mirror of
https://github.com/lancedb/lancedb.git
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ci(node): run examples in CI (#1796)
This is done as setup for a PR that will fix the OpenAI dependency issue. * [x] FTS examples * [x] Setup mock openai * [x] Ran `npm audit fix` * [x] sentences embeddings test * [x] Double check formatting of docs examples
This commit is contained in:
Will Jones
GitHub
57
nodejs/examples/ann_indexes.test.ts
Normal file
57
nodejs/examples/ann_indexes.test.ts
Normal file
@@ -0,0 +1,57 @@
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// SPDX-License-Identifier: Apache-2.0
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// SPDX-FileCopyrightText: Copyright The LanceDB Authors
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import { expect, test } from "@jest/globals";
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// --8<-- [start:import]
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import * as lancedb from "@lancedb/lancedb";
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import { VectorQuery } from "@lancedb/lancedb";
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// --8<-- [end:import]
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import { withTempDirectory } from "./util.ts";
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test("ann index examples", async () => {
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await withTempDirectory(async (databaseDir) => {
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// --8<-- [start:ingest]
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const db = await lancedb.connect(databaseDir);
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const data = Array.from({ length: 5_000 }, (_, i) => ({
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vector: Array(128).fill(i),
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id: `${i}`,
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content: "",
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longId: `${i}`,
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}));
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const table = await db.createTable("my_vectors", data, {
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mode: "overwrite",
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});
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await table.createIndex("vector", {
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config: lancedb.Index.ivfPq({
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numPartitions: 10,
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numSubVectors: 16,
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}),
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});
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// --8<-- [end:ingest]
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// --8<-- [start:search1]
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const search = table.search(Array(128).fill(1.2)).limit(2) as VectorQuery;
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const results1 = await search.nprobes(20).refineFactor(10).toArray();
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// --8<-- [end:search1]
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expect(results1.length).toBe(2);
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// --8<-- [start:search2]
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const results2 = await table
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.search(Array(128).fill(1.2))
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.where("id != '1141'")
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.limit(2)
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.toArray();
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// --8<-- [end:search2]
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expect(results2.length).toBe(2);
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// --8<-- [start:search3]
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const results3 = await table
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.search(Array(128).fill(1.2))
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.select(["id"])
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.limit(2)
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.toArray();
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// --8<-- [end:search3]
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expect(results3.length).toBe(2);
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});
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}, 100_000);
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@@ -1,49 +0,0 @@
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// --8<-- [start:import]
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import * as lancedb from "@lancedb/lancedb";
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// --8<-- [end:import]
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// --8<-- [start:ingest]
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const db = await lancedb.connect("/tmp/lancedb/");
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const data = Array.from({ length: 10_000 }, (_, i) => ({
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vector: Array(1536).fill(i),
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id: `${i}`,
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content: "",
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longId: `${i}`,
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}));
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const table = await db.createTable("my_vectors", data, { mode: "overwrite" });
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await table.createIndex("vector", {
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config: lancedb.Index.ivfPq({
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numPartitions: 16,
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numSubVectors: 48,
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}),
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});
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// --8<-- [end:ingest]
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// --8<-- [start:search1]
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const _results1 = await table
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.search(Array(1536).fill(1.2))
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.limit(2)
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.nprobes(20)
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.refineFactor(10)
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.toArray();
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// --8<-- [end:search1]
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// --8<-- [start:search2]
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const _results2 = await table
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.search(Array(1536).fill(1.2))
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.where("id != '1141'")
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.limit(2)
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.toArray();
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// --8<-- [end:search2]
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// --8<-- [start:search3]
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const _results3 = await table
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.search(Array(1536).fill(1.2))
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.select(["id"])
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.limit(2)
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.toArray();
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// --8<-- [end:search3]
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console.log("Ann indexes: done");
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175
nodejs/examples/basic.test.ts
Normal file
175
nodejs/examples/basic.test.ts
Normal file
@@ -0,0 +1,175 @@
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// SPDX-License-Identifier: Apache-2.0
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// SPDX-FileCopyrightText: Copyright The LanceDB Authors
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import { expect, test } from "@jest/globals";
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// --8<-- [start:imports]
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import * as lancedb from "@lancedb/lancedb";
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import * as arrow from "apache-arrow";
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import {
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Field,
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FixedSizeList,
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Float16,
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Int32,
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Schema,
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Utf8,
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} from "apache-arrow";
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// --8<-- [end:imports]
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import { withTempDirectory } from "./util.ts";
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test("basic table examples", async () => {
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await withTempDirectory(async (databaseDir) => {
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// --8<-- [start:connect]
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const db = await lancedb.connect(databaseDir);
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// --8<-- [end:connect]
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{
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// --8<-- [start:create_table]
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const _tbl = await db.createTable(
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"myTable",
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[
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{ vector: [3.1, 4.1], item: "foo", price: 10.0 },
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{ vector: [5.9, 26.5], item: "bar", price: 20.0 },
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],
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{ mode: "overwrite" },
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);
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// --8<-- [end:create_table]
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const data = [
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{ vector: [3.1, 4.1], item: "foo", price: 10.0 },
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{ vector: [5.9, 26.5], item: "bar", price: 20.0 },
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];
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{
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// --8<-- [start:create_table_exists_ok]
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const tbl = await db.createTable("myTable", data, {
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existOk: true,
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});
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// --8<-- [end:create_table_exists_ok]
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expect(await tbl.countRows()).toBe(2);
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}
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{
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// --8<-- [start:create_table_overwrite]
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const tbl = await db.createTable("myTable", data, {
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mode: "overwrite",
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});
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// --8<-- [end:create_table_overwrite]
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expect(await tbl.countRows()).toBe(2);
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}
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}
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await db.dropTable("myTable");
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{
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// --8<-- [start:create_table_with_schema]
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const schema = new arrow.Schema([
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new arrow.Field(
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"vector",
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new arrow.FixedSizeList(
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2,
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new arrow.Field("item", new arrow.Float32(), true),
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),
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),
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new arrow.Field("item", new arrow.Utf8(), true),
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new arrow.Field("price", new arrow.Float32(), true),
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]);
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const data = [
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{ vector: [3.1, 4.1], item: "foo", price: 10.0 },
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{ vector: [5.9, 26.5], item: "bar", price: 20.0 },
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];
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const tbl = await db.createTable("myTable", data, {
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schema,
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});
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// --8<-- [end:create_table_with_schema]
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expect(await tbl.countRows()).toBe(2);
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}
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{
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// --8<-- [start:create_empty_table]
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const schema = new arrow.Schema([
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new arrow.Field("id", new arrow.Int32()),
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new arrow.Field("name", new arrow.Utf8()),
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]);
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const emptyTbl = await db.createEmptyTable("empty_table", schema);
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// --8<-- [end:create_empty_table]
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expect(await emptyTbl.countRows()).toBe(0);
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}
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{
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// --8<-- [start:open_table]
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const _tbl = await db.openTable("myTable");
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// --8<-- [end:open_table]
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}
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{
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// --8<-- [start:table_names]
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const tableNames = await db.tableNames();
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// --8<-- [end:table_names]
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expect(tableNames).toEqual(["empty_table", "myTable"]);
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}
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const tbl = await db.openTable("myTable");
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{
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// --8<-- [start:add_data]
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const data = [
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{ vector: [1.3, 1.4], item: "fizz", price: 100.0 },
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{ vector: [9.5, 56.2], item: "buzz", price: 200.0 },
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];
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await tbl.add(data);
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// --8<-- [end:add_data]
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}
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{
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// --8<-- [start:vector_search]
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const res = await tbl.search([100, 100]).limit(2).toArray();
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// --8<-- [end:vector_search]
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expect(res.length).toBe(2);
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}
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{
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const data = Array.from({ length: 1000 })
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.fill(null)
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.map(() => ({
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vector: [Math.random(), Math.random()],
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item: "autogen",
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price: Math.round(Math.random() * 100),
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}));
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await tbl.add(data);
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}
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// --8<-- [start:create_index]
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await tbl.createIndex("vector");
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// --8<-- [end:create_index]
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// --8<-- [start:delete_rows]
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await tbl.delete('item = "fizz"');
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// --8<-- [end:delete_rows]
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// --8<-- [start:drop_table]
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await db.dropTable("myTable");
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// --8<-- [end:drop_table]
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await db.dropTable("empty_table");
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{
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// --8<-- [start:create_f16_table]
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const db = await lancedb.connect(databaseDir);
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const dim = 16;
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const total = 10;
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const f16Schema = new Schema([
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new Field("id", new Int32()),
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new Field(
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"vector",
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new FixedSizeList(dim, new Field("item", new Float16(), true)),
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false,
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),
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]);
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const data = lancedb.makeArrowTable(
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Array.from(Array(total), (_, i) => ({
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id: i,
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vector: Array.from(Array(dim), Math.random),
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})),
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{ schema: f16Schema },
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);
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const _table = await db.createTable("f16_tbl", data);
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// --8<-- [end:create_f16_table]
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await db.dropTable("f16_tbl");
|
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}
|
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});
|
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});
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@@ -1,162 +0,0 @@
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// --8<-- [start:imports]
|
||||
import * as lancedb from "@lancedb/lancedb";
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import * as arrow from "apache-arrow";
|
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import {
|
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Field,
|
||||
FixedSizeList,
|
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Float16,
|
||||
Int32,
|
||||
Schema,
|
||||
Utf8,
|
||||
} from "apache-arrow";
|
||||
|
||||
// --8<-- [end:imports]
|
||||
|
||||
// --8<-- [start:connect]
|
||||
const uri = "/tmp/lancedb/";
|
||||
const db = await lancedb.connect(uri);
|
||||
// --8<-- [end:connect]
|
||||
{
|
||||
// --8<-- [start:create_table]
|
||||
const tbl = await db.createTable(
|
||||
"myTable",
|
||||
[
|
||||
{ vector: [3.1, 4.1], item: "foo", price: 10.0 },
|
||||
{ vector: [5.9, 26.5], item: "bar", price: 20.0 },
|
||||
],
|
||||
{ mode: "overwrite" },
|
||||
);
|
||||
// --8<-- [end:create_table]
|
||||
|
||||
const data = [
|
||||
{ vector: [3.1, 4.1], item: "foo", price: 10.0 },
|
||||
{ vector: [5.9, 26.5], item: "bar", price: 20.0 },
|
||||
];
|
||||
|
||||
{
|
||||
// --8<-- [start:create_table_exists_ok]
|
||||
const tbl = await db.createTable("myTable", data, {
|
||||
existsOk: true,
|
||||
});
|
||||
// --8<-- [end:create_table_exists_ok]
|
||||
}
|
||||
{
|
||||
// --8<-- [start:create_table_overwrite]
|
||||
const _tbl = await db.createTable("myTable", data, {
|
||||
mode: "overwrite",
|
||||
});
|
||||
// --8<-- [end:create_table_overwrite]
|
||||
}
|
||||
}
|
||||
|
||||
{
|
||||
// --8<-- [start:create_table_with_schema]
|
||||
const schema = new arrow.Schema([
|
||||
new arrow.Field(
|
||||
"vector",
|
||||
new arrow.FixedSizeList(
|
||||
2,
|
||||
new arrow.Field("item", new arrow.Float32(), true),
|
||||
),
|
||||
),
|
||||
new arrow.Field("item", new arrow.Utf8(), true),
|
||||
new arrow.Field("price", new arrow.Float32(), true),
|
||||
]);
|
||||
const data = [
|
||||
{ vector: [3.1, 4.1], item: "foo", price: 10.0 },
|
||||
{ vector: [5.9, 26.5], item: "bar", price: 20.0 },
|
||||
];
|
||||
const _tbl = await db.createTable("myTable", data, {
|
||||
schema,
|
||||
});
|
||||
// --8<-- [end:create_table_with_schema]
|
||||
}
|
||||
|
||||
{
|
||||
// --8<-- [start:create_empty_table]
|
||||
|
||||
const schema = new arrow.Schema([
|
||||
new arrow.Field("id", new arrow.Int32()),
|
||||
new arrow.Field("name", new arrow.Utf8()),
|
||||
]);
|
||||
|
||||
const empty_tbl = await db.createEmptyTable("empty_table", schema);
|
||||
// --8<-- [end:create_empty_table]
|
||||
}
|
||||
{
|
||||
// --8<-- [start:open_table]
|
||||
const _tbl = await db.openTable("myTable");
|
||||
// --8<-- [end:open_table]
|
||||
}
|
||||
|
||||
{
|
||||
// --8<-- [start:table_names]
|
||||
const tableNames = await db.tableNames();
|
||||
console.log(tableNames);
|
||||
// --8<-- [end:table_names]
|
||||
}
|
||||
|
||||
const tbl = await db.openTable("myTable");
|
||||
{
|
||||
// --8<-- [start:add_data]
|
||||
const data = [
|
||||
{ vector: [1.3, 1.4], item: "fizz", price: 100.0 },
|
||||
{ vector: [9.5, 56.2], item: "buzz", price: 200.0 },
|
||||
];
|
||||
await tbl.add(data);
|
||||
// --8<-- [end:add_data]
|
||||
}
|
||||
{
|
||||
// --8<-- [start:vector_search]
|
||||
const _res = tbl.search([100, 100]).limit(2).toArray();
|
||||
// --8<-- [end:vector_search]
|
||||
}
|
||||
{
|
||||
const data = Array.from({ length: 1000 })
|
||||
.fill(null)
|
||||
.map(() => ({
|
||||
vector: [Math.random(), Math.random()],
|
||||
item: "autogen",
|
||||
price: Math.round(Math.random() * 100),
|
||||
}));
|
||||
|
||||
await tbl.add(data);
|
||||
}
|
||||
|
||||
// --8<-- [start:create_index]
|
||||
await tbl.createIndex("vector");
|
||||
// --8<-- [end:create_index]
|
||||
|
||||
// --8<-- [start:delete_rows]
|
||||
await tbl.delete('item = "fizz"');
|
||||
// --8<-- [end:delete_rows]
|
||||
|
||||
// --8<-- [start:drop_table]
|
||||
await db.dropTable("myTable");
|
||||
// --8<-- [end:drop_table]
|
||||
await db.dropTable("empty_table");
|
||||
|
||||
{
|
||||
// --8<-- [start:create_f16_table]
|
||||
const db = await lancedb.connect("/tmp/lancedb");
|
||||
const dim = 16;
|
||||
const total = 10;
|
||||
const f16Schema = new Schema([
|
||||
new Field("id", new Int32()),
|
||||
new Field(
|
||||
"vector",
|
||||
new FixedSizeList(dim, new Field("item", new Float16(), true)),
|
||||
false,
|
||||
),
|
||||
]);
|
||||
const data = lancedb.makeArrowTable(
|
||||
Array.from(Array(total), (_, i) => ({
|
||||
id: i,
|
||||
vector: Array.from(Array(dim), Math.random),
|
||||
})),
|
||||
{ schema: f16Schema },
|
||||
);
|
||||
const _table = await db.createTable("f16_tbl", data);
|
||||
// --8<-- [end:create_f16_table]
|
||||
await db.dropTable("f16_tbl");
|
||||
}
|
||||
76
nodejs/examples/custom_embedding_function.test.ts
Normal file
76
nodejs/examples/custom_embedding_function.test.ts
Normal file
@@ -0,0 +1,76 @@
|
||||
import { FeatureExtractionPipeline, pipeline } from "@huggingface/transformers";
|
||||
// SPDX-License-Identifier: Apache-2.0
|
||||
// SPDX-FileCopyrightText: Copyright The LanceDB Authors
|
||||
import { expect, test } from "@jest/globals";
|
||||
// --8<-- [start:imports]
|
||||
import * as lancedb from "@lancedb/lancedb";
|
||||
import {
|
||||
LanceSchema,
|
||||
TextEmbeddingFunction,
|
||||
getRegistry,
|
||||
register,
|
||||
} from "@lancedb/lancedb/embedding";
|
||||
// --8<-- [end:imports]
|
||||
import { withTempDirectory } from "./util.ts";
|
||||
|
||||
// --8<-- [start:embedding_impl]
|
||||
@register("sentence-transformers")
|
||||
class SentenceTransformersEmbeddings extends TextEmbeddingFunction {
|
||||
name = "Xenova/all-miniLM-L6-v2";
|
||||
#ndims!: number;
|
||||
extractor!: FeatureExtractionPipeline;
|
||||
|
||||
async init() {
|
||||
this.extractor = await pipeline("feature-extraction", this.name, {
|
||||
dtype: "fp32",
|
||||
});
|
||||
this.#ndims = await this.generateEmbeddings(["hello"]).then(
|
||||
(e) => e[0].length,
|
||||
);
|
||||
}
|
||||
|
||||
ndims() {
|
||||
return this.#ndims;
|
||||
}
|
||||
|
||||
toJSON() {
|
||||
return {
|
||||
name: this.name,
|
||||
};
|
||||
}
|
||||
async generateEmbeddings(texts: string[]) {
|
||||
const output = await this.extractor(texts, {
|
||||
pooling: "mean",
|
||||
normalize: true,
|
||||
});
|
||||
return output.tolist();
|
||||
}
|
||||
}
|
||||
// -8<-- [end:embedding_impl]
|
||||
|
||||
test("Registry examples", async () => {
|
||||
await withTempDirectory(async (databaseDir) => {
|
||||
// --8<-- [start:call_custom_function]
|
||||
const registry = getRegistry();
|
||||
|
||||
const sentenceTransformer = await registry
|
||||
.get<SentenceTransformersEmbeddings>("sentence-transformers")!
|
||||
.create();
|
||||
|
||||
const schema = LanceSchema({
|
||||
vector: sentenceTransformer.vectorField(),
|
||||
text: sentenceTransformer.sourceField(),
|
||||
});
|
||||
|
||||
const db = await lancedb.connect(databaseDir);
|
||||
const table = await db.createEmptyTable("table", schema, {
|
||||
mode: "overwrite",
|
||||
});
|
||||
|
||||
await table.add([{ text: "hello" }, { text: "world" }]);
|
||||
|
||||
const results = await table.search("greeting").limit(1).toArray();
|
||||
// -8<-- [end:call_custom_function]
|
||||
expect(results.length).toBe(1);
|
||||
});
|
||||
}, 100_000);
|
||||
@@ -1,64 +0,0 @@
|
||||
// --8<-- [start:imports]
|
||||
import * as lancedb from "@lancedb/lancedb";
|
||||
import {
|
||||
LanceSchema,
|
||||
TextEmbeddingFunction,
|
||||
getRegistry,
|
||||
register,
|
||||
} from "@lancedb/lancedb/embedding";
|
||||
import { pipeline } from "@xenova/transformers";
|
||||
// --8<-- [end:imports]
|
||||
|
||||
// --8<-- [start:embedding_impl]
|
||||
@register("sentence-transformers")
|
||||
class SentenceTransformersEmbeddings extends TextEmbeddingFunction {
|
||||
name = "Xenova/all-miniLM-L6-v2";
|
||||
#ndims!: number;
|
||||
extractor: any;
|
||||
|
||||
async init() {
|
||||
this.extractor = await pipeline("feature-extraction", this.name);
|
||||
this.#ndims = await this.generateEmbeddings(["hello"]).then(
|
||||
(e) => e[0].length,
|
||||
);
|
||||
}
|
||||
|
||||
ndims() {
|
||||
return this.#ndims;
|
||||
}
|
||||
|
||||
toJSON() {
|
||||
return {
|
||||
name: this.name,
|
||||
};
|
||||
}
|
||||
async generateEmbeddings(texts: string[]) {
|
||||
const output = await this.extractor(texts, {
|
||||
pooling: "mean",
|
||||
normalize: true,
|
||||
});
|
||||
return output.tolist();
|
||||
}
|
||||
}
|
||||
// -8<-- [end:embedding_impl]
|
||||
|
||||
// --8<-- [start:call_custom_function]
|
||||
const registry = getRegistry();
|
||||
|
||||
const sentenceTransformer = await registry
|
||||
.get<SentenceTransformersEmbeddings>("sentence-transformers")!
|
||||
.create();
|
||||
|
||||
const schema = LanceSchema({
|
||||
vector: sentenceTransformer.vectorField(),
|
||||
text: sentenceTransformer.sourceField(),
|
||||
});
|
||||
|
||||
const db = await lancedb.connect("/tmp/db");
|
||||
const table = await db.createEmptyTable("table", schema, { mode: "overwrite" });
|
||||
|
||||
await table.add([{ text: "hello" }, { text: "world" }]);
|
||||
|
||||
const results = await table.search("greeting").limit(1).toArray();
|
||||
console.log(results[0].text);
|
||||
// -8<-- [end:call_custom_function]
|
||||
96
nodejs/examples/embedding.test.ts
Normal file
96
nodejs/examples/embedding.test.ts
Normal file
@@ -0,0 +1,96 @@
|
||||
// SPDX-License-Identifier: Apache-2.0
|
||||
// SPDX-FileCopyrightText: Copyright The LanceDB Authors
|
||||
import { expect, test } from "@jest/globals";
|
||||
// --8<-- [start:imports]
|
||||
import * as lancedb from "@lancedb/lancedb";
|
||||
import "@lancedb/lancedb/embedding/openai";
|
||||
import { LanceSchema, getRegistry, register } from "@lancedb/lancedb/embedding";
|
||||
import { EmbeddingFunction } from "@lancedb/lancedb/embedding";
|
||||
import { type Float, Float32, Utf8 } from "apache-arrow";
|
||||
// --8<-- [end:imports]
|
||||
import { withTempDirectory } from "./util.ts";
|
||||
|
||||
const openAiTest = process.env.OPENAI_API_KEY == null ? test.skip : test;
|
||||
|
||||
openAiTest("openai embeddings", async () => {
|
||||
await withTempDirectory(async (databaseDir) => {
|
||||
// --8<-- [start:openai_embeddings]
|
||||
const db = await lancedb.connect(databaseDir);
|
||||
const func = getRegistry()
|
||||
.get("openai")
|
||||
?.create({ model: "text-embedding-ada-002" }) as EmbeddingFunction;
|
||||
|
||||
const wordsSchema = LanceSchema({
|
||||
text: func.sourceField(new Utf8()),
|
||||
vector: func.vectorField(),
|
||||
});
|
||||
const tbl = await db.createEmptyTable("words", wordsSchema, {
|
||||
mode: "overwrite",
|
||||
});
|
||||
await tbl.add([{ text: "hello world" }, { text: "goodbye world" }]);
|
||||
|
||||
const query = "greetings";
|
||||
const actual = (await tbl.search(query).limit(1).toArray())[0];
|
||||
// --8<-- [end:openai_embeddings]
|
||||
expect(actual).toHaveProperty("text");
|
||||
});
|
||||
});
|
||||
|
||||
test("custom embedding function", async () => {
|
||||
await withTempDirectory(async (databaseDir) => {
|
||||
// --8<-- [start:embedding_function]
|
||||
const db = await lancedb.connect(databaseDir);
|
||||
|
||||
@register("my_embedding")
|
||||
class MyEmbeddingFunction extends EmbeddingFunction<string> {
|
||||
toJSON(): object {
|
||||
return {};
|
||||
}
|
||||
ndims() {
|
||||
return 3;
|
||||
}
|
||||
embeddingDataType(): Float {
|
||||
return new Float32();
|
||||
}
|
||||
async computeQueryEmbeddings(_data: string) {
|
||||
// This is a placeholder for a real embedding function
|
||||
return [1, 2, 3];
|
||||
}
|
||||
async computeSourceEmbeddings(data: string[]) {
|
||||
// This is a placeholder for a real embedding function
|
||||
return Array.from({ length: data.length }).fill([
|
||||
1, 2, 3,
|
||||
]) as number[][];
|
||||
}
|
||||
}
|
||||
|
||||
const func = new MyEmbeddingFunction();
|
||||
|
||||
const data = [{ text: "pepperoni" }, { text: "pineapple" }];
|
||||
|
||||
// Option 1: manually specify the embedding function
|
||||
const table = await db.createTable("vectors", data, {
|
||||
embeddingFunction: {
|
||||
function: func,
|
||||
sourceColumn: "text",
|
||||
vectorColumn: "vector",
|
||||
},
|
||||
mode: "overwrite",
|
||||
});
|
||||
|
||||
// Option 2: provide the embedding function through a schema
|
||||
|
||||
const schema = LanceSchema({
|
||||
text: func.sourceField(new Utf8()),
|
||||
vector: func.vectorField(),
|
||||
});
|
||||
|
||||
const table2 = await db.createTable("vectors2", data, {
|
||||
schema,
|
||||
mode: "overwrite",
|
||||
});
|
||||
// --8<-- [end:embedding_function]
|
||||
expect(await table.countRows()).toBe(2);
|
||||
expect(await table2.countRows()).toBe(2);
|
||||
});
|
||||
});
|
||||
@@ -1,83 +0,0 @@
|
||||
// --8<-- [start:imports]
|
||||
import * as lancedb from "@lancedb/lancedb";
|
||||
import { LanceSchema, getRegistry, register } from "@lancedb/lancedb/embedding";
|
||||
import { EmbeddingFunction } from "@lancedb/lancedb/embedding";
|
||||
import { type Float, Float32, Utf8 } from "apache-arrow";
|
||||
// --8<-- [end:imports]
|
||||
|
||||
{
|
||||
// --8<-- [start:openai_embeddings]
|
||||
|
||||
const db = await lancedb.connect("/tmp/db");
|
||||
const func = getRegistry()
|
||||
.get("openai")
|
||||
?.create({ model: "text-embedding-ada-002" }) as EmbeddingFunction;
|
||||
|
||||
const wordsSchema = LanceSchema({
|
||||
text: func.sourceField(new Utf8()),
|
||||
vector: func.vectorField(),
|
||||
});
|
||||
const tbl = await db.createEmptyTable("words", wordsSchema, {
|
||||
mode: "overwrite",
|
||||
});
|
||||
await tbl.add([{ text: "hello world" }, { text: "goodbye world" }]);
|
||||
|
||||
const query = "greetings";
|
||||
const actual = (await (await tbl.search(query)).limit(1).toArray())[0];
|
||||
|
||||
// --8<-- [end:openai_embeddings]
|
||||
console.log("result = ", actual.text);
|
||||
}
|
||||
|
||||
{
|
||||
// --8<-- [start:embedding_function]
|
||||
const db = await lancedb.connect("/tmp/db");
|
||||
|
||||
@register("my_embedding")
|
||||
class MyEmbeddingFunction extends EmbeddingFunction<string> {
|
||||
toJSON(): object {
|
||||
return {};
|
||||
}
|
||||
ndims() {
|
||||
return 3;
|
||||
}
|
||||
embeddingDataType(): Float {
|
||||
return new Float32();
|
||||
}
|
||||
async computeQueryEmbeddings(_data: string) {
|
||||
// This is a placeholder for a real embedding function
|
||||
return [1, 2, 3];
|
||||
}
|
||||
async computeSourceEmbeddings(data: string[]) {
|
||||
// This is a placeholder for a real embedding function
|
||||
return Array.from({ length: data.length }).fill([1, 2, 3]) as number[][];
|
||||
}
|
||||
}
|
||||
|
||||
const func = new MyEmbeddingFunction();
|
||||
|
||||
const data = [{ text: "pepperoni" }, { text: "pineapple" }];
|
||||
|
||||
// Option 1: manually specify the embedding function
|
||||
const table = await db.createTable("vectors", data, {
|
||||
embeddingFunction: {
|
||||
function: func,
|
||||
sourceColumn: "text",
|
||||
vectorColumn: "vector",
|
||||
},
|
||||
mode: "overwrite",
|
||||
});
|
||||
|
||||
// Option 2: provide the embedding function through a schema
|
||||
|
||||
const schema = LanceSchema({
|
||||
text: func.sourceField(new Utf8()),
|
||||
vector: func.vectorField(),
|
||||
});
|
||||
|
||||
const table2 = await db.createTable("vectors2", data, {
|
||||
schema,
|
||||
mode: "overwrite",
|
||||
});
|
||||
// --8<-- [end:embedding_function]
|
||||
}
|
||||
42
nodejs/examples/filtering.test.ts
Normal file
42
nodejs/examples/filtering.test.ts
Normal file
@@ -0,0 +1,42 @@
|
||||
// SPDX-License-Identifier: Apache-2.0
|
||||
// SPDX-FileCopyrightText: Copyright The LanceDB Authors
|
||||
import { expect, test } from "@jest/globals";
|
||||
import * as lancedb from "@lancedb/lancedb";
|
||||
import { withTempDirectory } from "./util.ts";
|
||||
|
||||
test("filtering examples", async () => {
|
||||
await withTempDirectory(async (databaseDir) => {
|
||||
const db = await lancedb.connect(databaseDir);
|
||||
|
||||
const data = Array.from({ length: 10_000 }, (_, i) => ({
|
||||
vector: Array(1536).fill(i),
|
||||
id: i,
|
||||
item: `item ${i}`,
|
||||
strId: `${i}`,
|
||||
}));
|
||||
|
||||
const tbl = await db.createTable("myVectors", data, { mode: "overwrite" });
|
||||
|
||||
// --8<-- [start:search]
|
||||
const _result = await tbl
|
||||
.search(Array(1536).fill(0.5))
|
||||
.limit(1)
|
||||
.where("id = 10")
|
||||
.toArray();
|
||||
// --8<-- [end:search]
|
||||
|
||||
// --8<-- [start:vec_search]
|
||||
const result = await (
|
||||
tbl.search(Array(1536).fill(0)) as lancedb.VectorQuery
|
||||
)
|
||||
.where("(item IN ('item 0', 'item 2')) AND (id > 10)")
|
||||
.postfilter()
|
||||
.toArray();
|
||||
// --8<-- [end:vec_search]
|
||||
expect(result.length).toBe(0);
|
||||
|
||||
// --8<-- [start:sql_search]
|
||||
await tbl.query().where("id = 10").limit(10).toArray();
|
||||
// --8<-- [end:sql_search]
|
||||
});
|
||||
});
|
||||
@@ -1,34 +0,0 @@
|
||||
import * as lancedb from "@lancedb/lancedb";
|
||||
|
||||
const db = await lancedb.connect("data/sample-lancedb");
|
||||
|
||||
const data = Array.from({ length: 10_000 }, (_, i) => ({
|
||||
vector: Array(1536).fill(i),
|
||||
id: i,
|
||||
item: `item ${i}`,
|
||||
strId: `${i}`,
|
||||
}));
|
||||
|
||||
const tbl = await db.createTable("myVectors", data, { mode: "overwrite" });
|
||||
|
||||
// --8<-- [start:search]
|
||||
const _result = await tbl
|
||||
.search(Array(1536).fill(0.5))
|
||||
.limit(1)
|
||||
.where("id = 10")
|
||||
.toArray();
|
||||
// --8<-- [end:search]
|
||||
|
||||
// --8<-- [start:vec_search]
|
||||
await tbl
|
||||
.search(Array(1536).fill(0))
|
||||
.where("(item IN ('item 0', 'item 2')) AND (id > 10)")
|
||||
.postfilter()
|
||||
.toArray();
|
||||
// --8<-- [end:vec_search]
|
||||
|
||||
// --8<-- [start:sql_search]
|
||||
await tbl.query().where("id = 10").limit(10).toArray();
|
||||
// --8<-- [end:sql_search]
|
||||
|
||||
console.log("SQL search: done");
|
||||
45
nodejs/examples/full_text_search.test.ts
Normal file
45
nodejs/examples/full_text_search.test.ts
Normal file
@@ -0,0 +1,45 @@
|
||||
// SPDX-License-Identifier: Apache-2.0
|
||||
// SPDX-FileCopyrightText: Copyright The LanceDB Authors
|
||||
import { expect, test } from "@jest/globals";
|
||||
import * as lancedb from "@lancedb/lancedb";
|
||||
import { withTempDirectory } from "./util.ts";
|
||||
|
||||
test("full text search", async () => {
|
||||
await withTempDirectory(async (databaseDir) => {
|
||||
const db = await lancedb.connect(databaseDir);
|
||||
|
||||
const words = [
|
||||
"apple",
|
||||
"banana",
|
||||
"cherry",
|
||||
"date",
|
||||
"elderberry",
|
||||
"fig",
|
||||
"grape",
|
||||
];
|
||||
|
||||
const data = Array.from({ length: 10_000 }, (_, i) => ({
|
||||
vector: Array(1536).fill(i),
|
||||
id: i,
|
||||
item: `item ${i}`,
|
||||
strId: `${i}`,
|
||||
doc: words[i % words.length],
|
||||
}));
|
||||
|
||||
const tbl = await db.createTable("myVectors", data, { mode: "overwrite" });
|
||||
|
||||
await tbl.createIndex("doc", {
|
||||
config: lancedb.Index.fts(),
|
||||
});
|
||||
|
||||
// --8<-- [start:full_text_search]
|
||||
const result = await tbl
|
||||
.query()
|
||||
.nearestToText("apple")
|
||||
.select(["id", "doc"])
|
||||
.limit(10)
|
||||
.toArray();
|
||||
expect(result.length).toBe(10);
|
||||
// --8<-- [end:full_text_search]
|
||||
});
|
||||
});
|
||||
@@ -1,52 +0,0 @@
|
||||
// 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 lancedb from "@lancedb/lancedb";
|
||||
|
||||
const db = await lancedb.connect("data/sample-lancedb");
|
||||
|
||||
const words = [
|
||||
"apple",
|
||||
"banana",
|
||||
"cherry",
|
||||
"date",
|
||||
"elderberry",
|
||||
"fig",
|
||||
"grape",
|
||||
];
|
||||
|
||||
const data = Array.from({ length: 10_000 }, (_, i) => ({
|
||||
vector: Array(1536).fill(i),
|
||||
id: i,
|
||||
item: `item ${i}`,
|
||||
strId: `${i}`,
|
||||
doc: words[i % words.length],
|
||||
}));
|
||||
|
||||
const tbl = await db.createTable("myVectors", data, { mode: "overwrite" });
|
||||
|
||||
await tbl.createIndex("doc", {
|
||||
config: lancedb.Index.fts(),
|
||||
});
|
||||
|
||||
// --8<-- [start:full_text_search]
|
||||
let result = await tbl
|
||||
.search("apple")
|
||||
.select(["id", "doc"])
|
||||
.limit(10)
|
||||
.toArray();
|
||||
console.log(result);
|
||||
// --8<-- [end:full_text_search]
|
||||
|
||||
console.log("SQL search: done");
|
||||
6
nodejs/examples/jest.config.cjs
Normal file
6
nodejs/examples/jest.config.cjs
Normal file
@@ -0,0 +1,6 @@
|
||||
/** @type {import('ts-jest').JestConfigWithTsJest} */
|
||||
module.exports = {
|
||||
preset: "ts-jest",
|
||||
testEnvironment: "node",
|
||||
testPathIgnorePatterns: ["./dist"],
|
||||
};
|
||||
@@ -1,27 +0,0 @@
|
||||
{
|
||||
"compilerOptions": {
|
||||
// Enable latest features
|
||||
"lib": ["ESNext", "DOM"],
|
||||
"target": "ESNext",
|
||||
"module": "ESNext",
|
||||
"moduleDetection": "force",
|
||||
"jsx": "react-jsx",
|
||||
"allowJs": true,
|
||||
|
||||
// Bundler mode
|
||||
"moduleResolution": "bundler",
|
||||
"allowImportingTsExtensions": true,
|
||||
"verbatimModuleSyntax": true,
|
||||
"noEmit": true,
|
||||
|
||||
// Best practices
|
||||
"strict": true,
|
||||
"skipLibCheck": true,
|
||||
"noFallthroughCasesInSwitch": true,
|
||||
|
||||
// Some stricter flags (disabled by default)
|
||||
"noUnusedLocals": false,
|
||||
"noUnusedParameters": false,
|
||||
"noPropertyAccessFromIndexSignature": false
|
||||
}
|
||||
}
|
||||
5009
nodejs/examples/package-lock.json
generated
5009
nodejs/examples/package-lock.json
generated
File diff suppressed because it is too large
Load Diff
@@ -5,24 +5,29 @@
|
||||
"main": "index.js",
|
||||
"type": "module",
|
||||
"scripts": {
|
||||
"test": "echo \"Error: no test specified\" && exit 1"
|
||||
"//1": "--experimental-vm-modules is needed to run jest with sentence-transformers",
|
||||
"//2": "--testEnvironment is needed to run jest with sentence-transformers",
|
||||
"//3": "See: https://github.com/huggingface/transformers.js/issues/57",
|
||||
"test": "node --experimental-vm-modules node_modules/.bin/jest --testEnvironment jest-environment-node-single-context --verbose",
|
||||
"lint": "biome check *.ts && biome format *.ts",
|
||||
"lint-ci": "biome ci .",
|
||||
"lint-fix": "biome check --write *.ts && npm run format",
|
||||
"format": "biome format --write *.ts"
|
||||
},
|
||||
"author": "Lance Devs",
|
||||
"license": "Apache-2.0",
|
||||
"dependencies": {
|
||||
"@lancedb/lancedb": "file:../",
|
||||
"@xenova/transformers": "^2.17.2"
|
||||
"@huggingface/transformers": "^3.0.2",
|
||||
"@lancedb/lancedb": "file:../dist",
|
||||
"openai": "^4.29.2",
|
||||
"sharp": "^0.33.5"
|
||||
},
|
||||
"devDependencies": {
|
||||
"@biomejs/biome": "^1.7.3",
|
||||
"@jest/globals": "^29.7.0",
|
||||
"jest": "^29.7.0",
|
||||
"jest-environment-node-single-context": "^29.4.0",
|
||||
"ts-jest": "^29.2.5",
|
||||
"typescript": "^5.5.4"
|
||||
},
|
||||
"compilerOptions": {
|
||||
"target": "ESNext",
|
||||
"module": "ESNext",
|
||||
"moduleResolution": "Node",
|
||||
"strict": true,
|
||||
"esModuleInterop": true,
|
||||
"skipLibCheck": true,
|
||||
"forceConsistentCasingInFileNames": true
|
||||
}
|
||||
}
|
||||
|
||||
42
nodejs/examples/search.test.ts
Normal file
42
nodejs/examples/search.test.ts
Normal file
@@ -0,0 +1,42 @@
|
||||
// SPDX-License-Identifier: Apache-2.0
|
||||
// SPDX-FileCopyrightText: Copyright The LanceDB Authors
|
||||
import { expect, test } from "@jest/globals";
|
||||
// --8<-- [start:import]
|
||||
import * as lancedb from "@lancedb/lancedb";
|
||||
// --8<-- [end:import]
|
||||
import { withTempDirectory } from "./util.ts";
|
||||
|
||||
test("full text search", async () => {
|
||||
await withTempDirectory(async (databaseDir) => {
|
||||
{
|
||||
const db = await lancedb.connect(databaseDir);
|
||||
|
||||
const data = Array.from({ length: 10_000 }, (_, i) => ({
|
||||
vector: Array(128).fill(i),
|
||||
id: `${i}`,
|
||||
content: "",
|
||||
longId: `${i}`,
|
||||
}));
|
||||
|
||||
await db.createTable("my_vectors", data);
|
||||
}
|
||||
|
||||
// --8<-- [start:search1]
|
||||
const db = await lancedb.connect(databaseDir);
|
||||
const tbl = await db.openTable("my_vectors");
|
||||
|
||||
const results1 = await tbl.search(Array(128).fill(1.2)).limit(10).toArray();
|
||||
// --8<-- [end:search1]
|
||||
expect(results1.length).toBe(10);
|
||||
|
||||
// --8<-- [start:search2]
|
||||
const results2 = await (
|
||||
tbl.search(Array(128).fill(1.2)) as lancedb.VectorQuery
|
||||
)
|
||||
.distanceType("cosine")
|
||||
.limit(10)
|
||||
.toArray();
|
||||
// --8<-- [end:search2]
|
||||
expect(results2.length).toBe(10);
|
||||
});
|
||||
});
|
||||
@@ -1,38 +0,0 @@
|
||||
// --8<-- [end:import]
|
||||
import * as fs from "node:fs";
|
||||
// --8<-- [start:import]
|
||||
import * as lancedb from "@lancedb/lancedb";
|
||||
|
||||
async function setup() {
|
||||
fs.rmSync("data/sample-lancedb", { recursive: true, force: true });
|
||||
const db = await lancedb.connect("data/sample-lancedb");
|
||||
|
||||
const data = Array.from({ length: 10_000 }, (_, i) => ({
|
||||
vector: Array(1536).fill(i),
|
||||
id: `${i}`,
|
||||
content: "",
|
||||
longId: `${i}`,
|
||||
}));
|
||||
|
||||
await db.createTable("my_vectors", data);
|
||||
}
|
||||
|
||||
await setup();
|
||||
|
||||
// --8<-- [start:search1]
|
||||
const db = await lancedb.connect("data/sample-lancedb");
|
||||
const tbl = await db.openTable("my_vectors");
|
||||
|
||||
const _results1 = await tbl.search(Array(1536).fill(1.2)).limit(10).toArray();
|
||||
// --8<-- [end:search1]
|
||||
|
||||
// --8<-- [start:search2]
|
||||
const _results2 = await tbl
|
||||
.search(Array(1536).fill(1.2))
|
||||
.distanceType("cosine")
|
||||
.limit(10)
|
||||
.toArray();
|
||||
console.log(_results2);
|
||||
// --8<-- [end:search2]
|
||||
|
||||
console.log("search: done");
|
||||
@@ -1,50 +0,0 @@
|
||||
import * as lancedb from "@lancedb/lancedb";
|
||||
|
||||
import { LanceSchema, getRegistry } from "@lancedb/lancedb/embedding";
|
||||
import { Utf8 } from "apache-arrow";
|
||||
|
||||
const db = await lancedb.connect("/tmp/db");
|
||||
const func = await getRegistry().get("huggingface").create();
|
||||
|
||||
const facts = [
|
||||
"Albert Einstein was a theoretical physicist.",
|
||||
"The capital of France is Paris.",
|
||||
"The Great Wall of China is one of the Seven Wonders of the World.",
|
||||
"Python is a popular programming language.",
|
||||
"Mount Everest is the highest mountain in the world.",
|
||||
"Leonardo da Vinci painted the Mona Lisa.",
|
||||
"Shakespeare wrote Hamlet.",
|
||||
"The human body has 206 bones.",
|
||||
"The speed of light is approximately 299,792 kilometers per second.",
|
||||
"Water boils at 100 degrees Celsius.",
|
||||
"The Earth orbits the Sun.",
|
||||
"The Pyramids of Giza are located in Egypt.",
|
||||
"Coffee is one of the most popular beverages in the world.",
|
||||
"Tokyo is the capital city of Japan.",
|
||||
"Photosynthesis is the process by which plants make their food.",
|
||||
"The Pacific Ocean is the largest ocean on Earth.",
|
||||
"Mozart was a prolific composer of classical music.",
|
||||
"The Internet is a global network of computers.",
|
||||
"Basketball is a sport played with a ball and a hoop.",
|
||||
"The first computer virus was created in 1983.",
|
||||
"Artificial neural networks are inspired by the human brain.",
|
||||
"Deep learning is a subset of machine learning.",
|
||||
"IBM's Watson won Jeopardy! in 2011.",
|
||||
"The first computer programmer was Ada Lovelace.",
|
||||
"The first chatbot was ELIZA, created in the 1960s.",
|
||||
].map((text) => ({ text }));
|
||||
|
||||
const factsSchema = LanceSchema({
|
||||
text: func.sourceField(new Utf8()),
|
||||
vector: func.vectorField(),
|
||||
});
|
||||
|
||||
const tbl = await db.createTable("facts", facts, {
|
||||
mode: "overwrite",
|
||||
schema: factsSchema,
|
||||
});
|
||||
|
||||
const query = "How many bones are in the human body?";
|
||||
const actual = await tbl.search(query).limit(1).toArray();
|
||||
|
||||
console.log("Answer: ", actual[0]["text"]);
|
||||
59
nodejs/examples/sentence-transformers.test.ts
Normal file
59
nodejs/examples/sentence-transformers.test.ts
Normal file
@@ -0,0 +1,59 @@
|
||||
// SPDX-License-Identifier: Apache-2.0
|
||||
// SPDX-FileCopyrightText: Copyright The LanceDB Authors
|
||||
import { expect, test } from "@jest/globals";
|
||||
import { withTempDirectory } from "./util.ts";
|
||||
|
||||
import * as lancedb from "@lancedb/lancedb";
|
||||
import "@lancedb/lancedb/embedding/transformers";
|
||||
import { LanceSchema, getRegistry } from "@lancedb/lancedb/embedding";
|
||||
import { Utf8 } from "apache-arrow";
|
||||
|
||||
test("full text search", async () => {
|
||||
await withTempDirectory(async (databaseDir) => {
|
||||
const db = await lancedb.connect(databaseDir);
|
||||
const func = await getRegistry().get("huggingface").create();
|
||||
|
||||
const facts = [
|
||||
"Albert Einstein was a theoretical physicist.",
|
||||
"The capital of France is Paris.",
|
||||
"The Great Wall of China is one of the Seven Wonders of the World.",
|
||||
"Python is a popular programming language.",
|
||||
"Mount Everest is the highest mountain in the world.",
|
||||
"Leonardo da Vinci painted the Mona Lisa.",
|
||||
"Shakespeare wrote Hamlet.",
|
||||
"The human body has 206 bones.",
|
||||
"The speed of light is approximately 299,792 kilometers per second.",
|
||||
"Water boils at 100 degrees Celsius.",
|
||||
"The Earth orbits the Sun.",
|
||||
"The Pyramids of Giza are located in Egypt.",
|
||||
"Coffee is one of the most popular beverages in the world.",
|
||||
"Tokyo is the capital city of Japan.",
|
||||
"Photosynthesis is the process by which plants make their food.",
|
||||
"The Pacific Ocean is the largest ocean on Earth.",
|
||||
"Mozart was a prolific composer of classical music.",
|
||||
"The Internet is a global network of computers.",
|
||||
"Basketball is a sport played with a ball and a hoop.",
|
||||
"The first computer virus was created in 1983.",
|
||||
"Artificial neural networks are inspired by the human brain.",
|
||||
"Deep learning is a subset of machine learning.",
|
||||
"IBM's Watson won Jeopardy! in 2011.",
|
||||
"The first computer programmer was Ada Lovelace.",
|
||||
"The first chatbot was ELIZA, created in the 1960s.",
|
||||
].map((text) => ({ text }));
|
||||
|
||||
const factsSchema = LanceSchema({
|
||||
text: func.sourceField(new Utf8()),
|
||||
vector: func.vectorField(),
|
||||
});
|
||||
|
||||
const tbl = await db.createTable("facts", facts, {
|
||||
mode: "overwrite",
|
||||
schema: factsSchema,
|
||||
});
|
||||
|
||||
const query = "How many bones are in the human body?";
|
||||
const actual = await tbl.search(query).limit(1).toArray();
|
||||
|
||||
expect(actual[0]["text"]).toBe("The human body has 206 bones.");
|
||||
});
|
||||
});
|
||||
17
nodejs/examples/tsconfig.json
Normal file
17
nodejs/examples/tsconfig.json
Normal file
@@ -0,0 +1,17 @@
|
||||
{
|
||||
"include": ["*.test.ts"],
|
||||
"compilerOptions": {
|
||||
"target": "es2022",
|
||||
"module": "NodeNext",
|
||||
"declaration": true,
|
||||
"outDir": "./dist",
|
||||
"strict": true,
|
||||
"allowJs": true,
|
||||
"resolveJsonModule": true,
|
||||
"emitDecoratorMetadata": true,
|
||||
"experimentalDecorators": true,
|
||||
"moduleResolution": "NodeNext",
|
||||
"allowImportingTsExtensions": true,
|
||||
"emitDeclarationOnly": true
|
||||
}
|
||||
}
|
||||
16
nodejs/examples/util.ts
Normal file
16
nodejs/examples/util.ts
Normal file
@@ -0,0 +1,16 @@
|
||||
// SPDX-License-Identifier: Apache-2.0
|
||||
// SPDX-FileCopyrightText: Copyright The LanceDB Authors
|
||||
import * as fs from "fs";
|
||||
import { tmpdir } from "os";
|
||||
import * as path from "path";
|
||||
|
||||
export async function withTempDirectory(
|
||||
fn: (tempDir: string) => Promise<void>,
|
||||
) {
|
||||
const tmpDirPath = fs.mkdtempSync(path.join(tmpdir(), "temp-dir-"));
|
||||
try {
|
||||
await fn(tmpDirPath);
|
||||
} finally {
|
||||
fs.rmSync(tmpDirPath, { recursive: true });
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user