Weston Pace
8033a44d68
In order to add support for `add` we needed to migrate the rust `Table` trait to a `Table` struct and `TableInternal` trait (similar to the way the connection is designed). While doing this we also cleaned up some inconsistencies between the SDKs: * Python and Node are garbage collected languages and it can be difficult to trigger something to be freed. The convention for these languages is to have some kind of close method. I added a close method to both the table and connection which will drop the underlying rust object. * We made significant improvements to table creation in https://github.com/lancedb/lancedb/commit/cc5f2136a61234a9cdbe20e19c56459db8ca1051 for the `node` SDK. I copied these changes to the `nodejs` SDK. * The nodejs tables were using fs to create tmp directories and these were not getting cleaned up. This is mostly harmless but annoying and so I changed it up a bit to ensure we cleanup tmp directories. * ~~countRows in the node SDK was returning `bigint`. I changed it to return `number`~~ (this actually happened in a previous PR) * Tables and connections now implement `std::fmt::Display` which is hooked into python's `__repr__`. Node has no concept of a regular "to string" function and so I added a `display` method. * Python method signatures are changing so that optional parameters are always `Optional[foo] = None` instead of something like `foo = False`. This is because we want those defaults to be in rust whenever possible (though we still need to mention the default in documentation). * I changed the python `AsyncConnection/AsyncTable` classes from abstract classes with a single implementation to just classes because we no longer have the remote implementation in python. Note: this does NOT add the `add` function to the remote table. This PR was already large enough, and the remote implementation is unique enough, that I am going to do all the remote stuff at a later date (we should have the structure in place and correct so there shouldn't be any refactor concerns) --------- Co-authored-by: Will Jones <willjones127@gmail.com>
Developer-friendly, serverless vector database for AI applications
LanceDB is an open-source database for vector-search built with persistent storage, which greatly simplifies retrevial, filtering and management of embeddings.
The key features of LanceDB include:
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Production-scale vector search with no servers to manage.
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Store, query and filter vectors, metadata and multi-modal data (text, images, videos, point clouds, and more).
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Support for vector similarity search, full-text search and SQL.
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Native Python and Javascript/Typescript support.
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Zero-copy, automatic versioning, manage versions of your data without needing extra infrastructure.
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GPU support in building vector index(*).
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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()