dbdd0a7b4b
This PR adds support for passing through a set of ordering fields at index time (unsigned ints that tantivity can use as fast_fields) that at query time you can sort your results on. This is useful for cases where you want to get related hits, i.e by keyword, but order those hits by some other score, such as popularity. I.e search for songs descriptions that match on "sad AND jazz AND 1920" and then order those by number of times played. Example usage can be seen in the fts tests. --------- Co-authored-by: Nat Roth <natroth@Nats-MacBook-Pro.local> Co-authored-by: Chang She <759245+changhiskhan@users.noreply.github.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()