lancedb
Developer-friendly, serverless vector database for AI applications. Easily add long-term memory to your LLM apps!
Stars: 4258
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 π¦οΈπ, LlamaIndex π¦, Apache-Arrow, Pandas, Polars, DuckDB, and more on the way. LanceDB's core is written in Rust π¦ and is built using Lance, an open-source columnar format designed for performant ML workloads.
README:
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 π¦οΈπ, LlamaIndex π¦, Apache-Arrow, Pandas, Polars, DuckDB and more on the way.
LanceDB's core is written in Rust π¦ and is built using Lance, an open-source columnar format designed for performant ML workloads.
Javascript
npm install @lancedb/lancedb
import * as lancedb from "@lancedb/lancedb";
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 },
], {mode: 'overwrite'});
const query = table.vectorSearch([0.1, 0.3]).limit(2);
const results = await query.toArray();
// You can also search for rows by specific criteria without involving a vector search.
const rowsByCriteria = await table.query().where("price >= 10").toArray();
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()
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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 π¦οΈπ, LlamaIndex π¦, Apache-Arrow, Pandas, Polars, DuckDB, and more on the way. LanceDB's core is written in Rust π¦ and is built using Lance, an open-source columnar format designed for performant ML workloads.
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