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chore: update readme to point to lancedb package (#1470)
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28
README.md
28
README.md
@@ -7,8 +7,8 @@
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<a href='https://github.com/lancedb/vectordb-recipes/tree/main' target="_blank"><img alt='LanceDB' src='https://img.shields.io/badge/VectorDB_Recipes-100000?style=for-the-badge&logo=LanceDB&logoColor=white&labelColor=645cfb&color=645cfb'/></a>
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<a href='https://lancedb.github.io/lancedb/' target="_blank"><img alt='lancdb' src='https://img.shields.io/badge/DOCS-100000?style=for-the-badge&logo=lancdb&logoColor=white&labelColor=645cfb&color=645cfb'/></a>
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[](https://blog.lancedb.com/)
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[](https://discord.gg/zMM32dvNtd)
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[](https://blog.lancedb.com/)
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[](https://discord.gg/zMM32dvNtd)
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[](https://twitter.com/lancedb)
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</p>
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@@ -44,26 +44,24 @@ LanceDB's core is written in Rust 🦀 and is built using <a href="https://githu
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**Javascript**
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```shell
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npm install vectordb
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npm install @lancedb/lancedb
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```
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```javascript
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const lancedb = require('vectordb');
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const db = await lancedb.connect('data/sample-lancedb');
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import * as lancedb from "@lancedb/lancedb";
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const table = await db.createTable({
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name: 'vectors',
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data: [
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{ id: 1, vector: [0.1, 0.2], item: "foo", price: 10 },
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{ id: 2, vector: [1.1, 1.2], item: "bar", price: 50 }
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]
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})
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const db = await lancedb.connect("data/sample-lancedb");
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const table = await db.createTable("vectors", [
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{ id: 1, vector: [0.1, 0.2], item: "foo", price: 10 },
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{ id: 2, vector: [1.1, 1.2], item: "bar", price: 50 },
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], {mode: 'overwrite'});
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const query = table.search([0.1, 0.3]).limit(2);
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const results = await query.execute();
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const query = table.vectorSearch([0.1, 0.3]).limit(2);
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const results = await query.toArray();
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// You can also search for rows by specific criteria without involving a vector search.
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const rowsByCriteria = await table.search(undefined).where("price >= 10").execute();
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const rowsByCriteria = await table.query().where("price >= 10").toArray();
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```
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**Python**
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@@ -275,12 +275,15 @@ export abstract class Table {
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* of the given query vector
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* @param {string} query - the query. This will be converted to a vector using the table's provided embedding function
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* @note If no embedding functions are defined in the table, this will error when collecting the results.
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*
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* This is just a convenience method for calling `.query().nearestTo(await myEmbeddingFunction(query))`
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*/
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abstract search(query: string): VectorQuery;
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/**
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* Create a search query to find the nearest neighbors
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* of the given query vector
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* @param {IntoVector} query - the query vector
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* This is just a convenience method for calling `.query().nearestTo(query)`
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*/
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abstract search(query: IntoVector): VectorQuery;
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/**
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