(monkeypatch):
-    names = tuple(f"SECRET_{index}" for index in range(9))
-    values = {name: "x" * _MAX_FUNCTION_SECRET_VALUE_BYTES for name in names}
-    monkeypatch.setattr(
-        normalize_score,
-        "_request",
-        normalize_score._request._copy(update={"required_secrets": names}),
-    )
-
-    def fail_if_json_construction_starts(self):
-        pytest.fail("oversized aggregate reached JSON construction")
-
-    monkeypatch.setattr(
-        FunctionRegistrationRequest, "_known_dict", fail_if_json_construction_starts
-    )
-    with pytest.raises(ValueError, match=r"exceed.*524288-byte request limit"):
-        normalize_score._submission_json(values)
-
-
-@pytest.mark.asyncio
-async def test_async_remote_registration_submits_secret_values_only_once():
-    with _mock_remote_function_catalog() as (host, state):
-        db = await lancedb.connect_async(
-            "db://dev",
-            api_key="fake",
-            host_override=host,
-            client_config={"retry_config": {"retries": 0}},
-        )
-        registration = await db.create_function_async(
-            normalize_score, secrets={"API_TOKEN": "async-secret"}
-        )
-        created = await registration.wait()
-
-    assert state["requests"][0][1]["secret_values"] == {"API_TOKEN": "async-secret"}
-    assert not hasattr(created, "secret_values")
@@ -25,7 +25,6 @@ from lancedb.db import DBConnection
 from lancedb.index import FTS
 from lancedb.query import (
    BoostQuery,
-    DocumentGranularity,
    MatchQuery,
    MultiMatchQuery,
    PhraseQuery,
@@ -246,63 +245,6 @@ def test_create_inverted_index_rejects_invalid_block_size(table):
        table.create_index("text", config=FTS(block_size=129))


-def test_list_element_document_granularity(tmp_path):
-    docs_type = pa.list_(pa.struct([pa.field("content", pa.string())]))
-    docs = pa.array(
-        [
-            [
-                {"content": "alpha beta"},
-                None,
-                {"content": ""},
-                {"content": "the and"},
-                {"content": "alpha beta"},
-            ]
-        ],
-        type=docs_type,
-    )
-    table = ldb.connect(tmp_path).create_table(
-        "list_element_docs", pa.table({"id": [0], "docs": docs})
-    )
-    row_table = ldb.connect(tmp_path).create_table(
-        "row_docs", pa.table({"id": [0], "docs": docs})
-    )
-    row_table.create_index("docs.content", config=FTS())
-    row_result = row_table.search(MatchQuery("alpha", "docs.content")).to_arrow()
-    assert row_result.num_rows == 1
-    assert "_doc_index" not in row_result.column_names
-
-    granularity = DocumentGranularity.LIST_ELEMENT
-    table.create_index(
-        "docs.content",
-        config=FTS(with_position=True, document_granularity=granularity),
-    )
-    assert table.list_indices()[0].columns == ["docs.content"]
-
-    def coordinates(query):
-        result = table.search(query).limit(10).to_arrow()
-        doc_index_type = result.schema.field("_doc_index").type
-        assert pa.types.is_list(doc_index_type)
-        assert doc_index_type.value_type == pa.uint32()
-        return sorted(result["_doc_index"].to_pylist())
-
-    assert coordinates(
-        MatchQuery("alpha", "docs.content", document_granularity=granularity)
-    ) == [[0], [4]]
-    assert coordinates(
-        PhraseQuery("alpha beta", "docs.content", document_granularity=granularity)
-    ) == [[0], [4]]
-    assert coordinates(MatchQuery("alpha", "docs.content")) == [[0], [4]]
-    assert FTS().document_granularity is DocumentGranularity.ROW
-
-
-def test_create_inverted_index_respects_build_memory_limit(table):
-    with pytest.raises(ValueError, match="exceeds worker memory limit"):
-        table.create_index(
-            "text",
-            config=FTS(memory_limit=0, num_workers=1),
-        )
-
-
 def test_custom_stop_words_list(table):
    table.create_index(
        "text",
@@ -1139,20 +1081,6 @@ def test_fts_query_to_json():
    )
    assert json_str == expected

-    # Test MatchQuery with list-element document granularity
-    match_query = MatchQuery(
-        "hello world",
-        "text",
-        document_granularity=DocumentGranularity.LIST_ELEMENT,
-    )
-    json_str = match_query.to_json()
-    expected = (
-        '{"match":{"column":"text","terms":"hello world","boost":1.0,'
-        '"fuzziness":0,"max_expansions":50,"operator":"Or","prefix_length":0,'
-        '"document_granularity":"list_element"}}'
-    )
-    assert json_str == expected
-
    # Test MatchQuery with options
    match_query = MatchQuery("puppy", "text", fuzziness=2, boost=1.5, prefix_length=3)
    json_str = match_query.to_json()
@@ -1162,19 +1090,6 @@ def test_fts_query_to_json():
    )
    assert json_str == expected

-    # Test PhraseQuery with list-element document granularity
-    phrase_query = PhraseQuery(
-        "quick brown fox",
-        "title",
-        document_granularity=DocumentGranularity.LIST_ELEMENT,
-    )
-    json_str = phrase_query.to_json()
-    expected = (
-        '{"phrase":{"column":"title","terms":"quick brown fox","slop":0,'
-        '"document_granularity":"list_element"}}'
-    )
-    assert json_str == expected
-
    # Test PhraseQuery
    phrase_query = PhraseQuery("quick brown fox", "title")
    json_str = phrase_query.to_json()
@@ -88,7 +88,7 @@ async def binary_table(db_async):
 async def test_create_index_async_returns_done_job(some_table: AsyncTable):
    job = await some_table.create_index_async("id", config=BTree())
    assert job.id is None
-    assert await job.wait() is None
+    await job.wait()
    assert len(await some_table.list_indices()) == 1
    await job.cancel()

@@ -1,268 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright The LanceDB Authors
-
-import lancedb
-import pytest
-from lancedb.materialized_view import MaterializedViewDefinition
-
-
-STABLE_ROW_IDS = {"new_table_enable_stable_row_ids": "true"}
-
-
-def make_db(tmp_path):
-    db = lancedb.connect(tmp_path, storage_options=STABLE_ROW_IDS)
-    db.create_table(
-        "people",
-        [
-            {"name": "ada", "age": 36},
-            {"name": "kid", "age": 7},
-            {"name": "grace", "age": 85},
-        ],
-    )
-    return db
-
-
-def test_create_refresh_and_query(tmp_path):
-    db = make_db(tmp_path)
-    view = db.create_materialized_view(
-        "adults",
-        "people",
-        select=["name", ("shout", "upper(name)")],
-        where="age >= 18",
-    )
-    assert view.name == "adults"
-    assert view.table.count_rows() == 0
-
-    result = view.refresh()
-    assert result.mode == "rebuild"
-    assert result.rows_written == 2
-
-    rows = view.table.search().to_list()
-    assert sorted(row["shout"] for row in rows) == ["ADA", "GRACE"]
-
-
-def test_definition_round_trips(tmp_path):
-    db = make_db(tmp_path)
-    db.create_materialized_view("adults", "people", where="age >= 18")
-
-    view = db.open_materialized_view("adults")
-    assert view.definition == MaterializedViewDefinition(
-        source_table="people",
-        projections=[("name", "`name`"), ("age", "`age`")],
-        filter="age >= 18",
-        inputs=["age", "name"],
-    )
-
-
-def test_incremental_refresh_after_append(tmp_path):
-    db = make_db(tmp_path)
-    view = db.create_materialized_view("copy", "people")
-    view.refresh()
-
-    db.open_table("people").add([{"name": "alan", "age": 41}])
-    result = view.refresh()
-    assert result.mode == "incremental"
-    assert result.rows_written == 1
-    assert view.table.count_rows() == 4
-
-    assert view.refresh().mode == "no_op"
-
-
-def test_incremental_refresh_after_update(tmp_path):
-    db = make_db(tmp_path)
-    view = db.create_materialized_view("copy", "people")
-    view.refresh()
-
-    db.open_table("people").update(where="name = 'kid'", values={"age": 8})
-    result = view.refresh()
-    assert result.mode == "incremental"
-    assert result.rows_written == 1
-    rows = view.table.search().to_list()
-    assert sorted(row["age"] for row in rows) == [8, 36, 85]
-
-
-def test_legacy_storage_source_update_rebuilds(tmp_path):
-    db = lancedb.connect(
-        tmp_path,
-        storage_options={**STABLE_ROW_IDS, "new_table_data_storage_version": "legacy"},
-    )
-    db.create_table("people", [{"name": "ada", "age": 36}, {"name": "kid", "age": 7}])
-    view = db.create_materialized_view("copy", "people")
-    view.refresh()
-
-    db.open_table("people").update(where="name = 'kid'", values={"age": 8})
-    result = view.refresh()
-    assert result.mode == "rebuild"
-    rows = view.table.search().to_list()
-    assert sorted(row["age"] for row in rows) == [8, 36]
-
-
-def test_list_and_not_a_view(tmp_path):
-    db = make_db(tmp_path)
-    db.create_materialized_view("adults", "people", where="age >= 18")
-
-    assert db.list_materialized_views() == ["adults"]
-    with pytest.raises(ValueError, match="not a materialized view"):
-        db.open_materialized_view("people")
-
-
-def test_invalid_expression_fails_at_create(tmp_path):
-    db = make_db(tmp_path)
-    with pytest.raises(Exception, match="missing"):
-        db.create_materialized_view("bad", "people", select=[("x", "missing + 1")])
-    assert "bad" not in db.list_tables().tables
-
-
-@pytest.mark.asyncio
-async def test_async_create_refresh_and_open(tmp_path):
-    db = await lancedb.connect_async(tmp_path, storage_options=STABLE_ROW_IDS)
-    await db.create_table("people", [{"name": "ada", "age": 36}])
-
-    view = await db.create_materialized_view(
-        "shouts", "people", select=[("shout", "upper(name)")]
-    )
-    result = await view.refresh()
-    assert result.mode == "rebuild"
-    assert result.rows_written == 1
-
-    reopened = await db.open_materialized_view("shouts")
-    definition = await reopened.definition()
-    assert definition.projections == [("shout", "upper(name)")]
-    assert await db.list_materialized_views() == ["shouts"]
-
-
-@pytest.mark.asyncio
-async def test_async_incremental(tmp_path):
-    db = await lancedb.connect_async(tmp_path, storage_options=STABLE_ROW_IDS)
-    await db.create_table("people", [{"name": "ada", "age": 36}])
-    view = await db.create_materialized_view("copy", "people")
-    await view.refresh()
-
-    table = await db.open_table("people")
-    await table.add([{"name": "alan", "age": 41}])
-    result = await view.refresh()
-    assert result.mode == "incremental"
-    assert result.rows_written == 1
-
-
-def test_source_requires_stable_row_ids(tmp_path):
-    db = lancedb.connect(tmp_path)
-    db.create_table("plain", [{"x": 1}])
-    with pytest.raises(Exception, match="stable row ids"):
-        db.create_materialized_view("v", "plain")
-
-
-def test_bare_select_names_are_quoted(tmp_path):
-    db = lancedb.connect(tmp_path, storage_options=STABLE_ROW_IDS)
-    db.create_table("odd_names", [{"order item": "widget", "select": 2}])
-
-    view = db.create_materialized_view(
-        "quoted", "odd_names", select=["order item", "select"]
-    )
-    result = view.refresh()
-    assert result.rows_written == 1
-    rows = view.table.search().to_list()
-    assert rows[0]["order item"] == "widget"
-    assert rows[0]["select"] == 2
-
-
-@pytest.mark.asyncio
-async def test_async_remote_is_refused_without_network():
-    db = await lancedb.connect_async(
-        "db://nowhere", api_key="sk_test", region="us-east-1"
-    )
-    with pytest.raises(NotImplementedError, match="local"):
-        await db.create_materialized_view("v", "src")
-    with pytest.raises(NotImplementedError, match="local"):
-        await db.open_materialized_view("v")
-    with pytest.raises(NotImplementedError, match="local"):
-        await db.list_materialized_views()
-
-
-def test_scalar_select_is_one_column(tmp_path):
-    db = make_db(tmp_path)
-    view = db.create_materialized_view("just_name", "people", select="name")
-    view.refresh()
-    rows = view.table.search().to_list()
-    assert set(rows[0]) - {"__source_row_id"} == {"name"}
-    assert sorted(row["name"] for row in rows) == ["ada", "grace", "kid"]
-
-
-@pytest.mark.asyncio
-async def test_async_scalar_select_is_one_column(tmp_path):
-    db = await lancedb.connect_async(tmp_path, storage_options=STABLE_ROW_IDS)
-    await db.create_table("people", [{"name": "ada", "age": 36}])
-    view = await db.create_materialized_view("just_name", "people", select="name")
-    await view.refresh()
-    rows = await view.table.query().to_list()
-    assert set(rows[0]) - {"__source_row_id"} == {"name"}
-
-
-def test_limit_above_i64_max_is_refused(tmp_path):
-    db = make_db(tmp_path)
-    with pytest.raises(ValueError, match="exceeds the maximum"):
-        db.create_materialized_view("too_big", "people", limit=2**63)
-    # The boundary is fine, and zero still means an empty view.
-    db.create_materialized_view("at_max", "people", limit=2**63 - 1)
-    empty = db.create_materialized_view("none", "people", limit=0)
-    empty.refresh()
-    assert empty.table.count_rows() == 0
-
-
-def _namespace_db(tmp_path):
-    return lancedb.connect_namespace(
-        "dir",
-        {"root": str(tmp_path)},
-        storage_options=STABLE_ROW_IDS,
-    )
-
-
-def test_namespace_connection_materialized_views(tmp_path):
-    db = _namespace_db(tmp_path)
-    db.create_table(
-        "people",
-        [{"name": "ada", "age": 36}, {"name": "kid", "age": 7}],
-        storage_options=STABLE_ROW_IDS,
-    )
-
-    view = db.create_materialized_view("adults", "people", where="age >= 18")
-    view.refresh()
-    assert view.table.count_rows() == 1
-    assert db.list_materialized_views() == ["adults"]
-
-    reopened = db.open_materialized_view("adults")
-    assert reopened.definition.source_table == "people"
-    with pytest.raises(ValueError, match="not a materialized view"):
-        db.open_materialized_view("people")
-
-
-@pytest.mark.asyncio
-async def test_async_namespace_connection_materialized_views(tmp_path):
-    db = lancedb.connect_namespace_async(
-        "dir",
-        {"root": str(tmp_path)},
-        storage_options=STABLE_ROW_IDS,
-    )
-    await db.create_table(
-        "people",
-        [{"name": "ada", "age": 36}, {"name": "kid", "age": 7}],
-        storage_options=STABLE_ROW_IDS,
-    )
-
-    view = await db.create_materialized_view("adults", "people", where="age >= 18")
-    await view.refresh()
-    assert await view.table.count_rows() == 1
-    assert await db.list_materialized_views() == ["adults"]
-
-    reopened = await db.open_materialized_view("adults")
-    assert (await reopened.definition()).source_table == "people"
-
-    # The view's table came through the namespace, not straight from the
-    # inner connection: a bare inner table carries no namespace context, so
-    # its pushdown routing differs from a table the namespace opened.
-    through_namespace = await db.open_table("adults")
-    for handle in (view.table, reopened.table):
-        assert (
-            handle._route_pushdown_to_rust == through_namespace._route_pushdown_to_rust
-        )
-        assert handle._namespace_path == through_namespace._namespace_path
@@ -56,31 +56,6 @@ def test_execute_does_not_reenter_background_loop(tmp_path, monkeypatch):
    assert permutation_tbl._conn.read_consistency_interval is None


-def test_pickled_permutation_reads_pinned_version(tmp_path):
-    """An unpickled copy must still read the pinned version, which also covers the
-    version surviving the ``to_arrow()`` round trip in ``__getstate__``."""
-    import pickle
-
-    db = connect(tmp_path)
-    tbl = db.create_table("base", pa.table({"idx": range(20)}))
-    permutation_tbl = permutation_builder(tbl).execute()
-    perm = Permutation.from_tables(tbl, permutation_tbl)
-
-    payload = pickle.dumps(perm)
-
-    # Compact so the stored row addresses no longer describe these rows at latest.
-    tbl.delete("true")
-    tbl.optimize()
-    assert tbl.count_rows() == 0
-
-    # Unpickle after the mutation: __setstate__ reopens at latest, so this only
-    # passes if the recorded version is applied on reopen.
-    restored = pickle.loads(payload)
-    assert len(restored) == 20
-    rows = restored.__getitems__(list(range(20)))
-    assert sorted(row["idx"] for row in rows) == list(range(20))
-
-
 def test_split_random_counts(mem_db):
    """Test random splitting with absolute counts."""
    tbl = mem_db.create_table(
@@ -675,21 +675,6 @@ def test_distance_range(table: lancedb.table.Table):
    assert res["_distance"].to_pylist() == [min_dist, max_dist]


-@pytest.mark.parametrize("expression", ["1 - _distance", "1.0 - _distance"])
-def test_select_arithmetic_with_distance(table, expression):
-    result = (
-        table.search([10, 10])
-        .select({"similarity": expression, "_distance": "_distance"})
-        .distance_type("cosine")
-        .to_arrow()
-    )
-
-    assert result.schema.field("similarity").type == pa.float32()
-    assert result["similarity"].to_pylist() == pytest.approx(
-        [1 - distance for distance in result["_distance"].to_pylist()]
-    )
-
-
@pytest.mark.asyncio
 async def test_distance_range_async(table_async: AsyncTable):
    q = [0, 0]
@@ -912,23 +897,6 @@ 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")
@@ -242,8 +242,8 @@ def test_remote_table_branches_sync():
        table.branches.delete("exp")


-def test_remote_table_cherry_pick_defaults_to_execute():
-    cherry_pick_bodies = []
+def test_remote_table_branch_merge_defaults_to_execute():
+    merge_bodies = []
    diff = {
        "fromBranch": "exp",
        "parentVersion": 1,
@@ -265,7 +265,8 @@ def test_remote_table_cherry_pick_defaults_to_execute():
        "changedColumns": [],
        "addedIndexes": [],
        "removedIndexes": [],
-        "errors": [],
+        "mergeable": True,
+        "mergeBlockers": [],
    }

    def handler(request):
@@ -275,11 +276,11 @@ def test_remote_table_cherry_pick_defaults_to_execute():
        else:
            content_len = int(request.headers.get("Content-Length"))
            request_body = json.loads(request.rfile.read(content_len))
-            cherry_pick_bodies.append(request_body)
+            merge_bodies.append(request_body)
            dry_run = request_body["dry_run"]
            status = 200 if dry_run else 409
            body = {
-                "status": "ready" if dry_run else "failed",
+                "status": "ready" if dry_run else "rejected",
                "diff": diff,
                "preview": {"promotedColumns": []},
            }
@@ -291,10 +292,10 @@ def test_remote_table_cherry_pick_defaults_to_execute():

    with mock_lancedb_connection(handler) as db:
        branches = db.open_table("test").branches
-        assert branches.cherry_pick("exp")["status"] == "failed"
-        assert branches.cherry_pick("exp", dry_run=True)["status"] == "ready"
+        assert branches.merge("exp")["status"] == "rejected"
+        assert branches.merge("exp", dry_run=True)["status"] == "ready"

-    assert cherry_pick_bodies == [
+    assert merge_bodies == [
        {"from_branch": "exp", "dry_run": False},
        {"from_branch": "exp", "dry_run": True},
    ]
@@ -875,85 +876,11 @@ def test_remote_create_index_async_returns_job():
        table = db.create_table("test", [{"id": 1}])
        job = table.create_index_async("id", config=BTree())
        assert job.id == "job-1"
-        assert job.wait(timeout=timedelta(seconds=30)) is None
+        job.wait(timeout=timedelta(seconds=30))
        assert len(describe_calls) == 2
        job.cancel()


-def test_remote_refresh_async_returns_typed_terminal_result():
-    terminal_result = {
-        "rows_assigned": 12,
-        "rows_failed": 0,
-        "rows_remaining": 0,
-        "source_version": 7,
-        "published_version": 8,
-    }
-
-    def handler(request):
-        content_len = int(request.headers.get("Content-Length", 0))
-        body = request.rfile.read(content_len) if content_len > 0 else b""
-        if request.path == "/v1/table/test/backfill_column":
-            assert json.loads(body)["column"] == "derived"
-            request.send_response(202)
-            request.send_header("Content-Type", "application/json")
-            request.end_headers()
-            request.wfile.write(b'{"job_id": "refresh-1"}')
-        elif request.path == "/v1/jobs/describe":
-            assert json.loads(body)["job_id"] == "refresh-1"
-            request.send_response(200)
-            request.send_header("Content-Type", "application/json")
-            request.end_headers()
-            request.wfile.write(
-                json.dumps(
-                    {
-                        "job_id": "refresh-1",
-                        "job_type": "function_refresh",
-                        "job_state": "DONE",
-                        "result": terminal_result,
-                    }
-                ).encode()
-            )
-        elif request.path == "/v1/table/test/create/?mode=create":
-            request.send_response(200)
-            request.send_header("Content-Type", "application/json")
-            request.end_headers()
-            request.wfile.write(b"{}")
-        elif request.path == "/v1/table/test/describe/":
-            request.send_response(200)
-            request.send_header("Content-Type", "application/json")
-            request.end_headers()
-            request.wfile.write(
-                json.dumps(
-                    {
-                        "version": 1,
-                        "schema": {
-                            "fields": [
-                                {
-                                    "name": "id",
-                                    "type": {"type": "int64"},
-                                    "nullable": False,
-                                }
-                            ]
-                        },
-                    }
-                ).encode()
-            )
-        else:
-            request.send_response(404)
-            request.end_headers()
-
-    with mock_lancedb_connection(handler) as db:
-        table = db.create_table("test", [{"id": 1}])
-        job = table.refresh_column_async("derived")
-        assert job.id == "refresh-1"
-        result = job.wait(timeout=timedelta(seconds=30))
-
-    assert isinstance(result, lancedb.RefreshColumnResult)
-    assert result.model_dump() == terminal_result
-    assert result.rows_filled == 12
-    assert result.version == 8
-
-
 def test_remote_job_wait_raises_on_failure():
    from lancedb.exceptions import JobFailedError
    from lancedb.index import BTree
@@ -1618,49 +1545,6 @@ def test_query_sync_fts():
        )


-def test_query_sync_fts_document_granularity():
-    from lancedb.query import DocumentGranularity, MatchQuery
-
-    def handler(body):
-        assert body == {
-            "full_text_query": {
-                "query": {
-                    "match": {
-                        "column": "docs.content",
-                        "terms": "alpha",
-                        "boost": 1.0,
-                        "fuzziness": 0,
-                        "max_expansions": 50,
-                        "operator": "Or",
-                        "prefix_length": 0,
-                        "document_granularity": "list_element",
-                    }
-                }
-            },
-            "k": 10,
-            "prefilter": True,
-            "vector": [],
-            "version": None,
-        }
-        return pa.table(
-            {
-                "id": [1, 1],
-                "_doc_index": pa.array([[0], [4]], type=pa.list_(pa.uint32())),
-            }
-        )
-
-    with query_test_table(handler, server_version=Version("0.6.0")) as table:
-        result = table.search(
-            MatchQuery(
-                "alpha",
-                "docs.content",
-                document_granularity=DocumentGranularity.LIST_ELEMENT,
-            )
-        ).to_arrow()
-
-    assert result["_doc_index"].to_pylist() == [[0], [4]]
-
-
 def test_query_sync_hybrid():
    def handler(body):
        if "full_text_query" in body:
@@ -4,7 +4,6 @@

 import asyncio
 import copy
-from concurrent.futures import ThreadPoolExecutor
 from datetime import timedelta
 import threading

@@ -87,25 +86,6 @@ def test_s3_lifecycle(s3_bucket: str):
    asyncio.run(test())


-@pytest.mark.s3_test
-def test_concurrent_open_table(s3_bucket: str):
-    uri = f"s3://{s3_bucket}/test_concurrent_open_table"
-    db = lancedb.connect(uri, storage_options=copy.copy(CONFIG))
-    db.create_table("test", pa.table({"x": [1, 2, 3]}))
-
-    num_workers = 32
-    barrier = threading.Barrier(num_workers)
-
-    def open_and_count(_):
-        barrier.wait()
-        return db.open_table("test").count_rows()
-
-    with ThreadPoolExecutor(max_workers=num_workers) as pool:
-        row_counts = list(pool.map(open_and_count, range(num_workers)))
-
-    assert row_counts == [3] * num_workers
-
-
@pytest.fixture()
 def kms_key():
    kms = get_boto3_client("kms", endpoint_url=CONFIG["aws_endpoint"])
@@ -11,7 +11,6 @@ import warnings
 import weakref
 from concurrent.futures import ThreadPoolExecutor
 from datetime import date, datetime, timedelta
-from decimal import Decimal
 from time import sleep
 from typing import List
 from unittest.mock import patch
@@ -337,21 +336,6 @@ async def test_update_async(mem_db_async: AsyncConnection):
    assert await table.count_rows("id == 10") == 1


-@pytest.mark.asyncio
-async def test_update_expr_filter_literals_async(mem_db_async: AsyncConnection):
-    values = ["5", "4.66e-84", "it's"]
-    table = await mem_db_async.create_table(
-        "update_expr_literals",
-        data=[{"field": value, "result": "original"} for value in values],
-    )
-
-    for value in values:
-        update_res = await table.update({"result": value}, where=col("field") == value)
-        assert update_res.rows_updated == 1
-
-    assert (await table.to_arrow())["result"].to_pylist() == values
-
-
 def test_create_table(mem_db: DBConnection):
    schema = pa.schema(
        {
@@ -1483,7 +1467,7 @@ def test_create_index_async_returns_done_job(mem_db: DBConnection):
    table = mem_db.create_table("job_test", [{"id": i} for i in range(10)])
    job = table.create_index_async("id", config=BTree())
    assert job.id is None
-    assert job.wait() is None
+    job.wait()
    assert len(table.list_indices()) == 1
    job.cancel()

@@ -2359,148 +2343,6 @@ def test_update(mem_db: DBConnection):
    assert np.allclose(v, np.array([[1.2, 1.9], [1.1, 1.1]]))


-def test_update_expr_filter_literals(mem_db: DBConnection):
-    values = ["5", "4.66e-84", "it's"]
-    table = mem_db.create_table(
-        "update_expr_literals",
-        data=[{"field": value, "result": "original"} for value in values],
-    )
-
-    for value in values:
-        update_res = table.update(where=col("field") == value, values={"result": value})
-        assert update_res.rows_updated == 1
-
-    assert table.to_arrow()["result"].to_pylist() == values
-
-
-def test_update_expr_filter_preserves_typed_semantics(mem_db: DBConnection):
-    low = Decimal("1.234567890123456789")
-    high = Decimal("1.234567890123456790")
-    decimal_schema = pa.schema(
-        [("val", pa.decimal128(19, 18)), ("result", pa.string())]
-    )
-    decimal_table = mem_db.create_table(
-        "update_expr_decimal",
-        pa.table(
-            {"val": [low, high], "result": ["old", "old"]},
-            schema=decimal_schema,
-        ),
-    )
-    predicate = col("val") < lit(high)
-    assert decimal_table.search().where(predicate).to_arrow().num_rows == 1
-    result = decimal_table.update(where=predicate, values={"result": "new"})
-    assert result.rows_updated == 1
-
-    keyword_table = mem_db.create_table(
-        "update_expr_keyword", [{"null": 1, "result": "old"}]
-    )
-    predicate = col("null") == 1
-    assert keyword_table.search().where(predicate).to_arrow().num_rows == 1
-    result = keyword_table.update(where=predicate, values={"result": "new"})
-    assert result.rows_updated == 1
-
-    empty_in_table = mem_db.create_table(
-        "update_expr_empty_in", [{"id": 1, "result": "old"}]
-    )
-    predicate = col("id").isin([])
-    assert empty_in_table.search().where(predicate).to_arrow().num_rows == 0
-    result = empty_in_table.update(where=predicate, values={"result": "new"})
-    assert result.rows_updated == 0
-
-    marker = "__lancedb_binary_placeholder_0__"
-    binary_schema = pa.schema(
-        [("payload", pa.binary()), ("text", pa.string()), ("result", pa.string())]
-    )
-    binary_table = mem_db.create_table(
-        "update_expr_binary",
-        pa.table(
-            {
-                "payload": [b"\x01", b"\x02"],
-                "text": ["other", marker],
-                "result": ["old", "old"],
-            },
-            schema=binary_schema,
-        ),
-    )
-    predicate = (col("payload") == lit(b"\x01")) | (col("text") == marker)
-    assert binary_table.search().where(predicate).to_arrow().num_rows == 2
-    result = binary_table.update(where=predicate, values={"result": "new"})
-    assert result.rows_updated == 2
-
-    nonfinite_table = mem_db.create_table(
-        "update_expr_nonfinite",
-        [{"x": 1.0, "result": "old"}, {"x": 2.0, "result": "old"}],
-    )
-    predicate = col("x") < float("inf")
-    assert nonfinite_table.search().where(predicate).to_arrow().num_rows == 2
-    result = nonfinite_table.update(where=predicate, values={"result": "new"})
-    assert result.rows_updated == 2
-
-    float16_table = mem_db.create_table(
-        "update_expr_float16",
-        [{"x": 1.0, "result": "old"}, {"x": 3.0, "result": "old"}],
-    )
-    predicate = col("x").cast(pa.float16()) < 2.0
-    assert float16_table.search().where(predicate).to_arrow().num_rows == 1
-    result = float16_table.update(where=predicate, values={"result": "new"})
-    assert result.rows_updated == 1
-
-    string_cast_table = mem_db.create_table(
-        "update_expr_string_cast",
-        [{"x": 1, "result": "old"}, {"x": 2, "result": "old"}],
-    )
-    predicate = col("x").cast("string") == "1"
-    assert string_cast_table.search().where(predicate).to_arrow().num_rows == 1
-    result = string_cast_table.update(where=predicate, values={"result": "new"})
-    assert result.rows_updated == 1
-
-    quoted_identifier_schema = pa.schema(
-        [("payload", pa.binary()), ("odd'name", pa.int64()), ("result", pa.string())]
-    )
-    quoted_identifier_table = mem_db.create_table(
-        "update_expr_quoted_identifier",
-        pa.table(
-            {"payload": [b"\x01"], "odd'name": [1], "result": ["old"]},
-            schema=quoted_identifier_schema,
-        ),
-    )
-    predicate = (col("payload") == lit(b"\x01")) & (col("odd'name") == 1)
-    assert quoted_identifier_table.search().where(predicate).to_arrow().num_rows == 1
-    result = quoted_identifier_table.update(where=predicate, values={"result": "new"})
-    assert result.rows_updated == 1
-
-    decimal256_schema = pa.schema(
-        [("val", pa.decimal256(40, 2)), ("result", pa.string())]
-    )
-    decimal256_table = mem_db.create_table(
-        "update_expr_decimal256",
-        pa.table(
-            {
-                "val": [Decimal("1.00"), Decimal("3.00")],
-                "result": ["old", "old"],
-            },
-            schema=decimal256_schema,
-        ),
-    )
-    predicate = col("val") < lit(Decimal("2.00")).cast(pa.decimal256(40, 2))
-    assert decimal256_table.search().where(predicate).to_arrow().num_rows == 1
-    result = decimal256_table.update(where=predicate, values={"result": "new"})
-    assert result.rows_updated == 1
-
-    binary_empty_table = mem_db.create_table(
-        "update_expr_binary_empty",
-        pa.table(
-            {"payload": [b"\x01", b"\x02"], "result": ["old", "old"]},
-            schema=pa.schema([("payload", pa.binary()), ("result", pa.string())]),
-        ),
-    )
-    predicate = (col("payload") == lit(b"\x01")).isin([])
-    assert binary_empty_table.search().where(predicate).to_arrow().num_rows == 0
-    assert predicate.to_sql() == "false"
-    result = binary_empty_table.update(where=predicate, values={"result": "new"})
-    assert result.rows_updated == 0
-
-
 def test_update_with_arrow_scalar(mem_db: DBConnection):
    schema = pa.schema({"id": pa.int64(), "vector": pa.list_(pa.float32(), 4)})
    table = mem_db.create_table("my_table", schema=schema)
@@ -4105,21 +3947,10 @@ def test_refresh_column_async_returns_job(tmp_path):

    job = table.refresh_column_async("doubled")
    assert job.id is None  # in-process jobs have no server id
-    result = job.wait()
-    assert isinstance(result, lancedb.RefreshColumnResult)
-    assert result.rows_assigned == 2
-    assert result.rows_failed == 0
-    assert result.rows_remaining == 0
-    assert result.source_version == 2
-    assert result.published_version == 3
+    assert job.wait() is None
    assert job.status() == "finished"
    assert sorted(table.to_arrow()["doubled"].to_pylist()) == [2, 4]

-    no_op = table.refresh_column_async("doubled").wait()
-    assert no_op.rows_assigned == 0
-    assert no_op.source_version == 3
-    assert no_op.published_version is None
-
    # Bad input raises at the call, not through the job.
    with pytest.raises(Exception, match="not a computed column"):
        table.refresh_column_async("x")
@@ -4132,10 +3963,6 @@ async def test_refresh_column_async_job_async_table(tmp_path):
    await table.add_columns(computed={"tripled": "x * 3"})

    job = await table.refresh_column_async("tripled")
-    result = await job.wait()
-    assert isinstance(result, lancedb.RefreshColumnResult)
-    assert result.rows_assigned == 1
-    assert result.source_version == 2
-    assert result.published_version == 3
+    assert await job.wait() is None
    assert await job.status() == "finished"
    assert (await table.to_arrow())["tripled"].to_pylist() == [9]
@@ -7,7 +7,6 @@ import pathlib
 from typing import Optional

 import lance
-from lance.blob import BlobType as LanceBlobType
 from lancedb.conftest import MockTextEmbeddingFunction
 from lancedb.embeddings.base import EmbeddingFunctionConfig
 from lancedb.embeddings.registry import EmbeddingFunctionRegistry
@@ -908,165 +907,6 @@ def test_cast_to_target_schema():
    assert output == expected


-def test_cast_to_target_schema_coerces_binary_to_blob_v2():
-    data = pa.table({"image": pa.array([b"hello", None], type=pa.binary())})
-    target = pa.schema([lancedb.blob("image")])
-
-    output = _cast_to_target_schema(data.to_reader(), target).read_all()
-
-    image = output["image"].chunk(0)
-    assert type(image.type) is lancedb.BlobType
-    assert image.storage.to_pylist() == [
-        {"data": b"hello", "uri": None, "position": None, "size": None},
-        None,
-    ]
-
-
-def test_cast_to_target_schema_coerces_binary_to_metadata_blob_struct():
-    storage = lancedb.blob("image").type.storage_type
-    target = pa.schema(
-        [
-            pa.field(
-                "image",
-                storage,
-                metadata={
-                    b"ARROW:extension:name": b"lance.blob.v2",
-                    b"ARROW:extension:metadata": b"",
-                },
-            )
-        ]
-    )
-    data = pa.table({"image": pa.array([b"hello", None], type=pa.binary())})
-
-    output = _cast_to_target_schema(data.to_reader(), target).read_all()
-
-    image = output["image"].chunk(0)
-    assert not isinstance(image.type, pa.ExtensionType)
-    assert image.to_pylist() == [
-        {"data": b"hello", "uri": None, "position": None, "size": None},
-        None,
-    ]
-
-
-def test_cast_to_target_schema_coerces_nested_binary_blob():
-    data = pa.table(
-        {
-            "info": pa.array(
-                [{"blob": b"hello"}, {"blob": None}],
-                type=pa.struct([pa.field("blob", pa.binary())]),
-            )
-        }
-    )
-    target = pa.schema([pa.field("info", pa.struct([lancedb.blob("blob")]))])
-
-    output = _cast_to_target_schema(data.to_reader(), target).read_all()
-
-    blob = output["info"].chunk(0).field("blob")
-    assert type(blob.type) is lancedb.BlobType
-    assert blob.storage.to_pylist() == [
-        {"data": b"hello", "uri": None, "position": None, "size": None},
-        None,
-    ]
-
-
-def test_cast_to_target_schema_coerces_list_binary_blob_with_inferred_child_name():
-    data = pa.table(
-        {"images": pa.array([[b"a", b"b"], None], type=pa.list_(pa.binary()))}
-    )
-    target = pa.schema([pa.field("images", pa.list_(lancedb.blob("image")))])
-
-    output = _cast_to_target_schema(data.to_reader(), target).read_all()
-
-    images = output["images"].chunk(0)
-    assert images.type.value_field.name == "image"
-    assert type(images.type.value_type) is lancedb.BlobType
-    assert images.to_pylist()[1] is None
-    assert images.values.storage.to_pylist() == [
-        {"data": b"a", "uri": None, "position": None, "size": None},
-        {"data": b"b", "uri": None, "position": None, "size": None},
-    ]
-
-
-def test_list_blob_coercion_preserves_null_slots_with_nonzero_extent():
-    child = pa.field("image", pa.binary())
-    source = pa.ListArray.from_arrays(
-        pa.array([0, 2, 4], type=pa.int32()),
-        pa.array([b"a", b"b", b"dead", b"beef"], type=pa.binary()),
-        mask=pa.array([False, True]),
-    ).cast(pa.list_(child))
-    target = pa.schema([pa.field("images", pa.list_(lancedb.blob("image")))])
-
-    output = _cast_to_target_schema(
-        pa.table({"images": source}).to_reader(), target
-    ).read_all()
-
-    images = output["images"].chunk(0)
-    assert images.to_pylist()[1] is None
-    assert [b["data"] for b in images.to_pylist()[0]] == [b"a", b"b"]
-
-
-def test_fixed_size_list_blob_coercion_keeps_null_rows():
-    child = pa.field("frame", pa.binary())
-    source = (
-        pa.FixedSizeListArray.from_arrays(
-            pa.array([b"a", b"b", b"c", b"d"], type=pa.binary()), 2
-        )
-        .take(pa.array([0, None], type=pa.int32()))
-        .cast(pa.list_(child, 2))
-    )
-    target = pa.schema([pa.field("frames", pa.list_(lancedb.blob("frame"), 2))])
-
-    output = _cast_to_target_schema(
-        pa.table({"frames": source}).to_reader(), target
-    ).read_all()
-
-    frames = output["frames"].chunk(0)
-    assert frames.to_pylist()[1] is None
-    assert [b["data"] for b in frames.to_pylist()[0]] == [b"a", b"b"]
-
-
-def test_cast_to_target_schema_accepts_pylance_blob_v2():
-    target_type = lancedb.BlobType()
-    source = lance.blob_array([b"hello", None])
-    assert type(source.type) is LanceBlobType
-    assert type(source.type) is type(target_type)
-    data = pa.table({"image": source})
-    target = pa.schema([pa.field("image", target_type)])
-
-    output = _cast_to_target_schema(data.to_reader(), target).read_all()
-
-    image = output["image"].chunk(0)
-    assert type(image.type) is LanceBlobType
-    assert image.type == target_type
-    assert image.storage.to_pylist() == [
-        {"data": b"hello", "uri": None, "position": None, "size": None},
-        None,
-    ]
-
-
-def test_cast_to_target_schema_rejects_different_blob_v2_class():
-    class OtherBlobType(pa.ExtensionType):
-        def __init__(self):
-            super().__init__(lancedb.BlobType().storage_type, "lance.blob.v2")
-
-        def __arrow_ext_serialize__(self) -> bytes:
-            return b""
-
-        @classmethod
-        def __arrow_ext_deserialize__(
-            cls, storage_type: pa.DataType, serialized: bytes
-        ) -> "OtherBlobType":
-            return cls()
-
-    storage = lance.blob_array([b"hello"]).storage
-    source = pa.ExtensionArray.from_storage(OtherBlobType(), storage)
-    data = pa.table({"image": source})
-    target = pa.schema([lancedb.blob("image")])
-
-    with pytest.raises(pa.ArrowTypeError, match="different extension type"):
-        _cast_to_target_schema(data.to_reader(), target).read_all()
-
-
 def test_sanitize_data_stream():
    # Make sure we don't collect the whole stream when running sanitize_data
    schema = pa.schema({"a": pa.int32()})
@@ -333,40 +333,6 @@ impl Connection {
        })
    }

-    #[pyo3(signature = (name, source, projections=None, filter=None, limit=None))]
-    pub fn create_materialized_view(
-        self_: PyRef<'_, Self>,
-        name: String,
-        source: String,
-        projections: Option<Vec<(String, String)>>,
-        filter: Option<String>,
-        limit: Option<u64>,
-    ) -> PyResult<Bound<'_, PyAny>> {
-        let inner = self_.get_inner()?.clone();
-        future_into_py(self_.py(), async move {
-            let mut builder = inner.create_materialized_view(name, source);
-            if let Some(projections) = projections {
-                builder = builder.select(projections);
-            }
-            if let Some(filter) = filter {
-                builder = builder.only_if(filter);
-            }
-            if let Some(limit) = limit {
-                builder = builder.limit(limit);
-            }
-            let view = builder.execute().await.infer_error()?;
-            Ok(Table::new(view.table().clone()))
-        })
-    }
-
-    pub fn list_materialized_views(self_: PyRef<'_, Self>) -> PyResult<Bound<'_, PyAny>> {
-        let inner = self_.get_inner()?.clone();
-        future_into_py(self_.py(), async move {
-            let views = inner.list_materialized_views().await.infer_error()?;
-            Ok(views.into_iter().map(|view| view.name).collect::<Vec<_>>())
-        })
-    }
-
    #[pyo3(signature = (name, namespace_path=None))]
    pub fn drop_table(
        self_: PyRef<'_, Self>,
@@ -609,7 +575,7 @@ impl Connection {
                .create_function_async(request)
                .await
                .infer_error()
-                .map(crate::job::Job::new_typed)
+                .map(crate::job::FunctionJob::new)
        })
    }

--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Will Jones	10dac899e5	Merge branch 'main' into gatekeeper/fix-2369-1	2026-08-21 16:12:55 -07:00
Gatefixer	10c8894fe8	test: cover HNSW index optimization	2026-08-05 21:57:08 +00:00