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42 lines
1.4 KiB
JavaScript
42 lines
1.4 KiB
JavaScript
// Copyright 2023 Lance Developers.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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'use strict'
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async function example () {
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const lancedb = require('vectordb')
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// You need to provide an OpenAI API key, here we read it from the OPENAI_API_KEY environment variable
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const apiKey = process.env.OPENAI_API_KEY
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// The embedding function will create embeddings for the 'text' column(text in this case)
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const embedding = new lancedb.OpenAIEmbeddingFunction('text', apiKey)
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const db = await lancedb.connect('data/sample-lancedb')
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const data = [
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{ id: 1, text: 'Black T-Shirt', price: 10 },
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{ id: 2, text: 'Leather Jacket', price: 50 }
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]
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const table = await db.createTable('vectors', data, embedding)
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console.log(await db.tableNames())
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const results = await table
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.search('keeps me warm')
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.limit(1)
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.execute()
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console.log(results[0].text)
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}
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example().then(_ => { console.log('All done!') })
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