7dfb555fea
This PR adds an overview of embeddings docs: - 2 ways to vectorize your data using lancedb - explicit & implicit - explicit - manually vectorize your data using `wit_embedding` function - Implicit - automatically vectorize your data as it comes by ingesting your embedding function details as table metadata - Multi-modal example w/ disappearing embedding function
Developer-friendly, serverless vector database for AI applications
Documentation • Blog • Discord • Twitter
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('vectors',
[{ 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();
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()