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Developer-friendly, database for multimodal AI

LanceDBlancdbBlogDiscordTwitterGurubase

LanceDB Multimodal Search

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 🦜️🔗, 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 @lancedb/lancedb
import*aslancedbfrom"@lancedb/lancedb";constdb=awaitlancedb.connect("data/sample-lancedb");consttable=awaitdb.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'});constquery=table.vectorSearch([0.1,0.3]).limit(2);constresults=awaitquery.toArray();// You can also search for rows by specific criteria without involving a vector search.constrowsByCriteria=awaittable.query().where("price >= 10").toArray();

Python

pip install lancedb
importlancedburi="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()

Blogs, Tutorials & Videos

About

Developer-friendly, serverless vector database for AI applications. Easily add long-term memory to your LLM apps!

lancedb.github.io/lancedb/

Resources

Readme
License
Activity

Stars

0 stars

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0 watching

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0 forks
Report repository

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Contributors

Languages

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GitHub - H3zi/lancedb: Developer-friendly, serverless vector database for AI applications. Easily add long-term memory to your LLM apps! · GitHub
Skip to content
BranchesTags
Repository files navigation
LanceDB Logo
Developer-friendly, database for multimodal AI
LanceDBlancdbBlogDiscordTwitterGurubase
LanceDB Multimodal Search
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 🦜️🔗, 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 @lancedb/lancedb
import*aslancedbfrom"@lancedb/lancedb";constdb=awaitlancedb.connect("data/sample-lancedb");consttable=awaitdb.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'});constquery=table.vectorSearch([0.1,0.3]).limit(2);constresults=awaitquery.toArray();// You can also search for rows by specific criteria without involving a vector search.constrowsByCriteria=awaittable.query().where("price >= 10").toArray();
Python
pip install lancedb
importlancedburi="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()
Blogs, Tutorials & Videos
About
Developer-friendly, serverless vector database for AI applications. Easily add long-term memory to your LLM apps!
lancedb.github.io/lancedb/
Resources
Readme
License
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
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, 'i');
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var __re = new RegExp('^' + ".*" + '
GitHub - H3zi/lancedb: Developer-friendly, serverless vector database for AI applications. Easily add long-term memory to your LLM apps! · GitHub
Skip to content
BranchesTags
Repository files navigation
LanceDB Logo
Developer-friendly, database for multimodal AI
LanceDBlancdbBlogDiscordTwitterGurubase
LanceDB Multimodal Search
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 🦜️🔗, 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 @lancedb/lancedb
import*aslancedbfrom"@lancedb/lancedb";constdb=awaitlancedb.connect("data/sample-lancedb");consttable=awaitdb.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'});constquery=table.vectorSearch([0.1,0.3]).limit(2);constresults=awaitquery.toArray();// You can also search for rows by specific criteria without involving a vector search.constrowsByCriteria=awaittable.query().where("price >= 10").toArray();
Python
pip install lancedb
importlancedburi="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()
Blogs, Tutorials & Videos
About
Developer-friendly, serverless vector database for AI applications. Easily add long-term memory to your LLM apps!
lancedb.github.io/lancedb/
Resources
Readme
License
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
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, 'i');
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observer.observe(document.body, { childList: true, subtree: true });
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} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - H3zi/lancedb: Developer-friendly, serverless vector database for AI applications. Easily add long-term memory to your LLM apps! · GitHub
Skip to content
BranchesTags
Repository files navigation
LanceDB Logo
Developer-friendly, database for multimodal AI
LanceDBlancdbBlogDiscordTwitterGurubase
LanceDB Multimodal Search
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 🦜️🔗, 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 @lancedb/lancedb
import*aslancedbfrom"@lancedb/lancedb";constdb=awaitlancedb.connect("data/sample-lancedb");consttable=awaitdb.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'});constquery=table.vectorSearch([0.1,0.3]).limit(2);constresults=awaitquery.toArray();// You can also search for rows by specific criteria without involving a vector search.constrowsByCriteria=awaittable.query().where("price >= 10").toArray();
Python
pip install lancedb
importlancedburi="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()
Blogs, Tutorials & Videos
About
Developer-friendly, serverless vector database for AI applications. Easily add long-term memory to your LLM apps!
lancedb.github.io/lancedb/
Resources
Readme
License
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
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, 'i');
if (__m === '*' || __re.test(location.href)) {
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(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
GitHub - H3zi/lancedb: Developer-friendly, serverless vector database for AI applications. Easily add long-term memory to your LLM apps! · GitHub
Skip to content
BranchesTags
Repository files navigation
LanceDB Logo
Developer-friendly, database for multimodal AI
LanceDBlancdbBlogDiscordTwitterGurubase
LanceDB Multimodal Search
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 🦜️🔗, 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 @lancedb/lancedb
import*aslancedbfrom"@lancedb/lancedb";constdb=awaitlancedb.connect("data/sample-lancedb");consttable=awaitdb.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'});constquery=table.vectorSearch([0.1,0.3]).limit(2);constresults=awaitquery.toArray();// You can also search for rows by specific criteria without involving a vector search.constrowsByCriteria=awaittable.query().where("price >= 10").toArray();
Python
pip install lancedb
importlancedburi="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()
Blogs, Tutorials & Videos
About
Developer-friendly, serverless vector database for AI applications. Easily add long-term memory to your LLM apps!
lancedb.github.io/lancedb/
Resources
Readme
License
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
// Auto-enable theater mode on YouTube
(function() {
function tryTheater() {
var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
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(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - H3zi/lancedb: Developer-friendly, serverless vector database for AI applications. Easily add long-term memory to your LLM apps! · GitHub
Skip to content
BranchesTags
Repository files navigation
LanceDB Logo
Developer-friendly, database for multimodal AI
LanceDBlancdbBlogDiscordTwitterGurubase
LanceDB Multimodal Search
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 🦜️🔗, 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 @lancedb/lancedb
import*aslancedbfrom"@lancedb/lancedb";constdb=awaitlancedb.connect("data/sample-lancedb");consttable=awaitdb.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'});constquery=table.vectorSearch([0.1,0.3]).limit(2);constresults=awaitquery.toArray();// You can also search for rows by specific criteria without involving a vector search.constrowsByCriteria=awaittable.query().where("price >= 10").toArray();
Python
pip install lancedb
importlancedburi="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()
Blogs, Tutorials & Videos
About
Developer-friendly, serverless vector database for AI applications. Easily add long-term memory to your LLM apps!
lancedb.github.io/lancedb/
Resources
Readme
License
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
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, 'i');
if (__m === '*' || __re.test(location.href)) {
// Remove or un-stick sticky/fixed headers that block content
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function unstick() {
document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) {
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observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });
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(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - H3zi/lancedb: Developer-friendly, serverless vector database for AI applications. Easily add long-term memory to your LLM apps! · GitHub
Skip to content
BranchesTags
Repository files navigation
LanceDB Logo
Developer-friendly, database for multimodal AI
LanceDBlancdbBlogDiscordTwitterGurubase
LanceDB Multimodal Search
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 🦜️🔗, 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 @lancedb/lancedb
import*aslancedbfrom"@lancedb/lancedb";constdb=awaitlancedb.connect("data/sample-lancedb");consttable=awaitdb.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'});constquery=table.vectorSearch([0.1,0.3]).limit(2);constresults=awaitquery.toArray();// You can also search for rows by specific criteria without involving a vector search.constrowsByCriteria=awaittable.query().where("price >= 10").toArray();
Python
pip install lancedb
importlancedburi="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()
Blogs, Tutorials & Videos
About
Developer-friendly, serverless vector database for AI applications. Easily add long-term memory to your LLM apps!
lancedb.github.io/lancedb/
Resources
Readme
License
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
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var enabled = true;
function applyDarkMode() {
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var style = document.getElementById('universal-dark-mode-style');
if (!style) {
style = document.createElement('style');
style.id = 'universal-dark-mode-style';
document.head.appendChild(style);
}
// Dark mode CSS - inverts colors but preserves images/video
style.textContent = '
/* Invert everything except media */
html {
filter: invert(1) hue-rotate(180deg) !important;
background: #1a1a2e !important;
}
/* Restore images, videos, iframes, canvas */
img, video, iframe, canvas, svg, picture, [style*="background-image"] {
filter: invert(1) hue-rotate(180deg) !important;
}
/* Preserve specific elements that should not be inverted */
.no-dark-mode, .no-dark-mode *,
[data-theme="light"], [data-theme="light"],
.ace_editor, .ace_editor *,
.CodeMirror, .CodeMirror *,
.monaco-editor, .monaco-editor *,
.markdown-body pre, .markdown-body pre *,
.highlight, .highlight *,
pre code, pre code * {
filter: none !important;
}
/* Fix common UI elements */
.modal, .popup, .dropdown-menu, .tooltip, .popover {
filter: invert(1) hue-rotate(180deg) !important;
background: #2d2d44 !important;
border-color: #444 !important;
}
/* Scrollbars */
::-webkit-scrollbar { background: #1a1a2e !important; }
::-webkit-scrollbar-thumb { background: #444 !important; }
::-webkit-scrollbar-thumb:hover { background: #555 !important; }
/* Selection */
::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
';
}
function removeDarkMode() {
var style = document.getElementById('universal-dark-mode-style');
if (style) style.remove();
}
// Toggle with Alt+Shift+D
document.addEventListener('keydown', function(e) {
if (e.altKey && e.shiftKey && e.key === 'D') {
e.preventDefault();
enabled = !enabled;
if (enabled) {
applyDarkMode();
console.log('[Universal Dark Mode] Enabled');
} else {
removeDarkMode();
console.log('[Universal Dark Mode] Disabled');
}
}
});
// Apply on load
applyDarkMode();
// Re-apply on dynamic content
var observer = new MutationObserver(function(mutations) {
if (enabled && !document.getElementById('universal-dark-mode-style')) {
applyDarkMode();
}
});
observer.observe(document.head, { childList: true });
console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');
})();
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();
GitHub - H3zi/lancedb: Developer-friendly, serverless vector database for AI applications. Easily add long-term memory to your LLM apps! · GitHub
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LanceDB Logo
Developer-friendly, database for multimodal AI
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LanceDB Multimodal Search
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 🦜️🔗, 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 @lancedb/lancedb
import*aslancedbfrom"@lancedb/lancedb";constdb=awaitlancedb.connect("data/sample-lancedb");consttable=awaitdb.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'});constquery=table.vectorSearch([0.1,0.3]).limit(2);constresults=awaitquery.toArray();// You can also search for rows by specific criteria without involving a vector search.constrowsByCriteria=awaittable.query().where("price >= 10").toArray();
Python
pip install lancedb
importlancedburi="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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Developer-friendly, serverless vector database for AI applications. Easily add long-term memory to your LLM apps!
lancedb.github.io/lancedb/
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