9d2fb7d602
Todo: - [x] add proper documentation - [x] add unit tests - [x] better handling of the registry**1 - [x] allow user defined registry**2 **1 The python implementation just uses a global registry so it makes things a bit easier. I attached it to the db/connection to prevent future conflicts if running multiple connections/databases. I mostly modeled the registry & pattern off of datafusion's [FunctionRegistry](https://docs.rs/datafusion/latest/datafusion/execution/trait.FunctionRegistry.html). **2 Ideally, the user should be able to provide it's own registry entirely, but currently it just uses an in memory registry by default (_which isn't configurable_) `rust/lancedb/examples/embedding_registry.rs` provides a thorough example of expected usage. --- Some additional notes: This does not provide any of the out of box functionality that the python registry does. _i.e there are no built-in embedding functions._ You can think of this as the ground work for adding those built in functions, So while this is part of https://github.com/lancedb/lancedb/issues/994, it does not yet offer feature parity.
Developer-friendly, database for multimodal AI
LanceDB is an open-source database for vector-search built with persistent storage, which greatly simplifies retrieval, filtering and management of embeddings.
The key features of LanceDB include:
-
Production-scale vector search with no servers to manage.
-
Store, query and filter vectors, metadata and multi-modal data (text, images, videos, point clouds, and more).
-
Support for vector similarity search, full-text search and SQL.
-
Native Python and Javascript/Typescript support.
-
Zero-copy, automatic versioning, manage versions of your data without needing extra infrastructure.
-
GPU support in building vector index(*).
-
Ecosystem integrations with LangChain 🦜️🔗, LlamaIndex 🦙, Apache-Arrow, Pandas, Polars, DuckDB and more on the way.
LanceDB's core is written in Rust 🦀 and is built using Lance, an open-source columnar format designed for performant ML workloads.
Quick Start
Javascript
npm install vectordb
const lancedb = require('vectordb');
const db = await lancedb.connect('data/sample-lancedb');
const table = await db.createTable({
name: 'vectors',
data: [
{ id: 1, vector: [0.1, 0.2], item: "foo", price: 10 },
{ id: 2, vector: [1.1, 1.2], item: "bar", price: 50 }
]
})
const query = table.search([0.1, 0.3]).limit(2);
const results = await query.execute();
// You can also search for rows by specific criteria without involving a vector search.
const rowsByCriteria = await table.search(undefined).where("price >= 10").execute();
Python
pip install lancedb
import lancedb
uri = "data/sample-lancedb"
db = lancedb.connect(uri)
table = db.create_table("my_table",
data=[{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
{"vector": [5.9, 26.5], "item": "bar", "price": 20.0}])
result = table.search([100, 100]).limit(2).to_pandas()