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Hello team, I'm the maintainer of [Anteon](https://github.com/getanteon/anteon). We have created Gurubase.io with the mission of building a centralized, open-source tool-focused knowledge base. Essentially, each "guru" is equipped with custom knowledge to answer user questions based on collected data related to that tool. I wanted to update you that I've manually added the [LanceDB Guru](https://gurubase.io/g/lancedb) to Gurubase. LanceDB Guru uses the data from this repo and data from the [docs](https://lancedb.github.io/lancedb/) to answer questions by leveraging the LLM. In this PR, I showcased the "LanceDB Guru", which highlights that LanceDB now has an AI assistant available to help users with their questions. Please let me know your thoughts on this contribution. Additionally, if you want me to disable LanceDB Guru in Gurubase, just let me know that's totally fine. Signed-off-by: Kursat Aktas <kursat.ce@gmail.com>
3.8 KiB
Developer-friendly, database for multimodal AI
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:
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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 @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()