mirror of
https://github.com/lancedb/lancedb.git
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Move the public website pages into `docs/web` beside the SDK, with
shared navigation and executable example sources in `docs/web-tests`.
The combined website is assembled by
[lancedb/docs#355](https://github.com/lancedb/docs/pull/355), with six
Enterprise replacements from
[lancedb/sophon#7681](https://github.com/lancedb/sophon/pull/7681).
This includes Function guidance, runnable Quickstarts, regenerated
snippets, production contact/demo fixes, the legacy SQL route, and the
LeRobot redirect. LeRobot remains attributed to Geneva; all 34 Geneva
pages remain in the docs repository until Function officially launches.
Materialized-view guidance follows the full rebuild refresh introduced
by #4373: local refresh writes the complete result, has no `full`
argument, supports `source_version`, and no longer requires stable row
IDs. Deployment SQL refresh options and stable-ID setup are qualified by
version/configuration. Function input guidance distinguishes ordinary
stored columns from computed inputs supported by newer deployments.
**Validation:** independently accepted local checks include 95 Python
tests (real Enterprise excluded), eight TypeScript tests, Rust
Quickstart, snippet parity, typos, deterministic assembly, Mint
validation and links/anchors/redirects. The combined baseline retains
all 181 production routes and 17 legacy redirects. Examples ran locally
against source-built Python SDK 0.40.0b13; existing GitHub workflows do
not run `docs/web-tests`. New main at `069b2f7d` is 0.41.0b0 and adds
computed Function inputs; that SDK was not exercised by the accepted
tests. Latest prose corrections receive focused site validation.
**Merge/rollout:** merge this and #7681 before #355's combined CI, which
reads producer `main`. This is migration infrastructure: section
overlays, source-owned skills and automatic versioned paired releases
remain follow-ups. The current manual publisher is transitional. Hosted
validation, a supported SDK/Enterprise release pair and a real
Enterprise walkthrough remain pending.
Production Mintlify watches `lancedb/docs:deploy-freeze`, directory
`docs`; preserve that source through review. The separate SDK GitHub
Pages reference workflow rebuilds on main merges.
**Updated-head CI (9a50f8db):**
[Typos](https://github.com/lancedb/lancedb/actions/runs/37654185443/job/112904990558),
[PR
title](https://github.com/lancedb/lancedb/actions/runs/37654179507/job/112904974595)
and
[Aikido](https://app.aikido.dev/featurebranch/scan/215386145?groupId=74229)
pass. Critical findings in the example lockfiles were patched: all Sharp
copies use 0.35.5, protobufjs 7.6.6, tar 7.5.22, quinn-proto 0.11.19,
zerovec 0.11.8 and zerovec-derive 0.11.6. The SDK's product dependencies
are unchanged. The revised lockfiles pass eight TypeScript examples,
HuggingFace decode/resize/crop/rescale/normalize plus AVIF roundtrip,
and Rust Quickstart. Aikido passing does not mean every advisory is
cleared; npm still reports 29 high and five moderate advisories in the
example dependency graph. No scanner bypass or suppression was added.
The Function prose correction passes combined/standalone Mint,
deterministic assembly, routes and assets. The revised 325-file site
checksum is
`175bec06864abb400dd292e667ba93448c0479b8359fd7bf6146afabf9040647`. This
PR remains open for independent review; further merges are on hold.
---------
Co-authored-by: Brendan Clement <brendan.clement@lancedb.com>
177 lines
5.6 KiB
TypeScript
177 lines
5.6 KiB
TypeScript
// 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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import * as lancedb from "@lancedb/lancedb";
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import { withTempDirectory } from "./util.ts";
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test("quickstart example (async)", async () => {
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await withTempDirectory(async (databaseDir) => {
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const db = await lancedb.connect(databaseDir);
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// --8<-- [start:quickstart_data]
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const data = [
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{
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id: "1",
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name: "King Arthur",
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role: "King",
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description: "Leader of Camelot and wielder of Excalibur.",
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stats: { strength: 4, magic: 1, leadership: 5, wisdom: 4 },
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vector: [0.7, 0.1, 0.9, 0.7],
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},
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{
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id: "2",
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name: "Merlin",
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role: "Wizard",
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description: "Advisor and prophet with deep magical knowledge.",
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stats: { strength: 2, magic: 5, leadership: 4, wisdom: 5 },
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vector: [0.2, 0.9, 0.4, 0.9],
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},
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{
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id: "3",
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name: "Sir Lancelot",
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role: "Knight",
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description: "Legendary knight known for courage and combat skill.",
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stats: { strength: 5, magic: 1, leadership: 3, wisdom: 3 },
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vector: [0.9, 0.1, 0.5, 0.4],
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},
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];
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// --8<-- [end:quickstart_data]
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// --8<-- [start:quickstart_create_table]
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let table = await db.createTable("characters", data, { mode: "overwrite" });
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// --8<-- [end:quickstart_create_table]
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expect(await table.countRows()).toBe(3);
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await db.dropTable("characters");
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// --8<-- [start:quickstart_create_table_no_overwrite]
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table = await db.createTable("characters", data);
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// --8<-- [end:quickstart_create_table_no_overwrite]
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expect(await table.countRows()).toBe(3);
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// --8<-- [start:quickstart_vector_search_1]
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// Search for examples similar to a "wise magical advisor"
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let queryVector = [0.2, 0.8, 0.4, 0.9];
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let result = await table
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.search(queryVector)
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.select(["name", "role", "description", "_distance"])
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.limit(2)
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.toArray();
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console.table(result);
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// --8<-- [end:quickstart_vector_search_1]
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expect(result[0].name).toBe("Merlin");
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// --8<-- [start:quickstart_curate_with_metadata]
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const curated = await table
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.search(queryVector)
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.where("stats.magic >= 4")
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.select(["name", "role", "description", "_distance"])
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.limit(2)
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.toArray();
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console.table(curated);
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// --8<-- [end:quickstart_curate_with_metadata]
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expect(curated[0].name).toBe("Merlin");
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// --8<-- [start:quickstart_output_array]
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result = await table.search(queryVector).limit(2).toArray();
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console.table(result);
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// --8<-- [end:quickstart_output_array]
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expect(result[0].name).toBe("Merlin");
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// --8<-- [start:quickstart_add_feature]
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await table.addColumns([
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{
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name: "power_score",
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valueSql:
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"cast(((stats.strength + stats.magic + stats.leadership + stats.wisdom) / 4.0) as float)",
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},
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]);
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// --8<-- [end:quickstart_add_feature]
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const schemaWithFeature = await table.schema();
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expect(schemaWithFeature.fields.some((f) => f.name === "power_score")).toBe(
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true,
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);
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// --8<-- [start:quickstart_query_feature]
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const features = await table
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.query()
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.select(["name", "role", "power_score"])
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.toArray();
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console.table(features);
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// --8<-- [end:quickstart_query_feature]
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expect(features[0]).toHaveProperty("power_score");
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// --8<-- [start:quickstart_multimodal_bytes]
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const arrow = await import("apache-arrow");
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const path = await import("node:path");
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const { readFile } = await import("node:fs/promises");
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const imagePath = path.join(
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__dirname,
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"../../web/static/assets/images/quickstart/sir-lancelot.jpg",
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);
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const imageBytes = await readFile(imagePath);
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const imageSchema = new arrow.Schema([
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new arrow.Field("id", new arrow.Utf8()),
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new arrow.Field("description", new arrow.Utf8()),
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new arrow.Field("image", new arrow.Binary()),
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new arrow.Field(
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"vector",
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new arrow.FixedSizeList(
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4,
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new arrow.Field("item", new arrow.Float32(), true),
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),
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),
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]);
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const imageData = lancedb.makeArrowTable(
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[
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{
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id: "lancelot",
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description: "Portrait of Sir Lancelot",
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image: imageBytes,
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vector: [0.9, 0.1, 0.5, 0.4],
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},
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],
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{ schema: imageSchema },
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);
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const multimodalTable = await db.createTable(
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"character_images",
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imageData,
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{ mode: "overwrite" },
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);
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// --8<-- [end:quickstart_multimodal_bytes]
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expect(await multimodalTable.countRows()).toBe(1);
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// --8<-- [start:quickstart_open_table]
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table = await db.openTable("characters");
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// --8<-- [end:quickstart_open_table]
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// --8<-- [start:quickstart_add_data]
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const moreData = [
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{
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id: "4",
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name: "Morgana",
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role: "Sorceress",
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description: "Powerful sorceress of Avalon.",
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stats: { strength: 2, magic: 5, leadership: 4, wisdom: 4 },
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vector: [0.3, 0.9, 0.6, 0.8],
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power_score: 3.75,
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},
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];
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// Add data to table
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await table.add(moreData);
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// --8<-- [end:quickstart_add_data]
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expect(await table.countRows()).toBe(4);
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// --8<-- [start:quickstart_vector_search_2]
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// Search for examples similar to a "powerful sorceress"
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queryVector = [0.3, 0.9, 0.6, 0.8];
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const results = await table.search(queryVector).limit(2).toArray();
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console.table(results);
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// --8<-- [end:quickstart_vector_search_2]
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expect(results[0].name).toBe("Morgana");
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});
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});
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