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faf8973624
This PR adds support of multi-level namespace in a LanceDB database,
according to the Lance Namespace spec.
This allows users to create namespace inside a database connection,
perform create, drop, list, list_tables in a namespace. (other
operations like update, describe will be in a follow-up PR)
The 3 types of database connections behave like the following:
1 Local database connections will continue to have just a flat list of
tables for backwards compatibility.
2. Remote database connections will make REST API calls according to the
APIs in the Lance Namespace spec.
3. Lance Namespace connections will invoke the corresponding operations
against the specific namespace implementation which could have different
behaviors regarding these APIs.
All the table APIs now take identifier instead of name, for example
`/v1/table/{name}/create` is now `/v1/table/{id}/create`. If a table is
directly in the root namespace, the API call is identical. If the table
is in a namespace, then the full table ID should be used, with `$` as
the default delimiter (`.` is a special character and creates issues
with URL parsing so `$` is used), for example
`/v1/table/ns1$table1/create`. If a different parameter needs to be
passed in, user can configure the `id_delimiter` in client config and
that becomes a query parameter, for example
`/v1/table/ns1__table1/create?delimiter=__`
The Python and Typescript APIs are kept backwards compatible, but the
following Rust APIs are not:
1. `Connection::drop_table(&self, name: impl AsRef<str>) -> Result<()>`
is now `Connection::drop_table(&self, name: impl AsRef<str>, namespace:
&[String]) -> Result<()>`
2. `Connection::drop_all_tables(&self) -> Result<()>` is now
`Connection::drop_all_tables(&self, name: impl AsRef<str>) ->
Result<()>`
LanceDB JavaScript SDK
A JavaScript library for LanceDB.
Installation
npm install @lancedb/lancedb
This will download the appropriate native library for your platform. We currently support:
- Linux (x86_64 and aarch64 on glibc and musl)
- MacOS (Intel and ARM/M1/M2)
- Windows (x86_64 and aarch64)
Usage
Basic Example
import * as lancedb from "@lancedb/lancedb";
const db = await lancedb.connect("data/sample-lancedb");
const table = await db.createTable("my_table", [
{ id: 1, vector: [0.1, 1.0], item: "foo", price: 10.0 },
{ id: 2, vector: [3.9, 0.5], item: "bar", price: 20.0 },
]);
const results = await table.vectorSearch([0.1, 0.3]).limit(20).toArray();
console.log(results);
The quickstart contains a more complete example.
Development
See CONTRIBUTING.md for information on how to contribute to LanceDB.