73c69a6b9a
The synchronous table_names function in python lancedb relies on arrow's filesystem which behaves slightly differently than object_store. As a result, the function would not work properly in GCS. However, the async table_names function uses object_store directly and thus is accurate. In most cases we can fallback to using the async table_names function and so this PR does so. The one case we cannot is if the user is already in an async context (we can't start a new async event loop). Soon, we can just redirect those users to use the async API instead of the sync API and so that case will eventually go away. For now, we fallback to the old behavior.
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()