Files
lancedb/nodejs/__test__/table.test.ts
BubbleCal f9d5fa88a1 feat!: migrate FTS from tantivy to lance-index (#1483)
Lance now supports FTS, so add it into lancedb Python, TypeScript and
Rust SDKs.

For Python, we still use tantivy based FTS by default because the lance
FTS index now misses some features of tantivy.

For Python:
- Support to create lance based FTS index
- Support to specify columns for full text search (only available for
lance based FTS index)

For TypeScript:
- Change the search method so that it can accept both string and vector
- Support full text search

For Rust
- Support full text search

The others:
- Update the FTS doc

BREAKING CHANGE: 
- for Python, this renames the attached score column of FTS from "score"
to "_score", this could be a breaking change for users that rely the
scores

---------

Signed-off-by: BubbleCal <bubble-cal@outlook.com>
2024-08-08 15:33:15 +08:00

874 lines
26 KiB
TypeScript

// Copyright 2024 Lance Developers.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
import * as fs from "fs";
import * as path from "path";
import * as tmp from "tmp";
import * as arrow13 from "apache-arrow-13";
import * as arrow14 from "apache-arrow-14";
import * as arrow15 from "apache-arrow-15";
import * as arrow16 from "apache-arrow-16";
import * as arrow17 from "apache-arrow-17";
import { Table, connect } from "../lancedb";
import {
Table as ArrowTable,
Field,
FixedSizeList,
Float32,
Float64,
Int32,
Int64,
Schema,
makeArrowTable,
} from "../lancedb/arrow";
import {
EmbeddingFunction,
LanceSchema,
getRegistry,
register,
} from "../lancedb/embedding";
import { Index } from "../lancedb/indices";
describe.each([arrow13, arrow14, arrow15, arrow16, arrow17])(
"Given a table",
// biome-ignore lint/suspicious/noExplicitAny: <explanation>
(arrow: any) => {
let tmpDir: tmp.DirResult;
let table: Table;
const schema:
| import("apache-arrow-13").Schema
| import("apache-arrow-14").Schema
| import("apache-arrow-15").Schema
| import("apache-arrow-16").Schema
| import("apache-arrow-17").Schema = new arrow.Schema([
new arrow.Field("id", new arrow.Float64(), true),
]);
beforeEach(async () => {
tmpDir = tmp.dirSync({ unsafeCleanup: true });
const conn = await connect(tmpDir.name);
table = await conn.createEmptyTable("some_table", schema);
});
afterEach(() => tmpDir.removeCallback());
it("be displayable", async () => {
expect(table.display()).toMatch(
/NativeTable\(some_table, uri=.*, read_consistency_interval=None\)/,
);
table.close();
expect(table.display()).toBe("ClosedTable(some_table)");
});
it("should let me add data", async () => {
await table.add([{ id: 1 }, { id: 2 }]);
await table.add([{ id: 1 }]);
await expect(table.countRows()).resolves.toBe(3);
});
it("should overwrite data if asked", async () => {
await table.add([{ id: 1 }, { id: 2 }]);
await table.add([{ id: 1 }], { mode: "overwrite" });
await expect(table.countRows()).resolves.toBe(1);
});
it("should let me close the table", async () => {
expect(table.isOpen()).toBe(true);
table.close();
expect(table.isOpen()).toBe(false);
expect(table.countRows()).rejects.toThrow("Table some_table is closed");
});
it("should let me update values", async () => {
await table.add([{ id: 1 }]);
expect(await table.countRows("id == 1")).toBe(1);
expect(await table.countRows("id == 7")).toBe(0);
await table.update({ id: "7" });
expect(await table.countRows("id == 1")).toBe(0);
expect(await table.countRows("id == 7")).toBe(1);
await table.add([{ id: 2 }]);
// Test Map as input
await table.update(new Map(Object.entries({ id: "10" })), {
where: "id % 2 == 0",
});
expect(await table.countRows("id == 2")).toBe(0);
expect(await table.countRows("id == 7")).toBe(1);
expect(await table.countRows("id == 10")).toBe(1);
});
it("should let me update values with `values`", async () => {
await table.add([{ id: 1 }]);
expect(await table.countRows("id == 1")).toBe(1);
expect(await table.countRows("id == 7")).toBe(0);
await table.update({ values: { id: 7 } });
expect(await table.countRows("id == 1")).toBe(0);
expect(await table.countRows("id == 7")).toBe(1);
await table.add([{ id: 2 }]);
// Test Map as input
await table.update({
values: {
id: "10",
},
where: "id % 2 == 0",
});
expect(await table.countRows("id == 2")).toBe(0);
expect(await table.countRows("id == 7")).toBe(1);
expect(await table.countRows("id == 10")).toBe(1);
});
it("should let me update values with `valuesSql`", async () => {
await table.add([{ id: 1 }]);
expect(await table.countRows("id == 1")).toBe(1);
expect(await table.countRows("id == 7")).toBe(0);
await table.update({
valuesSql: {
id: "7",
},
});
expect(await table.countRows("id == 1")).toBe(0);
expect(await table.countRows("id == 7")).toBe(1);
await table.add([{ id: 2 }]);
// Test Map as input
await table.update({
valuesSql: {
id: "10",
},
where: "id % 2 == 0",
});
expect(await table.countRows("id == 2")).toBe(0);
expect(await table.countRows("id == 7")).toBe(1);
expect(await table.countRows("id == 10")).toBe(1);
});
// https://github.com/lancedb/lancedb/issues/1293
test.each([new arrow.Float16(), new arrow.Float32(), new arrow.Float64()])(
"can create empty table with non default float type: %s",
async (floatType) => {
const db = await connect(tmpDir.name);
const data = [
{ text: "hello", vector: Array(512).fill(1.0) },
{ text: "hello world", vector: Array(512).fill(1.0) },
];
const f64Schema = new arrow.Schema([
new arrow.Field("text", new arrow.Utf8(), true),
new arrow.Field(
"vector",
new arrow.FixedSizeList(512, new arrow.Field("item", floatType)),
true,
),
]);
const f64Table = await db.createEmptyTable("f64", f64Schema, {
mode: "overwrite",
});
try {
await f64Table.add(data);
const res = await f64Table.query().toArray();
expect(res.length).toBe(2);
} catch (e) {
expect(e).toBeUndefined();
}
},
);
it("should return the table as an instance of an arrow table", async () => {
const arrowTbl = await table.toArrow();
expect(arrowTbl).toBeInstanceOf(ArrowTable);
});
},
);
describe("merge insert", () => {
let tmpDir: tmp.DirResult;
let table: Table;
beforeEach(async () => {
tmpDir = tmp.dirSync({ unsafeCleanup: true });
const conn = await connect(tmpDir.name);
table = await conn.createTable("some_table", [
{ a: 1, b: "a" },
{ a: 2, b: "b" },
{ a: 3, b: "c" },
]);
});
afterEach(() => tmpDir.removeCallback());
test("upsert", async () => {
const newData = [
{ a: 2, b: "x" },
{ a: 3, b: "y" },
{ a: 4, b: "z" },
];
await table
.mergeInsert("a")
.whenMatchedUpdateAll()
.whenNotMatchedInsertAll()
.execute(newData);
const expected = [
{ a: 1, b: "a" },
{ a: 2, b: "x" },
{ a: 3, b: "y" },
{ a: 4, b: "z" },
];
expect(
JSON.parse(JSON.stringify((await table.toArrow()).toArray())),
).toEqual(expected);
});
test("conditional update", async () => {
const newData = [
{ a: 2, b: "x" },
{ a: 3, b: "y" },
{ a: 4, b: "z" },
];
await table
.mergeInsert("a")
.whenMatchedUpdateAll({ where: "target.b = 'b'" })
.execute(newData);
const expected = [
{ a: 1, b: "a" },
{ a: 2, b: "x" },
{ a: 3, b: "c" },
];
// round trip to arrow and back to json to avoid comparing arrow objects to js object
// biome-ignore lint/suspicious/noExplicitAny: test
let res: any[] = JSON.parse(
JSON.stringify((await table.toArrow()).toArray()),
);
res = res.sort((a, b) => a.a - b.a);
expect(res).toEqual(expected);
});
test("insert if not exists", async () => {
const newData = [
{ a: 2, b: "x" },
{ a: 3, b: "y" },
{ a: 4, b: "z" },
];
await table.mergeInsert("a").whenNotMatchedInsertAll().execute(newData);
const expected = [
{ a: 1, b: "a" },
{ a: 2, b: "b" },
{ a: 3, b: "c" },
{ a: 4, b: "z" },
];
// biome-ignore lint/suspicious/noExplicitAny: <explanation>
let res: any[] = JSON.parse(
JSON.stringify((await table.toArrow()).toArray()),
);
res = res.sort((a, b) => a.a - b.a);
expect(res).toEqual(expected);
});
test("replace range", async () => {
const newData = [
{ a: 2, b: "x" },
{ a: 4, b: "z" },
];
await table
.mergeInsert("a")
.whenMatchedUpdateAll()
.whenNotMatchedInsertAll()
.whenNotMatchedBySourceDelete({ where: "a > 2" })
.execute(newData);
const expected = [
{ a: 1, b: "a" },
{ a: 2, b: "x" },
{ a: 4, b: "z" },
];
// biome-ignore lint/suspicious/noExplicitAny: <explanation>
let res: any[] = JSON.parse(
JSON.stringify((await table.toArrow()).toArray()),
);
res = res.sort((a, b) => a.a - b.a);
expect(res).toEqual(expected);
});
test("replace range no condition", async () => {
const newData = [
{ a: 2, b: "x" },
{ a: 4, b: "z" },
];
await table
.mergeInsert("a")
.whenMatchedUpdateAll()
.whenNotMatchedInsertAll()
.whenNotMatchedBySourceDelete()
.execute(newData);
const expected = [
{ a: 2, b: "x" },
{ a: 4, b: "z" },
];
// biome-ignore lint/suspicious/noExplicitAny: test
let res: any[] = JSON.parse(
JSON.stringify((await table.toArrow()).toArray()),
);
res = res.sort((a, b) => a.a - b.a);
expect(res).toEqual(expected);
});
});
describe("When creating an index", () => {
let tmpDir: tmp.DirResult;
const schema = new Schema([
new Field("id", new Int32(), true),
new Field("vec", new FixedSizeList(32, new Field("item", new Float32()))),
]);
let tbl: Table;
let queryVec: number[];
beforeEach(async () => {
tmpDir = tmp.dirSync({ unsafeCleanup: true });
const db = await connect(tmpDir.name);
const data = makeArrowTable(
Array(300)
.fill(1)
.map((_, i) => ({
id: i,
vec: Array(32)
.fill(1)
.map(() => Math.random()),
})),
{
schema,
},
);
queryVec = data.toArray()[5].vec.toJSON();
tbl = await db.createTable("test", data);
});
afterEach(() => tmpDir.removeCallback());
it("should create a vector index on vector columns", async () => {
await tbl.createIndex("vec");
// check index directory
const indexDir = path.join(tmpDir.name, "test.lance", "_indices");
expect(fs.readdirSync(indexDir)).toHaveLength(1);
const indices = await tbl.listIndices();
expect(indices.length).toBe(1);
expect(indices[0]).toEqual({
name: "vec_idx",
indexType: "IvfPq",
columns: ["vec"],
});
// Search without specifying the column
let rst = await tbl
.query()
.limit(2)
.nearestTo(queryVec)
.distanceType("dot")
.toArrow();
expect(rst.numRows).toBe(2);
// Search using `vectorSearch`
rst = await tbl.vectorSearch(queryVec).limit(2).toArrow();
expect(rst.numRows).toBe(2);
// Search with specifying the column
const rst2 = await tbl
.query()
.limit(2)
.nearestTo(queryVec)
.column("vec")
.toArrow();
expect(rst2.numRows).toBe(2);
expect(rst.toString()).toEqual(rst2.toString());
});
it("should allow parameters to be specified", async () => {
await tbl.createIndex("vec", {
config: Index.ivfPq({
numPartitions: 10,
}),
});
// TODO: Verify parameters when we can load index config as part of list indices
});
it("should allow me to replace (or not) an existing index", async () => {
await tbl.createIndex("id");
// Default is replace=true
await tbl.createIndex("id");
await expect(tbl.createIndex("id", { replace: false })).rejects.toThrow(
"already exists",
);
await tbl.createIndex("id", { replace: true });
});
test("should create a scalar index on scalar columns", async () => {
await tbl.createIndex("id");
const indexDir = path.join(tmpDir.name, "test.lance", "_indices");
expect(fs.readdirSync(indexDir)).toHaveLength(1);
for await (const r of tbl.query().where("id > 1").select(["id"])) {
expect(r.numRows).toBe(298);
}
// should also work with 'filter' alias
for await (const r of tbl.query().filter("id > 1").select(["id"])) {
expect(r.numRows).toBe(298);
}
});
test("should be able to get index stats", async () => {
await tbl.createIndex("id");
const stats = await tbl.indexStats("id_idx");
expect(stats).toBeDefined();
expect(stats?.numIndexedRows).toEqual(300);
expect(stats?.numUnindexedRows).toEqual(0);
});
test("when getting stats on non-existent index", async () => {
const stats = await tbl.indexStats("some non-existent index");
expect(stats).toBeUndefined();
});
// TODO: Move this test to the query API test (making sure we can reject queries
// when the dimension is incorrect)
test("two columns with different dimensions", async () => {
const db = await connect(tmpDir.name);
const schema = new Schema([
new Field("id", new Int32(), true),
new Field("vec", new FixedSizeList(32, new Field("item", new Float32()))),
new Field(
"vec2",
new FixedSizeList(64, new Field("item", new Float32())),
),
]);
const tbl = await db.createTable(
"two_vectors",
makeArrowTable(
Array(300)
.fill(1)
.map((_, i) => ({
id: i,
vec: Array(32)
.fill(1)
.map(() => Math.random()),
vec2: Array(64) // different dimension
.fill(1)
.map(() => Math.random()),
})),
{ schema },
),
);
// Only build index over v1
await tbl.createIndex("vec", {
config: Index.ivfPq({ numPartitions: 2, numSubVectors: 2 }),
});
const rst = await tbl
.query()
.limit(2)
.nearestTo(
Array(32)
.fill(1)
.map(() => Math.random()),
)
.toArrow();
expect(rst.numRows).toBe(2);
// Search with specifying the column
await expect(
tbl
.query()
.limit(2)
.nearestTo(
Array(64)
.fill(1)
.map(() => Math.random()),
)
.column("vec")
.toArrow(),
).rejects.toThrow(/.* query dim=64, expected vector dim=32.*/);
const query64 = Array(64)
.fill(1)
.map(() => Math.random());
const rst64Query = await tbl.query().limit(2).nearestTo(query64).toArrow();
const rst64Search = await tbl
.query()
.limit(2)
.nearestTo(query64)
.column("vec2")
.toArrow();
expect(rst64Query.toString()).toEqual(rst64Search.toString());
expect(rst64Query.numRows).toBe(2);
});
});
describe("Read consistency interval", () => {
let tmpDir: tmp.DirResult;
beforeEach(() => {
tmpDir = tmp.dirSync({ unsafeCleanup: true });
});
afterEach(() => tmpDir.removeCallback());
// const intervals = [undefined, 0, 0.1];
const intervals = [0];
test.each(intervals)("read consistency interval %p", async (interval) => {
const db = await connect(tmpDir.name);
const table = await db.createTable("my_table", [{ id: 1 }]);
const db2 = await connect(tmpDir.name, {
readConsistencyInterval: interval,
});
const table2 = await db2.openTable("my_table");
expect(await table2.countRows()).toEqual(await table.countRows());
await table.add([{ id: 2 }]);
if (interval === undefined) {
expect(await table2.countRows()).toEqual(1);
// TODO: once we implement time travel we can uncomment this part of the test.
// await table2.checkout_latest();
// expect(await table2.countRows()).toEqual(2);
} else if (interval === 0) {
expect(await table2.countRows()).toEqual(2);
} else {
// interval == 0.1
expect(await table2.countRows()).toEqual(1);
await new Promise((r) => setTimeout(r, 100));
expect(await table2.countRows()).toEqual(2);
}
});
});
describe("schema evolution", function () {
let tmpDir: tmp.DirResult;
beforeEach(() => {
tmpDir = tmp.dirSync({ unsafeCleanup: true });
});
afterEach(() => {
tmpDir.removeCallback();
});
// Create a new sample table
it("can add a new column to the schema", async function () {
const con = await connect(tmpDir.name);
const table = await con.createTable("vectors", [
{ id: 1n, vector: [0.1, 0.2] },
]);
await table.addColumns([
{ name: "price", valueSql: "cast(10.0 as float)" },
]);
const expectedSchema = new Schema([
new Field("id", new Int64(), true),
new Field(
"vector",
new FixedSizeList(2, new Field("item", new Float32(), true)),
true,
),
new Field("price", new Float32(), false),
]);
expect(await table.schema()).toEqual(expectedSchema);
});
it("can alter the columns in the schema", async function () {
const con = await connect(tmpDir.name);
const schema = new Schema([
new Field("id", new Int64(), true),
new Field(
"vector",
new FixedSizeList(2, new Field("item", new Float32(), true)),
true,
),
new Field("price", new Float64(), false),
]);
const table = await con.createTable("vectors", [
{ id: 1n, vector: [0.1, 0.2] },
]);
// Can create a non-nullable column only through addColumns at the moment.
await table.addColumns([
{ name: "price", valueSql: "cast(10.0 as double)" },
]);
expect(await table.schema()).toEqual(schema);
await table.alterColumns([
{ path: "id", rename: "new_id" },
{ path: "price", nullable: true },
]);
const expectedSchema = new Schema([
new Field("new_id", new Int64(), true),
new Field(
"vector",
new FixedSizeList(2, new Field("item", new Float32(), true)),
true,
),
new Field("price", new Float64(), true),
]);
expect(await table.schema()).toEqual(expectedSchema);
});
it("can drop a column from the schema", async function () {
const con = await connect(tmpDir.name);
const table = await con.createTable("vectors", [
{ id: 1n, vector: [0.1, 0.2] },
]);
await table.dropColumns(["vector"]);
const expectedSchema = new Schema([new Field("id", new Int64(), true)]);
expect(await table.schema()).toEqual(expectedSchema);
});
});
describe("when dealing with versioning", () => {
let tmpDir: tmp.DirResult;
beforeEach(() => {
tmpDir = tmp.dirSync({ unsafeCleanup: true });
});
afterEach(() => {
tmpDir.removeCallback();
});
it("can travel in time", async () => {
// Setup
const con = await connect(tmpDir.name);
const table = await con.createTable("vectors", [
{ id: 1n, vector: [0.1, 0.2] },
]);
const version = await table.version();
await table.add([{ id: 2n, vector: [0.1, 0.2] }]);
expect(await table.countRows()).toBe(2);
// Make sure we can rewind
await table.checkout(version);
expect(await table.countRows()).toBe(1);
// Can't add data in time travel mode
await expect(table.add([{ id: 3n, vector: [0.1, 0.2] }])).rejects.toThrow(
"table cannot be modified when a specific version is checked out",
);
// Can go back to normal mode
await table.checkoutLatest();
expect(await table.countRows()).toBe(2);
// Should be able to add data again
await table.add([{ id: 2n, vector: [0.1, 0.2] }]);
expect(await table.countRows()).toBe(3);
// Now checkout and restore
await table.checkout(version);
await table.restore();
expect(await table.countRows()).toBe(1);
// Should be able to add data
await table.add([{ id: 2n, vector: [0.1, 0.2] }]);
expect(await table.countRows()).toBe(2);
// Can't use restore if not checked out
await expect(table.restore()).rejects.toThrow(
"checkout before running restore",
);
});
});
describe("when optimizing a dataset", () => {
let tmpDir: tmp.DirResult;
let table: Table;
beforeEach(async () => {
tmpDir = tmp.dirSync({ unsafeCleanup: true });
const con = await connect(tmpDir.name);
table = await con.createTable("vectors", [{ id: 1 }]);
await table.add([{ id: 2 }]);
});
afterEach(() => {
tmpDir.removeCallback();
});
it("compacts files", async () => {
const stats = await table.optimize();
expect(stats.compaction.filesAdded).toBe(1);
expect(stats.compaction.filesRemoved).toBe(2);
expect(stats.compaction.fragmentsAdded).toBe(1);
expect(stats.compaction.fragmentsRemoved).toBe(2);
});
it("cleanups old versions", async () => {
const stats = await table.optimize({ cleanupOlderThan: new Date() });
expect(stats.prune.bytesRemoved).toBeGreaterThan(0);
expect(stats.prune.oldVersionsRemoved).toBe(3);
});
});
describe.each([arrow13, arrow14, arrow15, arrow16, arrow17])(
"when optimizing a dataset",
// biome-ignore lint/suspicious/noExplicitAny: <explanation>
(arrow: any) => {
let tmpDir: tmp.DirResult;
beforeEach(() => {
getRegistry().reset();
tmpDir = tmp.dirSync({ unsafeCleanup: true });
});
afterEach(() => {
tmpDir.removeCallback();
});
test("can search using a string", async () => {
@register()
class MockEmbeddingFunction extends EmbeddingFunction<string> {
toJSON(): object {
return {};
}
ndims() {
return 1;
}
embeddingDataType() {
return new Float32();
}
// Hardcoded embeddings for the sake of testing
async computeQueryEmbeddings(_data: string) {
switch (_data) {
case "greetings":
return [0.1];
case "farewell":
return [0.2];
default:
return null as never;
}
}
// Hardcoded embeddings for the sake of testing
async computeSourceEmbeddings(data: string[]) {
return data.map((s) => {
switch (s) {
case "hello world":
return [0.1];
case "goodbye world":
return [0.2];
default:
return null as never;
}
});
}
}
const func = new MockEmbeddingFunction();
const schema = LanceSchema({
text: func.sourceField(new arrow.Utf8()),
vector: func.vectorField(),
});
const db = await connect(tmpDir.name);
const data = [{ text: "hello world" }, { text: "goodbye world" }];
const table = await db.createTable("test", data, { schema });
const results = await table.search("greetings").toArray();
expect(results[0].text).toBe(data[0].text);
const results2 = await table.search("farewell").toArray();
expect(results2[0].text).toBe(data[1].text);
});
test("rejects if no embedding function provided", async () => {
const db = await connect(tmpDir.name);
const data = [
{ text: "hello world", vector: [0.1, 0.2, 0.3] },
{ text: "goodbye world", vector: [0.4, 0.5, 0.6] },
];
const table = await db.createTable("test", data);
expect(table.search("hello", "vector").toArray()).rejects.toThrow(
"No embedding functions are defined in the table",
);
});
test("full text search if no embedding function provided", async () => {
const db = await connect(tmpDir.name);
const data = [
{ text: "hello world", vector: [0.1, 0.2, 0.3] },
{ text: "goodbye world", vector: [0.4, 0.5, 0.6] },
];
const table = await db.createTable("test", data);
await table.createIndex("text", {
config: Index.fts(),
});
const results = await table.search("hello").toArray();
expect(results[0].text).toBe(data[0].text);
});
test.each([
[0.4, 0.5, 0.599], // number[]
Float32Array.of(0.4, 0.5, 0.599), // Float32Array
Float64Array.of(0.4, 0.5, 0.599), // Float64Array
])("can search using vectorlike datatypes", async (vectorlike) => {
const db = await connect(tmpDir.name);
const data = [
{ text: "hello world", vector: [0.1, 0.2, 0.3] },
{ text: "goodbye world", vector: [0.4, 0.5, 0.6] },
];
const table = await db.createTable("test", data);
// biome-ignore lint/suspicious/noExplicitAny: test
const results: any[] = await table.search(vectorlike).toArray();
expect(results.length).toBe(2);
expect(results[0].text).toBe(data[1].text);
});
},
);
describe("when calling explainPlan", () => {
let tmpDir: tmp.DirResult;
let table: Table;
let queryVec: number[];
beforeEach(async () => {
tmpDir = tmp.dirSync({ unsafeCleanup: true });
const con = await connect(tmpDir.name);
table = await con.createTable("vectors", [{ id: 1, vector: [0.1, 0.2] }]);
});
afterEach(() => {
tmpDir.removeCallback();
});
it("retrieves query plan", async () => {
queryVec = Array(2)
.fill(1)
.map(() => Math.random());
const plan = await table.query().nearestTo(queryVec).explainPlan(true);
expect(plan).toMatch("KNN");
});
});
describe("column name options", () => {
let tmpDir: tmp.DirResult;
let table: Table;
beforeEach(async () => {
tmpDir = tmp.dirSync({ unsafeCleanup: true });
const con = await connect(tmpDir.name);
table = await con.createTable("vectors", [
{ camelCase: 1, vector: [0.1, 0.2] },
]);
});
test("can select columns with different names", async () => {
const results = await table.query().select(["camelCase"]).toArray();
expect(results[0].camelCase).toBe(1);
});
test("can filter on columns with different names", async () => {
const results = await table.query().where("`camelCase` = 1").toArray();
expect(results[0].camelCase).toBe(1);
});
});