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- Creates testing files `md_testing.py` and `md_testing.js` for testing python and nodejs code in markdown files in the documentation This listens for HTML tags as well: `<!--[language] code code code...-->` will create a set-up file to create some mock tables or to fulfill some assumptions in the documentation. - Creates a github action workflow that triggers every push/pr to `docs/**` - Modifies documentation so tests run (mostly indentation, some small syntax errors and some missing imports) A list of excluded files that we need to take a closer look at later on: ```javascript const excludedFiles = [ "../src/fts.md", "../src/embedding.md", "../src/examples/serverless_lancedb_with_s3_and_lambda.md", "../src/examples/serverless_qa_bot_with_modal_and_langchain.md", "../src/examples/youtube_transcript_bot_with_nodejs.md", ]; ``` Many of them can't be done because we need the OpenAI API key :(. `fts.md` has some issues with the library, I believe this is still experimental? Closes #170 --------- Co-authored-by: Will Jones <willjones127@gmail.com>
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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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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]);
query.limit = 20;
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_df()