npm install embedbase-js

import { createClient } from 'embedbase-js'
 
// you can find the api key at https://embedbase.xyz
const apiKey = 'your api key'
// this is using the hosted instance
const url = 'https://api.embedbase.xyz'
 
const embedbase = createClient(url, apiKey)

// fetching data
const data = await embedbase
  .dataset('test-amazon-product-reviews')
  .search('best hot dogs accessories', { limit: 3 })
 
console.log(data)
// [
//   {
//       "similarity": 0.810843349,
//       "data": "This nice little hot dog toaster is a great addition to our kitchen. It is easy to use and makes a great hot dog. It is also easy to clean. I would recommend this to anyone who likes hot dogs."
//       "metadata": {
//         "path": "https://amazon.com/hotdogtoaster",
//         "source": "amazon"
//       },
//       "embedding": [0.35332, 0.23423, ...]
//   },
//   {
//       "similarity": 0.294602573,
//       "data": "200 years ago, people would never have guessed that humans in the future would communicate by silently tapping on glass",
//       "embedding": [0.76532, 0.23423, ...]
//   },
//   {
//       "similarity": 0.192932034,
//       "data": "The average car in space is nicer than the average car on Earth",
//       "embedding": [0.52342, 0.23423, ...]
//   },
// ]

const data = await embedbase
  .dataset('test-amazon-product-reviews')
  .search('best hot dogs accessories')
  .where('source', '==', "amazon")

const data =
  await // embeddings are extremely good for retrieving unstructured data
  // in this example we store an unparsable html string
  embedbase.dataset('test-amazon-product-reviews').add(`
  <div>
    <span>Lightweight. Telescopic. Easy zipper case for storage. Didn't put in dishwasher. Still perfect after many uses.</span>
`)
 
console.log(data)
//
// {
//   "id": "eiew823",
//   "data": "Lightweight. Telescopic. Easy zipper case for storage.
//          Didn't put in dishwasher. Still perfect after many uses.",
//   "embedding": [0.1, 0.2, 0.3, ...]
// }

embedbase.dataset(datasetId).batchAdd([{
  data: 'some text',
}])

const batch = async (myList: any[], fn: (chunk: any[]) => Promise<any>) => {
    const batchSize = 100;
    return Promise.all(
        myList.reduce((acc: BatchAddDocument[][], chunk, i) => {
            if (i % batchSize === 0) {
                acc.push(myList.slice(i, i + batchSize));
            }
            return acc;
        }, []).map(fn)
    )
}
batch(chunks, (chunk) => embedbase.dataset(datasetId).batchAdd(chunk))

import { splitText } from 'embedbase-js/dist/main/split';
 
const text = 'some very long text...';
// ⚠️ note here that the value of maxTokens depends
// on the used embedder in embedbase.
// With models such as OpenAI's embeddings model, you can
// use a maxTokens of 500. With other models, you may need to
// use a lower maxTokens value.
// (embedbase cloud use openai model at the moment) ⚠️
const maxTokens = 500
// chunk_overlap is the number of tokens that will overlap between chunks
// it is useful to have some overlap to ensure that the context is not
// cut off in the middle of a sentence.
const chunkOverlap = 200
splitText(text, { maxTokens: maxTokens, chunkOverlap: chunkOverlap }, async ({ chunk, start, end }) =>
    embedbase.dataset('some-data-set').add(chunk)
)

// you can create a context to store data
const data = await embedbase
  .dataset('my-documentation')
  .createContext('my-context')
 
console.log(data)
[
 "Embedbase API allows to store unstructured data...",
 "Embedbase API has 3 main functions a) provides a plug and play solution to store embeddings b) makes it easy to connect to get the right data into llms c)..",
 "Embedabase API is self-hostable...",
]

const data = await embedbase
  .dataset('my-documentation')
  .createContext('my-context')
 
const question = 'How do I use Embedbase?'
const prompt =
`Based on the following context:\n${data.join('\n')}\nAnswer the user's question: ${question}`
 
for await (const res of embedbase.generate(prompt)) {
    console.log(res)
    // You, can, use, ...
}

const history = [
    {"role": "system", "content": "You are a helpful assistant that teach how to use Embedbase"},
    {"role": "user", "content": "How can I connect my data to LLMs using Embedbase?"},
    {"role": "assistant", "content": "You can npm i embedbase-js, write two lines of code, and..."},
    {"role": "user", "content": "how can i do this with my notion pages now using their api?"},
]
 
// ...
for await (const res of embedbase.generate(prompt, {history})) {
    console.log(res)
    // You, need, to, query, Notion, API, like, so:, ...
}

const data =
  await
  embedbase.dataset('test-amazon-product-reviews').add(`
  <div>
    <span>Lightweight. Telescopic. Easy zipper case for storage. Didn't put in dishwasher. Still perfect after many uses.</span>
    // metadata can be anything you want that will appear in the search results later
`, {path: 'https://www.amazon.com/dp/B00004OCNS'})
 
console.log(data)
//
// {
//   "id": "eiew823",
//   "data": "Lightweight. Telescopic. Easy zipper case for storage.
//          Didn't put in dishwasher. Still perfect after many uses.",
//   "metadata": {"path": "https://www.amazon.com/dp/B00004OCNS"},
//   "embedding": [0.1, 0.2, 0.3, ...]
// }

const data = await embedbase.datasets()
console.log(data)
// [{"datasetId": "test-amazon-product-reviews", "documentsCount": 2}]