docs(node): regenerate RRF reranker reference

.github/workflows/rust.yml

@@ -296,18 +296,16 @@ jobs:
           cargo update -p aws-types --precise 1.3.9
           cargo update -p aws-sigv4 --precise 1.3.5
           cargo update -p aws-credential-types --precise 1.2.8
-          # aws-smithy-checksums must stay at or above 0.63.13: OpenDAL's S3
-          # service needs crc-fast ~1.9, and older releases pin it to ~1.3.
-          cargo update -p aws-smithy-checksums --precise 0.63.13
+          cargo update -p aws-smithy-checksums --precise 0.63.9
           cargo update -p aws-smithy-runtime --precise 1.9.3
-          cargo update -p aws-smithy-http --precise 0.62.6
-          cargo update -p aws-smithy-eventstream --precise 0.60.14
+          cargo update -p aws-smithy-http --precise 0.62.4
+          cargo update -p aws-smithy-eventstream --precise 0.60.12
           cargo update -p aws-smithy-http-client --precise 1.1.3
           cargo update -p aws-smithy-observability --precise 0.1.4
           cargo update -p aws-smithy-query --precise 0.60.8
-          cargo update -p aws-smithy-runtime-api --precise 1.9.3
-          cargo update -p aws-smithy-async --precise 1.2.7
-          cargo update -p aws-smithy-types --precise 1.3.6
+          cargo update -p aws-smithy-runtime-api --precise 1.9.1
+          cargo update -p aws-smithy-async --precise 1.2.6
+          cargo update -p aws-smithy-types --precise 1.3.5
           cargo update -p aws-smithy-xml --precise 0.60.11
           cargo update -p home --precise 0.5.9
       - name: cargo +${{ matrix.msrv }} check

AGENTS.md

@@ -152,54 +152,3 @@ Please consider the following when reviewing code contributions.
 ### Documentation
 * New features must include updates to the rust documentation comments. Link to
   relevant structs and methods to increase the value of documentation.
-
-## Cursor Cloud specific instructions
-
-The VM snapshot already has the Rust `1.97.0` toolchain (auto-selected by
-`rust-toolchain.toml`), `protoc`, `uv` (on `PATH` via `~/.bashrc`), the Rust
-debug build artifacts, the Python editable extension, and `nodejs/node_modules`.
-The startup update script only refreshes dependencies (`uv sync` for Python and
-`pnpm install` for Node); it deliberately does NOT rebuild the native
-extensions. After changing Rust or PyO3/napi binding code you must rebuild the
-affected binding yourself (see per-binding rebuild commands below).
-
-Non-obvious caveats discovered during setup:
-
-* The documented Python bootstrap `uv run --extra tests --extra dev maturin
-  develop --extras tests,dev` does not work as-is here: `maturin` is not
-  installed as a CLI in the uv environment, and `maturin develop --extras`
-  runs its own dependency resolution that cannot find the prerelease
-  `pylance==9.0.0rc1` (it lacks the extra package index that `uv` uses via
-  `uv.lock`). Because `uv run --extra tests --extra dev` already installs those
-  extras, the working command is:
-  `cd python && uv run --extra tests --extra dev --with maturin maturin develop`
-  (note: `--with maturin`, and no `--extras`). This is the Python binding
-  rebuild command.
-* Rust core, the Python extension (maturin), and the Node addon (napi) all
-  compile into the SHARED `/workspace/target`. Cargo feature unification differs
-  between `maturin develop` and `pnpm build`, so alternating between building
-  the Python and Node bindings forces a full recompile of shared crates
-  (`lancedb`, `datafusion`, `lance-*`) — roughly 6-7 min each way on this
-  4-core VM. Build one binding at a time to avoid the churn.
-* The `_lancedb` release build (triggered when `uv run`/`uv sync` installs the
-  `lancedb` project itself) uses `lto = "fat"` + `opt-level = 3`, needs ~11 GB
-  RAM, and takes ~20 min cold on this VM. To avoid it, the update script uses
-  `uv sync --no-install-project --inexact` (the `--inexact` flag is required so
-  the sync does not uninstall the editable extension). Prefer the debug
-  `maturin develop` (~6 min cold, seconds when warm) for iteration.
-* `cargo check` only produces metadata, so the first `cargo run --example ...`
-  or `cargo test` after a check triggers a large codegen/link compile.
-* Node binding rebuild: `cd nodejs && pnpm build` (napi debug build + `tsc`).
-  The native addon lands at `nodejs/dist/lancedb.linux-x64-gnu.node`.
-
-Verified working (local backend, no cloud credentials needed):
-
-* Rust: `cargo check/clippy --features remote --tests --examples`,
-  `cargo test --features remote -p lancedb --lib`, `cargo run --features remote
-  --example simple`.
-* Python: `cd python && uv run --extra tests pytest python/tests/test_table.py`,
-  `uv run --directory python --extra dev ruff check python`.
-* Node: `cd nodejs && pnpm lint`, `pnpm test __test__/connection.test.ts`.
-
-Java (`java/`) is optional; its integration tests need LanceDB Cloud
-credentials (`LANCEDB_DB`, `LANCEDB_API_KEY`) and were not set up here.

Cargo.toml

@@ -13,20 +13,20 @@ categories = ["database-implementations"]
 rust-version = "1.91.0"
 
 [workspace.dependencies]
-lance = { "version" = "=11.0.0-beta.2", default-features = false, "tag" = "v11.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
-lance-core = { "version" = "=11.0.0-beta.2", "tag" = "v11.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
-lance-datagen = { "version" = "=11.0.0-beta.2", "tag" = "v11.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
-lance-file = { "version" = "=11.0.0-beta.2", "tag" = "v11.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
-lance-io = { "version" = "=11.0.0-beta.2", default-features = false, "tag" = "v11.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
-lance-index = { "version" = "=11.0.0-beta.2", "tag" = "v11.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
-lance-linalg = { "version" = "=11.0.0-beta.2", "tag" = "v11.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
-lance-namespace = { "version" = "=11.0.0-beta.2", "tag" = "v11.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
-lance-namespace-impls = { "version" = "=11.0.0-beta.2", default-features = false, "tag" = "v11.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
-lance-table = { "version" = "=11.0.0-beta.2", "tag" = "v11.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
-lance-testing = { "version" = "=11.0.0-beta.2", "tag" = "v11.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
-lance-datafusion = { "version" = "=11.0.0-beta.2", "tag" = "v11.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
-lance-encoding = { "version" = "=11.0.0-beta.2", "tag" = "v11.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
-lance-arrow = { "version" = "=11.0.0-beta.2", "tag" = "v11.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance = { "version" = "=10.1.0-beta.1", default-features = false, "tag" = "v10.1.0-beta.1", "git" = "https://github.com/lance-format/lance.git" }
+lance-core = { "version" = "=10.1.0-beta.1", "tag" = "v10.1.0-beta.1", "git" = "https://github.com/lance-format/lance.git" }
+lance-datagen = { "version" = "=10.1.0-beta.1", "tag" = "v10.1.0-beta.1", "git" = "https://github.com/lance-format/lance.git" }
+lance-file = { "version" = "=10.1.0-beta.1", "tag" = "v10.1.0-beta.1", "git" = "https://github.com/lance-format/lance.git" }
+lance-io = { "version" = "=10.1.0-beta.1", default-features = false, "tag" = "v10.1.0-beta.1", "git" = "https://github.com/lance-format/lance.git" }
+lance-index = { "version" = "=10.1.0-beta.1", "tag" = "v10.1.0-beta.1", "git" = "https://github.com/lance-format/lance.git" }
+lance-linalg = { "version" = "=10.1.0-beta.1", "tag" = "v10.1.0-beta.1", "git" = "https://github.com/lance-format/lance.git" }
+lance-namespace = { "version" = "=10.1.0-beta.1", "tag" = "v10.1.0-beta.1", "git" = "https://github.com/lance-format/lance.git" }
+lance-namespace-impls = { "version" = "=10.1.0-beta.1", default-features = false, "tag" = "v10.1.0-beta.1", "git" = "https://github.com/lance-format/lance.git" }
+lance-table = { "version" = "=10.1.0-beta.1", "tag" = "v10.1.0-beta.1", "git" = "https://github.com/lance-format/lance.git" }
+lance-testing = { "version" = "=10.1.0-beta.1", "tag" = "v10.1.0-beta.1", "git" = "https://github.com/lance-format/lance.git" }
+lance-datafusion = { "version" = "=10.1.0-beta.1", "tag" = "v10.1.0-beta.1", "git" = "https://github.com/lance-format/lance.git" }
+lance-encoding = { "version" = "=10.1.0-beta.1", "tag" = "v10.1.0-beta.1", "git" = "https://github.com/lance-format/lance.git" }
+lance-arrow = { "version" = "=10.1.0-beta.1", "tag" = "v10.1.0-beta.1", "git" = "https://github.com/lance-format/lance.git" }
 ahash = "0.8"
 # Note that this one does not include pyarrow
 arrow = { version = "58.0.0", optional = false }

docs/src/js/namespaces/rerankers/classes/RRFReranker.md

@@ -10,6 +10,24 @@ Reranks the results using the Reciprocal Rank Fusion (RRF) algorithm.
 
 ## Methods
 
+### outputSchema()
+
+```ts
+outputSchema(inputSchema): Promise<Schema<any>>
+```
+
+Declare the RRF output schema for vector-only query execution.
+
+#### Parameters
+
+* **inputSchema**: `Schema`&lt;`any`&gt;
+
+#### Returns
+
+`Promise`&lt;`Schema`&lt;`any`&gt;&gt;
+
+***
+
 ### rerankHybrid()
 
 ```ts

docs/src/js/namespaces/rerankers/interfaces/Reranker.md

@@ -8,6 +8,27 @@
 
 ## Methods
 
+### outputSchema()?
+
+```ts
+optional outputSchema(inputSchema): Promise<Schema<any>>
+```
+
+Declare the schema returned when reranking a vector-only query.
+
+This is required for vector-only reranking so query schema introspection
+and execution agree. Hybrid-only rerankers may omit it.
+
+#### Parameters
+
+* **inputSchema**: `Schema`&lt;`any`&gt;
+
+#### Returns
+
+`Promise`&lt;`Schema`&lt;`any`&gt;&gt;
+
+***
+
 ### rerankHybrid()
 
 ```ts

java/pom.xml

@@ -28,7 +28,7 @@
     <properties>
         <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
         <arrow.version>15.0.0</arrow.version>
-        <lance-core.version>11.0.0-beta.2</lance-core.version>
+        <lance-core.version>10.1.0-beta.1</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>

nodejs/__test__/arrow.test.ts

@@ -197,35 +197,6 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
         expect(table.getChild("d")?.toJSON()).toEqual([9n, 10n, null]);
       });
 
-      it("will use a provided FixedSizeList schema with typed array values", function () {
-        const schema = new Schema([
-          new Field("text", new Utf8(), false),
-          new Field(
-            "vector",
-            new FixedSizeList(3, new Field("item", new Float32(), false)),
-            false,
-          ),
-        ]);
-
-        const table = makeArrowTable(
-          [
-            {
-              text: "foo",
-              vector: new Float32Array([1, 2, 3]),
-            },
-          ],
-          { schema },
-        );
-
-        expect(table.getChild("text")?.toJSON()).toEqual(["foo"]);
-        expect(
-          table
-            .getChild("vector")
-            ?.toJSON()
-            .map((value) => value.toJSON()),
-        ).toEqual([[1, 2, 3]]);
-      });
-
       it("will assume the column `vector` is FixedSizeList<Float32> by default", async function () {
         const schema = new Schema([
           new Field("a", new Float(Precision.DOUBLE), true),

nodejs/__test__/remote.test.ts

@@ -170,38 +170,6 @@ describe("remote connection", () => {
     );
   });
 
-  it("surfaces JSON server errors from remote table operations", async () => {
-    await withMockDatabase(
-      (req, res) => {
-        const path = req.url ?? "";
-        if (path.endsWith("/describe/")) {
-          res.writeHead(200, { "Content-Type": "application/json" }).end(
-            JSON.stringify({
-              name: "broken_table",
-              version: 1,
-              schema: { fields: [] },
-            }),
-          );
-          return;
-        }
-
-        if (path.endsWith("/count_rows/")) {
-          res
-            .writeHead(400, { "Content-Type": "application/json" })
-            .end(JSON.stringify({ error: "count rows failed" }));
-          return;
-        }
-
-        res.writeHead(404).end();
-      },
-      async (db) => {
-        const table = await db.openTable("broken_table");
-
-        await expect(table.countRows()).rejects.toThrow("count rows failed");
-      },
-    );
-  });
-
   it("should pass on requested extra headers", async () => {
     await withMockDatabase(
       (req, res) => {

nodejs/__test__/rerankers.test.ts

@@ -79,6 +79,22 @@ describe("rerankers", function () {
     expect(result).toHaveLength(2);
   });
 
+  it("returns relevance scores when reranking a vector search", async function () {
+    const query = table
+      .vectorSearch([0.1, 0.1])
+      .limit(2)
+      .rerank(await RRFReranker.create());
+    const schema = await query.outputSchema();
+    const result = await query.toArray();
+
+    expect(schema.fields.map((field) => field.name)).toContain(
+      "_relevance_score",
+    );
+    expect(result).toHaveLength(2);
+    expect(result[0]._relevance_score).toBeCloseTo(1 / 60);
+    expect(result[1]._relevance_score).toBeCloseTo(1 / 61);
+  });
+
   it("does not keep process alive after rerank query", async function () {
     const script = `
 import * as lancedb from "./dist/index.js";

nodejs/__test__/table.test.ts

@@ -86,44 +86,6 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
       await expect(table.countRows()).resolves.toBe(3);
     });
 
-    it("should support a foreign Float64 vector schema end to end", async () => {
-      const conn = await connect(tmpDir.name);
-      const schema = new arrow.Schema([
-        new arrow.Field("resource_id", new arrow.Int32(), false),
-        new arrow.Field(
-          "vector",
-          new arrow.FixedSizeList(
-            3,
-            new arrow.Field("value", new arrow.Float64(), true),
-          ),
-          false,
-        ),
-      ]);
-      const data = [
-        {
-          // biome-ignore lint/style/useNamingConvention: matches the reported schema
-          resource_id: 0,
-          vector: [0.1, 0.1, 0.1],
-        },
-      ];
-
-      const resources = await conn.createTable("resources", data, { schema });
-
-      const existing = await resources
-        .query()
-        .where("resource_id = 0")
-        .limit(1)
-        .toArray();
-      expect(existing).toHaveLength(1);
-
-      const matched = await resources
-        .search(Float64Array.from(data[0].vector))
-        .limit(1)
-        .toArray();
-      expect(matched).toHaveLength(1);
-      expect(matched[0]["resource_id"]).toBe(0);
-    });
-
     it("should support branches", async () => {
       await table.add([{ id: 1 }]);
       expect(await table.countRows()).toBe(1);

nodejs/lancedb/query.ts

@@ -5,9 +5,11 @@ import {
   Table as ArrowTable,
   type IntoVector,
   RecordBatch,
+  createEmptyTable,
   extractVectorBuffer,
   fromBufferToRecordBatch,
   fromRecordBatchToBuffer,
+  fromTableToBuffer,
   tableFromIPC,
 } from "./arrow";
 import { type IvfPqOptions } from "./indices";
@@ -744,20 +746,30 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {
   }
 
   rerank(reranker: Reranker): VectorQuery {
-    super.doCall((inner) =>
-      inner.rerank(async (args) => {
-        const vecResults = await fromBufferToRecordBatch(args.vecResults);
-        const ftsResults = await fromBufferToRecordBatch(args.ftsResults);
-        const result = await reranker.rerankHybrid(
-          args.query,
-          vecResults as RecordBatch,
-          ftsResults as RecordBatch,
-        );
+    super.doCall((inner) => {
+      const outputSchema = reranker.outputSchema?.bind(reranker);
+      inner.rerank(
+        async (args) => {
+          const vecResults = await fromBufferToRecordBatch(args.vecResults);
+          const ftsResults = await fromBufferToRecordBatch(args.ftsResults);
+          const result = await reranker.rerankHybrid(
+            args.query,
+            vecResults as RecordBatch,
+            ftsResults as RecordBatch,
+          );
 
-        const buffer = fromRecordBatchToBuffer(result);
-        return buffer;
-      }),
-    );
+          const buffer = fromRecordBatchToBuffer(result);
+          return buffer;
+        },
+        outputSchema
+          ? async (args) => {
+              const inputSchema = tableFromIPC(args.inputSchema).schema;
+              const result = await outputSchema(inputSchema);
+              return fromTableToBuffer(createEmptyTable(result));
+            }
+          : undefined,
+      );
+    });
 
     return this;
   }

nodejs/lancedb/rerankers/index.ts

@@ -1,14 +1,22 @@
 // SPDX-License-Identifier: Apache-2.0
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
-import { RecordBatch } from "apache-arrow";
+import { RecordBatch, Schema } from "apache-arrow";
 
 export * from "./rrf";
 
-// Interface for a reranker. A reranker is used to rerank the results from a
-// vector and FTS search. This is useful for combining the results from both
-// search methods.
+// Interface for a reranker. A reranker is used to rerank vector and hybrid
+// search results. For vector-only searches, query is empty and ftsResults is an
+// empty batch with the same schema as vecResults.
 export interface Reranker {
+  /**
+   * Declare the schema returned when reranking a vector-only query.
+   *
+   * This is required for vector-only reranking so query schema introspection
+   * and execution agree. Hybrid-only rerankers may omit it.
+   */
+  outputSchema?(inputSchema: Schema): Promise<Schema>;
+
   rerankHybrid(
     query: string,
     vecResults: RecordBatch,

nodejs/lancedb/rerankers/rrf.ts

@@ -1,7 +1,7 @@
 // SPDX-License-Identifier: Apache-2.0
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
-import { RecordBatch } from "apache-arrow";
+import { Field, Float32, RecordBatch, Schema } from "apache-arrow";
 import { fromBufferToRecordBatch, fromRecordBatchToBuffer } from "../arrow";
 import { RrfReranker as NativeRRFReranker } from "../native";
 
@@ -24,6 +24,17 @@ export class RRFReranker {
     );
   }
 
+  /** Declare the RRF output schema for vector-only query execution. */
+  async outputSchema(inputSchema: Schema): Promise<Schema> {
+    return new Schema(
+      [
+        ...inputSchema.fields,
+        new Field("_relevance_score", new Float32(), false),
+      ],
+      inputSchema.metadata,
+    );
+  }
+
   async rerankHybrid(
     query: string,
     vecResults: RecordBatch,

nodejs/src/query.rs

@@ -6,8 +6,8 @@ use std::sync::Arc;
 use crate::error::NapiErrorExt;
 use crate::error::convert_error;
 use crate::iterator::RecordBatchIterator;
-use crate::rerankers::RerankHybridCallbackArgs;
 use crate::rerankers::Reranker;
+use crate::rerankers::{RerankHybridCallbackArgs, RerankOutputSchemaCallbackArgs};
 use crate::util::{parse_distance_type, schema_to_buffer};
 use arrow_array::{
     Array, Float16Array as ArrowFloat16Array, Float32Array as ArrowFloat32Array,
@@ -388,8 +388,9 @@ impl VectorQuery {
     pub fn rerank(
         &mut self,
         rerank_hybrid: Function<RerankHybridCallbackArgs, Promise<Buffer>>,
+        output_schema: Option<Function<RerankOutputSchemaCallbackArgs, Promise<Buffer>>>,
     ) -> napi::Result<()> {
-        let reranker = Reranker::new(rerank_hybrid)?;
+        let reranker = Reranker::new(rerank_hybrid, output_schema)?;
         self.inner = self.inner.clone().rerank(Arc::new(reranker));
         Ok(())
     }

nodejs/src/rerankers.rs

@@ -6,7 +6,7 @@ use async_trait::async_trait;
 use napi::{bindgen_prelude::*, threadsafe_function::ThreadsafeFunction};
 use napi_derive::napi;
 
-use lancedb::ipc::batches_to_ipc_file;
+use lancedb::ipc::{batches_to_ipc_file, ipc_file_to_schema, schema_to_ipc_file};
 use lancedb::rerankers::Reranker as LanceDBReranker;
 use lancedb::{error::Error, ipc::ipc_file_to_batches};
 
@@ -21,28 +21,72 @@ type RerankHybridFn = ThreadsafeFunction<
     true,
 >;
 
+type RerankOutputSchemaFn = ThreadsafeFunction<
+    RerankOutputSchemaCallbackArgs,
+    Promise<Buffer>,
+    RerankOutputSchemaCallbackArgs,
+    Status,
+    false,
+    true,
+>;
+
 /// Reranker implementation that "wraps" a NodeJS Reranker implementation.
 /// This contains references to the callbacks that can be used to invoke the
 /// reranking methods on the NodeJS implementation and handles serializing the
 /// record batches to Arrow IPC buffers.
 pub struct Reranker {
     rerank_hybrid: RerankHybridFn,
+    output_schema: Option<RerankOutputSchemaFn>,
 }
 
 impl Reranker {
     pub fn new(
         rerank_hybrid: Function<RerankHybridCallbackArgs, Promise<Buffer>>,
+        output_schema: Option<Function<RerankOutputSchemaCallbackArgs, Promise<Buffer>>>,
     ) -> napi::Result<Self> {
         let rerank_hybrid = rerank_hybrid
             .build_threadsafe_function()
             .weak::<true>()
             .build()?;
-        Ok(Self { rerank_hybrid })
+        let output_schema = output_schema
+            .map(|output_schema| {
+                output_schema
+                    .build_threadsafe_function()
+                    .weak::<true>()
+                    .build()
+            })
+            .transpose()?;
+        Ok(Self {
+            rerank_hybrid,
+            output_schema,
+        })
     }
 }
 
 #[async_trait]
 impl lancedb::rerankers::Reranker for Reranker {
+    async fn output_schema(
+        &self,
+        input: &arrow_schema::SchemaRef,
+    ) -> lancedb::error::Result<arrow_schema::SchemaRef> {
+        let output_schema = self.output_schema.as_ref().ok_or(Error::NotSupported {
+            message: "vector rerankers must declare their output schema".to_string(),
+        })?;
+        let callback_args = RerankOutputSchemaCallbackArgs {
+            input_schema: Buffer::from(schema_to_ipc_file(input.as_ref())?),
+        };
+        let promised_buffer: Promise<Buffer> = output_schema
+            .call_async(callback_args)
+            .await
+            .map_err(|e| Error::Runtime {
+                message: format!("napi error status={}, reason={}", e.status, e.reason),
+            })?;
+        let buffer = promised_buffer.await.map_err(|e| Error::Runtime {
+            message: format!("napi error status={}, reason={}", e.status, e.reason),
+        })?;
+        ipc_file_to_schema(buffer.to_vec())
+    }
+
     async fn rerank_hybrid(
         &self,
         query: &str,
@@ -86,6 +130,11 @@ pub struct RerankHybridCallbackArgs {
     pub fts_results: Buffer,
 }
 
+#[napi(object)]
+pub struct RerankOutputSchemaCallbackArgs {
+    pub input_schema: Buffer,
+}
+
 fn buffer_to_record_batch(buffer: Buffer) -> Result<RecordBatch> {
     let mut reader = ipc_file_to_batches(buffer.to_vec()).default_error()?;
     reader

python/Cargo.toml

@@ -26,7 +26,7 @@ lance-namespace-impls.workspace = true
 lance-io.workspace = true
 env_logger.workspace = true
 log.workspace = true
-pyo3 = { version = "0.28", features = ["extension-module", "abi3-py310", "chrono"] }
+pyo3 = { version = "0.28", features = ["extension-module", "abi3-py39", "chrono"] }
 chrono = { version = "0.4", default-features = false, features = ["clock"] }
 pyo3-async-runtimes = { version = "0.28", features = [
     "attributes",
@@ -43,7 +43,7 @@ libc = "0.2"
 [build-dependencies]
 pyo3-build-config = { version = "0.28", features = [
     "extension-module",
-    "abi3-py310",
+    "abi3-py39",
 ] }
 
 [features]

python/python/lancedb/embeddings/instructor.py

@@ -101,7 +101,8 @@ class InstructorEmbeddingFunction(TextEmbeddingFunction):
 
     @weak_lru(maxsize=1)
     def ndims(self):
-        return len(self.generate_embeddings([[self.source_instruction, "foo"]])[0])
+        model = self.get_model()
+        return model.encode("foo").shape[0]
 
     def compute_query_embeddings(self, query: str, *args, **kwargs) -> List[np.array]:
         return self.generate_embeddings([[self.query_instruction, query]])

python/python/lancedb/util.py

@@ -395,11 +395,6 @@ def _(value: dict):
     )
 
 
-@value_to_sql.register(pa.Scalar)
-def _(value: pa.Scalar):
-    return value_to_sql(value.as_py())
-
-
 @value_to_sql.register(np.ndarray)
 def _(value: np.ndarray):
     return value_to_sql(value.tolist())

python/python/tests/test_db.py

@@ -2,7 +2,6 @@
 # SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
 
-import inspect
 import re
 import sys
 from datetime import timedelta
@@ -63,23 +62,17 @@ def test_basic(tmp_path):
     assert db.open_table("test").name == db["test"].name
 
 
-def test_sync_debugger_inspection_does_not_use_background_loop(tmp_path, monkeypatch):
+def test_sync_repr_does_not_use_background_loop(tmp_path, monkeypatch):
     from lancedb.background_loop import LOOP
 
     db = lancedb.connect(tmp_path)
     table = db.create_table("test", data=[{"id": 1}])
 
     def fail_run(*args, **kwargs):
-        raise AssertionError("debugger inspection should not use the background loop")
+        raise AssertionError("repr should not use the Python background loop")
 
     monkeypatch.setattr(LOOP, "run", fail_run)
 
-    # Debuggers enumerate and evaluate every exposed attribute when expanding a
-    # variable. This must remain safe while their breakpoint suspends LOOP's thread.
-    members = dict(inspect.getmembers(db))
-
-    assert members["uri"] == str(tmp_path)
-    assert members["read_consistency_interval"] is None
     assert repr(db) == f"LanceDBConnection(uri={str(tmp_path)!r})"
     assert repr(table) == f"LanceTable(name='test', _conn={db!r})"
 

python/python/tests/test_embeddings.py

@@ -64,23 +64,6 @@ def test_embedding_function(tmp_path):
     assert np.allclose(actual, expected)
 
 
-def test_instructor_ndims_uses_instruction():
-    instructor = get_registry().get("instructor").create()
-    model = MagicMock()
-    model.encode.return_value = np.zeros((1, 384))
-
-    with patch.object(type(instructor), "get_model", return_value=model):
-        assert instructor.ndims() == 384
-
-    model.encode.assert_called_once_with(
-        [[instructor.source_instruction, "foo"]],
-        batch_size=instructor.batch_size,
-        show_progress_bar=instructor.show_progress_bar,
-        normalize_embeddings=instructor.normalize_embeddings,
-        device=instructor.device,
-    )
-
-
 def test_embedding_function_variables():
     @register("variable-testing")
     class VariableTestingFunction(TextEmbeddingFunction):
@@ -132,16 +115,34 @@ def test_embedding_function_variables():
     assert func.safe_model_dump()["secret_key"] == "$var:secret"
 
 
-def test_openai_variables_survive_metadata_round_trip():
+def test_parse_functions_with_variables():
+    @register("variable-parsing-test")
+    class VariableParsingFunction(TextEmbeddingFunction):
+        api_key: str
+        base_url: Optional[str] = None
+
+        @staticmethod
+        def sensitive_keys():
+            return ["api_key"]
+
+        def ndims(self):
+            return 10
+
+        def generate_embeddings(self, texts):
+            # Mock implementation that just returns random embeddings
+            # In real usage, this would use the api_key to call an API
+            return [np.random.rand(self.ndims()).tolist() for _ in texts]
+
     registry = EmbeddingFunctionRegistry.get_instance()
 
     registry.set_var("test_api_key", "sk-test-key-12345")
+    registry.set_var("test_base_url", "https://api.example.com")
 
     conf = EmbeddingFunctionConfig(
         source_column="text",
         vector_column="vector",
-        function=registry.get("openai").create(
-            api_key="$var:test_api_key", base_url="https://api.example.com"
+        function=registry.get("variable-parsing-test").create(
+            api_key="$var:test_api_key", base_url="$var:test_base_url"
         ),
     )
 
@@ -149,10 +150,7 @@ def test_openai_variables_survive_metadata_round_trip():
 
     # Create a mock arrow table with the metadata
     schema = pa.schema(
-        [
-            pa.field("text", pa.string()),
-            pa.field("vector", pa.list_(pa.float32(), 1536)),
-        ]
+        [pa.field("text", pa.string()), pa.field("vector", pa.list_(pa.float32(), 10))]
     )
     table = pa.table({"text": [], "vector": []}, schema=schema)
     table = table.replace_schema_metadata(metadata)
@@ -166,15 +164,13 @@ def test_openai_variables_survive_metadata_round_trip():
 
     assert parsed_func.api_key == "sk-test-key-12345"
     assert parsed_func.base_url == "https://api.example.com"
+
+    embeddings = parsed_func.generate_embeddings(["test text"])
+    assert len(embeddings) == 1
+    assert len(embeddings[0]) == 10
+
     assert parsed_func.safe_model_dump()["api_key"] == "$var:test_api_key"
 
-    with patch("lancedb.embeddings.openai.attempt_import_or_raise") as import_openai:
-        parsed_func._openai_client
-
-    import_openai.return_value.OpenAI.assert_called_once_with(
-        api_key="sk-test-key-12345", base_url="https://api.example.com"
-    )
-
 
 def test_embedding_with_bad_results(tmp_path):
     @register("null-embedding")

python/python/tests/test_hybrid_query.py

@@ -12,7 +12,7 @@ import pyarrow.compute as pc
 import pytest
 import pytest_asyncio
 
-from lancedb.index import BTree, FTS, IvfPq
+from lancedb.index import FTS
 from lancedb.table import AsyncTable, Table
 
 
@@ -99,86 +99,6 @@ async def test_async_hybrid_query_filters(table: AsyncTable):
     assert result["text"].to_pylist() == ["cat", "b"]
 
 
-@pytest.mark.asyncio
-async def test_hybrid_query_with_stale_fixed_size_binary_prefilter(
-    tmpdir_factory,
-):
-    tmp_path = str(tmpdir_factory.mktemp("stale_scalar_prefilter"))
-    db = await lancedb.connect_async(tmp_path)
-
-    def fixed_size_binary(value: int) -> bytes:
-        return value.to_bytes(16, byteorder="big")
-
-    num_rows = 1000
-    data = pa.table(
-        {
-            "space_id": pa.array(
-                [fixed_size_binary(i) for i in range(num_rows)],
-                type=pa.binary(16),
-            ),
-            "text": ["book"] * num_rows,
-            "vector": pa.array(
-                [[float(i), float(i)] for i in range(num_rows)],
-                type=pa.list_(pa.float32(), 2),
-            ),
-        }
-    )
-    table = await db.create_table("test", data)
-    await table.create_index(
-        "vector", config=IvfPq(num_partitions=4, num_sub_vectors=2)
-    )
-    await table.create_index("space_id", config=BTree())
-    await table.create_index("text", config=FTS(with_position=False))
-
-    # Advance the search indices without advancing the scalar index. This is the
-    # state that previously let hybrid search use an incomplete scalar prefilter.
-    await table.add(data)
-    lance_dataset = await table.to_lance()
-    lance_dataset.optimize.optimize_indices(index_names=["vector_idx", "text_idx"])
-    await table.checkout_latest()
-
-    scalar_stats = await table.index_stats("space_id_idx")
-    assert scalar_stats is not None
-    assert scalar_stats.num_indexed_rows == num_rows
-    assert scalar_stats.num_unindexed_rows == num_rows
-
-    for index_name in ["vector_idx", "text_idx"]:
-        search_stats = await table.index_stats(index_name)
-        assert search_stats is not None
-        assert search_stats.num_indexed_rows == num_rows * 2
-        assert search_stats.num_unindexed_rows == 0
-
-    matching_ids = [5, 10, 15, 20, 25, 30]
-    literals = [
-        f"arrow_cast(0x{fixed_size_binary(i).hex()}, 'FixedSizeBinary(16)')"
-        for i in matching_ids
-    ]
-    predicate = f"space_id IN ({', '.join(literals)})"
-    expected_ids = sorted(fixed_size_binary(i) for i in matching_ids for _ in range(2))
-
-    vector_query = (
-        table.query().where(predicate).nearest_to([5.0, 5.0]).limit(num_rows * 2)
-    )
-    vector_results = await vector_query.to_arrow()
-    assert sorted(vector_results["space_id"].to_pylist()) == expected_ids
-
-    fts_query = (
-        table.query().where(predicate).nearest_to_text("book").limit(num_rows * 2)
-    )
-    fts_results = await fts_query.to_arrow()
-    assert sorted(fts_results["space_id"].to_pylist()) == expected_ids
-
-    hybrid_results = await (
-        table.query()
-        .where(predicate)
-        .nearest_to([5.0, 5.0])
-        .nearest_to_text("book")
-        .limit(num_rows * 2)
-        .to_arrow()
-    )
-    assert sorted(hybrid_results["space_id"].to_pylist()) == expected_ids
-
-
 @pytest.mark.asyncio
 async def test_async_hybrid_query_default_limit(table: AsyncTable):
     # add 10 new rows

python/python/tests/test_import.py

@@ -1,33 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright The LanceDB Authors
-
-import re
-import shutil
-import subprocess
-import sys
-
-import lancedb._lancedb as _lancedb
-import pytest
-
-
-@pytest.mark.skipif(sys.platform != "linux", reason="ldd is Linux-specific")
-def test_native_extension_does_not_link_openssl():
-    """OpenSSL-linked wheels abort when imported on RHEL hosts in FIPS mode."""
-    ldd = shutil.which("ldd")
-    if ldd is None:
-        pytest.skip("ldd is not installed")
-
-    result = subprocess.run(
-        [ldd, _lancedb.__file__],
-        check=True,
-        capture_output=True,
-        text=True,
-    )
-    openssl_libraries = re.findall(
-        r"^\s*(lib(?:crypto|ssl)\S*)\s+=>", result.stdout, flags=re.MULTILINE
-    )
-
-    assert not openssl_libraries, (
-        "the LanceDB native extension must use rustls instead of linking OpenSSL: "
-        f"{openssl_libraries}"
-    )

python/python/tests/test_index.py

@@ -372,31 +372,6 @@ async def test_create_vector_index(some_table: AsyncTable):
     assert stats.num_indices == 1
 
 
-@pytest.mark.asyncio
-async def test_create_ivf_index_reports_unsplittable_partitions(db_async):
-    dim = 8
-    num_partitions = 300  # More than 256 selects hierarchical k-means.
-    base_vectors = [[float(row == column) for column in range(dim)] for row in range(5)]
-    vectors = pa.array(base_vectors * 200, pa.list_(pa.float32(), dim))
-    table = await db_async.create_table(
-        "unsplittable_partitions",
-        pa.table({"vector": vectors}),
-    )
-
-    error_pattern = (
-        rf"Cannot create {num_partitions} IVF partitions: k-means could only form"
-    )
-    with pytest.raises(RuntimeError, match=error_pattern):
-        await table.create_index(
-            "vector",
-            config=IvfFlat(
-                distance_type="dot",
-                num_partitions=num_partitions,
-                max_iterations=10,
-            ),
-        )
-
-
 @pytest.mark.asyncio
 async def test_create_4bit_ivfpq_index(some_table: AsyncTable):
     # Can create

python/python/tests/test_package_metadata.py

@@ -1,42 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright The LanceDB Authors
-
-import importlib
-import re
-import sys
-from pathlib import Path
-
-import pytest
-
-
-def test_pyo3_abi_matches_minimum_supported_python():
-    project_dir = Path(__file__).parents[2]
-    pyproject = (project_dir / "pyproject.toml").read_text()
-    cargo_manifest = (project_dir / "Cargo.toml").read_text()
-
-    minimum_python = re.search(
-        r'^requires-python\s*=\s*">=(\d+)\.(\d+)"$', pyproject, re.MULTILINE
-    )
-    assert minimum_python is not None
-
-    major, minor = minimum_python.groups()
-    expected_abi = f"abi3-py{major}{minor}"
-    configured_abis = re.findall(r'"(abi3-py\d+)"', cargo_manifest)
-
-    assert configured_abis == [expected_abi, expected_abi], (
-        "the pyo3 runtime and build ABI features must both match requires-python"
-    )
-
-
-@pytest.mark.skipif(sys.platform != "win32", reason="Windows wheel regression test")
-def test_windows_wheel_tag_and_native_import():
-    project_dir = Path(__file__).parents[2]
-    wheels = list((project_dir.parent / "target" / "wheels").glob("lancedb-*.whl"))
-    if not wheels:
-        pytest.skip("no wheel artifact is available in this development environment")
-
-    assert len(wheels) == 1
-    assert wheels[0].name.endswith("-cp310-abi3-win_amd64.whl")
-
-    native_module = importlib.import_module("lancedb._lancedb")
-    assert Path(native_module.__file__).suffix == ".pyd"

python/python/tests/test_remote_db.py

@@ -35,12 +35,6 @@ def make_mock_http_handler(handler):
     return MockLanceDBHandler
 
 
-@pytest.mark.parametrize("db_name", ["a" * 64, "invalid..database"])
-def test_connect_rejects_invalid_cloud_dns_hostname(db_name):
-    with pytest.raises(ValueError, match="DNS labels must contain 1 to 63 bytes"):
-        lancedb.connect(f"db://{db_name}", api_key="fake")
-
-
 @contextlib.contextmanager
 def mock_lancedb_connection(handler):
     with http.server.HTTPServer(

python/python/tests/test_table.py

@@ -2,13 +2,10 @@
 # SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
 
-import ctypes
-import gc
 import os
 import sys
 import threading
 import warnings
-import weakref
 from concurrent.futures import ThreadPoolExecutor
 from datetime import date, datetime, timedelta
 from time import sleep
@@ -102,30 +99,6 @@ def test_basic(mem_db: DBConnection):
     assert table.to_arrow() == expected_data
 
 
-def test_search_preserves_nulls_from_sliced_arrow_table(mem_db: DBConnection):
-    data = pa.table(
-        {
-            "id": [0, 1, 2, 3, 4],
-            "score_cn": [None, 22, None, 5, 8],
-            "score_mt": [None, 42, None, 5, 8],
-            "vector": [
-                [20, 19, -1, -1],
-                [41, 38, 22, 42],
-                [10, 10, -1, -1],
-                [5, 5, 5, 5],
-                [8, 8, 8, 8],
-            ],
-        }
-    ).slice(1)
-
-    table = mem_db.create_table("sliced_nullable", data=data)
-    result = table.search([41, 38, 22, 42]).limit(1).to_arrow()
-
-    assert result["id"].to_pylist() == [1]
-    assert result["score_cn"].to_pylist() == [22]
-    assert result["score_mt"].to_pylist() == [42]
-
-
 def test_table_to_pandas_default_matches_arrow(tmp_db: DBConnection):
     pd = pytest.importorskip("pandas")
     data = pa.table({"id": [1, 2], "text": ["one", "two"]})
@@ -462,38 +435,6 @@ def test_add(mem_db: DBConnection):
     _add(table, schema)
 
 
-def test_add_releases_arrow_buffers_without_gc(mem_db: DBConnection):
-    """Regression test for https://github.com/lancedb/lancedb/issues/2512."""
-    schema = pa.schema([pa.field("x", pa.int64())])
-    table = mem_db.create_table("test_add_releases_arrow_buffers", schema=schema)
-
-    class BufferOwner:
-        def __init__(self, size: int):
-            self.memory = ctypes.create_string_buffer(size)
-
-    owner_refs = []
-    gc_was_enabled = gc.isenabled()
-    gc.disable()
-    try:
-        for _ in range(3):
-            size = 8 * 1024
-            owner = BufferOwner(size)
-            arrow_buffer = pa.foreign_buffer(
-                ctypes.addressof(owner.memory), size, owner
-            )
-            array = pa.Array.from_buffers(pa.int64(), 1024, [None, arrow_buffer])
-            batch = pa.RecordBatch.from_arrays([array], schema=schema)
-            owner_refs.append(weakref.ref(owner))
-
-            table.add(batch)
-            del batch, array, arrow_buffer, owner
-
-        assert all(owner_ref() is None for owner_ref in owner_refs)
-    finally:
-        if gc_was_enabled:
-            gc.enable()
-
-
 def test_add_write_parallelism(mem_db: DBConnection):
     schema = pa.schema([pa.field("id", pa.int64())])
     table = mem_db.create_table("test", schema=schema)
@@ -1884,33 +1825,6 @@ def test_add_nullable_struct_with_none(mem_db: DBConnection):
     assert result.column("data").to_pylist() == [{"x": 1.0}, None]
 
 
-def test_read_mostly_null_list_v2_2_page_boundary(tmp_path):
-    # Regression test for #3194. This row/value count crosses a v2.2 structural
-    # encoding page boundary where Lance 3.0.0 sliced repetition/definition
-    # levels by row offset and decoded child arrays at different lengths.
-    num_rows = 64_885
-    num_values = 217
-    list_type = pa.list_(pa.float32())
-    source = pa.table(
-        {
-            "id": np.arange(num_rows, dtype=np.int64),
-            "coords": pa.array(
-                [[1.0, 2.0, 3.0, 4.0]] * num_values + [None] * (num_rows - num_values),
-                type=list_type,
-            ),
-        }
-    )
-    db = lancedb.connect(
-        tmp_path,
-        storage_options={"new_table_data_storage_version": "2.2"},
-    )
-    table = db.create_table("test_sparse_nullable_list", data=source)
-
-    result = table.search().select(["id", "coords"]).limit(num_rows).to_arrow()
-
-    assert result.equals(source)
-
-
 def test_add_with_integer_embeddings_preserves_casting(mem_db: DBConnection):
     class Schema(LanceModel):
         text: str
@@ -2282,20 +2196,6 @@ def test_update(mem_db: DBConnection):
     assert np.allclose(v, np.array([[1.2, 1.9], [1.1, 1.1]]))
 
 
-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)
-    table.add([{"id": 1, "vector": [1.0, 2.0, 3.0, 4.0]}])
-
-    value = table.search().select(["vector"]).limit(1).to_arrow()["vector"][0]
-    assert isinstance(value, pa.FixedSizeListScalar)
-
-    result = table.update(where="id == 1", values={"vector": value})
-
-    assert result.rows_updated == 1
-    assert table.to_arrow()["vector"].to_pylist() == [[1.0, 2.0, 3.0, 4.0]]
-
-
 def test_update_types(mem_db: DBConnection):
     table = mem_db.create_table(
         "my_table",
@@ -2463,55 +2363,6 @@ def test_merge_insert(mem_db: DBConnection):
         )
 
 
-def test_merge_insert_nullable_pandas_into_pydantic_schema(mem_db: DBConnection):
-    # Regression test for https://github.com/lancedb/lancedb/issues/2366
-    pd = pytest.importorskip("pandas")
-
-    class Document(LanceModel):
-        id: int
-        title: str
-        content: str
-
-    table = mem_db.create_table("documents", schema=Document)
-    table.add(
-        pd.DataFrame(
-            {
-                "title": ["Old title", "Unchanged"],
-                "id": [2, 3],
-                "content": ["Old content", "Keep this"],
-            }
-        )
-    )
-
-    # Pandas produces nullable Arrow fields, in an order that differs from the
-    # non-nullable Pydantic schema. This is valid as long as the data has no nulls.
-    new_data = pd.DataFrame(
-        {
-            "title": ["Inserted", "Updated"],
-            "id": [1, 2],
-            "content": ["New row", "New content"],
-        }
-    )
-    result = (
-        table.merge_insert("id")
-        .when_matched_update_all()
-        .when_not_matched_insert_all()
-        .execute(new_data)
-    )
-
-    assert result.num_inserted_rows == 1
-    assert result.num_updated_rows == 1
-    expected = pa.Table.from_pylist(
-        [
-            {"id": 1, "title": "Inserted", "content": "New row"},
-            {"id": 2, "title": "Updated", "content": "New content"},
-            {"id": 3, "title": "Unchanged", "content": "Keep this"},
-        ],
-        schema=Document.to_arrow_schema(),
-    )
-    assert table.to_arrow().sort_by("id") == expected
-
-
 def test_merge_insert_by_source_delete_expr(mem_db: DBConnection):
     table = mem_db.create_table(
         "my_table",
@@ -2612,36 +2463,6 @@ def test_merge_insert_subschema(mem_db: DBConnection, data_format):
     assert table.to_arrow().sort_by("id") == expected
 
 
-def test_repeated_partial_merge_insert_with_scalar_index(mem_db: DBConnection):
-    def make_batch(start: int) -> pa.Table:
-        return pa.table(
-            {
-                "id": [f"id-{i:04}" for i in range(start, start + 100)],
-                "category": ["A"] * 100,
-                "value_a": [float(i) for i in range(start, start + 100)],
-                "value_b": [float(i) / 10 for i in range(100)],
-            }
-        )
-
-    table = mem_db.create_table("my_table", data=make_batch(0))
-    table.add(make_batch(100))
-    table.add(make_batch(200))
-    table.create_index("id", config=BTree())
-
-    ids = [f"id-{i:04}" for i in range(100, 200)]
-    for value in (999.0, 888.0):
-        result = (
-            table.merge_insert("id")
-            .when_matched_update_all()
-            .execute(pa.table({"id": ids, "value_a": [value] * 100}))
-        )
-        assert result.num_updated_rows == 100
-
-    actual = table.to_arrow().sort_by("id")
-    assert actual.num_rows == 300
-    assert actual["value_a"].to_pylist()[100:200] == [888.0] * 100
-
-
 @pytest.mark.asyncio
 async def test_merge_insert_async(mem_db_async: AsyncConnection):
     data = pa.table({"a": [1, 2, 3], "b": ["a", "b", "c"]})
@@ -3668,8 +3489,8 @@ def test_create_table_empty_list_no_schema_error(mem_db: DBConnection):
         mem_db.create_table("test_empty_no_schema", data=[])
 
 
-def test_create_table_without_data_with_vector_schema(tmp_path):
-    """Test exact scenario from issue #1968.
+def test_add_table_with_empty_embeddings(tmp_path):
+    """Test exact scenario from issue #1968
 
     Regression test for issue #1968:
     https://github.com/lancedb/lancedb/issues/1968
@@ -3681,9 +3502,6 @@ def test_create_table_without_data_with_vector_schema(tmp_path):
         embedding: Vector(16)
 
     table = db.create_table("test", schema=MySchema)
-    assert table.count_rows() == 0
-    assert table.schema == MySchema.to_arrow_schema()
-
     table.add(
         [{"text": "bar", "embedding": [0.1] * 16}],
         on_bad_vectors="drop",

python/python/tests/test_voyageai_embeddings.py

@@ -75,22 +75,6 @@ class TestVoyageAIModelRegistration:
         with pytest.raises(ValueError, match="not supported"):
             func.ndims()
 
-    def test_voyage3_source_embeddings_use_text_api(self, mock_voyageai_client):
-        """Regression test for text table data being sent to the multimodal API."""
-        mock_voyageai_client.tokenize.return_value = [["hello", "world"]]
-        mock_voyageai_client.embed.return_value.embeddings = [[0.1] * 1024]
-
-        registry = get_registry()
-        func = registry.get("voyageai").create(name="voyage-3")
-
-        embeddings = func.compute_source_embeddings("hello world")
-
-        assert embeddings == [[0.1] * 1024]
-        mock_voyageai_client.embed.assert_called_once_with(
-            texts=["hello world"], model="voyage-3", input_type="document"
-        )
-        mock_voyageai_client.multimodal_embed.assert_not_called()
-
     @pytest.mark.parametrize(
         "model_name",
         [

rust/lancedb/Cargo.toml

@@ -49,8 +49,8 @@ lance-namespace = { workspace = true }
 lance-namespace-impls = { workspace = true }
 metrics = { workspace = true, optional = true }
 metrics-util = { workspace = true, optional = true }
-# Pin the GooseFS SDK to the version required by Lance's OpenDAL dependency.
-goosefs-sdk = { version = "=0.1.9", optional = true }
+# Pin the transitive GooseFS SDK until the 0.1.6 compile break is fixed upstream.
+goosefs-sdk = { version = "=0.1.5", optional = true }
 moka = { workspace = true }
 pin-project = { workspace = true }
 tokio = { version = "1.23", features = ["rt-multi-thread", "sync"] }
@@ -75,8 +75,6 @@ reqwest = { version = "0.12.0", default-features = false, features = [
     "http2",
     "json",
     "macos-system-configuration",
-    # Avoid linking OpenSSL into Python wheels, which breaks on FIPS hosts.
-    "rustls-tls-native-roots",
     "stream",
 ], optional = true }
 http = { version = "1", optional = true } # Matching what is in reqwest

rust/lancedb/src/blob.rs

@@ -17,7 +17,7 @@ use arrow_array::builder::LargeBinaryBuilder;
 use arrow_schema::{DataType, Field, Schema};
 use lance::dataset::{BlobRangeRequest as LanceBlobRangeRequest, Dataset, WriteParams};
 use lance_arrow::FieldExt;
-use lance_file::version::LanceFileVersion;
+use lance_encoding::version::LanceFileVersion;
 use lance_io::object_store::ObjectStore;
 use object_store::path::Path;
 

rust/lancedb/src/connection.rs

@@ -34,7 +34,7 @@ use crate::remote::{
     db::{OPT_REMOTE_API_KEY, OPT_REMOTE_HOST_OVERRIDE, OPT_REMOTE_REGION},
 };
 use lance::io::ObjectStoreParams;
-pub use lance_file::version::LanceFileVersion;
+pub use lance_encoding::version::LanceFileVersion;
 #[cfg(feature = "remote")]
 use lance_io::object_store::StorageOptions;
 use lance_io::object_store::{StorageOptionsAccessor, StorageOptionsProvider};

rust/lancedb/src/connection/create_table.rs

@@ -202,17 +202,6 @@ mod tests {
         assert_eq!(table.count_rows(None).await.unwrap(), 0);
     }
 
-    #[tokio::test]
-    async fn create_table_in_named_memory_database() {
-        let db = connect("memory://foo").execute().await.unwrap();
-        let batch = record_batch!(("id", Int64, [1, 2, 3])).unwrap();
-
-        let table = db.create_table("my_table", batch).execute().await.unwrap();
-
-        assert_eq!(table.uri().await.unwrap(), "memory://foo/my_table.lance");
-        assert_eq!(table.count_rows(None).await.unwrap(), 3);
-    }
-
     async fn test_create_table_with_data<T>(data: T)
     where
         T: Scannable + 'static,

rust/lancedb/src/database/listing.rs

@@ -12,7 +12,7 @@ use lance::dataset::refs::Ref;
 use lance::dataset::{ReadParams, WriteMode, builder::DatasetBuilder};
 use lance::io::{ObjectStore, ObjectStoreParams, WrappingObjectStore};
 use lance_datafusion::utils::StreamingWriteSource;
-use lance_file::version::LanceFileVersion;
+use lance_encoding::version::LanceFileVersion;
 use lance_io::object_store::{StorageOptionsAccessor, StorageOptionsProvider};
 use lance_table::io::commit::commit_handler_from_url;
 use object_store::local::LocalFileSystem;
@@ -1376,68 +1376,6 @@ mod tests {
         assert!(!tempdir.path().join("__manifest").exists());
     }
 
-    /// Regression test for https://github.com/lancedb/lancedb/issues/1600.
-    ///
-    /// Opening a table used to create a separate object-store client instead of
-    /// reusing the one that successfully connected to the database.  Repeating
-    /// credential discovery made S3 table opens intermittent, especially in AWS
-    /// Lambda, and the failed open was reported as `TableNotFound`.
-    #[tokio::test]
-    async fn test_open_table_reuses_connection_object_store() {
-        let tempdir = tempdir().unwrap();
-        let uri = tempdir.path().to_str().unwrap();
-        let registry = Arc::new(lance_io::object_store::ObjectStoreRegistry::default());
-        let session = Arc::new(lance::session::Session::new(16, 16, registry.clone()));
-
-        let request = ConnectRequest {
-            uri: uri.to_string(),
-            #[cfg(feature = "remote")]
-            client_config: Default::default(),
-            options: Default::default(),
-            namespace_client_properties: Default::default(),
-            manifest_enabled: false,
-            read_consistency_interval: None,
-            session: Some(session),
-        };
-        let db = ListingDatabase::connect_with_options(&request)
-            .await
-            .unwrap();
-
-        let schema = Arc::new(Schema::new(vec![Field::new("id", DataType::Int32, false)]));
-        db.create_table(CreateTableRequest {
-            name: "test".to_string(),
-            namespace_path: vec![],
-            data: Box::new(RecordBatch::new_empty(schema)) as Box<dyn Scannable>,
-            mode: CreateTableMode::Create,
-            write_options: Default::default(),
-            location: None,
-            namespace_client: None,
-        })
-        .await
-        .unwrap();
-
-        let before_open = registry.stats();
-        for _ in 0..3 {
-            let table = db
-                .open_table(OpenTableRequest {
-                    name: "test".to_string(),
-                    namespace_path: vec![],
-                    index_cache_size: None,
-                    lance_read_params: None,
-                    location: None,
-                    namespace_client: None,
-                    managed_versioning: None,
-                })
-                .await
-                .unwrap();
-            assert_eq!(table.count_rows(None).await.unwrap(), 0);
-        }
-
-        let after_open = registry.stats();
-        assert_eq!(after_open.misses, before_open.misses);
-        assert!(after_open.hits >= before_open.hits + 3);
-    }
-
     #[tokio::test]
     async fn test_clone_table_basic() {
         let (_tempdir, db) = setup_database().await;

rust/lancedb/src/database/namespace.rs

@@ -201,7 +201,7 @@ impl LanceNamespaceDatabase {
         &self,
         request: &DbCreateTableRequest,
     ) -> Result<(
-        Option<lance_file::version::LanceFileVersion>,
+        Option<lance_encoding::version::LanceFileVersion>,
         Option<bool>,
         Option<bool>,
     )> {
@@ -214,7 +214,7 @@ impl LanceNamespaceDatabase {
 
         let storage_version_override = storage_options
             .and_then(|opts| opts.get(OPT_NEW_TABLE_STORAGE_VERSION))
-            .map(|s| s.parse::<lance_file::version::LanceFileVersion>())
+            .map(|s| s.parse::<lance_encoding::version::LanceFileVersion>())
             .transpose()?;
 
         let v2_manifest_override = storage_options

rust/lancedb/src/io/object_store.rs

@@ -132,14 +132,9 @@ impl ObjectStore for MirroringObjectStore {
         if to.primary_only() {
             self.primary.copy_opts(from, to, options).await
         } else {
-            // The secondary store can be process-local and less durable than the
-            // primary, so a source written by another process may not exist here
-            // or may be evicted before the copy begins.
-            match self.secondary.copy_opts(from, to, options.clone()).await {
-                Ok(()) | Err(Error::NotFound { .. }) => {}
-                Err(err) => return Err(err),
-            }
-            self.primary.copy_opts(from, to, options).await
+            self.secondary.copy_opts(from, to, options.clone()).await?;
+            self.primary.copy_opts(from, to, options).await?;
+            Ok(())
         }
     }
 }
@@ -197,8 +192,7 @@ mod test {
     use futures::TryStreamExt;
     use lance::{dataset::WriteParams, io::ObjectStoreParams};
     use lance_testing::datagen::{BatchGenerator, IncrementingInt32, RandomVector};
-    use object_store::{local::LocalFileSystem, memory::InMemory};
-    use std::time::Duration;
+    use object_store::local::LocalFileSystem;
     use tempfile;
 
     use crate::{
@@ -207,139 +201,6 @@ mod test {
         table::WriteOptions,
     };
 
-    #[derive(Debug)]
-    struct EvictBeforeCopyStore {
-        inner: Arc<dyn ObjectStore>,
-    }
-
-    impl std::fmt::Display for EvictBeforeCopyStore {
-        fn fmt(&self, f: &mut Formatter<'_>) -> std::fmt::Result {
-            write!(f, "EvictBeforeCopyStore")
-        }
-    }
-
-    #[async_trait]
-    impl ObjectStore for EvictBeforeCopyStore {
-        async fn put_opts(
-            &self,
-            location: &Path,
-            payload: PutPayload,
-            options: PutOptions,
-        ) -> Result<PutResult> {
-            self.inner.put_opts(location, payload, options).await
-        }
-
-        async fn put_multipart_opts(
-            &self,
-            location: &Path,
-            options: PutMultipartOptions,
-        ) -> Result<Box<dyn MultipartUpload>> {
-            self.inner.put_multipart_opts(location, options).await
-        }
-
-        async fn get_opts(&self, location: &Path, options: GetOptions) -> Result<GetResult> {
-            self.inner.get_opts(location, options).await
-        }
-
-        fn delete_stream(
-            &self,
-            locations: BoxStream<'static, Result<Path>>,
-        ) -> BoxStream<'static, Result<Path>> {
-            self.inner.delete_stream(locations)
-        }
-
-        fn list(&self, prefix: Option<&Path>) -> BoxStream<'static, Result<ObjectMeta>> {
-            self.inner.list(prefix)
-        }
-
-        async fn list_with_delimiter(&self, prefix: Option<&Path>) -> Result<ListResult> {
-            self.inner.list_with_delimiter(prefix).await
-        }
-
-        async fn copy_opts(&self, from: &Path, to: &Path, options: CopyOptions) -> Result<()> {
-            self.inner.delete(from).await?;
-            self.inner.copy_opts(from, to, options).await
-        }
-    }
-
-    #[tokio::test]
-    async fn test_copy_when_source_is_missing_from_secondary() {
-        let primary_dir = tempfile::tempdir().unwrap();
-        let secondary_dir = tempfile::tempdir().unwrap();
-        let primary: Arc<dyn ObjectStore> =
-            Arc::new(LocalFileSystem::new_with_prefix(primary_dir.path()).unwrap());
-        let secondary: Arc<dyn ObjectStore> =
-            Arc::new(LocalFileSystem::new_with_prefix(secondary_dir.path()).unwrap());
-        let store = MirroringObjectStore {
-            primary: primary.clone(),
-            secondary: secondary.clone(),
-        };
-        let staging = Path::from("_versions/1.manifest-staging");
-        let finalized = Path::from("_versions/1.manifest");
-
-        primary
-            .put(&staging, "manifest contents".into())
-            .await
-            .unwrap();
-
-        tokio::time::timeout(Duration::from_secs(5), store.copy(&staging, &finalized))
-            .await
-            .expect("copy should not hang when the secondary source is missing")
-            .unwrap();
-
-        let copied = primary
-            .get(&finalized)
-            .await
-            .unwrap()
-            .bytes()
-            .await
-            .unwrap();
-        assert_eq!(copied, "manifest contents");
-        assert!(matches!(
-            secondary.head(&finalized).await,
-            Err(Error::NotFound { .. })
-        ));
-    }
-
-    #[tokio::test]
-    async fn test_copy_when_secondary_source_disappears_after_head() {
-        let primary: Arc<dyn ObjectStore> = Arc::new(InMemory::new());
-        let secondary_inner: Arc<dyn ObjectStore> = Arc::new(InMemory::new());
-        let secondary: Arc<dyn ObjectStore> = Arc::new(EvictBeforeCopyStore {
-            inner: secondary_inner.clone(),
-        });
-        let store = MirroringObjectStore {
-            primary: primary.clone(),
-            secondary,
-        };
-        let staging = Path::from("_versions/1.manifest-staging");
-        let finalized = Path::from("_versions/1.manifest");
-
-        primary
-            .put(&staging, "manifest contents".into())
-            .await
-            .unwrap();
-        secondary_inner
-            .put(&staging, "manifest contents".into())
-            .await
-            .unwrap();
-
-        store.copy(&staging, &finalized).await.unwrap();
-
-        let copied = primary
-            .get(&finalized)
-            .await
-            .unwrap()
-            .bytes()
-            .await
-            .unwrap();
-        assert_eq!(copied, "manifest contents");
-        assert!(matches!(
-            secondary_inner.head(&finalized).await,
-            Err(Error::NotFound { .. })
-        ));
-    }
-
     // This test is ignored because lance 3.0 introduced LocalWriter optimization
     // that bypasses the object store wrapper for local writes. The mirroring feature
     // still works for remote/cloud storage, but can't be tested with local storage.

rust/lancedb/src/query.rs

@@ -511,7 +511,9 @@ pub trait QueryBase {
 
     /// Rerank the results using the specified reranker.
     ///
-    /// This is currently only supported for Hybrid Search.
+    /// For vector-only searches, the reranker receives the vector results and an
+    /// empty full-text result set and must declare its output schema. Reranking
+    /// multiple query vectors in one query is not supported.
     fn rerank(self, reranker: Arc<dyn Reranker>) -> Self;
 
     /// The method to normalize the scores. Can be "rank" or "Score". If "Rank",
@@ -1138,6 +1140,44 @@ pub struct VectorQuery {
 }
 
 impl VectorQuery {
+    fn check_vector_rerank_supported(&self) -> Result<()> {
+        if self.request.query_vector.len() > 1 {
+            return Err(Error::NotSupported {
+                message: "reranking multiple query vectors is not supported; execute one query per vector"
+                    .to_string(),
+            });
+        }
+        Ok(())
+    }
+
+    async fn vector_rerank_output_schema(&self) -> Result<SchemaRef> {
+        self.check_vector_rerank_supported()?;
+
+        // Rerankers receive row IDs internally. Apply their schema transform to
+        // that exact input and then hide the row ID from the declared public
+        // schema unless it was explicitly requested.
+        let vector_query = self.clone().with_row_id();
+        let plan = vector_query
+            .create_plan(QueryExecutionOptions::default())
+            .await?;
+        let reranker = self
+            .request
+            .base
+            .reranker
+            .as_ref()
+            .expect("vector_rerank_output_schema requires a reranker");
+        let input_schema = plan.schema();
+        let output_schema = reranker.output_schema(&input_schema).await?;
+
+        if self.request.base.with_row_id {
+            Ok(output_schema)
+        } else {
+            Ok(RecordBatch::new_empty(output_schema)
+                .drop_column(ROW_ID)?
+                .schema())
+        }
+    }
+
     fn new(base: Query) -> Self {
         Self {
             parent: base.parent,
@@ -1443,6 +1483,61 @@ impl VectorQuery {
         Ok(single_batch_stream(results, max_batch_length))
     }
 
+    async fn execute_vector_rerank(
+        &self,
+        options: QueryExecutionOptions,
+    ) -> Result<SendableRecordBatchStream> {
+        self.check_vector_rerank_supported()?;
+
+        let max_batch_length = options.max_batch_length as usize;
+        let internal_options = options.without_output_batch_length_limit();
+
+        // RRF needs row IDs to assign and preserve scores. Keep them internal unless
+        // the caller explicitly requested them.
+        let vector_query = self.clone().with_row_id();
+        let vector_results = vector_query
+            .inner_execute_with_options(internal_options)
+            .await?;
+        let schema = vector_results.schema();
+        let vector_results = vector_results.try_collect::<Vec<_>>().await?;
+        let vector_results = concat_batches(&schema, vector_results.iter())?;
+        let vector_schema = vector_results.schema();
+        let fts_results = RecordBatch::new_empty(vector_schema.clone());
+
+        let reranker = self
+            .request
+            .base
+            .reranker
+            .as_ref()
+            .expect("execute_vector_rerank requires a reranker");
+        let expected_schema = reranker.output_schema(&vector_schema).await?;
+        let mut results = reranker
+            .rerank_hybrid("", vector_results, fts_results)
+            .await?;
+
+        check_reranker_result(&results)?;
+        if results.schema() != expected_schema {
+            return Err(Error::Schema {
+                message: format!(
+                    "reranker returned schema {:?}, but declared {:?}",
+                    results.schema(),
+                    expected_schema
+                ),
+            });
+        }
+
+        let limit = self.request.base.limit.unwrap_or(DEFAULT_TOP_K);
+        if results.num_rows() > limit {
+            results = results.slice(0, limit);
+        }
+
+        if !self.request.base.with_row_id {
+            results = results.drop_column(ROW_ID)?;
+        }
+
+        Ok(single_batch_stream(results, max_batch_length))
+    }
+
     async fn inner_execute_with_options(
         &self,
         options: QueryExecutionOptions,
@@ -1495,6 +1590,23 @@ impl ExecutableQuery for VectorQuery {
             return Ok(hybrid_result);
         }
 
+        if self.request.base.reranker.is_some() {
+            let timeout = options.timeout;
+            let mut rerank_options = options;
+            // A single outer deadline covers planning, candidate collection,
+            // schema declaration, and the complete reranker callback.
+            rerank_options.timeout = None;
+            let execution = self.execute_vector_rerank(rerank_options);
+            return match timeout {
+                Some(timeout) => tokio::time::timeout(timeout, execution)
+                    .await
+                    .map_err(|_| Error::Timeout {
+                        message: format!("Query timeout after {} ms", timeout.as_millis()),
+                    })?,
+                None => execution.await,
+            };
+        }
+
         self.inner_execute_with_options(options).await
     }
 
@@ -1507,6 +1619,15 @@ impl ExecutableQuery for VectorQuery {
         let query = AnyQuery::VectorQuery(self.request.clone());
         self.parent.analyze_plan(&query, options).await
     }
+
+    async fn output_schema(&self) -> Result<SchemaRef> {
+        if self.request.base.full_text_search.is_none() && self.request.base.reranker.is_some() {
+            self.vector_rerank_output_schema().await
+        } else {
+            let plan = self.create_plan(QueryExecutionOptions::default()).await?;
+            Ok(plan.schema())
+        }
+    }
 }
 
 impl HasQuery for VectorQuery {
@@ -1643,7 +1764,13 @@ impl ExecutableQuery for TakeQuery {
 
 #[cfg(test)]
 mod tests {
-    use std::{collections::HashSet, sync::Arc};
+    use std::{
+        collections::HashSet,
+        sync::{
+            Arc,
+            atomic::{AtomicBool, Ordering},
+        },
+    };
 
     use super::*;
     use arrow::{array::downcast_array, compute::concat_batches, datatypes::Int32Type};
@@ -1659,10 +1786,41 @@ mod tests {
 
     use crate::{Table, connect, database::CreateTableMode, index::Index};
 
+    #[derive(Debug)]
+    struct SlowReranker {
+        invoked: Arc<AtomicBool>,
+    }
+
+    #[async_trait::async_trait]
+    impl Reranker for SlowReranker {
+        async fn output_schema(&self, input: &SchemaRef) -> Result<SchemaRef> {
+            RRFReranker::default().output_schema(input).await
+        }
+
+        async fn rerank_hybrid(
+            &self,
+            query: &str,
+            vector_results: RecordBatch,
+            fts_results: RecordBatch,
+        ) -> Result<RecordBatch> {
+            self.invoked.store(true, Ordering::SeqCst);
+            tokio::time::sleep(Duration::from_secs(2)).await;
+            RRFReranker::default()
+                .rerank_hybrid(query, vector_results, fts_results)
+                .await
+        }
+    }
+
     #[tokio::test]
     async fn test_setters_getters() {
+        // TODO: Switch back to memory://foo after https://github.com/lancedb/lancedb/issues/1051
+        // is fixed
+        let tmp_dir = tempdir().unwrap();
+        let dataset_path = tmp_dir.path().join("test.lance");
+        let uri = dataset_path.to_str().unwrap();
+
         let batches = make_test_batches();
-        let conn = connect("memory://foo").execute().await.unwrap();
+        let conn = connect(uri).execute().await.unwrap();
         let table = conn
             .create_table("my_table", batches)
             .execute()
@@ -1757,8 +1915,14 @@ mod tests {
 
     #[tokio::test]
     async fn test_execute() {
+        // TODO: Switch back to memory://foo after https://github.com/lancedb/lancedb/issues/1051
+        // is fixed
+        let tmp_dir = tempdir().unwrap();
+        let dataset_path = tmp_dir.path().join("test.lance");
+        let uri = dataset_path.to_str().unwrap();
+
         let batches = make_non_empty_batches();
-        let conn = connect("memory://foo").execute().await.unwrap();
+        let conn = connect(uri).execute().await.unwrap();
         let table = conn
             .create_table("my_table", batches)
             .execute()
@@ -1877,8 +2041,14 @@ mod tests {
 
     #[tokio::test]
     async fn test_select_with_transform() {
+        // TODO: Switch back to memory://foo after https://github.com/lancedb/lancedb/issues/1051
+        // is fixed
+        let tmp_dir = tempdir().unwrap();
+        let dataset_path = tmp_dir.path().join("test.lance");
+        let uri = dataset_path.to_str().unwrap();
+
         let batches = make_non_empty_batches();
-        let conn = connect("memory://foo").execute().await.unwrap();
+        let conn = connect(uri).execute().await.unwrap();
         let table = conn
             .create_table("my_table", batches)
             .execute()
@@ -1975,9 +2145,15 @@ mod tests {
 
     #[tokio::test]
     async fn test_execute_no_vector() {
+        // TODO: Switch back to memory://foo after https://github.com/lancedb/lancedb/issues/1051
+        // is fixed
+        let tmp_dir = tempdir().unwrap();
+        let dataset_path = tmp_dir.path().join("test.lance");
+        let uri = dataset_path.to_str().unwrap();
+
         // test that it's ok to not specify a query vector (just filter / limit)
         let batches = make_non_empty_batches();
-        let conn = connect("memory://foo").execute().await.unwrap();
+        let conn = connect(uri).execute().await.unwrap();
         let table = conn
             .create_table("my_table", batches)
             .execute()
@@ -2346,6 +2522,71 @@ mod tests {
         // We don't guarantee order.
         assert!(query_index.values().contains(&0));
         assert!(query_index.values().contains(&1));
+
+        let reranked = query.rerank(Arc::new(RRFReranker::default()));
+        let Err(execute_error) = reranked.execute().await else {
+            panic!("multi-vector reranking should be rejected");
+        };
+        assert!(
+            execute_error
+                .to_string()
+                .contains("reranking multiple query vectors is not supported")
+        );
+        let schema_error = reranked.output_schema().await.unwrap_err();
+        assert!(
+            schema_error
+                .to_string()
+                .contains("reranking multiple query vectors is not supported")
+        );
+    }
+
+    #[tokio::test]
+    async fn test_vector_rerank_timeout_covers_reranker() {
+        let tmp_dir = tempdir().unwrap();
+        let table = make_test_table(&tmp_dir).await;
+        let invoked = Arc::new(AtomicBool::new(false));
+        let reranker = SlowReranker {
+            invoked: invoked.clone(),
+        };
+
+        let result = table
+            .vector_search(&[0.1, 0.2, 0.3, 0.4])
+            .unwrap()
+            .limit(1)
+            .rerank(Arc::new(reranker))
+            .execute_with_options(QueryExecutionOptions {
+                timeout: Some(Duration::from_secs(1)),
+                ..Default::default()
+            })
+            .await;
+
+        assert!(invoked.load(Ordering::SeqCst));
+        assert!(matches!(result, Err(Error::Timeout { .. })));
+    }
+
+    #[tokio::test]
+    async fn test_vector_rerank_output_schema_matches_execution() {
+        let tmp_dir = tempdir().unwrap();
+        let table = make_test_table(&tmp_dir).await;
+        let query = table
+            .vector_search(&[0.1, 0.2, 0.3, 0.4])
+            .unwrap()
+            .limit(1)
+            .rerank(Arc::new(RRFReranker::default()));
+
+        let promised = query.output_schema().await.unwrap();
+        let actual = query
+            .execute()
+            .await
+            .unwrap()
+            .next()
+            .await
+            .unwrap()
+            .unwrap()
+            .schema();
+
+        assert_eq!(promised, actual);
+        assert!(promised.column_with_name("_relevance_score").is_some());
     }
 
     #[tokio::test]

rust/lancedb/src/remote/client.rs

@@ -373,37 +373,6 @@ pub fn parse_db_url(db_url: &str) -> Result<ParsedDbUrl> {
     Ok(ParsedDbUrl { db_name, db_prefix })
 }
 
-fn validate_dns_hostname(hostname: &str) -> Result<()> {
-    let ascii_hostname = match url::Host::parse(hostname) {
-        Ok(url::Host::Domain(hostname)) => hostname,
-        Ok(_) => {
-            return Err(Error::InvalidInput {
-                message: "LanceDB Cloud database URI or region produced a non-DNS hostname"
-                    .to_string(),
-            });
-        }
-        Err(err) => {
-            return Err(Error::InvalidInput {
-                message: format!(
-                    "LanceDB Cloud database URI or region produced an invalid hostname: {err}"
-                ),
-            });
-        }
-    };
-
-    if ascii_hostname.len() > 253
-        || ascii_hostname
-            .split('.')
-            .any(|label| label.is_empty() || label.len() > 63)
-    {
-        return Err(Error::InvalidInput {
-            message: "LanceDB Cloud database URI or region produced an invalid hostname: DNS labels must contain 1 to 63 bytes and the full hostname must not exceed 253 bytes".to_string(),
-        });
-    }
-
-    Ok(())
-}
-
 impl RestfulLanceDbClient<Sender> {
     fn get_timeout(passed: Option<Duration>, env_var: &str) -> Result<Option<Duration>> {
         if let Some(passed) = passed {
@@ -511,11 +480,7 @@ impl RestfulLanceDbClient<Sender> {
 
         let host = match host_override {
             Some(host_override) => host_override,
-            None => {
-                let hostname = format!("{}.{}.api.lancedb.com", parsed_url.db_name, region);
-                validate_dns_hostname(&hostname)?;
-                format!("https://{hostname}")
-            }
+            None => format!("https://{}.{}.api.lancedb.com", parsed_url.db_name, region),
         };
         debug!("Created client for host: {}", host);
         let retry_config = client_config.retry_config.clone().try_into()?;
@@ -1192,29 +1157,6 @@ mod tests {
         assert_eq!(headers.get("x-api-key").unwrap(), "api-key");
     }
 
-    #[test]
-    fn test_rejects_invalid_cloud_dns_hostname() {
-        let invalid_database_names = ["a".repeat(64), "invalid..database".to_string()];
-
-        for db_name in invalid_database_names {
-            let parsed_url = parse_db_url(&format!("db://{db_name}")).unwrap();
-            let error = RestfulLanceDbClient::<Sender>::try_new(
-                &parsed_url,
-                "us-east-1",
-                None,
-                HeaderMap::new(),
-                ClientConfig::default(),
-                None,
-            )
-            .unwrap_err();
-
-            assert!(
-                matches!(error, Error::InvalidInput { ref message } if message.contains("DNS labels must contain 1 to 63 bytes")),
-                "unexpected error: {error}"
-            );
-        }
-    }
-
     // Test implementation of HeaderProvider
     #[derive(Debug, Clone)]
     struct TestHeaderProvider {

rust/lancedb/src/remote/table.rs

@@ -2791,10 +2791,9 @@ impl<S: HttpSend> BaseTable for RemoteTable<S> {
     }
 
     async fn index_stats(&self, index_name: &str) -> Result<Option<IndexStatistics>> {
-        let encoded_name = urlencoding::encode(index_name);
         let mut request = self.post_read(&format!(
-            "/v1/table/{}/index/{encoded_name}/stats/",
-            self.identifier
+            "/v1/table/{}/index/{}/stats/",
+            self.identifier, index_name
         ));
         let version = self.current_version().await;
         let mut body = serde_json::json!({ "version": version });
@@ -2821,10 +2820,9 @@ impl<S: HttpSend> BaseTable for RemoteTable<S> {
     }
 
     async fn drop_index(&self, index_name: &str) -> Result<()> {
-        let encoded_name = urlencoding::encode(index_name);
         let request = self.apply_branch_query(self.client.post(&format!(
-            "/v1/table/{}/index/{encoded_name}/drop/",
-            self.identifier
+            "/v1/table/{}/index/{}/drop/",
+            self.identifier, index_name
         )));
         let (request_id, response) = self.send(request, true).await?;
         if response.status() == StatusCode::NOT_FOUND {
@@ -2837,10 +2835,9 @@ impl<S: HttpSend> BaseTable for RemoteTable<S> {
     }
 
     async fn prewarm_index(&self, index_name: &str) -> Result<()> {
-        let encoded_name = urlencoding::encode(index_name);
         let request = self.client.post(&format!(
-            "/v1/table/{}/index/{encoded_name}/prewarm/",
-            self.identifier
+            "/v1/table/{}/index/{}/prewarm/",
+            self.identifier, index_name
         ));
         let (request_id, response) = self.send(request, true).await?;
         if response.status() == StatusCode::NOT_FOUND {
@@ -2942,7 +2939,7 @@ impl<S: HttpSend> BaseTable for RemoteTable<S> {
 }
 
 #[derive(Serialize, Clone, Debug)]
-pub struct MergeInsertRequest {
+pub(crate) struct MergeInsertRequest {
     on: String,
     when_matched_update_all: bool,
     when_matched_update_all_filt: Option<String>,
@@ -5907,18 +5904,16 @@ mod tests {
             .await
             .unwrap();
 
-        // Positions are relative to the first retained token, so dropping the
-        // leading "hello" stop word does not shift the remaining tokens.
         assert_eq!(
             tokens,
             vec![
                 FtsToken {
                     text: "こんにちは".to_string(),
-                    position: 0,
+                    position: 1,
                 },
                 FtsToken {
                     text: "世界".to_string(),
-                    position: 1,
+                    position: 2,
                 },
             ]
         );
@@ -6494,41 +6489,6 @@ mod tests {
         assert!(matches!(e, Error::IndexNotFound { .. }));
     }
 
-    /// Index names are unvalidated, so reserved characters must be
-    /// percent-encoded or they restructure the request path.
-    #[tokio::test]
-    async fn test_per_index_paths_encode_reserved_characters() {
-        const NAME: &str = "my/index?a#b c";
-        const PREFIX: &str = "/v1/table/my_table/index/my%2Findex%3Fa%23b%20c";
-
-        let table = Table::new_with_handler("my_table", |request| {
-            assert_eq!(request.url().path(), format!("{PREFIX}/stats/"));
-            let body = serde_json::json!({
-              "num_indexed_rows": 1,
-              "num_unindexed_rows": 0,
-              "index_type": "IVF_PQ",
-              "distance_type": "l2"
-            });
-            http::Response::builder()
-                .status(200)
-                .body(serde_json::to_string(&body).unwrap())
-                .unwrap()
-        });
-        assert!(table.index_stats(NAME).await.unwrap().is_some());
-
-        let table = Table::new_with_handler("my_table", |request| {
-            assert_eq!(request.url().path(), format!("{PREFIX}/drop/"));
-            http::Response::builder().status(200).body("{}").unwrap()
-        });
-        table.drop_index(NAME).await.unwrap();
-
-        let table = Table::new_with_handler("my_table", |request| {
-            assert_eq!(request.url().path(), format!("{PREFIX}/prewarm/"));
-            http::Response::builder().status(200).body("{}").unwrap()
-        });
-        table.prewarm_index(NAME).await.unwrap();
-    }
-
     #[tokio::test]
     async fn test_set_lsm_write_spec_unsharded() {
         let table = Table::new_with_handler("my_table", |request| {

rust/lancedb/src/remote/table/blobs.rs

@@ -90,7 +90,7 @@ struct RemoteBlobState {
 
 /// Seekable Cloud blob handle over HTTP Range.
 #[derive(Debug)]
-pub struct RemoteBlobFile {
+pub(crate) struct RemoteBlobFile {
     requester: Arc<dyn BlobRangeRequester>,
     state: Mutex<RemoteBlobState>,
     closed: AtomicBool,

rust/lancedb/src/remote/table/insert.rs

@@ -33,7 +33,7 @@ use crate::table::{AddResult, MergeResult};
 /// same Arrow-IPC streaming body and error side-channel; only the target
 /// endpoint, query parameters, and parsed result type differ.
 #[derive(Debug, Clone)]
-pub enum WriteOp {
+pub(crate) enum WriteOp {
     /// `add`: stream to `/v1/table/{id}/insert/`, optionally overwriting.
     Insert { overwrite: bool },
     /// `merge_insert`: stream to `/v1/table/{id}/merge_insert/` with the merge
@@ -49,7 +49,7 @@ pub enum WriteOp {
 /// The parsed server response for a completed write, discriminated by the
 /// operation that produced it.
 #[derive(Debug, Clone)]
-pub enum WriteResult {
+pub(crate) enum WriteResult {
     Add(AddResult),
     Merge(MergeResult),
 }

rust/lancedb/src/rerankers.rs

@@ -8,6 +8,7 @@ use arrow::{
     compute::{concat_batches, filter_record_batch},
 };
 use arrow_array::{BooleanArray, RecordBatch, UInt64Array};
+use arrow_schema::SchemaRef;
 use async_trait::async_trait;
 use lance::dataset::ROW_ID;
 
@@ -47,16 +48,28 @@ impl std::fmt::Display for NormalizeMethod {
     }
 }
 
-/// Interface for a reranker. A reranker is used to rerank the results from a
-/// vector and FTS search. This is useful for combining the results from both
-/// search methods.
+/// Interface for a reranker. A reranker is used to rerank vector and hybrid
+/// search results. This is useful for combining results from multiple search
+/// methods or assigning a relevance score to vector search results.
 #[async_trait]
 pub trait Reranker: std::fmt::Debug + Sync + Send {
-    // TODO support vector reranking and FTS reranking. Currently only hybrid reranking is supported.
+    /// Declare the schema returned by [`Self::rerank_hybrid`] for a vector-only
+    /// query.
+    ///
+    /// Vector reranking validates the returned batch against this schema so
+    /// [`crate::query::ExecutableQuery::output_schema`] and execution cannot
+    /// disagree. Rerankers that only support hybrid search do not need to
+    /// implement this method.
+    async fn output_schema(&self, _input: &SchemaRef) -> Result<SchemaRef> {
+        Err(Error::NotSupported {
+            message: "vector rerankers must declare their output schema".to_string(),
+        })
+    }
 
     /// Rerank function receives the individual results from the vector and FTS search
     /// results. You can choose to use any of the results to generate the final results,
-    /// allowing maximum flexibility.
+    /// allowing maximum flexibility. For a vector-only search, `query` is empty and
+    /// `fts_results` is an empty batch with the same schema as `vector_results`.
     async fn rerank_hybrid(
         &self,
         query: &str,

rust/lancedb/src/rerankers/rrf.rs

@@ -9,7 +9,7 @@ use arrow::{
     compute::{sort_to_indices, take},
 };
 use arrow_array::{Float32Array, RecordBatch, UInt64Array};
-use arrow_schema::{DataType, Field, Schema, SortOptions};
+use arrow_schema::{DataType, Field, Schema, SchemaRef, SortOptions};
 use async_trait::async_trait;
 use lance::dataset::ROW_ID;
 
@@ -44,6 +44,19 @@ impl Default for RRFReranker {
 
 #[async_trait]
 impl Reranker for RRFReranker {
+    async fn output_schema(&self, input: &SchemaRef) -> Result<SchemaRef> {
+        let mut fields = input.fields().to_vec();
+        fields.push(Arc::new(Field::new(
+            RELEVANCE_SCORE,
+            DataType::Float32,
+            false,
+        )));
+        Ok(Arc::new(Schema::new_with_metadata(
+            fields,
+            input.metadata().clone(),
+        )))
+    }
+
     async fn rerank_hybrid(
         &self,
         _query: &str,
@@ -135,15 +148,9 @@ impl Reranker for RRFReranker {
             .collect();
 
         // add relevance score to schema
-        let mut fields = combined_results.schema().fields().to_vec();
-        fields.push(Arc::new(Field::new(
-            RELEVANCE_SCORE,
-            DataType::Float32,
-            false,
-        )));
-        let schema = Schema::new(fields);
+        let schema = self.output_schema(&combined_results.schema()).await?;
 
-        let combined_results = RecordBatch::try_new(Arc::new(schema), columns)?;
+        let combined_results = RecordBatch::try_new(schema, columns)?;
 
         Ok(combined_results)
     }

rust/lancedb/src/table/merge.rs

@@ -315,10 +315,7 @@ pub(crate) async fn execute_merge_insert(
 
 #[cfg(test)]
 mod tests {
-    use arrow_array::builder::FixedSizeBinaryBuilder;
-    use arrow_array::{
-        Int32Array, RecordBatch, RecordBatchIterator, RecordBatchReader, StringArray, UInt64Array,
-    };
+    use arrow_array::{Int32Array, RecordBatch, RecordBatchIterator, RecordBatchReader};
     use arrow_schema::{DataType, Field, Schema};
     use std::sync::Arc;
 
@@ -340,42 +337,6 @@ mod tests {
         Box::new(RecordBatchIterator::new(vec![Ok(batch)], schema))
     }
 
-    fn fixed_size_binary_merge_batch(
-        id_range: std::ops::Range<u64>,
-        price: u64,
-    ) -> Box<dyn RecordBatchReader + Send> {
-        let ids = id_range.collect::<Vec<_>>();
-        let mut id_builder = FixedSizeBinaryBuilder::new(16);
-        for id in &ids {
-            let mut bytes = [0; 16];
-            bytes[..8].copy_from_slice(&id.to_le_bytes());
-            id_builder.append_value(bytes).unwrap();
-        }
-
-        let schema = Arc::new(Schema::new(vec![
-            Field::new("id", DataType::FixedSizeBinary(16), false),
-            Field::new("id_as_int", DataType::UInt64, false),
-            Field::new("name", DataType::Utf8, false),
-            Field::new("market", DataType::Utf8, false),
-        ]));
-        let batch = RecordBatch::try_new(
-            schema.clone(),
-            vec![
-                Arc::new(id_builder.finish()),
-                Arc::new(UInt64Array::from_iter_values(ids.iter().copied())),
-                Arc::new(StringArray::from_iter_values(
-                    ids.iter().map(|id| format!("name{id}")),
-                )),
-                Arc::new(StringArray::from_iter_values(std::iter::repeat_n(
-                    format!("market_{price}"),
-                    ids.len(),
-                ))),
-            ],
-        )
-        .unwrap();
-        Box::new(RecordBatchIterator::new(vec![Ok(batch)], schema))
-    }
-
     #[tokio::test]
     async fn test_merge_insert() {
         let conn = connect("memory://").execute().await.unwrap();
@@ -427,36 +388,6 @@ mod tests {
         );
     }
 
-    #[tokio::test]
-    async fn test_merge_insert_fixed_size_binary_non_nullable() {
-        // Regression test for #2869: an unrelated FixedSizeBinary column used to corrupt the
-        // outer join that implements when_not_matched_by_source_delete.
-        let conn = connect("memory://").execute().await.unwrap();
-        let table = conn
-            .create_table(
-                "fixed_size_binary_merge",
-                fixed_size_binary_merge_batch(0..256, 100),
-            )
-            .execute()
-            .await
-            .unwrap();
-
-        let mut merge_insert = table.merge_insert(&["id_as_int"]);
-        merge_insert
-            .when_matched_update_all(None)
-            .when_not_matched_insert_all()
-            .when_not_matched_by_source_delete(None);
-        let result = merge_insert
-            .execute(fixed_size_binary_merge_batch(100..356, 200))
-            .await
-            .unwrap();
-
-        assert_eq!(result.num_updated_rows, 156);
-        assert_eq!(result.num_inserted_rows, 100);
-        assert_eq!(result.num_deleted_rows, 100);
-        assert_eq!(table.count_rows(None).await.unwrap(), 256);
-    }
-
     #[tokio::test]
     async fn test_merge_insert_use_index() {
         let conn = connect("memory://").execute().await.unwrap();

rust/lancedb/src/table/optimize.rs

@@ -214,17 +214,12 @@ pub(crate) async fn execute_optimize(
 
 #[cfg(test)]
 mod tests {
-    use arrow_array::{
-        Array, FixedSizeListArray, Float32Array, Int32Array, RecordBatch, StringArray,
-    };
+    use arrow_array::{Int32Array, RecordBatch, StringArray};
     use arrow_schema::{DataType, Field, Schema};
-    use lance_arrow::FixedSizeListArrayExt;
     use rstest::rstest;
     use std::sync::Arc;
 
     use crate::connect;
-    use crate::database::listing::OPT_NEW_TABLE_ENABLE_STABLE_ROW_IDS;
-    use crate::index::vector::IvfRqIndexBuilder;
     use crate::index::{Index, scalar::BTreeIndexBuilder};
     use crate::query::ExecutableQuery;
     use crate::table::{CompactionOptions, OptimizeAction, OptimizeStats};
@@ -309,96 +304,6 @@ mod tests {
         assert_eq!(all_values, expected);
     }
 
-    #[tokio::test]
-    async fn test_compact_with_concurrent_add() {
-        const NUM_FRAGMENTS: usize = 5;
-        const ROWS_PER_FRAGMENT: i32 = 300;
-
-        let tmpdir = tempfile::tempdir().unwrap();
-        let conn = connect(tmpdir.path().to_str().unwrap())
-            .execute()
-            .await
-            .unwrap();
-        let schema = Arc::new(Schema::new(vec![Field::new("id", DataType::Int32, false)]));
-        let batch = RecordBatch::try_new(
-            schema,
-            vec![Arc::new(Int32Array::from_iter_values(0..ROWS_PER_FRAGMENT))],
-        )
-        .unwrap();
-
-        let table = conn
-            .create_table("test_concurrent_compact", batch.clone())
-            .execute()
-            .await
-            .unwrap();
-        table
-            .create_index(&["id"], Index::BTree(BTreeIndexBuilder::default()))
-            .execute()
-            .await
-            .unwrap();
-        for _ in 0..NUM_FRAGMENTS {
-            table.add(batch.clone()).execute().await.unwrap();
-        }
-
-        // Use separate handles so the two writes actually overlap, as they can
-        // when different Node connections operate on the same S3 table.
-        let compact_table = conn
-            .open_table("test_concurrent_compact")
-            .execute()
-            .await
-            .unwrap();
-        let append_table = conn
-            .open_table("test_concurrent_compact")
-            .execute()
-            .await
-            .unwrap();
-        let compact_task = tokio::spawn(async move {
-            compact_table
-                .optimize(OptimizeAction::Compact {
-                    options: CompactionOptions {
-                        target_rows_per_fragment: 1_000,
-                        ..Default::default()
-                    },
-                    remap_options: None,
-                })
-                .await
-        });
-        tokio::task::yield_now().await;
-        for _ in 0..NUM_FRAGMENTS {
-            append_table.add(batch.clone()).execute().await.unwrap();
-        }
-        compact_task.await.unwrap().unwrap();
-
-        let table = conn
-            .open_table("test_concurrent_compact")
-            .execute()
-            .await
-            .unwrap();
-        let dataset = table.dataset().unwrap().get().await.unwrap();
-        let fragment_ids = dataset
-            .get_fragments()
-            .iter()
-            .map(|fragment| fragment.id())
-            .collect::<Vec<_>>();
-        assert!(fragment_ids.windows(2).all(|ids| ids[0] < ids[1]));
-
-        // A second compaction exposed the original out-of-order row-id bug.
-        table
-            .optimize(OptimizeAction::Compact {
-                options: CompactionOptions {
-                    target_rows_per_fragment: 1_000,
-                    ..Default::default()
-                },
-                remap_options: None,
-            })
-            .await
-            .unwrap();
-        assert_eq!(
-            table.count_rows(None).await.unwrap(),
-            ROWS_PER_FRAGMENT as usize * (NUM_FRAGMENTS * 2 + 1)
-        );
-    }
-
     #[tokio::test]
     async fn test_optimize_prune_versions() {
         let conn = connect("memory://").execute().await.unwrap();
@@ -537,58 +442,6 @@ mod tests {
         assert_eq!(final_row_count, 200);
     }
 
-    #[tokio::test]
-    async fn test_optimize_vector_index_after_delete_with_stable_row_ids() {
-        const NUM_ROWS: i32 = 400;
-        const DIMENSION: i32 = 32;
-
-        let conn = connect("memory://").execute().await.unwrap();
-        let vectors = FixedSizeListArray::try_new_from_values(
-            Float32Array::from_iter_values((0..NUM_ROWS).flat_map(|id| {
-                (0..DIMENSION).map(move |offset| ((id as f32 * 0.1) + (offset as f32 * 0.3)).sin())
-            })),
-            DIMENSION,
-        )
-        .unwrap();
-        let schema = Arc::new(Schema::new(vec![
-            Field::new("id", DataType::Int32, false),
-            Field::new("vector", vectors.data_type().clone(), false),
-        ]));
-        let batch = RecordBatch::try_new(
-            schema,
-            vec![
-                Arc::new(Int32Array::from_iter_values(0..NUM_ROWS)),
-                Arc::new(vectors),
-            ],
-        )
-        .unwrap();
-        let table = conn
-            .create_table("test_vector_index_optimize_after_delete", batch)
-            .storage_option(OPT_NEW_TABLE_ENABLE_STABLE_ROW_IDS, "true")
-            .execute()
-            .await
-            .unwrap();
-
-        table
-            .create_index(
-                &["vector"],
-                Index::IvfRq(IvfRqIndexBuilder::default().num_partitions(4)),
-            )
-            .execute()
-            .await
-            .unwrap();
-        table.delete("id % 3 = 0").await.unwrap();
-
-        // Regression test for #3330: deleted stable row IDs used to become
-        // misaligned with row addresses while joining small IVF partitions.
-        table
-            .optimize(OptimizeAction::Index(Default::default()))
-            .await
-            .unwrap();
-
-        assert_eq!(table.count_rows(None).await.unwrap(), 266);
-    }
-
     #[tokio::test]
     async fn test_optimize_all() {
         let conn = connect("memory://").execute().await.unwrap();

rust/lancedb/tests/blob_integration.rs

@@ -10,7 +10,7 @@ use arrow_array::{
 use arrow_schema::{DataType, Field, Fields, Schema};
 use futures::TryStreamExt;
 use lance::Dataset;
-use lance_file::version::LanceFileVersion;
+use lance_encoding::version::LanceFileVersion;
 use lancedb::{
     Connection, Error, Result, Table,
     blob::{BlobRangeRequest, blob},