mirror of
https://github.com/lancedb/lancedb.git
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Wyatt AltandWill Jones
0b45ef93c0
This includes a handful of minor edits I made while reading the docs. In addition to a few spelling fixes, * standardize on "rerank" over "re-rank" in prose * terminate sentences with periods or colons as appropriate * replace some usage of dashes with colons, such as in "Try it yourself - <link>" All changes are surface-level. No changes to semantics or structure. --------- Co-authored-by: Will Jones <willjones127@gmail.com>
125 lines
3.6 KiB
Markdown
125 lines
3.6 KiB
Markdown
# Rust-backed Client Migration Guide
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In an effort to ensure all clients have the same set of capabilities we have
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migrated the Python and Node clients onto a common Rust base library. In Python,
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both the synchronous and asynchronous clients are based on this implementation.
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In Node, the new client is available as `@lancedb/lancedb`, which replaces
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the existing `vectordb` package.
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This guide describes the differences between the two Node APIs and will hopefully assist users
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that would like to migrate to the new API.
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## TypeScript/JavaScript
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For JS/TS users, we offer a brand new SDK [@lancedb/lancedb](https://www.npmjs.com/package/@lancedb/lancedb)
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We tried to keep the API as similar as possible to the previous version, but there are a few small changes. Here are the most important ones:
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### Creating Tables
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[CreateTableOptions.writeOptions.writeMode](./javascript/interfaces/WriteOptions.md#writemode) has been replaced with [CreateTableOptions.mode](./js/interfaces/CreateTableOptions.md#mode)
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=== "vectordb (deprecated)"
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```ts
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db.createTable(tableName, data, { writeMode: lancedb.WriteMode.Overwrite });
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```
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=== "@lancedb/lancedb"
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```ts
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db.createTable(tableName, data, { mode: "overwrite" })
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```
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### Changes to Table APIs
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Previously `Table.schema` was a property. Now it is an async method.
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#### Creating Indices
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The `Table.createIndex` method is now used for creating both vector indices
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and scalar indices. It currently requires a column name to be specified (the
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column to index). Vector index defaults are now smarter and scale better with
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the size of the data.
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=== "vectordb (deprecated)"
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```ts
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await tbl.createIndex({
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column: "vector", // default
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type: "ivf_pq",
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num_partitions: 2,
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num_sub_vectors: 2,
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});
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```
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=== "@lancedb/lancedb"
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```ts
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await table.createIndex("vector", {
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config: lancedb.Index.ivfPq({
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numPartitions: 2,
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numSubVectors: 2,
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}),
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});
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```
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### Embedding Functions
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The embedding API has been completely reworked, and it now more closely resembles the Python API, including the new [embedding registry](./js/classes/embedding.EmbeddingFunctionRegistry.md):
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=== "vectordb (deprecated)"
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```ts
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const embeddingFunction = new lancedb.OpenAIEmbeddingFunction('text', API_KEY)
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const data = [
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{ id: 1, text: 'Black T-Shirt', price: 10 },
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{ id: 2, text: 'Leather Jacket', price: 50 }
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]
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const table = await db.createTable('vectors', data, embeddingFunction)
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```
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=== "@lancedb/lancedb"
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```ts
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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 { LanceSchema, getRegistry } from "@lancedb/lancedb/embedding";
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const func = getRegistry().get("openai").create({apiKey: API_KEY});
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const data = [
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{ id: 1, text: 'Black T-Shirt', price: 10 },
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{ id: 2, text: 'Leather Jacket', price: 50 }
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]
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const table = await db.createTable('vectors', data, {
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embeddingFunction: {
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sourceColumn: "text",
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function: func,
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}
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})
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```
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You can also use a schema driven approach, which parallels the Pydantic integration in our Python SDK:
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```ts
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const func = getRegistry().get("openai").create({apiKey: API_KEY});
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const data = [
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{ id: 1, text: 'Black T-Shirt', price: 10 },
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{ id: 2, text: 'Leather Jacket', price: 50 }
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]
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const schema = LanceSchema({
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id: new arrow.Int32(),
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text: func.sourceField(new arrow.Utf8()),
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price: new arrow.Float64(),
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vector: func.vectorField()
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})
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const table = await db.createTable('vectors', data, {schema})
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```
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